A1 Refereed original research article in a scientific journal
Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia
Authors: Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, Almusa H, Bespalov MM, Ellonen P, Elonen E, Gjertsen BT, Karjalainen R, Kulesskiy E, Lagstrom S, Lehto A, Lepisto M, Lundan T, Majumder MM, Marti JML, Mattila P, Murumagi A, Mustjoki S, Palva A, Parsons A, Pirttinen T, Ramet ME, Suvela M, Turunen L, Vastrik I, Wolf M, Knowles J, Aittokallio T, Heckman CA, Porkka K, Kallioniemi O, Wennerberg K
Publisher: AMER ASSOC CANCER RESEARCH
Publishing place: PHILADELPHIA; 615 CHESTNUT ST, 17TH FLOOR, PHILADELPHIA, PA 19106-4404 USA
Publication year: 2013
Journal: Cancer Discovery
Journal name in source: Cancer Discovery
Journal acronym: Cancer Discov.
Volume: 3
Issue: 12
First page : 1416
Last page: 1429
Number of pages: 14
ISSN: 2159-8274
DOI: https://doi.org/10.1158/2159-8290.CD-13-0350
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 sub-types 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. (C) 2013 AACR.
