Abstract 5377: An integrative prognostic machine learning model in mantle cell lymphoma

Abstract

Abstract Background: Mantle cell lymphoma (MCL) is an uncommon B-cell lymphoma. The clinical course is highly variable: some patients have aggressive disease and relapse after treatment, while others have indolent disease or respond exceptionally to frontline therapy. Prognostication of MCL patients is dynamic and continues to evolve as novel therapies develop. Current prognostic indicators, such as the MCL international prognostic index (MIPI), were primarily designed with patients treated with chemo-immunotherapies. Using machine learning (ML) and molecular data, we provide a novel predictive method to improve upon conventional clinical markers. Methods: We studied 785 MCL patients diagnosed at MD Anderson since 2014 and retrospectively classified them as “aggressive MCL” (n=311): relapsed or refractory to frontline treatment, and “mild MCL” (n=474): those who did not relapse after the first treatment (exceptional response) or had indolent disease never requiring treatment. After data extraction and feature engineering, 195 baseline features comprised of clinical, genomic, pathology, and cytogenetic data were integrated into an extreme gradient-boosted ensemble ML model (XGBoost). The dataset containing all patients was split (75/25) into a training and test set. Hyperparameters for the model were tuned using a grid-based, space-filling (Latin hypercube) technique and resampled 10-fold cross-validation from the training set. Training, validation, and testing sets were split using stratification of the classification variable to avoid class imbalance. Results: Our integrative model achieved area under the curve (AUC) = .82 and accuracy = .76 on the test set and outperformed an XGBoost model using only clinical features (AUC = .78, accuracy = .68). Additionally, the fully integrated model improved on metrics from a similar multivariate logistic model including all patients (AUC = .72, accuracy =.72). Univariate logistic models were fit on the classification variable using the MIPI and other prognostic indices. The integrated ML model significantly outperformed the MIPI (AUC = .62, accuracy = .60) and other indices in predicting patient class. Clinical, pathological, cytogenetic, and genomic data were all represented as impactful features in a variable importance plot (VIP) and Shapley (SHAP) additive values of the fully integrated ML model. This model was used to launch a rest application programming interface (API) in which important features could be entered and a prediction returned. Conclusion: Our study demonstrates that the current paradigm of using limited features in disease prognostics should be replaced with more advanced ML models that utilize genomic and other molecular data. Future work will include expanding the features included in the model and using the rest API to construct a graphical user interface accessible to clinicians and other researchers to make treatment decisions in precision oncology. Citation Format: Holly A. Hill, Preetesh Jain, Michael L. Wang, Ken Chen. An integrative prognostic machine learning model in mantle cell lymphoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5377.