Personalized Medicine: Clinical Aspiration of Microarrays

Reporter, Writer: Stephen J. Williams, Ph.D.

 In this month’s Science, Mike May (at http://www.sciencemag.org/site/products/lst_20130215.xhtml) describes some of the challenges and successes in introducing microarray analysis to the clinical setting.  Traditionally used for investigational research, microarray is now being developed, customized and used for biomarker analysis, prognostic and predictive value, in a disease-specific manner.

Challenges in data interpretation

      In an interview with Seth Crosby, director of the Genome Technology Access Center at Washington University School of Medicine in St. Louis, “the biggest challenge” in moving microarray to the clinical setting is data interpretation.  The current technology makes it possible to evaluate expression of thousands of genes from a patient’s sample however as Crosby describes is assigning clinical relevance to the data.  For example Crosby explains that Washington University had validated a panel of 45 oncology genes by next generation sequencing and are using these genes to develop diagnostic tests to screen patient tumors for the purpose of determining a personalized therapeutic strategy. Seth Crosby noted it took “hundreds of Ph.D. and M.D. hours” to sift through the hundreds of papers to determine which genes were relevant to a specific cancer type. However, he notes, that once we better understand which changes in the patient’s genome are related to a specific disease we will be able to narrow down the list and be able to produce both economical and more disease-relevant microarrays.

Is this aberration pathogenic or not?

     Microarrays are becoming an invaluable tool in cytogenetics, as eluded by Andy Last, executive vice president of the genetic analysis business unit at Affymetrix.  Certain diseases like Down syndrome have well characterized chromosomal alterations like additions or deletions of parts or entire chromosomes.  According to Affymetrix, the most common use of microarrays is for determining copy number variation.  However according to James Clough, vice president of clinical and genomic services at Oxford Gene Technology, given the hundreds of syndromes associated with chromosomal rearrangements, the challenge will be to determine if a small chromosomal aberration has pathologic significance, given that microarray affords much higher diagnostic yield and speed of analysis than traditional microscopic techniques.  To address this challenge, Oxford Gene Technologies, PerkinElmer, Affymetrix, and Agilent all have custom designed microarrays to evaluate disease specific copy number and SNP (single nucleotide polymorphism) microarrays.  For example PerkinElmer designed OncoChip™ to evaluate copy number variation in more than 1.800 cancer genes.  Agilent makes microarrays that evaluates both copy number variation such as its CGH (comparative genomic hybridization) plus SNP microarrays.  Patricia Barco, product manager for cytogenetics at Agilent, notes these arrays can be used in prenatal and postnatal research and cancer, and “can be customized from more than 28 million probes in our library”.

Custom Tools and Software to Handle the Onslaught of Big Data

     There is a need for FDA approved diagnostic tools based on microarrays. Pathwork Diagnostic’s has one such tool (the Pathwork Tissue of Origin test), which uses 2,000 transcript markers and a proprietary computational algorithm to determine from expression analysis, the tissue of origin of a patient’s tumor.  Pathwork also provides a fast, custom turn-around analytical service for pathologists who encounter difficult to interpret samples.  Illumina provides the Infinium HumanCore BeadChip family of microarrays, which can determine genetic variations for purposes of biological tissue banking.  This system uses a set of over 300,000 SNP probes plus 240,000 exome-based markers.

     Tools have also been developed to validate microarray results.  A common validation strategy is the use of quantitative real-time PCR to verify the expression changes seen on the microarray.  Life Technologies developed the TaqMan OpenArray Real Time PCR plates, which have 3,072 wells and can be custom-formatted using their library of eight million validated TaqMan assays.

Making Sense of the Big Data: Bridging the Knowledge Gap using Bioinformatics

          The use of microarray has spurned industries devoted to developing the bioinformatics software to analyze the massive amounts of data and provide clinical significance.  For example companies such as Expression Analysis use their bioinformatics software to provide pathway analysis for microarray data in order to translate the data into the biology.  Using such strategies can also validate the design of microarrays for various diseases.

Foundation Medicine, Inc., a molecular information company, provides cancer genomics test solutions. It offers FoundationOne, an informative genomic profile to identify a patient’s individual molecular alterations and match them with relevant targeted therapies and clinical trials. The company’s product enables physicians to recommend treatment options for patients based on the molecular subtype of their cancer.

