A Systematic Literature Review and Meta-Analysis Describing the Prevalence of <i>KRAS, NRAS</i>, and <i>BRAF</i> Gene Mutations in Metastatic Colorectal Cancer
Bibliographic record
Abstract
Background: Tumors of the metastatic colorectal cancer (mCRC) patients that are wildtype (WT) for KRAS or NRAS mutations respond more favorably to anti-epidermal growth factor receptor (EGFR) treatments. Treatment guidelines now recommend that all mCRC patients have WT KRAS and NRAS tumor status confirmed prior to initiating anti-EGFR therapy. Evidence also suggests that BRAF mutations may predict lack of response to anti-EGFR therapy. As such, there is now a need for comprehensive data on the prevalence of KRAS , NRAS , and BRAF mutations among patients with mCRC. Methods: A systematic literature review was conducted among studies that described the prevalence of KRAS , NRAS , and BRAF gene mutations in mCRC patients. Observational cohort studies and standard of care arm of randomized clinical trials were included. Random effects meta-analysis models were used to create summary prevalence estimates for each of the mutation types. Subgroup analyses were also conducted to identify potential sources of heterogeneity. Exploratory analyses of overall and progression-free survival by mutation status were also conducted. Results: This systematic review and meta-analysis included 275 studies comprising 77,104 mCRC patients. The summary prevalence estimate was 35.9% for KRAS mutations, 7.1% for BRAF mutations, and 4.1% for NRAS mutations. Female patients had significantly more KRAS and BRAF mutations than males, and significant variation by study location was observed for both KRAS and BRAF mutation prevalence. Overall survival was significantly decreased for patients with KRAS , BRAF , and NRAS mutations compared to those with WT tumors. Progression-free survival was also significantly decreased among patients with KRAS and BRAF mutations. Conclusions: KRAS , NRAS , and BRAF mutation statuses in patients with mCRC are important predictors of treatment success and may also have prognostic value. In this paper we present the first systematic and comprehensive literature review and meta-analysis of the prevalence of KRAS , BRAF , and NRAS mutations and demonstrate the prognostic impact of mutation status on survival. Gastroenterol Res. 2020;13(5):184-198 doi: https://doi.org/10.14740/gr1167
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".