Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

Akanksha Farswan; Anubha Gupta; Krishnamachari Sriram; Atul Sharma; Lalit Kumar; Ritu Gupta

doi:10.3389/fonc.2021.720932

Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

Front Oncol. 2021 Nov 9:11:720932. doi: 10.3389/fonc.2021.720932. eCollection 2021.

Authors

Akanksha Farswan¹, Anubha Gupta¹, Krishnamachari Sriram², Atul Sharma³, Lalit Kumar³, Ritu Gupta⁴

Affiliations

¹ Signal Processing and Biomedical Imaging Lab (SBILab), Department of Electronics and Communication, Indraprastha Institute of Information Technology-Delhi, New Delhi, India.
² Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, New Delhi, India.
³ Department of Medical Oncology, Dr. B.R.A. IRCH, AIIMS, New Delhi, India.
⁴ Laboratory Oncology Unit, Dr. Bhim Rao Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences (Dr. B.R.A. IRCH, AIIMS), New Delhi, India.

Abstract

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 cutoffs of key prognostic parameters.

Methods: K-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets-the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the 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 with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity.

Conclusion: Our methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the 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 the R-ISS reported in 2015.

Keywords: AI in cancer research; GMM clustering in cancer; ML in cancer survival; consensus clustering in cancer; hematological malignancy; risk stratification of multiple myeloma.