Population-based screening for <i>BRAF</i> V600E in metastatic colorectal cancer (mCRC) to reveal true prognosis.
Bibliographic record
Abstract
3579 Background: BRAFV600E ( BRAF) mutations (mts) portend poor prognosis in mCRC and patients (pts) may die before ascertainment. Since 2014, Vancouver Coastal Health (VCH) has performed reflex hereditary screening of CRCs with BRAF and mismatch repair (MMR) immunohistochemistry (IHC). We evaluated this BRAF mt population-based cohort ( BRAFPOP) to establish the true prognosis of BRAF mts in mCRC. Methods: We reviewed all mCRCs from VCH between 4/2014 and 5/2018 for BRAF by IHC (VE1 antibody). Overall survival (OS) from stage IV diagnosis was compared to mCRCs with next generation sequencing (NGS) determined BRAF mts ( BRAFNGS) from BC Cancer & MD Anderson. BRAFNGS OS did not differ by center (p = 0.77). Results: See table for BRAF cohort baseline characteristic comparison. BRAFPOP pts had worse OS than BRAFNGS pts (HR 2.5, 95% CI 1.6 – 3.9, P < 0.0001). Median OS for all BRAF mt pts was 17.9 mos. Both groups had worse OS than wild type pts (P < 0.0001). 52 (81%) of BRAFPOP pts were referred to oncology, 40 (63%) received chemotherapy, and 12 (19%) had NGS BRAF testing. BRAFPOP pts who had NGS testing with BRAF mts had OS comparable to other BRAFNGS pts (P = 0.89) and better OS than BRAFPOP pts that never had NGS testing (HR 0.37, 95% CI 0.18-0.76, P = 0.030). Pts with BRAF mts and MMR deficiency (dMMR) (n = 40) had worse OS than MMR proficiency (pMMR, n = 202) (1.6, 95% CI 1.0-2.5, P = 0.011). This was driven by BRAFPOP dMMR pts (HR 1.9, 95% CI 0.9-4.0, P = 0.036) as no difference was seen by MMR in BRAFNGS pts (HR 1.3, 95% CI 0.8-2.2, P = 0.30). Conclusions: Current estimates of prognosis for mCRC with BRAF mts likely underestimate its impact due to referral bias for NGS testing. BRAF mts with dMMR are associated with worse prognosis than pMMR. This appears driven by BRAFPOP pts. [Table: see text]
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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 | 0.002 |
| 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 | 0.000 | 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".