Literature DB >> 29483097

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

Liye He1, Jing Tang1,2, Emma I Andersson3, Sanna Timonen1, Steffen Koschmieder4, Krister Wennerberg1, Satu Mustjoki3, Tero Aittokallio5,2.   

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 targeting multiple 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. Cancer Res; 78(9); 2407-18. ©2018 AACR. ©2018 American Association for Cancer Research.

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Year:  2018        PMID: 29483097     DOI: 10.1158/0008-5472.CAN-17-3644

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  26 in total

1.  Prediction of drug combination effects with a minimal set of experiments.

Authors:  Aleksandr Ianevski; Anil K Giri; Prson Gautam; Alexander Kononov; Swapnil Potdar; Jani Saarela; Krister Wennerberg; Tero Aittokallio
Journal:  Nat Mach Intell       Date:  2019-12-09

2.  Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells.

Authors:  Paschalis Athanasiadis; Aleksandr Ianevski; Sigrid S Skånland; Tero Aittokallio
Journal:  Methods Mol Biol       Date:  2022

3.  Combination of ERK2 and STAT3 Inhibitors Promotes Anticancer Effects on Acute Lymphoblastic Leukemia Cells.

Authors:  Ewa Jasek-Gajda; Halina Jurkowska; MaŁgorzata JasiŃska; Jan A Litwin; Grzegorz J Lis
Journal:  Cancer Genomics Proteomics       Date:  2020 Sep-Oct       Impact factor: 4.069

4.  Machine learning methods, databases and tools for drug combination prediction.

Authors:  Lianlian Wu; Yuqi Wen; Dongjin Leng; Qinglong Zhang; Chong Dai; Zhongming Wang; Ziqi Liu; Bowei Yan; Yixin Zhang; Jing Wang; Song He; Xiaochen Bo
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

5.  Development of HDAC Inhibitors Exhibiting Therapeutic Potential in T-Cell Prolymphocytic Leukemia.

Authors:  Krimo Toutah; Nabanita Nawar; Sanna Timonen; Helena Sorger; Yasir S Raouf; Shazreh Bukhari; Jana von Jan; Aleksandr Ianevski; Justyna M Gawel; Olasunkanmi O Olaoye; Mulu Geletu; Ayah Abdeldayem; Johan Israelian; Tudor B Radu; Abootaleb Sedighi; Muzaffar N Bhatti; Muhammad Murtaza Hassan; Pimyupa Manaswiyoungkul; Andrew E Shouksmith; Heidi A Neubauer; Elvin D de Araujo; Tero Aittokallio; Oliver H Krämer; Richard Moriggl; Satu Mustjoki; Marco Herling; Patrick T Gunning
Journal:  J Med Chem       Date:  2021-06-08       Impact factor: 7.446

6.  Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles.

Authors:  ZiaurRehman Tanoli; Zaid Alam; Markus Vähä-Koskela; Balaguru Ravikumar; Alina Malyutina; Alok Jaiswal; Jing Tang; Krister Wennerberg; Tero Aittokallio
Journal:  Database (Oxford)       Date:  2018-01-01       Impact factor: 3.451

7.  SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

Authors:  Aleksandr Ianevski; Anil K Giri; Tero Aittokallio
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

8.  Hijacking the Pathway: Perspectives in the Treatment of Mature T-cell Leukemias.

Authors:  Linus Wahnschaffe; Marco Herling
Journal:  Hemasphere       Date:  2021-06-01

9.  Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data.

Authors:  Maxim Sorokin; Roman Kholodenko; Maria Suntsova; Galina Malakhova; Andrew Garazha; Irina Kholodenko; Elena Poddubskaya; Dmitriy Lantsov; Ivan Stilidi; Petr Arhiri; Andreyan Osipov; Anton Buzdin
Journal:  Cancers (Basel)       Date:  2018-09-29       Impact factor: 6.639

Review 10.  Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance.

Authors:  Peter T Harrison; Paul H Huang
Journal:  Essays Biochem       Date:  2018-10-26       Impact factor: 8.000

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