1 Title: the BRAF and MEK Inhibitors Dabrafenib and Trametinib

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Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Title: The BRAF and MEK Inhibitors Dabrafenib and Trametinib: Effects on Immune Function and in Combination with Immunomodulatory Antibodies Targeting PD1, PD-L1 and CTLA-4

Authors: Li Liu1*, Patrick A Mayes1*, Stephen Eastman1, Hong Shi1, Sapna Yadavilli1, Tianqian Zhang1, Jingsong Yang1, Laura Seestaller-Wehr1, Shu-Yun Zhang1, Chris Hopson1, Lyuben Tsvetkov1, Junping Jing2, Shu Zhang3 , James Smothers1, and Axel Hoos1

Affiliations: GlaxoSmithKline, 1Immuno-Oncology & Combination DPU, 2Molecular Medicine Unit, Oncology R&D, 3Statistical Science, Collegeville, PA19426, Pennsylvania, USA, * These authors contributed equally

Running title: Dabrafenib and trametinib effect on immune function

Financial support: This study was financially supported by GlaxoSmithKline.

Corresponding author:

Axel Hoos, GlaxoSmithKline, Immuno-Oncology & Combination DPU, 1250 S. Collegeville Rd., Collegeville, PA, USA, 19426. Tel: +1 610 427 3733. Email: [email protected].

Conflict of interest disclosure: L. Liu, P. Mayes, S. Eastman, H. Shi, S. Yadavilli, J. Yang, L. Seestaller-Wehr, C. Hopson, J. Jing, S. Zhang and J. Smothers are employees and stockholders of GlaxoSmithKline. T. Zhang, S-Y. Zhang, L. Tsvetkov and A. Hoos are employees of GlaxoSmithKline.

Word count: 4985

Total number of figures and tables: 6

Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract Purpose: To assess the immunological effects of dabrafenib and trametinib in vitro and to test whether trametinib potentiates or antagonizes the activity of immunomodulatory antibodies in vivo. Experimental Design: Immune effects of dabrafenib and trametinib were evaluated in human CD4+ and CD8+ T cells from healthy volunteers, a panel of human tumor cell lines, and in vivo using a CT26 mouse model. Results: Dabrafenib enhanced pERK expression levels and did not suppress human CD4+ or CD8+ T cell function. Trametinib reduced pERK levels, and resulted in partial/transient inhibition of T cell proliferation/expression of a cytokine and immunomodulatory gene subset, which is context dependent. Trametinib effects were partially offset by adding dabrafenib. Dabrafenib and trametinib in BRAF V600E/K, and trametinib in BRAF wild type tumor cells induced apoptosis markers, up-regulated HLA molecule expression, and down-regulated certain immunosuppressive factors such as PD-L1, IL1, IL8, NT5E, and VEGFA. PD-L1 expression in tumor cells was up-regulated after acquiring resistance to BRAF inhibition in vitro. Combinations of trametinib with immunomodulators targeting PD1, PD-L1, or CTLA4 in a CT26 model were more efficacious than any single agent. The combination of trametinib with anti-PD1 increased tumor-infiltrating CD8+ T cells in CT26 tumors. Concurrent or phased sequential treatment, defined as trametinib lead-in followed by trametinib plus anti-PD1 antibody, demonstrated superior efficacy compared with anti-PD1 antibody followed by anti-PD1 plus trametinib. Conclusion: These findings support the potential for synergy between targeted therapies dabrafenib and trametinib and immunomodulatory antibodies. Clinical exploration of such combination regimens is under way.

Translational Relevance Combining MAPK pathway inhibitors, such as dabrafenib and trametinib, with immunomodulatory antibodies targeting CTLA-4 or PD-L1/PD1 is of high interest in clinical development. However, comprehensive in vitro/in vivo preclinical translational studies for these agents, especially MEK inhibitors such as trametinib, their mechanisms of action around immunomodulation, and their impact on combination sequences are lacking. In this study, we assessed the immunological effects of dabrafenib and trametinib at clinically relevant exposures on both immune and tumor cells in vitro, and tested antitumor efficacy and/or pharmacodynamic markers for trametinib alone, and in combination with several immunomodulatory antibodies, in a CT26 immunocompetent syngeneic mouse model. Our in vivo data demonstrate the superior efficacy by the combination of trametinib with anti-PD1 antibody concurrently or sequentially phased, when first treated with trametinib followed by trametinib plus anti- PD1 antibody. These findings support clinical exploration of both trametinib and dabrafenib in combination with specific immunomodulatory antibodies.

Introduction

Immunotherapies and targeted therapies have distinctly different mechanisms of action and have both been shown to be efficacious in patients with advanced cancers (1, 2). It is expected that combinations of both modalities may create synergies with increased benefit for cancer patients. In order to enable such combinations, it is critical to determine how targeted therapies affect immune function in the tumor- microenvironment and peripheral systems. Immunogenic cell death, characterized by secretion of cell damage-associated hallmark molecules consisting of calreticulin (CRT), HSP70 and 90 proteins, HMGB1 and ATP, increased expression of tumor antigens and HLA molecules, and decreased expression of immunosuppression factors are desirable features for potential immune sensitization (3, 4). These effects may allow targeted agents to not only directly inhibit tumor growth, but also further enhance immune response by immunotherapy, through either tumor cell intrinsic or extrinsic immunomodulatory mechanisms, thus making the cancer therapy more effective and durable.

Inhibition of oncogenic MAPK signaling by dabrafenib (D), trametinib (T), or the combination of D and T has been an effective strategy and approved for the treatment of metastatic melanoma tumors bearing BRAF V600E and V600K mutations (5). The first generation of checkpoint immunomodulatory antibodies targeting either CTLA-4 or PD-L1/PD1 have demonstrated impressive clinical activity resulting in durable responses in subsets of patients with various cancer types (1, 6, 7). BRAF inhibitors such as vemurafenib have been shown to increase immune response and efficacy in combination with immunomodulators in preclinical models (8, 9). However, MEK inhibitors, including T, have been reported to be immunosuppressive in vitro, which has limited the in vivo assessment of MEK inhibitor combinations with immunotherapies (10). In this study, we assessed the immunological effects of D and T at clinically relevant exposures on both immune and tumor cells in vitro. The effect on immune cells by D was consistent with the literature reports for BRAF inhibitors (10, 11). However, we found that the effect of T on immune cells was both complex and context dependent.

The antiproliferative effect of T on T cells was partial and transient in vitro. Furthermore, we tested T alone and in combination with several immunomodulatory antibodies in an immunocompetent syngeneic mouse model. Our in vivo data demonstrates that T had minimal inhibitory effects on circulating immune cells, and enhanced efficacy and/or tumor infiltration of CD8+ effective cells. These findings support clinical exploration of both T and D in combination with specific immunomodulatory antibodies.

Materials and Methods

Cell lines and reagents

Human melanoma cell line, A375PF11, was derived from a clonal isolate of the A375 cell line obtained from the American Type Culture Collection (ATCC). Human melanoma line YUSIT1 was obtained from Yale Dermatology Cell Culture Facility (12). Human melanoma lines: SK-MEL-24, CHL-1, HMVII, SK-MEL-2; human non-small cell lung cancer (NSCLC) lines: Calu6, A549, and H358; and mouse colon carcinoma line CT26 were obtained from ATCC and cultured in RPMI with 10% fetal bovine serum (FBS) media. All cell lines were characterized by genotypic and RNA expression analyses using the Affymetrix 500K SNP chip and HG-U133Plus2 chip respectively (Affymetrix, Inc., Sunnyvale, CA) for human lines, and using Exome Seq and RNA-Seq (Illumina, San Diego, CA) for CT26, and kept in culture for <3 months. 12R5-1, 12R5-3, 16R6-3, 16R5-5, and 16R6-4 were D resistant clones derived from A375PF11 (referred to henceforth as A375) (13).

