Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects

Nature Communications
Joske UbelsJeroen de Ridder

Abstract

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient's treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.

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Citations

May 20, 2020·British Journal of Haematology·Ciara L Freeman, Joseph Mikhael
Nov 9, 2018·International Journal of Molecular Sciences·Chia-Hung Yen, Hui-Hua Hsiao
Sep 12, 2020·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Joske UbelsJeroen de Ridder
Apr 7, 2021·Briefings in Bioinformatics·Marzia Settino, Mario Cannataro

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Datasets Mentioned

BETA
GSE2658
GSE19784

Methods Mentioned

BETA
chromosomal aberration
RNAseq

Software Mentioned

CoMMpass
R package randomForest
patiEnts
Cufflinks
R package
RNAseq
Bioconductor package biomaRt
ComBat
hgu133plus2
GESTURE

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