Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data - Farswan, Akanksha; Gupta, Anubha; Sriram, Krishnamachari; Sharma, Atul; Kumar, Lalit; Gupta, Ritu (Frontiers in Oncology, 2021)
Introduction: Current risk predictors of Multiple Myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust Artificial Intelligence (AI) enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cut-offs of key prognostic parameters. Methods: K-adaptive partitioning is used to propose new cut-offs of parameters for two different datasets- MMIn (MM Indian dataset) dataset and MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. Consensus based Risk-Stratification System (CRSS) is designed using Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan-Meier analysis and Log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS. Results: There is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared to R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF dataset. An online calculator has been built that can predict the risk-stage of a MM patient based on parameters’ values and ethnicity. Conclusion: Our methodology discovers changes in cut-offs with ethnicities from the established cut-offs of prognostic features. The best predictor model for both the cohorts was obtained with the new ethnicity specific cut-offs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in R-ISS reported in 2015.