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

doi:10.21203/rs.3.rs-3160616/v1

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

https://doi.org/10.21203/rs.3.rs-3160616/v1

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published 19 Jan, 2024

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RAC1 ^P29S 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 RAC1^P29S 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 RAC1^P29S-harboring melanomas. To investigate the RAC1^P29S-driven effect on downstream molecular signaling pathways, we generated an inducible RAC1^P29S 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 RAC1^P29S-mutant melanoma cells. In vitro, CDK9 inhibition impeded the proliferation of in RAC1^P29S-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 RAC1^P29S 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.

Biological sciences/Cancer/Oncogenes

Biological sciences/Cell biology/Cell division/Mitosis

RAC1 is a member of the RAS superfamily of small GTPases. Like other small GTPases, RAC1 functions as a molecular switch, cycling between a GTP-bound “on” state and a GDP-bound “off” state. Whole exome sequencing studies of large patient cohorts have identified 5–9% of cutaneous melanomas to harbor a RAC1^P29S mutation, establishing it as the third most common hotspot mutation behind BRAF^V600E and NRAS^Q61K/R/L (1, 2). The RAC1^P29S mutation is a UV-induced activating mutation, resulting in preferential GTP-bound active form and sustained activation of downstream signaling (2, 3). The RAC1 mutation is most often found in combination with activating mutations in BRAF or NRAS, or inactivating mutations in NF1 (4, 5). In the case of BRAF^V600E, the most common genetic driver, current first-line clinical treatments consist of combination therapy of a BRAF inhibitor and MEK inhibitor, with or without immunotherapy (6, 7). Although BRAF-mutant melanomas initially respond to this combination therapy and have greatly improved clinical outcomes, adaptive resistance arises after several months of administration (6). However, when the RAC1^P29S mutation is present, melanomas display primary resistance to BRAF and MEK inhibitors (8). The RAC1-driven mechanism of resistance remains unclear.

Compared to other cancer subtypes, melanoma has one of the highest response rates to immune checkpoint inhibition (ICI). However, predicting which patients will respond remains inaccurate, resulting in a high percentage of patient non-responders. To improve the clinical success of immunotherapy, many efforts combining ICI with adjuvant agents are currently underway. To date, there are limited studies that examine the responsiveness of tumors harboring the RAC1^P29S mutation to immune therapeutic agents. Previous studies have indicated that RAC1^P29S expression leads to increased PD-L1 expression (9). In addition, there are clinical data that suggest that immunotherapy is beneficial in metastatic RAC1-mutant melanoma (10).

This work explores the functional consequences of RAC1^P29S expression in melanocytes, melanoma cells, and melanoma syngeneic grafts. To characterize the RAC1^P29S mutation, we performed proteogenomic analysis that identified G2/M cell cycle progression as an upregulated process. Validation of our enriched pathways led to identification of CDK9 as a novel target in RAC1^P29S-mutant melanoma. Furthermore, targeting CDK9 with the clinical agent dinaciclib augmented the anti-tumor immune response generated by anti-PD-1 immune checkpoint inhibition in vivo. These results offer a new potential treatment strategy in RAC1-mutant melanoma.

Cell Culture

Melanocytes obtained from C57BL/6 mice were gifted by Ruth Halaban (Yale University). Melanocytes were cultured in OptiMEM supplemented with 7% horse serum, 1% FBS, 1% antibiotic, and 0.01% TPA. YUMM1.7 cells (RRID:CVCL_JK16) were purchased from ATCC (Manassas, VA, USA) and cultured in DMEM/F12 medium supplemented with 10% FBS, 1% sodium pyruvate, 1% non-essential amino acids, 1% L-glutamine, and 1% antibiotic. Cells were tested for mycoplasma every 6 weeks with MycoDect Mycoplasma Detection Kit (Alstem, Richmond, CA, USA).

501 mel (RRID:CVCL_4633), YUROB (RRID:CVCL_B486), and YURIF (RRID:CVCL_B485) human melanoma lines were cultured in OptiMEM with 10% FBS and 1% antibiotic. IGR1 cells (RRID:CVCL_1303) were gifted from Gaudenz Danuser (University of Texas Southwestern Medical Center).

All experiments were done on cells under 15 passages. All cells were cultured at 37 degrees C, 5% CO₂.

Cloning

pBMN-FKBP-YFP destabilization domain plasmid was purchased from Addgene (Watertown, MA, USA) RRID:Addgene_31763. Human RAC1^P29S cDNA was cloned into the vector using EcoRI and XhoI restriction enzymes (New England Biolabs, Ipswich, MA, USA) and ligated using T4 ligase (New England Biolabs). Transformations were performed in Stellar competent cells (Takara, San Jose, CA, USA). 2 µL of reaction was added to 50 µL Stellar Competent cells on ice for 30 mins followed by 45 second heat shock at 42 degrees C and resuspended in 500 µL SOC medium (Takara) and placed 1 hour shaking 200 rpm at 30 degrees C. 200 µL bacteria was then plated onto LB/AMP plate and incubated overnight at 30 degrees C. Colonies were mini-prepped with NucleoSpin Plasmid mini prep kit (Machery-Nagel, Duren, Nordrhein-Westfalen, Germany). Sequences were verified by Genewiz (Azenta, Chemsford, MA, USA) plasmid sequencing services with primer sequence 5’ – TTC TGG AGC GGC GAG CAT GCA TGG AAC AGA AAC TCA TCT CTG AAG AGG − 3’. The destabilization domain was removed from the plasmid via restriction enzymes BamHI and EcoRI (New England Biolabs) for constitutive expression in the YUMM1.7 cell lines.

Cell Transductions

pBMN-FKBP-RAC1^P29S was transduced into cells through generation of a retrovirus using ϕNX cells (RRID:CVCL_H716) (purchased from Fox Chase Cancer Center Tissue Culture Facility) with Lipofectamine 2000 (Invitrogen, ThermoFisher Scientific, Waltham, MA, USA). ϕNX were seeded 0.5 X 10⁶ cells on a 6 well cell culture plate in optiMEM (Gibco, ThermoFisher Scientific). In one Eppendorf tube, 200 µL optiMEM was mixed with 6 µL Lipofectamine 2000 Reagent. In another Eppendorf tube, 700 µL was mixed with 1 ng DNA. After 15 minutes, these tubes were mixed and incubated an additional 10 minutes. 800 µL Lipofectamine-DNA mix was added to a well with 1 mL optiMEM. Virus was collected at 24 hours and filter through a 0.45 µM filter. 4 µg/mL polybrene (Sigma-Aldrich, ST. Louis, MO, USA) was added to the filtered virus, and subsequently added to target cells. Selection of positively transduced cells was enriched by sorting for HcRed positive cells on an Aria II flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA).

RAC1 Mutant Induction by Shield-1

Protein expression was induced by treatment of 1 µM Shield-1 compound (AOBIOUS, Gloucester, MA, USA) into cell culture medium for 24 hours before experiments were performed.

Cell Proliferation

Melanocytes were seeded 100,000 cells on a 6 well plate. Cells were trypsinized with Tryp-LE express (Gibco). Cells were centrifuged and resuspended in optiMEM medium and counted on a TC 20 automated cell counter (Bio-Rad, Hercules, CA, USA). 3 wells were counted for each timepoint. Cells were counted every 24 hours post-seeding until 96 hours.

