Immunoscore Is Prognostic in Low-Risk and High-Risk Stage III Colon Carcinomas Treated With Adjuvant Infusional Fluorouracil, Leucovorin, and Oxaliplatin in a Phase III Trial
Bibliographic record
Abstract
PURPOSE The recommended duration of adjuvant fluoropyrimidine and oxaliplatin chemotherapy for patients with stage III colon cancer is based on tumor classification into clinically low-risk (T 1-3 N 1 ) and high-risk (T 4 or N 2 ) groups. We determined whether Immunoscore can enhance prognostication within these risk groups. MATERIALS AND METHODS Patients with stage III colon carcinomas (N = 600) were randomly selected from the infusional fluorouracil, leucovorin, and oxaliplatin arm of adjuvant trial NCCTG N0147 (Alliance for Clinical Trials in Oncology). Tumors were evaluated for Immunoscore that quantifies CD3 + and CD8 + T-cell densities in the tumor center and invasive margin by digital image analysis. Disease-free survival (DFS) by Immunoscore was analyzed using a multivariable Cox regression model in each risk group with adjustment for covariates including KRAS, BRAF V600E , and mismatch repair status. RESULTS Of 559 cancers with Immunoscore data, 299 (53.5%) were classified as clinically low-risk (T 1-3 N 1 ) and 260 (46.5%) as clinically high-risk (T 4 and/or N 2 ). Among patients with low-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors had significantly worse 5-year DFS rates (77.5% v 91.8%; hazard ratio, 1.70; 95% CI, 1.03 to 2.79; P = .037). Among patients with high-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors also had significantly worse DFS (55.3% v 70.3%; hazard ratio, 1.65; 95% CI, 1.11 to 2.47; P = .013). Tumors that were low-risk/Immunoscore-Low had similar outcomes as did tumors that were high-risk/Immunoscore-High ( P = .174). Prognostication was significantly improved in multivariable models where Immunoscore was added to clinical risk parameters and limited biomarkers (likelihood ratio test P = .0003). CONCLUSION Immunoscore can refine patient prognosis beyond clinical risk group classification, suggesting its potential utility for adjuvant decision making.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".