Machine learning (ML)–enabled, circulating tumor cell–based classification of patients for non-prerequisite adjuvant therapy.

Abstract

1547 Background: Oncology implicates highest precision using next generation diagnostics and progressive therapies assisted by predictive tools. If validated clinically, machine learning (ML) can provide better insights in precision oncology. Furthermore, it longitudinally may stratify the progression of cancer disease burden in a real time. We have developed, Circulating Tumor Cells (CTCs) driven ML model as a predictor for the treatment decision strategy for both surgery and adjuvant therapy in head and neck squamous cell carcinoma (HNSCC) patients. Methods: In this study, a total of 380 HNSCC patients who underwent either surgery alone or surgery plus adjuvant therapy were accounted for. CTCs in patients were stratified based on clinicopathological parameters and using OncoDiscover platform having anti EpCAM antibody system regulated by the Drug Controller of India. Following this, we explored the predictive performance of the ML model on the usefulness of adjuvant therapy in HNSCC patients after the surgery. The available data was randomly divided into two subsets. First, 75%, of the original data was applied for Training the ML, and rest 25% of the data was used as a Test set. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test. Results: XGBoost machine learning classifier was superior to Random Forest and SVM-based analyses in predicting the usefulness of adjuvant therapy post-surgery using CTC alone or in combination with other clinical parameters in HNSCC patients. Machine learning algorithms were compared for predicting the accuracy of patients stratification. The results for each model were: XGBoost model (Accuracy = 0.84, ROC value = 0.73, Kappa = 0.43); Random Forest model (Accuracy = 0.81 ROC value = 0.70, Kappa = 0.41); SVM model (Accuracy = 0.76, ROC value = 0. 69, Kappa = 0.40). The ROC value of the XGBoost model was highest (0.73) while the ROC value for the SVM model was lower (0.69). We observed that when CTCs were combined with clinicopathological parameters, the accuracy, kappa values and AUC-ROC drastically improved in predicting the usefulness of adjuvant therapy post-surgery. A similar trend was observed when CTCs were combined with clinicopathological parameters in predicting the line of chemotherapy, post-surgery. Conclusions: ML-enabled, CTCs driven predictions can be highly accurate and ascertain the patient treatments. CTCs can be a positive predictor for selecting patient’s treatment regimen in both surgery as well in type of treatment (e.g. surgery alone or surgery + adjuvant therapy). It can also implicate to classify the patients and determine who necessitates an additional adjuvant therapy. Further investigations in this direction are necessary to predict the treatment options based on ML that may improve the overall survival of cancer patients.