Scratch Motility Assay

Cells were seeded on a density 2.5 X 10⁴ cells for well on a 12 well plate. RAC1^P29S was induced and cells were serum starved for 16 hours before a 20 µL pipette tip was used to scratch a vertical line across the monolayer of cells. Pictures were taken at 0 hrs and 24 hours post scratch on an EVOS fluorescent microscope on the brightfield setting. 4 wells were performed per condition and 3 pictures of each well were taken to analyze. Area was determined by ImageJ and percent cell migration was calculated using the formula: %Cell Migration = ((area_0HR - area_24HR)/ area_0HR)*100. Results were graphed in GraphPad Prism9 and statistics were calculated by a student’s t-test.

Transwell Migration Assay

8 µM Polycarbonate membrane transwell inserts were purchased from costar and inserted into 24 well plates. Cells were seeded in serum free media in the inserts, and full serum media was placed in the well. After 24 hours, media was aspirated from the assay plate and transwell inserts. The inside of each insert was gently swabbed with a cotton round to remove non-migrated cells. Apical inserts and receiver wells were then washed in PBS and stained with crystal violet solution (6% (vol/vol) glutaraldehyde and 0.5% (wt/vol) crystal violet in H₂0) for 10 minutes. Plates were then rinsed with tap water and visualized on a microscope. Number of stained cells was counted in 5 different views of each well (4 wells per condition). To calculate number of migrated cells the average number of cells within each field of view was divided by the microscope viewing field and then multiplied by the area of the transwell insert (0.33 cm³). This value was then divided by the number of cells seeded and multiplied by 100.

Phalloidin Staining

Cells were seeded on 18 mm² glass coverslip at 1 X 10⁵ inside a well on a 6 well plate. Cells were washed in PBS and fixed in 4% paraformaldehyde (Electron Microscopy Sciences, 15710) in the dark for 15 minutes. Slides were wash 2 times in PBS followed by permeabilization with 0.1% Triton X-100 in PBS for 15 minutes. Slides were washed 2 times in PBS and incubated in Alexa Fluor 488 Phalloidin (ThermoFisher Scientific, A12379) at 1:400 diluted in PBS with 1% BSA. Cells were mounted in ProLong Gold Antifade Mountant with DAPI (Thermo Fisher Scientific) and allowed to dry overnight. Images were acquired on a laser scanning confocal microscope (Leica TSC SP8 Confocal).

CRISPR-mediated CDK9 Knockout and Cell Electroporation

Cas9 nuclease alone or Cas9 nuclease and sgCDK9 were electroporated into YUMM1.7 cells at a ratio of 9:1. sgCDK9 pooled sequences: (C*U*C*CCUUCAGAUCCUGCACA), (U*G*G*GCUGGCUAUUCUUAGCU), (C*G*A*GCAGCAGCUCCGGAGGC). Cas9 nuclease and sgCDK9 pool were purchased from Synthego. RNP complexes were assembled by mixing 30 µM CDK9 sgRNA with 20 µM Cas9 and 3.5 Resuspension buffer (Invitrogen, ThermoFisher Scientific). RNP mix was incubated at room temperature for 10 minutes before electroporation. 1 X 10⁶ YUMM1.7 cells were resuspended in 50 µL resuspension buffer R and cell-RNP mixture was produced by mixing 5 µL cell suspension with 7 µL RNP mixture. 10 µL cell-RNP mixture was aspirated into a 10 µL Neon tip and electroporated by a Neon Transfection System (Invitrogen, ThermoFisher Scientific). YUMM1.7 cells were electroporated at 1500 V, 20 ms width, for 2 pulses.

RNA Sequencing

Total RNA was isolated with TRI-reagent (Invitrogen, ThermoFisher Scientific) and chloroform (Sigma-Aldrich) per manufacturer’s instructions. RNA was sequenced by the Next Generation Sequencing Facilities at Fox Chase Cancer Center. Samples were sequenced on an Illumina HiSeq 2500 sequencer. Raw data was analyzed by Fox Chase Cancer Center Biostatistics and Bioinformatics Facility.

Multiplex Inhibitor Beads and Mass Spectrometry

MIBs/MS was performed in collaboration with the James Duncan lab at Fox Chase Cancer Center.

Lysate Preparation

Cells were scraped from 150 mm plates with MIBs Lysis buffer (50mM HEPES pH 7.5, 150 mM NaCl, 0.5% Triton X-100, 1 mM EDTA, 1 mM EGTA, 10 mM NaF, 2.5 mM NaVO4, Protease inhibitor cocktail (Roche), phosphatase inhibitor cocktail 2 (Sigma P5726), Phosphatase inhibitor cocktail 3 (Sigma-P0044)) and added to a 15 mL conical tube. Lysates were sonicated using a tip sonicator at 3 x 30 pulses at 20% duty cycle. Lysates were then transferred to Eppendorf tubes and centrifuged at 4 degrees for 15 minutes at max speed. Supernatants were then filtered through 0.45 µm CA membrane filter. Protein concertation was determined by BCA assay. 5 mg of protein was used for each replicate for subsequent MIBs/MS steps (n = 3 per condition).

MIBs/MS

Kinases were isolated by flowing lysates over kinase inhibitor-conjugated Sepharose beads (purvalanol B, VI16832, PP58 and CTx-0294885 beads) in 10 mL gravity flow columns. Columns were washed 2 times in high salt buffer (50mM HEPES pH 7.5, 1 M NaCl, 0.5% Triton X-100, 1 mM EDTA, 1 mM EGTA) followed by 1 time in low salt buffer (50 mM HEPES pH 7.5, 150 mM NaCl, 0.5% Triton X-100, 1 mM EDTA, 1 mM EGTA). Kinases were eluted from the column with elution buffer (0.5% SDS, 1% β-mercaptoethanol in 0.1 M Tris-HCl pH 6.8) and boiled. Eluted peptides were incubated with 5 mM DTT at 65 degrees C followed by 20 mM iodoacetamide at room temp for reduction and alkylation. Alkylation was quenched with DTT. Samples were then concentrated to 100 µL with Millipore 10kD cutoff spin concentrators. Detergent was removed by chloroform/methanol extraction, and protein pellet was resuspended in 50 mM ammonium bicarbonate and digested with sequencing grade trypsin (Promega) overnight at 37 degrees C. Digested peptides were then cleaned with PepClean C18 spin columns (ThermoFisher Scientific) and dried in a speed-vacuum and resuspended in 50 µL 0.1% formic acid and extracted by ethyl acetate. Any additional ethyl acetate was allowed to evaporate, and peptides were dried in a speed vacuum, followed by LC- /MS/MS analysis.

Cell Cycle Profile Analysis

Cells were trypsinized in TrpLE-express (Gibco) and fixed dropwise while vortexing in ice cold 70% ethanol and stored at -20°C. Cells were then stained with 1 mg/mL propidium iodide (Invitrogen) with 1 mg/mL RNase A (Sigma Aldrich) in PBS. Cell cycle was measured on a FACSymphony A5 cytometer (BD Biosciences). Data was analyzed in FlowJo FlowJo (RRID:SCR_008520) and graphed on GraphPad Prism 9.