The Canadian Bioinformatics Workshops series recently offered a course on using bioinformatic approaches to analyze clinical data generated from microarray approaches (http://bioinformatics.ca/workshops/2012/bioinformatics-cancer-genomics-bicg).   The course objectives are described below:

Course Objectives

Cancer research has rapidly embraced high throughput technologies into its research, using various microarray, tissue array, and next generation sequencing platforms. The result has been a rapid increase in cancer data output and data types. Now more than ever, having the bioinformatic skills and knowledge of available bioinformatic resources specific to cancer is critical. The CBW will host a 5-day workshop covering the key bioinformatics concepts and tools required to analyze cancer genomic data sets. Participants will gain experience in genomic data visualization tools which will be applied throughout the development of the skills required to analyze cancer -omic data for gene expression, genome rearrangement, somatic mutations and copy number variation. The workshop will conclude with analyzing and conducting pathway analysis on the resultant cancer gene list and integration of clinical data.

Successful Examples of Clinical Ventures Integrating Bioinformatics in Cancer Treatment Decision –Making

The University of Pavia, Italy developed a fully integrated oncology bioinformatics workflow as described on their website and at the ESMO 2012 Congress meeting:

http://abstracts.webges.com/viewing/view.php?congress=esmo2012&congress_id=370&publication_id=2530

ONCO-I2B2 PROJECT: A BIOINFORMATICS TOOL INTEGRATING –OMICS AND CLINICAL DATA TO SUPPORT TRANSLATIONAL RESEARCH

Abstract:	2530
Congress:	ESMO 2012
Type:	Abstract
Topic:	Translational research
Authors:	A. Zambelli, D. Segagni, V. Tibollo, A. Dagliati, A. Malovini, V. Fotia, S. Manera, R. Bellazzi; Pavia/IT

• Body

The ONCO-i2b2 project, supported by the University of Pavia and the Fondazione Salvatore Maugeri (FSM), aims at supporting translational research in oncology and exploits the software solutions implemented by the Informatics for Integrating Biology and the Bedside (i2b2) research centre, an initiative funded by the NIH Roadmap National Centres for Biomedical Computing. The ONCO-i2b2 software is designed to integrate the i2b2 infrastructure with the FSM hospital information system and the Bruno Boerci Biobank, in order to provide well-characterized cancer specimens along with an accurate patients clinical data-base. The i2b2 infrastructure provides a web-based access to all the electronic medical records of cancer patients, and allow researchers analyzing the vast amount of biological and clinical information, relying on a user-friendly interface. Data coming from multiple sources are integrated and jointly queried.

In 2011 at AIOM Meeting we reported the preliminary experience of the ONCO-i2b2 project, now we’re able to present the up and running platform and the extended data set. Currently, more than 4400 specimens are stored and more than 600 of breast cancer patients give the consent for the use of specimens in the context of clinical research, in addition, more than 5000 histological reports are stored in order to integrate clinical data.

Within the ONCO-i2b2 project is possible to query and merge data regarding:

• Anonymous patient personal data;

• Diagnosis and therapy ICD9-CM subset from the hospital information system;

• Histological data (tumour SNOMED and TNM codes) and receptor profile testing (Her2, Ki67) from anatomic pathology database;

• Specimen molecular characteristics (DNA, RNA, blood, plasma and cancer tissues) from the Bruno Boerci Biobank management system.

The research infrastructure will be completed by the development of new set of components designed to enhance the ability of an i2b2 hive to utilize data generated by NGS technology, providing a mechanism to apply custom genomic annotations. The translational tool created at FSM is a concrete example regarding how the integration of different information from heterogeneous sources could bring scientific research closer to understand the nature of disease itself and to create novel diagnostics through handy interfaces.

Disclosure

All authors have declared no conflicts of interest.

NCI has under-taken a similar effort under the Recovery Act (the full text of the latest report is taken from their website http://www.cancer.gov/aboutnci/recovery/recoveryfunding/investmentreports/bioinformatics:

Cancer Bioinformatics: Recovery Act Investment Report

November 2009

Public Health Burden of Cancer

Cancer is the second leading cause of death in the United States after heart disease. In 2009, it is estimated that nearly 1.5 million new cases of invasive cancer will be diagnosed in this country and more than 560,000 people will die of the disease.

