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Drug-Induced Death Signaling Strategy Rapidly Predicts Cancer Response to Chemotherapy
Author(s) -
Joan Montero,
Kristopher A. Sarosiek,
Joseph D. DeAngelo,
Ophélia Maertens,
Jeremy Ryan,
Dalia Ercan,
Huiying Piao,
Neil S. Horowitz,
Ross S. Berkowitz,
Ursula A. Matulonis,
Pasi A. Jänne,
Philip C. Amrein,
Karen Cichowski,
Ronny Drapkin,
Anthony Letai
Publication year - 2015
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2015.01.042
Subject(s) - biology , ex vivo , drug , profiling (computer programming) , in vivo , phenotype , chemotherapy , efficacy , systems pharmacology , biomarker , cytotoxic t cell , cancer , cancer cell , computational biology , bioinformatics , cancer research , pharmacology , in vitro , gene , genetics , computer science , operating system
There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo.

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