H2B-GFP Live Cell Imaging

Cells were transduced with an H2B-GFP plasmid (Addgene 11680) and were seeded 0.25 X 10⁵ cells onto 6 well plates and treated with +/- SHLD 1 ligand. Brightfield and GFP photos were taken every 15 minutes for 24 hours to track the mitotic cell cycle phase. Images were analyzed on ImageJ and mitosis was quantified by onset of mitosis (visible condensation of chromosomes) to anaphase (clear separation of sister chromatids).

Pharmacologic Inhibitor Treatments and Viability Assays

Cells were seeded from 750–2000 cells per well on a white 96 well plated and allowed to adhere overnight. 24 hours after transgene induction (when necessary), cells were treated with a dose titration of pharmacologic inhibitors. After an incubation time of 48 hours, viability was measured by Cell Titer Glo 2.0 assay (Promega, Madison, WI, USA) per manufacturer’s protocol. Plates were read on a luminescent plate reader (Perkin Elmer Envision Plate Reader). All drug compounds were purchased from Selleck Chemicals (Houston, TX, USA). Drug treatment dose ranges included Alisertib (Cat. No. S1133): 0.0001-50 µM, Volasertib (Cat. No. S2235) : 0.0001-50 µM, Tozasertib (Cat. No. S1048): 0.0001-50 µM, Dinaciclib (Cat. No. S2768): 1-100 nM, R03306 (Cat. No. S7747): 0.1–50 µM, NU2058 (Cat. No. S5316): 0.1–100 µM, SU9516: 0.1–75 µM, Abemaceclib (Cat. No. S7158): 0.01–325 µM, AT519 (Cat. No. S1524): 0.1-4 µM, NVP2 (Cat. No. S8981): 0.1–1000 nM, JQ-1 (Cat. No. S7110): 1-1000 nM.

Immunoblot

Protein was isolated using RIPA buffer with protease (Sigma Aldrich) and phosphatase cocktail inhibitors (Sigma Aldrich). Cells were washed twice with 1X PBS on ice and incubated for 5 minutes in RIPA buffer. Cells were then scraped off and sonicated for 3 pulses at 20% (550 sonic dismembrator, Fisher Scientific). Cells were then centrifuged for 15 minutes at 15,000 x g at 4 degrees. The supernatant was transferred to a new Eppendorf tube and protein was quantified with Quick Start Bradford 1X Dye Reagent (BioRad). Samples were added to 4X Laemmli Sample Buffer (BioRad) plus 10% β-mercaptoethanol and boiled for 5 minutes. 20 µg of protein was loaded on a Mini-PROTEAN TGX gel 4–20% gradient pre-cast gels (BioRad). Protein was transferred to 0.45 µm nitrocellulose membrane (BioRad) and blocked per manufacturer’s instructions in either 5% milk-TBST or 5% BSA-TBST. Antibody dilutions were prepared in 5% BSA-TBST and incubated overnight with gentle agitation at the following dilutions phospho-AKT T308 1:2000 (Cell Signaling Technology Cat# 13038, RRID:AB_2629447), AKT 1:1000 (Cell Signaling Technology Cat# 9272, RRID:AB_329827), phospho-ERK 1/2 Y202/204 1:2000 (Cell Signaling Technology Cat# 4370, RRID:AB_2315112), ERK 1:1000 (Cell Signaling Technology Cat# 4695, RRID:AB_390779), phospho-PAK 1/2 S199/204, S192/197 1:1000 (Cell Signaling Technology Cat# 2605, RRID:AB_2160222), PAK 1/2/3 1:1000 (Cell Signaling Technology Cat# 2604, RRID:AB_2160225), Myc-Tag 1:1000 (Cell Signaling Technology Cat# 2276, RRID:AB_331783), GAPDH 1:1000 (Cell Signaling Technology Cat# 2118, RRID:AB_561053), Vinculin 1:1000 (Cell Signaling Technology Cat# 13901, RRID:AB_2728768), CDK9 1:1000 (Cell Signaling Technology Cat# 2316, RRID:AB_2291505), phospho-RNA Polymerase II S2 1:1000 (Millipore Cat# 04-1571, RRID:AB_11212363), PTEN 1:1000 (Cell Signaling Technology Cat# 9552, RRID:AB_10694066).

Secondary antibodies were Peroxidase AffiniPure Goat Anti-Mouse IgG (H + L) 1:10000 (Jackson ImmunoResearch Labs Cat# 115-035-003, RRID:AB_10015289) and Peroxidase AffiniPure Goat Anti-Rabbit IgG (H + L) 1:10000 (Jackson ImmunoResearch Labs Cat# 111-035-003, RRID:AB_2313567).

Live/Dead Assays

Cells were suspended by trypsinization with Tryp-LE Express (Gibco). Cells were stained using LIVE/DEAD Viability/Cytotoxicity Kit (Invitrogen, L3224) per manufacturer’s protocol. Cells were analyzed via flow cytometry on a Symphony A5 cytometer (BD Biosciences). Compensation controls were used to gate for live and dead cells on PE and BB630 fluorescent channels. Data analysis was performed on FlowJo and graphed on GraphPad Prism 9.

Crystal Violet Staining

Crystal violet solution fixation solution consisted of 6% (vol/vol) glutaraldehyde and 0.5% (wt/vol) crystal violet in H₂0. Solution was added to cells in a 6 well plate and incubated for 30 mins. Cells were then rinsed in tap water and allowed to dry.

Cell Surface Staining by Flow Cytometry

Cells were suspended using Tryp-LE Express (Gibco). Cells were fixed in 4% PFA (Electron Microscopy Sciences) for 15 minutes and washed 3 times in PBS before staining. Cells were stained with fluorescent-conjugated primary antibodies rotating overnight at 4 degrees Celsius. Cells were then washed 3 times in PBS and analyzed by flow cytometry using a Symphony A5 cytometer (BD Biosciences). Data was analyzed with FlowJo software and graphed on GraphPad Prism 9. Statistics were calculated by GraphPad Prism.

Antibodies used: PD-L1 0.125 µg/test (Thermo Fisher Scientific Cat# 53-5982-82, RRID:AB_2811871), MHC Class I 1 µg/test (Thermo Fisher Scientific Cat# 11-5958-82, RRID:AB_11149502), IgG Isotype Control 0.125 µg/test (Thermo Fisher Scientific Cat# 11-4888-81, RRID:AB_470037).