To learn more, visit:

• caBIG®
• Recovery funding to redesign informatics infrastructure

Cancer Bioinformatics Program Overview

Over the past five years, NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) has led the effort to develop and deploy the cancer Biomedical Informatics Grid® (caBIG) in partnership with the broader cancer community.  The caBIG network is designed to enable the integration and exchange of data among researchers in the laboratory and the clinic, simplify collaboration, and realize the potential of information-based (personalized) medicine in improving patient outcomes. caBIG has connected major components of the cancer community, including NCI-designated Cancer Centers, participating institutions of the NCI Community Cancer Centers Program (NCCCP), and numerous large-scale scientific endeavors, as well as basic, translational, and clinical researchers at public and private institutions across the United States and around the world.  Beyond cancer research, caBIG capabilities—infrastructure, standards, and tools—provide a prototype for linking other disease communities and catalyzing a new 21st-century biomedical ecosystem that unifies research and care. ARRA funding will allow NCI to accelerate the ongoing development of the Cancer Knowledge Cloud and Oncology Electronic Health Records (EHRs) initiatives, thereby providing for continued job creation in the areas of biomedical informatics development and application as well as healthcare delivery.

The caBIG Cancer Knowledge Cloud: Extending the Research Infrastructure

The Cancer Knowledge Cloud is a virtual biomedical capability that utilizes caBIG tools, infrastructure, and security frameworks to integrate distributed individual and organizational data, software applications, and computational capacity throughout the broad cancer research and treatment community. The Cancer Knowledge Cloud connects, integrates, and facilitates sharing of the diverse primary data generated through basic and clinical research and care delivery to enable personalized medicine. The cloud includes information generated through large-scale research projects such as The Cancer Genome Atlas (TCGA), the cancer Human Biobank (caHUB) tissue acquisition network, the NCI Functional Biology Consortium, the NCI Patient Characterization Center, and the NCI Preclinical Development Pipeline, academic and industry counterparts to these projects, and clinical observations (from entities such as the NCCCP) captured in oncology-extended Electronic Health Records.  Through the use of the caBIG Data Sharing and Security Framework, the Cloud will support appropriate sharing of information, supporting in silico hypothesis generation and testing, and enabling a learning healthcare system.

A caBIG-Based Rapid-Learning Healthcare System: Incorporating Oncology-Extended Electronic Healthcare Records (EHRs)

The 21st-century Cancer Knowledge Cloud will connect individuals, organizations, institutions, and their associated information within an information technology-enabled cycle of discovery, development, and clinical care—the paradigm of a rapid-learning healthcare system. This will transform these disconnected sectors into a system that is personalized, preventive, pre-emptive, and patient-participatory.  To be realized, this model requires the adoption of standards-based EHRs. Presently, however, no certified oncology-based EHR exists, and fewer than 3 percent of oncologists with outpatient-based practices utilize EHRs. caBIG has recently established a collaboration with the American Society of Clinical Oncology (ASCO) to develop an oncology-specific EHR (caEHR) specification based on open standards already in use in the oncology community that will utilize caBIG standards for interoperability. NCI will implement an open-source version of this specification to validate the specification and to provide a free alternative to sites that choose not to purchase a commercial system. The launch customer for the caEHR will be NCCCP participating sites. NCI will work with appropriate entities to provide a mechanism for certifying that caEHR implementations are consistent with the NCI/ASCO specification.

Bards Cancer Institute has another clinical bioinformatics program to support their clinical efforts:

Clinical Bioinformatics Program in Oncology at Barts Cancer Institute at Barts and the London School of Medicine

http://www.bci.qmul.ac.uk/cancer-bioinformatics

BCI HomeCancer Bioinformatics

Why we focus on Cancer Bioinformatics

Bioinformatics is a new interdisciplinary area involving biological, statistical and computational sciences. Bioinformatics will enable cancer researchers not only to manage, analyze, mine and understand the currently accumulated, valuable, high-throughput data, but also to integrate these in their current research programs. The need for bioinformatics will become ever more important as new technologies increase the already exponential rate at which cancer data are generated.

