Clinical trial designs using unselected populations often fail to identify significant responses that may be relevant only to a subset of tumors. By using prior information, clinical trials can be rationally designed to test therapeutic regimens in tumor types identified as most likely to be responsive. However, prior information is not always available. Therefore, we developed MATCH as an unbiased genomic approach to predicting drug sensitivity in individual tumors.
MATCH leverages the large amount of publicly available genomic data to investigate optimal targeted therapy for individual cancers. The example of VPA in breast cancer shows the superiority of individualized genomic predictions over simply using traditional cancer subtypes. Current methods to classify breast cancer phenotypes utilize tumor pathology and signaling pathway status to place the tumor into a molecular subtype (Perou et al, 2000
; Sorlie et al, 2001
; Hu et al, 2006
; Desmedt et al, 2008
; Wirapati et al, 2008
; Parker et al, 2009
). However, the existing molecular subtypes are not ideal as the sole categorization for defining breast cancer therapy, as heterogeneity exists within each subtype (Di Cosimo and Baselga, 2010
; Foulkes et al, 2010
). In fact, we are learning that there is increased complexity among subtypes in clinical course and response to therapy. For example, HER2/ERBB2-overexpressing tumors differ in their natural history and/or response to therapy based on lymphocyte-associated genes, PTEN status, and PI3K pathway status (Alexe et al, 2007
; Rody et al, 2009
; Esteva et al, 2010
). Thus, significant heterogeneity remains uncharacterized even within breast cancer subtypes, highlighting the need for approaches investigating drug sensitivity or clinical phenotypes at the individual patient level.
Although VPA had enriched sensitivity in the more aggressive breast cancer subtypes, our studies show that there is great variation in sensitivity of patient tumors to treatment with VPA alone but that there is a definable population of breast tumors that are markedly sensitive to VPA. VPA is known to be a HDAC inhibitor, a class without any well-defined biomarkers of response. VPA is of considerable interest because it is oral, already FDA-approved for treatment of epilepsy, and well tolerated over the long term. The low toxicity of VPA makes this drug an ideal candidate to further assess its potential in the treatment of selected tumors within aggressive breast cancer subtypes, such as triple negative tumors. VPA is currently being studied in clinical trials against breast, lung, ovarian, thyroid, melanoma, prostate, and cervical cancers (Michaelis et al, 2007
). The fact that this drug is being tested against so many different cancers without a clear indication of potential efficacy highlights the need for a more targeted approach to finding the populations of patients who will respond. For example, trials of another HDAC inhibitor, SAHA, in lung cancer have shown stable disease but no responses, perhaps due to the small number of subjects receiving therapeutic doses (Vansteenkiste et al, 2008
; Traynor et al, 2009
). The ability to increase trial efficiency by excluding people who will not respond could improve such trials. By using MATCH to couple personalized computational prediction with in vitro
and in vivo
validation work, as presented in this study, a systematic methodology for streamlining identification of optimal therapeutic regimens can be leveraged in the clinical setting. We use the genomic predictions to guide our pharmacological studies, followed by experimental validation in actual patient tumor cells to determine clinical significance before initiating a clinical trial. We then rapidly verify the accuracy of these predictions using 2D in vitro
cell lines and 3D cell cultures generated from fresh tumors. In the VPA example, we found significant correlation between predicted and actual sensitivity in all experimental conditions.
MATCH has some limitations. First, effective doses of drugs in vitro may not be the same as in vivo due to factors such as increased protein binding in culture media or organ metabolism. Thus, MATCH gives the relative sensitivity of individual tumors but cannot determine absolute doses for use. Second, MATCH cannot predict all off target effects and side effects of drugs. Third, not all tumors predicted to be sensitive by MATCH will be sufficiently sensitive to clinical doses of drugs. MATCH is useful for identifying patient populations most likely to respond (or not) to a drug. Identifying relevant populations for a drug boosts the proportion of possible responders in a trial population, decreasing the chance of falsely negative trials.
MATCH leverages high-throughput pharmacogenomic and individual-based tumor drug response analyses, and serves to identify the appropriate cancer patients for specific drug therapies. This rational approach to drug selection has the potential to streamline preclinical testing, optimize clinical trial design, and identify clinically useful biomarkers early in drug development.