In vivo Experiments

6-week-old male C57Bl/6J mice were purchased from Jackson Laboratories (Bar Harbor, ME, USA) (RRID:IMSR_JAX:000664) and allowed to acclimate for 2 weeks. Male mice were used in this study as the YUMM1.7 cell line originated from a male mouse, and female hosts can reject grafts due to antigens from the Y-chromosome. Post acclimatization, syngeneic grafts were established by subcutaneous injections of 1,000,000 cells in PBS. Cells injected were either the YUMM1.7 cell line or YUMM1.7 + RAC1^P29S. Tumor sizes were monitored daily until tumors reached 100 mm³. Tumors were measured with vernier calipers and tumor volume was calculated by ((length x width²) x 0.5). Mice were randomly divided into 4 cohorts respective of tumor genotype: Vehicle, dinaciclib, anti-PD1 antibody, or dinaciclib + anti-PD1 antibody. Vehicle consisted of 10mg/mL rat IgG2a isotype control diluted in InVivoPure 6.5 dilution buffer and 20% hydroxypropyl-β-cyclodextrin. Dinaciclib was dissolved in 20% hydroxypropyl-β-cyclodextrin and dosed at 30 mg/kg via intraperitoneal injection every three days. Anti-mouse PD-1 (RMP1-14) was diluted in InVivoPure pH 7.0 dilution buffer and dosed 10mg/kg every 4 days via intraperitoneal injection. Tumors and weight were measured every three days post regimen start for 21 days. Humane endpoints included tumor mass over 10% of body weight, tumor length greater than 20 mm, or tumor ulceration. Dinaciclib (SCH727965) was purchased from Selleck Chemicals (S2768). Dinaciclib was diluted in 20% hydroxypropyl-β-cyclodextrin (w/v) purchased from Sigma-Aldrich (H107-5G). The following reagents were purchased from BioXCell (Lebanon, NH, USA): InVivoMAb anti-mouse PD-1 (Clone RMP1-14) (BE0146) which was diluted in InVivoPure pH 7.0 dilution buffer (IP0070). InVivoMAb rat IgG2a isotype control, anti-trinitrophenol (Clone 2A3) (BE0089) and was diluted in InVivoPure pH 6.5 dilution buffer (IP0065).

Study Approval

All animal experiments were performed according to the approved protocols of the Fox Chase Cancer Center Institutional Animal Care and Use Committee.

Immunohistochemical (IHC) staining

Mouse tumor tissues were collected and fixed in 10% neutral buffered formalin for 24–48 hrs, dehydrated and embedded in paraffin. Hematoxylin and eosin (H&E) stained sections were used for morphological evaluation purposes and 5-µm unstained sections for immunohistochemical (IHC) studies.

Immunohistochemical staining was performed on a VENTANA Discovery XT automated staining instrument (Ventana Medical Systems) using VENTANA reagents according to the manufacturer's instructions. Briefly, slides were de-paraffinized using EZ Prep solution (cat # 950–102) for 16 min at 72°C. Epitope retrieval was accomplished with CC1 solution (EDTA, ph 9.0. cat # 950–224) at high temperature (eg, 95–100°C) for 32 min. Rabbit primary antibodies (anti-mouse CD8, 1:50, (Cell Signaling Technology Cat# 98941, RRID:AB_2756376); CD11C: 1:100 (Cell Signaling Technology Cat# 39143, RRID:AB_2924836); CD86 (Cell Signaling Technology Cat# 19589, RRID:AB_2892094), tittered with a TBS antibody diluent into user fillable dispensers for use on the automated stainer. Immune complex was detected using the Ventana OmniMap anti-Rabbit detection kit (760–4311) and developed using the VENTANA ChromMap DAB detection kit (cat # 760 − 159) according to the manufacturer’s instructions. Slides were then counterstained with hematoxylin II (cat # 790–2208) for 8 min, followed by Bluing reagent (cat # 760–2037) for 4 min.

The slides were then dehydrated with ethanol series, cleared in xylene, and mounted. As a negative control, the primary antibody was replaced with normal rabbit IgG to confirm absence of specific staining.

Quantitative Image analysis

Immunostained slides were scanned using an Aperio ScanScope CS 5 slide scanner (Aperio, Vista, CA, USA). Scanned images were then viewed with Aperio's image viewer software (ImageScope, version 11.1.2.760, Aperio). Selected regions of interest were outlined manually by a pathologist. The immune cells quantifications were performed with Aperio V9 or positive pixel count (PPC) algorithm.

Statistical Analysis

Statistical Analysis was done in GraphPad Prism (RRID:SCR_002798). For comparisons of two groups, statistical analysis was performed using Student’s t-test: * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001. Immunoblot results are representative images of three experiments and quantifications were analyzed by ImageJ (RRID:SCR_003070). Analysis of multiple groups was performed using ordinary two-way ANOVA, Tukey’s multiple comparisons test.

Characterization of RAC1 ^P29S oncogenic properties

To characterize the RAC1^P29S effect on downstream signaling, morphology, and cell proliferation, we generated an inducible cell system of RAC1^P29S expression. Melanocytes isolated from C57BL/6 mice were obtained and genetically altered via CRISPR/Cas technology to knock out Pten, since this is a commonly altered tumor suppressor in cutaneous melanoma and mouse models of combined Braf/Pten mutations have been well-studied (4, 5) (Supplemental Fig. 1). A plasmid containing a destabilization domain (DD) fused to RAC1^P29S was then introduced into these C57BL/6 Pten^−/− melanocytes (Fig. 1A). Under basal conditions, the DD results in translation of an unstable protein, which is immediately targeted and degraded by the proteasome. However, upon treatment with Shield1 ligand (SHLD1), the DD is stabilized, and the recombinant protein is expressed (11). This expression system was originally produced in a non-transformed C57BL/6 cell line to delineate molecular consequences specific to the RAC1^P29S mutation without confounding signaling inputs from other potential oncogenes. After establishing cellular RAC1^P29S expression, we examined the RAC1^P29S effect on downstream MAPK and AKT signaling, both of which lead to cell proliferation and survival. RAC1^P29S resulted in a slight increase in phospho-PAK, phospho-ERK and phospho-AKT T308 (Fig. 1B). Melanocytes that expressed RAC1^P29S also displayed faster proliferation rates than non P29S-expressing counterparts (Fig. 1C). As RAC1 is a master regulator of actin dynamics, we explored the effect on motility, migration, and actin structure. To further examine the effects of RAC1^P29S on cell motility and migration, we performed scratch and transwell assays. RAC1^P29S-expressing melanocytes exhibited higher motility, as a significantly higher number of cells migrated into the scratch area (Fig. 1D). Additionally, we observed higher cell migration in a transwell assay (Fig. 1E). Phalloidin staining of actin displayed increased membrane ruffling and lamellipodia formation compared to melanocytes that did not express the P29S mutation (Fig. 1F). This data concludes that RAC1^P29S is an activating mutation that leads to MAPK activation, lamellipodia formation, and increased motility and migration. However, as these effects are modest, there may be additional mechanisms that contribute to RAC1-driven oncogenicity.

Proteogenomic analysis of RAC1 ^P29S melanocytic expression reveals upregulation of cell cycle processes.

To determine the molecular changes induced by RAC1^P29S expression, we performed RNA-sequencing (RNA-seq) coupled with multiplexed inhibitor bead-mass spectrometry (MIBs/MS). RNA-seq analysis of expressed recombinant RAC1^P29S mouse-derived melanocytes resulted in 600 differentially expressed genes. E2F_TARGETS and G2M_CHECKPOINT were the top two enriched hallmark gene sets (Fig. 2A, B). Other cell cycle related processes included MYC_TARGETS_V1 and MITOTIC_SPINDLE (Fig. 2A). The top 10 enriched REACTOME gene sets were all cell cycle events, indicating RAC1^P29S has a role in influencing cell cycle progression, consistent with previous studies on RAC1 (Fig. 2C) (12, 13). In addition, INFLAMMATORY_RESPONSE and IL-2_STAT5_SIGNALING hallmark gene sets were enriched, suggesting a potential function in modulating inflammation (Fig. 2A). Ingenuity Pathway Analysis (IPA) identified the top altered upstream regulators in response to RAC1^P29S expression (Fig. 2D). Top activated upstream regulators included mainly transcription factors that have been reported to promote expression of cell cycle regulating genes such as E2F proteins, YAP1, CCND1, and FOXM1. Accordingly, the top inactive upstream regulators were tumor suppressors and cell cycle inhibitors including COPS5, SMARCB1, CDKN2A, and RB1 (Fig. 2D). Interestingly, the activity of several transcriptional regulators was altered as well, including MED1 (a subunit of the mediator complex which helps position RNA polymerase II on DNA promoter regions), HDAC1 (histone deacetylase), and PRDM16 (histone methyltransferase) (Fig. 2D). MIBs/MS further implicated cell cycle progression as an enriched pathway in RAC1^P29S-expressing cells through identification of 7 kinases involved specifically in G2/M transition (Fig. 2E).

