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Supplementary Table S1. Clinicopathologic characteristics of the cohort separated by death at the 5-year time point. Supplementary Table S2. Primary and secondary antibody information and TSA dye pairings for multiplex IF panel. Supplementary Table S3. Twenty highest ranking variables associating with overall survival by univariate Cox regression analysis. Supplementary Table S4. Density and proximity variables selected by least absolute shrinkage and selection operator (LASSO) methods using a 5-fold cross-validation. Supplementary Figure S1. Image cytometry to quantify immune cell biomarkers in metastatic melanoma. Supplementary Figure S2. The expression of PD-L1 on tumor cells and 'Other' cells is determined by their proximity to CD8+ cells. Supplementary Figure S3. Characterization of immune neighborhoods associated with overall survival. Supplementary Computational Methods. Survival and clinical statistical analysis - R markdown document.

Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R2 > 0.8). The associated spatial UMAP highlighted “immune neighborhoods” and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded—the highest PD-L1 expression intensity was observed on CD163+ cells in neighborhoods with high CD8+ cell density, and the highest PD-1 expression intensity was observed on CD8+ cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.

Melanocytic archival specimens analyzed for PD-L1 expression, BRAF mutational status, and inflammatory infiltrate. PD-L1 and MHC I induction by IFN-g on cultured human melanoma cell lines, according to BRAF codon 600 genotype.

Supplementary Table S1: PD-L1+ RCC specimens used in this study. Supplementary Table S2: Genes differentially expressed in RCC based on whole genome microarray analysis, in patients responding or not to anti-PD-1 therapy (234 probe sets corresponding to 223 genes). Supplementary Table S3: Sixty genes included in custom multiplex qRT-PCR array to assess candidates from RCC whole genome microarray profiling.

PD-L1 expression in melanoma correlates with response to PD-1 pathway–blocking antibodies. Aberrant tumor-cell PD-L1 expression may be oncogene driven and/or induced by IFNγ. Melanomas express PD-L1 in association with tumor-infiltrating lymphocytes (TIL), but the potential contribution of the BRAF V600E mutation (BRAFmut) to induced PD-L1 expression has not been determined. Fifty-two archival melanocytic lesions were assessed for PD-L1 expression, TIL infiltration, and BRAFmut simultaneously. IFNγ-induced PD-L1 expression in cultured melanomas was assessed in parallel according to BRAF status. Melanocyte PD-L1 expression was observed in 40% of specimens, and BRAFmut was observed in 42% of specimens, but no significant concordance was found between these variables. Almost all melanocytes displaying PD-L1 expression were observed to be adjacent to TILs, irrespective of BRAF status. TIL− lesions were not more likely to be associated with BRAFmut, when compared with TIL+ lesions. Baseline expression of PD-L1 by melanoma cell lines was virtually nil, regardless of BRAFmut status, and the intensity of IFN-induced PD-L1 expression in melanoma cell lines likewise did not correlate with BRAF mutational status. PD-L1 expression in melanocytic lesions does not correlate with the BRAFmut. Thus, distinct populations of melanoma patients will likely benefit from BRAF inhibitors versus PD-1 pathway blockade. Cancer Immunol Res; 3(2); 110–5. ©2014 AACR.

Figure S1: Extraction of paraffin-embedded PD-L1+ RCC tissues for RNA isolation. Figure S2: Molecules previously found to be up-regulated in PD-L1+ vs. PD-L1(-) melanomas are not differentially expressed in PD-L1+ RCCs from patients with divergent clinical outcomes after anti-PD-1 therapy. Figure S3: Gene expression in RCC cell lines. Figure S4: In silico analyses of TCGA RCC data do not demonstrate significant associations between UGT1A6 gene expression and overall survival or clinical stage.

Supplementary Materials and Methods

Supplementary methods

Supplementary Table S4: DAVID clustering of 1660 Illumina probes up-regulated at least 2-fold in PD-L1+ melanomas

