Choosing the right strategy based on individualized treatment effect predictions: combination versus sequential chemotherapy in patients with metastatic colorectal cancer

Background: Translating results from randomized trials to individual patients is challenging, since treatment effects may vary due to heterogeneous prognostic characteristics. We aimed to demonstrate model development for individualized treatment effect predictions in cancer patients. We used data from two randomized trials that investigated sequential versus combination chemotherapy in unresectable metastatic colorectal cancer (mCRC) patients.

Material and methods: We used data from 803 patients included in CAIRO for prediction model development and internal validation, and data from 1423 patients included in FOCUS for external validation. A Weibull model with pre-specified patient and tumour characteristics was developed for a prediction of gain in median overall survival (OS) by upfront combination versus sequential chemotherapy. Decision curve analysis with net benefit was used. A nomogram was built using logistic regression for estimating the probability of receiving second-line treatment after the first-line monochemotherapy.

Results: Median-predicted gain in OS for the combination versus sequential chemotherapy was 2.3 months (IQR: −1.1 to 3.7 months). A predicted gain in favour of sequential chemotherapy was found in 231 patients (29%) and a predicted gain of >3 months for combination chemotherapy in 294 patients (37%). Patients with benefit from sequential chemotherapy had metachronous metastatic disease and a left-sided primary tumour. Decision curve analyses showed improvement in a net benefit for treating all patients according to prediction-based treatment compared to treating all patients with combination chemotherapy. Multiple characteristics were identified as prognostic variables which identify patients at risk of never receiving second-line treatment if treated with initial monochemotherapy. External validation showed good calibration with moderate discrimination in both models (C-index 0.66 and 0.65, respectively).

Conclusions: We successfully developed individualized prediction models including prognostic characteristics derived from randomized trials to estimate treatment effects in mCRC patients. In times where the heterogeneity of CRC becomes increasingly evident, such tools are an important step towards personalized treatment.