Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival
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 o...
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Published in | NPJ precision oncology Vol. 5; no. 1; p. 33 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
28.04.2021
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | 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). |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2397-768X 2397-768X |
DOI: | 10.1038/s41698-021-00171-6 |