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 &lt; 0.0001], which persisted in multivariate analysis (P &lt; 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".