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Posts Tagged ‘biomarker’


Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.

 

Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.

 

Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).

 

References:

 

http://link.springer.com/chapter/10.1007%2F978-3-319-31973-5_12

 

http://onlinelibrary.wiley.com/doi/10.1111/andr.12276/abstract;jsessionid=F805A89DCC94BDBD42D6D60C40AD4AB0.f03t03

 

http://www.sciencedirect.com/science/article/pii/S0009279716302241

 

http://link.springer.com/article/10.1007%2Fs10815-016-0657-9

 

http://www.nature.com/articles/srep24976

 

 

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Personalized Medicine – The California Initiative

Curator: Demet Sag, PhD, CRA, GCP

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

It was necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data. It was called Gene Ontology Consortium. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more:

·         the budding yeast, Saccharomyces cerevisiae, completed in 1996

·         the nematode worm Caenorhabditis elegans, completed in 1998

·         the fruitfly Drosophila melanogaster,

·         the flowering plant Arabidopsis thaliana

·         fission yeast Schizosaccharomyces pombe

·         the mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of commondiseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with un-relevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component incomplex diseases and
  • to fully understand the genetic pathways contributing tocomplex disease

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a key:

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Table 1.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6 (18), which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.

Published online 2009 May 27. doi:  10.1073/pnas.0903103106

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Table 2

Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

Table 3

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.

Published online 2005 Nov 16. doi:  10.1086/499287

Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1. Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

In 2002The International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed. In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population. In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability; these are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots3,6,36 (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence (Supplementary Fig. 6).

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

 

FUNCTIONAL GENOMICS AND DATA FOR MEDICINE:  BIOINFORMATICS/COMPUTER BIOLOGY

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily to recognize and annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database).

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured:

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases: A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

Schematic illustration of the process for building PANTHER families.

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

 

Further Reading

Human Phenome Project: Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

 

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

 

Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

Aitken A. Protein consensus sequence motifs. Mol Biotechnol. 1999 Oct;12(3):241-53. Review.

Bork P, Koonin EV. Protein sequence motifs. Curr Opin Struct Biol. 1996 Jun;6(3):366-76. Review.

Hodgman TC. The elucidation of protein function by sequence motif analysis.  Comput Appl Biosci. 1989 Feb;5(1):1-13. Review.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Stoch Models. 1998;14:273–295. doi: 10.1080/15326349808807471.

Gauderman WJ. Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002;21(1):35–50. doi: 10.1002/sim.973.

Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293. [PubMed]

Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., et al. 2003. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31: 334–341.

HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.

 

Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

Dayhoff, M.O., Barker, W.C., and McLaughlin, P.J. 1974. Inferences from protein and nucleic acid sequences: Early molecular evolution, divergence of kingdoms and rates of change. Orig. Life 5: 311–330.

Joseph Lachance Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations BMC Med Genomics. 2010; 3: 57.

Joseph Lachance, Sarah A. Tishkoff  Biased Gene Conversion Skews Allele Frequencies in Human Populations, Increasing the Disease Burden of Recessive Alleles  Am J Hum Genet. 2014 October 2; 95(4): 408-420.

Hemalatha Kuppusamy, Helga M. Ogmundsdottir, Eva Baigorri, Amanda Warkentin, Hlif Steingrimsdottir, Vilhelmina Haraldsdottir, Michael J. Mant, John Mackey, James B. Johnston, Sophia Adamia, Andrew R. Belch, Linda M. Pilarski Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer  PLoS One. 2014; 9(6): e100691.

Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

Annu Rev Ecol Evol Syst. Author manuscript; available in PMC 2014 November 5.

Published in final edited form as: Annu Rev Ecol Evol Syst. 2013 November; 44: 123–143

Joseph Lachance, Sarah A. Tishkoff SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it  Bioessays.

Erik Corona, Rong Chen, Martin Sikora, Alexander A. Morgan, Chirag J. Patel, Aditya Ramesh, Carlos D. Bustamante, Atul J. Butte Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration PLoS Genet. 2013 May; 9(5): e1003447.

Olga Y. Gorlova, Jun Ying, Christopher I. Amos, Margaret R. Spitz, Bo Peng, Ivan P. Gorlov J Derived SNP Alleles Are Used More Frequently Than Ancestral Alleles As Risk-Associated Variants In Common Human Diseases Bioinform Comput Biol.

Ani Manichaikul, Wei-Min Chen, Kayleen Williams, Quenna Wong, Michèle M. Sale, James S. Pankow, Michael Y. Tsai, Jerome I. Rotter, Stephen S. Rich, Josyf C. Mychaleckyj  Analysis of Family- and Population-Based Samples in Cohort Genome-Wide Association Studies Hum Genet.

Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008; 322(5903):881–888. doi: 10.1126/science.1156409.

Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678. doi: 10.1038/nature05911.

Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC, Hobbs HH. A spectrum of PCSK9 Alleles contributes to plasma levels of low-density lipoprotein cholesterol. American Journal of Human Genetics.2006;78(3):410–422. doi: 10.1086/500615.

Tomlinson I, Webb E, Carvajal-Carmona L, Broderick P, Kemp Z, Spain S, Penegar S, Chandler I, Gorman M, Wood W. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nature Genetics. 2007;39(8):984–988. doi: 10.1038/ng2085.

Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, Bailey R, Nejentsev S, Field SF, Payne F. et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nature Genetics. 2007;39(7):857–864. doi: 10.1038/ng2068.

Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A. et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. doi: 10.1038/nature08494.

Maher B. Personal genomes: The case of the missing heritability. Nature.2008;456(7218):18–21. doi: 10.1038/456018a.

Clark AG, Boerwinkle E, Hixson J, Sing CF. Determinants of the success of whole-genome association testing. Genome Res. 2005;15(11):1463–1467. doi: 10.1101/gr.4244005.

Clarke AJ, Cooper DN. GWAS: heritability missing in action? European Journal of Human Genetics. 2010;18:859–861. doi: 10.1038/ejhg.2010.35.

Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am J Hum Genet. 2009;85(3):309–320. doi: 10.1016/j.ajhg.2009.08.006.

Goldstein DB. Common genetic variation and human traits. N Engl J Med.2009;360(17):1696–1698. doi: 10.1056/NEJMp0806284.

Hirschhorn JN. Genomewide association studies–illuminating biologic pathways. N Engl J Med. 2009;360(17):1699–1701. doi: 10.1056/NEJMp0808934.

Iles MM. What can genome-wide association studies tell us about the genetics of common disease? PLoS Genet. 2008;4(2):e33. doi: 10.1371/journal.pgen.0040033.

Myles S, Davison D, Barrett J, Stoneking M, Timpson N. Worldwide population differentiation at disease-associated SNPs. BMC Med Genomics.2008;1:22. doi: 10.1186/1755-8794-1-22.

Lohmueller KE, Mauney MM, Reich D, Braverman JM. Variants associated with common disease are not unusually differentiated in frequency across populations. Am J Hum Genet. 2006;78(1):130–136. doi: 10.1086/499287.

Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA.2009;106(23):9362–9367. doi: 10.1073/pnas.0903103106.

Wang WYS, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: Theoretical and practical concerns. Nature Reviews Genetics.2005;6(2):109–118. doi: 10.1038/nrg1522.

Hacia JG, Fan JB, Ryder O, Jin L, Edgemon K, Ghandour G, Mayer RA, Sun B, Hsie L, Robbins C. et al. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nat Genet. 1999;22(2):164–167. doi: 10.1038/9674.

Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet. 2003;33(2):177–182. doi: 10.1038/ng1071.

Wang WY, Pike N. The allelic spectra of common diseases may resemble the allelic spectrum of the full genome. Med Hypotheses. 2004;63(4):748–751. doi: 10.1016/j.mehy.2003.12.057.

HapMart. http://hapmart.hapmap.org/BioMart/martview/

Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal S. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature.2007;449(7164):851–861. doi: 10.1038/nature06258.

Rotimi CN, Jorde LB. Ancestry and disease in the age of genomic medicine. N Engl J Med. 2010;363(16):1551–1558. doi: 10.1056/NEJMra0911564.

Ganapathy G, Uyenoyama MK. Site frequency spectra from genomic SNP surveys. Theor Popul Biol. 2009;75(4):346–354. doi: 10.1016/j.tpb.2009.04.003.

Nielsen R, Hubisz MJ, Clark AG. Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics.2004;168(4):2373–2382. doi: 10.1534/genetics.104.031039.

Watterson GA, Guess HA. Is the most frequent allele the oldest? Theor Popul Biol. 1977;11(2):141–160. doi: 10.1016/0040-5809(77)90023-5.

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–311. doi: 10.1093/nar/29.1.308.

Spencer CC, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G. The influence of recombination on human genetic diversity. PLoS Genet. 2006;2(9):e148. doi: 10.1371/journal.pgen.0020148.

Kimura M. The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics. 1969;61(4):893–903.

Johnson AD, O’Donnell CJ. An open access database of genome-wide association results. BMC Med Genet. 2009;10:6. doi: 10.1186/1471-2350-10-6.

Kimura M, Ohta T. The age of a neutral mutant persisting in a finite population. Genetics. 1973;75(1):199–212.

McVean GA, et al. The fine-scale structure of recombination rate variation in the human genome. Science. 2004;304:581–584.

Slatkin M, Rannala B. Estimating the age of alleles by use of intraallelic variability. Am J Hum Genet. 1997;60(2):447–458.

Green R, Krause J, Briggs A, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz M. et al. A Draft Sequence of the Neanderthal Genome. Science. 2010;328:710–722. doi: 10.1126/science.1188021.

Bamshad M, Wooding SP. Signatures of natural selection in the human genome. Nat Rev Genet. 2003;4(2):99–111. doi: 10.1038/nrg999.

Hernandez RD, Williamson SH, Bustamante CD. Context dependence, ancestral misidentification, and spurious signatures of natural selection. Mol Biol Evol. 2007;24(8):1792–1800. doi: 10.1093/molbev/msm108.

Bustamante CD, et al. Natural selection on protein-coding genes in the human genome. Nature. 2005;437:1153–1157.

 

 

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The Future of Translational Medicine with Smart Diagnostics and Therapies: PharmacoGenomics

Curator: Demet Sag, PhD

Since Human Genome project is completed we saw several projects to understand function and how they relate to personal health.  These advancements hope to improve diagnostics in preventive medicine. The future of medicine may involve a personal wireless unit to detect the vital records with genomics changes and compare the assumed “healthy” state to “unhealthy” to suggest options to treat in a palm of hand.

Pharmacogenomics is the study of how genes affect a person’s response to drugs. This relatively new field combines pharmacology (the science of drugs) and genomics (the study of genes and their functions) to develop effective, safe medications and doses that will be tailored to a person’s genetic makeup.

The American Medical Association and  Critical Path Institute and the Arizona Center for Education and Research on Therapeutics developed a brochure for health care providers on pharmacogenomics. The man purpose is to help physicians to ue this information correctly by case based approach.   View an electronic version of the brochure.

Like always, there are debates and controversies but the positives outweighs the negatives in this case such as some patients with the same gene abnormality may not benefit due to his or her deficiency or polymorphisms in another connected gene so it is a system approach including origin of pathways during development. There is nothing simply white or black but like Goethe said “there are shades of gray”. This shade is light compared to one size fits all drug making.

The main idea is create safer, effective and perfect dose medication to gain health for a quality life with less expense but more beneficial outcomes.

At the same token these developments decreases the cost of making drugs since they are specific to a small population or group so there are less clinical trial time, less time for approval, less adverse affects.

Functional genomics suggests how piece of information utilized in body in a nut shell. However, use of these knowledge to develop new drugs created a new area called Pharmacogenomics. Thus, FDA included the terminology for drug labeling that contain biomarkers along with several other factors containing variation of clinical response to drug exposure, possible side or adverse effects, genotype-specific dosing, drug action mechanism,  polymorphic drug target and disposition genes.

What can be on the label: Age, Sex, Origin/Ethinicity (Asian, Caucasian, African, South Asian), gene of interest, possible SNPs, variation/polymorphisms warnings, dose etc.

Here are the FDA-approved drugs with pharmacogenomic information in their labeling:

Pharmacogenomic Biomarkers in Drug Labeling

Drug Therapeutic Area HUGO Symbol Referenced Subgroup Labeling Sections
Abacavir Infectious Diseases HLA-B HLA-B*5701 allele carriers Boxed Warning, Contraindications, Warnings and Precautions, Patient Counseling Information
Ado-Trastuzumab Emtansine Oncology ERBB2 HER2 protein overexpression or gene amplification positive Indications and Usage, Warnings and Precautions, Adverse Reactions, Clinical Pharmacology, Clinical Studies
Afatinib Oncology EGFR EGFR exon 19 deletion or exon 21 substitution (L858R) mutation positive Indications and Usage, Dosage and Administration, Adverse Reactions, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Amitriptyline Psychiatry CYP2D6 CYP2D6 poor metabolizers Precautions
Anastrozole Oncology ESR1, PGR Hormone receptor positive Indications and Usage, Clinical Pharmacology, Clinical Studies
Aripiprazole Psychiatry CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Dosage and Administration
Arsenic Trioxide Oncology PML/RARA PML/RARα (t(15;17)) gene expression positive Boxed Warning, Clinical Pharmacology, Indications and Usage, Warnings
Atomoxetine Psychiatry CYP2D6 CYP2D6 poor metabolizers Dosage and Administration, Warnings and Precautions, Drug Interactions, Clinical Pharmacology
Atorvastatin Endocrinology LDLR Homozygous familial hypercholesterolemia Indications and Usage, Dosage and Administration, Warnings and Precautions, Clinical Pharmacology, Clinical Studies
Azathioprine Rheumatology TPMT TPMT intermediate or poor metabolizers Dosage and Administration, Warnings and Precautions, Drug Interactions, Adverse Reactions, Clinical Pharmacology
Belimumab Autoimmune Diseases BAFF/TNFSF13B CD257 positive Clinical Pharmacology, Clinical Studies
Boceprevir Infectious Diseases IFNL3 IL28B rs12979860 T allele carriers Clinical Pharmacology
Bosutinib Oncology BCR/ABL1 Philadelphia chromosome (t(9;22)) positive Indications and Usage, Adverse Reactions, Clinical Studies
Brentuximab Vedotin Oncology TNFRSF8 CD30 positive Indications and Usage, Description, Clinical Pharmacology
Busulfan Oncology Ph Chromosome Ph Chromosome negative Clinical Studies
Capecitabine Oncology DPYD DPD deficient Contraindications, Warnings and Precautions, Patient Information
Carbamazepine (1) Neurology HLA-B HLA-B*1502 allele carriers Boxed Warning, Warnings and Precautions
Carbamazepine (2) Neurology HLA-A HLA-A*3101 allele carriers Boxed Warning, Warnings and Precautions
Carglumic Acid Metabolic Disorders NAGS N-acetylglutamate synthase deficiency Indications and Usage, Warnings and Precautions, Special Populations, Clinical Pharmacology, Clinical Studies
Carisoprodol Rheumatology CYP2C19 CYP2C19 poor metabolizers Clinical Pharmacology, Special Populations
Carvedilol Cardiology CYP2D6 CYP2D6 poor metabolizers Drug Interactions, Clinical Pharmacology
Celecoxib Rheumatology CYP2C9 CYP2C9 poor metabolizers Dosage and Administration, Drug Interactions, Use in Specific Populations, Clinical Pharmacology
Cetuximab (1) Oncology EGFR EGFR protein expression positive Indications and Usage, Warnings and Precautions, Description, Clinical Pharmacology, Clinical Studies
Cetuximab (2) Oncology KRAS KRAS codon 12 and 13 mutation negative Indications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Clinical Pharmacology, Clinical Studies
Cevimeline Dermatology CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Chloroquine Infectious Diseases G6PD G6PD deficient Precautions
Chlorpropamide Endocrinology G6PD G6PD deficient Precautions
Cisplatin Oncology TPMT TPMT intermediate or poor metabolizers Clinical Pharmacology, Warnings, Precautions
Citalopram (1) Psychiatry CYP2C19 CYP2C19 poor metabolizers Drug Interactions, Warnings
Citalopram (2) Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Clobazam Neurology CYP2C19 CYP2C19 poor metabolizers Clinical Pharmacology, Dosage and Administration, Use in Specific Populations
Clomipramine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Clopidogrel Cardiology CYP2C19 CYP2C19 intermediate or poor metabolizers Boxed Warning, Dosage and Administration, Warnings and Precautions, Drug Interactions, Clinical Pharmacology
Clozapine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions, Clinical Pharmacology
Codeine Anesthesiology CYP2D6 CYP2D6 poor metabolizers Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology
Crizotinib Oncology ALK ALK gene rearrangement positive Indications and Usage, Dosage and Administration, Drug Interactions, Warnings and Precautions, Adverse Reactions, Clinical Pharmacology, Clinical Studies
Dabrafenib (1) Oncology BRAF BRAF V600E mutation positive Indications and Usage, Dosage and Administration, Warnings and Precautions, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Dabrafenib (2) Oncology G6PD G6PD deficient Warnings and Precautions, Adverse Reactions, Patient Counseling Information
Dapsone (1) Dermatology G6PD G6PD deficient Indications and Usage, Precautions, Adverse Reactions, Patient Counseling Information
Dapsone (2) Infectious Diseases G6PD G6PD deficient Precautions, Adverse Reactions, Overdosage
Dasatinib Oncology BCR/ABL1 Philadelphia chromosome (t(9;22)) positive; T315I mutation-positive Indications and Usage, Clinical Studies, Patient Counseling Information
Denileukin Diftitox Oncology IL2RA CD25 antigen positive Indications and Usage, Warnings and Precautions, Clinical Studies
Desipramine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Dexlansoprazole (1) Gastroenterology CYP2C19 CYP2C19 poor metabolizers Clinical Pharmacology, Drug Interactions
Dexlansoprazole (2) Gastroenterology CYP1A2 CYP1A2 genotypes Clinical Pharmacology
Dextromethorphan and Quinidine Neurology CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Warnings and Precautions, Drug Interactions
Diazepam Psychiatry CYP2C19 CYP2C19 poor metabolizers Drug Interactions, Clinical Pharmacology
Doxepin Psychiatry CYP2D6 CYP2D6 poor metabolizers Precautions
Drospirenone and Ethinyl Estradiol Neurology CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Warnings and Precautions, Drug Interactions
Eltrombopag (1) Hematology F5 Factor V Leiden carriers Warnings and Precautions
Eltrombopag (2) Hematology SERPINC1 Antithrombin III deficient Warnings and Precautions
Erlotinib (1) Oncology EGFR EGFR protein expression positive Clinical Pharmacology
Erlotinib (2) Oncology EGFR EGFR exon 19 deletion or exon 21 substitution (L858R) positive Indications and Usage, Dosage and Administration, Clinical Pharmacology, Clinical Studies
Esomeprazole Gastroenterology CYP2C19 CYP2C19 poor metabolizers Drug Interactions, Clinical Pharmacology
Everolimus (1) Oncology ERBB2 HER2 protein overexpression negative Indications and Usage, Boxed Warning, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical Studies
Everolimus (2) Oncology ESR1 Estrogen receptor positive Clinical Pharmacology, Clinical Studies
Exemestane Oncology ESR1 Estrogen receptor positive Indications and Usage, Dosage and Administration, Clinical Studies, Clinical Pharmacology
Fluorouracil (1) Dermatology DPYD DPD deficient Contraindications, Warnings, Patient Information
Fluorouracil (2) Oncology DPYD DPD deficient Warnings
Fluoxetine Psychiatry CYP2D6 CYP2D6 poor metabolizers Warnings, Precautions, Clinical Pharmacology
Flurbiprofen Rheumatology CYP2C9 CYP2C9 poor metabolizers Clinical Pharmacology, Special Populations
Fluvoxamine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Fulvestrant Oncology ESR1 Estrogen receptor positive Indications and Usage, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Galantamine Neurology CYP2D6 CYP2D6 poor metabolizers Special Populations
Glimepiride Endocrinology G6PD G6PD deficient Warning and Precautions
Glipizide Endocrinology G6PD G6PD deficient Precautions
Glyburide Endocrinology G6PD G6PD deficient Precautions
Ibritumomab Tiuxetan Oncology MS4A1 CD20 positive Indications and Usage, Clinical Pharmacology, Description
Iloperidone Psychiatry CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Dosage and Administration, Drug Interactions, Specific Populations, Warnings and Precautions
Imatinib (1) Oncology KIT c-KIT D816V mutation negative Indications and Usage, Dosage and Administration Clinical Pharmacology, Clinical Studies
Imatinib (2) Oncology BCR/ABL1 Philadelphia chromosome (t(9;22)) positive Indications and Usage, Dosage and Administration, Clinical Pharmacology, Clinical Studies
Imatinib (3) Oncology PDGFRB PDGFR gene rearrangement positive Indications and Usage, Dosage and Administration, Clincal Studies
Imatinib (4) Oncology FIP1L1/PDGFRA FIP1L1/PDGFRα fusion kinase (or CHIC2 deletion) positive Indications and Usage, Dosage and Administration, Clinical Studies
Imipramine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Indacaterol Pulmonary UGT1A1 UGT1A1 *28 allele homozygotes Clinical Pharmacology
Irinotecan Oncology UGT1A1 UGT1A1*28 allele carriers Dosage and Administration, Warnings, Clinical Pharmacology
Isosorbide and Hydralazine Cardiology NAT1-2 Slow acetylators Clinical Pharmacology
Ivacaftor Pulmonary CFTR CFTR G551D carriers Indications and Usage, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical Studies
Lansoprazole Gastroenterology CYP2C19 CYP2C19 poor metabolizer Drug Interactions, Clinical Pharmacology
Lapatinib Oncology ERBB2 HER2 protein overexpression positive Indications and Usage, Clinical Pharmacology, Patient Counseling Information
Lenalidomide Hematology del (5q) Chromosome 5q deletion Boxed Warning, Indications and Usage, Clinical Studies, Patient Counseling
Letrozole Oncology ESR1, PGR Hormone receptor positive Indications and Usage, Adverse Reactions, Clinical Studies, Clinical Pharmacology
Lomitapide Endocrinology LDLR Homozygous familial hypercholesterolemia and LDL receptor mutation deficient Indication and Usage, Adverse Reactions, Clinical Studies
Mafenide Infectious Diseases G6PD G6PD deficient Warnings, Adverse Reactions
Maraviroc Infectious Diseases CCR5 CCR5 positive Indications and Usage, Warnings and Precautions, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Mercaptopurine Oncology TPMT TPMT intermediate or poor metabolizers Dosage and Administration, Contraindications, Precautions, Adverse Reactions, Clinical Pharmacology
Methylene Blue Hematology G6PD G6PD deficient Precautions
Metoclopramide Gastroentrology CYB5R1-4 NADH cytochrome b5 reductase deficient Precautions
Metoprolol Cardiology CYP2D6 CYP2D6 poor metabolizers Precautions, Clinical Pharmacology
Mipomersen Endocrinology LDLR Homozygous familial hypercholesterolemia and LDL receptor mutation deficient Indication and Usage, Clinical Studies, Use in Specific Populations
Modafinil Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Mycophenolic Acid Transplantation HPRT1 HGPRT deficient Precautions
Nalidixic Acid Infectious Diseases G6PD G6PD deficient Precautions, Adverse Reactions
Nefazodone Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Nilotinib (1) Oncology BCR/ABL1 Philadelphia chromosome (t(9 :22)) positive Indications and Usage, Patient Counseling Information
Nilotinib (2) Oncology UGT1A1 UGT1A1*28 allele homozygotes Warnings and Precautions, Clinical Pharmacology
Nitrofurantoin Infectious Diseases G6PD G6PD deficient Warnings, Adverse Reactions
Nortriptyline Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Ofatumumab Oncology MS4A1 CD20 positive Indications and Usage, Clinical Pharmacology
Omacetaxine Oncology BCR/ABL1 BCR-ABL T315I Clinical Pharmacology
Omeprazole Gastroenterology CYP2C19 CYP2C19 poor metabolizers Dosage and Administration, Warnings and Precautions, Drug Interactions
Panitumumab (1) Oncology EGFR EGFR protein expression positive Indications and Usage, Warnings and Precautions, Clinical Pharmacology, Clinical Studies
Panitumumab (2) Oncology KRAS KRAS codon 12 and 13 mutation negative Indications and Usage, Clinical Pharmacology, Clinical Studies
Pantoprazole Gastroenterology CYP2C19 CYP2C19 poor metabolizers Clinical Pharmacology, Drug Interactions, Special Populations
Paroxetine Psychiatry CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Drug Interactions
Pazopanib Oncology UGT1A1 (TA)7/(TA)7 genotype (UGT1A1*28/*28) Clinical Pharmacology, Warnings and Precautions
PEG-3350, Sodium Sulfate, Sodium Chloride, Potassium Chloride, Sodium Ascorbate, and Ascorbic Acid Gastroenterology G6PD G6PD deficient Warnings and Precautions
Peginterferon alfa-2b Infectious Diseases IFNL3 IL28B rs12979860 T allele carriers Clinical Pharmacology
Pegloticase Rheumatology G6PD G6PD deficient Contraindications, Patient Counseling Information
Perphenazine Psychiatry CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Drug Interactions
Pertuzumab Oncology ERBB2 HER2 protein overexpression positive Indications and Usage, Warnings and Precautions, Adverse Reactions, Clinical Studies, Clinical Pharmacology
Phenytoin Neurology HLA-B HLA-B*1502 allele carriers Warnings
Pimozide Psychiatry CYP2D6 CYP2D6 poor metabolizers Warnings, Precautions, Contraindications, Dosage and Administration
Ponatinib Oncology BCR/ABL1 Philadelphia chromosome (t(9;22)) positive, BCR –ABL T315I mutation Indications and Usage, Warnings and Precautions, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical Studies
Prasugrel Cardiology CYP2C19 CYP2C19 poor metabolizers Use in Specific Populations, Clinical Pharmacology, Clinical Studies
Pravastatin Endocrinology LDLR Homozygous familial hypercholesterolemia and LDL receptor deficient Clinical Studies, Use in Specific Populations
Primaquine Infectious Diseases G6PD G6PD deficient Warnings and Precautions, Adverse Reactions
Propafenone Cardiology CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology
Propranolol Cardiology CYP2D6 CYP2D6 poor metabolizers Precautions, Drug Interactions, Clinical Pharmacology
Protriptyline Psychiatry CYP2D6 CYP2D6 poor metabolizers Precautions
Quinidine Cardiology CYP2D6 CYP2D6 poor metabolizers Precautions
Quinine Sulfate Infectious Diseases G6PD G6PD deficient Contraindications, Patient Counseling Information
Rabeprazole Gastroenterology CYP2C19 CYP2C19 poor metabolizers Drug Interactions, Clinical Pharmacology
Rasburicase Oncology G6PD G6PD deficient Boxed Warning, Contraindications
Rifampin, Isoniazid, and Pyrazinamide Infectious Diseases NAT1-2 Slow inactivators Adverse Reactions, Clinical Pharmacology
Risperidone Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions, Clinical Pharmacology
Rituximab Oncology MS4A1 CD20 positive Indication and Usage, Clinical Pharmacology, Description, Precautions
Rosuvastatin Endocrinology LDLR Homozygous and Heterozygous familial hypercholesterolemia Indications and Usage, Dosage and Administration, Clinical Pharmacology, Clinical Studies
Sodium Nitrite Antidotal Therapy G6PD G6PD deficient Warnings and Precautions
Succimer Hematology G6PD G6PD deficient Clinical Pharmacology
Sulfamethoxazole and Trimethoprim Infectious Diseases G6PD G6PD deficient Precautions
Tamoxifen (1) Oncology ESR1, PGR Hormone receptor positive Indications and Usage, Precautions, Medication Guide
Tamoxifen (2) Oncology F5 Factor V Leiden carriers Warnings
Tamoxifen (3) Oncology F2 Prothrombin mutation G20210A Warnings
Telaprevir Infectious Diseases IFNL3 IL28B rs12979860 T allele carriers Clinical Pharmacology
Terbinafine Infectious Diseases CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Tetrabenazine Neurology CYP2D6 CYP2D6 poor metabolizers Dosage and Administration, Warnings, Clinical Pharmacology
Thioguanine Oncology TPMT TPMT poor metabolizer Dosage and Administration, Precautions, Warnings
Thioridazine Psychiatry CYP2D6 CYP2D6 poor metabolizers Precautions, Warnings, Contraindications
Ticagrelor Cardiology CYP2C19 CYP2C19 poor metabolizers Clinical Studies
Tolterodine Urology CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology, Drug Interactions, Warnings and Precautions
Tositumomab Oncology MS4A1 CD20 antigen positive Indications and Usage, Clinical Pharmacology
Tramadol Analgesic CYP2D6 CYP2D6 poor metabolizers Clinical Pharmacology
Trametinib Oncology BRAF BRAF V600E/K mutation positive Indications and Usage, Dosage and Administration, Adverse Reactions, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Trastuzumab Oncology ERBB2 HER2 protein overexpression positive Indications and Usage, Warnings and Precautions, Clinical Pharmacology, Clinical Studies
Tretinoin Oncology PML/RARA PML/RARα (t(15;17)) gene expression positive Clinical Studies, Indications and Usage, Warnings
Trimipramine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Valproic Acid (1) Neurology POLG POLG mutation positive Boxed Warning, Contraindications, Warnings and Precautions
Valproic Acid (2) Neurology NAGS, CPS1, ASS1, OTC, ASL, ABL2 Urea cycle enzyme deficient Contraindications, Warnings and Precautions, Adverse Reactions, Medication Guide
Velaglucerase Alfa Metabolic Disorders GBA Lysosomal glucocerebrosidase enzyme Indication and Usage, Description, Clinical Pharmacology, Clinical Studies
Vemurafenib Oncology BRAF BRAF V600E mutation positive Indications and Usage, Warning and Precautions, Clinical Pharmacology, Clinical Studies, Patient Counseling Information
Venlafaxine Psychiatry CYP2D6 CYP2D6 poor metabolizers Drug Interactions
Voriconazole Infectious Diseases CYP2C19 CYP219 intermediate or poor metabolizers Clinical Pharmacology, Drug Interactions
Warfarin (1) Cardiology or Hematology CYP2C9 CYP2C9 intermediate or poor metabolizers Dosage and Administration, Drug Interactions, Clinical Pharmacology
Warfarin (2) Cardiology or Hematology VKORC1 VKORC1 rs9923231 A allele carriers Dosage and Administration, Clinical Pharmacology

