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Unique vulnerability of RAC1-mutant melanoma to combined inhibition of CDK9 and immune checkpoints

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Abstract

RAC1P29S is the third most prevalent hotspot mutation in sun-exposed melanoma. RAC1 alterations in cancer are correlated with poor prognosis, resistance to standard chemotherapy, and insensitivity to targeted inhibitors. Although RAC1P29S mutations in melanoma and RAC1 alterations in several other cancers are increasingly evident, the RAC1-driven biological mechanisms contributing to tumorigenesis remain unclear. Lack of rigorous signaling analysis has prevented identification of alternative therapeutic targets for RAC1P29S-harboring melanomas. To investigate the RAC1P29S-driven effect on downstream molecular signaling pathways, we generated an inducible RAC1P29S expression melanocytic cell line and performed RNA-sequencing (RNA-seq) coupled with multiplexed kinase inhibitor beads and mass spectrometry (MIBs/MS) to establish enriched pathways from the genomic to proteomic level. Our proteogenomic analysis identified CDK9 as a potential new and specific target in RAC1P29S-mutant melanoma cells. In vitro, CDK9 inhibition impeded the proliferation of in RAC1P29S-mutant melanoma cells and increased surface expression of PD-L1 and MHC Class I proteins. In vivo, combining CDK9 inhibition with anti-PD-1 immune checkpoint blockade significantly inhibited tumor growth only in melanomas that expressed the RAC1P29S mutation. Collectively, these results establish CDK9 as a novel target in RAC1-driven melanoma that can further sensitize the tumor to anti-PD-1 immunotherapy.

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Fig. 1: Characterization of RAC1P29S oncogenic properties.
Fig. 2: Proteogenomic analysis of RAC1P29S melanocyte expression reveals upregulation of cell cycle processes.
Fig. 3: RAC1P29S expression confers increased sensitivity to CDK9 inhibitors.
Fig. 4: RAC1P29S expression confers increased sensitivity to CDK9 inhibition and genetic knockout in melanoma cells.
Fig. 5: RAC1P29S expression confers increased activity and sensitivity to CDK9 inhibitors.
Fig. 6: CDK9 inhibition increase surface expression of PD-L1 and Class I MHC in RAC1 mutant melanocytes.
Fig. 7: Combination therapy of CDK9 inhibition and anti-PD-1 immunotherapy significantly decreases tumor growth in vivo.
Fig. 8: RAC1P29S expression increases tumor immune cell infiltration.

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Data availability

All raw sequencing data has been deposited in the Gene Expression Omnibus database under accession #GSE248218. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [49] partner repository with the dataset identifier PXD046960. All other raw data generated from this study are available from the corresponding author upon request.

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Acknowledgements

We would like to thank Ruth Halaban and Gaudenz Danuser for cell lines. We would also like to thank Fox Chase Cancer Center core facilities including flow cytometry, biological imaging, histopathology, laboratory animal and biostatistics facilities. This work was supported by grants from the NIH R01 CA2271844 (JC), the Melanoma Research Foundation (JC), the F.M. Kirby Foundation (CUA), NIH R01 CA211670 (JSD), NIH T32 CA009035 (AMK), and NCI Core Grant P30 CA06927 (to Fox Chase Cancer Center).

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Author notes

  1. Suraj Peri

    Present address: Merck, Bioinformatics Oncology Discovery, Boston, MA, USA

Authors and Affiliations

  1. Cancer Signaling and Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA, USA

    Alexa C. Cannon, Konstantin Budagyan, Cristina Uribe-Alvarez, Alison M. Kurimchak, Daniela Araiza-Olivera, James S. Duncan & Jonathan Chernoff

  2. Drexel University College of Medicine, Philadelphia, PA, USA

    Alexa C. Cannon & Konstantin Budagyan

  3. Histopathology Facility, Fox Chase Cancer Center, Philadelphia, PA, USA

    Kathy Q. Cai

  4. Biostatistics-Bioinformatics, Fox Chase Cancer Center, Philadelphia, PA, USA

    Suraj Peri & Yan Zhou

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Contributions

AC: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, supervision, visualization, writing – original draft, writing – review and editing. KB: Conceptualization, formal analysis, investigation, writing – review and editing. CU-A: Funding acquisition, methodology, writing – review and editing. AK: Formal analysis, investigation, software. DA-O: Methodology QC: Formal analysis, investigation, methodology, software. SP: Data Curation, formal analysis, software. YZ: Data Curation, formal analysis, software JD: Methodology, resources, supervision, formal analysis. JC: Conceptualization, funding acquisition, methodology, project administration, resources, supervision, visualization, writing – original draft, writing – review and editing.

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Correspondence to Jonathan Chernoff.

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Cannon, A.C., Budagyan, K., Uribe-Alvarez, C. et al. Unique vulnerability of RAC1-mutant melanoma to combined inhibition of CDK9 and immune checkpoints. Oncogene 43, 729–743 (2024). https://doi.org/10.1038/s41388-024-02947-z

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