Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials
Bibliographic record
Abstract
PURPOSE Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One known predictive factor for CET efficacy is the presence of CET-related rash; other putative toxicity factors include fatigue and nausea. Analysis of early CET-associated toxicities may reveal patient subpopulations that clinically benefit from long-term CET treatment. METHODS We analyzed data from CO.20 (ClinicalTrials.gov identifier: NCT00640471 ) trial arms, CET + brivanib alaninate (BRIV) (n = 376) and CET + placebo (n = 374), and CO.17 (ClinicalTrials.gov identifier: NCT00079066 ) trial arms, CET (+best supportive care [BSC]; n = 287) and BSC only (n = 285). Patients were clustered into subpopulations using KmL3D, a machine learning method, to analyze 14 joint longitudinal toxicity trajectories from weeks 0 to 8 of treatment. Landmark survival analyses were performed from 8 weeks after treatment initiation. Regression analyses assessed the relationship between subpopulations and plasma CET concentrations. Three supervised machine learning models were developed to assign patients in the CO.20-CET trial arm into subpopulations, which were then validated using CO.20-CET-BRIV and CO.17-CET trial arm data. RESULTS Joint longitudinal toxicity clustering revealed dichotomous high- and low-toxicity clusters, with all CET-containing arms showing consistent toxicity trajectories and characteristics. High-toxicity clusters were associated with male predilection, fewer metastatic sites, fewer colon-only primaries, and higher body mass indices. In CO.20 trial samples, higher toxicity clusters were associated with improved overall survival and progression-free survival outcomes (adjusted hazard ratios ranging from 2.21 to 4.36) and higher CET concentrations ( P = .003). The random forest predictive model performed the best, with an AUC of 0.981 (0.963-0.999). CONCLUSION We used an innovative machine learning approach to analyze longitudinal joint drug toxicities, demonstrating their role in predicting patient outcomes through a putative pharmacokinetic mechanism.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".