Jan 19 2016

Cancer ‘Moonshot’ needs Informatics

by at 10:33 am

Many of us who work in the interface of Cancer Clinical Research and Biomedical informatics were thrilled to hear about the cancer moonshot program from President Obama announced in his final State of the Union Address on Tuesday, Jan 12’16.

VP Biden, the nominated leader for this effort, has pledged to increase the resources available to combat the disease, and to find ways for the cancer community to work together and share information, the operative word being “share” (after ‘resources’).

In this post, I briefly review (by no means comprehensive; just a Saturday morning project while brunch cooks in the Instant pot) four thematic areas where informatics is already playing a key role to help realize cancer moonshot goals and identify challenges and opportunities.

  • Immunotherapies: Recent approvals of ipilimumab (Yervoy), sipuleucel-T (Provenge), Nivolmab (Opdivo) and Pembrolizumab (Keytruda) represent important clinical advances for the field of active immunotherapy in oncology and for patients with melanoma and prostate cancer, respectively. Immunoinformatics has played a critical role in B- and T- cell epitope prediction during the course of development of these therapies. New predictive computational models to describe the time-dependent relationships of cancer, immunity, and immunotherapies have emerged over the last few years. Using next gen sequencing approaches such as whole genome, exome and RNA sequencing, it is now possible to characterize with high accuracy the individual set of Human Lymphocyte Antigen (HLA) alleles of an individual patient leading to personalized immunotherapies. The biggest challenge in immunoinformatics arises from the routine sequencing of individual human genomes. We need new informatics tools to study the impact of natural genomic variation on the immune system and how to tap into it for new therapies. Click here for further reading.
  • Precision medicine: President Obama’s precision medicine initiative and the $215M investment have brought precision medicine to the forefront of many organizations. The cost of cancer care is estimated at $200 Billion each year and only on the rise as our population increases and lives longer. Many pundits see Precision Medicine as a way to deliver value-based cancer care. Thanks to high throughput technology, including genomic testing of each tumor, and each patient’s inherited DNA— along with proteomics in the future—oncologists are able to tailor regimens for gene mutations in each patient thus avoiding high cost of drugs that may not work. A key informatics challenge is to figure out which of the thousands of mutations in a patient’s tumor are drivers or actionable markers. There is a race in both academic and commercial space to develop software that will tease out the ‘drivers’ from the ‘passengers’. Furthermore, mutations have to be categorized by levels of evidence: high evidence – where the gene mutation has been tested in a randomized controlled trial (RCT) setting, medium evidence – retrospective gene mutation analysis of RCTs- and finally low level evidence with pre-clinical data only on the mutation. We need better evidence modeling approaches to categorize actionable mutations if clinicians are to use these in routine patient care. Click here for further reading.
  • Cell free DNA/blood tests: While molecular profiling in solid tumors remains routine practice in cancer diagnostics, modern technologies have enabled detection of biomarkers in stray cells, exosomes and traces of DNA in blood and other body fluids. This offers a low cost method to obtain cancer-profiling data for diagnosis and treatment when invasive tissue biopsies may be clinically difficult. While technologies and informatics methods for detecting very small amounts of tumor DNA are on the rise, there are many biological issues that need to be addressed. If the tumor cell did not shed a single piece of variant DNA, even the most sensitive technology will be unable to detect it. Commercial interest in this space is enormous. The Genomics/Informatics Company Illumina has just launched a new startup, GRAIL, in collaboration with Jeff Bezos and Bill Gates to develop a single blood test that could detect cancer early. Now, that is a moonshot goal! Click here for further reading.
  • Organizing cancer data: Now on to my favorite topic of organizing cancer data to power new discovery. Secondary use of EHR data for observational studies is improving through clinical research networks. As large biorepositories linked to electronic health records become more common, informatics is enabling researchers to identify cohorts that meet study criteria and have requisite consents.
    Modified from Thomas Wilckens, MD

    Modified from Thomas Wilckens, MD

    While there have been significant efforts in sharing molecular data sets publicly, less progress has been made on sharing healthcare data. Many standards exist today to facilitate data sharing and interoperability. We need more training of existing standards to consumers (app developers, scientists) of standards. We also need a comprehensive knowledgebase ecosystem that supports federated queries across cancer subtypes, risk, molecular features, diagnosis, therapy and outcomes at an individual level to advance biomarker discovery and better clinical decision support. Real-world Big Data on claims, outcomes, drug labels, research publications, clinical trials are now available and ready to be linked and analyzed to develop better cancer treatments. NCI’s TCGA and Rembrandt, Georgetown Lombardi Cancer Center’s G-DOC, Global Alliance for Genomic Health (GA4GH), ASCO’s CancerLinQ are all efforts in this direction. Let’s unleash cancer big data in effective ways to collectively make the moonshot program a reality! Click here for further reading.

