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Posts Tagged ‘clinical genomics’


 

THE 3RD STAT4ONC ANNUAL SYMPOSIUM

APRIL 25-27, 2019

HILTON, HARTFORD, CONNECTICUT
315 Trumbull St, Hartford, CT 06103
Reporter: Stephen J. Williams, Ph.D.

SYMPOSIUM OBJECTIVES

The three-day symposium aims to bring oncologists and statisticians together to share new research, discuss novel ideas, ask questions and provide solutions for cancer clinical trials. In the era of big data, precision medicine, and genomics and immune-based oncology, it is crucial to provide a platform for interdisciplinary dialogues among clinical and quantitative scientists. The Stat4Onc Annual Symposium serves as a venue for oncologists and statisticians to communicate their views on trial design and conduct, drug development, and translations to patient care. To be discussed includes big data and genomics for oncology clinical trials, novel dose-finding designs, drug combinations, immune oncology clinical trials, and umbrella/basket oncology trials. An important aspect of Stat4Onc is the participation of researchers across academia, industry, and regulatory agency.

Meeting Agenda will be announced coming soon. For Updated Agenda and Program Speakers please CLICK HERE

The registration of the symposium is via NESS Society PayPal. Click here to register.

Other  2019 Conference Announcement Posts on this Open Access Journal Include:

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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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PrecisionFDA Consistency Challenge supports projects to validate and increase reproduceability of genomic testing methods

Reporter: Stephen J. Williams, Ph.D.

 

PrecisionFDA
Consistency Challenge

Engage and improve DNA test results with our first community challenge

JOIN THE CHALLENGE

ABOUT 1 MONTH REMAINING
PrecisionFDA Consistency Challenge
The Food and Drug Administration (FDA) calls on the genomics community to further assess, compare, and improve techniques used in DNA testing by launching the first precisionFDA challenge.

President Obama’s Precision Medicine Initiative envisions a day when an individual’s medical care will be tailored in part based on their unique characteristics and genetic make-up.

The goal of the FDA’s first precisionFDA challenge is to engage the genomics community in advancing the quality standards in order to achieve more consistent results in the context of genetic tests (related to whole human genome sequencing), advancing the goal of better personalized care.

PrecisionFDA invites all innovators to take the challenge and assess their software on the supplied reference human datasets. Participation is voluntary, but instrumental in helping the community prepare for the coming genomic data revolution.


Challenge Time Period

February 25, 2016 through April 25, 2016


AT A GLANCE

In the context of whole human genome sequencing, software pipelines typically rely on mapping sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878.

By supplying NA12878 whole-genome sequencing read datasets (FASTQ), and a framework for comparing variant call format (VCF) results, this challenge provides a common frame of reference for measuring some of the aspects of reproducibility and accuracy of participants’ pipelines.


PrecisionFDA Consistency Challenge

The challenge begins with two precisionFDA-provided input datasets, corresponding to whole-genome sequencing of the NA12878 human sample at two different sequencing sites. Your mission is to process these FASTQ files through your mapping and variation calling pipeline and create VCF files. For one of the datasets, you are required to do a rerun of your pipeline and obtain a rerun VCF as well. You can generate those results on your own environment, and upload them to precisionFDA, or you can reconstruct your pipeline on precisionFDA and run it on the cloud.

Regardless of how you generate your VCF files, you will subsequently use the precisionFDA comparison framework to conduct several pairwise comparisons:

  • By comparing the rerun VCF to the original one, you will evaluate your pipeline’s reproducibility with respect to the same exact input file.
  • By comparing the VCF files of the two datasets, you will evaluate reproducibility on the same sample across different sites.
  • By comparing each of your three VCF files to the NIST (Genome in a Bottle) benchmark VCF, you will get estimates for accuracy.

The complete set of these five comparisons constitutes your submission entry to the challenge. Each comparison outputs several metrics (such as precision*, recall*, f-measure, or number of non-common variants). Selected participants and winners** will be recognized on the precisionFDA website. Therefore, we hope you are willing to share your experience with others to further enhance the community’s effort to ensure consistency of tests.

The challenge runs until April 25, 2016.


CHALLENGE DETAILS

Last updated: March 2nd, 2016

If you do not yet have a contributor account on precisionFDA, file an access request with your complete information, and indicate that you are entering the challenge. The FDA acts as steward to providing the precisionFDA service to the community and ensuring proper use of the resources, so your request will be initially pending. In the meantime, you will receive an email with a link to access the precisionFDA website in browse (guest) mode. Once approved, you will receive another email with your contributor account information.

With your contributor account you can use the features required to participate in the challenge (such as transfer files or run comparisons). Everything you do on precisionFDA is initially private to you (not accessible to the FDA or the rest of the community) until you choose to publicize it. So you can immediately start working on the challenge in private, and whenever you are ready you can officially publish your results as your challenge entry.


Footnotes

* The terminology currently used in the precisionFDA comparison output (such as “precision” and “recall”) is not necessarily harmonized with definitions used by ISO, CLSI, or FDA, but are terms commonly used by NGS software developers.

** Winning a precisionFDA challenge is an acknowledgement by the precisionFDA community and does not imply FDA endorsement of any organization, tool, software, etc.

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Cambridge Healthtech Institute’s Third Annual

Clinical NGS Assays

Addressing Validation, Standards, and Clinical Relevance for Improved Outcomes

August 23-24, 2016 | Grand Hyatt Hotel | Washington, DC


View Preliminary Agenda

Molecular diagnostics, particularly next-generation sequencing (NGS), have become an integral component of disease diagnosis. Still, there is work to be done to establish these tools as the standard of care. The Third Annual Clinical NGS Assays event will address NGS assay validation, establishing NGS standards, and determining clinical relevance. The pros and cons of various techniques such as gene panels, whole exome, and whole genome sequencing will also be debated with regards to depth of coverage, clinical utility, and reimbursement. Overall, this event will address the needs of both researchers and clinicians while exploring strategies to increase collaboration for improved patient outcomes.

