Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients

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http://hdl.handle.net/10138/301885

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He , L , Tang , J , Andersson , E I , Timonen , S , Koschmieder , S , Wennerberg , K , Mustjoki , S & Aittokallio , T 2018 , ' Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients ' , Cancer Research , vol. 78 , no. 9 , pp. 2407-2418 . https://doi.org/10.1158/0008-5472.CAN-17-3644

Title: Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients
Author: He, Liye; Tang, Jing; Andersson, Emma I.; Timonen, Sanna; Koschmieder, Steffen; Wennerberg, Krister; Mustjoki, Satu; Aittokallio, Tero
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Department of Diagnostics and Therapeutics
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Medicum
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2018-05-01
Language: eng
Number of pages: 12
Belongs to series: Cancer Research
ISSN: 0008-5472
URI: http://hdl.handle.net/10138/301885
Abstract: The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targetingmultiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo. The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL. Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. (C) 2018 AACR.
Subject: BREAST-CANCER CELLS
EX-VIVO PLATFORM
CLINICAL-RESPONSE
MEDICINE
NETWORK
INHIBITOR
REVEALS
SYNERGY
IDENTIFICATION
HETEROGENEITY
3122 Cancers
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