Programs such as the cancer moonshot are a journey, not a destination and if directed appropriately, can inevitably better the practice of cancer medicine.

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May 16 2014

Highlights from TCGA 3rd Annual Symposium

by at 4:54 pm

The Cancer Genome Atlas’ 3rd annual scientific symposium – a report

Earlier this month, I had the opportunity to attend the 3rd annual TCGA symposium at NIH, Bethesda. The TCGA symposium is an open scientific meeting that invites all scientists, who use or wish to use TCGA data, to share and discuss their novel research findings using this data. Although a frequent user of TCGA data, this was my first visit to the symposium and I was excited to see so many other researchers using these datasets to create new knowledge in cancer research. Here I have highlighted a few talks from the symposium.

Dr. Christopher C. Benz and team studied mutations across 12 different cancer types and found P1K3CA occurring in 8 types of cancer. Their analysis showed that breast and kidney cancers favor kinase domain mutations to enhance PI3K catalytic activity and drive cell proliferation, while lung and hand-and-neck squamous cancers favor helical domain mutations to preferentially enhance their malignant cell motility. It was interesting to see how different pathways are affected based on the domain of mutation, and such insights could help understand these mechanisms better.

Samir B. Amin and team profiled long intergenic non-coding RNA (lincRNA) interactions in cancer. The results of profiling show that cancer samples could be stratified/clustered according to cancer type and or cancer stage based on lincRNA expression data.

Another interesting talk was by Dr. Rehan Akbani whose team profiled proteomics data across multiple cancer types using reverse phase protein arrays (RPPA) to analyze more than 3000 patients from 11 TCGA diseases using 181 antibodies that target a panel of known cancer related proteins. Their findings identify several novel and potentially actionable single-tumor and cross-tumor targets and pathways. Their analyses also show that tumor samples demonstrate a much more complex regulation of protein expression than cell lines, most likely due to microenvironment i.e. stroma-tumor interactions and or immune cells – tumor interactions.

Gastric cancer (GC) is the third leading cause of death worldwide, after lung and liver cancers, respectively. Most clinical trials currently recruit patients with stomach cancer and find that all patients do not respond the same way to treatment, implying an underlying heterogeneity in the tumors.  Adam Bass’s group at Dana Farber Cancer Institute did a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas. Using a cluster of clusters and iCluster methods, they have separated GC into four subtypes:

  1. Tumors positive for Epstein-Barr virus – displaying recurrent PIK3CA mutations and extreme DNA hypermethylation.
  2. Microsatellite unstable tumors – showing elevated mutation rates, including mutations of genes encoding targetable oncogenic signaling proteins.
  3. Genomically stable tumors – enriched for the diffuse histologic variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins.
  4. Tumors with chromosomal instability – showing marked aneuploidy and focal amplification of receptor tyrosine kinases.

They also found that tumor characteristics vary based on the tumor site in the stomach – tumors found in the middle of the stomach have more EBV positive and have strong methylation differences. Here’s hoping that understanding these tumor subtypes in GC will help develop treatments specific to each subtype and eventually improve gastric cancer survival in the future.

Even though the TCGA data analysis is synonymous with integrative analyses on multi-omics data, it was interesting to see in-depth analyses of single data types – including associations with viral DNA and yeast models; in-depth analysis of splicing, mRNA splicing mutations and copy number aberrations respectively. The TCGA data collection has not only compiled multi-omics data for various cancer types, but also imaging and pathology images for many samples that could be used for validation of results from ‘omics’ analyses.

Like a kid in a candy show, I was most surprised and excited to see a number of online portals and freely available software and tools showcased in the posters that take advantage of the TCGA big data collection. Some of them are highlighted below.

Online tools/portals:

  • CRAVAT 3.0 – predicts the functional effect of variant on their translated protein, predicts whether the submitted variants are cancer drivers or not.
  • MAGI – For mutation annotation and gene interpretation
  • SpliceSeq – Allows users to interactively explore splicing variation across TCGA tumor types
  • TCGA Compass – Allows users to explore clinical data, methylation, miRNA and mRNA seq data from TCGA

Online resources:

Downloadable tools from Github/R:

  • THetA – Program for tumor Heterogeneity Analysis
  • ABRA – Tool for improved indel detection
  • Hotnet2 algorithm – Identifies significantly mutated sub-networks in a PPI network
  • Switch plus – An R package in the making that uses segment copy number data on various cancer types to show differences in human and mouse models

It is energizing to see the collective efforts being taken to make this data collection more readable and parsable. I’m sure the biomedical informatics community will be more than pleased to know that it is becoming easier to explore and find what one is looking for within the TCGA data collection.

Comments by Krithika Bhuvaneshwar with contributions by Dr. Yuriy Gusev

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