Lynda Chin, MD Anderson Cancer Center, USA
Cancer as a disease of the genome. Discovery of the BRAF mutation by Wellcome Trust as the poster child of what genome analysis can do (proof of concept). Know the target, what it does, have the right target, and identify the right patient population subset should lead to therapeutic success — in theory. A large scale catalogue of mutations insufficient. Overview of RAC1 mutations, biological evidence of activating function of the mutation, still need better understanding to translate this into aims for a drug design.
Another example, Prex2 in melanoma, mutated and highly re-arranged in different patients. Unclear whether this is a driver mutation or noise — large number of mutations, but scattered all over the gene, no hotspots. Need to engineer mutations and test in vivo model system. That still does not define what it does or how, and more importantly is it rate limiting for the tumor?
All model systems with strengths and weaknesses; need to run several independent tests that should converge on the same result before trusting the results to not be an artifact.
Case study: landscape of somatic mutations in melanoma (Hodis, Watson; Cell 2012). Number of patients without BRAF or NRAS mutations, no treatment for this group. Start with genetic model, NRAS mouse promoter can be turned on and off in a tumor. NRAS initiates tumor, mutations in TRRAP, GRM3, SETD2 present. Switch NRAS off, tumor shrinks — NRAS is required for maintenance and a valid therapeutic target. Genetic ablation of NRAS induces tumor regression, not achieved by inhibition of MEK. Identified genes significantly altered by MEK inhibitor and the effect of NRAS (plenty of overlap, MEKi represents partial inhibition of NRAS activity). Majority of NRAS-associated genes not affected by MEK inhibitors though.
Tested for pathway enriched by RAS-specific genes (cell cycle proliferation, opposite of expectations). P53 decreased upon NRAS extinction, not after MEK inhibition. Network modeling (TRAP) to analyze difference what key regulators are responsible for the pathway differences, identified CDK4 as a putative key driver (proliferation checkpoint).
Missing step: pharmacological validation with a CDK4 inhibitor (commercially available). Only works in combination with MEK inhibitor (synergistic effect); confirmed in ex vivo test. Partial inhibition uncouples apoptosis and cell cycle arrest.
Model system vital to understand how the signaling of the pathway works, bypass redundant / complex feedback systems. Enables a wider therapeutic window. Systems approach to collect data at will crucial to develop combination therapies.
Matthew Ellis - WUSTL
Need for controlled screens rather randomly sampling frozen samples from patients treated in different ways as part of a broad mutation discovery program. Relate cancer phenotype and genotype through recurrence and validation efforts, identify drug-able matches, validation studies and clinical trials.
Summary of a breast cancer clinical trial (3 drugs for ER+ patients for patients where degree of required surgery not feasible; smaller surgery possible if tumor shrinks in size). Use samples of well-controlled trial for sequencing analysis. Split discovery set of 50 cases into half based on whether a drug worked or didn’t, 2 samples each, 70+% tumor cell content, compare to germline, again tiered annotation of somatic mutations. Prioritized list of genes, familiar and novel [not published yet and asks not to share examples].
Knockdown of target genes (with and without estrogen depletion) and tracing apoptosis to zoom in on candidates for therapy followed by pharmacological targeting of pathways (PIK3 in this case); study sensitivity versus mutation status. Genome-forward trial with upfront sequencing for targets, mutation status. Adjusting trials difficult for loss-of-function mutations.
[He goes through a number of additional genes and pathways, druggable status, etc., but I’m unclear on what can or cannot be blogged. Erring on the side of caution here.]
Extend from SNPs to areas of gene amplifications with potential targets, preferably drivers of higher prevalence rates (putative resistance genes). Yields even more druggable targets (‘lots of ‘em’).
Forward: do genome upfront, all patients then have a theoretical target and a realistic chance of responding to the drug.