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

Bibliographic Details
Title: Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival
Authors: Vladimir Lazar, Shai Magidi, Nicolas Girard, Alexia Savignoni, Jean-François Martini, Giorgio Massimini, Catherine Bresson, Raanan Berger, Amir Onn, Jacques Raynaud, Fanny Wunder, Ioana Berindan-Neagoe, Marina Sekacheva, Irene Braña, Josep Tabernero, Enriqueta Felip, Angel Porgador, Claudia Kleinman, Gerald Batist, Benjamin Solomon, Apostolia Maria Tsimberidou, Jean-Charles Soria, Eitan Rubin, Razelle Kurzrock, Richard L. Schilsky
Source: npj Precision Oncology, Vol 5, Iss 1, Pp 1-12 (2021)
Publisher Information: Nature Portfolio, 2021.
Publication Year: 2021
Collection: LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Subject Terms: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
More Details: 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).
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2397-768X
Relation: https://doaj.org/toc/2397-768X
DOI: 10.1038/s41698-021-00171-6
Access URL: https://doaj.org/article/03d1a19c53bf406b8f14275e04c89c95
Accession Number: edsdoj.03d1a19c53bf406b8f14275e04c89c95
Database: Directory of Open Access Journals
More Details
ISSN:2397768X
DOI:10.1038/s41698-021-00171-6
Published in:npj Precision Oncology
Language:English