Human CD4+ and CD8+ T cells were isolated from whole blood using Stemcell Technologies RosetteSep Human T Cell Enrichment Cocktails (STEMCELL Technologies, Vancouver, BC) and Lymphoprep density gradient medium according to manufacturer’s protocol. Human whole blood was obtained from GlaxoSmithKline’s blood donation unit (Upper Providence site, PA) under the IRB approval.

In vitro T cell assays

Human CD4+ and CD8+ cells were activated with anti-CD3/anti-CD28 antibodies either in bead- or plate-bound forms. T and D were added at same time or sequentially with activation. T cell proliferation, cytokine secretion and apoptosis induction, cell signaling, surface markers, and gene expression levels were measured. Protocols are described in the Supplementary Methods.

Human and mouse tumor cell assays

The expression levels of immunomodulators, HLA molecules and tumor-associated antigens from tumor cells were determined by NanoString, RT-PCR, flow cytometry, and/or Western blot analyses. Procedures, antibodies and reagents are described in the Supplementary Methods.

In vivo evaluation in CT26 murine carcinoma syngeneic mouse model

Female BALB/C mice (Charles River) received food and water ad libitum and were housed in GSK in compliance with the recommendations of the Guide for Care and Use of Laboratory Animals. Tumors were established by subcutaneously implanting 5 x 104 CT26 cells in suspension into the right flank of mice. Tumor weights were calculated using the equation (l x w2)/2, where l and w refer to the larger and smaller dimensions collected at each measurement. Treatments began at Day 11 or 12 with tumor size 40– 100 mm3. Tumors were monitored and mice were euthanized when an endpoint was reached, defined as tumor volume greater than 2,000 mm3, tumor ulceration, or study end (21 or 68 days after initial dosing), whichever came first. Tumor regressions, median tumor volume, and treatment tolerability were also considered.

Percent tumor growth inhibition (% TGI) was defined as the difference between the mean tumor volume (MTV) of the designated control group and the MTV of the drug- treated group, expressed as a percentage of the MTV of the designated control group:

% TGI = [1–(MTVdrug-treated/MTVcontrol)] x 100. Kaplan–Meier (KM) method was carried out to estimate the survival probability of different treatment groups at a given time. The median time to endpoint (TTE) and its corresponding 95% confidence interval (CI) was calculated.

For pharmacodynamic analysis, fresh tumors, lymph nodes, spleen, serum, and whole blood were collected 4 and 24 hours post last dose on day 7 or day 8. Flow cytometric analysis of lymphocytes from mouse blood, tumor tissues, lymph nodes, and spleens, cytokine analysis from serum, and gene expression and immunohistochemistry (IHC) analyses from tumor tissues are described in the Supplementary Methods.

Statistical analysis of the results was performed by contrast analysis following one-way ANOVA, and described in the Supplementary Methods.

Results

T, but not D, partially and transiently inhibits T cell proliferation and cytokine production in vitro

We used CD4+ and CD8+ cells isolated from healthy volunteers to assess if D and T could affect T cell proliferation and function in vitro. T cells were activated with anti- CD3/anti-CD28 antibodies coated either on beads or plates. T and D were added either simultaneously or sequentially with activation. At clinically relevant exposures, both D and T had little to no effect on naïve T cells (data not shown). At clinically relevant concentrations, D alone did not significantly inhibit proliferation, apoptosis, or cytokine production of activated CD4+ and CD8+ T cells (Fig. 1). Unlike D, T alone resulted in partial inhibition of CD4+ T cell proliferation after 3 days, but not after 7 days of treatment if CD4+ T cells were dosed with compound 24 hours before activation (Fig. 1A, Drug>Act., top panel). At 7 days, partial growth inhibition was apparent only at T concentrations greater than 100 nM, which is above clinical exposure levels. The partial inhibitory effect was not observed if CD4+ T cells were activated prior to adding T (Fig. 1A, Act.>Drug, bottom panel). In addition, treatment with T alone (Drug>Act. and Act.>Drug) and in combination with D (Act.>Drug) for 24 hours resulted in decreased activation-induced apoptosis (AID) measured by Caspase 3/7 activity following T cell activation (Fig. 1B). This was despite similar levels of proliferation in T- and D/T-treated cells at the time and concentration tested. The effects of T on cytokine production were

8 variable depending on the cytokine analyzed, resulting in little to no change (IFNγ, IL-5, IL-10), or partial inhibition of some cytokines (IL2, TNFa, IL8), while inducing expression of others (IL4) when it was added prior to (Fig. 1B, Drug>Act.) or simultaneously (data not shown) with T cell activators. Observed cell growth inhibition and cytokine changes by T were transient and minimized if CD4+ T cells were activated first (Fig. 1, Act.>Drug). In the setting of T and D combination, the effects of T appeared to dominate but were partially offset by D in some instances. Similar results were also observed in CD8+ cells (data not shown).

D and T differentially affect the expression levels of pERK and a subset of genes/proteins in human activated T cells in vitro

Cell signaling critical to MAPK/PI3K/mTOR pathways was measured in CD4+ and CD8+ cells and representative data are shown in Fig. 2A. D alone enhanced pERK expression levels, an observation consistent with previously reported paradoxical effects of BRAF inhibitors in BRAF WT cells (14); with no observed changes in pAKT and pS6 protein levels (Fig. 2A and data not shown). In contrast, T alone reduced pERK levels, but not pAKT and pS6 expression levels as compared with controls (Fig. 2A and data not shown).

Furthermore, immune gene expression profiling using the NanoString nCounter gene expression system demonstrated unique gene signatures associated with both CD4+ and CD8+ T cell activation by anti-CD3/CD28 antibodies (Fig. 2B). We identified genes with expression levels changed equal to or greater than 3-fold upon activation. Of the 525 genes in the panel, in CD4+ T cells, 88 genes (17%) were up-regulated and 55 genes (10%) were down-regulated; however, in CD8+ T cells, 39 genes (7%) were up- regulated and 88 genes (17%) were down-regulated. Interestingly, more genes were down-regulated in CD8 cells upon activation whereas more genes were up-regulated in CD4 cells. Ontology enrichment indicated activation modulated sets of genes with specific functions related to the activity of the T cell receptors, accompanied by genes encoding cytokines, chemokines, as well as genes involved in cell proliferation, transcription, and growth (Tables S2A, S2B, and S2C). When T was added

9 simultaneously with T cell activators, it partially offset the up-regulation of 10 genes, CCL3, CCL4, GZMB, IL2, IL3, IL9, IL10, IL17A, IL17F, and IL23R, mostly cytokines and chemokines. It also enhanced the expression of three up-regulated cytokines, IL4, IL5, and IL13, while it attenuated the down-regulation of FCER1A, MX1, and RARRES3 genes in CD4+ cells. Similarly, T showed an offset of 11 up-regulated genes, CCL3, CCL4, GZMB, IFNG, IL2, IL3, LTA, CD82, IL1R1, TNF and XCL1, and three down- regulated genes CD244, CXCR4, and SIGIRR in CD8+ cells in response to T cell activation. D alone had little to no effect on activated CD4+ and CD8+ T cells. No apoptosis genes, including CASP1, CASP8, BCL2, and TNFSF10 (TRAIL), were modulated by T and D alone and in combination. Other immunomodulatory genes associated with activated T cells (FOXP3, CD274 [PD-L1], TNFRSF4 [OX40], ICOS, CTLA4, TNFRSF9 [4-1BB], CD25, and IFNG) were not affected substantially by D and T (< 2-fold) in CD4+ cells. However, OX40, ICOS, CTLA4, 4-1-BB, and IFNG were partially reduced by T alone and in combination with D (≈2-fold) in CD8+ cells. Both D and T had little-to-no effect on naïve CD4+ and CD8+ T cells (data not shown).