Purpose: PD-L1 expression in the pretreatment tumor microenvironment enriches for response to anti-PD-1/PD-L1 therapies. The purpose of this study was to quantitatively compare the performance of five monoclonal anti-PD-L1 antibodies used in recent landmark publications.Experimental Design: PD-L1 IHC was performed on 34 formalin-fixed paraffin-embedded archival melanoma samples using the 5H1, SP142, 28-8, 22C3, and SP263 clones. The percentage of total cells (including melanocytes and immune cells) demonstrating cell surface PD-L1 staining, as well as intensity measurements/H-scores, were assessed for each melanoma specimen using a computer-assisted platform. Staining properties were compared between antibodies.Results: Strong correlations were observed between the percentage of PD-L1(+) cells across all clones studied (R2 = 0.81–0.96). When present, discordant results were attributable to geographic heterogeneity of the melanoma tissue section rather than differences in PD-L1 antibody staining characteristics. PD-L1 intensity/H-scores strongly correlated with percentage of PD-L1(+) cells (R2 > 0.78, all clones).Conclusions: The 5H1, SP142, 28-8, 22C3, and SP263 clones all demonstrated similar performance characteristics when used in a standardized IHC assay on melanoma specimens. Reported differences in PD-L1 IHC assays using these antibodies are thus most likely due to assay characteristics beyond the antibody itself. Our findings also argue against the inclusion of an intensity/H-score in chromogenic PD-L1 IHC assays. Clin Cancer Res; 23(16); 4938–44. ©2017 AACR.

Cells were first gated on size and singularity by forward scatter and side scatter. Nonviable cells were excluded by live/dead gating. CD3+ live cells were gated on CD4 and CD8. CD3+FoxP3+CD4+ cells were designated as Tregs. TIM-3 vs. PD-1 expression was assessed for each lymphocyte population.

Purpose: Immunomodulatory drugs differ in mechanism-of-action from directly cytotoxic cancer therapies. Identifying factors predicting clinical response could guide patient selection and therapeutic optimization.Experimental Design: Patients (N = 41) with melanoma, non–small cell lung carcinoma (NSCLC), renal cell carcinoma (RCC), colorectal carcinoma, or castration-resistant prostate cancer were treated on an early-phase trial of anti–PD-1 (nivolumab) at one institution and had evaluable pretreatment tumor specimens. Immunoarchitectural features, including PD-1, PD-L1, and PD-L2 expression, patterns of immune cell infiltration, and lymphocyte subpopulations, were assessed for interrelationships and potential correlations with clinical outcomes.Results: Membranous (cell surface) PD-L1 expression by tumor cells and immune infiltrates varied significantly by tumor type and was most abundant in melanoma, NSCLC, and RCC. In the overall cohort, PD-L1 expression was geographically associated with infiltrating immune cells (P < 0.001), although lymphocyte-rich regions were not always associated with PD-L1 expression. Expression of PD-L1 by tumor cells and immune infiltrates was significantly associated with expression of PD-1 on lymphocytes. PD-L2, the second ligand for PD-1, was associated with PD-L1 expression. Tumor cell PD-L1 expression correlated with objective response to anti–PD-1 therapy, when analyzing either the specimen obtained closest to therapy or the highest scoring sample among multiple biopsies from individual patients. These correlations were stronger than borderline associations of PD-1 expression or the presence of intratumoral immune cell infiltrates with response.Conclusions: Tumor PD-L1 expression reflects an immune-active microenvironment and, while associated other immunosuppressive molecules, including PD-1 and PD-L2, is the single factor most closely correlated with response to anti–PD-1 blockade. Clin Cancer Res; 20(19); 5064–74. ©2014 AACR.

Supplemental Figures S1 Figure S2, Figure S3 and Table S1 and Table S2

Cells were first gated on size and singularity by forward scatter and side scatter. Nonviable cells were excluded by live/dead gating. Live cells were gated on CD11c and CD11b, then on CD45 and F4/80 to identify macrophage and dendritic cell phenotypes. Finally myeloid cells were gated for expression of TIM-3. Final populations were (A) F4/80+CD45hiCD11c-CD11bhi (B) F4/80+CD45dimCD11bhi (C) F4/80+CD45hiCD11c+CD11b+ and (D) F4/80-CD45dimCD11c+CD11b-.

Supplementary Table S1, Supplementary Table S3, Supplementary Table S5. Supplementary Table S1: Genes selected for multiplex qRT-PCR array Supplementary Table S3: Functionally annotated gene categories from DAVID analysis of whole genome microarray results comparing PD-L1+ to PD-L1(-) melanomas. Supplementary Table S5: Genes over-expressed in PD-L1+ vs. PD-L1(-) melanomas, assessed by qRT-PCR.