Special Early Registration Savings Available
Register Now to Save up to $450

Preliminary Agenda

ASSAY VALIDATION AND ANALYSIS

Best Practices for Using Genome in a Bottle Reference Materials to Benchmark Variant Calls
Justin Zook, National Institute of Standards and Technology

NGS in Clinical Diagnosis: Aspects of Quality Management
Pinar Bayrak-Toydemir, M.D., Ph.D., FACMG, Associate Professor, Pathology, University of Utah; Medical Director, Molecular Genetics and Genomics, ARUP Laboratories

Thorough Validation and Implementation of Preimplantation Genetic Screening for Aneuploidy by NGS
Rebekah Zimmerman, Ph.D., Laboratory Director, Clinical Genetics, Foundation for Embryonic Competence

EXOME INTERPRETATION CHALLENGES

Are We There Yet? The Odyssey of Exome Analysis and Interpretation
Avni B. Santani, Ph.D., Director, Genomic Diagnostics, Pathology and Lab Medicine, The Children’s Hospital of Philadelphia

Challenges in Exome Interpretation: Intronic Variants
Rong Mao, M.D., Associate Professor, Pathology, University of Utah; Medical Director, Molecular Genetics and Genomics, ARUP Laboratories

Exome Sequencing: Case Studies of Diagnostic and Ethical Challenges
Lora J. H. Bean, Ph.D., Assistant Professor, Human Genetics, Emory University

ESTABLISHING STANDARDS

Implementing Analytical and Process Standards
Karl V. Voelkerding, M.D., Professor, Pathology, University of Utah; Medical Director for Genomics and Bioinformatics, ARUP Laboratories

Assuring the Quality of Next-Generation Sequencing in Clinical Laboratory Practice
Shashikant Kulkarni, M.S., Ph.D., Professor, Pathology and Immunology; Head of Clinical Genomics, Genomics and Pathology Services; Director, Cytogenomics and Molecular Pathology, Washington University at St. Louis

Sponsored Presentation to be Announced by Genection

PANEL DISCUSSION: GENE PANEL VS. WHOLE EXOME VS. WHOLE GENOME

Panelists:
John Chiang, Ph.D., Director, Casey Eye Institute, Oregon Health & Science University
Avni B. Santani, Ph.D., Director, Genomic Diagnostics, Pathology and Lab Medicine, The Children’s Hospital of Philadelphia
Additional Panelist to be Announced

DETERMINING CLINICAL SIGNIFICANCE AND RETURNING RESULTS

Utility of Implementing Clinical NGS Assays as Standard-of-Care in Oncology
Helen Fernandes, Ph.D., Pathology & Laboratory Medicine, Weill Cornell Medical College

An NGS Inter-Laboratory Study to Assess Performance and QC – Sponsored by Seracare
Andrea Ferreira-Gonzalez, Ph.D., Chair, Molecular Diagnostics Division, Pathology, Virginia Commonwealth University Medical School

This conference is part of the Eighth Annual Next-Generation Dx Summit.


Track Sponsor: SeraCare


For exhibit & sponsorship opportunities, please contact:

Joseph Vacca, M.Sc.
Associate Director, Business Development
Cambridge Healthtech Institute
T: (+1) 781-972-5431
E: jvacca@healthtech.com

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Roche is developing a high-throughput low cost sequencer for NGS

Reporter: Stephen J. Williams, PhD

 

Reported from Diagnostic World News

Long-Read Sequencing in the Age of Genomic Medicine

 

 

By Aaron Krol

December 16, 2015 | This September, Pacific Biosciences announced the creation of the Sequel, a DNA sequencer half the cost and seven times as powerful as its previous RS II instrument. PacBio, with its unique long-read sequencing technology, had already secured a place in high-end research labs, producing finished, highly accurate genomes and helping to explore the genetic “dark matter” that other next-generation sequencing (NGS) instruments miss. Now, in partnership with Roche Diagnostics, PacBio is repositioning itself as a company that can serve hospitals as well.

“Pseudogenes, large structural variants, validation, repeat disorders, polymorphic regions of the genome―all those are categories where you practically need PacBio,” says Bobby Sebra, Director of Technology Development at the Icahn School of Medicine at Mount Sinai. “Those are gaps in the system right now for short-read NGS.”

Mount Sinai’s genetic testing lab owns three RS II sequencers, running almost around the clock, and was the first lab to announce it had bought a Sequel just weeks after the new instruments were launched. (It arrived earlier this month and has been successfully tested.) Sebra’s group uses these sequencers to read parts of the genome that, thanks to their structural complexity, can only be assembled from long, continuous DNA reads.

There are a surprising number of these blind spots in the human genome. “HLA is a huge one,” Sebra says, referring to a highly variable region of the genome involved in the immune system. “It impacts everything from immune response, to pharmacogenomics, to transplant medicine. It’s a pretty important and really hard-to-genotype locus.”

Nonetheless, few clinical organizations are studying PacBio or other long-read technologies. PacBio’s instruments, even the Sequel, come with a relatively high price tag, and research on their value in treating patients is still tentative. Mount Sinai’s confidence in the technology is surely at least partly due to the influence of Sebra―an employee of PacBio for five years before coming to New York―and Genetics Department Chair Eric Schadt, at one time PacBio’s Chief Scientific Officer.

Even here, the sequencers typically can’t be used to help treat patients, as the instruments are sold for research use only. Mount Sinai is still working on a limited number of tests to submit as diagnostics to New York State regulators.

Physician Use

Roche Diagnostics, which invested $75 million in the development of the Sequel, wants to change that. The company is planning to release its own, modified version of the instrument in the second half of 2016, specifically for diagnostic use. Roche will initially promote the device for clinical studies, and eventually seek FDA clearance to sell it for routine diagnosis of patients.

In an email to Diagnostics World, Paul Schaffer, Lifecycle Leader for Roche’s sequencing platforms division, wrote that the new device will feature an integrated software pipeline to interpret test results, in support of assays that Roche will design and validate for clinical indications. The instrument will also have at least minor hardware modifications, like near field communication designed to track Roche-branded reagents used during sequencing.

