Download This Paper
Preprints with The Lancet is a collaboration between The Lancet Group of journals and SSRN to facilitate the open sharing of preprints for early engagement, community comment, and collaboration. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early-stage research papers that have not been peer-reviewed. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. The findings should not be used for clinical or public health decision-making or presented without highlighting these facts. For more information, please see the FAQs.
Leveraging Transcriptional Dynamics to Improve BRAF Inhibitor Responses in Melanoma
45 Pages Posted: 27 Jun 2019
More...Abstract
Melanoma is known to be a heterogeneous tumor, but the impact of this heterogeneity upon therapeutic response is not well understood. In the current study, we used single cell mRNA analysis and our Single Cell HETerogeneity (SinCHET) platform to define the transcriptional diversity of melanoma and its dynamic response to both BRAF inhibitor therapy and treatment holidays. Our analyses showed melanoma cell lines and patient specimens to be composed of >3 co-existent, transcriptionally distinct states. Among these, State #1 had increased expression of cyclin D1, ERBB3, STAT3/5, MITF and β-catenin and lower expression of c-JUN and RTKs such as Axl and EGFR. State #2 was characterized by higher expression of ERBB3, Axl and the transcription factor c-JUN. State #3 was characterized by high Axl, c-JUN, E2F1, WEE1, c-MET and EGFR expression and lower expression of MITF, ERBB3 and SMAD9. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy, with State #1 declining and States #2 and #3 increasing in response to drug. The percentage of cells in State #1 recovered following a drug holiday, allowing for a successful therapy rechallenge. Other melanomas that lacked State #1 had varying degrees of intrinsic drug resistance and were not amenable to rechallenge. We next leveraged the differences in fitness between the different transcriptional states in the absence and presence of drug to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation of the mathematical model demonstrated that the personalized adaptive dosing schedules were better at suppressing tumor growth than either continuous or fixed intermittent BRAF inhibitor schedules. Together our studies provide the first preclinical evidence that transcriptional heterogeneity at the single cell level predicts for the initial sensitivity to BRAF inhibitor therapy. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules that can delay the time to resistance.
Funding Statement: Supported by SPORE grant P50 CA168536, R21 CA198550, R21 CA216756 (to KSMS) K99 CA226679 (to IS), Moffitt Cancer Center PSOC, U54 CA193489 (EK & AA) and the Cancer Center Support Grant P30 CA076292 from the NIH (NCI/NIH). We also acknowledge the support Miami Center for AIDS Research (CFAR) at the University of Miami Miller School of Medicine funded by P30AI073961.
Declaration of Interests: We have no competing interest to declare.
Ethics Approval Statement: All animal experiments were carried out in compliance with ethical regulations and protocols approved by the University of South Florida Institutional Animal Care and Use Committee.
Keywords: melanoma, MITF, resistance, heterogeneity, mathematical modeling
Suggested Citation: Suggested Citation