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 c...
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Nature Portfolio
2021
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oai:doaj.org-article:03d1a19c53bf406b8f14275e04c89c952021-12-02T17:38:32ZDigital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival10.1038/s41698-021-00171-62397-768Xhttps://doaj.org/article/03d1a19c53bf406b8f14275e04c89c952021-04-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00171-6https://doaj.org/toc/2397-768XAbstract 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).Vladimir LazarShai MagidiNicolas GirardAlexia SavignoniJean-François MartiniGiorgio MassiminiCatherine BressonRaanan BergerAmir OnnJacques RaynaudFanny WunderIoana Berindan-NeagoeMarina SekachevaIrene BrañaJosep TaberneroEnriqueta FelipAngel PorgadorClaudia KleinmanGerald BatistBenjamin SolomonApostolia Maria TsimberidouJean-Charles SoriaEitan RubinRazelle KurzrockRichard L. SchilskyNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-12 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 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 Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| description |
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). |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Vladimir Lazar |
| title |
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| title_short |
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| title_full |
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| title_fullStr |
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| title_full_unstemmed |
Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| title_sort |
digital display precision predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/03d1a19c53bf406b8f14275e04c89c95 |
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