This new version of the Sequel will probably not be the first instrument clinical labs turn to when they decide to start running NGS. Short-read sequencers are sure to outcompete the Roche machine on price, and can offer a pretty useful range of assays, from co-diagnostics in cancer to carrier testing for rare genetic diseases. But Roche can clear away some of the biggest barriers to entry for hospitals that want to pursue long-read sequencing.

Today, institutions like Mount Sinai that use PacBio typically have to write a lot of their own software to interpret the data that comes off the machines. Off-the-shelf analysis, with readable diagnostic reports for doctors, will make it easier for hospitals with less research focus to get on board. To this end, Roche acquired Bina, an NGS analysis company that handles structural variants and other PacBio specialties, in late 2014.

The next question will be whether Roche can design a suite of tests that clinical labs will want to run. Long-read sequencing is beloved by researchers because it can capture nearly complete genomes, finding the correct order and orientation of DNA reads. “The long-read technologies like PacBio’s are going to be, in the future, the showcase that ties it all together,” Sebra says. “You need those long reads as scaffolds to bring it together.”

But that envisions a future in which doctors will want to sequence their patients’ entire genomes. When it comes to specific medical tests, targeting just a small part of the genome connected to disease, Roche will have to content itself with some niche applications where PacBio stands out.

Early Applications

“At this time we are not releasing details regarding the specific assays under development,” Schaffer told Diagnostics World in his email. “However, virology and genetics are a key focus, as they align with other high-priority Roche Diagnostics products.”

Genetic disease is the obvious place to go with any sequencing technology. Rare hereditary disorders are much easier to understand on a genetic level than conditions like diabetes or heart disease; typically, the pathology can be traced back to a single mutation, making it easy to interpret test results.

Some of these mutations are simply intractable for short-read sequencers. A whole class of diseases, the PolyQ disorders and other repeat disorders, develop when a patient has too many copies of a single, repetitive sequence in a gene region. The gene Huntingtin, for example, contains a long stretch of the DNA code CAG; people born with 40 or more CAG repeats in a row will develop Huntington’s disease as they reach early adulthood.

These disorders would be a prime target for Roche’s sequencer. The Sequel’s long reads, spanning thousands of DNA letters at a stretch, can capture the entire repeat region of Huntingtin at a stretch, unlike short-read sequencers that would tend to produce a garbled mess of CAG reads impossible to count or put in order.

Nonetheless, the length of reads is not the only obstacle to understanding these very obstinate diseases. “The entire category of PolyQ disorders, and Fragile X and Huntington’s, is really important,” says Sebra. “But to be frank, they’re the most challenging even with PacBio.” He suggests that, even without venturing into the darkest realms of the genome, a long-read sequencer might actually be useful for diagnosing many of the same genetic diseases routinely covered by other instruments.

That’s because, even when the gene region involved in a disease is well known, there’s rarely only one way for it to go awry. “An example of that is Gaucher’s disease, in a gene called GBA,” Sebra says. “In that gene, there are hundreds of known mutations, some of which you can absolutely genotype using short reads. But others, you would need to phase the entire block to really understand.” Long-read sequencing, which is better at distinguishing maternal from paternal DNA and highlighting complex rearrangements within a gene, can offer a more thorough look at diseases with many genetic permutations, especially when tracking inheritance through a family.

“You can think of long-read sequencing as a really nice way to supplement some of the inherited panels or carrier screening panels,” Sebra says. “You can also use PacBio to verify variants that are called with short-read sequencing.”

Virology is, perhaps, a more surprising focus for Roche. Diagnosing a viral (or bacterial, or fungal) infection with NGS only requires finding a DNA read unique to a particular species or strain, something short-read sequencers are perfectly capable of.

But Mount Sinai, which has used PacBio in pathogen surveillance projects, has seen advantages to getting the full, completely assembled genomes of the organisms it’s tracking. With bacteria, for instance, key genes that confer resistance to antibiotics might be found either in the native genome, or inside plasmids, small packets of DNA that different species of bacteria freely pass between each other. If your sequencer can assemble these plasmids in one piece, it’s easier to tell when there’s a risk of antibiotic resistance spreading through the hospital, jumping from one infectious species to another.

Viruses don’t share their genetic material so freely, but a similar logic can still apply to viral infections, even in a single person. “A virus is really a mixture of different quasi-species,” says Sebra, so a patient with HIV or influenza likely has a whole constellation of subtly different viruses circulating in their body. A test that assembles whole viral genomes—which, given their tiny size, PacBio can often do in a single read—could give physicians a more comprehensive view of what they’re dealing with, and highlight any quasi-species that affect the course of treatment or how the virus is likely to spread.

The Broader View

These applications are well suited to the diagnostic instrument Roche is building. A test panel for rare genetic diseases can offer clear-cut answers, pointing physicians to any specific variants linked to a disorder, and offering follow-up information on the evidence that backs up that call.

That kind of report fits well into the workflows of smaller hospital labs, and is relatively painless to submit to the FDA for approval. It doesn’t require geneticists to puzzle over ambiguous results. As Schaffer says of his company’s overall NGS efforts, “In the past two years, Roche has been actively engaged in more than 25 partnerships, collaborations and acquisitions with the goal of enabling us to achieve our vision of sample in to results out.”

But some of the biggest ways medicine could benefit from long-read sequencing will continue to require the personal touch of labs like Mount Sinai’s.

Take cancer, for example, a field in which complex gene fusions and genetic rearrangements have been studied for decades. Tumors contain multitudes of cells with unique patchworks of mutations, and while long-read sequencing can pick up structural variants that may play a role in prognosis and treatment, many of these variants are rarely seen, little documented, and hard to boil down into a physician-friendly answer.

An ideal way to unravel a unique cancer case would be to sequence the RNA molecules produced in the tumor, creating an atlas of the “transcriptome” that shows which genes are hyperactive, which are being silenced, and which have been fused together. “When you run something like IsoSeq on PacBio and you can see truly the whole transcriptome, you’re going to figure out all possible fusions, all possible splicing events, and the true atlas of reads,” says Sebra. “Cancer is so diverse that it’s important to do that on an individual level.”