References and Further Readings:

 

There are several practical applications pharmacogenomics in cancer, depression, cardiovascular disease and drug metabolism that is used today.  Some of these included in the following references:

JAMA 2004; 291(23) 2821-2827.

Useful Links:

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Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

Larry H Bernstein, MD, FCAP, Author and Curator

This article has two parts:

  • Part 1: Novel Genomics Industry Trends in Diagnostics and Biomarkers vs Present Market Transient Conditions

and

  • Part 2: Historical Scientific Leaders Memoirs

 

Part 1: Novel Genomics Industry Trends in Diagnostics and Biomarkers vs Present Market Transient Conditions

 

Based on “Forging a path from companion diagnostics to holistic decision support”, L.E.K.

Executive Insights, 2013;14(12). http://www.LEK.com

Companion diagnostics and their companion therapies is defined here as a method enabling

  • LIKELY responders to therapies that are specific for patients with ma specific molecular profile.

The result of this statement is that the diagnostics permitted to specific patient types gives access to

  • novel therapies that may otherwise not be approve or reimbursed in other, perhaps “similar” patients
  • who lack a matching identification of the key identifier(s) needed to permit that therapy,
  • thus, entailing a poor expected response.

The concept is new because:

(1) The diagnoses may be closely related by classical criteria, but at the same time they are
not alike with respect to efficacy of treatment with a standard therapy.
(2) The companion diagnostics is restricted to dealing with a targeted drug-specific question
without regard to other clinical issues.
(3) The efficacy issue it clarifies is reliant on a deep molecular/metabolic insight that is not available, except through
emergent genomic/proteomic analysis that has become available and which has rapidly declining cost to obtain.

The limitation example given is HER2 testing for use of Herceptin in therapy for non-candidates (HER2 negative patients).
The problem is that the current format is a “one test/one drug” match, but decision support  may require a combination of

  • validated biomakers obtained on a small biopsy sample (technically manageable) with confusing results.

While HER2 negative patients are more likely to be pre-menopausal with a more aggressive tumor than postmenopausal,

  • the HER2 negative designation does not preclude treatment with Herceptin.

So the Herceptin would be given in combination, but with what other drug in a non-candidate?

The point that L.E.K. makes is that providing highly validated biomarkers linked to approved therapies, it is necessary to pursue more holistic decision support tests that interrogate multiple biomarkers (panels of companion diagnostic markers) and discovery of signatures for treatments that are also used with a broad range of information, such as,

  • traditional tests,
  • imaging,
  • clinical trials,
  • outcomes data,
  • EMR data,
  • reimbursement and coverage data.

A comprehensive solution of this nature appears to be a distance from realization.  However, is this the direction that will lead to tomorrows treatment decision support approaches?

 Surveying the Decision Support Testing Landscape

As a starting point, L.E.K. characterized the landscape of available tests in the U.S. that inform treatment decisions compiled from ~50 leading diagnostics companies operating in the U.S. between 2004-2011. L.E.K. identified more than 200 decision support tests that were classified by test purpose, and more specifically,  whether tests inform treatment decisions for a single drug/class (e.g., companion diagnostics) vs. more holistic treatment decisions across multiple drugs/classes (i.e., multiagent response tests).

 Treatment Decision Support Tests

Companion Diagnostics
Single drug/class
Predict response/safety or guide dosing of a single drug or class

HercepTest   Dako
Determines HER2 protein overexpression for Herceptin treatment selection

Multiple drugs/classes

Vysis ALK Break
Apart FISH
Abbott Labs Predicts the NSCLC patient response to Xalkori

Other Decision Support
Provide prognostic and predictive information on the benefit of treatment

Oncotype Dx    Genomic Health, Inc.
Predicts both recurrence of breast cancer and potential patient benefit to chemotherapy regimens

PML-RARα     Clarient, Inc.
Predicts response to all-trans retinoic acid (ATRA) and other chemotherapy agents

TRUGENE    Siemens
Measures resistence to multiple  HIV-1 anti-retroviral agents

Multi-agent Response

Inform targeted therapy class selection by interrogating a panel of biomarkers
Target Now  Caris Life Sciences
Examines tumor’s molecular profile to tailor treatment options

ResponseDX: Lung    Response Genetics, Inc.
Examines multiple biomarkers to guide therapeutic treatment decisions for NSCLC patients

Source: L.E.K. Analysis

Includes IVD and LDT tests from

  1. top-15 IVD test suppliers,
  2. top-four large reference labs,
  3. top-five AP labs, and
  4. top-20 specialty reference labs.

For descriptive purposes only, may not map to exact regulatory labeling

Most tests are companion diagnostics and other decision support tests that provide guidance on

  • single drug/class therapy decisions.

However, holistic decision support tests (e.g., multi-agent response) are growing the fastest at 56% CAGR.
The emergence of multi-agent response tests suggests diagnostics companies are already seeing the need to aggregate individual tests (e.g., companion diagnostics) into panels of appropriate markers addressing a given clinical decision need. L.E.K. believes this trend is likely to continue as

  • increasing numbers of  biomarkers become validated for diseases and multiplexing tools
  • enabling the aggregation of multiple biomarker interrogations into a single test

to become deployed in the clinic.

Personalized Medicine Partnerships

L.E.K. also completed an assessment of publicly available personalized medicine partnership activity from 2009-2011 for ~150 leading organizations operating in the U.S. to look at broader decision support trends and emergence of more holistic solutions beyond diagnostic tests.

Survey of partnerships deals was conducted for

  • top-10 academic medical centers research institutions,
  • top-25 biopharma,
  • top-four healthcare IT companies,
  • top-three healthcare imaging companies,
  • top-20 IVD manufacturers,
  • top-20 laboratories,
  • top-10 payers/PBMs,
  • top-15 personalized healthcare companies,
  • top-10 regulatory/guideline entities, and
  • top-20 tools vendors for the period of 01/01/2009 – 12/31/2011.
    Source: Company websites, GenomeWeb, L.E.K. analysis

Across the sample we identified 189 publicly announced partnerships of which ~65% focused on more traditional areas (biomarker discovery, companion diagnostics and targeted therapies). However, a significant portion (~30%) included elements geared towards creating more holistic decision support models.

Partnerships categorized as holistic decision support by L.E.K. were focused on

  • mining large patient datasets (e.g., from payers or providers),
  • molecular profiling (e.g., deploying next-generation sequencing),
  • creating information technology (IT) infrastructure needed to enable holistic decision support models and
  • integrating various datasets to create richer decision support solutions.

Interestingly, holistic decision support partnerships often included stakeholders outside of biopharma and diagnostics such as

  • research tools,
  • payers/PBMs,
  • healthcare IT companies as well as
  • emerging personalized healthcare (PHC) companies (e.g., Knome, Foundation Medicine and 23andMe).

This finding suggests that these new stakeholders will be increasingly important in influencing care decisions going forward.

Holistic Treatment Decision Support

Holistic Decision   Support Focus

Technology Provider Partners
Stakeholder Deploying the Solution

Holistic Decision
Support Activities
Molecular Profiling

Life Technologies

TGEN/US
Oncology

Sequencing of triple-negative breast  cancer patients to identify potential treatment strategies

Foundation Medicine

Novartis

Deployment of cancer genomics analysis platform to support Novartis clinical research efforts
Predictive genomics

Clarient, Inc.
(GE Healthcare)

Acorn
Research

Biomarker profiling of patients within Acorn’s network of providers to support clinical research efforts

GenomeQuest

Beth Israel Deaconess
Medical Center

Whole genome analysis and to guide patient management
Outcomes Data Mining

AstraZeneca

WellPoint

Evaluate comparative effectiveness of selected marketed therapies

23andMe

NIH

Leverage information linking drug response and CYP2C9/CYP2C19 variation

Pfizer

Medco

Leverage patient genotype, phenotype and outcome for treatment decisions and target therapeutics
Healthcare IT Infrastructure

IBM

WellPoint

Deploy IBM’s Watson-based solution to evidence-based healthcare decision-making support

Oracle

Moffitt Cancer Center

Deploy Oracle’s informatics platform to store and manage patient medical information
Data Integration

Siemens Diagnostics

Susquehanna Health

Integration of imaging and laboratory diagnostics

Cernostics

Geisinger
Health

Integration of advanced tissue diagnostics, digital pathology, annotated biorepository and EMR
to create solutions
next-generation treatment decision support solutions

CardioDx

GE Healthcare

Integration of genomics with imaging data in CVD

Implications

L.E.K. believes the likely debate won’t center on which models and companies will prevail. It appears that the industry is now moving along the continuum to a truly holistic capability.
The mainstay of personalized medicine today will become integrated and enhanced by other data.

The companies that succeed will be able to capture vast amounts of information

  • and synthesize it for personalized care.

Holistic models will be powered by increasingly larger datasets and sophisticated decision-making algorithms.
This will require the participation of an increasingly broad range of participants to provide the

  • science, technologies, infrastructure and tools necessary for deployment.

There are a number of questions posed by this study, but only some are of interest to this discussion:

Group A.    Pharmaceuticals and Devices

  •  How will holistic decision support impact the landscape ?
    (e.g., treatment /testing algorithms, decision making, clinical trials)

Group B.     Diagnostics and   Decision Support

  •   What components will be required to build out holistic solutions?

– Testing technologies

– Information (e.g., associations, outcomes, trial databases, records)

– IT infrastructure for data integration and management, simulation and reporting

  •  How can various components be brought together to build seamless holistic  decision support solutions?

Group C.      Providers and Payers

  •  In which areas should models be deployed over time?
  • Where are clinical and economic arguments  most compelling?

Part 2: Historical Scientific Leaders Memoirs – Realtime Clinical Expert Support

Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman,
in the Yale University Applied Mathematics Program,

A software system that is the equivalent of an intelligent Electronic Health Records Dashboard that

  • provides empirical medical reference and
  • suggests quantitative diagnostics options.

The current design of the Electronic Medical Record (EMR) is a linear presentation of portions of the record

  • by services
  • by diagnostic method, and
  • by date, to cite examples.

This allows perusal through a graphical user interface (GUI) that partitions the information or necessary reports

  • in a workstation entered by keying to icons.

This requires that the medical practitioner finds the

  • history,
  • medications,
  • laboratory reports,
  • cardiac imaging and
  • EKGs, and
  • radiology in different workspaces.

The introduction of a DASHBOARD has allowed a presentation of

  • drug reactions
  • allergies
  • primary and secondary diagnoses, and
  • critical information

about any patient the care giver needing access to the record.

The advantage of this innovation is obvious.  The startup problem is what information is presented and

  • how it is displayed, which is a source of variability and a key to its success.

We are proposing an innovation that supercedes the main design elements of a DASHBOARD and utilizes

  • the conjoined syndromic features of the disparate data elements.

So the important determinant of the success of this endeavor is that

  • it facilitates both the workflow and the decision-making process with a reduction of medical error.

Continuing work is in progress in extending the capabilities with model datasets, and sufficient data because

  • the extraction of data from disparate sources will, in the long run, further improve this process.

For instance, the finding of  both ST depression on EKG coincident with an elevated cardiac biomarker (troponin), particularly in the absence of substantially reduced renal function. The conversion of hematology based data into useful clinical information requires the establishment of problem-solving constructs based on the measured data.

The most commonly ordered test used for managing patients worldwide is the hemogram that often incorporates

  • the review of a peripheral smear.

While the hemogram has undergone progressive modification of the measured features over time the subsequent expansion of the panel of tests has provided a window into the cellular changes in the

  • production
  • release
  • or suppression

of the formed elements from the blood-forming organ into the circulation. In the hemogram one can view

  • data reflecting the characteristics of a broad spectrum of medical conditions.

Progressive modification of the measured features of the hemogram has delineated characteristics expressed as measurements of

  • size
  • density, and
  • concentration,

resulting in many characteristic features of classification. In the diagnosis of hematological disorders

  • proliferation of marrow precursors, the
  • domination of a cell line, and features of
  • suppression of hematopoiesis

provide a two dimensional model.  Other dimensions are created by considering

  • the maturity of the circulating cells.

The application of rules-based, automated problem solving should provide a valid approach to

  • the classification and interpretation of the data used to determine a knowledge-based clinical opinion.

The exponential growth of knowledge since the mapping of the human genome enabled by parallel advances in applied mathematics that have not been a part of traditional clinical problem solving.

As the complexity of statistical models has increased

  • the dependencies have become less clear to the individual.

Contemporary statistical modeling has a primary goal of finding an underlying structure in studied data sets.
The development of an evidence-based inference engine that can substantially interpret the data at hand and

  • convert it in real time to a “knowledge-based opinion”

could improve clinical decision-making by incorporating

  • multiple complex clinical features as well as duration of onset into the model.

An example of a difficult area for clinical problem solving is found in the diagnosis of SIRS and associated sepsis. SIRS (and associated sepsis) is a costly diagnosis in hospitalized patients.   Failure to diagnose sepsis in a timely manner creates a potential financial and safety hazard.  The early diagnosis of SIRS/sepsis is made by the application of defined criteria by the clinician.

  • temperature
  • heart rate
  • respiratory rate and
  • WBC count

The application of those clinical criteria, however, defines the condition after it has developed and

  • has not provided a reliable method for the early diagnosis of SIRS.

The early diagnosis of SIRS may possibly be enhanced by the measurement of proteomic biomarkers, including

  • transthyretin
  • C-reactive protein
  • procalcitonin
  • mean arterial pressure

Immature granulocyte (IG) measurement has been proposed as a

  • readily available indicator of the presence of granulocyte precursors (left shift).

The use of such markers, obtained by automated systems

  • in conjunction with innovative statistical modeling, provides
  • a promising approach to enhance workflow and decision making.

Such a system utilizes the conjoined syndromic features of

  • disparate data elements with an anticipated reduction of medical error.

How we frame our expectations is so important that it determines

  • the data we collect to examine the process.

In the absence of data to support an assumed benefit, there is no proof of validity at whatever cost.
This has meaning for

  • hospital operations,
  • for nonhospital laboratory operations,
  • for companies in the diagnostic business, and
  • for planning of health systems.

The problem stated by LL  WEED in “Idols of the Mind” (Dec 13, 2006): “ a root cause of a major defect in the health care system is that, while we falsely admire and extol the intellectual powers of highly educated physicians, we do not search for the external aids their minds require”.  HIT use has been

  • focused on information retrieval, leaving
  • the unaided mind burdened with information processing.

We deal with problems in the interpretation of data presented to the physician, and how through better

  • design of the software that presents this data the situation could be improved.

The computer architecture that the physician uses to view the results is more often than not presented

  • as the designer would prefer, and not as the end-user would like.

In order to optimize the interface for physician, the system would have a “front-to-back” design, with
the call up for any patient ideally consisting of a dashboard design that presents the crucial information

  • that the physician would likely act on in an easily accessible manner.