Purpose: Checkpoint molecules like programmed death-1 (PD-1) and T-cell immunoglobulin mucin-3 (TIM-3) are negative immune regulators that may be upregulated in the setting of glioblastoma multiforme. Combined PD-1 blockade and stereotactic radiosurgery (SRS) have been shown to improve antitumor immunity and produce long-term survivors in a murine glioma model. However, tumor-infiltrating lymphocytes (TIL) can express multiple checkpoints, and expression of ≥2 checkpoints corresponds to a more exhausted T-cell phenotype. We investigate TIM-3 expression in a glioma model and the antitumor efficacy of TIM-3 blockade alone and in combination with anti-PD-1 and SRS.Experimental Design: C57BL/6 mice were implanted with murine glioma cell line GL261-luc2 and randomized into 8 treatment arms: (i) control, (ii) SRS, (iii) anti-PD-1 antibody, (iv) anti-TIM-3 antibody, (v) anti-PD-1 + SRS, (vi) anti-TIM-3 + SRS, (vii) anti-PD-1 + anti-TIM-3, and (viii) anti-PD-1 + anti-TIM-3 + SRS. Survival and immune activation were assessed.Results: Dual therapy with anti-TIM-3 antibody + SRS or anti-TIM-3 + anti-PD-1 improved survival compared with anti-TIM-3 antibody alone. Triple therapy resulted in 100% overall survival (P < 0.05), a significant improvement compared with other arms. Long-term survivors demonstrated increased immune cell infiltration and activity and immune memory. Finally, positive staining for TIM-3 was detected in 7 of 8 human GBM samples.Conclusions: This is the first preclinical investigation on the effects of dual PD-1 and TIM-3 blockade with radiation. We also demonstrate the presence of TIM-3 in human glioblastoma multiforme and provide preclinical evidence for a novel treatment combination that can potentially result in long-term glioma survival and constitutes a novel immunotherapeutic strategy for the treatment of glioblastoma multiforme. Clin Cancer Res; 23(1); 124–36. ©2016 AACR.

Supplementary Table S2: 1660 Illumina probes up-regulated at least 2-fold in PD-L1+ melanomas

Each mouse was individually imaged with cone beam computed tomography (CBCT) using the SARRP with a 65 kVp and 0.7 mA beam. Using the treatment planning system from the SARRP system (Muriplan) and the CBCT, the target was exactly placed 3mm below the skull's burr hole. The planning system calculated the x-ray beam's (220kVp and 13mA) time of exposure according to the prescribed dose and moved the motorized couch to its target location, after which a 3-mm beam centered on the burr hole and underlying tumor was used to administer a total of 10 Gy radiation per animal at a rate of 1.9 Gy/min (18). The isodose distribution is shown on the figure provided. The dose to adjacent organs or rest of the brain is insignificant as the beam's penumbra demonstrates complete drop off on the edge of the field (17, 50, 51)

Purpose: Blocking the immunosuppressive PD-1/PD-L1 pathway has antitumor activity in multiple cancer types, and PD-L1 expression on tumor cells and infiltrating myeloid cells correlates with the likelihood of response. We previously found that IFNG (interferon-gamma) was overexpressed by tumor-infiltrating lymphocytes in PD-L1+ versus PD-L1(−) melanomas, creating adaptive immune resistance by promoting PD-L1 display. This study was undertaken to identify additional factors in the PD-L1+ melanoma microenvironment coordinately contributing to immunosuppression.Experimental Design: Archived, formalin-fixed paraffin-embedded melanoma specimens were assessed for PD-L1 protein expression at the tumor cell surface with IHC. Whole-genome expression analysis, quantitative (q)RT-PCR, IHC, and functional in vitro validation studies were used to assess factors differentially expressed in PD-L1+ versus PD-L1(−) melanomas.Results: Functional annotation clustering based on whole-genome expression profiling revealed pathways upregulated in PD-L1+ melanomas, involving immune cell activation, inflammation, and antigen processing and presentation. Analysis by qRT-PCR demonstrated overexpression of functionally related genes in PD-L1+ melanomas, involved in CD8+ T-cell activation (CD8A, IFNG, PRF1, and CCL5), antigen presentation (CD163, TLR3, CXCL1, and LYZ), and immunosuppression [PDCD1 (PD-1), CD274 (PD-L1), and LAG3, IL10]. Functional studies demonstrated that some factors, including IL10 and IL32-gamma, induced PD-L1 expression on monocytes but not tumor cells.Conclusions: These studies elucidate the complexity of immune checkpoint regulation in the tumor microenvironment, identifying multiple factors likely contributing to coordinated immunosuppression. These factors may provide tumor escape mechanisms from anti–PD-1/PD-L1 therapy, and should be considered for cotargeting in combinatorial immunomodulation treatment strategies. Clin Cancer Res; 21(17); 3969–76. ©2015 AACR.

