Location of colon cancer (right-sided [RC] versus left-sided [LC]) as a predictor of benefit from cetuximab (CET): NCIC CTG CO.17.
Bibliographic record
Abstract
3528 Background: RC and LC differ with respect to biology, pathology, and epidemiology. Further, recent SEER data suggests a mortality difference between RC and LC that varies by stage: stage II and III RC having lower and higher mortality, respectively. We examined if the primary tumour site can also predict for outcome in pre-treated, chemotherapy refractory, metastatic colon cancer (MCC). We compared RC vs. LC as a predictor for efficacy of EGFR inhibition with CET. Methods: Using CO.17 (CET vs. BSC), we coded the primary tumour site for 399 pts as RC (cecum to transverse colon) or LC (splenic flexure to rectosigmoid). Fisher’s exact test assessed the association between site of cancer and baseline characteristics. Univariate and multivariate analyses of overall survival (OS) and progression free survival (PFS) by site of cancer were performed using Cox regression models. Results: Pts with RC (150/399) had more poorly differentiated tumours, mutant KRAS status, and peritoneal rather than liver and lung metastases, and they more often entered the study less than two years after initial diagnosis. Among pts receiving BSC, tumour location (RC vs. LC) was not prognostic for PFS (HR 1.07 [0.79, 1.44], p=0.67) or OS (HR 0.96 [0.70, 1.31], p=0.78). Among pts with KRAS wild type tumour status, site of cancer was a predictor of benefit from CET, with much greater PFS observed for LC (interaction p=0.002). Conclusions: In refractory MCC, tumour location within the colon (RC vs. LC) is not a prognostic factor, but is a strong predictor of PFS benefit from CET therapy. Additional research is needed to understand the molecular differences between RC and LC and their interaction with EGFR inhibition. [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 | 0.000 | 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) | 0.003 | 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".