The key point is that each item used has to be closely related to a corresponding criterion needed for a decision.

Feature Extraction.

This further breakdown in the modern era is determined by genetically characteristic gene sequences
that are transcribed into what we measure.  Eugene Rypka contributed greatly to clarifying the extraction
of features in a series of articles, which

  • set the groundwork for the methods used today in clinical microbiology.

The method he describes is termed S-clustering, and

  • will have a significant bearing on how we can view laboratory data.

He describes S-clustering as extracting features from endogenous data that

  • amplify or maximize structural information to create distinctive classes.

The method classifies by taking the number of features

  • with sufficient variety to map into a theoretic standard.

The mapping is done by

  • a truth table, and each variable is scaled to assign values for each: message choice.

The number of messages and the number of choices forms an N-by N table.  He points out that the message

  • choice in an antibody titer would be converted from 0 + ++ +++ to 0 1 2 3.

Even though there may be a large number of measured values, the variety is reduced

  • by this compression, even though there is risk of loss of information.

Yet the real issue is how a combination of variables falls into a table with meaningful information. We are concerned with accurate assignment into uniquely variable groups by information in test relationships. One determines the effectiveness of each variable by

  • its contribution to information gain in the system.

The reference or null set is the class having no information.  Uncertainty in assigning to a classification is

  • only relieved by providing sufficient information.

The possibility for realizing a good model for approximating the effects of factors supported by data used

  • for inference owes much to the discovery of Kullback-Liebler distance or “information”, and Akaike
  • found a simple relationship between K-L information and Fisher’s maximized log-likelihood function.

In the last 60 years the application of entropy comparable to

  • the entropy of physics, information, noise, and signal processing,
  • has been fully developed by Shannon, Kullback, and others, and has been integrated with modern statistics,
  • as a result of the seminal work of Akaike, Leo Goodman, Magidson and Vermunt, and work by Coifman.

Gil David et al. introduced an AUTOMATED processing of the data available to the ordering physician and

  • can anticipate an enormous impact in diagnosis and treatment of perhaps half of the top 20 most common
  • causes of hospital admission that carry a high cost and morbidity.

For example: anemias (iron deficiency, vitamin B12 and folate deficiency, and hemolytic anemia or myelodysplastic syndrome); pneumonia; systemic inflammatory response syndrome (SIRS) with or without bacteremia; multiple organ failure and hemodynamic shock; electrolyte/acid base balance disorders; acute and chronic liver disease; acute and chronic renal disease; diabetes mellitus; protein-energy malnutrition; acute respiratory distress of the newborn; acute coronary syndrome; congestive heart failure; disordered bone mineral metabolism; hemostatic disorders; leukemia and lymphoma; malabsorption syndromes; and cancer(s)[breast, prostate, colorectal, pancreas, stomach, liver, esophagus, thyroid, and parathyroid].

Rudolph RA, Bernstein LH, Babb J: Information-Induction for the diagnosis of myocardial infarction. Clin Chem 1988;34:2031-2038.

Bernstein LH (Chairman). Prealbumin in Nutritional Care Consensus Group.

Measurement of visceral protein status in assessing protein and energy malnutrition: standard of care. Nutrition 1995; 11:169-171.

Bernstein LH, Qamar A, McPherson C, Zarich S, Rudolph R. Diagnosis of myocardial infarction: integration of serum markers and clinical descriptors using information theory. Yale J Biol Med 1999; 72: 5-13.

Kaplan L.A.; Chapman J.F.; Bock J.L.; Santa Maria E.; Clejan S.; Huddleston D.J.; Reed R.G.; Bernstein L.H.; Gillen-Goldstein J. Prediction of Respiratory Distress Syndrome using the Abbott FLM-II amniotic fluid assay. The National Academy of Clinical Biochemistry (NACB) Fetal Lung Maturity Assessment Project.  Clin Chim Acta 2002; 326(8): 61-68.

Bernstein LH, Qamar A, McPherson C, Zarich S. Evaluating a new graphical ordinal logit method (GOLDminer) in the diagnosis of myocardial infarction utilizing clinical features and laboratory data. Yale J Biol Med 1999; 72:259-268.

Bernstein L, Bradley K, Zarich SA. GOLDmineR: Improving models for classifying patients with chest pain. Yale J Biol Med 2002; 75, pp. 183-198.

Ronald Raphael Coifman and Mladen Victor Wickerhauser. Adapted Waveform Analysis as a Tool for Modeling, Feature Extraction, and Denoising. Optical Engineering, 33(7):2170–2174, July 1994.

R. Coifman and N. Saito. Constructions of local orthonormal bases for classification and regression. C. R. Acad. Sci. Paris, 319 Série I:191-196, 1994.

Realtime Clinical Expert Support and validation System

We have developed a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides empirical medical reference and suggests quantitative diagnostics options.

The primary purpose is to

  1. gather medical information,
  2. generate metrics,
  3. analyze them in realtime and
  4. provide a differential diagnosis,
  5. meeting the highest standard of accuracy.

The system builds its unique characterization and provides a list of other patients that share this unique profile, therefore utilizing the vast aggregated knowledge (diagnosis, analysis, treatment, etc.) of the medical community. The

  • main mathematical breakthroughs are provided by accurate patient profiling and inference methodologies
  • in which anomalous subprofiles are extracted and compared to potentially relevant cases.

As the model grows and its knowledge database is extended, the diagnostic and the prognostic become more accurate and precise. We anticipate that the effect of implementing this diagnostic amplifier would result in

  • higher physician productivity at a time of great human resource limitations,
  • safer prescribing practices,
  • rapid identification of unusual patients,
  • better assignment of patients to observation, inpatient beds,
    intensive care, or referral to clinic,
  • shortened length of patients ICU and bed days.

The main benefit is a real time assessment as well as diagnostic options based on

  • comparable cases,
  • flags for risk and potential problems

as illustrated in the following case acquired on 04/21/10. The patient was diagnosed by our system with severe SIRS at a grade of 0.61 .

Graphical presentation of patient status

The patient was treated for SIRS and the blood tests were repeated during the following week. The full combined record of our system’s assessment of the patient, as derived from the further hematology tests, is illustrated below. The yellow line shows the diagnosis that corresponds to the first blood test (as also shown in the image above). The red line shows the next diagnosis that was performed a week later.

Progression changes in patient ICU stay with SIRS

Chemistry of Herceptin [Trastuzumab] is explained with images in

http://www.chm.bris.ac.uk/motm/herceptin/index_files/Page450.htm

 

REFERENCES

The Cost Burden of Disease: U.S. and Michigan CHRT Brief. January 2010.
@www.chrt.org

The National Hospital Bill: The Most Expensive Conditions by Payer, 2006. HCUP Brief #59.

Rudolph RA, Bernstein LH, Babb J: Information-Induction for the diagnosis of myocardial infarction. Clin Chem 1988;34:2031-2038.

Bernstein LH, Qamar A, McPherson C, Zarich S, Rudolph R. Diagnosis of myocardial infarction: integration of serum markers and clinical descriptors using information theory. Yale J Biol Med 1999; 72: 5-13.

Kaplan L.A.; Chapman J.F.; Bock J.L.; Santa Maria E.; Clejan S.; Huddleston D.J.; Reed R.G.; Bernstein L.H.; Gillen-Goldstein J. Prediction of Respiratory Distress Syndrome using the Abbott FLM-II amniotic fluid assay. The National Academy of Clinical Biochemistry (NACB) Fetal Lung Maturity Assessment Project.  Clin Chim Acta 2002; 326(8): 61-68.

Bernstein LH, Qamar A, McPherson C, Zarich S. Evaluating a new graphical ordinal logit method (GOLDminer) in the diagnosis of myocardial infarction utilizing clinical features and laboratory data. Yale J Biol Med 1999; 72:259-268.

Bernstein L, Bradley K, Zarich SA. GOLDmineR: Improving models for classifying patients with chest pain. Yale J Biol Med 2002; 75, pp. 183-198.

Ronald Raphael Coifman and Mladen Victor Wickerhauser. Adapted Waveform Analysis as a Tool for Modeling, Feature Extraction, and Denoising. Optical Engineering 1994; 33(7):2170–2174.

  1. Coifman and N. Saito. Constructions of local orthonormal bases for classification and regression. C. R. Acad. Sci. Paris, 319 Série I:191-196, 1994.

W Ruts, S De Deyne, E Ameel, W Vanpaemel,T Verbeemen, And G Storms. Dutch norm data for 13 semantic categories and 338 exemplars. Behavior Research Methods, Instruments, & Computers 2004; 36 (3): 506–515.

De Deyne, S Verheyen, E Ameel, W Vanpaemel, MJ Dry, WVoorspoels, and G Storms.  Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts.  Behavior Research Methods 2008; 40 (4): 1030-1048

Landauer, T. K., Ross, B. H., & Didner, R. S. (1979). Processing visually presented single words: A reaction time analysis [Technical memorandum].  Murray Hill, NJ: Bell Laboratories. Lewandowsky, S. (1991).

Weed L. Automation of the problem oriented medical record. NCHSR Research Digest Series DHEW. 1977;(HRA)77-3177.

Naegele TA. Letter to the Editor. Amer J Crit Care 1993:2(5):433.

Retinal prosthetic strategy with the capacity to restore normal vision, Sheila Nirenberg and Chethan Pandarinath

http://www.pnas.org/content/109/37/15012

 

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Read Full Post »


microRNA Biomarker

Reporter: Larry H Bernstein, MD, FCAP

MicroRNA Molecule May Serve as Biomarker

miRNA molecule called miR-7 decreased in highly metastatic cancer stem-like cells.
February 18, 2013
Researchers have identified two molecules that could potentially serve as biomarkers in

MicroRNAs are involved in

  • tumor initiation and
  • progression, and
  • may play a role in metastasis, particularly in relation to
  • cancer stem-like cells.
miR-7 is a metastasis

  • suppressor in cancer stem-like cells, and when they
  • increased expression of miR-7 in cancer stem-like cells from
    • it suppressed their metastatic properties.

miR-7 suppressed ………….expression of KLF4.
However, miR-7 significantly suppressed the ability of cancer stem-like cells to metastasize to the brain but not the bone.

A gram illustrating the disctinction between c...

A gram illustrating the disctinction between cancer stem cell targeted (above) and conventional (below) cancer therapies (Photo credit: Wikipedia)

Related articles

 

Read Full Post »


Reporter: Aviva Lev-Ari, PhD, RN

http://www.biomarkerworldcongress.com/

DOWNLOADS

http://www.biomarkerworldcongress.com/bmc_content.aspx?id=95326

Conference Programs

 

May 6 – 7, 2013 

 

Track 1: Translational Biomarkers in Drug Development

Track 2: Clinical Assay Development

Track 3: Cancer Tissue Diagnostics

 

May 6 – 8, 2013 

Track 4: Executive Summit: Companion Diagnostics

 

May 7 – 8, 2013

Track 5: Biomarkers for Patient Selection

Track 6: Cancer Drug Resistance

Track 7: Exosomes and Microvesicles as Biomarkers 

               and Diagnostics

 
SPEAKERS

SPEAKERS

Jason M. Aliotta, M.D., Assistant Professor, Medicine, Warren Alpert Medical School, Brown University

John L. Allinson, FIBMS, Vice President, Biomarker Laboratory Services, ICON Development Solutions

Maria E. Arcila, M.D., Department of Pathology, Memorial Sloan-Kettering Cancer Center

Khusru Asadullah, M.D., Vice President and Head, Global Biomarkers, Bayer Pharma

Jiri Aubrecht, Pharm.D., Ph.D., Senior Director, Safety Biomarker Group Lead, Drug Safety Research & Development, Pfizer

M.J. Finley Austin, Ph.D., Personalized Healthcare & Biomarker Strategy Director, AstraZeneca

Nazneen Aziz, Ph.D., Director, Molecular Medicine, Transformation Program Office, College of American Pathologists

Geoffrey Stuart Baird, M.D., Ph.D., Assistant Professor, Laboratory Medicine, University of Washington

Robert A. Beckman, M.D., External Faculty, Center for Evolution and Cancer, Helen Diller Family Cancer Center, UCSF; Executive Director, Clinical Development Oncology, Daiichi Sankyo Pharma Development

Darrell R. Borger, Ph.D., Co-Director, Translational Research Laboratory, Massachusetts General Hospital Cancer Center

Mark Broenstrup, Ph.D., Director, Biomarker and Diagnostics, R&D Diabetes Division, Sanofi

Michael Burczynski, Ph.D., Executive Director, Biomarker Technologies, Discovery Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Claudio Carini, M.D., Global Clinical Immunology and Biomarkers Lead, Bioenhancement Development Unit, Pfizer

Luigi Catanzariti, Ph.D., Executive Director and Global Program Director, Diagnostics, Novartis

David Chen, Ph.D., Senior Director, Correlative Sciences, Oncology Clinical Development, Novartis Pharmaceuticals

Carol Cheung, M.D., Ph.D., Department of Pathology, University Health Network

Atish Choudhury, M.D., Instructor in Medicine, Medical Oncology, Dana-Farber Cancer Institute

Seth Crosby, M.D., Director, Partnerships & Alliances, Washington University School of Medicine

Mark E. Curran, Ph.D., Vice President, Immunology Biomarkers, Janssen Research & Development

Stephen P. Day, Ph.D., Director, Medical Affairs, Hologic

Viswanath Devanarayan, Ph.D., Global Head, Exploratory Statistics, AbbVie, Inc.

Luis Alberto Diaz, M.D., Associate Professor of Oncology, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center

Max Diem, Ph.D., Professor, Chemistry and Chemical Biology, Northeastern University

Nicholas C. Dracopoli, Ph.D., Vice President, Janssen R&D, Johnson & Johnson

Crislyn D’Souza-Schorey, Ph.D., Professor, Biological Sciences, University of Notre Dame

Dominik Duelli, Ph.D., Assistant Professor, Cellular and Molecular Pharmacology, Rosalind Franklin University of Medicine & Science, Chicago Medical School

Daniel Edelman, Ph.D., Facility Head, Clinical Molecular Profiling Core, National Cancer Institute, NIH

Reyna Favis, Ph.D., Scientific Director, Clinical Research & Development, Janssen Pharmaceutical Companies of Johnson & Johnson

Andrea Ferreira-Gonzalez, Ph.D., Professor and Chair, Division of Molecular Diagnostics; Director, Molecular Diagnostics Laboratory, Department of Pathology, Virginia Commonwealth University

Andrew Fish, Executive Director, AdvaMedDx

Herbert A. Fritsche, Ph.D., Senior Vice President and CSO, Health Discovery Corporation

Felix Frueh, Entrepreneur-in-Residence, Third Rock Ventures

Steve Furlong, Ph.D., Safety Science Lead, AstraZeneca Clinical Development

George A. Green IV, Ph.D., Director, Pharmacodiagnostics, Bristol-Myers Squibb

Patrick Groody, Ph.D., Divisional Vice President, Research & Development, Abbott

Steven Gutman, M.D., MBA, Strategic Advisor, Myraqa

Abdel Halim, Pharm.D., Ph.D., DABCC-MDx, DABCC-TOX, DABCC-CC, FACB, Director, Clinical Biomarkers, Daiichi Sankyo Pharma Development

Sam Hanash, M.D., Ph.D., Director, McCombs Institute for Cancer Early Detection and Treatment, MD Anderson Cancer Center

Charles R. Handorf, M.D., Ph.D., Professor and Chair, Pathology and Laboratory Medicine, University of Tennessee

Amir Handzel, Ph.D., Associate Director, Translational and Clinical Sciences, OSI Pharma (Astellas)

Chunhai “Charlie” Hao, M.D., Ph.D., Associate Professor, Neuropathology Attending, Department of Pathology & Laboratory Medicine, Emory University School of Medicine

Madhuri Hegde, Ph.D., Executive Director, Emory Genetics Laboratory; Associate Professor, Human Genetics, Emory University School of Medicine

Philip Hewitt, Ph.D., Head, Early Non-Clinical Safety (Liver and Kidney), Merck Serono

Stephen M. Hewitt, M.D., Ph.D., Clinical Investigator, Laboratory of Pathology, National Cancer Institute, NIH

Holly Hilton, Ph.D., Head, Disease and Translational Genomics, Hoffmann-La Roche; Adjunct Professor, University of Medicine and Dentistry New Jersey

Fred H. Hochberg, M.D., Associate Professor, Neurology, Massachusetts General Hospital

Shidong Jia, Ph.D., Scientist, Oncology Biomarker Development, Genentech

Chris Jowett, General Manager, Commercial Operations, Abbott Molecular

Jingfang Ju, Ph.D., Co-Director of Translational Research, Pathology, Stony Brook University

Peter M. Kazon, General Counsel, American Clinical Laboratory Association

Eric Lai, Ph.D., Senior Vice President and Head, Pharmacogenomics, Takeda Pharmaceuticals International

Ira M. Lubin, Ph.D., Team Lead, Genetics Laboratory Research and Evaluation Branch, CDC

Johan Luthman, D.D.S., Ph.D., Senior Program Leader, Neuroscience & Ophthalmology Research & Development Franchise Integrator, Merck

Elaine Lyon, Ph.D., Medical Director, Molecular Genetics, ARUP Laboratories

Ron Mazumder, Ph.D., MBA, Global Head, Research and Product Development, Janssen Diagnostics, Janssen Pharmaceutical Companies of Johnson & Johnson

Duncan McHale, Ph.D., Vice President, Global Exploratory Development at UCB Pharma

Alan Mertz, President, American Clinical Laboratory Association

Yoshi Oda, Ph.D., President, Biomarkers and Personalized Medicine Core Function Unit, Eisai

Lorraine O’Driscoll, Ph.D., Associate Professor, Pharmacology; Director, Research, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin

Carol S. Palackdharry, M.D., MS, Medical Director, ActiveHealth Management; Clinical Lead, Oncology Condition Analysis, Aetna

Saumya Pant, Ph.D., Research Fellow, Merck

Liron Pantanowitz, M.D., Associate Professor, Pathology and Biomedical Informatics, University of Pittsburgh Medical Center

Scott D. Patterson, Ph.D., Executive Director, Medical Sciences, Amgen

Sonia Pearson-White, Ph.D., Scientific Program Manager, Oncology, The Biomarkers Consortium, Foundation for the National Institutes of Health

Emanuel Petricoin III, Ph.D., Co-Director, The Center for Applied Proteomics and Molecular Medicine, George Mason University

Suso Platero, Ph.D., Director, Oncology Biomarkers, Janssen Pharmaceuticals

Mark Priebe, MT(ASCP)SBB, Managing Director, QualityStar Quality Consortium

Debra Rasmussen, MBA, Senior Director, Regulatory Affairs, Johnson & Johnson

Hallgeir Rui, M.D., Ph.D., Professor, Cancer Biology, Medical Oncology, and Pathology, Thomas Jefferson University

Hakan Sakul, Ph.D., Executive Director and Head, Diagnostics, Worldwide R&D, Clinical Research and Precision Medicine, Pfizer

Kurt A. Schalper, M.D., Ph.D., Associate Research Scientist, Pathology, Yale School of Medicine

Stephen C. Schmechel, M.D., Ph.D., Associate Professor, Pathology, University of Washington School of Medicine

Robert Schupp, Ph.D., Executive Director, Diagnostics Hematology/Oncology, Celgene Corporation

Jason S. Simon, Ph.D., Director, Immuno-Oncology Biomarkers, Discovery Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Sharon Sokolowski, Ph.D., Principal Scientist, Pfizer Global Research & Development

Gyongyi Szabo, M.D., Ph.D., Professor, Gastroenterology, University of Massachusetts Medical School

Douglas D. Taylor, Ph.D., Professor, Obstetrics and Gynecology, University of Louisville School of Medicine

Meghna Das Thakur, Ph.D., Presidential PostDoctoral Fellow, Novartis Institutes for BioMedical Research

Emina Torlakovic, M.D., Ph.D., Associate Professor, Laboratory Medicine and Pathobiology, University of Toronto

Jessie Villanueva, Ph.D., Assistant Professor, Molecular & Cellular Oncogenesis Program, The Wistar Institute

Glen J. Weiss, M.D., Co-Head, Lung Cancer Unit, The Translational Genomics Research Institute (TGen); Director, Clinical Research, Cancer Treatment Centers of America; CMO, CRAB-Clinical Trials Consortium

David Wholley, Director, Biomarkers Consortium, Foundation for the NIH

David T.W. Wong, D.M.D., D.M.Sc., Professor, Associate Dean of Research, UCLA School of Dentistry and Director of Dental Research Institute

Janghee Woo, M.D., Albert Einstein Medical Center

Wen Jin Wu, M.D., Ph.D., Principal Investigator, Division of Monoclonal Antibodies, Office of Biotechnology Products, Center for Drug Evaluation and Research, FDA

Brenda Yanak, Ph.D., Precision Medicine Leader, Clinical Innovation, Pfizer

Tammie Yeh, Ph.D., Molecular Biomarkers, Oncology Lead, Merck

Eunhee S. Yi, M.D., Consultant, Anatomic Pathology, Mayo Clinic; Professor, Pathology, Mayo Clinic College of Medicine

Malcolm York, MPhil, Director and Head, Clinical Pathology and Safety Assessment, GlaxoSmithKline R&D

Theresa Zhang, Ph.D., Associate Director, Exploratory and Translational Sciences, Merck

 

BIOMARKERS

& DIAGNOSTICS

world congress 2013

MAY 6 – 8, 2013 | LOEWS PHILADELPHIA HOTEL | PHILADELPHIA, PA

Cambridge Healthtech Institute’s Ninth Annual

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The Leading Annual Meeting Dedicated to Biomarkers

and Diagnostics Research and Implementation

Dinner Courses:

Fit-for-Purpose Biomarker Assay

Development and Validation

Next-Generation Sequencing as

a Clinical Test

Laboratory-Developed Tests

Conference Programs:

May 6 – 7, 2013

Track 1: Translational

Biomarkers in Drug Development

Track 2: Clinical Assay

Development

Track 3: Cancer Tissue

Diagnostics

May 6 – 8, 2013

Track 4: Executive Summit:

Companion Diagnostics

May 7 – 8, 2013

Track 5: Biomarkers for

Patient Selection

Track 6: Cancer Drug Resistance

Track 7: Exosomes and

Microvesicles as Biomarkers

and Diagnostics

Register by March 29th and SAVE up to $250!