Multicolor flow cytometry confirmed cell surface expression changes of CD69, CD25, PD1, OX40 and CTLA4 in CD4+ and CD8+ cells (Fig. 2C and data not shown). D effects were similar to vehicle control treated samples. Treatment with T decreased the expression of CD25, CD69, OX40, and PD-1 in CD4+ and CD8+ T cells, and CTLA4 in CD4+ T cells only. However, the expression levels of CD69 and OX40 in T treated cells were still well above non-activated T cells. Combining D with T partially offset the inhibitory effects seen with T alone (Fig. 2C and Tables S3A and S3B).

D and T alone and in combination reduce the expression of tumor suppression factors and increase the expression of HLA-class I molecules and tumor antigens in BRAF V600 mutant melanoma cell lines

To determine how MAPK pathway inhibition affects the expression levels of immunoregulatory genes/proteins in BRAF V600E or V600K mutant melanoma cells, we first treated A375 melanoma cells with D and T either alone or in combination with the absence and presence of IFNγ. As shown in Fig. 3A, INFγ was able to induce both PD-

L1 and HLA-A expression in A375 cells. D and T (either alone or in combination) decreased PD-L1 and increased HLA-A regardless of IFNγ exposure. Interestingly, the inhibition of PD-L1 by D, T and D+T was transient in vitro, and did not track with the activation status of the pathway over time. In A375 cells treated with D, T or D+T over a 30-day timecourse, PD-L1 mRNA levels increased steadily out to day 30 after an initial reduction through day 8 (Fig. S3A). In contrast, DUSP6 mRNA levels, a reliable surrogate of MAPK activation, remained low throughout the 30-day timecourse (Fig. S3B), indicating an uncoupling of PD-L1 expression and MAPK activation status in cancer cells chronically exposed to D, T or D+T. In addition, we observed that a number of BRAF inhibitor resistant clones (12R5-1, 12R5-3, 16R6-3, 16R5-5, and 16R6-4) (13), which developed from the parental line A375, expressed high levels of PD-L1 as determined by Western blot analysis, flow cytometry and RT-PCR (Fig. 3B-C and data not shown). In A375 dabrafenib-resistant clones, increased PD-L1 protein expression tended to correlate with increased pSTAT levels (Fig. 3C), a result which was consistent with a previous report (15). IFNγ expression was below the level of detection in all cell samples tested and adding IFNγ did not further increase PD-L1 expression in 12R5-1 resistant line (data not shown).. In the 12R5-1 cell line, PD-L1 expression was still partially responsive to MAPK pathway inhibition as PD-L1 mRNA levels were reduced by 39%, 34%, and 77%, respectively in response to T, D and D+T treatment.. We also used a NanoString customer built codeset (302 genes) to measure the expression of genes including tumor antigens, HLA molecules as well as markers associated with immunomodulation, apoptosis, and MAPK signaling in both A375 and SK-MEL-24 BRAF V600E mutant cells. As shown in Fig. 3C and Table S4A for A375 cells, MAPK inhibition by D and T up-regulated tumor antigens NY-ESO-1, BAGE, TRP1, gp100, HLA-class I and class II molecules, immunomodulation factors such as CD40, ICOSLG, IL15, IRF1, OX40L, SPP1, STAT1/3, TOX, B7-H3, PDCD2, and pro-apoptosis/tumor suppression genes including BCL2L11 (BIM1), DPP4, PIK3IP1, RARRES3 and TP53IP, and down-regulated a subset of immunosuppressive factors such as PD-L1, VEGFA, IL1A, and NT5E (CD73). Similar results were seen in SK-MEL-24 cells (Table 1 and Table S4B). In addition, down-regulation of PD-L1 and up-regulation of HLA-I and II molecules were confirmed in A375 and SK-MEL-24 and observed in 12R5-1, but not in

YUSIT BRAF V600K mutant cells by RT-PCR and/or flow cytometry analyses (Table 1 and data not shown). Furthermore, out of these four tested lines, tumor antigen NY- ESO-1 mRNA was increased ≈2-fold in A375 cells measured by NanoString, but not by Taqman, and at the protein level by Western blot analysis; whereas MART1 up- regulation by MAPK inhibition was observed at mRNA level (≈6-fold) in YUSIT and both mRNA (37–93-fold) and protein levels in SK-MEL-24 cells.

T increases apoptosis markers and the expression of HLA-molecules in non- BRAF mutant tumor cells

To evaluate the immunomodulatory effects of T on non-BRAF mutant tumor cells, we monitored the same key markers with the NanoString customer built codeset in three BRAF wild type melanoma lines, HMVII and SK-MEL-2 (both have a NRAS mutation and are sensitive to T), CHL1 (resistant to T), and three KRAS mutant NSCLC lines, Calu6, H358 and A549 (sensitive to T). The representative genes, PD-L1, DUSP4, DUSP6, HLA-I (A, B, C), HLA-II (DMA, DPA, DRA), IL6, and IL8 were confirmed with RT-PCR. In addition, surface protein expression levels of PD-L1 and HLA-I molecules were evaluated by flow cytometry analysis. The data are shown in Table S4B (Nanostring) and summarized in Table 1. Baseline level of PD-L1 is low in most of the lines, except H358 with moderate expression. T displayed mixed effects on PD-L1 expression with up-regulation in Calu-6 and A549 (only detected by RT-PCR), down- regulation in H358 NSCLC line, and no change in three out of three melanoma lines tested. IFNγ increased the expression levels of PD-L1 and HLA-I in all tested cell lines (data not shown). DUSP4 and DUSP6 expression level was monitored as a measure of MAPK pathway inhibition by compound treatment. T induced an up-regulation of apoptosis markers, PIK3IP1, TP53INP1, BCL2L11 (BIM1), and HLA-I and/or II expression in five out of six cell lines tested except in the CHL-1. Whereas it decreased IL-8 levels in two out of three melanoma lines (HMVII and SK-MEL-2) and reduced the expression of NTE5 (CD73) and/or VEGFA in all three lung lines. It also reduced TGFA, EREG and AREG genes in two out of three lung lines (H358 and Calu6). Baseline level of IL6 was extremely low in all lines except in CHL1. RT-PCR, but not Nanostring, detected some level of IL6 induction by T in two out of the six lines (A549 and Calu6).

All cell lines except CHL-1 showed a reduction in DUSP4/6 levels by compound treatment, indicating that up-regulation of markers involved in apoptosis, HLA-I/II, and down-regulation a subset of immunosuppression IL8, NTE5, VEGFA may be associated with MAPK pathway inhibition by T. In addition, T dose dependently increased MART1, GP100, TRP-1 and TYR (3–10-fold at 3–10 nM) tumor antigen gene expression in HMVII BRAF WT melanoma line, but not in the rest of the lines tested.

Combinations of T with immunomodulatory antibodies targeting PD1, PD-L1 or CTLA4 in CT26 murine syngeneic tumor models are superior to single agents.

The in vivo antitumor effect of T was evaluated in the murine immunocompetent BALB/C syngeneic CT26 tumor model. CT26 mouse colorectal tumor cells harbor the homozygous KRAS G12D mutation and MAPK1 and MET amplifications (16). In vitro, T

caused dose-dependent inhibition of cell proliferation with a mean IC50 value of 20 nM and blocked MAPK signaling measured by pERK (Fig. 4A). In vivo, as shown in Fig. 4B, 18 days of daily T monotherapy at 1 mg/kg resulted in moderate antitumor activity with 61% TGI. Anti-mouse PD1, PD-L1, and CTLA4 antibodies dosed alone showed minimal to low efficacy with 2%, 18%, and 32% TGI respectively. However, concurrent combinations beginning with the first dose of T with anti-mouse PD1, PD-L1, or CTLA4 antibodies demonstrated much more profound activity with 80%, 81%, and 84% TGI respectively. No overt toxicity, as defined by weight loss, unkempt appearance, mortality and behavior, was observed in any of the groups during the course of treatment. The above data indicate that concurrent combinations of T with immunomodulatory antibodies targeting PD1, PD-L1, and CTLA4 potentiate antitumor activity as compared with the single agents at their tolerated doses.

