Treatment of BRAF‐mutant melanomas with MAP kinase pathway inhibitors is paradigmatic of the promise of precision cancer therapy but also highlights problems with drug resistance that limit patient benefit. We use live‐cell imaging, single‐cell analysis, and molecular profiling to show that exposure of tumor cells to RAF/MEKinhibitors elicits a heterogeneous response in which some cells die, some arrest, and the remainder adapt to drug. Drug‐adapted cells up‐regulate markers of the neural crest (e.g., NGFR), a melanocyte precursor, and grow slowly. This phenotype is transiently stable, reverting to the drug‐naïve state within 9 days of drug withdrawal. Transcriptional profiling of cell lines and human tumors implicates a c‐Jun/ECM/FAK/Src cascade in de‐differentiation in about one‐third of cell lines studied; drug‐induced changes in c‐Jun and NGFR levels are also observed in xenograft and human tumors. Drugs targeting the c‐Jun/ECM/FAK/Src cascade as well as BETbromodomain inhibitors increase the maximum effect (Emax) of RAF/MEK kinase inhibitors by promoting cell killing. Thus, analysis of reversible drug resistance at a single‐cell level identifies signaling pathways and inhibitory drugs missed by assays that focus on cell populations.
Greg is a Postdoctoral Research Fellow in the Laboratory of Systems Pharmacology (LSP), part of the Harvard Program in Therapeutic Science (HiTS) at Harvard Medical School. His research focuses on the elucidation of multi-scale, system-wide biomolecular pathways mediating tumor-induced immunosuppression in glioblastoma (GBM) – a devastating cancer of the central nervous system for which there is no cure.
Since joining the LSP in the Fall of 2015, Greg has employed mouse models in the development of an extensible in vivo screening platform designed to holistically determine how GBM affects host cellular immune organization. Using a 12-color optimized multicolor immunofluorescence panel (OMIP), Greg is able to flow cytometrically identify all major mouse leukocyte lineages within multiple lymphoid tissues, allowing him to contextualize and temporally resolve known aspects of GBM immuno-oncology while simultaneously permitting identification of novel network-level immunosuppression mechanisms. His flow-based immunoprofiling strategy has already provided novel insight into how these tumors influence global leukocyte composition; however, an expected transition to cytometry by time-of-flight (CyTOF) as an alternative single-cell analysis technique will dramatically enhance immunoprofiling resolution, allowing Greg to concomitantly measure over 50 cell surface markers and intracellular proteins of interest.
Greg is now applying his screening platform to better understand how immunotherapy targeting the PD-1/PD-L1 immune checkpoint axis acts to alleviate tumor-induced immunosuppression in GBM and other solid cancers. Here the platform is being exploited to achieve three goals: (1) identify immune signatures that correlate with response or resistance to therapy, (2) infer mechanisms of drug action, and (3) identify immune cell subsets of novel prognostic value.
Greg next aims to collect both cytokine and mRNA transcript data across multiple immune cell subsets over the course of tumor progression. This information – together with previously collected data on system-wide leukocyte composition – will be used to assemble data-driven, network-level influence diagrams. These diagrams will help guide downstream hypotheses and permit cellular dynamics inferencing. Also on the horizon is the incorporation of genetically-induced tumor models; the study of these systems will allow Greg to interrogate the immunosuppressive influence of clinically-relevant GBM oncogenes.