Figure S1, Figure S2, Figure S3. Supplementary Figure S1: Overlay of PD-L1 and LAG-3 IHC demonstrates close geographic association of these cell-surface markers in a melanoma lymph node metastasis. Supplementary Figure S2: Comparison of immunohistochemistry (IHC) and in situ hybridization (ISH) for LAG-3 expression in a melanoma lymph node metastasis. Supplementary Figure S3: IL-32-g activates CD3+ T cells and induces their expression of PD-L1.

Antibodies used for flow cytometry (clones and dilutions)

Gating strategy to assess for surface expression of TIM-3 on (A) CD4+ and (B) CD8+ T cells isolated from peripheral lymph nodes, lungs, livers, spleens, and brains.

Supplementary Table and Figure Legends

Supplemental Table 1. Clinical and pathologic characteristics of patients included in tissue microarrays Supplementary Figure 1. B7-H4 expression is low in human and mouse lung cancer cell lines in vitro Supplementary Figure 2. B7-H1 expression does not change with MAPK or proliferation inhibition Supplementary Figure 3. Scoring of PD-L1 and pS6 staining in lung adenocarcinoma and squamous cell carcinoma TMAs Supplementary Figure 4. Co-expression of pS6 and PD-L1 staining in TMAs Supplemental Figure 5. The combination of rapamycin and anti-PD-1 antibody decreases KRAS-driven lung tumor growth Supplementary Figure 6. The combination of rapamycin and αPD-1 blockade decreases NNK-derived syngeneic lung tumor growth.

Alterations in EGFR, KRAS, and ALK are oncogenic drivers in lung cancer, but how oncogenic signaling influences immunity in the tumor microenvironment is just beginning to be understood. Immunosuppression likely contributes to lung cancer, because drugs that inhibit immune checkpoints like PD-1 and PD-L1 have clinical benefit. Here, we show that activation of the AKT–mTOR pathway tightly regulates PD-L1 expression in vitro and in vivo. Both oncogenic and IFNγ-mediated induction of PD-L1 was dependent on mTOR. In human lung adenocarcinomas and squamous cell carcinomas, membranous expression of PD-L1 was significantly associated with mTOR activation. These data suggest that oncogenic activation of the AKT–mTOR pathway promotes immune escape by driving expression of PD-L1, which was confirmed in syngeneic and genetically engineered mouse models of lung cancer where an mTOR inhibitor combined with a PD-1 antibody decreased tumor growth, increased tumor-infiltrating T cells, and decreased regulatory T cells. Cancer Res; 76(2); 227–38. ©2015 AACR.

Pretreatment tumor PD-L1 expression has been shown to correlate with response to anti–PD-1/PD-L1 therapies. Yet, most patients with PD-L1+ tumors do not respond to treatment. The current study was undertaken to investigate mechanisms underlying the failure of PD-1–targeted therapies in patients with advanced renal cell carcinoma (RCC) whose tumors express PD-L1. Formalin-fixed, paraffin-embedded pretreatment tumor biopsies expressing PD-L1 were derived from 13 RCC patients. RNA was isolated from PD-L1+ regions and subjected to whole genome microarray and multiplex quantitative (q)RT-PCR gene expression analysis. A balance between gene expression profiles reflecting metabolic pathways and immune functions was associated with clinical outcomes following anti–PD-1 therapy. In particular, the expression of genes involved in metabolic and solute transport functions such as UGT1A family members, also found in kidney cancer cell lines, was associated with treatment failure in patients with PD-L1+ RCC. Conversely, tumors from responding patients overexpressed immune markers such as BACH2, a regulator of CD4+ T-cell differentiation, and CCL3 involved in leukocyte migration. These findings suggest that tumor cell–intrinsic metabolic factors may contribute to treatment resistance in RCC, thus serving as predictive markers for treatment outcomes and potential new targets for combination therapy regimens with anti–PD-1. Cancer Immunol Res; 4(9); 726–33. ©2016 AACR. See related Spotlight by Ohashi, p. 719.

