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

Joske Ubels, Pieter Sonneveld, Erik H. van Beers, Annemiek Broijl, Martin H. van Vliet, Jeroen de Ridder

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Article number2943
JournalNature Communications
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Dec 2018
Externally publishedYes

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