Targeted cancer therapies have produced substantial clinical results in the last decade, but most tumours eventually develop resistance to these drugs.
Benes, Engelman and colleagues have recently described in Science the development of a pharmacogenomic platform that facilitates rapid discovery of new drug combinations starting from human cancer samples.
This new approach could in the future help direct therapeutic choices for individual patients.
The study focused on non-small-cell lung cancers (NSCLCs) with activating mutations in epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK). This tumours are now routinely treated with specific tyrosine kinase inhibitors (TKIs), although the tumours eventually develops resistance within 1–2 years, through 2 main mechanisms:
‘gatekeeper’ mutations, which prevent target inhibition by the TKI;
‘bypass track’ mutations, which activate compensatory signalling pathways.
The authors generated cell lines directly from tumour biopsy samples using recent advances in cell culture methods. The cells were then subjected to a screen that combined the original TKI (against which cells had become resistant) with a panel of 76 drugs targeted at key regulators of cell proliferation and survival (Fig 1).
Fig 1. Schematic of the screen workflow
The pharmacological screen identified multiple effective drug combinations and a number of previously undescribed mechanisms of resistance. For example, a cell line derived from an ALK-mutated cancer was resensitised to ALK inhibitors when these were combined with a MET tyrosine kinase inhibitor. Furthermore, the screen identified resistance mechanisms that would have been difficult to discover by genetic analysis alone — for example, it was found that ALK-mutated NSCLCs often exhibit upregulated SRC tyrosine kinases signalling, without any evidence of mutations in SRC.
Several combination therapies identified in vitro were subsequently tested in xenograft models using the same cells and shown to be effective, indicating that the screen may indeed be predictive for in vivo activity.
Both the success rate of cell-line generation from biopsy specimens (50% in this study) and the time scale for establishing cell lines (2–6 months) will need to be improved for this approach to become clinically useful. However, once optimized, it may be used not only for NSCLC but also for other types of cancer, allowing truly personalized cancer therapy.
Original paper: Crystal, A. S. et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science, 2014, 1480-1486.