We had a very interesting talk from Lynda Chin, a researcher from the Dana-Farber Cancer institute who played a key role in the analysis of our TCGA glioblastoma publication. The attendees were an esoteric group, including key players from our genome center (Rick Wilson, Elaine Mardis, Tim Ley) and every cancer researcher here that I’ve met (Paul Goodfellow, Brian Suarez, Greg Longmore). The presenter’s talk – Mining and Translating the Cancer Genome – came in two logical parts: first, a discussion of “why study the genome” of cancer, and then a fascinating overview of her research into the determinants of cancer metastasis.
Why Study the Cancer Genome?
I wonder if people are still asking this question. Dr. Chin hit us with a few high-profile examples of how studying cancer genetics/genomics has improved our knowledge of and ability to treat the disease. There was the 2004 Science publication by Matt Meyerson’s group showing a correlation between patient mutations in the EGFR genes and their response to gefitinib therapy in lung cancer. A year later in the New England Journal of Medicine, Mellinghoff et al showed a significant association between PTEN/EGFR co-expression and response to EGFR kinase inhibitors (erlotinib) in glioblastoma. In 2007, Stommel et al published some interesting (and slightly frightening) findings in Science. They found that certain GBM tumors – particularly those deficient in PTEN – had multiple co-activated receptor tyrosine kinases (RTKs). Treatment with a single RTK inhibitor had little effect in these tumors, but combined therapy to target several RTKs (with Iressa/Tarceva/Gleevec, etc.) decreased signaling, cell survival, and anchorage-independent growth in GBM.
Dr. Chin was also kind enough to acknowledge work by Li Ding’s group on MGMT methylation and PIK3R1 mutations in glioblastoma, published in the pilot TCGA study published a few months ago in Nature. She emphasized several times the value of TCGA’s GBM sequencing as a reference cancer genome, with high-quality mutations already profiled for mutations, gene expression, etc.
Current Cancer Therapy: Medical Whack-A-Mole
The speaker likened our current approaches to cancer therapy to the game of Whack-A-Mole; numerous different targeted therapies are applied across a wide spectrum of cancer patients, each therapy proving effective in as few as 40% of patients with the same disease. The other 60% of patients might show no benefit, a possible explanation for the failure of so many Phase III clinical trials. Cancer, she argued, is likely to be the result of a network of interacting genes, pathways, and environmental factors. Only when we functionalize the entire network can we begin to treat individual patients with rational drug combinations based on their unique tumor genome – the promise of personalized medicine.
Model Systems for Cancer Metastasis
The second half of the talk focused on work done in mouse models. Throughout her talk, Dr. Chin championed model systems as critical components to studying cancer. Her group set out to answer the question, how can we distinguish at an early stage if a cancer will metastasize? The ability to do so, obviously, would have major implications for diagnosis and treatment; metastasis is generally what kills cancer patients, and being able to predict who’s at risk can inform the plan for therapy. The idea was to identify genes that were metastasis determinants by correlating gene expression with some kind of invasion assay that measured tumor progression. They whittled a 1,597 gene dataset down to 360 MD suspects, which went through a knowledge-based pathway analysis using IPA. The filtered dataset highlighted several key cancer processes, including those involved in cell motility and adhesion.
Metastasis Determinants in Other Cancers
When they narrowed down their list of metastatic determinants to the top 20 or so, things got pretty interesting. It turned out that mutations in these MD candidates were prevalent across many types of cancer. Twelve of their MD’s were mutated in breast cancer, around 8 or so in colon cancer, and so on. One particular gene of interest that Dr. Chin presented was HOXA1, a gene involved in development. When expressed, HOXA1 activated the TFGB network which, according to their IPA analysis, was centered on SMAD3. Thus, the take-home finding was that tumors whose expression profiles show HOXA1 over-expression might benefit from TGFB inhibitors.
Early Metastatic Genes: Probably Oncogenes
The presenter pointed out that genes that confer advantages for metastasis (invasion) alone have no Darwinian selective advantage in early tumor development. Thus, she proposed that many determinants of metastasis are likely to be oncogenes as well. If this is true, then finding mutations that confer both tumorigenesis and metastasis may provide biomarkers for prognosis. Metastatic potential is probably hard-wired, Dr. Chin said, and thus may eventually be predictable from the genetics of the primary tumor.