Alterations in EGFR, KRAS, and ALK are oncogenic drivers in lung cancer, but how oncogenic signaling influences immunity in the tumor microenvironment is just beginning to be understood. Immunosuppression likely contributes to lung cancer, because drugs that inhibit immune checkpoints like PD-1 and PD-L1 have clinical benefit. Here, we show that activation of the AKT–mTOR pathway tightly regulates PD-L1 expression in vitro and in vivo. Both oncogenic and IFNγ-mediated induction of PD-L1 was dependent on mTOR. In human lung adenocarcinomas and squamous cell carcinomas, membranous expression of PD-L1 was significantly associated with mTOR activation. These data suggest that oncogenic activation of the AKT–mTOR pathway promotes immune escape by driving expression of PD-L1, which was confirmed in syngeneic and genetically engineered mouse models of lung cancer where an mTOR inhibitor combined with a PD-1 antibody decreased tumor growth, increased tumor-infiltrating T cells, and decreased regulatory T cells. Cancer Res; 76(2); 227–38. ©2015 AACR.

Background: Multidimensional, spatially resolved analyses of immune and tumor cells within the TME of patients treated with checkpoint inhibitors will provide clinically translatable mechanistic insights and potentiate biomarker discovery. To achieve this goal, information from pathology specimens needs to be captured at a single cell level with high fidelity and in meaningfully sized cohorts. To date, efforts have been limited by inadequate tissue sampling and previously unrecognized errors in staining, imaging and data analysis. Here we describe the ‘AstroPath' platform, where strategies from the field of astronomy were adapted to study pathology specimens and generate large high quality mIF data. Methods: Potential error was identified and addressed at each stage of 6-plex (PD-1, PD-L1, FoxP3, CD163, CD8, tumor marker) mIF assay development. Whole slides from formalin-fixed paraffin embedded tissue specimens were stained with the optimized assay and imaged using a multispectral microscope (Vectra 3.0) with 20% overlap of high power fields (HPFs). The overlaps were used to quantify and correct optical lens distortion, HPF alignment, and illumination variation. Errors from cell segmentation algorithms, batch-to-batch staining variation, and HPF sampling were also addressed. The resultant mIF data were organized and analyzed using a large, relational database. Results: The optimized mIF assay captured equivalent signal compared to gold standard chromogenic immunohistochemistry and 2x more signal for PD-1, PD-L1 and FoxP3 compared to the manufacturer's recommended protocol. Errors and corrections for imaging included: pixel alignment error reduced from ~10 to Conclusion: Here we present an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF datasets using lessons derived from the field of astronomy. Such approaches will vastly improve standardization and scalability of mIF technologies, enabling cross-site comparisons and eventual clinical translation as biomarker discovery platforms or standard diagnostic tests. Citation Format: Sneha Berry, Nicolas Giraldo, Benjamin Green, Elizabeth Engle, Haiying Xu, Aleksandra Ogurtsova, Daphne Wang, Julie E. Stein, Peter Nguyen, Suzanne Topalian, Angelo DeMarzo, Drew M. Pardoll, Robert A. Anders, Tricia R. Cottrell, Alexander S. Szalay, Janis M. Taube. The ‘AstroPath' platform for spatially resolved, single cell analysis of the tumor microenvironment (TME) using multispectral immunofluorescence (mIF) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6584.

Merkel cell carcinoma (MCC) is a lethal, virus-associated cancer that lacks effective therapies for advanced disease. Agents blocking the PD-1/PD-L1 pathway have shown objective, durable tumor regressions in patients with advanced solid malignancies and efficacy has been linked to PD-L1 expression in the tumor microenvironment. To investigate whether MCC might be a target for PD-1/PD-L1 blockade, we examined MCC PD-L1 expression, its association with tumor-infiltrating lymphocytes (TIL), Merkel cell polyomavirus (MCPyV), and overall survival. Sixty-seven MCC specimens from 49 patients were assessed with immunohistochemistry for PD-L1 expression by tumor cells and TILs, and immune infiltrates were characterized phenotypically. Tumor cell and TIL PD-L1 expression were observed in 49% and 55% of patients, respectively. In specimens with PD-L1(+) tumor cells, 97% (28/29) showed a geographic association with immune infiltrates. Among specimens with moderate-severe TIL intensities, 100% (29/29) showed PD-L1 expression by tumor cells. Significant associations were also observed between the presence of MCPyV DNA, a brisk inflammatory response, and tumor cell PD-L1 expression: MCPyV(-) tumor cells were uniformly PD-L1(-). Taken together, these findings suggest that a local tumor-specific and potentially MCPyV-specific immune response drives tumor PD-L1 expression, similar to previous observations in melanoma and head and neck squamous cell carcinomas. In multivariate analyses, PD-L1(-) MCCs were independently associated with worse overall survival [HR 3.12; 95% confidence interval, 1.28–7.61; P = 0.012]. These findings suggest that an endogenous immune response promotes PD-L1 expression in the MCC microenvironment when MCPyV is present, and provide a rationale for investigating therapies blocking PD-1/PD-L1 for patients with MCC. Cancer Immunol Res; 1(1); 54–63. ©2013 AACR.