To further investigate the RAC1^P29S influence on the cell cycle, the cell cycle profile was examined by flow cytometry. RAC1^P29S-expressing melanocytes showed a lower percentage of cells in G2/M phase, indicating the RAC1^P29S mutation may lead to accelerated transition through the G2/M cell cycle checkpoint (Supplemental Fig. 2A). P29S-expressing melanocytes also exhibited a higher percentage of cells in S phase, indicating increased DNA replication, consistent with gene set enrichment analysis (GSEA) (Supplemental Fig. 2A). Additionally, live H2B-GFP cell tracking resulted in less time spent in the prophase to anaphase stages of mitosis in the RAC1-mutant cells, further suggesting a role in mitotic cell cycle progression (Supplemental Fig. 2B).

RAC1 ^P29S -expressing melanocytes show increased sensitivity to CDK9 inhibition.

Due to the strong G2/M cell cycle phase transition signature yielded by the proteogenomic and cell cycle analysis, we next further examined the kinase signaling cascade that promotes G2 to M phase transition to investigate potential targeting vulnerabilities of RAC1^P29S-expressing melanocytes. PAK1 phosphorylates Aurora kinases (AURK) and Polo-like kinase 1 (PLK1), which in turn phosphorylates CDC25, ultimately leading to CDK1 phosphorylation, activation, and G2/M phase transition (14, 15) (Fig. 3A). While there were no differential sensitivities to alisertib (AURKA inhibitor), tozasertib (pan-AURK inhibitor), or volasertib (PLK1 inhibitor), melanocytes expressing a RAC1^P29S mutation were significantly more sensitive to dinaciclib (CDK1,2,5,9 inhibitor) (Fig. 3B). However, as dinaciclib is not specific to CDK1, we performed a CDK inhibitor viability panel to determine which CDK target conferred the observed sensitivity (Fig. 3C). Interestingly, the compounds that targeted CDK9 were all significantly more cytotoxic to melanocytes expressing the RAC1^P29S mutation (Fig. 3C,D).

To further examine RAC1^P29S-mediated sensitivity to CDK9, we generated a constitutive RAC1^P29S-expressing melanoma cell line. YUMM1.7 cell lines (Braf^V600E, Pten^null, Cdkn2d^null) were transfected with myc-tagged RAC1^P29S (Fig. 4A). YUMM1.7 cells that expressed RAC1^P29S were significantly more sensitive to both dinaciclib and the CDK9 specific inhibitor NVP2 (Fig. 4B,C). To confirm this sensitivity was indeed due to CDK9 inhibition we knocked out CDK9 using CRISPR-Cas9 (Fig. 4D). Either Cas9 plus sgCDK9 or Cas9 alone were electroporated into YUMM1.7 or YUMM1.7 plus RAC1^P29S cells. 48 hours post electroporation, YUMM1.7 cells expressing RAC1^P29S had a significantly higher percent of dead cells compared to those that did not express mutant RAC1 (Fig. 4E). After 120 hours, crystal violet staining showed RAC1^P29S expressing YUMM1.7 melanoma cells were no longer viable, further implicating CDK9 as a vital protein in RAC1^P29S-expressing cells (Fig. 4F).

CDK9 is a non-canonical CDK, which has a primary function in transcriptional regulation through phosphorylation of Serine 2 on the C terminal domain (CTD) tail of RNA polymerase II (16, 17). In cancer, aberrant activity of CDK9 promotes transcription of genes that facilitate cell survival, such as those that promote cell cycle progression (18). To confirm that sensitivity of cells harboring a RAC1^P29S mutation is transferrable across multiple melanoma cell lines and genetic backgrounds, we examined CDK9 sensitivity across several human melanoma lines, including those that harbor endogenous RAC1^P29S mutations. The melanoma cells bearing P29S mutations were significantly more sensitive to dinaciclib and NVP2 (Fig. 4G-H).

We also tested the effects of a BRD4 inhibitor, as active CDK9 and Cyclin T form the positive transcription elongation factor b (P-TEFb) complex, which associates with bromodomain-containing protein 4 (BRD4) to activate RNA polymerase II transcriptional elongation (19). We found that human melanoma lines harboring a RAC1^P29S mutation are also more sensitive to inhibition of BRD4 with JQ-1 (Fig. 4I).

Although CDK9 does not primarily regulate the cell cycle as a classic CDK, previous studies have linked CDK9 with promotion of transcription of G2/M genes (18). Accordingly, melanocytes trend toward G2/M arrest after CDK9 inhibition with dinaciclib or NVP2 for 16 hours, with a lower dose necessary for G2/M arrest in RAC1^P29S-expressing cells (Supplemental Fig. 3A-B).

These data suggest that RAC1^P29S induces molecular changes that lead to increased dependency on CDK9, presenting a potential therapeutic target in this subset of melanoma. Additionally, the cell cycle and G2/M signature seen in the RNA-seq and MIBs/MS datasets may be influenced by CDK9 modulation of RNA polymerase II transcribed gene regulation.

CDK9 inhibition increase surface expression of PD-L1 and Class I MHC in RAC1 mutant melanocytes

Several recent studies have reported pharmacologic inhibition of CDKs to regulate PD-L1 expression and sensitize the tumor microenvironment to immunotherapy (20–26). Given the unique sensitivity of RAC1^P29S mutant melanoma cells to CDK9 inhibition, we next explored the consequences of CDK9 inhibition on cellular immunogenicity. Dinaciclib and NVP2 both decreased phosphorylated RNA polymerase II at serine 2 (Fig. 5A,D). Following dinaciclib treatment, both surface expression of PD-L1 and major histocompatibility complex (MHC) Class I are significantly more elevated in P29S-expressing melanocytes (Fig. 5B). Cells treated with NVP2 also exhibited an increase in surface PD-L1 and MHC Class I expression (Fig. 5C). The same increase in PD-L1 and MHC Class I was also seen upon dinaciclib treatment in YUMM1.7 plus RAC1^P29S melanoma cell lines (Fig. 5E) and upon NVP2 treatment (Fig. 5F).

MHC class I is found on the cell surface of all nucleated cells and plays an instrumental role in antigen presentation and T-cell activation. Upregulation of MHC Class I could lead to more antigen presentation and a more robust anti-tumor immune response. These results indicate CDK9 inhibition could increase the immunogenicity of the tumor microenvironment and potentially enhance immune checkpoint inhibition response.