Next, we explored whether the combination of T with the anti-PD1 antibody in different lead-in sequences would affect in vivo efficacy. As shown in Fig. 4C, 4D and Fig. S1 (tumor growth curves from individual mice), three combination regimens were evaluated: T and anti-PD1 antibody given concurrently starting in the first week (MEK- 1st+PD1-1st); T given in the first week as single agent followed by adding anti-PD1 antibody in the second week with continued T dosing (MEK-1st+PD1-2nd); and anti-

PD1 antibody given in the first week as single agent followed by adding T in the second week with continued anti-PD1 dosing (PD1-1st+MEK-2nd). All three combination regimens showed inhibition of tumor growth more effectively than their single agent controls during the initial 2–3 weeks of treatment (Fig. 4C). However, only two of three combination treatment groups, concurrent MEK-1st+PD1-1st and T lead-in followed by T + PD-1 (MEK-1st+PD1-2nd) produced profound delay in median tumor growth with TTE of 39 and 49 days, two out of 10 and four out of 10 68-day survivors, respectively, at the end of the study, and differed significantly from untreated and vehicle/isotype controls by log-rank survival analysis (P < 0.05) (Fig. 4D). Conversely, all PD1- 1st+MEK-2nd showed minimal benefit in long-term survival. Finally, it is worth noting that both PD1-1st monotherapy and PD1-1st+MEK-2nd treated groups had one out of 10 68-day survivors although majority of the groups showed no response. All treatments were well tolerated.

T alone or in combination with anti-PD1 antibody in vivo reduced immunosuppression factors, increased HLA-class II genes and lead to increased intratumoral CD4+ and CD8+ T cells

Given that T can potentiate the efficacy of immunomodulators in vivo, we investigated potential immunomodulatory mechanisms of T in combination with an anti-PD1 blocking antibody in lymphocyte tissues and CT26 tumors in vivo. We collected whole blood, tumors, spleens, and lymph nodes after 7 days of treatment with T and anti-PD1 antibody alone, or in concurrent combination. In tumors, T alone and in combination with anti-PD1 antibody, but not anti-PD1 antibody alone, significantly increased CD4+ T cells by 4.3- and 3.4-fold, respectively, compared with untreated and non-specific IgG2a controls (P < 0.05) measured by flow cytometry (Fig. 5A, 5B and Table S5). Only the combination of T and anti-PD1 antibody significantly increased CD8+ T cells by 4.7-fold vs. untreated (P < 0.05) and 3.6-fold vs. non-specific IgG2a control (P = 0.05) (Fig. 5B). CD69+ cells from CD4+ population were reduced by 53% from T treatment (P < 0.05 vs. untreated). CD69+ cells from CD8+ population were reduced by 57% from the

14 combination of T with anti-PD1 antibody (P < 0.05 vs. untreated). Other immune cell subtypes such as Treg measured by Foxp3+/CD25+ from CD4+ population, PD1+, PD- L1+, OX40+, and ICOS+ from both CD4+ and CD8+ cells were changed less than 2-fold or not statistically significant (P > 0.05) by any treatment (Table S5). There were no significant alterations in the numbers and expression levels of CD3+, CD4+, CD25+, CD69+, and PD1+ cells from spleens and lymph nodes (data not shown). Of note, these immune surface markers and circulating cytokines from peripheral blood samples were variable from mouse to mouse, and marginally (< 2-fold) or not significantly affected by any treatment in the study (data not shown). IHC analysis revealed that treatment with T alone and in combination with anti-PD1 antibody led to 70–75% inhibition of pERK in the tumor (Fig. 5C, Tables S5A and S5B), demonstrating effective MAPK signaling inhibition by T in the CT26 tumor model in immunocompetent mice.

In addition, tumor gene expression was profiled using a NanoString-based immunology panel and follow up with quantitative RT-PCR (qRT-PCR). Among the 561 mouse genes profiled, 77 showed a ≥ 1.5-fold change by T, anti-PD1, or the combination of the two in comparison with untreated and non-specific IgG2a controls (Table S6). A heatmap was generated by gene clustering of these 77 genes (Fig. 5D). The group I genes are those up-regulated by T alone and/or T in combination with anti-PD1 but not by anti-PD1 alone. Interestingly, three MHC class II genes, H2-Ea-ps, CD74, and H2- Eb1, along with CD4 and IL12b were up-regulated most significantly by the combination treatment. Group II includes 12 genes mainly associated with T cell IFNγ inducible cytotoxic factor (GZMB), immunoregulators (CD274-PD-L1and IL2RA-CD25), chemokines (CXCL9, CXCL11, CXCL10, CXCL12, CCL5-RENTES, and CCL8-MCP-1), and other immune factors (CFD, CD36, C3). Although not statistically significant, tumors from 2 out of three mice treated with anti-PD1 antibody alone showed unique and noticeable up-regulation of all of these genes, implying a gene signature of anti-PD1 effect on tumors. Group III has 42 genes which were down-regulated by T alone and in combination with anti-PD1. These genes includes inflammation factors (e.g., SELE-E- selectin, PTGS2-COX-2, MIF, TNF, PLAUR), chemokines (CCL2, CCL3, CCL4, CCL6, CCL7, CCL9, CCL11, CCL12, CXCL1, CXCL3, CXCR2), pro-inflammatory IL1 family cytokines mostly associated with immunosuppression (IL33, IL1Rl1, IL1RN, IL1B,

IL1R2, IL18RAP), tumor metastasis factors (S100A8 and S100a9, PLAU, ITGA6), markers associated with immunosuppression (TGFB1), and markers for monocytes (CD163, CD14), macrophage and dendritic cells (MSR1, CD209G) and myeloid cells (TREM1). A few selected genes were further evaluated using qRT-PCR which has more sensitive detection and showed consistent results with NanoString. As illustrated in Fig. S2, anti-PD1 antibody increased IFNG and GZMB by itself, and PD-L1, ICOS and CTLA4 with and without T. Only the combination of T and anti-PD1 increased CD4, OX40 and PD1. T alone and in combination with anti-PD1 showed reduction of CCL2 (consistent with Nanostring data), DUSP6 (correlated with pERK IHC) and IL6 (only detected by RT-PCR) (Fig. S2).

All data generated demonstrated that T in concurrent combination with anti-PD1 down- regulated immunosuppression factors, up-regulated HLA molecules and increased immune response in tumors with little or no immune phenotypical/functional changes from circulating cell populations and immune organs in vivo. All of these effects contribute to and support the rationale that T in combination with immunomodulatory antibodies targeting PD1, PD-L1, and CTLA4 is a more effective antitumor therapeutic approach.

Discussion Although the mechanisms of action for BRAF and MEK inhibitors such as D and T regarding tumor-growth inhibition are well studied, their impact on immune cells and the tumor microenvironment is less understood. Preclinical and limited clinical findings have suggested that immunomodulatory effects in the tumor microenvironment and on circulating immune cells by a BRAF inhibitor alone and the combination of a BRAF inhibitor with a MEK inhibitor is context-dependent (8, 17-20). On the other hand, concerns have been raised regarding the potential immunosuppressive activity of MEK inhibitors due to their immunosuppressive activity in vitro (10). However, the studies have been limited in scope and lack in vivo validation in mouse models. Here, we present data showing that the immunomodulatory effects of T on activated T cells were

