A Phase II Exploratory Study to Identify Biomarkers Predictive of Clinical Response to Regorafenib in Patients with Metastatic Colorectal Cancer Who Have Failed First-Line Therapy
Bibliographic record
Abstract
Single-agent regorafenib is approved in Canada for metastatic colorectal cancer (mCRC) patients who have failed previous lines of therapy. Identifying prognostic biomarkers is key to optimizing therapeutic strategies for these patients. In this clinical study (NCT01949194), we evaluated the safety and efficacy of single-agent regorafenib as a second-line therapy for mCRC patients who received it after failing first-line therapy with an oxaliplatin or irinotecan regimen with or without bevacizumab. Using various omics approaches, we also investigated putative biomarkers of response and resistance to regorafenib in metastatic lesions and blood samples in the same cohort. Overall, the safety profile of regorafenib seemed similar to the CORRECT trial, where regorafenib was administered as ≥ 2 lines of therapy. While the mutational landscape showed typical mutation rates for the top five driver genes (APC, KRAS, BRAF, PIK3CA, and TP53), KRAS mutations were enriched in intrinsically resistant lesions. Additional exploration of genomic-phenotype associations revealed several biomarker candidates linked to unfavorable prognoses in patients with mCRC using various approaches, including pathway analysis, cfDNA profiling, and copy number analysis. However, further research endeavors are necessary to validate the potential utility of these promising genes in understanding patients' responses to regorafenib treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".