Integrating complex genomic datasets and tumour cell sensitivity profiles to address a 'simple' question: which patients should get this drug?
It is becoming increasingly apparent that cancer drug therapies can only reach their full potential through appropriate patient selection. Matching drugs and cancer patients has proven to be a complex challenge, due in large part to the substantial molecular heterogeneity inherent to human cancers. This is not only a major hurdle to the improvement of the use of current treatments but also for the development of novel therapies and the ability to steer them to the relevant clinical indications. In this commentary we discuss recent studies from Kuo et al., published this month in BMC Medicine, in which they used a panel of cancer cell lines as a model for capturing patient heterogeneity at the genomic and proteomic level in order to identify potential biomarkers for predicting the clinical activity of a novel candidate chemotherapeutic across a patient population. The findings highlight the ability of a 'systems approach' to develop a better understanding of the properties of novel candidate therapeutics and to guide clinical testing and application.See the associated research paper by Kuo et al: http://www.biomedcentral.com/1741-7015/7/77.
Benes
Cyril
C
Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center and Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA. cbenes@bidmc.harvard.edu
Settleman
Jeff
J
D000970:Antineoplastic Agents / Q000627:therapeutic use; D045744:Cell Line, Tumor; D003658:Decision Making, Computer-Assisted; D023281:Genomics / Q000379:methods; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods
D000970:Antineoplastic Agents