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Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival.

ABSTRACT: The expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the literature by their association with MTOR and angiogenesis pathways, respectively, and their expression in tumor versus normal tissues was associated with the progression-free survival (PFS) of patients treated with everolimus or axitinib (respectively) using DDPP. A specific eight-gene set best correlated with PFS in six patients treated with everolimus: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB (r = 0.99, p = 5.67E-05). A two-gene set best correlated with PFS in five patients treated with axitinib: KIT and KITLG (r = 0.99, p = 4.68E-04). Leave-one-out experiments demonstrated significant concordance between observed and DDPP-predicted PFS (r = 0.9, p = 0.015) for patients treated with everolimus. Notwithstanding the small cohort and pending further prospective validation, the prototype of DDPP offers the potential to transform patients' treatment selection with a tumor- and treatment-agnostic predictor of outcomes (duration of PFS).

SUBMITTER: Lazar V

PROVIDER: S-EPMC8080819 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival.

Lazar Vladimir V Magidi Shai S Girard Nicolas N Savignoni Alexia A Martini Jean-François JF Massimini Giorgio G Bresson Catherine C Berger Raanan R Onn Amir A Raynaud Jacques J Wunder Fanny F Berindan-Neagoe Ioana I Sekacheva Marina M Braña Irene I Tabernero Josep J Felip Enriqueta E Porgador Angel A Kleinman Claudia C Batist Gerald G Solomon Benjamin B Tsimberidou Apostolia Maria AM Soria Jean-Charles JC Rubin Eitan E Kurzrock Razelle R Schilsky Richard L RL

NPJ precision oncology 20210428 1

The expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the ...[more]

PMID: 33911192

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Dataset Information

Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival.

Publications

Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival.

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