Colorectal cancer (CRC) KRAS mutational status and response to chemotherapy in absence of influence of epidermal growth factor receptor (EGFR) monoclonal antibody (MAb) therapy.
Bibliographic record
Abstract
504 Background: KRAS mutations (MUT), present in ~40% of CRC, predict benefit from EGFR MAb treatment. The effect of mutation status on benefit from chemotherapy alone is less clear. Methods: Following ethics approval, 223 CRC patients with known KRAS status treated at a single institution were retrospectively analyzed for response to chemotherapy prior to initiation of EGFR MAb therapy. Tumour response rate and progression-free survival (PFS) were determined retrospectively. Given chemotherapy sequencing variability, a measure of the time to chemotherapy-refractory state (TTCR) was defined as time from start of first line therapy to progression on all drugs; ie fluoropyrimidines (F), irinotecan (I), and oxaliplatin (O), +/- bevacizumab (B). Results: Patients were median age 60 years (25-86), 58/42% male/female, 38% rectal or rectosigmoid, 18% liver-only metastases, 45.6% stage I-III and 54.4% stage IV at initial diagnosis, 43% prior adjuvant chemotherapy, 43/57% KRAS MUT/wild-type(WT) status. With a median follow-up of 27 months, 64 (29%) are alive. TTCR did not vary by KRAS status, with median 16.7 vs 14.8 months in WT vs MUT status patients, respectively, HR 0.85 [0.65-1.12], p=0.26. Overall survival (which now includes influence of any subsequent EGFR MAb therapy, received by 76/126 (60%) of KRAS WT status patients) did not differ significantly by KRAS status, with median survival 34.3 vs 29.2 months for WT vs MUT status, respectively, 0.79 [0.58-1.08], p=0.14. Conclusions: For most treatment strategies KRAS status did not affect 1st line PFS or time to chemotherapy-refractory state. However, for patients who received triple combination therapy, MUT status was associated with early progression. Small sample size, chance or some yet-to-be-elucidated molecular interaction may account for this finding. [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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".