Somatic pharmacogenomics in the treatment prognosis of locally advanced rectal cancer patients: a narrative review of the literature
Bibliographic record
Abstract
Introduction Standard treatment for patients with locally advanced rectal cancer (LARC) includes neoadjuvant chemoradiotherapy (nCRT) with fluoropyrimidines, followed by surgical excision. The newly introduced therapeutic strategies propose intensified regimens or more conservative approaches based on risk stratification algorithms that currently include clinicoradiological criteria but not molecular variables. How to better stratify patients is a burning clinical question, and pharmacogenomics may prove useful in identifying new genetic markers that could be incorporated into clinical algorithms to personalize nCRT. An emerging area could be the evaluation of somatic mutations as potential genetic markers that correlate with patient prognosis. Tumor mutations in the RAS/BRAF genes, as well as microsatellite instability (MSI) status, are currently used in treatment selection for colorectal cancer (CRC); however, their clinical value in LARC is still unclear.Area covered This literature review discusses the relevant findings on the prognostic role of mutations in the key oncogenes RAS, KRAS, BRAF, PIK3CA, SMAD4 and TP53, including MSI status in LARC patients treated with nCRT.Expert opinion KRAS proved to be the most promising marker, consistently associated with poorer disease-free survival and overall survival. Therefore, KRAS could be a good candidate for integration into the risk stratification algorithm to develop a personalized 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".