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A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

by Valyi-Nagy, Istvan T. , Le Tourneau, Christophe , Bieche, Ivan , Tresca, Patricia , du Rusquec, Pauline , Filotas, Peter , Dupain, Celia , Deri, Julia , Bello Roufai, Diana , Tihanyi, Dora , Servois, Vincent , Dirner, Anna , Varkondi, Edit , Lakatos, Dora , Servant, Nicolas , Doczi, Robert , Gentien, David , Vodicska, Barbara , Schwab, Richard , Petak, Istvan , Mariani, Odette , Salomon, Anne , Hegedus, Csilla , Kamal, Maud

in 631/67/1059 / 631/67/70 / Artificial intelligence / Biomarkers / Cancer Research / Clinical trials / Gene Therapy / Genetics / Human Genetics / Internal Medicine / Medicine / Medicine & Public Health / Oncology / Precision medicine / Targeted cancer therapy

2021

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A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

2021

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2021

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Journal Article

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Valyi-Nagy, Istvan T.,

Le Tourneau, Christophe,

Bieche, Ivan,

Tresca, Patricia,

du Rusquec, Pauline,

Filotas, Peter,

Dupain, Celia,

Deri, Julia,

Bello Roufai, Diana,

Tihanyi, Dora,

Servois, Vincent,

Dirner, Anna,

Varkondi, Edit,

Lakatos, Dora,

Servant, Nicolas,

Doczi, Robert,

Gentien, David,

Vodicska, Barbara,

Schwab, Richard,

Petak, Istvan,

Mariani, Odette,

Salomon, Anne,

Hegedus, Csilla,

Kamal, Maud

2021

Overview

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

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Publisher

Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio

Subject

631/67/1059

/ 631/67/70

/ Artificial intelligence