Literature DB >> 24056683

Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia.

Tea Pemovska1, Mika Kontro, Bhagwan Yadav, Henrik Edgren, Samuli Eldfors, Agnieszka Szwajda, Henrikki Almusa, Maxim M Bespalov, Pekka Ellonen, Erkki Elonen, Bjørn T Gjertsen, Riikka Karjalainen, Evgeny Kulesskiy, Sonja Lagström, Anna Lehto, Maija Lepistö, Tuija Lundán, Muntasir Mamun Majumder, Jesus M Lopez Marti, Pirkko Mattila, Astrid Murumägi, Satu Mustjoki, Aino Palva, Alun Parsons, Tero Pirttinen, Maria E Rämet, Minna Suvela, Laura Turunen, Imre Västrik, Maija Wolf, Jonathan Knowles, Tero Aittokallio, Caroline A Heckman, Kimmo Porkka, Olli Kallioniemi, Krister Wennerberg.   

Abstract

UNLABELLED: We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. ©2013 AACR.

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Year:  2013        PMID: 24056683     DOI: 10.1158/2159-8290.CD-13-0350

Source DB:  PubMed          Journal:  Cancer Discov        ISSN: 2159-8274            Impact factor:   39.397


  148 in total

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2.  Ovarian Cancer Treatment Stratification Using Ex Vivo Drug Sensitivity Testing.

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3.  Dasatinib and navitoclax act synergistically to target NUP98-NSD1+/FLT3-ITD+ acute myeloid leukemia.

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Journal:  Leukemia       Date:  2018-12-19       Impact factor: 11.528

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Review 5.  A survey of current trends in computational drug repositioning.

Authors:  Jiao Li; Si Zheng; Bin Chen; Atul J Butte; S Joshua Swamidass; Zhiyong Lu
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6.  Overcoming resistance to targeted therapies in chronic lymphocytic leukemia.

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7.  Cancer: A precision approach to tumour treatment.

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8.  Personalized therapy for acute myeloid leukemia.

Authors:  Christopher S Hourigan; Judith E Karp
Journal:  Cancer Discov       Date:  2013-12       Impact factor: 39.397

9.  A tumor deconstruction platform identifies definitive end points in the evaluation of drug responses.

Authors:  R R Naik; A K Singh; A M Mali; M F Khirade; S A Bapat
Journal:  Oncogene       Date:  2015-04-27       Impact factor: 9.867

Review 10.  Personalized Cancer Models for Target Discovery and Precision Medicine.

Authors:  Carla Grandori; Christopher J Kemp
Journal:  Trends Cancer       Date:  2018-08-08
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