What we do

• We work alongside clinical and basic scientists to support the cancer projects within BCI.  This is an ideal partnership between scientific experts, who know the research questions that will be relevant from a cancer biologist or clinician’s perspective, and bioinformatics experts, who know how to develop the proposed methods to provide answers.
• We also conduct independent bioinformatics research, focusing on the development of computational and integrative methods, algorithms, databases and tools to tackle the analysis of the high volumes of cancer data.
• We also are actively involved in the development of bioinformatics educational courses at BCI. Our courses offer a unique opportunity for biologists to gain a basic understanding in the use of bioinformatics methods to access and harness large complicated high-throughput data and uncover meaningful information that could be used to understand molecular mechanisms and develop novel targeted therapeutics/diagnostic tools.

Developing Criteria for Genomic Profiling in Lung Cancer:

A Report from U.S. Cancer Centers

In a report by Pao et. al., a group of clinicians organized a meeting to standardize some protocols for the integration of microarray and genomic data from lung cancer patients into the clinical setting.[1]  There has been ample evidence that adenocarcinomas could be classified into “clinically relevant molecular subsets” based on distinct genomic changes.  For example EGFR (epidermal growth factor receptor) exon 19 deletions and exon 21 point mutations predict sensitivity to tyrosine kinase inhibitors (TKIs) like gefitinib, whereas exon 20 insertions predict primary resistance[2].

However, as the authors note, “mutational profiling has not been widely accepted or adopted into practice in thoracic oncology”.  

     Therefore, a multi-institutional workshop was held in 2009 among participants from Massachusetts General Hospital (MGH) Cancer Center, Memorial Sloan-Kettering Cancer Center (MSKCC), the Dana-Farber/Bingham & Women’s Cancer Center (DF/BWCC), the M.D. Anderson Cancer Center (VICC), and the Vanderbilt-Ingram Cancer Center (VICC) to discuss their institutes molecular profiling programs with emphasis on:

·         Organization/workflow

·         Mutation detection technologies

·         Clinical protocols and reporting

·         Patient consent

In addition to the aforementioned challenges, the panel discussed further issues for developing improved science-driven criteria for determining targeted therapies including:

1)      Including pathologists into criteria development as pathology departments are usually the main repositories for specimens

2)      Developing integrated informatics systems

3)      Standardizing new target validation methodology across cancer centers

 References

1.            Pao W, Kris MG, Iafrate AJ, Ladanyi M, Janne PA, Wistuba, II, Miake-Lye R, Herbst RS, Carbone DP, Johnson BE et al: Integration of molecular profiling into the lung cancer clinic. Clinical cancer research : an official journal of the American Association for Cancer Research 2009, 15(17):5317-5322.

2.            Wu JY, Wu SG, Yang CH, Gow CH, Chang YL, Yu CJ, Shih JY, Yang PC: Lung cancer with epidermal growth factor receptor exon 20 mutations is associated with poor gefitinib treatment response. Clinical cancer research : an official journal of the American Association for Cancer Research 2008, 14(15):4877-4882.

Other posts on this website on Cancer and Genomics include:

• http://pharmaceuticalintelligence.com/2012/07/14/merck-demonstrates-odanacatib-effectiveness-ends-study-early-2/
• http://pharmaceuticalintelligence.com/2013/02/17/what-is-the-future-for-genomics-in-clinical-medicine/
• http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/
• http://pharmaceuticalintelligence.com/2012/11/04/reboot-evidence-based-medicine-and-reconsider-the-randomized-placebo-controlled-clinical-trial/
• http://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/
• http://pharmaceuticalintelligence.com/2013/02/15/thymosin-alpha1-in-melanoma/
• http://pharmaceuticalintelligence.com/2013/02/14/prostate-cancer-androgen-driven-pathomechanism-in-early-onset-forms-of-the-disease/
• http://pharmaceuticalintelligence.com/2013/02/07/prostate-cancer-and-nanotecnology/
• http://pharmaceuticalintelligence.com/2013/02/05/winning-over-cancer-progression-new-oncology-drugs-to-suppress-driver-mutations-vs-passengers-mutations/
• • The Initiation and Growth of Molecular Biology and Genomics (pharmaceuticalintelligence.com)
  • CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics (pharmaceuticalintelligence.com)
  • 2013 Genomics: The Era Beyond the Sequencing Human Genome: Francis Collins, Craig Venter, Eric Lander, et al. (pharmaceuticalintelligence.com)