Combination therapy of CDK9 inhibition and anti-PD-1 immunotherapy significantly decreases tumor growth in vivo

As our in vitro data indicated CDK9 inhibition as a potential enhancer of immune checkpoint inhibition, we next tested the efficacy of CDK9 inhibition with anti-PD-1 ICI in a syngeneic murine model. Subcutaneous (s.c.) tumors were established in immune competent C57BL/6 mice with YUMM1.7 (Braf^V600E, Pten^null, Cdkn2d^null) melanoma cells, or YUMM1.7 cells constitutively expressing the RAC1^P29S mutation. Once tumors reached approximately 100 mm³, mice were randomly allocated to 4 treatment groups and either treated with vehicle (20% (2-Hydroxypropyl)-β-cyclodextrin) + IgG isotype control, 30 mg/kg dinaciclib, 10 mg/kg anti-PD-1 Ab, or a combination of dinaciclib plus anti-PD-1 Ab. Dinaciclib was treated every 3 days, while anti-PD-1 Ab was administered every 4 days (Fig. 6A). YUMM1.7 tumors did not significantly respond to either monotherapies or the combination therapy (Fig. 6B). Tumor growth inhibition (TGI) of dinaciclib alone was 1%, anti-PD1 antibody alone was 0%, and combination treatment was 16%. Contrastingly, YUMM1.7 tumors that expressed RAC1^P29S were significantly repressed with treatment of either monotherapies and overall inhibited by combination treatment (Fig. 6C). Dinaciclib alone had a TGI of 72%, anti-PD-1 Ab alone had a TGI of 66%, and dinaciclib and anti-PD-1 Ab combination had a TGI of 97%, with one complete responder. YUMM1.7 tumor response greatly varied to dinaciclib and anti-PD1 Ab combination, while YUMM1.7 + RAC1^P29S tumors were all suppressed, with 4/5 tumors smaller than their size at the beginning of drug administration (Fig. 6F,G).

RAC1^P29S expression increases tumor immune cell infiltration

To determine the effect of the treatment regimen on tumor immune cell infiltration, we performed immunohistochemistry (IHC) on tumors fixed from the mice at the experiment endpoint. Overall, there was more T cell (CD8) and myeloid (CD11c) immune cell infiltration in RAC1^P29S-expressing melanomas and expression of these was further increased upon treatment with dinaciclib (Fig. 7A,B). Additionally, there was increased CD86 expression in the RAC1 mutant tumors, which is found on monocytes, activated B cells, and dendritic cells and is necessary for T cell activation (27) (Fig. 7C). In contrast, in parental YUMM 1.7 grafts, neither dinaciclib nor anti-PD-1 Ab elicited an increase in CD8, CD11c, or CD86 (Fig. 7A-C). Surprisingly, there was no significant difference in caspase-3 positivity in control vs. treated tumors, suggesting that either these cells cannot proliferate or that a non-apoptotic mode of programmed cell death is evoked by combined anti-CDK9 and anti-PD1 therapy.

RAC1-mutant melanomas display primary resistance to targeted BRAF and MEK inhibitors, higher metastatic propensity, and overall worse survival compared to RAC1-wild type melanomas (4, 5, 8). There are currently no approved treatments specific for RAC1-mutant melanoma. Thus, there is a clinical need to identify novel targets that can combat this melanoma subtype or increase efficacy of existing treatments that would extend clinical benefits to a larger patient population. Immune checkpoint inhibitors have significantly improved patient prognosis in general in the field of melanoma, however, limited studies have investigated the RAC1 influence on sensitivity to these immune therapy agents. Previous studies have identified RAC1^P29S-expressing melanocytes to express higher PD-L1 (9), indicating RAC1^P29S melanomas may be more sensitive to anti-PD-1 or PD-L1 ICI. Additional clinical data suggests systemic immunotherapies are beneficial to RAC1^P29S metastatic melanoma (10).

There have been recent mechanistic investigations into RAC1^P29S-mediated primary BRAF and MEK inhibitor resistance. Mohan et al. demonstrate RAC1-driven elongation of lamellipodia sustains cell survival after treatment with MEK or BRAF inhibitors (28). Furthermore, Lionarons et al. reported that RAC1^P29S drives an SRF/MRTF-mediated melanocytic to mesenchymal switch to promote BRAF inhibitor resistance. They further showed this resistance can be reversed with a SRF/MRTF inhibitor (29). These works argue a role of actin cytoskeleton remodeling in propagation of BRAF inhibitor resistance. While our RNA-sequencing data was strongly enriched in cell cycle processes, which Lionarons et al. did not observe, our data uncovered many overlapping enriched gene sets, including TNFA_SIGNALING_VIA_NFKB, KRAS_SIGNALING_UP, and IL2_STAT5_SIGNALING (29).

The idea of employing CDK inhibitors to promote tumor immunogenicity has emerged over the past few years. These reports have established CDK inhibitors as pro-immunogenic by increasing both immune cell infiltration and increasing expression of molecules important in antigen presentation and T cell activation. Most of these works investigate combinations of CDK4/6 inhibitors with anti-PD-1 or anti-PD-L1 ICI (22–26). In agreement with our data, additional prior reports demonstrated anti-tumor effects in vivo after combination of CDK9 inhibition via dinaciclib or other agents with anti PD-1 ICI (20, 21). However, in in vivo models used by Zhang et al. and Hossain et al., RAC1 wild type tumors benefited from CDK9 and anti-PD-1 combination, while only RAC1-mutant tumors were inhibited in our model. This could be explained by the high microsatellite instability status (MSI-H) of the engrafted tumor cells in the other studies. Supporting our observations, YUMM1.7 grafts have previously been shown to be insensitive to anti-PD-1 blockade (30). Although PD-1 blockade combined with BRAF and MEK inhibition has demonstrated increased overall survival in BRAF-mutant melanoma, anti-PD-1 monotherapy response greatly varies in clinic (31). Furthermore, contrary to Hossain et al., our findings indicate dinaciclib and PD-1 ICI-induced cell death is not solely immune-cell mediated in our syngeneic model (21). This suggests an altered dependence of RAC1 mutant cells on CDK9, which could further be exploited for therapeutic intervention. The mechanism by which RAC1^P29S is linked to CDK9 remains unclear, however previous studies have alluded to a RAC1 influence on CDK9 activity, as RAC1 knockdown by siRNA resulted in decreased phosphorylation of RNA polymerase II at Ser 2 (32).

In this work, we establish CDK9 as a novel target in RAC1-mutant melanoma. Both non-transformed melanocytes and melanoma cells that overexpress RAC1^P29S and melanoma cell lines that harbor an endogenous RAC1^P29S mutation displayed higher sensitivity to CDK9 inhibition by dinaciclib, NVP2, or a CRISPR-mediated CDK9 knockout. Inhibition of CDK9 induced higher surface expression of PD-L1 and MHC Class I in vitro. Combination of dinaciclib with anti-PD-1 Ab ICI resulted in tumor regression uniquely in syngeneic YUMM1.7 tumor grafts that express RAC1^P29S. While few CDK9-specific inhibitors are currently in clinical trials, a potent multi-CDK inhibitor, such as dinaciclib, may offer additional anti-tumor benefits by inhibiting both the transcription regulating CDK9 and cell cycle regulating CDKs 1 and 2.