16 multifaceted and context dependent. Changes which could be considered immunosuppressive were only observed in vitro, were transient, and did not translate in vivo using an immunocompetent mouse tumor model. The differences between data from this study and those of prior reports may be explained by a number of factors, including: 1) the in vitro experiments done by others had a short period of time exposure to MEK inhibitors without monitoring longer-term effects, therefore only the transient suppressive activity by MAPK inhibition was captured; 2) MEK inhibitors transiently block the MAPK signaling during the initial T cell activation, thus delay the kinetics of T cell activation and cytokine secretion in vitro; 3) the circulating T cells collected in vivo are mostly in a naïve state, reflected in this study by the low number of activated T cells from blood and immune organs with and without anti-PD1 antibody treatment in mice and much less active than T cells activated by anti-CD3/CD28 in vitro. Our preclinical data are complementary to the recent report showing that both D and T alone or the combination increased tumor infiltration of lymphocytes, and enhanced the antitumor effect with adoptive T cell immunotherapy in the syngeneic murine model of BRAF V600E mutant melanoma (21). Our studies indicate that T alone increases tumor infiltrating CD4+ lymphocytes (TILs), does not negatively affect the prevalence of CD8+ TILS while significantly increasing CD8+ TILs when combined with anti-PD1 antibody. Most importantly, combining T with anti-PD1 either concurrently or in phased sequence, with T administered first followed by T plus anti-PD1, resulted in much more effective and durable antitumor activity than both single agents in the KRAS mutant CT26 colorectal tumor syngeneic mouse model. Interestingly, phased sequence of anti-PD1 antibody dosed first, followed by anti-PD1 antibody plus T showed minimal benefit in long-term survival compared with single agent treatment. The observation suggests that blockage of oncogenic MAPK signaling by T is critical and effective to prime and synergize tumors in response to immunotherapy through the induction of apoptosis markers, up-regulation of HLA molecules, and reduction of immunosuppression factors from tumors. Our data support the rationale to evaluate the sequencing of these agents, including giving the targeted therapy as a lead-in to an immunomodulatory antibody or concurrently in clinical trial design to maximize antitumor efficacy and with the hope of minimizing side effects.

Our data show that PD-L1 was not only induced by IFN-γ in all cell lines tested, but was also up-regulated in cell lines with acquired resistance to dabrafenib. Conversely, D, T and the combination partially down-regulated PD-L1 expression in a subset, but not all cell lines tested, an observation similar to data from recent publications (15, 22, 23). Interestingly we also observed that T could induce PD-L1 expression in two of the NSCLC lines with low baseline level of PD-L1 expression. Our data in A375 cells chronically exposed to D, T or D+T, suggests that PD-L1 expression may be regulated via a compensatory pathway in response to sustained inhibition of MAPK signaling. Although the mechanisms of PD-L1 regulation appear to be multifactorial, MAPK signaling and feedback regulation by MAPK inhibition likely contribute, in part, to the expression levels of PD-L1 in tumors. However, PD-L1 expression level could be affected by multiple pathways including the MAPK pathway, and the change of PD-L1 expression is accompanied by multiple other genetic and morphological changes that collectively contribute to tumor growth and metastasis (24). As a result, PD-L1 may serve as a marker, but may not be the driver determining patient response to BRAF inhibitor treatment Future studies should further investigate the compensatory pathways responsible for PD-L1 regulation in the context of MAPK inhibition. As reported, during the initial treatment with either BRAF inhibitor alone or BRAF + MEK inhibitor in BRAF mutant melanoma patients, both PD-L1 and CD8+ T cell infiltrates were increased in treated tumors (18). However, CD8+ T cell infiltrates declined when these patients progressed from the treatment. It was also noted that PD-L1 expression increased when patients became refractory to BRAF or BRAF + MEK inhibitor treatment. The evidence of PD-L1 modulation data from both preclinical and clinical studies indicates that up- regulation of PD-L1 may be a marker of acquired resistance to BRAF, MEK or BRAF+MEK inhibition, and may provide rationale for PD-L1 inhibitor use in BRAF- resistant patients.

In this study, we also demonstrated that inhibition of MAPK by T, D or the combination of T and D in BRAF mutant melanoma and T in BRAF wild type melanoma and NSCLC lines increased the expression of apoptosis markers and HLA-class I and/or II

18 molecules and decreased a subset of immunosuppression factors in vitro. The increase of apoptosis markers PIK3IP1, TP53INP1, and BCL2L11 (BIM1) may sensitize tumor cell killing by cytotoxic T cells. Tumor cells secrete IL1, IL8 and VEGFA soluble factors to induce immunosuppression (25, 26). NTE5 (CD73) gene is highly expressed in many human solid tumors. It encodes an ectoenzyme generating extracellular adenosine which induces potent immunosuppressive effects and impaired anti-tumor T cell responses (27, 28). EGFR ligands such as TGFA, EREG, AREG can activate EGFR, promote tumor metastasis and enhance regulatory T cell-suppressive function via the EGFR (29). HLA-class I molecules are often down-regulated or lost in tumor cells and play a key role in immune escape (30-32). A study by Carreteto, et al. (33) showed that higher HLA class I gene expression was observed in regressing but not in progressing metastases from microdissected tumor regions, supporting the idea that the nature of HLA class I alterations in tumor cells may contribute to antitumor effects of therapeutic interventions. Our observation of MAPK inhibition induced apoptosis, increase of MHC class I and/or II along with the decrease of immunosuppression factors in most of the human tumor cell lines, and in CT26 tumors when combined with anti-PD1 antibody in vivo is intriguing. These effects may potentially increase immune response when MAPK pathway inhibitors combine with immunotherapy and may lead to therapeutic synergy.

These data taken together further expand the understanding of BRAF and MEK inhibitor effects on the immune system and provide scientific evidence to support the investigation of the combination of T, with or without D, with immunomodulators in the clinic. Clinical studies are under way to test the combination of these important agents in patients with metastatic melanoma.

Disclosure of Potential Conflicts of Interest L. Liu, P.A. Mayes, S. Eastman, H. Shi, S. Yadavilli, J. Yang, L. Seestaller-Wehr, C. Hopson, J. Jing, S. Zhang and J. Smothers are employees and stockholders of GlaxoSmithKline. T. Zhang, S-Y. Zhang, L. Tsvetkov and A. Hoos are employees of GlaxoSmithKline.

Authors’ Contributions Conception and design: L. Liu, P.A. Mayes, S. Eastman, H. Shi, T. Zhang, J. Yang, L. Seestaller-Wehr, C. Hopson, J. Smothers, A. Hoos. Collection and assembly of data: L. Liu, P.A. Mayes, S. Eastman, H. Shi, S. Yadavilli, T. Zhang, J. Yang, L. Seestaller-Wehr, S-Y. Zhang, L. Tsvetkov. Data analysis and interpretation: L. Liu, P.A. Mayes, S. Eastman, H. Shi, S. Yadavilli, T. Zhang, J. Yang, L. Seestaller-Wehr, S-Y. Zhang, C. Hopson, L. Tsvetkov, J. Jing, S. Zhang, A. Hoos. Financial support: A. Hoos. Administrative support: L. Liu. Provision of study material or patients: L. Liu. Manuscript writing: All authors. Final approval of manuscript: All authors.

Acknowledgements We thank Amber Anderson, Vivian Zhang, Bao Hoang, Yaobin Liu, Meixia Bi, David Kilian (GSK), Xiaoyu Pan, Drew M Pardoll (Johns Hopkins University), and Lisa Dauffenbach (Mosaic Laboratories) for their technical and consultation assistance.

Grant Support This study was financially supported by GlaxoSmithKline.

References

1. Mackiewicz-Wysocka M, Zolnierek J, Wysocki PJ. New therapeutic options in systemic treatment of advanced cutaneous melanoma. Expert Opin Investig Drugs

2013;22(2):181-90.

2. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nature

Reviews Cancer 2012;12(4):252-64.

3. Dudek AM, Garg AD, Krysko DV, De Ruysscher D, Agostinis P. Inducers of immunogenic cancer cell death. Cytokine Growth Factor Rev 2013;24(4):319-33.

4. Yaguchi T, Sumimoto H, Kudo-Saito C, Tsukamoto N, Ueda R, Iwata-Kajihara T, et

al. The mechanisms of cancer immunoescape and development of overcoming

strategies. Int J Hematol 2011;93(3):294-300.