Occasionally, looking at the whole transcriptome, and seeing how a mutation in one gene affects an entire network of related genes, can reveal an unexpected treatment option―repurposing a drug usually reserved for other cancer types. But that takes a level of attention and expertise that is hard to condense into a mass-market assay.

And, Sebra suggests, there’s another reason for medical centers not to lean too heavily on off-the-shelf tests from vendors like Roche.

Devoted as he is to his onetime employer, Sebra is also a fan of other technologies now emerging to capture some of the same long-range, structural information on the genome. “You’ve now got 10X Genomics, BioNano, and Oxford Nanopore,” he says. “Often, any two or even three of those technologies, when you merge them together, can get you a much more comprehensive story, sometimes faster and sometimes cheaper.” At Mount Sinai, for example, combining BioNano and PacBio data has produced a whole human genome much more comprehensive than either platform can achieve on its own.

The same is almost certainly true of complex cases like cancer. Yet, while companies like Roche might succeed in bringing NGS diagnostics to a much larger number of patients, they have few incentives to make their assays work with competing technologies the way a research-heavy institute like Mount Sinai does.

“It actually drives the commercialization of software packages against the ability to integrate the data,” Sebra says.

Still, he’s hopeful that the Sequel can lead the industry to pay more attention to long-read sequencing in the clinic. “The RS II does a great job of long-read sequencing, but the throughput for the Sequel is so much higher that you can start to achieve large genomes faster,” he says. “It makes it more accessible for people who don’t own the RS II to get going.” And while the need for highly specialized genetics labs won’t be falling off anytime soon, most patients don’t have the luxury of being treated in a hospital with the resources of Mount Sinai. NGS companies increasingly see physicians as some of their most important customers, and as our doctors start checking into the health of our genomes, it would be a shame if ubiquitous short-read sequencing left them with blind spots.

Source: http://diagnosticsworldnews.com/2015/12/16/long-read-sequencing-age-genomic-medicine.aspx

 

 

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How Will FDA’s new precisionFDA Science 2.0 Collaboration Platform Protect Data?

Reporter: Stephen J. Williams, Ph.D.

As reported in MassDevice.com

FDA launches precisionFDA to harness the power of scientific collaboration

FDA VoiceBy: Taha A. Kass-Hout, M.D., M.S. and Elaine Johanson

Imagine a world where doctors have at their fingertips the information that allows them to individualize a diagnosis, treatment or even a cure for a person based on their genes. That’s what President Obama envisioned when he announced his Precision Medicine Initiative earlier this year. Today, with the launch of FDA’s precisionFDA web platform, we’re a step closer to achieving that vision.

PrecisionFDA is an online, cloud-based, portal that will allow scientists from industry, academia, government and other partners to come together to foster innovation and develop the science behind a method of “reading” DNA known as next-generation sequencing (or NGS). Next Generation Sequencing allows scientists to compile a vast amount of data on a person’s exact order or sequence of DNA. Recognizing that each person’s DNA is slightly different, scientists can look for meaningful differences in DNA that can be used to suggest a person’s risk of disease, possible response to treatment and assess their current state of health. Ultimately, what we learn about these differences could be used to design a treatment tailored to a specific individual.

The precisionFDA platform is a part of this larger effort and through its use we want to help scientists work toward the most accurate and meaningful discoveries. precisionFDA users will have access to a number of important tools to help them do this. These tools include reference genomes, such as “Genome in the Bottle,” a reference sample of DNA for validating human genome sequences developed by the National Institute of Standards and Technology. Users will also be able to compare their results to previously validated reference results as well as share their results with other users, track changes and obtain feedback.

Over the coming months we will engage users in improving the usability, openness and transparency of precisionFDA. One way we’ll achieve that is by placing the code for the precisionFDA portal on the world’s largest open source software repository, GitHub, so the community can further enhance precisionFDA’s features.Through such collaboration we hope to improve the quality and accuracy of genomic tests – work that will ultimately benefit patients.

precisionFDA leverages our experience establishing openFDA, an online community that provides easy access to our public datasets. Since its launch in 2014, openFDA has already resulted in many novel ways to use, integrate and analyze FDA safety information. We’re confident that employing such a collaborative approach to DNA data will yield important advances in our understanding of this fast-growing scientific field, information that will ultimately be used to develop new diagnostics, treatments and even cures for patients.

fda-voice-taha-kass-1x1Taha A. Kass-Hout, M.D., M.S., is FDA’s Chief Health Informatics Officer and Director of FDA’s Office of Health Informatics. Elaine Johanson is the precisionFDA Project Manager.

 

The opinions expressed in this blog post are the author’s only and do not necessarily reflect those of MassDevice.com or its employees.

So What Are the Other Successes With Such Open Science 2.0 Collaborative Networks?

In the following post there are highlighted examples of these Open Scientific Networks and, as long as

  • transparancy
  • equal contributions (lack of heirarchy)

exists these networks can flourish and add interesting discourse.  Scientists are already relying on these networks to collaborate and share however resistance by certain members of an “elite” can still exist.  Social media platforms are now democratizing this new science2.0 effort.  In addition the efforts of multiple biocurators (who mainly work for love of science) have organized the plethora of data (both genomic, proteomic, and literature) in order to provide ease of access and analysis.

Science and Curation: The New Practice of Web 2.0

Curation: an Essential Practice to Manage “Open Science”

The web 2.0 gave birth to new practices motivated by the will to have broader and faster cooperation in a more free and transparent environment. We have entered the era of an “open” movement: “open data”, “open software”, etc. In science, expressions like “open access” (to scientific publications and research results) and “open science” are used more and more often.

Curation and Scientific and Technical Culture: Creating Hybrid Networks

Another area, where there are most likely fewer barriers, is scientific and technical culture. This broad term involves different actors such as associations, companies, universities’ communication departments, CCSTI (French centers for scientific, technical and industrial culture), journalists, etc. A number of these actors do not limit their work to popularizing the scientific data; they also consider they have an authentic mission of “culturing” science. The curation practice thus offers a better organization and visibility to the information. The sought-after benefits will be different from one actor to the next.

