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 () and Jeroen de Ridder ()
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Joske Ubels: University Medical Center Utrecht
Pieter Sonneveld: Erasmus MC Cancer Institute
Erik H. van Beers: SkylineDx
Annemiek Broijl: Erasmus MC Cancer Institute
Martin H. van Vliet: SkylineDx
Jeroen de Ridder: University Medical Center Utrecht

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-05348-5

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