5. Menzies AM, Long GV. Dabrafenib and trametinib, alone and in combination for

BRAF-mutant metastatic melanoma. Clin Cancer Res 2014;20(8):2035-43.

6. Forde PM, Reiss KA, Zeidan AM, Brahmer JR. What lies within: Novel strategies in immunotherapy for non-small cell lung cancer. Oncologist 2013;18(11):1203-13.

7. Merelli B, Massi D, Cattaneo L, Mandalà M. Targeting the PD1/PD-L1 axis in melanoma: Biological rationale, clinical challenges and opportunities. Crit Rev Oncol

2014;89(1):140-65.

8. Hong DS, Vence L, Falchook G, Radvanyi LG, Liu C, Goodman V, et al. BRAF(V600) inhibitor GSK2118436 targeted inhibition of mutant BRAF in cancer patients does not impair overall immune competency. Clin Cancer Res 2012;18(8):2326-35.

9. Koya RC, Mok S, Otte N, Blacketor KJ, Comin-Anduix B, Tumeh PC, et al. BRAF inhibitor vemurafenib improves the antitumor activity of adoptive cell immunotherapy.

Cancer Res 2012;72(16):3928-37.

10. Vella LJ, Pasam A, Dimopoulos N, Andrews M, Knights A, Puaux AL, et al. MEK inhibition, alone or in combination with BRAF inhibition, affects multiple functions of isolated normal human lymphocytes and dendritic cells. Cancer Immunol Res

2014;2(4):351-60.

11. Boni A, Cogdill AP, Dang P, Udayakumar D, Njauw CN, Sloss CM, et al. Selective

BRAFV600E inhibition enhances T-cell recognition of melanoma without affecting lymphocyte function. Cancer Res 2010;70(1538-7445; 0008-5472; 13):5213-9.

12. Halaban R, Zhang W, Bacchiocchi A, Cheng E, Parisi F, Ariyan S, et al. PLX4032, a

selective BRAFV600E kinase inhibitor, activates the ERK pathway and enhances cell

migration and proliferation of BRAFWT melanoma cells. Pigment cell & melanoma

research 2010;23(2):190-200.

13. Greger JG, Eastman SD, Zhang V, Bleam MR, Hughes AM, Smitheman KN, et al.

Combinations of BRAF, MEK, and PI3K/mTOR inhibitors overcome acquired resistance

to the BRAF inhibitor GSK2118436 dabrafenib, mediated by NRAS or MEK mutations.

Mol Cancer Ther 2012;11(4):909-20.

14. Callahan MK, Masters G, Pratilas CA, Ariyan C, Katz J, Kitano S, et al. Paradoxical activation of T cells via augmented ERK signaling mediated by a RAF inhibitor. Cancer

Immunol Res 2014;2(1):70-9.

15. Jiang X, Zhou J, Giobbie-Hurder A, Wargo J, Hodi FS. The activation of MAPK in melanoma cells resistant to BRAF inhibition promotes PD-L1 expression that is reversible by MEK and PI3K inhibition. Clin Cancer Res 2013;19(3):598-609.

16. Castle JC, Loewer M, Boegel S, de Graaf J, Bender C, Tadmor AD, et al.

Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma.

BMC Genomics 2014;15(1):190.

17. Cooper ZA, Frederick DT, Juneja VR, Sullivan RJ, Lawrence DP, Piris A, et al.

BRAF inhibition is associated with increased clonality in tumor-infiltrating lymphocytes.

Oncoimmunology 2013;2(10).

18. Frederick DT, Piris A, Cogdill AP, Cooper ZA, Lezcano C, Ferrone CR, et al. BRAF inhibition is associated with enhanced melanoma antigen expression and a more favorable tumor microenvironment in patients with metastatic melanoma. Clin Cancer

Res 2013;19(5):1225-31.

19. Knight DA, Ngiow SF, Li M, Parmenter T, Mok S, Cass A, et al. Host immunity contributes to the anti-melanoma activity of BRAF inhibitors. J Clin Invest

2013;123(3):1371-81.

20. Wilmott JS, Long GV, Howle JR, Haydu LE, Sharma RN, Thompson JF, et al.

Selective BRAF inhibitors induce marked T-cell infiltration into human metastatic melanoma. Clin Cancer Res 2012;18(5):1386-94.

21. Hu-Lieskovan S, Mok S, Faja LR, Goedert L, Comin-Anduix B, Koya RC, et al.

Combinatorial effect of dabrafenib, trametinib, and adoptive cell transfer (ACT) in an immune-competent murine model of BRAFV600E mutant melanoma. J Clin Oncol.

2014;32 (Suppl. 15):abstract 2512.

22. Atefi M, Avramis E, Lassen A, Wong DJ, Robert L, Foulad D, et al. Effects of MAPK and PI3K pathways on PD-L1 expression in melanoma. Clin Cancer Res

2014;20(13):3446-57.

23. Iwai Y, Ishida M, Tanaka Y, Okazaki T, Honjo T, Minato N. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-

L1 blockade. Proc Natl Acad Sci U S A 2002;99(19):12293-7.

24. Massi D, Brusa D, Merelli B, Ciano M, Audrito V, Serra S, et al. PD-L1 marks a

subset of melanomas with a shorter overall survival and distinct genetic and

morphological characteristics. Ann Oncol 2014.

25. Liu C, Peng W, Xu C, Lou Y, Zhang M, Wargo JA, et al. BRAF inhibition increases

tumor infiltration by T cells and enhances the antitumor activity of adoptive

immunotherapy in mice. Clin Cancer Res 2013;19(2):393-403.

26. Khalili JS, Liu S, Rodriguez-Cruz TG, Whittington M, Wardell S, Liu C, et al.

Oncogenic BRAF(V600E) promotes stromal cell-mediated immunosuppression via induction of interleukin-1 in melanoma. Clin Cancer Res 2012;18(19):5329-40.

27. Jin D, Fan J, Wang L, Thompson LF, Liu A, Daniel BJ, et al. CD73 on tumor cells impairs antitumor T-cell responses: A novel mechanism of tumor-induced immune suppression. Cancer Res 2010;70(6):2245-55.

28. Muller-Haegele S, Muller L, Whiteside TL. Immunoregulatory activity of adenosine and its role in human cancer progression. Expert review of clinical immunology

2014(0):1-18.

29. Zaiss DM, van Loosdregt J, Gorlani A, Bekker CP, Gröne A, Sibilia M, et al.

Amphiregulin enhances regulatory T cell-suppressive function via the epidermal growth factor receptor. Immunity 2013;38(2):275-84.

30. Garrido C, Paco L, Romero I, Berruguilla E, Stefansky J, Collado A, et al. MHC class I molecules act as tumor suppressor genes regulating the cell cycle gene expression, invasion and intrinsic tumorigenicity of melanoma cells. Carcinogenesis

2012;33(3):687-93.

31. Garcia‐Lora A, Algarra I, Garrido F. MHC class I antigens, immune surveillance, and tumor immune escape. J Cell Physiol 2003;195(3):346-55.

32. Algarra I, García-Lora A, Cabrera T, Ruiz-Cabello F, Garrido F. The selection of

tumor variants with altered expression of classical and nonclassical MHC class I

25 molecules: Implications for tumor immune escape. Cancer Immunology, Immunotherapy

2004;53(10):904-10.

33. Wilmott JS, Haydu LE, Menzies AM, Lum T, Hyman J, Thompson JF, et al.

Dynamics of chemokine, cytokine, and growth factor serum levels in BRAF-mutant melanoma patients during BRAF inhibitor treatment. J Immunol 2014;192(5):2505-13.

