Population-based Screening for <i>BRAF</i> V600E in Metastatic Colorectal Cancer Reveals Increased Prevalence and Poor Prognosis
Bibliographic record
Abstract
Abstract Purpose: BRAF V600E mutations portend poor prognosis in metastatic colorectal cancer (mCRC); however, the true prevalence and prognosis are unknown, as unwell patients may not undergo BRAF sequencing. Experimental Design: We reviewed a population-based cohort of 1,898 patients with colorectal cancer that underwent reflexive IHC mismatch repair (MMR) and BRAFV600E testing. Outcomes among IHC-detected BRAFV600E mCRC (BRAFIHC) were compared with patients with next-generation sequencing (NGS)–identified BRAFV600E-mutated mCRC from two institutions (BRAFNGS) with patients spanning from 2004 to 2018. Results: All-stage population prevalence of BRAFV600E was 12.5% (238/1,898) and did not differ between early and metastatic stages (P = 0.094). Prevalence among mCRC was 10.6% (61/575), of whom 51 (83.6%) were referred to oncology and 26 (42.6%) had NGS testing. BRAFIHC had worse median overall survival (mOS) than BRAFNGS [5.5 vs. 20.4 months; HR, 2.90; 95% confidence interval (CI), 1.89–4.45; P < 0.0001], which persisted in multivariate analysis (P < 0.0001). Across a combined NGS and IHC cohort, BRAFV600E tumors with deficient MMR showed worse mOS compared with MMR proficient tumors (8.9 vs. 17.2 months; HR, 1.46; 95% CI, 0.96–2.27; P = 0.043). In this combined cohort, first-line progression-free survival was 5.9 months, with minimal differences between regimens. Within the population-based cohort, attrition between treatment lines was high with only 60.7% receiving first-line chemotherapy and 26.2% receiving second line. Conclusions: Patients with BRAFV600E-mutated mCRC have a worse prognosis than previously suggested, potentially arising from referral bias for testing. High attrition between lines of therapy suggests efficacious therapies need to be prioritized early for patients to benefit.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".