Premier Sponsor

Featured Speakers

Hakan Sakul

Head, Diagnostics

Pfizer

Yoshi Oda

President, Biomarkers & Personalized Medicine

Eisai

David Wholley

Director

Biomarkers Consortium

Khusru Asadullah

VP, Head, Global Biomarkers

Bayer

Nicholas C. Dracopoli

VP, Janssen R&D

Johnson & Johnson

Eric Lai

SVP, Head, Pharmacogenomics

Takeda

2 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

BIOMARKERS & DIAGNOSTICS

world congress 2013

Track 1: Translational Biomarkers

in Drug Development

Track 2: Clinical

Assay Development

Track 3: Cancer Tissue Diagnostics Track 4: Executive Summit:

Companion Diagnostics*

Sunday, May 5

5:00-6:00 Conference Pre-Registration

Monday, May 6

8:30-10:00 Biomarkers in Translational Medicine From Research Biomarkers to

Clinical Assays

Whole-Slide Imaging and

Digital Pathology

Commercialization of

Companion Diagnostics

10:00-10:30 Networking Coffee Break

10:30-11:50 Biomarkers in Translational Medicine From Research Biomarkers to

Clinical Assays

Whole-Slide Imaging and

Digital Pathology

Commercialization of

Companion Diagnostics

11:50-1:20 Luncheon Presentation

Sponsored by

Lunch on Your Own

1:20-2:40 Biomarker Utility in Clinical Development NGS in Clinical Use Strategies for Rx-Dx Partnerships

2:40-3:40 Refreshment Break in the Exhibit Hall with Poster Viewing

3:40-5:00 Biomarker Utility in Clinical Development NGS in Clinical Use Strategies for Rx-Dx Partnerships

5:00-6:00 Networking Reception in the Exhibit Hall with Poster Viewing

6:00-9:00 Dinner Courses (Separate registration required)

Fit-for-Purpose Biomarker Assay Development and Validation

Next-Generation Sequencing as a Clinical Test

Tuesday, May 7

7:30-8:15 Breakfast Presentation Sponsored by

8:25-10:00 Biomarkers for Safety Assessment Choosing a Platform for

Companion Diagnostics

Advances in IHC: Guiding

Therapy Decisions

Choosing a Platform for

Companion Diagnostics

10:00-11:00 Coffee Break in the Exhibit Hall with Poster Viewing

11:00-12:15 Biomarker Collaborations and Consortia Multiplexed Assays Tissue Biomarkers for Targeted Therapy Panel Discussion: Next-Generation

CDx Platforms

12:15-1:45 Lunch on Your Own and Conference Registration for Tracks 5-7

Track 5: Biomarkers for

Patient Selection

Track 6: Cancer Drug Resistance Track 7: Exosomes and Microvesicles

as Biomarkers and Diagnostics

12:15-1:45 Conference Registration

1:45-2:40 Biomarkers to Diagnostics Exosome Biomarkers in

Drug Development

Biomarkers to Diagnostics

2:40-3:45 Refreshment Break in the Exhibit Hall with Poster Viewing

3:45-5:30 Molecular Profiling of Tumor Heterogeneity to Guide Therapy Exosome Biomarkers in

Drug Development

Timeline for CDx Development

6:00-9:00 Dinner Course (Separate registration required)

Laboratory-Developed Tests

Wednesday, May 8

7:30-8:15 Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

8:25-10:30 Advancing Personalized Medicine 8:05 Secondary Resistance to Targeted

Cancer Therapy

Exosomes as Disease Markers Advancing Personalized Medicine

10:30-11:30 Coffee Break in the Exhibit Hall with Poster Viewing

11:30-12:45 Advancing Personalized Medicine 11:30-1:15 Resistance to

Various Therapies: Cancer Does

Not Discriminate

11:30-1:15 Exosomes as Novel

Cancer Biomarkers

Advancing Personalized Medicine

12:45 Close of Conference 1:15 Close of Conference 1:15 Close of Conference Close of Conference

*Executive pricing registration required

Conference-at-a-Glance

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 3

BIOMARKERS & DIAGNOSTICS

world congress 2013

Distinguished Faculty

Jason M. Aliotta, M.D., Assistant Professor,

Medicine, Warren Alpert Medical School,

Brown University

John L. Allinson, FIBMS, Vice President,

Biomarker Laboratory Services, ICON

Development Solutions

Maria E. Arcila, M.D., Department of Pathology,

Memorial Sloan-Kettering Cancer Center

Khusru Asadullah, M.D., Vice President and

Head, Global Biomarkers, Bayer Pharma AG

Jiri Aubrecht, Pharm.D., Ph.D., Senior Director,

Safety Biomarker Group Lead, Drug Safety

Research & Development, Pfizer

M.J. Finley Austin, Ph.D., Personalized

Healthcare & Biomarker Strategy

Director, AstraZeneca

Nazneen Aziz, Ph.D., Director, Molecular

Medicine, Transformation Program Office,

College of American Pathologists

Geoffrey Stuart Baird, M.D., Ph.D., Assistant

Professor, Laboratory Medicine, University

of Washington

Robert A. Beckman, M.D., External Faculty,

Center for Evolution and Cancer, Helen Diller

Family Cancer Center, UCSF; Executive

Director, Clinical Development Oncology,

Daiichi Sankyo Pharma Development

Darrell R. Borger, Ph.D., Co-Director,

Translational Research Laboratory,

Massachusetts General Hospital Cancer Center

Mark Broenstrup, Ph.D., Director, Biomarker

and Diagnostics, R&D Diabetes Division, Sanofi

Michael Burczynski, Ph.D., Executive

Director, Biomarker Technologies, Discovery

Medicine and Clinical Pharmacology,

Bristol-Myers Squibb

Claudio Carini, M.D., Global Clinical

Immunology and Biomarkers Lead,

Bioenhancement Development Unit, Pfizer

Luigi Catanzariti, Ph.D., Executive Director and

Global Program Director, Diagnostics, Novartis

David Chen, Ph.D., Senior Director, Correlative

Sciences, Oncology Clinical Development,

Novartis Pharmaceuticals

Carol Cheung, M.D., Ph.D., Department of

Pathology, University Health Network

Atish Choudhury, M.D., Instructor in

Medicine, Medical Oncology, Dana-Farber

Cancer Institute

Seth Crosby, M.D., Director, Partnerships

& Alliances, Washington University School

of Medicine

Mark E. Curran, Ph.D., Vice President,

Immunology Biomarkers, Janssen Research &

Development

Stephen P. Day, Ph.D., Director, Medical

Affairs, Hologic

Viswanath Devanarayan, Ph.D., Global Head,

Exploratory Statistics, AbbVie, Inc

Luis Alberto Diaz, M.D., Associate Professor

of Oncology, Johns Hopkins Sidney Kimmel

Comprehensive Cancer Center

Max Diem, Ph.D., Professor, Chemistry and

Chemical Biology, Northeastern University

Nicholas C. Dracopoli, Ph.D., Vice President,

Janssen R&D, Johnson & Johnson

Crislyn D’Souza-Schorey, Ph.D., Professor,

Biological Sciences, University of Notre Dame

Dominik Duelli, Ph.D., Assistant Professor,

Cellular and Molecular Pharmacology, Rosalind

Franklin University of Medicine & Science,

Chicago Medical School

Daniel Edelman, Ph.D., Facility Head, Clinical

Molecular Profiling Core, National Cancer

Institute, NIH

Reyna Favis, Ph.D., Scientific Director,

Clinical Research & Development, Janssen

Pharmaceutical Companies of Johnson &

Johnson

Andrea Ferreira-Gonzalez, Ph.D., Professor

and Chair, Division of Molecular Diagnostics;

Director, Molecular Diagnostics Laboratory,

Department of Pathology, Virginia

Commonwealth University

Andrew Fish, Executive Director, AdvaMedDx

Herbert A. Fritsche, Ph.D., Senior

Vice President and CSO, Health

Discovery Corporation

Felix Frueh, Entrepreneur-in-Residence, Third

Rock Ventures

Steve Furlong, Ph.D., Safety Science Lead,

AstraZeneca Clinical Development

George A. Green IV, Ph.D., Director,

Pharmacodiagnostics, Bristol-Myers Squibb

Patrick Groody, Ph.D., Divisional Vice President,

Research & Development, Abbott

Steven Gutman, M.D., MBA, Strategic

Advisor, Myraqa

Abdel Halim, Pharm.D., Ph.D., DABCCMDx,

DABCC-TOX, DABCC-CC, FACB,

Director, Clinical Biomarkers, Daiichi Sankyo

Pharma Development

Sam Hanash, M.D., Ph.D., Director, McCombs

Institute for Cancer Early Detection and

Treatment, MD Anderson Cancer Center

Charles R. Handorf, M.D., Ph.D., Professor

and Chair, Pathology and Laboratory Medicine,

University of Tennessee

Amir Handzel, Ph.D., Associate Director,

Translational and Clinical Sciences, OSI

Pharma (Astellas)

Chunhai “Charlie” Hao, M.D., Ph.D., Associate

Professor, Neuropathology Attending,

Department of Pathology & Laboratory

Medicine, Emory University School of Medicine

Madhuri Hegde, Ph.D., Associate Professor,

Human Genetics; Senior Director, Emory

Genetics Laboratory, Emory University

Philip Hewitt, Ph.D., Head, Early Non-Clinical

Safety (Liver and Kidney), Merck Serono

Stephen M. Hewitt, M.D., Ph.D., Clinical

Investigator, Laboratory of Pathology, National

Cancer Institute, NIH

Holly Hilton, Ph.D., Head, Disease and

Translational Genomics, Hoffmann-La Roche;

Adjunct Professor, University of Medicine and

Dentistry New Jersey

Fred H. Hochberg, M.D., Associate Professor,

Neurology, Massachusetts General Hospital

Shidong Jia, Ph.D., Scientist, Oncology

Biomarker Development, Genentech

Chris Jowett, General Manager, Commercial

Operations, Abbott Molecular

Jingfang Ju, Ph.D., Co-Director of Translational

Research, Pathology, Stony Brook University

Peter M. Kazon, General Counsel, American

Clinical Laboratory Association

Eric Lai, Ph.D., Senior Vice President

and Head, Pharmacogenomics, Takeda

Pharmaceuticals International

Ira M. Lubin, Ph.D., Team Lead, Genetics

Laboratory Research and Evaluation

Branch, CDC

Johan Luthman, D.D.S., Ph.D., Senior Program

Leader, Neuroscience & Ophthalmology

Research & Development Franchise

Integrator, Merck

Elaine Lyon, Ph.D., Medical Director, Molecular

Genetics, ARUP Laboratories

Ron Mazumder, Ph.D., MBA, Global Head,

Research and Product Development, Janssen

Diagnostics, Janssen Pharmaceutical Companies

of Johnson & Johnson

Duncan McHale, Ph.D., Vice President, Global

Exploratory Development at UCB Pharma

Alan Mertz, President, American Clinical

Laboratory Association

Yoshi Oda, Ph.D., President, Biomarkers and

Personalized Medicine Core Function Unit, Eisai

Lorraine O’Driscoll, Ph.D., Associate Professor,

Pharmacology; Director, Research, School of

Pharmacy and Pharmaceutical Sciences, Trinity

College Dublin

Carol S. Palackdharry, M.D., MS, Medical

Director, ActiveHealth Management; Clinical

Lead, Oncology Condition Analysis, Aetna

Saumya Pant, Ph.D., Research Fellow, Merck

Liron Pantanowitz, M.D., Associate Professor,

Pathology and Biomedical Informatics,

University of Pittsburgh Medical Center

Scott D. Patterson, Ph.D., Executive Director,

Medical Sciences, Amgen

Sonia Pearson-White, Ph.D., Scientific

Program Manager, Oncology, The Biomarkers

Consortium, Foundation for the National

Institutes of Health

Emanuel Petricoin III, Ph.D., Co-Director, The

Center for Applied Proteomics and Molecular

Medicine, George Mason University

Suso Platero, Ph.D., Director, Oncology

Biomarkers, Janssen Pharmaceuticals

Mark Priebe, MT(ASCP)SBB, Managing

Director, QualityStar Quality Consortium

Debra Rasmussen, MBA, Senior Director,

Regulatory Affairs, Johnson & Johnson

Hallgeir Rui, M.D., Ph.D., Professor, Cancer

Biology, Medical Oncology, and Pathology,

Thomas Jefferson University

Hakan Sakul, Ph.D., Executive Director and

Head, Diagnostics, Worldwide R&D, Clinical

Research and Precision Medicine, Pfizer

Kurt A. Schalper, M.D., Ph.D., Associate

Research Scientist, Pathology, Yale School

of Medicine

Stephen C. Schmechel, M.D., Ph.D., Associate

Professor, Pathology, University of Washington

School of Medicine

Robert Schupp, Ph.D., Executive Director,

Diagnostics Hematology/Oncology,

Celgene Corporation

Jason S. Simon, Ph.D., Director, Immuno-

Oncology Biomarkers, Discovery Medicine and

Clinical Pharmacology, Bristol-Myers Squibb

Sharon Sokolowski, Ph.D., Principal Scientist,

Pfizer Global Research & Development

Gyongyi Szabo, M.D., Ph.D., Professor,

Gastroenterology, University of Massachusetts

Medical School

Douglas D. Taylor, Ph.D., Professor, Obstetrics

and Gynecology, University of Louisville School

of Medicine

Meghna Das Thakur, Ph.D., Presidential

PostDoctoral Fellow, Novartis Institutes for

BioMedical Research

Emina Torlakovic, M.D., Ph.D., Associate

Professor, Laboratory Medicine and

Pathobiology, University of Toronto

Jessie Villanueva, Ph.D., Assistant Professor,

Molecular & Cellular Oncogenesis Program,

The Wistar Institute

Glen J. Weiss, M.D., Co-Head, Lung Cancer

Unit, The Translational Genomics Research

Institute (TGen); Director, Thoracic Oncology,

Virginia G. Piper Cancer Center Clinical Trials

at Scottsdale Healthcare; CMO, CRAB-Clinical

Trials Consortium

David Wholley, Director, Biomarkers

Consortium, Foundation for the NIH

David T.W. Wong, D.M.D., D.M.Sc., Professor,

Associate Dean of Research, UCLA

School of Dentistry and Director of Dental

Research Institute

Janghee Woo, M.D., Albert Einstein

Medical Center

Wen Jin Wu, M.D., Ph.D., Principal Investigator,

Division of Monoclonal Antibodies, Office

of Biotechnology Products, Center for Drug

Evaluation and Research, FDA

Brenda Yanak, Ph.D., Precision Medicine

Leader, Clinical Innovation, Pfizer

Tammie Yeh, Ph.D., Molecular Biomarkers,

Oncology Lead, Merck

Eunhee S. Yi, M.D., Consultant, Anatomic

Pathology, Mayo Clinic; Professor, Pathology,

Mayo Clinic College of Medicine

Malcolm York, MPhil, Director and Head,

Clinical Pathology and Safety Assessment,

GlaxoSmithKline R&D

Theresa Zhang, Ph.D., Associate Director,

Exploratory and Translational Sciences, Merck

Premier Sponsor Corporate

Sponsors

4 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Monday, May 6, 6:00-9:00 pm

Fit-for-Purpose Biomarker Assay Development

and Validation

Instructors:

John L. Allinson, FIBMS, Vice President, Biomarker Laboratory Services, ICON

Development Solutions

Viswanath Devanarayan, Ph.D., Global Head, Exploratory Statistics, AbbVie, Inc

This tutorial will provide recommendations on the “fit-for-purpose” best

practices in the development and validation of biomarker assays for

exploratory or advanced biomarker applications. Strategies for different

applications at various phases of biomarker development will be described.

Key elements in the method of development and validation will be illustrated

with examples, including reference to standard material, sample stability and

collection integrity, validation and QC samples, validity of reference standards,

calibration curve fitting methods, method optimization and feasibility studies.

Special challenges in protein biomarker assays will be discussed, including

strategies for moving from biomarker panels in the exploratory phase to the

few markers chosen to support clinical trials, cross-validation of biomarker

assays, etc.

Outline:

1. Introduction: Nomenclature, types of biomarker methods/assays, method

development and validation road-map, fundamental validity, similarity and

differences from PK assays and diagnostic applications

2. Pre-analytical and bioanalytical elements: Target range, standards,

validation and QC samples, stability, matrix effect, specificity and

relative selectivity

3. Calibration curve model selection, evaluation and weighting

4. Method feasibility and optimization with precision profiles

5. Evaluation of some pre-study validation characteristics such as precision,

bias, sensitivity and quantification limits

6. Use of sample controls for in-study performance monitoring and

conformance testing among laboratories

7. Special considerations for multiplex assays, cross-validation of assays, etc.

8. Method comparisons

Next-Generation Sequencing as a Clinical Test:

It Takes a Community

Instructors:

Nazneen Aziz, Ph.D., Director, Molecular Medicine, Transformation Program

Office, College of American Pathologists

Madhuri Hegde, Ph.D., Associate Professor, Human Genetics; Senior Director,

Emory Genetics Laboratory, Emory University

Next-Generation Sequencing (NGS) is used widely in clinical research for

the discovery of disease-associated genes and the clinical community

is beginning to embrace this technology for diagnostic testing. The rapid

evolution of NGS technologies presents significant opportunities and

challenges for researchers and clinicians for improving health outcomes,

particularly with respect to an increased emphasis on personalized and

preventive medicine. Adoption of NGS in the clinical laboratory setting

requires the adoption of many processes and procedures, such as

the analytic and clinical validation of the test, CLIA certification/CAP

accreditation, standards for reference materials, availability for proficiency

testing, and questions regarding reimbursement and informed consent.

The success of NGS as a viable diagnostic modality depends on many

branches of the health care community working together. This session will

be informative and practical for the researcher and laboratorians who are

considering launching NGS as a clinical test.

Tuesday, May 7, 6:00-9:00 pm

Laboratory-Developed Tests

Regulatory Issues Facing Laboratory-Developed Tests

Peter M. Kazon, General Counsel, American Clinical Laboratory Association

The development of molecular diagnostics has been accompanied by a host

of regulatory issues, including coding, billing and FDA issues. This session will

review recent changes affecting these codes as well as the position of Medicare

on how to pay for these tests and tests including an algorithm, also referred

to as MAAAs. It will review the latest developments from the FDA concerning

whether such tests will require FDA pre-market approval or clearance, and what

action FDA is likely to take in the future. It will also review other actions that affect

these tests, such as the new MolDx program being overseen by Palmetto GBA, a

Medicare contractor.

Laboratory-Developed Tests in the Genomic Medicine Era: Validation, Regulation

and Challenges Faced by New Technologies and Clinical Applications

Andrea Ferreira-Gonzalez, Ph.D., Professor and Chair, Division of Molecular

Diagnostics; Director, Molecular Diagnostics Laboratory, Department of

Pathology, Virginia Commonwealth University

Laboratory-developed tests are those tests developed, validated and

performed by clinical laboratories. There are standards and regulations in

place for the validation of these tests before they are introduced into clinical

practice. This presentation will discuss the process of validation under the

current regulatory framework, and regulatory challenges posed by new

technologies such as NGS and its clinical applications.

LDTs in the Context of CLIA: An NIH Experience

Daniel Edelman, Ph.D., Facility Head, Clinical Molecular Profiling Core, National

Cancer Institute, NIH

The mission of the Clinical Molecular Profiling Core (CMPC) of the National

Cancer Institute (NCI) is to provide state of the art genomic testing for specimens

obtained from NCI clinical trials. The greatest impact is effected where test results

have immediate clinical application for personalized cancer care for individual

patients enrolled in these trials. To that end, the CMPC is CLIA certified and

provides a growing set of clinical test modalities. In this talk we’ll discuss the

challenges of meeting CLIA regulations in this new age of genomics at NIH for

high-complexity assays that did not exist as diagnostic tests when the federal

guidelines were written.

LDT Regulation Guidance from the FDA: Where Does It Stand after Three Years?

Stephen P. Day, Ph.D., Director, Medical Affairs, Hologic

The FDA’s announced intent to further regulate laboratory developed tests

(LDTs) enters its third year without the issuance of the anticipated guidance.

This presentation will summarize what the FDA has made publicly available

on the subject up to this time, the positions and recommendations put

forward by medical and industry professional societies, and how it will

potentially affect clinical laboratories offering LDTs and the delivery of quality

medical care.

Next-Generation Sequencing Assays as Laboratory-Developed Tests

Elaine Lyon, Ph.D., Medical Director, Molecular Genetics; Co-Medical Director,

Pharmacogenomics, ARUP Laboratories; Associate Professor, University of Utah

As next-generation sequencing (NGS) technologies improve in accuracy

and cost effectiveness, they will become standard in clinical laboratories.

Multi-gene panels, exome or genome analysis are alternative approaches.

With the complexity of genomic scale sequencing, implementing NGS

assays into clinical laboratories requires expertise in laboratory techniques,

informatics and interpretation. CLIA-certified clinical laboratories are

developing NGS assays as laboratory-developed tests (LDTs). The

presentation will discuss how NGS assays are “procedures” involving input

from health care professionals, and how they fit under the category of high

complexity LDTs.

*Separate registration required

Dinner Courses*

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 5

Sunday, May 5

5:00-6:00 pm Conference Pre-Registration

Monday, May 6

7:30-8:30 am Conference Registration and Morning Coffee

8:30-8:40 Welcome Remarks from Conference Director

Julia Boguslavsky, Executive Director, Conferences, Cambridge Healthtech Institute

Biomarkers in Translational Medicine

8:40-8:45 Chairperson’s Opening Remarks

8:45-9:10 Translating Biological Data into Predictive Biomarker

Development Strategies

Nicholas C. Dracopoli, Ph.D., Vice President, Janssen R&D, Johnson & Johnson

A decade after completion of the human genome sequence, the translation

of complex genomic data into widely used clinical tests has been slower

than anticipated. Three complex tests (in vitro diagnostic multiplex index

assays – IVDMIA) have been approved as prognostic tests, but there has

still not been a single approval of an IVDMIA to predict response to therapy.

Retrospective analyses of the development of predictive biomarkers for

first-in-class oncology drugs over the last ten years shows that 1) insufficient

patients have been exposed to an efficacious dose to support complex

statistical analyses to correlate high-content data against clinical endpoints,

and 2) biomarkers that correlate to response in Phase II studies are not

always good predictors of overall survival in Phase III trials. We will need to

modify the clinical development paradigm for first-in-class agents to support

the efficient co-development of predictive markers.

9:10-9:35 Application of Next-Generation Sequencing in Phase III

Oncology Trials

David Chen, Ph.D., Senior Director, Oncology Correlative Sciences, Novartis

Analysis of tumor samples by next-generation sequencing (NGS) has

increased dramatically in the last 2 years. Most of the reported results

are genetic landscapes generated on samples collected outside clinical

trials or from early phase trials. Application of this technology in large

global Phase III trials provides an excellent opportunity for treatment

efficacy predictive biomarker explorations. Study design considerations

and analysis strategies for the implementation of complex and resource

demanding NGS analysis in Phase III trials will be discussed.