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

  • Using Curation and Science 2.0 to build Trusted, Expert Networks of Scientists and Clinicians

Given the aforementioned problems of:

        I.            the complex and rapid deluge of scientific information

      II.            the need for a collaborative, open environment to produce transformative innovation

    III.            need for alternative ways to disseminate scientific findings

CURATION MAY OFFER SOLUTIONS

        I.            Curation exists beyond the review: curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY (discussed below) while NOT acting as mere reiteration, regurgitation

 

      II.            Curation providing insights from WHOLE scientific community on multiple WEB 2.0 platforms

 

    III.            Curation makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings (shown in Examples below)

 

Therefore a discussion is given on methodologies, definitions of best practices, and tools developed to assist the content curation community in this endeavor

which has created a need for more context-driven scientific search and discourse.

However another issue would be Individual Bias if these networks are closed and protocols need to be devised to reduce bias from individual investigators, clinicians.  This is where CONSENSUS built from OPEN ACCESS DISCOURSE would be beneficial as discussed in the following post:

Risk of Bias in Translational Science

As per the article

Risk of bias in translational medicine may take one of three forms:

  1. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
  2. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
  3. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).

This post highlights many important points related to bias but in summarry there can be methodologies and protocols devised to eliminate such bias.  Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.

  • Information dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation
  • threats to internal and external validity  represent specific aspects of systematic errors (i.e., bias)in design, methodology and analysis

So what about the safety and privacy of Data?

A while back I did a post and some interviews on how doctors in developing countries are using social networks to communicate with patients, either over established networks like Facebook or more private in-house networks.  In addition, these doctor-patient relationships in developing countries are remote, using the smartphone to communicate with rural patients who don’t have ready access to their physicians.

Located in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

I discuss some of these problems in the following paragraph and associated posts below:

Mobile Health Applications on Rise in Developing World: Worldwide Opportunity

According to International Telecommunication Union (ITU) statistics, world-wide mobile phone use has expanded tremendously in the past 5 years, reaching almost 6 billion subscriptions. By the end of this year it is estimated that over 95% of the world’s population will have access to mobile phones/devices, including smartphones.

This presents a tremendous and cost-effective opportunity in developing countries, and especially rural areas, for physicians to reach patients using mHealth platforms.

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

In Summary, although there are restrictions here in the US governing what information can be disseminated over social media networks, developing countries appear to have either defined the regulations as they are more dependent on these types of social networks given the difficulties in patient-physician access.

Therefore the question will be Who Will Protect The Data?

For some interesting discourse please see the following post

Atul Butte Talks on Big Data, Open Data and Clinical Trials

 

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Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond

Curator: Stephen J. Williams, Ph.D.

Updated 04/27/2019

Signatures of Mutational Processes in Human Cancer (from COSMIC)

From The COSMIC Database

summary_circos_cosmic_38_380

The genomic landscape of cancer. The COSMIC database has a fully curated and annotated database of recurrent genetic mutations founds in various cancers (data taken form cancer sequencing projects). For interactive map please go to the COSMIC database here: http://cancer.sanger.ac.uk/cosmic

 

 

Somatic mutations are present in all cells of the human body and occur throughout life. They are the consequence of multiple mutational processes, including the intrinsic slight infidelity of the DNA replication machinery, exogenous or endogenous mutagen exposures, enzymatic modification of DNA and defective DNA repair. Different mutational processes generate unique combinations of mutation types, termed “Mutational Signatures”.

In the past few years, large-scale analyses have revealed many mutational signatures across the spectrum of human cancer types [Nik-Zainal S. et al., Cell (2012);Alexandrov L.B. et al., Cell Reports (2013);Alexandrov L.B. et al., Nature (2013);Helleday T. et al., Nat Rev Genet (2014);Alexandrov L.B. and Stratton M.R., Curr Opin Genet Dev (2014)]. However, as the number of mutational signatures grows the need for a curated census of signatures has become apparent. Here, we deliver such a resource by providing the profiles of, and additional information about, known mutational signatures.

The current set of mutational signatures is based on an analysis of 10,952 exomes and 1,048 whole-genomes across 40 distinct types of human cancer. These analyses are based on curated data that were generated by The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and a large set of freely available somatic mutations published in peer-reviewed journals. Complete details about the data sources will be provided in future releases of COSMIC.

The profile of each signature is displayed using the six substitution subtypes: C>A, C>G, C>T, T>A, T>C, and T>G (all substitutions are referred to by the pyrimidine of the mutated Watson–Crick base pair). Further, each of the substitutions is examined by incorporating information on the bases immediately 5’ and 3’ to each mutated base generating 96 possible mutation types (6 types of substitution ∗ 4 types of 5’ base ∗ 4 types of 3’ base). Mutational signatures are displayed and reported based on the observed trinucleotide frequency of the human genome, i.e., representing the relative proportions of mutations generated by each signature based on the actual trinucleotide frequencies of the reference human genome version GRCh37. Note that only validated mutational signatures have been included in the curated census of mutational signatures.

Additional information is provided for each signature, including the cancer types in which the signature has been found, proposed aetiology for the mutational processes underlying the signature, other mutational features that are associated with each signature and information that may be relevant for better understanding of a particular mutational signature.

The set of signatures will be updated in the future. This will include incorporating additional mutation types (e.g., indels, structural rearrangements, and localized hypermutation such as kataegis) and cancer samples. With more cancer genome sequences and the additional statistical power this will bring, new signatures may be found, the profiles of current signatures may be further refined, signatures may split into component signatures and signatures

See their COSMIC tutorial page here for instructional videos

Updated News: COSMIC v75 – 24th November 2015

COSMIC v75 includes curations across GRIN2A, fusion pair TCF3-PBX1, and genomic data from 17 systematic screen publications. We are also beginning a reannotation of TCGA exome datasets using Sanger’s Cancer Genome Project analyis pipeline to ensure consistency; four studies are included in this release, to be expanded across the next few releases. The Cancer Gene Census now has a dedicated curator, Dr. Zbyslaw Sondka, who will be focused on expanding the Census, enhancing the evidence underpinning it, and developing improved expert-curated detail describing each gene’s impact in cancer. Finally, as we begin to streamline our ever-growing website, we have combined all information for each gene onto one page and simplified the layout and design to improve navigation

may be found in cancer types in which they are currently not detected.

mutational signatures across human cancer

Mutational signatures across human cancer

Patterns of mutational signatures [Download signatures]

 COSMIC database identifies 30 mutational signatures in human cancer

Please goto to COSMIC site to see bigger .png of mutation signatures

Signature 1

Cancer types:

Signature 1 has been found in all cancer types and in most cancer samples.

