TY - JOUR
T1 - Digital Display Precision Predictor
T2 - the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival
AU - Lazar, Vladimir
AU - Magidi, Shai
AU - Girard, Nicolas
AU - Savignoni, Alexia
AU - Martini, Jean François
AU - Massimini, Giorgio
AU - Bresson, Catherine
AU - Berger, Raanan
AU - Onn, Amir
AU - Raynaud, Jacques
AU - Wunder, Fanny
AU - Berindan-Neagoe, Ioana
AU - Sekacheva, Marina
AU - Braña, Irene
AU - Tabernero, Josep
AU - Felip, Enriqueta
AU - Porgador, Angel
AU - Kleinman, Claudia
AU - Batist, Gerald
AU - Solomon, Benjamin
AU - Tsimberidou, Apostolia Maria
AU - Soria, Jean Charles
AU - Rubin, Eitan
AU - Kurzrock, Razelle
AU - Schilsky, Richard L.
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/4/28
Y1 - 2021/4/28
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85116954646&partnerID=8YFLogxK
U2 - 10.1038/s41698-021-00171-6
DO - 10.1038/s41698-021-00171-6
M3 - Article
C2 - 33911192
SN - 2397-768X
VL - 5
JO - npj Precision Oncology
JF - npj Precision Oncology
M1 - 33
ER -