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

IntroductionCurrent 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 efficien...

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Autores principales:	Akanksha Farswan, Anubha Gupta, Krishnamachari Sriram, Atul Sharma, Lalit Kumar, Ritu Gupta
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	AI in cancer research ML in cancer survival risk stratification of multiple myeloma GMM clustering in cancer consensus clustering in cancer hematological malignancy Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/b8c1c7b8e3b04c54af5dd4d58f9c53a7
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spelling	oai:doaj.org-article:b8c1c7b8e3b04c54af5dd4d58f9c53a72021-11-09T06:39:04ZDoes Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data2234-943X10.3389/fonc.2021.720932https://doaj.org/article/b8c1c7b8e3b04c54af5dd4d58f9c53a72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.720932/fullhttps://doaj.org/toc/2234-943XIntroductionCurrent 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.MethodsK-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.ResultsThere 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.ConclusionOur 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.Akanksha FarswanAnubha GuptaKrishnamachari SriramAtul SharmaLalit KumarRitu GuptaFrontiers Media S.A.articleAI in cancer researchML in cancer survivalrisk stratification of multiple myelomaGMM clustering in cancerconsensus clustering in cancerhematological malignancyNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AI in cancer research ML in cancer survival risk stratification of multiple myeloma GMM clustering in cancer consensus clustering in cancer hematological malignancy Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	AI in cancer research ML in cancer survival risk stratification of multiple myeloma GMM clustering in cancer consensus clustering in cancer hematological malignancy Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Akanksha Farswan Anubha Gupta Krishnamachari Sriram Atul Sharma Lalit Kumar Ritu Gupta Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
description	IntroductionCurrent 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.MethodsK-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.ResultsThere 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.ConclusionOur 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.
format	article
author	Akanksha Farswan Anubha Gupta Krishnamachari Sriram Atul Sharma Lalit Kumar Ritu Gupta
author_facet	Akanksha Farswan Anubha Gupta Krishnamachari Sriram Atul Sharma Lalit Kumar Ritu Gupta
author_sort	Akanksha Farswan
title	Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
title_short	Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
title_full	Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
title_fullStr	Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
title_full_unstemmed	Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data
title_sort	does ethnicity matter in multiple myeloma risk prediction in the era of genomics and novel agents? evidence from real-world data
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b8c1c7b8e3b04c54af5dd4d58f9c53a7
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Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

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