Proposed aetiology:

Signature 1 is the result of an endogenous mutational process initiated by spontaneous deamination of 5-methylcytosine.

Additional mutational features:

Signature 1 is associated with small numbers of small insertions and deletions in most tissue types.

Comments:

The number of Signature 1 mutations correlates with age of cancer diagnosis.

Signature 2

Cancer types:

Signature 2 has been found in 22 cancer types, but most commonly in cervical and bladder cancers. In most of these 22 cancer types, Signature 2 is present in at least 10% of samples.

Proposed aetiology:

Signature 2 has been attributed to activity of the AID/APOBEC family of cytidine deaminases. On the basis of similarities in the sequence context of cytosine mutations caused by APOBEC enzymes in experimental systems, a role for APOBEC1, APOBEC3A and/or APOBEC3B in human cancer appears more likely than for other members of the family.

Additional mutational features:

Transcriptional strand bias of mutations has been observed in exons, but is not present or is weaker in introns.

Comments:

Signature 2 is usually found in the same samples as Signature 13. It has been proposed that activation of AID/APOBEC cytidine deaminases is due to viral infection, retrotransposon jumping or to tissue inflammation. Currently, there is limited evidence to support these hypotheses. A germline deletion polymorphism involving APOBEC3A and APOBEC3B is associated with the presence of large numbers of Signature 2 and 13 mutations and with predisposition to breast cancer. Mutations of similar patterns to Signatures 2 and 13 are commonly found in the phenomenon of local hypermutation present in some cancers, known as kataegis, potentially implicating AID/APOBEC enzymes in this process as well.

Signature 3

Cancer types:

Signature 3 has been found in breast, ovarian, and pancreatic cancers.

Proposed aetiology:

Signature 3 is associated with failure of DNA double-strand break-repair by homologous recombination.

Additional mutational features:

Signature 3 associates strongly with elevated numbers of large (longer than 3bp) insertions and deletions with overlapping microhomology at breakpoint junctions.

Comments:

Signature 3 is strongly associated with germline and somatic BRCA1 and BRCA2 mutations in breast, pancreatic, and ovarian cancers. In pancreatic cancer, responders to platinum therapy usually exhibit Signature 3 mutations.

Signature 4

Cancer types:

Signature 4 has been found in head and neck cancer, liver cancer, lung adenocarcinoma, lung squamous carcinoma, small cell lung carcinoma, and oesophageal cancer.

Proposed aetiology:

Signature 4 is associated with smoking and its profile is similar to the mutational pattern observed in experimental systems exposed to tobacco carcinogens (e.g., benzo[a]pyrene). Signature 4 is likely due to tobacco mutagens.

Additional mutational features:

Signature 4 exhibits transcriptional strand bias for C>A mutations, compatible with the notion that damage to guanine is repaired by transcription-coupled nucleotide excision repair. Signature 4 is also associated with CC>AA dinucleotide substitutions.

Comments:

Signature 29 is found in cancers associated with tobacco chewing and appears different from Signature 4.

Signature 5

Cancer types:

Signature 5 has been found in all cancer types and most cancer samples.

Proposed aetiology:

The aetiology of Signature 5 is unknown.

Additional mutational features:

Signature 5 exhibits transcriptional strand bias for T>C substitutions at ApTpN context.

Comments:

Signature 6

Cancer types:

Signature 6 has been found in 17 cancer types and is most common in colorectal and uterine cancers. In most other cancer types, Signature 6 is found in less than 3% of examined samples.

Proposed aetiology:

Signature 6 is associated with defective DNA mismatch repair and is found in microsatellite unstable tumours.

Additional mutational features:

Signature 6 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 6 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 15, 20, and 26.

Signature 7

Cancer types:

Signature 7 has been found predominantly in skin cancers and in cancers of the lip categorized as head and neck or oral squamous cancers.

Proposed aetiology:

Based on its prevalence in ultraviolet exposed areas and the similarity of the mutational pattern to that observed in experimental systems exposed to ultraviolet light Signature 7 is likely due to ultraviolet light exposure.

Additional mutational features:

Signature 7 is associated with large numbers of CC>TT dinucleotide mutations at dipyrimidines. Additionally, Signature 7 exhibits a strong transcriptional strand-bias indicating that mutations occur at pyrimidines (viz., by formation of pyrimidine-pyrimidine photodimers) and these mutations are being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 8

Cancer types:

Signature 8 has been found in breast cancer and medulloblastoma.

Proposed aetiology:

The aetiology of Signature 8 remains unknown.

Additional mutational features:

Signature 8 exhibits weak strand bias for C>A substitutions and is associated with double nucleotide substitutions, notably CC>AA.

Comments:

Signature 9

Cancer types:

Signature 9 has been found in chronic lymphocytic leukaemias and malignant B-cell lymphomas.

Proposed aetiology:

Signature 9 is characterized by a pattern of mutations that has been attributed to polymerase η, which is implicated with the activity of AID during somatic hypermutation.

Additional mutational features:

Comments:

Chronic lymphocytic leukaemias that possess immunoglobulin gene hypermutation (IGHV-mutated) have elevated numbers of mutations attributed to Signature 9 compared to those that do not have immunoglobulin gene hypermutation.

Signature 10

Cancer types:

Signature 10 has been found in six cancer types, notably colorectal and uterine cancer, usually generating huge numbers of mutations in small subsets of samples.

Proposed aetiology:

It has been proposed that the mutational process underlying this signature is altered activity of the error-prone polymerase POLE. The presence of large numbers of Signature 10 mutations is associated with recurrent POLE somatic mutations, viz., Pro286Arg and Val411Leu.