Collectively, these data indicate combination therapy of CDK9 inhibition with anti-PD-1 ICI may be particularly beneficial to melanoma patients that harbor the RAC1^P29S mutation. Furthermore, this work suggests the presence of RAC1-driven epigenetic reprograming events which might respond to epigenetic agents such as BRD4i. P-TEFb (CDK9-Cyclin T complex) binds to BRD4 and is subsequently recruited to promoter regions on chromosomes via BRD4’s interactions with acetylated histones (33, 34). It has previously been reported that BRD4 binds to the promoter regions of cell cycle genes (35). Furthermore, RNAi knockdown of BRD4 resulted in downregulation of genes involved in mitotic cell cycle, cell division, and DNA replication (36). This could explain the strong cell cycle enrichment displayed in the RNA-seq and MIBs/MS. Since BRD4 is necessary for P-TEFb driven gene transcription, BRD4 may also yield a potential therapeutic target in RAC1-mutant melanoma. Several studies have shown promising anti-tumor effects by targeting BRD4 with JQ-1 in melanoma (37–40). Relatedly, it has also been reported that JQ-1, along with other bromodomain and Extra-Terminal motif (BET) inhibitors, modulate the tumor immune microenvironment and sensitize the tumor to PD-1 or PD-L1 blockade (41, 42). However, the efficacy of BRD4 inhibition in RAC1-mutant melanoma remains unexplored.

The benefits of combining CDK9 inhibition and anti-PD-1 immunotherapy in RAC-driven malignancies may extend beyond RAC1^P29S melanoma. RAC1 is mutated in approximately 3% of head and neck squamous cell carcinomas (HNSCC), most commonly at the A159 (A159V) and P29 (P29S) loci (4, 5). The current standard of care in HNSCC includes surgery and radiotherapy, although immunotherapies have been approved and are widely implemented (43). In addition, HNSCCs typically bear a higher tumor mutational burden and immune infiltration relative to other cancer subtypes (44, 45). These aspects make RAC1-mutant HNSCCs good candidates to benefit from immunotherapy, and combination with a CDK9 inhibitor may further enhance this efficacy. While the response of the RAC1^A159V mutant to immunotherapy has yet to be investigated, it is presumed that A159V would behave similarly to the P29S mutation, as both are activating mutations that result in “fast-cycling” GTPases (46). Moreover, both inhibition of RAC1 or depletion of CDK9 enhanced radiosensitivity in HNSCC cells in two independent studies, further implicating RAC1-mediated therapy resistance is influenced by CDK9 (47, 48).

Overall, these results demonstrate a novel treatment strategy for RAC1^P29S-mutant melanoma. This therapeutic approach builds on the strategy of creating a “hotter” tumor microenvironment, which may improve clinical options for tumors that would not benefit from traditional treatment regimens.

Authors’ Disclosures

The authors declare no competing financial interests.

Data Availability Statement

All raw sequencing data will be submitted to NCBI GEO for access. All other raw data generated from this study are available from the corresponding author upon request.

Authors’ Contributions

A. Cannon: Conceptualization, data curation,formal analysis, investigation, methodology, project administration, supervision, visualization, writing – original draft, writing – review and editing K. Budagyan: Conceptualization, formal analysis, investigation, writing – review and editing C. Uribe-Alvarez: Funding acquisition, methodology, writing – review and editing A. Kurimchak: Formal analysis, investigation, software D. Araiza-Olivera: Methodology Q. Cai: Formal analysis, investigation, methodology, software S. Peri: Data Curation, formal analysis, software Y. Zhou: Data Curation, formal analysis, software J. Duncan: Methodology, resources, supervision, formal analysis J. Chernoff: Conceptualization, funding acquisition, methodology, project administration, resources, supervision, visualization, writing – original draft, writing – review and editing

Acknowledgments

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, 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).

Competing Interests

The authors declare no competing financial interests.

Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat JP, et al. A landscape of driver mutations in melanoma. Cell. 2012;150(2):251-63.
Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker JP, et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet. 2012;44(9):1006-14.
Davis MJ, Ha BH, Holman EC, Halaban R, Schlessinger J, Boggon TJ. RAC1P29S is a spontaneously activating cancer-associated GTPase. Proc Natl Acad Sci U S A. 2013;110(3):912-7.
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-4.
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.
Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364(26):2507-16.
Giugliano F, Crimini E, Tarantino P, Zagami P, Uliano J, Corti C, et al. First line treatment of BRAF mutated advanced melanoma: Does one size fit all? Cancer Treat Rev. 2021;99:102253.
Watson IR, Li L, Cabeceiras PK, Mahdavi M, Gutschner T, Genovese G, et al. The RAC1 P29S hotspot mutation in melanoma confers resistance to pharmacological inhibition of RAF. Cancer Res. 2014;74(17):4845-52.
Vu HL, Rosenbaum S, Purwin TJ, Davies MA, Aplin AE. RAC1 P29S regulates PD-L1 expression in melanoma. Pigment Cell Melanoma Res. 2015;28(5):590-8.
Lodde GC, Jansen P, Herbst R, Terheyden P, Utikal J, Pfohler C, et al. Characterisation and outcome of RAC1 mutated melanoma. Eur J Cancer. 2023;183:1-10.
Banaszynski LA, Chen LC, Maynard-Smith LA, Ooi AG, Wandless TJ. A rapid, reversible, and tunable method to regulate protein function in living cells using synthetic small molecules. Cell. 2006;126(5):995-1004.
Moore KA, Sethi R, Doanes AM, Johnson TM, Pracyk JB, Kirby M, et al. Rac1 is required for cell proliferation and G2/M progression. Biochem J. 1997;326 ( Pt 1)(Pt 1):17-20.
Michaelson D, Abidi W, Guardavaccaro D, Zhou M, Ahearn I, Pagano M, et al. Rac1 accumulates in the nucleus during the G2 phase of the cell cycle and promotes cell division. J Cell Biol. 2008;181(3):485-96.
Maroto B, Ye MB, von Lohneysen K, Schnelzer A, Knaus UG. P21-activated kinase is required for mitotic progression and regulates Plk1. Oncogene. 2008;27(36):4900-8.
Zhao ZS, Lim JP, Ng YW, Lim L, Manser E. The GIT-associated kinase PAK targets to the centrosome and regulates Aurora-A. Mol Cell. 2005;20(2):237-49.
Marshall NF, Peng J, Xie Z, Price DH. Control of RNA polymerase II elongation potential by a novel carboxyl-terminal domain kinase. J Biol Chem. 1996;271(43):27176-83.
Anshabo AT, Milne R, Wang S, Albrecht H. CDK9: A Comprehensive Review of Its Biology, and Its Role as a Potential Target for Anti-Cancer Agents. Front Oncol. 2021;11:678559.
Guhan SM, Shaughnessy M, Rajadurai A, Taylor M, Kumar R, Ji Z, et al. The Molecular Context of Vulnerability for CDK9 Suppression in Triple Wild-Type Melanoma. J Invest Dermatol. 2021;141(8):2018-27 e4.
Itzen F, Greifenberg AK, Bosken CA, Geyer M. Brd4 activates P-TEFb for RNA polymerase II CTD phosphorylation. Nucleic Acids Res. 2014;42(12):7577-90.
Zhang H, Pandey S, Travers M, Sun H, Morton G, Madzo J, et al. Targeting CDK9 Reactivates Epigenetically Silenced Genes in Cancer. Cell. 2018;175(5):1244-58 e26.
Hossain DMS, Javaid S, Cai M, Zhang C, Sawant A, Hinton M, et al. Dinaciclib induces immunogenic cell death and enhances anti-PD1-mediated tumor suppression. J Clin Invest. 2018;128(2):644-54.
Goel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J, Li BB, et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature. 2017;548(7668):471-5.
Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984-97 e24.
Deng J, Wang ES, Jenkins RW, Li S, Dries R, Yates K, et al. CDK4/6 Inhibition Augments Antitumor Immunity by Enhancing T-cell Activation. Cancer Discov. 2018;8(2):216-33.
Schaer DA, Beckmann RP, Dempsey JA, Huber L, Forest A, Amaladas N, et al. The CDK4/6 Inhibitor Abemaciclib Induces a T Cell Inflamed Tumor Microenvironment and Enhances the Efficacy of PD-L1 Checkpoint Blockade. Cell Rep. 2018;22(11):2978-94.
Zhang J, Bu X, Wang H, Zhu Y, Geng Y, Nihira NT, et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance. Nature. 2018;553(7686):91-5.
Adams AB, Ford ML, Larsen CP. Costimulation Blockade in Autoimmunity and Transplantation: The CD28 Pathway. J Immunol. 2016;197(6):2045-50.
Mohan AS, Dean KM, Isogai T, Kasitinon SY, Murali VS, Roudot P, et al. Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1(P29S) Melanoma Cells. Dev Cell. 2019;49(3):444-60 e9.
Lionarons DA, Hancock DC, Rana S, East P, Moore C, Murillo MM, et al. RAC1(P29S) Induces a Mesenchymal Phenotypic Switch via Serum Response Factor to Promote Melanoma Development and Therapy Resistance. Cancer Cell. 2019;36(1):68-83 e9.
Gorgun FM, Widen SG, Tyler DS, Englander EW. Enhanced Antitumor Response to Immune Checkpoint Blockade Exerted by Cisplatin-Induced Mutagenesis in a Murine Melanoma Model. Front Oncol. 2021;11:701968.
Ribas A, Lawrence D, Atkinson V, Agarwal S, Miller WH, Jr., Carlino MS, et al. Combined BRAF and MEK inhibition with PD-1 blockade immunotherapy in BRAF-mutant melanoma. Nat Med. 2019;25(6):936-40.
Zhang B, Zhong X, Sauane M, Zhao Y, Zheng ZL. Modulation of the Pol II CTD Phosphorylation Code by Rac1 and Cdc42 Small GTPases in Cultured Human Cancer Cells and Its Implication for Developing a Synthetic-Lethal Cancer Therapy. Cells. 2020;9(3).
Filippakopoulos P, Picaud S, Mangos M, Keates T, Lambert JP, Barsyte-Lovejoy D, et al. Histone recognition and large-scale structural analysis of the human bromodomain family. Cell. 2012;149(1):214-31.
Vollmuth F, Blankenfeldt W, Geyer M. Structures of the dual bromodomains of the P-TEFb-activating protein Brd4 at atomic resolution. J Biol Chem. 2009;284(52):36547-56.
Dey A, Nishiyama A, Karpova T, McNally J, Ozato K. Brd4 marks select genes on mitotic chromatin and directs postmitotic transcription. Mol Biol Cell. 2009;20(23):4899-909.
Du Z, Song X, Yan F, Wang J, Zhao Y, Liu S. Genome-wide transcriptional analysis of BRD4-regulated genes and pathways in human glioma U251 cells. Int J Oncol. 2018;52(5):1415-26.
Li L, Meng Y, Wu X, Li J, Sun Y. Bromodomain-containing protein 4 inhibitor JQ1 promotes melanoma cell apoptosis by regulating mitochondrial dynamics. Cancer Sci. 2021;112(10):4013-25.
Segura MF, Fontanals-Cirera B, Gaziel-Sovran A, Guijarro MV, Hanniford D, Zhang G, et al. BRD4 sustains melanoma proliferation and represents a new target for epigenetic therapy. Cancer Res. 2013;73(20):6264-76.
Dar AA, Bezrookove V, Nosrati M, Ice R, Patino JM, Vaquero EM, et al. Bromodomain inhibition overcomes treatment resistance in distinct molecular subtypes of melanoma. Proc Natl Acad Sci U S A. 2022;119(34):e2206824119.
Echevarria-Vargas IM, Reyes-Uribe PI, Guterres AN, Yin X, Kossenkov AV, Liu Q, et al. Co-targeting BET and MEK as salvage therapy for MAPK and checkpoint inhibitor-resistant melanoma. EMBO Mol Med. 2018;10(5).
Nikbakht N, Tiago M, Erkes DA, Chervoneva I, Aplin AE. BET Inhibition Modifies Melanoma Infiltrating T Cells and Enhances Response to PD-L1 Blockade. J Invest Dermatol. 2019;139(7):1612-5.
Kagoya Y, Nakatsugawa M, Yamashita Y, Ochi T, Guo T, Anczurowski M, et al. BET bromodomain inhibition enhances T cell persistence and function in adoptive immunotherapy models. J Clin Invest. 2016;126(9):3479-94.
Shibata H, Saito S, Uppaluri R. Immunotherapy for Head and Neck Cancer: A Paradigm Shift From Induction Chemotherapy to Neoadjuvant Immunotherapy. Front Oncol. 2021;11:727433.
Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, et al. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol Cancer Ther. 2017;16(11):2598-608.
Mandal R, Senbabaoglu Y, Desrichard A, Havel JJ, Dalin MG, Riaz N, et al. The head and neck cancer immune landscape and its immunotherapeutic implications. JCI Insight. 2016;1(17):e89829.
Senyuz S, Jang H, Nussinov R, Keskin O, Gursoy A. Mechanistic Differences of Activation of Rac1(P29S) and Rac1(A159V). J Phys Chem B. 2021;125(15):3790-802.
Storch K, Cordes N. The impact of CDK9 on radiosensitivity, DNA damage repair and cell cycling of HNSCC cancer cells. Int J Oncol. 2016;48(1):191-8.
Skvortsov S, Dudas J, Eichberger P, Witsch-Baumgartner M, Loeffler-Ragg J, Pritz C, et al. Rac1 as a potential therapeutic target for chemo-radioresistant head and neck squamous cell carcinomas (HNSCC). Br J Cancer. 2014;110(11):2677-87.

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

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Journal Publication

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Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Cell Culture

Cloning

Cell Transductions

RAC1 Mutant Induction by Shield-1

Cell Proliferation

Scratch Motility Assay

Transwell Migration Assay

Phalloidin Staining

CRISPR-mediated CDK9 Knockout and Cell Electroporation

RNA Sequencing

Multiplex Inhibitor Beads and Mass Spectrometry

Cell Cycle Profile Analysis

H2B-GFP Live Cell Imaging

Pharmacologic Inhibitor Treatments and Viability Assays

Immunoblot

Live/Dead Assays

Crystal Violet Staining

Cell Surface Staining by Flow Cytometry

Study Approval

Immunohistochemical (IHC) staining

Quantitative Image analysis

Statistical Analysis

RESULTS

CDK9 inhibition increase surface expression of PD-L1 and Class I MHC in RAC1 mutant melanocytes

RAC1P29S expression increases tumor immune cell infiltration

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

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RAC1^P29S expression increases tumor immune cell infiltration