9:35-10:00 Can Biomarkers Recover Drug Development from the Ditch?

Abdel Halim, Pharm.D., Ph.D., DABCC-MDx, DABCC-TOX, DABCC-CC, FACB,

Director, Clinical Biomarkers, Daiichi Sankyo Pharma Development

Despite all the potential benefits of using biomarkers to advance the

pharmaceutical industry, discrepant results can pose a threat to development

programs by triggering false decisions. This talk will highlight the following

topics: biomarkers and their potential utility in drug development, limitations,

major reasons behind discrepant results and possibility of its mitigation.

10:00-10:30 Networking Coffee Break

10:30-10:55 Advancing Biomarkers for Alzheimer’s Disease—From

Target Engagement to Diagnostics

Johan Luthman, D.D.S., Ph.D., Senior Program Leader, Neuroscience &

Ophthalmology Research & Development Franchise Integrator, Merck

Measuring pathophysiology associated factors, such as Aβ peptide

and tau protein in cerebrospinal fluid, and imaging brain function with

fluorodeoxyglucose PET or functional MRI, or pathology with amyloid

PET or MRI, allows us to detect and follow the progression of very early,

pre-dementia stages of AD. While the use of pathophysiology associated

biomarkers allows pharmacodynamics monitoring of putative disease

modifying therapeutics, further qualification efforts are paving the way for

diagnostic and prognostic readouts.

10:55-11:20 Perspectives of Target and Marker Discovery in Multiple Myeloma

Robert Schupp, Ph.D., Executive Director, Diagnostics Hematology/Oncology,

Celgene Corporation

While major advances in therapeutic treatments have almost quadrupled the

overall survival of patients with multiple myeloma (MM) within the last decade,

the clinical challenges are now to optimize clinical utility of drugs and their

therapeutic sequence. The immunomodulatory drugs (IMiDs) thalidomide,

lenalidomide, and pomalidomide comprise an essential treatment modality in

MM and have shown to needfully bind to a newly identified protein in order

to exert their antimyeloma activity. Since this potential new target protein

may qualify as a biomarker predicting clinical response and the emergence of

resistance, significant challenges remain with regards to validating a robust

assay. This talk will highlight key challenges in the methodology and further

development of measurements leading to establishing a new biomarker in MM.

11:20-11:35 Measure for Measure: Assaying the Sponsored by

Rise and Fall of Protein Biomarkers across the Proteome

Steve Williams, M.D., Ph.D., CMO, SomaLogic

Shakespeare’s proteome: “Oh what may man within him hide, though angel on

the outward side!” – Early disease markers. “Our doubts are traitors and make

us lose the good we oft might win, by fearing to attempt” – Now is proteomics’

time. “Truth is truth to the end of reckoning” – Truth standards and biomarkers.

“The miserable have no other medicine but only hope” – Proteomics applied.

“What’s mine is yours, and what is yours is mine.” – Working with us.

11:35-11:50 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

11:50-1:20 pm Luncheon Presentation Sponsored by

Obtaining NAT Sensitivity with ELISA: Results from

Application of Simoa to Blood Screening

David Wilson, Ph.D., Vice President, Product Development, Quanterix

Until recently, nucleic acid testing (NAT) represented the most sensitive

method for early acute HIV infection, when individuals are most

contagious. Using Single Molecule Arrays (Simoa), a digital ELISA

technique, researchers were able to demonstrate a 3000x sensitivity

improvement over conventional ELISA and equivalence with the NAT gold

standard but at a fraction of the cost. This ground-breaking research has

significant implications for blood banking, HIV detection and beyond.

Biomarker Utility in Clinical Development

1:20-1:25 Chairperson’s Remarks

1:25-1:50 Implementing Biomarkers in Clinical Trials

Suso Platero, Ph.D., Director, Oncology Biomarkers, Janssen Pharmaceuticals

Finding biomarkers is relatively easy nowadays. One has only to open

any journal and find dozens of articles showing the discovery of new

biomarkers. The bottleneck in the development of biomarkers is in the

correlation of the appropriate biomarkers to each specific drug. This is done

in the context of clinical trials. Several strategies will be presented of how to

better accomplish this task in an efficient and time sensitive manner.

1:50-2:15 Clinical Innovation in Precision Medicine

Brenda Yanak, Precision Medicine Leader, Clinical Innovation, Pfizer

This presentation will give examples of how Pfizer is innovating in the

clinical development space to aid in the advancement of precision medicine.

2:15-2:40 Discovering Oncology Biomarkers and Translating into

Clinical Trials

Theresa Zhang, Ph.D., Associate Director, Exploratory and Translational

Sciences, Merck

This talk will present a platform for discovering oncology response

biomarkers using a large panel of tumor cell lines, validating them in

selected in vivo models, and refining and estimating biomarker prevalence

in a large human tumor reference dataset. The predictive signature

will then be converted into an analytically validated assay that will be

performed in a CLIA- or CAP-certified laboratory in order to enroll patients

for clinical trials. The process will be illustrated by examples.

Track 1: Translational Biomarkers in Drug Development

6 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

2:40-3:40 Refreshment Break in the Exhibit Hall with Poster Viewing

3:40-4:05 Biomarker Discovery for Immuno-Oncology Agents

Jason S. Simon, Ph.D., Director, Immuno-Oncology Biomarkers, Discovery

Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Tumor cells can use escape mechanisms to avoid or suppress the

natural immune response, ultimately resulting in tumor growth; in fact,

avoiding immune destruction is one of the emerging hallmarks of cancer.

Therefore, understanding and dismantling key immune escape mechanisms

(“checkpoints”) is a key focus of immuno-oncology research. In concert with

identifying agents to regulate the immune checkpoint is working to understand

which tumor types and patient characteristics will respond best to this

treatment approach. This talk will review our strategy to identify biomarkers

which help support clinical development and commercialization strategies.

4:05-4:30 Accelerating and Personalizing Clinical Trials with

Biomarkers and Adaptive Design, the I-SPY 2 Example

Sonia Pearson-White, Ph.D., Scientific Program Manager, Oncology, The

Biomarkers Consortium, Foundation for the National Institutes of Health

I-SPY 2 is a unique clinical trial managed as a public/private partnership

by the Foundation for the NIH (FNIH) Biomarkers Consortium. I-SPY

2 employs an innovative adaptive trial design testing multiple drugs in

high-risk breast cancers in the neoadjuvant setting, and will advance the

understanding of which drugs work best with tumor types with different

biomarker profiles, and the drive toward personalized medicine.

4:30 Metabolomic Profiling for NMR Based Clinical Sponsored by

Assay Development

Thomas O’Connell, Ph.D., Senior Director, Assay Research &

Development, LipoScience, Inc.

Metabolomic profiling yields a unique picture of the downstream phenotype

taking into account genetic influences as well as environmental factors such

as diet, lifestyle and the microbiome. In this presentation it will be shown how

NMR technology is used in both the discovery and translation of biomarkers

into the clinical laboratory. Applications include the prediction, diagnosis and

prognosis of disease as well as the guidance of pharmaceutical interventions.

5:00-6:00 Networking Reception in the Exhibit Hall with Poster Viewing

6:00-9:00 Dinner Courses

Fit-for-Purpose Biomarker Assay Development and Validation

Next-Generation Sequencing as a Clinical Test

(Separate registration required. See Page 4 for additional information.)

Tuesday, May 7

7:30-8:15 am Breakfast Presentation Sponsored by

Identifying Non-Invasive Biomarkers of Smoking-

Related Parenchymal Lung Disease (i.e. COPD and

IPF) to Detect Subclinical Lung Disease

Ivan O. Rosas, M.D., Assistant Professor, Medicine Division, Pulmonary &

Critical Care Medicine, Brigham & Women’s Hospital, Harvard Medical School

Biomarkers for Safety Assessment

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Pre-Clinical Safety Biomarkers and Translation to Clinical

Testing: Current Perspectives

Malcolm York, MPhil, Director and Head, Clinical Pathology and Safety

Assessment, GlaxoSmithKline R&D

Significant efforts have been conducted into the analytical validation,

characterization and qualification of safety biomarkers, notably for the

assessment and prediction of cardiac, kidney and liver safety. However,

significant challenges are still evident in the interpretation of toxicity

biomarker signals above defined thresholds, which report apparent injury

(but in the absence of the histopathology) and their relevance for clinical

safety testing. Current perspectives on the measurement and use of toxicity

biomarkers/multiple biomarkers, along with integration with other measures

of biological response, to improve definition of safety margins and riskbenefit

characterization of exploratory new medicines will be discussed.

8:55-9:20 Biomarkers of Acute Kidney Injury: From Pre-Clinical Species

to Human Patients

Jiri Aubrecht, Pharm.D., Ph.D., Senior Director, Safety Biomarker Group Lead,

Drug Safety Research & Development, Pfizer

Acute kidney injury provides a significant challenge to drug development.

Recently, new biomarkers of acute kidney injury have been developed. In this

presentation we will review the recent progress in applying emerging biomarkers

of acute kidney injury across pre-clinical species and human subjects.

9:20-9:45 Preparing for Safety Biomarkers to Support Clinical Trials

Stephen T. Furlong, Ph.D., Safety Science Lead, AstraZeneca Patient Safety

Many new biomarkers are being considered for use in clinical trials to

monitor drug-induced organ toxicity. However, deciding which biomarkers

to use, selecting a vendor to perform the assays, establishing sample

handling protocols, preparing for statistical analysis of the data and

deciding how to use the data all represent significant challenges. This talk

will review these topics, provide examples from specific biomarkers and

provide suggestions for overcoming some of these challenges.

9:45-10:10 Identifying Biomarkers of Kidney and Liver Toxicity by

Integrating Toxicogenomics Datasets with Biological Networks

Philip Hewitt, Ph.D., Head, Early Non-Clinical Safety (Liver and Kidney), Merck Serono

Candidate nephrotoxicity biomarkers were identified by interrogating

profiles from hundreds of publicly available toxicogenomics datasets,

including datasets from the EU PredTox and Japanese TG-GATEs

projects. Application of multiple bioinformatics approaches identified

43 significant candidates. These findings were corroborated by testing

model nephrotoxic compounds using whole genome expression profile

experiments both in vivo and in vitro. This in silico approach greatly

enriched candidates for those likely to be true biomarkers.

10:10-11:00 Coffee Break in the Exhibit Hall with Poster Viewing

Biomarker Collaborations and Consortia

11:00-11:25 From Promise to Progress: An Update on the Biomarkers

Consortium

David Wholley, Director, Biomarkers Consortium, Foundation for the NIH

11:25-11:50 Open Innovation in Biomarker Discovery: Experiences

from Our Grants for Targets and Biomarkers Initiative

Khusru Asadullah, M.D., Vice President and Head, Global Biomarkers, Bayer

HealthCare

To combine expertise Bayer Healthcare has set up a novel open innovation

approach called Grants4Targets. After a review process, grants are

provided to perform focused experiments to further validate the proposed

targets/biomarkers. In addition to financial support, specific know-how on

target validation and drug discovery is provided. Experienced scientists

are nominated as project partners and, depending on the project, tools

or specific models are provided. More than 600 applications have been

received and 77 projects granted so far.

11:50-12:15 pm Biomarker Discovery—The Power of Collaborative

Networks

Duncan McHale, Ph.D., Vice President, Global Exploratory Development, UCB

Pharma

Clinically useful, predictive biomarkers have been very elusive despite

the growth of Big Biology. Individual technology solutions are commonly

touted as being able to identify drug response biomarkers but are rarely

successful. It is likely that to be successful a network of collaborators will

be needed bringing together technology discipline experts with disease

biology experts. A case example is given in rheumatoid arthritis.

12:15-1:45 Enjoy Lunch on Your Own

Track 1: Translational Biomarkers in Drug Development

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 7

Track 2: Clinical Assay Development

Sunday, May 5

5:00-6:00 pm Conference Pre-Registration

Monday, May 6

7:30-8:30 am Conference Registration and Morning Coffee

8:30-8:40 Welcome Remarks from Conference Director

Julia Boguslavsky, Executive Director, Conferences, Cambridge Healthtech Institute

From Research Biomarkers to Clinical Assays

8:40-8:45 Chairperson’s Opening Remarks

8:45-9:10 Biomarkers and the Quest for Clinical Utility—Obstacles,

Challenges and Opportunities

Steven Gutman, M.D., MBA, Strategic Advisor, Myraqa

Over the past ten years there has been an explosive increase in the number

of biomarker assays available for the study and evaluation of human

disease. To ensure stakeholders are able to use this growing menu of tests

responsibly, there is a compelling need to understand the clinical utility of

these assays. Unfortunately a surprising number of tests are plagued by

inadequate information on clinical utility. This talk will focus on obstacles,

challenges and opportunities for addressing this problem.

9:10-9:35 Clinical Assay Development—The Process and Considerations

Herbert A. Fritsche, Ph.D., Senior Vice President and CSO, Health Discovery Corporation

The process for successful development of a clinical laboratory test begins

with a strict definition of the test concept and its clinical utility; design of an

accurate and robust assay for the analyte; analytical validation followed by

clinical validation; and lastly, translation of the new test from the research

lab to routine clinical use, which includes validation of the new test outside

of the research setting. Success of development is defined as acceptance of

the new test by the medical community as the “standard-of-care.”

9:35-10:00 Bridging Research and “Clinical” Assays in Pharmaceutical

Research & Development

John L. Allinson, FIBMS, Vice President, Biomarker Laboratory Services, ICON

Development Solutions

Many biomarker assays used in drug development are research assays

(i.e., not accredited diagnostic devices). This presentation will look at the

following: basic validation experiments across assays in research and

diagnostics; differences and assay evolution as methods progress through

different uses of results data; the requirements for accreditation of assays to

be used in diagnostics; and a brief look at the development of a companion

diagnostic and its implications from the laboratory perspective.

10:00-10:30 Networking Coffee Break

10:30-10:55 Key Considerations for Choosing and Transitioning a

Research Grade Assay to the Clinical Setting

Tammie C. Yeh, Ph.D., Molecular Biomarkers, Oncology Lead, Merck

Developing a biomarker assay with the clinical perspective in mind is critical to

the success of the biomarker. Identifying/choosing a robust biomarker readout

is as important as developing a robust analytical assay to ensure clinical utility.

It is important to understand the inherent biological variability as well as the

clinical feasibility of a biomarker readout, both of which will depend on tissue

type, tissue processing and the specific clinical setting. Both patient selection

and pharmacodynamic biomarkers will be addressed in this presentation.

10:55-11:20 Clinical Assay Development for Cancer Protein Biomarkers:

What Works and What Does Not Work

Samir Hanash, Ph.D., Program Head, Molecular Diagnostics, Fred Hutchinson

Cancer Research Center

The breadth and depth of proteomics technologies for the discovery of

biomarkers has increased substantially over the past decade, covering

a dynamic range of more than 7 logs in protein abundance. As a result,

numerous cancer biomarker candidates have emerged from discovery

studies. There remains a need for the development of high-throughput

technologies that allow testing the utility of these biomarkers for their

intended clinical application to meet regulatory requirements. Current

opportunities and challenges will be presented.

11:20-11:45 Will Regulation of Laboratory-Developed Tests Stifle Innovation?

Alan Mertz, President, American Clinical Laboratory Association

Laboratory developed tests (LDTs) are regulated by Federal law (CLIA),

state law, and industry standards established by the College of American

Pathologists. For many years, FDA has maintained that LDTs are medical

devices. FDA’s legal authority has been questioned, however, and Congress,

in July 2011, considered legislation that would enhance the CLIA framework

for regulating LDTs. FDA’s work plan for 2013 does not mention new

guidance on LDTs, but remains a possibility. Closing the LDT pathway would

have substantial effects on clinical laboratories, health care providers, and

patients. This presentation will examine the role of LDTs in creating new

tests, diagnosing rare diseases, and including the most up-to-date clinical

information in diagnostic tests.

11:45-1:20 pm Enjoy Lunch on Your Own

NGS in Clinical Use

1:20-1:25 Chairperson’s Remarks

1:25-1:50 College of American Pathologists’ Standards and Proficiency

Testing for Next-Generation Sequencing for the Clinical Laboratory

Nazneen Aziz, Ph.D., Director, Molecular Medicine, Transformation Program

Office, College of American Pathologists

The rapid and ongoing advances in the genetic test market, spurred by the

opportunities of Next-Generation Sequencing (NGS), necessitate many facets

of the health care industry to work cohesively. Adoption of NGS as a clinical test

requires the adoption of many processes and procedures, such as the analytic

and clinical validation of the test, CLIA certification/CAP accreditation, standards

for reference materials, availability for proficiency testing, genetic counseling,

and questions regarding reimbursement, informed consent and incidental

findings. This talk will focus on the laboratory requirements developed at CAP

for CLIA/CAP accreditation and the plans for proficiency testing for NGS.

1:50-2:15 Assuring the Quality of Next-Generation Sequencing in

Clinical Laboratory Practice

Ira M. Lubin, Ph.D., Team Lead, Genetics Laboratory Research and Evaluation

Branch, Division of Laboratory Science and Standards, Laboratory Science,

Policy, and Practice Program Office, Office of Surveillance, Epidemiology, and

Laboratory Services, Centers for Disease Control and Prevention

Integration of next-generation sequencing (NGS) into the clinical laboratory

requires test validation, establishment of quality control procedures, and

the independent assessment of test performance by proficiency testing

or alternate approaches. Existing regulatory requirements and professional

guidance do not adequately address these quality issues for clinical NGS

testing. This talk will describe the outcomes of a national workgroup

organized by the Centers for Disease Control and Prevention tasked to

identify principles and develop guidance to promote good clinical laboratory

practices for NGS and meet regulatory and professional standards.

2:15-2:40 Clinical NGS: Validation, Reporting and Economics

Seth Crosby, M.D., Director, Partnerships & Alliances, Washington University

School of Medicine

As NGS enters the clinic, matters of analytic and clinical validation are just

the start of the medical director’s worries. How should results be quickly

generated and communicated to a physician in a meaningful and actionable

manner? What are the new rules for billing and reimbursement?

2:40-3:40 Refreshment Break in the Exhibit Hall with Poster Viewing

3:40-4:05 Exome Sequencing in a Clinical Setting to Guide Patient Care

Madhuri Hegde, Ph.D., Associate Professor, Human Genetics; Senior Director,

Emory Genetics Laboratory, Emory University

8 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 2: Clinical Assay Development

Advances in genomic medicine have made it necessary for clinical laboratories

to rapidly implement new technologies to guide patient care. Exome

sequencing is rapidly being implemented across different specialties such as

inherited diseases, cancer and infectious diseases. This talk will focus on the

clinical utility of exome sequencing in patient care with real case examples.

4:05-4:30 Interpreting Clinical Next-Generation Sequencing Data:

Current Challenges and Hope for the Future

Elaine Lyon, Ph.D., Medical Director, Molecular Genetics; Co-Medical Director,

Pharmacogenomics, ARUP Laboratories; Associate Professor, University of Utah

With the complexity of genomic scale sequencing (next-generation

sequencing or NGS) and the massive amounts of data obtained, informatics

is essential. Two challenges in evaluating a variant are 1) is it real and 2)

is it clinically significant. Informatics allow alignment and variant calling

(differences from a reference sequence), and sifting of probable clinically

insignificant variants. More challenging is prioritizing variants that are likely

to be associated with the clinical symptoms. In addition to the symptomguided

analysis approach, NGS data can reveal variants in genes related to

drug metabolism that may affect efficacy or response. This presentation will

discuss approaches to prioritize symptom-related variants as well as the

potential of NGS data for companion diagnostics or therapeutics.

4:30-5:00 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

5:00-6:00 Networking Reception in the Exhibit Hall with Poster Viewing

6:00-9:00 Dinner Courses

Fit-for-Purpose Biomarker Assay Development and Validation

Next-Generation Sequencing as a Clinical Test

(Separate registration required. See Page 4 for additional information.)

Tuesday, May 7

7:30-8:15 am Breakfast Presentation Sponsored by

Identifying Non-Invasive Biomarkers of Smoking-

Related Parenchymal Lung Disease (i.e. COPD and

IPF) to Detect Subclinical Lung Disease

Ivan O. Rosas, M.D., Assistant Professor, Medicine Division, Pulmonary &

Critical Care Medicine, Brigham & Women’s Hospital, Harvard Medical School

Choosing a Platform for Companion Diagnostics

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Validating Biomarker Assays as a Prelude to Companion

Diagnostic Development: Emerging Platform-Specific Considerations

Michael Burczynski, Ph.D., Executive Director, Biomarker Technologies, Discovery

Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Timely implementation of companion diagnostics alongside therapeutic

products has amplified the need to validate predictive biomarkers in earlier

phases of drug development. Today, biomarker strategies are more complex

and require more diverse platforms than ever before. Ensuring that analytical

validation strategies for these exploratory predictive biomarker assays

are aligned with the downstream requirements for full-blown companion

diagnostic development is a critical activity that ultimately helps determine

the efficiency with which targeted medicines can be brought to market.

8:55-9:20 Choosing a Platform for Companion Diagnostic Development

Ron Mazumder, Ph.D., MBA, Global Head, Research and Product Development,

Janssen Diagnostics, Janssen Pharmaceutical Companies of Johnson & Johnson

One of the early considerations in developing a companion diagnostic is

choice of platform. Several factors, such as technical performance, regulatory

and reimbursement path, and commercial access will be discussed in this

context. Examples from the literature and case studies will be presented.

9:20-9:45 Thoughts and Considerations for Choosing a Companion

Diagnostic Technology and Platform Delivery System

Patrick Groody, Ph.D., Divisional Vice President, Research & Development, Abbott

Choosing a diagnostic technology and testing platform for the

development of a companion diagnostic test can be a significant challenge.

A wide variety of factors including the development time, capabilities of

potential partners and the ability of laboratories and physicians to access

and perform the test routinely in a clinical setting are key factors in

developing a companion diagnostic program. This talk will focus on variety

of strategies for developing commercial companion diagnostic tests.

9:45-10:00 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

10:00-11:00 Coffee Break in the Exhibit Hall with Poster Viewing

Multiplexed Assays

11:00-11:25 Measurement of Telomere Repeats in Human Cancer Cell Lines

and Tissues Using a Monochrome Multiplex Quantitative PCR Assay

Daniel Edelman, Ph.D., Facility Head, Clinical Molecular Profiling Core, National

Cancer Institute, NIH

This talk will describe our efforts for the development and validation of a

QPCR multiplex assay to enable the quantitation of overall telomere length

(TL) in cancerous cell lines and tissues. A TL pattern between cancers

might provide valuable diagnostic or prognostic information to promote

a better understanding of the molecular or pathogenic characteristics of

specific cancer types.