Additional mutational features:

Signature 10 exhibits strand bias for C>A mutations at TpCpT context and T>G mutations at TpTpT context.

Comments:

Signature 10 is associated with some of most mutated cancer samples. Samples exhibiting this mutational signature have been termed ultra-hypermutators.

Signature 11

Cancer types:

Signature 11 has been found in melanoma and glioblastoma.

Proposed aetiology:

Signature 11 exhibits a mutational pattern resembling that of alkylating agents. Patient histories have revealed an association between treatments with the alkylating agent temozolomide and Signature 11 mutations.

Additional mutational features:

Signature 11 exhibits a strong transcriptional strand-bias for C>T substitutions indicating that mutations occur on guanine and that these mutations are effectively repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 12

Cancer types:

Signature 12 has been found in liver cancer.

Proposed aetiology:

The aetiology of Signature 12 remains unknown.

Additional mutational features:

Signature 12 exhibits a strong transcriptional strand-bias for T>C substitutions.

Comments:

Signature 12 usually contributes a small percentage (<20%) of the mutations observed in a liver cancer sample.

Signature 13

Cancer types:

Signature 13 has been found in 22 cancer types and seems to be commonest in cervical and bladder cancers. In most of these 22 cancer types, Signature 13 is present in at least 10% of samples.

Proposed aetiology:

Signature 13 has been attributed to activity of the AID/APOBEC family of cytidine deaminases converting cytosine to uracil. On the basis of similarities in the sequence context of cytosine mutations caused by APOBEC enzymes in experimental systems, a role for APOBEC1, APOBEC3A and/or APOBEC3B in human cancer appears more likely than for other members of the family. Signature 13 causes predominantly C>G mutations. This may be due to generation of abasic sites after removal of uracil by base excision repair and replication over these abasic sites by REV1.

Additional mutational features:

Transcriptional strand bias of mutations has been observed in exons, but is not present or is weaker in introns.

Comments:

Signature 2 is usually found in the same samples as Signature 13. It has been proposed that activation of AID/APOBEC cytidine deaminases is due to viral infection, retrotransposon jumping or to tissue inflammation. Currently, there is limited evidence to support these hypotheses. A germline deletion polymorphism involving APOBEC3A and APOBEC3B is associated with the presence of large numbers of Signature 2 and 13 mutations and with predisposition to breast cancer. Mutations of similar patterns to Signatures 2 and 13 are commonly found in the phenomenon of local hypermutation present in some cancers, known as kataegis, potentially implicating AID/APOBEC enzymes in this process as well.

Signature 14

Cancer types:

Signature 14 has been observed in four uterine cancers and a single adult low-grade glioma sample.

Proposed aetiology:

The aetiology of Signature 14 remains unknown.

Additional mutational features:

Comments:

Signature 14 generates very high numbers of somatic mutations (>200 mutations per MB) in all samples in which it has been observed.

Signature 15

Cancer types:

Signature 15 has been found in several stomach cancers and a single small cell lung carcinoma.

Proposed aetiology:

Signature 15 is associated with defective DNA mismatch repair.

Additional mutational features:

Signature 15 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 15 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 20, and 26.

Signature 16

Cancer types:

Signature 16 has been found in liver cancer.

Proposed aetiology:

The aetiology of Signature 16 remains unknown.

Additional mutational features:

Signature 16 exhibits an extremely strong transcriptional strand bias for T>C mutations at ApTpN context, with T>C mutations occurring almost exclusively on the transcribed strand.

Comments:

Signature 17

Cancer types:

Signature 17 has been found in oesophagus cancer, breast cancer, liver cancer, lung adenocarcinoma, B-cell lymphoma, stomach cancer and melanoma.

Proposed aetiology:

The aetiology of Signature 17 remains unknown.

Additional mutational features:

Comments:

Signature 1Signature 18

Cancer types:

Signature 18 has been found commonly in neuroblastoma. Additionally, Signature 18 has been also observed in breast and stomach carcinomas.

Proposed aetiology:

The aetiology of Signature 18 remains unknown.

Additional mutational features:

Comments:

Signature 19

Cancer types:

Signature 19 has been found only in pilocytic astrocytoma.

Proposed aetiology:

The aetiology of Signature 19 remains unknown.

Additional mutational features:

Comments:

Signature 20

Cancer types:

Signature 20 has been found in stomach and breast cancers.

Proposed aetiology:

Signature 20 is believed to be associated with defective DNA mismatch repair.

Additional mutational features:

Signature 20 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 20 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 15, and 26.

Signature 21

Cancer types:

Signature 21 has been found only in stomach cancer.

Proposed aetiology:

The aetiology of Signature 21 remains unknown.

Additional mutational features:

Comments:

Signature 21 is found only in four samples all generated by the same sequencing centre. The mutational pattern of Signature 21 is somewhat similar to the one of Signature 26. Additionally, Signature 21 is found only in samples that also have Signatures 15 and 20. As such, Signature 21 is probably also related to microsatellite unstable tumours.

Signature 22

Cancer types:

Signature 22 has been found in urothelial (renal pelvis) carcinoma and liver cancers.

Proposed aetiology:

Signature 22 has been found in cancer samples with known exposures to aristolochic acid. Additionally, the pattern of mutations exhibited by the signature is consistent with the one previous observed in experimental systems exposed to aristolochic acid.

Additional mutational features:

Signature 22 exhibits a very strong transcriptional strand bias for T>A mutations indicating adenine damage that is being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 22 has a very high mutational burden in urothelial carcinoma; however, its mutational burden is much lower in liver cancers.

Signature 23

Cancer types:

Signature 23 has been found only in a single liver cancer sample.

Proposed aetiology:

The aetiology of Signature 23 remains unknown.

Additional mutational features:

Signature 23 exhibits very strong transcriptional strand bias for C>T mutations.

Comments:

Signature 24

Cancer types:

Signature 24 has been observed in a subset of liver cancers.

Proposed aetiology:

Signature 24 has been found in cancer samples with known exposures to aflatoxin. Additionally, the pattern of mutations exhibited by the signature is consistent with that previous observed in experimental systems exposed to aflatoxin.

