Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer

Abstract

PURPOSE A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. METHODS We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS– (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. RESULTS Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS–. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS– patients did not (COLOXIS– HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). CONCLUSION The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

“…Machine learning has great potential for disease risk prediction and diagnosis. In colorectal cancer, machine learning models can accurately predict the risk of undesired postoperative return to surgery by comprehensively analyzing multidimensional data on surgical approaches, and a patient's clinical characteristics and comorbidities[ 14 ]. The ability of such techniques to learn and adapt to new data means that their predictive accuracy continues to improve over time and data accumulation, reducing unnecessary reoperations, optimizing patient prognosis, and improving quality of life.…”