11:25-11:50 Multiplexed Immunoassays on Formalin-Fixed, Paraffin-

Embedded Tissue Homogenates as Cancer Diagnostics

Geoffrey Stuart Baird, M.D., Ph.D., Assistant Professor, Laboratory Medicine,

University of Washington

Multiplex immunoassays (MIs) performed on formalin-fixed, paraffinembedded

(FFPE) tissue homogenates offer several advantages

over immunohistochemistry as cancer diagnostics. In contrast to

immunohistochemistry, MIs offer absolute quantitation and improved

sensitivity and specificity through the use of sandwich assay geometries.

Moreover, MI instrumentation has already been adopted in the clinical

laboratory, and is much less expensive than a mass spectrometer. MIs have

been validated as a clinical diagnostic for pituitary adenoma classification in

FFPE tissue, with current work focused on breast carcinoma.

11:50-12:05 pm Diagnostic Classifiers for the Detection Sponsored by

of Bladder Cancer

Mark Ruddock, Ph.D., Team Leader, Molecular Biology, Randox

Pharma Services

Patients presenting with hematuria require investigations, including

cystoscopy and imaging of their upper urinary tracts, to identify the source

of bleeding. This is a significant health burden, which is set to increase

because of our aging population. Using biochip array technology, we have

identified diagnostic classifiers for detecting bladder cancer.

12:05-12:30 Development of Multiplexed Protein Pathway Activation

Mapping Clinical Assays for Personalized Cancer Therapy

Emanuel Petricoin III, Ph.D., Co-Director, The Center for Applied Proteomics and

Molecular Medicine, George Mason University

Cellular signaling pathways are a protein-based network, and the intended

drug effect is to disrupt aberrant protein phosphorylation-based enzymatic

activity, and epigenetic phenomena. The reverse-phase protein microarray

platform provides detailed information about the state of the cellular

“circuitry” from small samples. Measurements of dozens to hundreds of

specific phosphorylated proteins that represent most of the targets for

targeted therapeutics can be obtained at once from only a few thousand

cells. This information helps select specific therapy(ies) tailored to the

patient’s tumor activated protein “circuitry.”

12:30-1:45 Enjoy Lunch on Your Own

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 9

Track 3: Cancer Tissue Diagnostics

Sunday, May 5

5:00-6:00 pm Conference Pre-Registration

Monday, May 6

7:30-8:30 am Conference Registration and Morning Coffee

8:30-8:40 Welcome Remarks from Conference Director

Julia Boguslavsky, Executive Director, Conferences, Cambridge Healthtech Institute

Whole-Slide Imaging and Digital Pathology

8:40-8:45 Chairperson’s Opening Remarks

8:45-9:10 Validation of Whole Slide Imaging in Pathology

Liron Pantanowitz, M.D., Associate Professor, Pathology and Biomedical

Informatics, University of Pittsburgh Medical Center

Validation of whole slide imaging (WSI) is important to ensure that digitized

slides are at least equivalent to that of glass slides. The College of American

Pathologists (CAP) Pathology and Laboratory Quality Center convened a

panel to recommend validation requirements for WSI systems to be used

for clinical diagnostic purposes employing a combination of evidence-based

evaluation of the literature, expert consensus and public commentary. The

recommendations are comprehensive and address technical, interpretation

components and administrative issues related to WSI in pathology providing

practical guidance for all types of laboratories who are using or plan to

utilize WSI systems for diagnostic clinical work. This session will educate

participants about WSI in pathology, the regulatory issues surrounding digital

pathology, and review the validation guidelines developed by the CAP.

9:10-9:35 New Applications Utilizing Whole Slide Digital Imaging

for Anatomic Pathology Inter- and Intra-Lab Peer Review and

Benchmarking Quality Assurance

Mark Priebe, MT(ASCP)SBB, Managing Director, QualityStar Quality Consortium

Although application of Whole Slide Digital Imagining (WSI) for primary

diagnosis is limited by the FDA at this time, WSI is a significant enabling

technology for anatomic pathology (AP) quality assurance (QA) initiatives

both inter- and intra-laboratory. This presentation will review current AP/

QA programs and the application of WSI to a novel approach of gaining

longitudinal benchmarking data for quality review. The presentation

will focus on understanding design requirements for development and

implementation, investment requirements, confidentiality considerations

and methods to encourage pathologist participation and acceptance.

9:35-10:00 Label-Free Infrared Spectral Histopathology: Diagnostics and

Prognostics

Max Diem, Ph.D., Professor, Chemistry and Chemical Biology, Northeastern University

Infrared spectral histopathology is a method in which the biochemical

composition of a histopathological sample is used, rather than

morphometric criteria, to diagnose disease. To this end, thousands of

infrared spectra are collected from pixels about 10 μm on edge, and

analyzed to produce spectral images that detect abnormality based on

variations in composition. The accuracy of this method is comparable to

multi-panel immunohistochemistry.

10:00-10:30 Networking Coffee Break

10:30-10:55 Tumor Heterogeneity Assessed by Immunohistochemistry

of Multiplexed Protein Biomarkers

Steve Schmechel, M.D., Ph.D., Associate Professor, Pathology, University of

Washington School of Medicine

Intratumoral heterogeneity of protein expression may be linked to the

biological aggressiveness of tumors and selection of therapies. Analytical

and statistical methods to quantify heterogeneity are needed, particularly

for multiplexed assays. This presentation will discuss novel methods to

measure tumor heterogeneity.

10:55-11:20 Application of WSI in Consensus Review for Clinical Trials

Stephen M. Hewitt, M.D., Ph.D., Clinical Investigator, Laboratory of Pathology,

National Cancer Institute, NIH

Whole Slide Imaging is an enabling technology within pathology, altering all

aspects of current practice. Consensus review processes for clinical trials have

previously been expensive, slow, and complicated by issues of reproducibility.

Whole Slide Imaging and distributed review overcome many of these issues,

and provide new opportunities that have previously not been feasible.

11:20-11:50 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

11:50-1:20 pm Enjoy Lunch on Your Own

NGS in Clinical Use

1:20-1:25 Chairperson’s Remarks

1:25-1:50 College of American Pathologists’ Standards and Proficiency

Testing for Next-Generation Sequencing for the Clinical Laboratory

Nazneen Aziz, Ph.D., Director, Molecular Medicine, Transformation Program

Office, College of American Pathologists

The rapid and ongoing advances in the genetic test market, spurred by the

opportunities of Next-Generation Sequencing (NGS), necessitate many

facets of the health care industry to work cohesively. Adoption of NGS as

a clinical test requires the adoption of many processes and procedures,

such as the analytic and clinical validation of the test, CLIA certification/CAP

accreditation, standards for reference materials, availability for proficiency

testing, genetic counseling, and questions regarding reimbursement,

informed consent and incidental findings. This talk will focus on the

laboratory requirements developed at CAP for CLIA/CAP accreditation and

the plans for proficiency testing for NGS.

1:50-2:15 Assuring the Quality of Next-Generation Sequencing in

Clinical Laboratory Practice

Ira M. Lubin, Ph.D., Team Lead, Genetics Laboratory Research and Evaluation

Branch, Division of Laboratory Science and Standards, Laboratory Science,

Policy, and Practice Program Office, Office of Surveillance, Epidemiology, and

Laboratory Services, Centers for Disease Control and Prevention

Integration of next-generation sequencing (NGS) into the clinical laboratory

requires test validation, establishment of quality control procedures, and

the independent assessment of test performance by proficiency testing

or alternate approaches. Existing regulatory requirements and professional

guidance do not adequately address these quality issues for clinical NGS

testing. This talk will describe the outcomes of a national workgroup

organized by the Centers for Disease Control and Prevention tasked to

identify principles and develop guidance to promote good clinical laboratory

practices for NGS and meet regulatory and professional standards.

2:15-2:40 Clinical NGS: Validation, Reporting and Economics

Seth Crosby, M.D., Director, Partnerships & Alliances, Washington University

School of Medicine

As NGS enters the clinic, matters of analytic and clinical validation are just

the start of the medical director’s worries. How should results be quickly

generated and communicated to a physician in a meaningful and actionable

manner? What are the new rules for billing and reimbursement?

2:40-3:40 Refreshment Break in the Exhibit Hall with Poster Viewing

3:40-4:05 Exome Sequencing in a Clinical Setting to Guide Patient Care

Madhuri Hegde, Ph.D., Associate Professor, Human Genetics; Senior Director,

Emory Genetics Laboratory, Emory University

Advances in genomic medicine have made it necessary for clinical

laboratories to rapidly implement new technologies to guide patient care.

Exome sequencing is being rapidly being implemented across different

specialties such as inherited diseases, cancer and infectious diseases. This

talk will focus on the clinical utility of exome sequencing in patient care

with real case examples.

10 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 3: Cancer Tissue Diagnostics

4:05-4:30 Interpreting Clinical Next-Generation Sequencing Data:

Current Challenges and Hope for the Future

Elaine Lyon, Ph.D., Medical Director, Molecular Genetics; Co-Medical Director,

Pharmacogenomics, ARUP Laboratories; Associate Professor, University of Utah

With the complexity of genomic scale sequencing (next-generation

sequencing or NGS) and the massive amounts of data obtained, informatics

is essential. Two challenges in evaluating a variant are 1) is it real and 2)

is it clinically significant. Informatics allow alignment and variant calling

(differences from a reference sequence), and sifting of probable clinically

insignificant variants. More challenging is prioritizing variants that are likely

to be associated with the clinical symptoms. In addition to the symptomguided

analysis approach, NGS data can reveal variants in genes related to

drug metabolism that may affect efficacy or response. This presentation will

discuss approaches to prioritize symptom-related variants as well as the

potential of NGS data for companion diagnostics or therapeutics.

4:30-5:00 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

5:00-6:00 Networking Reception in the Exhibit Hall with Poster Viewing

6:00-9:00 Dinner Courses

Fit-for-Purpose Biomarker Assay Development and Validation

Next-Generation Sequencing as a Clinical Test

(Separate registration required. See Page 4 for additional information.)

Tuesday, May 7

7:30-8:15 am Breakfast Presentation Sponsored by

Identifying Non-Invasive Biomarkers of Smoking-

Related Parenchymal Lung Disease (i.e. COPD and

IPF) to Detect Subclinical Lung Disease

Ivan O. Rosas, M.D., Assistant Professor, Medicine Division, Pulmonary &

Critical Care Medicine, Brigham & Women’s Hospital, Harvard Medical School

Advances in Immunohistochemistry:

Guiding Therapy Decisions

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Quality Assurance/Quality Control for Immunohistochemistry

in the Era of Personalized Medicine

Emina Torlakovic, M.D., Ph.D., Associate Professor, Laboratory Medicine and

Pathobiology, University of Toronto

Immunohistochemistry (IHC) enables in situ detection of protein expression

(right tissue, right cells, right cellular compartment) and evaluation of expression

levels. Biomarker discovery increases demands on biomarker testing by IHC.

IHC incorporates >20 parameters and requires expert interpretation. Key

challenges include clinical trial study design, tissue processing parameters

and parameters related to expert interpretation. IHC testing challenges remain

significant due to widely spread lack of awareness that IHC QA/QC needs to

evolve to match IHC intended use in personalized medicine.

8:55-9:20 Detection of ALK Gene Rearrangement (ALK+) in Non-Small

Cell Lung Cancers

Eunhee S. Yi, M.D., Consultant, Anatomic Pathology, Mayo Clinic; Professor,

Pathology, Mayo Clinic College of Medicine

Currently, ALK FISH is regarded as the gold standard to select the ALK+

patients eligible for crizotinib therapy, and FISH confirmation is required for

“on-label” crizotinib treatment. ALK IHC can be useful to limit the number

of patients to be tested for ALK FISH by identification of a high probability

population whose tumors are likely to be ALK+. Current status of ALK IHC will

be reviewed along with the data from a molecular study on discordant cases

for ALK status by ALK IHC and FISH in a Mayo Clinic Lung Cancer Cohort.

9:20-9:45 Molecular Profiling and Immunohistochemistry: The Interface

for Identification of Tissue of Origin in Occult Primary Cancers

Charles R. Handorf, M.D., Ph.D., Professor and Chair, Pathology and Laboratory

Medicine, University of Tennessee

Metastatic tumors with an uncertain primary site can be a difficult clinical

problem. In thousands of patients every year, no confident diagnosis is ever

issued making standard-of-care treatment difficult. Newer gene expression

profiling (GEP) tests currently available to analyze these difficult-to-diagnose

tumors are now being compared head-to-head with immunochemistry (IHC),

which has long been held as a gold standard. The interface between these

techniques will be discussed and practical approaches will be explored.

9:45-10:00 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

Tissue Biomarkers for Targeted Therapy

10:00-11:00 Coffee Break in the Exhibit Hall with Poster Viewing

11:00-11:25 In situ Measurement of Tissue Biomarkers for Companion

Diagnostics in Cancer

Kurt A. Schalper, M.D., Ph.D., Associate Research Scientist, Pathology, Yale

School of Medicine

Measurement of tissue biomarkers has been shown to be a valuable tool

for companion diagnostics and is an essential component of personalized

cancer medicine. Several technical limitations surround commonly used

testing methods. In situ measurement of protein and mRNA transcripts

using automated quantitative immunofluorescence and novel hybridization

techniques provides increased sensitivity, specificity and reproducibility.

More quantitative approaches could open new opportunities for biomarker

discovery and patient selection for anti-cancer treatments.

11:25-11:50 Biomarkers and Targeted Therapy for Kaposi Sarcoma

Liron Pantanowitz, M.D., Associate Professor, Pathology and Biomedical

Informatics, University of Pittsburgh Medical Center

Kaposi sarcoma (KS) is an enigmatic vascular neoplasm that arises from

the initial infection of an endothelial or progenitor cell by Kaposi Sarcoma

Herpesvirus/Human Herpesvirus-8 (KSHV/HHV8). KS represents an ideal

model to investigate the interplay between viral oncogenesis, angiogenesis

and host immunity. The discovery of KSHV and related data about the

pathogenesis of KS has resulted in the identification of multiple novel

therapeutic targets. This talk will educate participants about KS biomarkers

being applied for diagnostic work, and also address newer therapeutic

agents aimed at molecular targets being evaluated in clinical trials.

11:50-12:15 pm Access to Human Tissue in the Age of Targeted

Therapies—Impact on Patient Care and Drug Development

Carol Cheung, M.D., Ph.D., Department of Pathology, Canadian University Health

Network

Access to human tissue is paramount in this age of targeted therapies.

Demand for this biological substrate, which is necessary for development

of innovative new tests and potentially blockbuster new therapies, is ever

increasing. The distinction between the two broad classes of excised human

tissue, research tissue that resides in research biobanks and diagnostic

tissue that resides in the clinical archives of institutional departments of

pathology, is important because the rules governing access differ depending

on this fundamental classification.

12:15-1:45 Enjoy Lunch on Your Own

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 11

Track 4: Executive Summit: Companion Diagnostics

Sunday, May 5

5:00-6:00 pm Conference Pre-Registration

Monday, May 6

7:30-8:30 am Conference Registration and Morning Coffee

8:30-8:40 Welcome Remarks from Conference Director

Julia Boguslavsky, Executive Director, Conferences, Cambridge Healthtech Institute

Commercialization of Companion Diagnostics

8:40-8:45 Chairperson’s Opening Remarks

8:45-9:10 Companion Diagnostic Success: Biomarker Discovery to

Global Commercialization

Chris Jowett, General Manager, Commercial Operations, Abbott Molecular

Developing a successful global commercialization strategy for a companion

diagnostic can be a significant challenge. Critical capability factors need

to be discussed prior to entering into the partnership to minimize risk.

Understanding the IVD manufacturers’ capabilities to develop, manage the

required clinical trials, navigate the regulatory environment for approval,

and drive sales and marketing efforts in all targeted countries for the

therapeutic launch is essential. This talk will focus on a variety of strategies

to support a successful launch of a companion diagnostic program.

9:10-9:35 The Payor’s Role in Personalized Medicine

Carol S. Palackdharry, M.D., MS, Medical Director, ActiveHealth Management;

Clinical Lead, Oncology Condition Analysis, Aetna

Targeted cancer treatment is already changing the standard of care for many

cancers. Personalized therapies are costly and generally have anti-tumor activity

only in patients with the specific targeted abnormality. Most targeted agents

require pre-certification, with coverage dependent on appropriate results on

approved companion diagnostic tests. Development of rigorous, evidencebased

recommendations for usage of such tests, as well as new contracting

strategies with high-quality laboratories, will avoid wasted expenditures and

assure access to personalized therapies for all qualified patients.

9:35-10:00 Meeting Evidence Demands for Diagnostics in an Evolving

Payment Environment

Andrew C. Fish, Executive Director, AdvaMedDx

Payer reimbursement of diagnostics is critical to ensuring a robust

market for innovation. As advanced molecular diagnostics proliferate, a

growing appreciation of the importance of these tests is tempered by

rising payer concerns about coding transparency, evidence of clinical

utility, and utilization of and payment for these tests. This talk will review

the reimbursement challenges faced by test developers and initiatives

underway by payers and in Congress to address those challenges.

10:00-10:30 Networking Coffee Break

10:30-10:55 Creating a Companion Diagnostic Regulatory Strategy:

Biomarker to Commercial Test

Debra Rasmussen, Senior Director, Regulatory Affairs, Johnson & Johnson

Validated biomarkers (diagnostic tests) that can serve as intermediate or

surrogate endpoints to acquire rapid regulatory approval are needed to

help move research into the clinic. This is especially true if such biomarkers

could be measured easily, rapidly and were generally accessible.

Pharmaceutical companies could gain from biomarkers and diagnostic

co-development efforts. In an increasingly challenging regulatory

environment, diagnostic led treatment can improve the chance that drugs

are reimbursed or approved in the first place. As companion diagnostics

these could also potentially identify patient benefits from a novel

therapeutic strategy earlier, assist in early discontinuation of ineffective

strategies, and identify active drugs more efficiently. New concerns could

include: 1) designing a definitive clinical study for a joint therapeutic–

diagnostic that allows for assessment of the therapeutic’s safety and

efficacy, as well as for validation of the clinical utility of the biomarker

guiding the therapeutic’s use or 2) regulatory bodies requiring a diagnostic

test before a prescription may be written for a patient.

10:55-11:25 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

11:25-11:50 Panel Discussion: Strategies for Regulatory and

Reimbursement Challenges in Commercialization of CDx

Panelists:

Chris Jowett, General Manager, Commercial Operations, Abbott Molecular

Carol S. Palackdharry, M.D., MS, Medical Director, ActiveHealth Management;

Clinical Lead, Oncology Condition Analysis, Aetna

Debra Rasmussen, Senior Director, Regulatory Affairs, Johnson & Johnson

Andrew C. Fish, Executive Director, AdvaMedDx

11:50-1:20 pm Enjoy Lunch on Your Own

Strategies for Rx-Dx Partnerships

1:20-1:25 Chairperson’s Remarks

1:25-1:50 Synchronizing Drug Development and Companion

Diagnostics: Challenges and Solutions

Hakan Sakul, Ph.D., Executive Director and Head, Diagnostics, Worldwide R&D,

Clinical Research and Precision Medicine, Pfizer

Last year witnessed simultaneous regulatory approvals of Rx and Dx and

it is expected that such approvals will be more commonplace in the near

future. Synchronizing the drug development phases with those for Dx

development presents many challenges. This talk will attempt to outline

these challenges and offer solutions based on the Xalkori Rx/Dx program.

1:50-2:15 Managing Pharma/Diagnostic Partnerships in Companion

Diagnostic Development

George A. Green IV, Ph.D., Director, Pharmacodiagnostics, Bristol-Myers Squibb

The development of CDx assays minimally requires a partnership between a

pharmaceutical and a diagnostic company. It is not uncommon for the drug

to be developed through an alliance of two pharmaceutical companies, and

diagnostic assay development programs may include separate companies

for design of the assay and development of the platform. To ensure effective

delivery of the CDx within this complex environment, highly matrixed teams

must be formed to address strategic and technical issues, and to deliver a

quality product coordinated with the drug development schedule.

2:15-2:40 Presentation to be Announced

2:40-3:40 Refreshment Break in the Exhibit Hall with Poster Viewing

3:40-4:10 Key Considerations for Selecting a Diagnostic Sponsored by

Partner in Rx-Dx Program Commercialization

Jeremy Bridge-Cook, Ph.D., Senior Vice President, Research &

Development, Luminex

What are the optimal capabilities and expertise required of diagnostic

partners for the development and commercialization of companion

diagnostic devices? Key considerations include prototype assay

development, analytical and clinical validation, regulatory filing, approval

and market launch. The speaker will discuss how each of these elements

can impact the success of a companion diagnostic program.

4:10-4:25 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

12 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 4: Executive Summit: Companion Diagnostics

4:25-5:00 Panel Discussion: Strategies for Initiating and Managing

Successful Rx-Dx Partnerships

Panelists:

Hakan Sakul, Ph.D., Executive Director and Head, Diagnostics, Worldwide R&D,

Clinical Research and Precision Medicine, Pfizer

George A. Green IV, Ph.D., Director, Pharmacodiagnostics, Bristol-Myers Squibb

Panelist to be Announced

5:00-6:00 Networking Reception in the Exhibit Hall with Poster Viewing

6:00-9:00 Dinner Courses

Fit-for-Purpose Biomarker Assay Development and Validation

Next-Generation Sequencing as a Clinical Test

(Separate registration required. See Page 4 for additional information.)

Tuesday, May 7

7:30-8:15 am Breakfast Presentation Sponsored by

Identifying Non-Invasive Biomarkers of Smoking-

Related Parenchymal Lung Disease (i.e. COPD and

IPF) to Detect Subclinical Lung Disease

Ivan O. Rosas, M.D., Assistant Professor, Medicine Division, Pulmonary &

Critical Care Medicine, Brigham & Women’s Hospital, Harvard Medical School

Choosing a Platform for Companion Diagnostics

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Validating Biomarker Assays as a Prelude to Companion

Diagnostic Development: Emerging Platform-Specific Considerations

Michael Burczynski, Ph.D., Executive Director, Biomarker Technologies,

Discovery Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Timely implementation of companion diagnostics alongside therapeutic

products has amplified the need to validate predictive biomarkers in earlier

phases of drug development. Today, biomarker strategies are more complex

and require more diverse platforms than ever before. Ensuring that analytical

validation strategies for these exploratory predictive biomarker assays

are aligned with the downstream requirements for full-blown companion

diagnostic development is a critical activity that ultimately helps determine

the efficiency with which targeted medicines can be brought to market.