Additional mutational features:

Signature 24 exhibits a very strong transcriptional strand bias for C>A mutations indicating guanine damage that is being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 25

Cancer types:

Signature 25 has been observed in Hodgkin lymphomas.

Proposed aetiology:

The aetiology of Signature 25 remains unknown.

Additional mutational features:

Signature 25 exhibits transcriptional strand bias for T>A mutations.

Comments:

This signature has only been identified in Hodgkin’s cell lines. Data is not available from primary Hodgkin lymphomas.

Signature 26

Cancer types:

Signature 26 has been found in breast cancer, cervical cancer, stomach cancer and uterine carcinoma.

Proposed aetiology:

Signature 26 is believed to be associated with defective DNA mismatch repair.

Additional mutational features:

Signature 26 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 26 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 15 and 20.

Signature 27

Cancer types:

Signature 27 has been observed in a subset of kidney clear cell carcinomas.

Proposed aetiology:

The aetiology of Signature 27 remains unknown.

Additional mutational features:

Signature 27 exhibits very strong transcriptional strand bias for T>A mutations. Signature 27 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 28

Cancer types:

Signature 28 has been observed in a subset of stomach cancers.

Proposed aetiology:

The aetiology of Signature 28 remains unknown.

Additional mutational features:

Comments:

Signature 29

Cancer types:

Signature 29 has been observed only in gingivo-buccal oral squamous cell carcinoma.

Proposed aetiology:

Signature 29 has been found in cancer samples from individuals with a tobacco chewing habit.

Additional mutational features:

Signature 29 exhibits transcriptional strand bias for C>A mutations indicating guanine damage that is most likely repaired by transcription-coupled nucleotide excision repair. Signature 29 is also associated with CC>AA dinucleotide substitutions.

Comments:

The Signature 29 pattern of C>A mutations due to tobacco chewing appears different from the pattern of mutations due to tobacco smoking reflected by Signature 4.

Signature 30

Cancer types:

Signature 30 has been observed in a small subset of breast cancers.

Proposed aetiology:

The aetiology of Signature 30 remains unknown.

 


 

Examples in the literature of deposits into or analysis from the COSMIC database

The Genomic Landscapes of Human Breast and Colorectal Cancers from Wood 318 (5853): 11081113 Science 2007

“analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene “mountains” and a much larger number of gene “hills” that are mutated at low frequency. ”

  • found cellular pathways with multiple pathways
  • analyzed a highly curated database (Metacore, GeneGo, Inc.) that includes human protein-protein interactions, signal transduction and metabolic pathways
  • There were 108 pathways that were found to be preferentially mutated in breast tumors. Many of the pathways involved phosphatidylinositol 3-kinase (PI3K) signaling
  • the cancer genome landscape consists of relief features (mutated genes) with heterogeneous heights (determined by CaMP scores). There are a few “mountains” representing individual CAN-genes mutated at high frequency. However, the landscapes contain a much larger number of “hills” representing the CAN-genes that are mutated at relatively low frequency. It is notable that this general genomic landscape (few gene mountains and many gene hills) is a common feature of both breast and colorectal tumors.
  • developed software to analyze multiple mutations and mutation frequencies available from Harvard Bioinformatics at

 

http://bcb.dfci.harvard.edu/~gp/software/CancerMutationAnalysis/cma.htm

 

 

R Software for Cancer Mutation Analysis (download here)

 

CancerMutationAnalysis Version 1.0:

R package to reproduce the statistical analyses of the Sjoblom et al article and the associated Technical Comment. This package is build for reproducibility of the original results and not for flexibility. Future version will be more general and define classes for the data types used. Further details are available in Working Paper 126.

CancerMutationAnalysis Version 2.0:

R package to reproduce the statistical analyses of the Wood et al article. Like its predecessor, this package is still build for reproducibility of the original results and not for flexibility. Further details are available in Working Paper 126

 

 

 

 

 

 

 

 

 

Update 04/27/2019

Review 2018. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Z. Sondka et al. Nature Reviews. 2018.

The Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) reevaluates the cancer genome landscape periodically and curates the findings into a database of genetic changes occurring in various tumor types.  The 2018 CGC describes in detail the effect of 719 cancer driving genes.  The recent expansion includes functional and mechanistic descriptions of how each gene contributes to disease etiology and in terms of the cancer hallmarks as described by Hanahan and Weinberg.  These functional characteristics show the complexity of the cancer mutational landscape and genome and suggest ” multiple cancer-related functions for many genes, which are often highly tissue-dependent or tumour stage-dependent.”  The 2018 CGC expands a second tier of genes, expanding the list of cancer related genes.

Criteria for curation of genes into CGC (curation process)

  • choosing candidate genes are selected from published literature, conference abstracts, large cancer genome screens deposited in databases, and analysis of current COSMIC database
  • COSMIC data are analyzed to determine presence of patterns of somatic mutations and frequency of such mutations in cancer
  • literature review to determine the role of the gene in cancer
  • Minimum evidence

– at least two publications from different groups shows increased mutation frequency in at least one type of cancer (PubMed)

–  at least two publications from different groups showing experimental evidence of functional involvement in at least one hallmark of cancer in order to classify the mutant gene as oncogene, tumor suppressor, or fusion partner (like BCR-Abl)

  • independent assessment by at least two postdoctoral fellows
  • gene must be classified as either Tier 1 of Tier 2 CGC gene
  • inclusion in database
  • continued curation efforts

definitions:

Tier 1 gene: genes which have strong evidence from both mutational and functional analysis as being involved in cancer

Tier 2 gene: genes with mutational patterns typical of cancer drivers but not functionally characterized as well as genes with published mechanistic description of involvement in cancer but without proof of somatic mutations in cancer

Current Status of Tier 1 and Tier 2 genes in CGC

Tier 1 genes (574 genes): include 79 oncogenes, 140 tumor suppressor genes, 93 fusion partners

Tier 2 genes (719 genes): include 103 oncogenes, 181 tumor suppressors, 134 fusion partners and 31 with unknown function

 

 

 

 

 

 

 

 

 

 

 

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