Table 1. In vitro effects on human tumor cell lines

Cell lines PD-L1 T Gene expression changes by T at 10 nM, 48 hours of (IC50, * baseline nM) treatment (RT-PCR) CTG Ct Level Apopto sis IL1A, s (PIK3I HLA-II MAPK HLA-I IL1B, TGFA, P1, (DMA, PD-L1 (DUSP (A, B, IL8 VEGF EREG, TP53I DPA, 4/6) C) A, AREG NP1, DRA) NTE5 BCL2L 11)

YUSI 28 Low 1 – ↓ ND – ↓ ↓ ND ND T‡ 12R5 ‡ 22 High 366 ↓ ↓ ND – ↑ ↓ ND ND melanoma -1

ant Mode A375 24 2 ↓ ↓ ↑ ↑ ↑ ↓ ↓ ↓

mut rate

SK- Very MEL 30 10 – ↓ ↑ ↑ ↑ ↓ ↓ ↓

BRAF low

– -24

SK-

MEL 26 Low 4 – ↓ ↑ ↑ ↑ ↓ – – ype t -2 ild

w HMV 29 Low 2 – ↓ ↑ ↑ ↑ ↓ – – II melanoma CHL- Very >50

BRAF 31 – – – – – – – – 1 low 0

Mode † H358 23 29 ↓ ↓ ↑ - - – ↓ ↓ rate

† NSCLC KRAS Calu6 29 Low 21 ↑ ↓ ↑ ↑ - – ↓ ↓ ant

–––– A549 29 Low 26 ↑ ↓ ↑ ↑ -† – ↓ – mut Gene expression levels were evaluated by NanoString and/or RT-PCR. *Cells were treated with T (10nM) alone in all lines or in combination with D (300nM) in 12R5-1 line for 48 hours. †Data from RT-PCR only. ‡Expression increase by T treatment was only detected by RT-PCR, but not NanoString due to below detection range. ↓ designated decrease with ≤ 0.5-fold of control. ↑ designated increase with ≥ 2-fold of control. – designated no change, with less than 2-fold of changes vs. control or below the detection range. CTG: cell titer glow assay for cell growth after 3 days of T treatment.

ND, Not determined.

Figure legend:

Figure 1. T transiently inhibits T cell proliferation and reduces activation-induced apoptosis. (A) The proliferation of CD4+ T cells as measured by CFSE following treatment with increasing concentrations of D and T at the indicated time points following addition of compounds. (B) Caspase 3/7 activity in CD4+ T cells following treatment with D (370 nM), T (37 nM) or, or the combination of D and T (D+T, 370nM/37 nM) for 24 hours. (C) Levels of soluble cytokines in the media of CD4+ T cells after treatment with D (370 nM), T (37 nM) or the combination of D and T (370 nM/37 nM) for 72 hours. (Drug>Act.) signifies the addition of drug 24 hours prior to addition of CD3/CD28 activation beads. (Act.>Drug) signifies the addition of drug 24 hours after activation with CD3/CD28 activation beads. Average of six individual donors are shown.

Figure 2. D and T differentially changed pERK and expression levels of a subset of genes/proteins, however showed no/minimal impact on pS6 in human activated T cells in vitro. (A) p-ERK and p-S6 levels were measured by MSD in CD4+ T cells treated with D (300nM), T(10 nM) or the combination of D and T (D+T, D/T = 300 nM/10 nM) in the absence (Unact.) and presence of anti-CD3/CD28 activation bead for 2 and 24 hours. (B) Heatmap from representative genes. NanoString nCounter GX Human Immunology v2 Kit was used. D (300nM), T (10 nM) or D+T (300 nM/10 nM) were added concurrently with CD3/CD28 activation beads to CD4+ and CD8+ T cells for 24 hours. (C) Time course of T cell surface marker expression in CD4+ T cells following treatment with D (100nM), T (10 nM) or D+T (100 nM/10 nM). (Drug>Act.) signifies the addition of drug 16 hours prior to activation; (Act.>Drug) signifies the addition of drug 16 hours after activation. Un, Non-activated T cells; Act, Activated T cells.

Figure 3. Immunomodulation by D and T in A375 BRAF mutant melanoma cells. (A) RT-PCR quantification of PD-L1 and HLA-A in A375 cells treated with T/D with and without IFNγ for 48 hours. (B) PD-L1 protein expression from flow cytometry and (C) Western Blot analyses in A375 parental and D acquired resistant cell lines. (D)

Differential gene expression from A375 cells treated with T (10nM), D (300nM) and the combination of T and D (10nM/300nM) for 48 hours (P < 0.05; ≥ 2-fold change).

Figure 4. Antitumor activity by T alone and/or in combination with immunomodulators targeting PD1, PD-L1, or CTLA4 in the CT26 murine syngeneic model. (A) In vitro cell growth and pERK inhibition by T in CT26 cells. (B) In vivo antitumor growth efficacy. Treatments began at day 12 after cell implant. Mice (n = 10 per group) were treated with vehicle (0.5% HPMC, 0.2% Tween-80, pH7.0) or T at 1 mg/kg orally once daily for 21days, or with antibodies rat-IgG2a, α-mouse PD-1 (RMP1-14 clone, rat IgG2a), α- mouse PD-L1 (10F.9G2 clone, rat IgG2b), or α-mouse CTLA-4 (9D9 clone, mouse IgG2b) at 10 mg/kg, i.p. twice weekly for 3 weeks. (C) Tumor growth inhibition by treatment after initial 3 weeks of treatment. (D) KM survival curves of different treatment groups. Treatments began on Day 11 post-tumor cell implantation, indicated as Day 1 of drug treatment for T (MEK-1st) and α-PD1 (PD1-1st), or on Day 18 post-tumor cell implantation, indicated as Day 8 of drug treatment for T (MEK-2st) and α-PD1 (PD1-2st). Mice (n = 10 per group) were treated with T at 1 mg/kg orally one daily, or with antibodies rat-IgG2a or α-mouse PD-1 (RMP1-14 clone) at 10 mg/kg i.p. twice weekly until tumors reached the end point of 2,000 mm3 or by study end.

Figure 5. T alone and/or in combination with anti-PD1 increased intratumoral CD4+ and CD8+ T cells in CT26 murine model. (A) Representative figures of flow cytometry gating and quantification for CD4+ and CD8+ (top panel), and Treg (CD25+/FoxP3+) (bottom panel) in tumors from each treatment group. (B) Flow cytometry quantification of tumor- infiltrating immune cells (Mean ± SEM, n = 3, *P < 0.05 vs. untreated and IgG2a+vehicle controls). (C) Representative pERK and total ERK IHC staining tumor sections from each treatment group. (D) Heatmap generated by clustering of 77 genes with ≥1.5-fold of tumor gene expression changes by any treatment group. Un; untreated; IgG2a, non- specific isotype control for anti-PD1; α-PD1, anti-PD1 antibody treatment; MEK, T treatment.

Figure 1 Un. Act. D T D+T A C Time = 72 hours Time = 168 hours 2.5 2.5 150,000 25,000

2.0 2.0 20,000 100,000 1.5 1.5 15,000 10,000 1.0 1.0 50,000

Drug>Act. 5,000 0.5 0.5 0 0 0.0 0.0 Drug>Act. Act.>Drug Drug>Act. Act.>Drug IFN-γ IL-2

0.014/0.0010.123/0.0121.111/0.111 0.014/0.0010.123/0.0121.111/0.111 15,000 8,000 CONTROLS0.002/0.0002 10.000/1.000 CONTROLS0.002/0.0002 10.000/1.000

6,000 10,000

Average cell division cell Average 4 4 4,000 3 3 5,000 2,000

2 2 0 0 Drug>Act. Act.>Drug Drug>Act. Act.>Drug Act.>Drug 1 1 TFN-α IL-8

0 1,500 3,000 0 (pg/ml) Concentration

1,000 2,000 0.014/0.0010.123/0.0121.111/0.111 0.014/0.0010.123/0.0121.111/0.111 CONTROLS0.002/0.0002 10.000/1.000 CONTROLS0.002/0.0002 10.000/1.000

D+T (µM) 500 1,000

0 0 B Drug>Act. Act.>Drug Drug>Act. Act.>Drug Drug>Act. Act.>Drug IL-4 IL-10 10,000 300,000 800 6,000