8:55-9:20 Choosing a Platform for Companion Diagnostic Development

Ron Mazumder, Ph.D., MBA, Global Head, Research and Product Development,

Janssen Diagnostics, Janssen Pharmaceutical Companies of Johnson &

Johnson

One of the early considerations in developing a companion diagnostic

is choice of platform. Several factors, such as technical performance,

regulatory and reimbursement path, and commercial access will be

discussed in this context. Examples from the literature and case studies

will be presented.

9:20-9:45 Thoughts and Considerations for Choosing a Companion

Diagnostic Technology and Platform Delivery System

Patrick Groody, Ph.D., Divisional Vice President, Research & Development,

Abbott

Choosing a diagnostic technology and testing platform for the

development of a companion diagnostic test can be a significant challenge.

A wide variety of factors including the development time, capabilities of

potential partners and the ability of laboratories and physicians to access

and perform the test routinely in a clinical setting are key factors in

developing a companion diagnostic program. This talk will focus on variety

of strategies for developing commercial companion diagnostic tests.

9:45-10:00 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

10:00-11:00 Coffee Break in the Exhibit Hall with Poster Viewing

11:00-12:00 pm Panel Discussion: Next-Generation CDx Platforms

Panelists:

Michael Burczynski, Ph.D., Executive Director, Biomarker Technologies,

Discovery Medicine and Clinical Pharmacology, Bristol-Myers Squibb

Ron Mazumder, Ph.D., MBA, Global Head, Research and Product Development,

Janssen Diagnostics, Janssen Pharmaceutical Companies of Johnson & Johnson

Elaine Lyon, Ph.D., Medical Director, Molecular Genetics; Co-Medical Director,

Pharmacogenomics, ARUP Laboratories; Associate Professor, University of Utah

Patrick Groody, Ph.D., Divisional Vice President, Quality Assurance and

Operations, Abbott

12:00-1:45 Enjoy Lunch on Your Own

Biomarkers to Diagnostics

1:45-1:50 Chairperson’s Remarks

1:50-2:15 Investing in Biomarkers and Turning Them into Diagnostics

Felix Frueh, Entrepreneur-in-Residence, Third Rock Ventures

The translation of biomarkers into useful clinical diagnostics requires the

demonstration of clinical benefit and cost effectiveness. Investing in new

technology is not sufficient without the realization that discovery and

development of a new biomarker needs to include the demonstration that

the biomarker makes a difference in clinical outcomes or decision-making,

preferably tested in the environment in which ultimately a diagnostic will

be used.

2:15-2:40 From Biomarker Research to Diagnostic Development—Our

Challenges

Yoshi Oda, Ph.D., President, Biomarkers and Personalized Medicine Core

Function Unit, Eisai

Biomarkers play important roles for drug development as a part of

translational research. Several examples about biomarkers for 1) the

evidence of target engagement, 2) patient stratification, 3) drug efficacy

and 4) disease diagnostics will be discussed.

2:40-3:45 Refreshment Break in the Exhibit Hall with Poster Viewing

Timeline for CDx Development

3:45-3:50 Chairperson’s Remarks

3:50-4:15 Timeline Considerations for Incorporating a Companion

Diagnostic into the Drug Development Process

Luigi Catanzariti, Ph.D., Executive Director and Global Program Director,

Diagnostics, Novartis

Rx/Dx co-development provides new opportunities for Pharma with respect to

targeted therapeutics. It also comes with considerable clinical, technical and

regulatory challenges. While both drug and diagnostic development processes

have their own rules and regulations, this new codependency requires

significant adjustments in what can be considered quintessentially clinical

(Rx) and technical (Dx) development cultures. Mutual understanding and

integration of both cultures early in the development process is an important

aspect for minimizing development timelines and achieving success.

4:15-4:40 Nothing Ventured, Nothing Gained: The Timeline Challenge

for Companion Diagnostics

Scott Patterson, Ph.D., Executive Director, Medical Sciences, Amgen

The identification of patients who are most likely to benefit from therapy

is an important component of any drug development strategy. Other

than when the target of the therapeutic is also the diagnostic for patient

selection, the generation of evidence to test a biomarker patient selection

hypothesis occurs during the drug development process. That data may

not become available until late in the development process. Strategies that

could be pursued to address this issue, with examples, will be presented.

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 13

Track 4: Executive Summit: Companion Diagnostics

4:40-5:05 Strategic and Computational Considerations in

Development of Complex Companion Diagnostics

Amir Handzel, Ph.D., Associate Director, Translational and Clinical Sciences, OSI

Pharma (Astellas)

Successful development of CDx requires special attention to diverse

factors, as well as to their seamless integration. These challenges in

developing validated complex diagnostic biomarkers have been highlighted

by several failures in the last decade. The universe of molecular entities

from which markers can be chosen is rich, comprising genetic mutations,

the transcriptome, proteins and emerging non-coding RNA and epigenetic

entities. Their extremely large numbers present difficult problems of

selection and validation in a statistically robust and consistent way.

In order to address them, an array of technical, as well as operational

and organizational approaches must be employed. For example, the

characteristics of the experimental platforms used to acquire data

influence biomarker selection and design and these in turn necessitate a

multidisciplinary team structure. I will discuss these strategic and technical

elements while pointing to pitfalls and how to avoid them to reach the

desired goal.

5:05-5:30 Companion Diagnostics: Challenges in Bridging the Chasm

between Diagnostics and Drugs

Steven Gutman, M.D., MBA, Strategic Advisor, Myraqa

An IVD companion diagnostic device is an in vitro diagnostic device

that provides information essential for the safe and effective use of a

corresponding therapeutic product. This pairing of products has generated

intense interest because 1) it offers a clear model for the implementation

of personalized health care and 2) it may contribute to more informed

choices about how to manage the pipeline for new drugs. This talk will

focus on potential roadmaps for use in drug-diagnostic co-development.

6:00-9:00 Dinner Course

Laboratory-Developed Tests

(Separate registration required. See Page 4 for additional information.)

Wednesday, May 8

7:30-8:15 am Breakfast Presentation or Morning Coffee

(Sponsorship opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

Advancing Personalized Medicine

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Personalized Health Care: ‘One Size Does Not Fit All’ Applies

to Patients and Products

M.J. Finley Austin, Ph.D., Personalized Healthcare & Biomarker Strategy

Director, AstraZeneca

The essence of Personalized Health Care (PHC) is identifying,

understanding and partioning drug response variation to improve clinical

outcomes. Existing PHC examples demonstrate diversity in source of

variation, path to market as well as market delivery and uptake. Current

examples will be used to elucidate the implications of differing sources

and degrees of variation, clinical utility, and timing of discovery all have for

clinical trial design, regulatory strategy and market delivery.

8:55-9:20 Molecular Subtyping of Patients for Drug Development

Eric Lai, Ph.D., Senior Vice President and Head, Pharmacogenomics, Takeda

Pharmaceuticals International

While the concept of drug-diagnostic co-development (CDx) has been

around for awhile, most companion diagnostics are still an afterthought

and not an integrated component of drug development. To benefit from

the full potential of CDx, we have to change the strategy of drug target

identification from the single target approach to systematic understanding

of a patient’s disease phenotypes. I will discuss some of the potentials

steps that we have made to the drug development process.

9:20-9:45 Co-Diagnostics in Autoimmune Disorders: Improving

Outcomes in RA and IBD

Mark E. Curran, Ph.D., Vice President, Immunology Biomarkers, Janssen

Research & Development

Rheumatoid arthritis and inflammatory bowel disease are severe immune

diseases with significantly reduced quality of life for patients. Despite

advances in treatment with the evolution of antibody and recombinant

protein based therapeutics, there remains a significant unmet clinical need

for new therapies and integrated treatment solutions. At Janssen we are

focused on transforming therapy in these diseases by applying systems

pharmacology, precision medicine principles and developing companion

diagnostics to create new treatment paradigms. Our objective is to

provide for higher response rates, deeper remission, early interception and

eventually prevention of these diseases. Progress toward these objectives

will be discussed.

9:45-10:15 Complex microRNA Signatures of Response Sponsored by

and Resistance as Powerful Biomarkers

E. Robert Wassman, M.D., CMO, Rosetta Genomics

10:15-10:30 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

10:30-11:30 Coffee Break in the Exhibit Hall with Poster Viewing

11:30-11:55 Precision Medicine: Triumphs and Tribulations

Claudio Carini, M.D., Global Clinical Immunology and Biomarkers Lead,

Bioenhancement Development Unit, Pfizer

The current model for drug development is failing. Failures often occur

either during the phase II trials, where either the candidate drug did not

meet the expected pharmacological requirements or the targeted drug

mechanism did not play a role in the patients population studied. Thus,

a new “personalized medicine” strategy is needed to develop predictive

biomarkers to assist in the decision making process during the pre-clinical

phase of drug development and use biomarkers as companion diagnostics

for stratifying patients in hypothesis-driven clinical trials.

11:55-12:20 pm Towards Personalized Medicine in Metabolic Diseases

Mark Broenstrup, Ph.D., Director, Biomarker and Diagnostics, R&D Diabetes

Division, Sanofi

Currently, more than 346 million people worldwide have diabetes. The

identification of the most effective drug(s) for the individual patient

is guided by a few selection criteria and a trial-and-error approach.

Consequently, the introduction of personalized approaches, accounting

for the heterogeneity of the disease, is regarded as a key enabler for

improved health care. An overview on biomarkers for assessing risk,

monitoring disease progression and predicting response to drugs is

provided, with a focus on beta cell imaging and systems biology solutions.

Finally, major public-private partnerships aiming at personalized solutions in

diabetes will be highlighted.

12:20-12:45 Translating Molecular Targets for Cancer Therapeutics

Glen J. Weiss, M.D., Co-Head, Lung Cancer Unit, The Translational Genomics

Research Institute (TGen); Director, Clinical Research, Cancer Treatment

Centers of America; CMO, CRAB-Clinical Trials Consortium

The presentation will focus on why there is a push to individualize cancer

therapy, past failures and successes, and how to define the tumor context

of vulnerability (COV). The talk will also describe the steps from pre-clinical

to new drug application and show how to optimize the drug development

path with knowledge of biomarker-based COV.

12:45 Close of Conference

14 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 5: Biomarkers for Patient Selection

tuesday, May 7

12:15-1:45 Conference Registration

Biomarkers to Diagnostics

1:45-1:50 Chairperson’s Opening Remarks

1:50-2:15 Investing in Biomarkers and Turning Them into Diagnostics

Felix Frueh, Entrepreneur-in-Residence, Third Rock Ventures

The translation of biomarkers into useful clinical diagnostics requires the

demonstration of clinical benefit and cost effectiveness. Investing in new

technology is not sufficient without the realization that discovery and

development of a new biomarker needs to include the demonstration that the

biomarker makes a difference in clinical outcomes or decision-making, preferably

tested in the environment in which ultimately a diagnostic will be used.

2:15-2:40 From Biomarker Research to Diagnostic Development—Our

Challenges

Yoshi Oda, Ph.D., President, Biomarkers and Personalized Medicine Core

Function Unit, Eisai

Biomarkers play important roles for drug development as a part of

translational research. Several examples about biomarkers for 1) the

evidence of target engagement, 2) patient stratification, 3) drug efficacy

and 4) disease diagnostics will be discussed.

2:40-3:45 Refreshment Break in the Exhibit Hall with Poster Viewing

Molecular Profiling of Tumor Heterogeneity to

Guide Therapy

3:45-3:50 Chairperson’s Remarks

3:50-4:15 Liquid Biopsies to Monitor Response and Resistance to

Targeted Therapies

Luis Alberto Diaz, M.D., Associate Professor of Oncology, Johns Hopkins

Sidney Kimmel Comprehensive Cancer Center

The simplest hypothesis to account for the development of resistance

to EGFR blockade is that rare cells with KRAS mutations pre-exist at low

levels in tumors with ostensibly wild-type KRAS genes. Although this

hypothesis would seem readily testable, there is no evidence in preclinical

models to support it, nor is there data from patients. To test this

hypothesis, we determined whether mutant KRAS DNA could be detected

in the circulation of 28 patients receiving monotherapy with panitumumab,

a therapeutic anti-EGFR antibody. The results suggest that the emergence

of KRAS mutations is a mediator of acquired resistance to EGFR blockade

and that these mutations can be detected in a non-invasive manner. They

explain why solid tumors develop resistance to targeted therapies in a

highly reproducible fashion.

4:15-4:40 Application of Clinical Tumor Genotyping in Targeted Cancer

Therapy

Darrell R. Borger, Ph.D., Co-Director, Translational Research Laboratory,

Massachusetts General Hospital Cancer Center

Multiplexed tumor genotyping has been offered as a physician-ordered

clinical test at a major U.S. cancer center. Over 3,000 patients have been

evaluated and these new capabilities have fostered a genotype-directed

approach to clinical trial design. By testing a broad spectrum of tumor

types, new molecular signatures have been revealed and mechanisms

of de novo and acquired resistance to targeted therapies have been

uncovered. This has provided the foundation for expanding clinical cancer

genotyping approaches for personalizing cancer care.

4:40-5:05 Quantitative Tumor Protein Profiling for Therapy-Relevant

Stratification of Breast Cancer Patients

Hallgeir Rui, M.D., Ph.D., Professor, Cancer Biology, Medical Oncology and

Pathology; Scientific Director, Jefferson Breast Care Center; Program Leader,

Biology of Breast Cancer, Kimmel Cancer Center; Co-Director, Pathology

Translational Research Core, Thomas Jefferson University

Breast cancer is a heterogeneous group of malignancies driven by diverse

oncogenic pathways. Ongoing consortium efforts are to map breast cancer

subtypes at high resolution based on quantitative immunofluorescence

(QIF) profiling of druggable target proteins within carcinoma cells of a

panel of 5,000 untreated primary breast cancer specimens. Progress

with prolactin-receptor-Jak-Stat pathway profiling will be highlighted using

complementary QIF technologies. Utility of resulting protein-based breast

cancer subclassification maps for rational recruitment of patients into

biomarker-driven, adaptive clinical trials will be discussed.

5:05-5:30 Clinical Validation of Predictive Biomarkers and Next-

Generation Personalized Medicine Treatment Strategies Incorporating

Genetic Dynamics

Robert A. Beckman, M.D., External Faculty, Center for Evolution and Cancer,

Helen Diller Family Cancer Center, University of California at San Francisco;

Executive Director, Clinical Development Oncology, Daiichi Sankyo Pharma

Development

The future of oncology drug development lies in personalized therapy

using predictive biomarkers. However, examples of the failure of predictive

biomarkers also exist. In these cases the use of biomarkers increased

the costs, complexity and duration of clinical trials, and narrowed the

treated population unnecessarily. We present methods to adaptively

integrate predictive biomarkers into clinical programs in a data-driven

manner, wherein these biomarkers are emphasized in exact proportion

to the evidence supporting their clinical predictive value. Next-generation

personalized treatment strategies, which emphasize tumor heterogeneity,

evolutionary dynamics and possible future tumor states, will also

be presented.

6:00-9:00 Dinner Course

Laboratory-Developed Tests

(Separate registration required. See Page 4 for additional information.)

Wednesday, May 8

7:30-8:15 am Breakfast Presentation or Morning Coffee

(Sponsorship opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

Advancing Personalized Medicine

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Personalized Health Care: ‘One Size Does Not Fit All’ Applies

to Patients and Products

M.J. Finley Austin, Ph.D., Personalized Healthcare & Biomarker Strategy

Director, AstraZeneca

The essence of Personalized Health Care (PHC) is identifying,

understanding and partioning drug response variation to improve clinical

outcomes. Existing PHC examples demonstrate diversity in source of

variation, path to market as well as market delivery and uptake. Current

examples will be used to elucidate the implications of differing sources

and degrees of variation, clinical utility, and timing of discovery all have for

clinical trial design, regulatory strategy and market delivery.

8:55-9:20 Molecular Subtyping of Patients for Drug Development

Eric Lai, Ph.D., Senior Vice President and Head, Pharmacogenomics, Takeda

Pharmaceuticals International

While the concept of drug-diagnostic co-development (CDx) has been

around for awhile, most companion diagnostics are still an afterthought

and not an integrated component of drug development. To benefit from

the full potential of CDx, we have to change the strategy of drug target

identification from the single target approach to systematic understanding

of a patient’s disease phenotypes. I will discuss some of the potentials

steps that we have made to the drug development process.

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 15

Track 5: Biomarkers for Patient Selection

9:20-9:45 Co-Diagnostics in Autoimmune Disorders: Improving

Outcomes in RA and IBD

Mark E. Curran, Ph.D., Vice President, Immunology Biomarkers, Janssen

Research & Development

Rheumatoid arthritis and inflammatory bowel disease are severe immune

diseases with significantly reduced quality of life for patients. Despite

advances in treatment with the evolution of antibody and recombinant

protein based therapeutics, there remains a significant unmet clinical need

for new therapies and integrated treatment solutions. At Janssen we are

focused on transforming therapy in these diseases by applying systems

pharmacology, precision medicine principles and developing companion

diagnostics to create new treatment paradigms. Our objective is to

provide for higher response rates, deeper remission, early interception and

eventually prevention of these diseases. Progress toward these objectives

will be discussed.

9:45-10:15 Complex microRNA Signatures of Response

Sponsored by

and Resistance as Powerful Biomarkers

E. Robert Wassman, M.D., CMO, Rosetta Genomics

10:15-10:30 Sponsored Presentation

(Opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com)

10:30-11:30 Coffee Break in the Exhibit Hall with Poster Viewing

11:30-11:55 Precision Medicine: Triumphs and Tribulations

Claudio Carini, M.D., Global Clinical Immunology and Biomarkers Lead,

Bioenhancement Development Unit, Pfizer

The current model for drug development is failing. Failures often occur

either during the Phase II trials, where either the candidate drug did not

meet the expected pharmacological requirements or the targeted drug

mechanism did not play a role in the patients population studied. Thus,

a new “personalized medicine” strategy is needed to develop predictive

biomarkers to assist in the decision making process during the pre-clinical

phase of drug development and use biomarkers as companion diagnostics

for stratifying patients in hypothesis-driven clinical trials.

11:55-12:20 pm Towards Personalized Medicine in Metabolic Diseases

Mark Broenstrup, Ph.D., Director, Biomarker and Diagnostics, R&D Diabetes

Division, Sanofi

Currently, more than 346 million people worldwide have diabetes. The

identification of the most effective drug(s) for the individual patient

is guided by a few selection criteria and a trial-and-error approach.

Consequently, the introduction of personalized approaches, accounting

for the heterogeneity of the disease, is regarded as a key enabler for

improved health care. An overview on biomarkers for assessing risk,

monitoring disease progression and predicting response to drugs is

provided, with a focus on beta cell imaging and systems biology solutions.

Finally, major public-private partnerships aiming at personalized solutions in

diabetes will be highlighted.

12:20-12:45 Translating Molecular Targets for Cancer Therapeutics

Glen J. Weiss, M.D., Co-Head, Lung Cancer Unit, The Translational Genomics

Research Institute (TGen); Director, Thoracic Oncology, Virginia G. Piper Cancer

Center Clinical Trials at Scottsdale Healthcare; CMO, CRAB-Clinical Trials

Consortium

The presentation will focus on why there is a push to individualize cancer

therapy, past failures and successes, and how to define the tumor context

of vulnerability (COV). The talk will also describe the steps from pre-clinical

to new drug application and show how to optimize the drug development

path with knowledge of biomarker-based COV.

12:45 Close of Conference

SPONSORSHIP, EXHIBIT & LEAD GENERATION OPPORTUNITIES

CHI offers comprehensive sponsorship packages which include presentation

opportunities, exhibit space and branding, as well as the use of the pre and postshow

delegate lists. Customizable sponsorship packages allow you to achieve

your objectives before, during, and long after the event. Signing on early will

allow you to maximize exposure to hard-to-reach decision makers!

Agenda Presentations

Showcase your solutions to a guaranteed, highly-targeted audience. Package

includes a 15- or 30-minute podium presentation within the scientific agenda,

exhibit space, on-site branding and access to cooperative marketing efforts

by CHI.

Breakfast & Luncheon Presentations

Opportunity includes a 30-minute podium presentation. Boxed lunches are

delivered into the main session room, which guarantees audience attendance

and participation. A limited number of presentations are available for sponsorship

and they will sell out quickly. Sign on early to secure your talk!

Invitation-Only VIP Dinner/Hospitality Suite

Sponsors will select their top prospects from the conference pre-registration

list for an evening of networking at the hotel or at a choice local venue. CHI will

extend invitations and deliver prospects. Evening will be customized according to

sponsor’s objectives i.e.:

• Purely social

• Focus group

• Reception style

• Plated dinner with specific

conversation focus

Exhibit

Exhibitors will enjoy facilitated networking opportunities with high-level

conference delegates. Speak face-to-face with prospective clients and showcase

your latest product, service, or solution.

*Inquire about additional branding opportunities!

Looking for additional ways to drive leads to your sales team?

Cambridge Healthtech Institute can help!

We offer clients numerous options for custom lead generation programs to

address their marketing and sales needs, including:

• Live Webinars

• White Papers

• Market Surveys

• Podcasts

• And More!

Benefits of working with Cambridge Healthtech Institute for your lead

generation needs:

• Your campaign will receive targeted promotion to Cambridge Healthtech

Institute’s unparalleled database of over 800,000 individuals, all of which are

involved in all sectors of the life sciences – lists can be segmented based on

geography, research area, title and industry.

• All custom lead generation programs are promoted through our experienced

marketing team that will develop and drive targeted campaigns to drive

awareness and leads to your lead generation program.

• For our webinar programs, we offer assistance in procuring speakers for

your web symposia through our extensive roster of industry recognized

speakers across multiple disciplines within life sciences, as well as provide

an experienced moderator and dedicated operations team who will

coordinate all efforts.

• If choosing a white paper program, we can offer editorial experience and

provide an industry recognized author to write your white paper.

To customize your participation at this event, please contact:

Ilana Quigley – Business Development Manager

781-972-5457 | iquigley@healthtech.com

16 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 6: Cancer Drug Resistance

Tuesday, May 8

12:15-1:45 Conference Registration

Biomarkers to Diagnostics

1:45-1:50 Chairperson’s Opening Remarks

1:50-2:15 Investing in Biomarkers and Turning Them into Diagnostics

Felix Frueh, Entrepreneur-in-Residence, Third Rock Ventures

The translation of biomarkers into useful clinical diagnostics requires the

demonstration of clinical benefit and cost effectiveness. Investing in new

technology is not sufficient without the realization that discovery and

development of a new biomarker needs to include the demonstration that the

biomarker makes a difference in clinical outcomes or decision-making, preferably

tested in the environment in which ultimately a diagnostic will be used.

