c-MET and KRAS: Signalling and Clinical Implications in Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third leading cause of death from cancer in North America. The KRAS gene is mutated in approximately 40-50% of all CRC, and this mutation precludes treatment with promising targeted therapeutics. c-MET is a receptor tyrosine kinase that is overexpressed in ~70% of CRCs, and expression is correlated with disease progression. We hypothesized that high c-MET plus mutant KRAS would result poor survival of CRC patients, by activating unique signalling pathways that may be targeted for therapeutic purposes. To this end, we used phosphoproteomics in a KRAS mutant cell line, and identified proteins phosphorylated on tyrosine in response to HGF stimulation, including a subset of those that contain SRC family kinase consensus motifs. Small molecule inhibitors of either SRC or c-MET reduced tyrosine phosphorylation of both proteins, indicating reciprocal signalling. We chose the c-MET target p190RhoGAP for future study, as it is often ubiquitously bound to p120RasGAP via phosphorylated tyrosine. We found that RasGAP expression is mediated in part by KRAS signalling, and that expression of RasGAP could partly rescue tumourigenicity of a CRC cell line where the mutant KRAS allele has been inactivated, indicating the requirement of both mutant KRAS and RasGAP expression in this model. We then conclude by looking at CRC patient samples to determine the role of KRAS mutation in the progression and survival of CRC. We found that both KRAS and c-MET copy number are correlated to KRAS mutation status, and that c-MET polysomy plus KRAS mutation leads to worse overall survival than KRAS mutation alone. Overall, we identified novel targets of c-MET and KRAS oncogenic signaling, and identify a population which may derive the most benefit from treatments targeting both of these lesions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".