8,000 600 200,000 4,000 6,000 400

(RLU) 4,000 100,000 2,000 200 2,000 CaspaseGLO 3/7 0 0 0 0 Act. DDownloaded D+T T from clincancerres.aacrjournals.orgAct. D D+T T on SeptemberDrug>Act. 27,Act.>Drug 2021. © 2015 Drug>Act.AmericanAct.>Drug Association for Cancer Research. IL-5 IL-13 Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 2

A 10 B Unact. CD4 CD8 8 2 hr 24 hr Un. Act. D T D+T Un. Act. D T D+T 6 4

pERK/tERK 2 +3.0 (% normalized) (% 0 T T T V V V D D D D+T D+T D+T

80 Unact. 60 2 hr 24 hr 40 -3.0 pS6/tS6 20 (% normalized) (% 0 T T T V V V D D D D+T D+T D+T C Act. D T D+T

90 CD25 120 CD69 45 PD-1 35 CTLA-4 80 40 100 30 70 35 25 60 80 30 50 25 20 Drug>Act. 60 40 20 15 30 40 15 10

CD4+ CD25+(%) 20 10 20 10 5 5 0 0 0 0

0 hr 6 hr 0 hr 6 hr 0 hr 6 hr 0 hr 6 hr 24 hr48 hr 24 hr48 hr 24 hr48 hr 24 hr48 hr Baseline Baseline Baseline Baseline 100 120 90 80 90 80 70 80 100 70 60 70 80 60 60 50 50 50 60 40 Act.>Drug 40 40 30 30 40 30 20 20 CD4+ CD25+(%) 20 20 10 10 10 0 0 0 0

0 hr 6 hr 0 hr 6 hr 0 hr 6 hr 0 hr 6 hr 24Downloaded hr48 hr from clincancerres.aacrjournals.org24 hr48 hr 24 hron48 hrSeptember 27, 2021.24 hr 48© hr2015 American Association for Cancer Baseline Baseline Baseline Research.Baseline Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 3

A B

3.0 x 105 PDL-1 48 hours 1,500 Flow cytometry

2.0 x 105 1,000

RT-PCR 1.0 x 10 500

0 ( α -PDL1-APC) I F M 0

0nM A375 DMSO 12R5-1 12R5-3 16R5-2 16R5-3 MEK-1 INFg+MEKi BRAF-300nMBRAFi+MEKiINFg-3ng/ml INFg+BRAFi Western blot INFg+BRAFi+Meki 9.0 x 106 HLA-A PDL1 pSTAT3 6.0 x 106 (Y705)

6 STAT3

RT-PCR 3.0 x 10 Actin 0

DMSO A375 2R5-3 MEK-10nM INFg+MEKi 12R5-1 1 16R6-3 16R5-5 16R6-4 BRAF-300nMBRAFi+MEKiINFg-3ng/ml INFg+BRAFi INFg+BRAFi+Meki C

Tumor antigens NY-ESO-1, BAGE, TRP1, gp100

HLA class I and II +4.0 HLA-A,B,C,E, HLA-DMA,DPA, DRA, DRB3

Immunomodulation CD40, CD68, CD70, CD83, GBP1, ICOSLG, IL15, IRF1, OX40L, SPP1, STAT1, STAT3, TOX, B7-H3, PDCD2

Immunosuppression PD-L1, IL1A, IL8, NT5E, VEGFA -4.0 Apoptosis/tumor suppressors BIM1, DPP4, PIK3IP1, RARRES3,TP53IP

MAPK pathway Ki67,AREG, CDKN3, DUSP4/6, ETV4/5,SPRY4 TIMP1,TRIB2 Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer DMSO T D D+T Research. Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 4

A C 2,500 Rat-I gG2b 100 α-PD1

) α-PD-L1

3 2,000 α-CTLA4 75 IC = 20 ± 16 nM T 50 1,500 T+α-PD1 T+α-PD-L1 50 0 3 10 nM 1,000 T+α-CTLA4 pErk Cell proliferation Cell

25 (mm volume Tumor (% vehicle control) vehicle(% 500 0.05 < * P Erk 0 0 0 0.1 1 10 100 0 7 14 21 Trametinib (nM) Days of treatment C D 100 2,000 80 ) 3 1,500 60

1,000 40 Survival (%)Survival * < 0.05 < * P 500 20 Tumor volume (mm volume Tumor

0 0 0 7 14 21 0 14 28 42 56 70 Days of treatment Days of treatment Untreated PD1-2nd PD1-1st+MEKi-2nd Untreated PD1-2nd PD1-1st+MEK-2nd Veh+IgG2a MEKi-1st MEK-1st+PD1-1st Veh+IgG2a MEK-1st MEK-1st+PD1-1st PD1-1st MEKi-2nd MEK-2nd+PD1-2nd PD1-1st MEK-2nd MEK-2nd+PD1-2nd

MEK-1st PD1-2nd PD1-1st MEK-2nd End of study -11 1 8 68 (days)

CT26 cell 68 days of treatment implant Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer 61 days ofResearch. treatment Author Manuscript Published OnlineFirst on January 14, 2015; DOI: 10.1158/1078-0432.CCR-14-2339 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 5

A Untreated IgG2a MEK α-PD1 MEK/α-PD1 5 5 5 5 5

10 17.7% 10 20.6% 10 30.5% 10 18.7% 10 21.7% 4 4 4 4 4 10 10 10 10 10 35.9% 33.2% 25.5% 24% 58.2% 3 3 3 3 3 10 10 10 10 10 2 2 2 2 2 10 10 10 10 10 CD4FITC-A

102 103 104 105 102 103 104 105 102 103 104 105 102 103 104 105 102 103 104 105 CD8 APC-Cv7-A

5 40.4% 5 41.7% 5 34.1% 5 41.1% 5 36.6% 10 10 10 10 10 4 4 4 4 4 10 10 10 10 10 3 3 3 3 3 10 10 10 10 10 2 2 2 2 2 10 10 10 10 10 CD25 APC-A CD25

102 103 104 105 102 103 104 105 102 103 104 105 102 103 104 105 102 103 104 105 Foxp3 PE-A Lymphocytes % in Tumor B C 16 α-PD1 Untreated Untreated IgG2a α-PD1 +MEK MEK IgG2a 12 T α-PD1 pERK 8 T+α-PD1 * 4 tERK * *

Total cell from tumor (%) tumor from cell Total 0 Lymphocytes CD3+ CD4+ CD8+ D

Untreated

IgG2a

α-PD1

α-PD1 +MEK

MEK C3 Cfd Il2ra Mif * Mif Ccl5 Ccl8 Ccl3 Cd36 Ccl11 Cxcl9 Cxcl3 Cxcl1 Tnf ** Tnf Cd4* Cd7* Il1rn* Il1r2* Gzmb Il33** Il1b** Icosl * Icosl Sell** Ccr7* C1s** Trem1 Cd274 Cxcl11 Batf3 * Batf3 Csf1r * Csf1r Ccl6** Ccl9** Ccl7** Cxcl10 Cxcl12 Nfil3** Ccl4** Ccl2** Cd74* Sele** Plau** Cx3cr1 Ccl22* C1ra** Il1rl1** Il12a** Cxcr3* Msr1 **Msr1 Tgfbi **Tgfbi II12b **II12b Itga6 **Itga6 Fcgrt ** Fcgrt Tnfaip6 Plaur** Cd14** Cd22** Cd44** Cd209g Ccl12** Ptgs2 **Ptgs2 Tgfb3 **Tgfb3 Cxcr2** Tnfrsf9 * Tnfrsf9 Ptpn22* Ltb4r1** Pdgfrb** Cd79b** Cd163** H2-Eb1* II11ra1 ** II11ra1 Il18rap** Clec4e** H2Eaps* Cdkn1a** S100a9** S100a8** Mapk11 ** Mapk11 Ceacam1*

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Clin Cancer Res Published OnlineFirst January 14, 2015.

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