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MULTIPLE MYELOMA, GAMMOPATHIES

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Abstract

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

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Fig. 1: Outline of the research workflow.
Fig. 2: Predicted individual survival curves according to the best random forests model.
Fig. 3: Comparison of optimal vs suboptimal treatment outcomes.

Code availability

The code to train and validate IAC-50 of survival can be requested by e-mail to the corresponding author.

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Acknowledgements

The authors wish to thank the Supercomputing Center of Galicia (CESGA) and the Multiple Myeloma Research Foundation (MMRF) for technical support and facilitating access to the data from the CoMMpass consortium, respectively.

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Correspondence to Maria Victoria Mateos Manteca.

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Conflict of interest

M-VM has received honoraria for lectures and participation in advisory boards from Janssen, Celgene-BMS, Amgen, Takeda, Abbvie, GSK, Adaptive, Roche, Seatle Genetics, Pfizer, and Regeneron. AMO has received honoraria for lectures and participation in advisory boards from Janssen and AstraZeneca. AMO has received research grants from Roche and Celgene-BMS. MSGP has received honoraria for lectures and participation in advisory boards from Janssen, Amgen, Celgene-BMS, Takeda, Sanofi, and GSK.

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Mosquera Orgueira, A., González Pérez, M.S., Díaz Arias, J.Á. et al. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. Leukemia (2021). https://doi.org/10.1038/s41375-021-01286-2

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