Influence of molecular alterations on site-specific (ss) time to recurrence (TTR) following adjuvant therapy in resected colon cancer (CC) (Alliance Trial N0147).
Bibliographic record
Abstract
3590 Background: Influence of tumor molecular alterations on ssTTR after resection of stage 3 CC has not been well studied. Phase 3 trial N0147 (adjuvant FOLFOX +/- cetuximab) provided an opportunity to assess possible correlations. Methods: 3098 stage 3 CC pts were enrolled and sites of all recurrences were reviewed centrally: liver, lung, peritoneal, local ( < 5 cm of anastomosis), regional, and other metastatic. Genetic markers include MMR, mutations (mut) in KRAS exon 2 (codons 12, 13), BRAF V600E exon 15. Cumulative incidence rate (CIR) was estimated for ss recurrences. Associations between biomarkers and TTR across sites (interaction) were assessed by a frailty Cox model. Association between biomarker and ssTTR were evaluated by multivariable Cox model when the site-biomarker interaction effect presents, adjusting for age, T/N stage, type of surgery, tumor sidedness, and treatment. Results: Most common recur sites were liver, regional, peritoneal, and lung, with 3 yr CIR of 8.4%, 8.3%, 5.5% and 5.4%, respectively, with no difference between treatment arms (p = 0.9). Interactions between sites of recurrence and BRAF (p = .006) or MMR (p = .018) status were significant and marginal for combined KRAS/BRAF (p = .10). Compared to wild type (wt) pts, BRAF mut pts had shorter TTR for peritoneal HR2.14; 95% CI, 1.36-3.37; padj= .001] and metastatic sites [HR, 1.19; 95 CI, 1.07-3.42; padj= .033], but not for liver or lung. KRAS mut/BRAF wt pts had shorter TTR for liver (HR, 1.55; 95% CI, 1.19-2.03) and lung (HR, 1.81, 95% CI, 1.32-2.50), and KRAS wt/BRAF mut pts had shorter TTR for peritoneal (HR, 2.14; 95% CI, 1.36-3.37) compared to double wt pts. dMMR pts had longer TTR for liver (HR, 0.44; 95% CI, 0.25-0.78; padj= .002 ) and peritoneal (HR, 0.46; 95% CI, 0.25-0.82; padj= .004) compared to pMMR pts. Conclusions: Status of MMR and BRAF, but not KRAS (alone), influences site of recurrence and TTR. Accordingly, these biomarkers may assist in treatment and follow-up approaches. Clinical trial information: NCT00079274.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".