Classifying Colorectal Cancer by Tumor Location Rather than Sidedness Highlights a Continuum in Mutation Profiles and Consensus Molecular Subtypes
Bibliographic record
Abstract
Abstract Purpose: Colorectal cancers are classified as right/left-sided based on whether they occur before/after the splenic flexure, with established differences in molecular subtypes and outcomes. However, it is unclear if this division is optimal and whether precise tumor location provides further information. Experimental Design: In 1,876 patients with colorectal cancer, we compared mutation prevalence and overall survival (OS) according to side and location. Consensus molecular subtype (CMS) was compared in a separate cohort of 608 patients. Results: Mutation prevalence differed by side and location for TP53, KRAS, BRAFV600, PIK3CA, SMAD4, CTNNB1, GNAS, and PTEN. Within left- and right-sided tumors, there remained substantial variations in mutation rates. For example, within right-sided tumors, RAS mutations decreased from 70% for cecal, to 43% for hepatic flexure location (P = 0.0001), while BRAFV600 mutations increased from 10% to 22% between the same locations (P < 0.0001). Within left-sided tumors, the sigmoid and rectal region had more TP53 mutations (P = 0.027), less PIK3CA (P = 0.0009), BRAF (P = 0.0033), or CTNNB1 mutations (P < 0.0001), and less MSI (P < 0.0001) than other left-sided locations. Despite this, a left/right division preceding the transverse colon maximized prognostic differences by side and transverse colon tumors had K-modes mutation clustering that appeared more left than right sided. CMS profiles showed a decline in CMS1 and CMS3 and rise in CMS2 prevalence moving distally. Conclusions: Current right/left classifications may not fully recapitulate regional variations in tumor biology. Specifically, the sigmoid-rectal region appears unique and the transverse colon is distinct from other right-sided locations. Clin Cancer Res; 24(5); 1062–72. ©2017 AACR. See related commentary by Dienstmann, p. 989
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".