2:15-2:40 From Biomarker Research to Diagnostic Development—Our

Challenges

Yoshi Oda, Ph.D., President, Biomarkers and Personalized Medicine Core

Function Unit, Eisai

Biomarkers play important roles for drug development as a part of

translational research. Several examples about biomarkers for 1) the

evidence of target engagement, 2) patient stratification, 3) drug efficacy

and 4) disease diagnostics will be discussed.

2:40-3:45 Refreshment Break in the Exhibit Hall with Poster Viewing

Molecular Profiling of Tumor Heterogeneity to

Guide Therapy

3:45-3:50 Chairperson’s Opening Remarks

3:50-4:15 Liquid Biopsies to Monitor Response and Resistance to

Targeted Therapies

Luis Alberto Diaz, M.D., Associate Professor of Oncology, Johns Hopkins

Sidney Kimmel Comprehensive Cancer Center

The simplest hypothesis to account for the development of resistance

to EGFR blockade is that rare cells with KRAS mutations pre-exist at low

levels in tumors with ostensibly wild-type KRAS genes. Although this

hypothesis would seem readily testable, there is no evidence in preclinical

models to support it, nor is there data from patients. To test this

hypothesis, we determined whether mutant KRAS DNA could be detected

in the circulation of 28 patients receiving monotherapy with panitumumab,

a therapeutic anti-EGFR antibody. The results suggest that the emergence

of KRAS mutations is a mediator of acquired resistance to EGFR blockade

and that these mutations can be detected in a non-invasive manner. They

explain why solid tumors develop resistance to targeted therapies in a

highly reproducible fashion.

4:15-4:40 Application of Clinical Tumor Genotyping in Targeted Cancer

Therapy

Darrell R. Borger, Ph.D., Co-Director, Translational Research Laboratory,

Massachusetts General Hospital Cancer Center

Multiplexed tumor genotyping has been offered as a physician-ordered

clinical test at a major U.S. cancer center. Over 3,000 patients have been

evaluated and these new capabilities have fostered a genotype-directed

approach to clinical trial design. By testing a broad spectrum of tumor

types, new molecular signatures have been revealed and mechanisms

of de novo and acquired resistance to targeted therapies have been

uncovered. This has provided the foundation for expanding clinical cancer

genotyping approaches for personalizing cancer care.

4:40-5:05 Quantitative Tumor Protein Profiling for Therapy-Relevant

Stratification of Breast Cancer Patients

Hallgeir Rui, M.D., Ph.D., Professor, Cancer Biology, Medical Oncology and

Pathology; Scientific Director, Jefferson Breast Care Center; Program Leader,

Biology of Breast Cancer, Kimmel Cancer Center; Co-Director, Pathology

Translational Research Core, Thomas Jefferson University

Breast cancer is a heterogeneous group of malignancies driven by diverse

oncogenic pathways. Ongoing consortium efforts are to map breast cancer

subtypes at high resolution based on quantitative immunofluorescence

(QIF) profiling of druggable target proteins within carcinoma cells of a

panel of 5,000 untreated primary breast cancer specimens. Progress

with prolactin-receptor-Jak-Stat pathway profiling will be highlighted using

complementary QIF technologies. Utility of resulting protein-based breast

cancer subclassification maps for rational recruitment of patients into

biomarker-driven, adaptive clinical trials will be discussed.

5:05-5:30 Clinical Validation of Predictive Biomarkers and Next-

Generation Personalized Medicine Treatment Strategies Incorporating

Genetic Dynamics

Robert A. Beckman, M.D., External Faculty, Center for Evolution and Cancer,

Helen Diller Family Cancer Center, University of California at San Francisco;

Executive Director, Clinical Development Oncology, Daiichi Sankyo Pharma

Development

The future of oncology drug development lies in personalized therapy

using predictive biomarkers. However, examples of the failure of predictive

biomarkers also exist. In these cases the use of biomarkers increased

the costs, complexity and duration of clinical trials, and narrowed the

treated population unnecessarily. We present methods to adaptively

integrate predictive biomarkers into clinical programs in a data-driven

manner, wherein these biomarkers are emphasized in exact proportion

to the evidence supporting their clinical predictive value. Next-generation

personalized treatment strategies, which emphasize tumor heterogeneity,

evolutionary dynamics and possible future tumor states, will also

be presented.

6:00-9:00 Dinner Course

Laboratory-Developed Tests

(Separate registration required. See Page 4 for additional information.)

Wednesday, May 8

7:30-8:05 am Morning Coffee

Secondary Resistance to Targeted Cancer Therapy

8:05-8:30 Biomarkers and Trastuzumab Resistance

Wen Jin Wu, M.D., Ph.D., Principal Investigator, Division of Monoclonal Antibodies,

Office of Biotechnology Products, Center for Drug Evaluation and Research, FDA

Trastuzumab is an anti-HER2 antibody indicated for the treatment of

HER2-positive breast cancer. Approximately two-thirds of HER2-positive

breast cancers show primary resistance to trastuzumab treatment, and

a majority of patients who achieve an initial response to trastuzumab

acquire resistance to trastuzumab within one year. However, there are

no clinically useful predictive biomarkers that can be used in conjunction

with HER2 expression to predict the outcome of trastuzumab treatment

in the HER2-positive breast cancer patients. We recently found that the

phosphorylation of HER2-Y1248 was associated with the sensitivity of

trastuzumab treatment, suggesting that the phosphorylation status of

HER2-Y1248 may be a predictive biomarker for trastuzumab treatment.

8:30-8:55 Resistance to MAPK Pathway Inhibitors in Melanoma:

Insights and Future Challenges

Jessie Villanueva, Ph.D., Assistant Professor, Molecular and Cellular

Oncogenesis Program, The Wistar Institute

The mitogen-activated protein kinase (MAPK) pathway is a key

therapeutic target for melanoma due to its activation in the majority of

tumors. Numerous small molecule inhibitors aimed at controlling MAPK

activity, such as BRAF and MEK inhibitors, are currently undergoing

clinical investigation. However, their therapeutic success is limited by

the development of drug resistance. To develop effective therapies for

melanoma patients, it is critical to uncover the mechanisms of resistance

to BRAF and MEK inhibitors. This presentation will discuss recent studies

on the molecular mechanisms of resistance to inhibitors of the MAPK

pathway and potential strategies to treat drug-resistant melanomas.

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 17

8:55-9:20 A Pre-Clinical Model of BRAF Inhibitor Resistance in

Melanoma Reveals a Novel Approach to Forestall Drug Resistance

Meghna Das Thakur, Ph.D., Presidential Postdoctoral Fellow, Novartis Institutes

for BioMedical Research

BRAF inhibitors such as vemurafenib have shown promising effects in

patients with mutant BRAF(V600E) melanomas, but the tumors generally

develop resistance. Interestingly, the vemurafenib-resistant melanomas

become drug dependent for their continued proliferation, such that

cessation of drug administration leads to regression of established drugresistant

tumors. Thus, a discontinuous dosing strategy exploiting the

fitness disadvantage shown by drug-resistant cells in the absence of the

drug forestalls the onset of lethal drug-resistant disease.

9:20-9:45 Non Cell-Autonomous Mechanisms of Resistance against

Anti-EGFR Therapy

Janghee Woo, M.D., Albert Einstein Medical Center; Recipient of AACRGlaxoSmithKline

Clinical Cancer Research Scholar Award and Dana-Farber/

Harvard Cancer Center Award

Our findings suggest that stroma-derived MMP9 may help tumors bypass

common mutational mechanisms for constitutive growth factor pathway

activation and confer resistance to anti-EGFR therapy through activation of

the ERBB2/ERK/JUN pathway. Stromal MMP9 expression may therefore

have value as a predictive marker for cetuximab response and in stratifying

patients before treatment.

9:45-10:30 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457 or

iquigley@healthtech.com)

10:30-11:30 Coffee Break in the Exhibit Hall with Poster Viewing

11:30-11:55 Managing Secondary Drug Resistance in the Clinic: The

Memorial Sloan-Kettering Approach

Maria E. Arcila, M.D., Department of Pathology, Memorial Sloan-Kettering

Cancer Center

Resistance to Various Therapies:

Cancer Does Not Discriminate

11:55-12:00 Chairperson’s Remarks

12:00-12:25 A20 Ubiquitin E3 Ligase is a Biomarker of the Cancer

Stem Cell Resistance to Apoptotic Drugs

Chunhai “Charlie” Hao, M.D., Ph.D., Associate Professor, Neuropathology

Attending, Department of Pathology and Laboratory Medicine, Emory

University School of Medicine

The TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)

apoptosis pathway has emerged as a cancer therapeutic target; however,

Phase II trials recently completed have showed limited if any antitumor

activities of TRAIL pathway-targeted therapies. Molecular and functional

examination of patients’ glioblastoma tissues and derived cancer stem

cells reveals the resistance mechanism by which the ubiquitin E3 ligase

A20 mediated poly-ubiquitination inhibits the cleavage of apoptosisinitiating

caspase-8 and the initiation of TRAIL-induced apoptosis. The

study suggests that the full characterization of patients’ cancer tissues

and derived cancer stem cells can predict the cancer responsiveness

to treatment and thus should be a critical pre-clinical trial step in

drug development.

12:25-12:50 pm Molecular Determinants of Hormone-Refractory

Prostate Cancer

Atish Choudhury, M.D., Instructor in Medicine, Medical Oncology, Dana-Farber

Cancer Institute

To identify novel genes that can confer androgen independence to

prostate cancer cells in vivo, we performed an unbiased screen for kinases

conferring androgen-independent tumor formation to androgen-dependent

transformed prostate epithelial cells in vivo. These kinases are likely

to activate signaling pathways that are relevant for conferring castrate

resistance in patients with advanced prostate cancer, and inhibiting these

genes is likely to result in inhibition of cancer cell proliferation and/or

restoration of hormone sensitivity. Integration of our ambitious functional

studies with gene expression and sequencing data in CRPC from tumor

samples being generated through collaborations between DFCI and the

Broad Institute will provide us a more comprehensive understanding of the

development of castrate resistance and novel targets for therapy.

12:50-1:15 Impact of microRNAs in Chemoresistance

Jingfang Ju, Ph.D., Co-Director, Translational Research, Pathology, Stony Brook

University

Non-coding miRNAs contribute to both intrinsic and extrinsic

chemoresistance mechanism, particularly in colon cancer stem cells.

We first discovered several miRNAs suppressing the expression of both

thymidylate synthase and dihydrofolate reductase to impact 5-FU and MTX

sensitivity. The expression of miR-215 was significantly associated with

colorectal cancer patient survival. Our recent studies also show miRNAs

impact intrinsic apoptotic pathways and autophagy. We believe miRNA

based therapeutics, diagnosis and prognosis may emerge in the near

future to benefit patients.

1:15 Close of Conference

Track 6: Cancer Drug Resistance

Lead Media Partners

Media Partners Web Partner

Lead Sponsoring Publications

Sponsoring Publications

18 | Biomarkers & Diagnostics World Congress BiomarkerWorldCongress.com

Track 7: Exosomes and Microvesicles as Biomarkers and Diagnostics

Tuesday, May 8

12:15-1:45 Conference Registration

Exosome Biomarkers in Drug Development

1:45-1:50 Chairperson’s Opening Remarks

1:50-2:15 Exosomes as Biomarkers for Translational Medicine

Holly Hilton, Ph.D., Head, Disease and Translational Genomics, Hoffmann-La

Roche; Adjunct Professor, Graduate School of Biomedical Sciences, University

of Medicine and Dentistry New Jersey

The need for new, relevant biomarkers for translational drug discovery

research is critical. Exosomes are small microvesicles secreted by a wide

range of mammalian cell types under normal and pathological conditions.

The unique signature of exosomal membrane and cytoplasmic proteins as

well as mRNAs and miRNAs can reveal the cell of origin and the condition

of those cells. Isolation and profiling of exosomes from accessible patient

biofluids, such as urine, blood, BALF and CSF, make them ideal candidates

as biomarkers. Examples of their utility as disease biomarkers of chronic

kidney disease and Alzheimer’s as well as possible applications of patient

stratification will be discussed. The current state of challenges to the

widespread use of fluid-based biomarkers will be explored.

2:15-2:40 Investigation of Microparticles as Potential Translatable

Biomarkers of Vascular Injury

Sharon Sokolowski, Ph.D., Principal Scientist, Pfizer Global Research &

Development

Endothelial cells (EC) are thin, flattened cells that line blood and lymph vessel

walls. Endothelial microparticles (EMPs) are small vesicles (0.1-1 mm) that are

released into circulating blood from activated, injured or apoptotic endothelial

cells and are found at elevated levels in a number of diseases associated with

vascular/endothelial dysfunction. The EMPs are being investigated as potential

translatable biomarkers of drug-induced vascular injury.

2:40-3:45 Refreshment Break in the Exhibit Hall with Poster Viewing

3:45-4:10 Utilization of Next-Gen Genomics Technologies for

Unraveling Exosomal Biomarker Potential

Saumya Pant, Ph.D., Research Fellow, Merck

4:10-4:35 CNS Exosomes and the Art of Eavesdropping

Reyna Favis, Ph.D., Scientific Director, Janssen Pharmaceutical Companies of

Johnson & Johnson

Gaining insight into both genomic changes and differences in the central

nervous system of living humans is currently pursued via investigation of

post mortem brain tissue and lymphocytes from living donors. Analyses

of both tissue types suffer from numerous caveats. There is an urgent

need to develop non-invasive methods that can accurately report temporal

changes, as well as inter-individual differences, in the CNS that may

elucidate neurological and neuropsychiatric disease and drug response.

4:35-5:00 Technology Assessment for Evaluation of Exosomal

microRNA as Novel Biomarkers

Shidong Jia, Ph.D., Scientist, Oncology Biomarker Development, Genentech

Dr. Jia’s lab has developed working procedures to evaluate exosomal

microRNA as novel biomarkers for cancer prognosis, prediction and patient

stratification. In particular, their work has refreshed current practice and

demonstrated a new approach for studying microRNA signature in patient

blood samples.

5:00-5:25 The Exosome Factor in Cancer

Lorraine O’Driscoll, Ph.D., Associate Professor, Pharmacology; Director, Research,

School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin

Our research at Trinity College Dublin supports exosomes cargo having

relevance as diagnostic, prognostic and predictive biomarkers. Evidence

indicates they are also causative in cancer spread and drug resistance.

Here we will discuss examples of this research in relation to breast cancer

and prostate cancer.

6:00-9:00 Dinner Course

Laboratory-Developed Tests

(Separate registration required. See Page 4 for additional information.)

Wednesday, May 8

7:30-8:15 am Breakfast Presentation or Morning Coffee

(Sponsorship opportunity available. Contact Ilana Quigley at 781-972-5457

or iquigley@healthtech.com).

Exosomes as Disease Markers

8:25-8:30 Chairperson’s Opening Remarks

8:30-8:55 Salivary Exosomes and Biomarkers Development

David T.W. Wong, D.M.D., D.M.Sc., Professor, Associate Dean, Research, UCLA

School of Dentistry and Director of Dental Research Institute

Extracellular RNA is an emerging concept in cellular communication

and biomarker development. Salivary extracellular RNA, microRNA and

snoRNA have recently been shown to be contained within exosomes

and can be developed to be discriminatory biomarkers for oral as well as

systemic diseases.

8:55-9:20 Circulating Exosomes in Liver Disease

Gyongyi Szabo, M.D., Ph.D., Professor, Gastroenterology, University of

Massachusetts Medical School

microRNAs (miRNAs) are fine tuners of diverse biological responses and

are expressed in various cell types of the liver. They can also serve as

biomarkers of liver damage and inflammation. We studied miRNA-122 that

is abundant in hepatocytes and miR-155, -146a and -125b that regulate

inflammation in immune cells in mouse models of various types of liver

diseases and found that serum/plasma miR-122 correlated with ALT

increases in the liver damage. miR-155, a regulator of inflammation, was

increased in serum/plasma liver injury associated with inflammation.

Depending on the type of liver injury, circulating miRNAs showed

association either with the exosome-rich or protein-rich compartments.

Our results suggest that circulating miRNAs may serve as biomarkers

to differentiate between hepatocyte injury and inflammation and the

exosome versus protein association of miRNAs may provide further

specificity to mechanisms of liver pathology.

9:20-9:45 Microvesicles: Linking the Bone Marrow and Endothelium in

Pulmonary Vascular Disease

Jason M. Aliotta, M.D., Assistant Professor, Medicine, Warren Alpert Medical

School, Brown University

Extracellular vesicles (EVs) represent potentially important mediators of

cell-to-cell communication and, depending on their source, facilitate tissue

repair or remodeling. We’ve demonstrated that EVs isolated from mice

with monocrotaline-induced pulmonary hypertension (PH) induce features

of PH in normal mice. This may be due to EV-induced apoptosis resistance

of pulmonary vascular endothelial cells or EV-induced differentiation

of marrow cells into progenitor cells which, in turn, induce vascular

remodeling. Conversely, we have found that mesenchymal stem cellderived

EVs may reverse monocrotaline-induced PH.

9:45-10:30 Sponsored Presentations

(Opportunities available. Contact Ilana Quigley at 781-972-5457 or

iquigley@healthtech.com)

10:30-11:30 Coffee Break in the Exhibit Hall with Poster Viewing

BiomarkerWorldCongress.com Biomarkers & Diagnostics World Congress | 19

Track 7: Exosomes and Microvesicles as Biomarkers and Diagnostics

Exosomes as Novel Cancer Biomarkers

11:30-11:35 Chairperson’s Remarks

11:35-12:00 pm The Exosome Platform as a Real-Time Tumor Status Monitor

Douglas D. Taylor, Ph.D., Professor, Obstetrics and Gynecology, University of

Louisville School of Medicine

12:00-12:25 Customized Heterogeneity of Breast Cancer Microvesicles

Dominik Duelli, Ph.D., Assistant Professor, Cellular and Molecular

Pharmacology, Rosalind Franklin University of Medicine & Science, Chicago

Medical School

Breast cancer cells, unlike normal cells, release a heterogeneous

population of circulating microvesicles. Resolving this heterogeneity

suggests that individual microvesicle subclasses have different subcellular

origin, different contents, and different destinations. Each subclass

contains mutually exclusive, functional marker microRNA species, and

some proteins with different functions in docking and lysis resistance in

blood plasma. Additionally, organ-site of metastasis influences the ratio of

these proteins, suggesting that these differences could be used to detect

the presence of malignant cells in the body.

12:25-12:50 Tumor-Derived Microvesicles: Biology and Clinical Potential

Crislyn D’Souza-Schorey, Ph.D., Professor, Biological Sciences, University of

Notre Dame

Tumor-derived microvesicles (TMVs) are heterogeneous membrane-bound

sacs that are shed from tumor cells into the extracellular environment.

The formation of these shed vesicles likely involves the vertical trafficking

of intracellular cargo to the cell surface. The complexity of bioactive

cargo contained in TMVs suggests multi-pronged mechanisms by which

shed TMVs can condition the extracellular milieu to facilitate disease

progression. It also demonstrates the potential to translate this knowledge

into innovative approaches for cancer diagnostics and therapy.

12:50-1:15 Exosome Biomarkers of Brain Tumors

Fred H. Hochberg, M.D., Associate Professor, Neurology, Massachusetts

General Hospital

We explore technology for detection of plasma and CSF exosomal

mutations specific to brain tumors. The analytics for mutations EGFrvIII

and IDH1.132 offer the potential to provide a diagnostic biomarker for

low grade and high grade gliomas. An eighteen member consortium,

collaborating with the ABC2 Foundation and the company Exosome

Diagnostics, will validate the sensitivity of these biomarker assays. The

presentation will include discussion of pre-clinical detection, SOPs for

specimen handling and the rationale for use of these biomarkers.

1:15 Close of Conference

Conference Hotel :

Loews Philadelphia Hotel

1200 Market Street

Philadelphia, PA 19107

Phone: 215-627-1200

HOTEL & TRAVEL

INFORMATION

Discounted Room Rate: $229 s/d

Discounted Room Rate Cut-off Date:

April 8, 2013

Please visit our conference website

to make your reservation online or

call the hotel directly to reserve your

sleeping accommodations. You will need

to identify yourself as a Cambridge

Healthtech Institute conference attendee

to receive the discounted room rate with

the host hotel. Reservations made after

the cut-off date or after the group room

block has been filled (whichever comes

first) will be accepted on a space and

rate-availability basis. Rooms are limited,

so please book early.

Flight Discounts:

Special discount rentals have been established with American Airlines

for this conference.

• Call American Airlines 1-800-433-1790 and use Conference code 8353BL.

• Go to http://www.aa.com/group and enter Conference code 8353BL in promotion

discount box.

• Contact our dedicated travel agents at 1-877-559-5549 or chi@protravelinc.com.

Car Rental Discounts:

Special discount rentals have been established with Hertz for this conference.

• Call Hertz 1-800-654-3131 and use our Hertz Convention Number (CV): 04KL0003

• Go to http://www.hertz.com and use our Hertz Convention Number (CV): 04KL0003

Top Reasons to Stay at The Loews Philadelphia

• Minutes from Amtrak 30th Street Station and 20 minutes from

Philadelphia Airport

• Complimentary wireless internet in your guest room

• Close to many of Philadelphia’s historical sites, including the Liberty

Bell and Independence Hall

• Steps from Reading Terminal Market, which offers an exhilarating selection

of baked goods, meats, poultry, seafood, produce, flowers and more

• Pet-friendly accommodations including specialty pet menus, gifts

upon arrival and dog-walking services

• Located in the historic PSFS Building: A 20th Century Masterpiece

BIOMARKERS & DIAGNOSTICS

world congress 2013

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May 6, 2013 May 7, 2013

Fit-for-Purpose Biomarker Assay Development and Validation Laboratory-Developed Tests

Next-Generation Sequencing as a Clinical Test: It Takes a Community

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ALL ACCESS Executive Pricing: Includes access to entire 3-days of Congress programs, including Executive Summit. (Does not include

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May 6-7, 2013 May 7-8, 2013

Track 1: Translational Biomarkers in Drug Development Track 5: Biomarkers for Patient Selection

Track 2: Clinical Assay Development Track 6: Cancer Drug Resistance

Track 3: Cancer Tissue Diagnostics Track 7: Exosomes and Microvesicles as Biomarkers and Diagnostics

Track 4: Executive Summit: Companion Diagnostics (May 6-8, 2013)

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