We recently reported a 56% objective response rate in patients with advanced MCC receiving pembrolizumab (anti-PD-1) as first-line therapy. However, a tumor biomarker predicting clinical response was not defined. The purpose of this study was to determine potential associations of anti-PD-1 response (RECIST 1.1, analysis 8/1/16) and the presence of MCPyV, with the density and distribution of CD8+, PD-1+ and PD-L1+ cell populations in the tumor microenvironment (TME). Pretreatment FFPE tumor specimens were stained for CD8 (n=23) and PD-L1 (n=16, 22C3 assay) with immunohistochemistry (IHC). Immunofluorescence (IF) was used to detect PD-1 (n=16). Intratumoral (IT), peritumoral (PT, 100 um zone) and total (PT+IT) densities of PD-1+ and CD8+ immune cells (IC) were determined with digital image analysis. PD-L1+ cases had >1% tumor cells (TC) expressing PD-L1. Tumors from patients who responded to anti-PD-1 showed higher densities of PD-1+ cells in the IT, PT, and IT+PT regions when compared to non-responders (Mann-Whitney test, p-value=0.03, 0.06, 0.03, respectively). There was no significant association of response with CD8+ IC densities (IT, PT or total) or TC PD-L1 expression. Similarly, when we quantified the number of PD-1+ IC located within 15 um from a PD-L1+ cell (TC or IC), an association was observed between PD-1 and PD-L1 proximity and clinical response (Mann-Whitney test, p-value =0.04), but not CD8 and PD-L1 proximity. Because viral neoantigens can elicit a strong immune response, we also studied these TME factors for their potential associations with MCPyV. We found the presence of virus was significantly associated with increased densities of PT CD8+ cells (Mann-Whitney test, p-value=0.008) and TC PD-L1 expression (Fisher's test, p-value=0.04), but not PD-1+ IC. A more extensive multiplex IF panel (CD8, PD-1, PD-L1, CD68, FoxP3, NSE) analysis was performed on select pre-treatment specimens using the Vectra multispectral imaging system (Perkin Elmer) to explore this divergence. We found that only ~60% of observed total PD-1 expression was displayed by CD8+ cells. Although preliminary, our results suggest a relationship between PD-1+ cells and response to anti-PD-1 therapy and highlight that lymphocyte subsets other than CD8+ T-cells may contribute to the observed response. Further studies in larger cohorts are needed to validate these results. Citation Format: Nicolas A. Giraldo, Genevieve J. Kaunitz, Tricia R. Cottrell, Sneha Berry, Joel C. Sunshine, Peter Nguyen, Haiying Xu, Aleksandra Orgutsova, Candice D. Church, Natalie J. Miller, Jennifer H. Yearley, Evan J. Lipson, Ludmila Danilova, Paul T. Nghiem, Suzanne L. Topalian, Janis M. Taube. The differential association of PD-1, PD-L1, and CD8+ cells with response to pembrolizumab and presence of Merkel cell polyomavirus (MCPyV) in patients with Merkel cell carcinoma (MCC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 662. doi:10.1158/1538-7445.AM2017-662

Background: Blockade of the immunosuppressive PD-1/PD-L1 pathway has shown promising clinical results in patients with treatment-refractory solid tumors including melanoma. PD-L1, a protein displayed by many tumors, ligates the PD-1 co-inhibitory receptor on activated T cells. We previously found that IFN-γ, a potent inducer of PD-L1, was expressed by TILs in PD-L1(+) but not PD-L1(-) melanomas, creating an immunosuppressive microenvironment by a mechanism that we term “adaptive immune resistance” (Taube et al., Science Transl Med 2012). In the current study, we assessed other factors associated with PD-L1 expression in melanoma through differential gene expression analysis, in order to better understand the biology of this pathway and identify potential targets for combination immunotherapies. Methods: PD-L1(+) vs. (-) melanomas including immune cell infiltrates (n=11) were laser-capture microdissected (LCM) from formalin-fixed paraffin embedded (FFPE) specimens and analyzed by whole genome array analysis with cDNA-mediated Annealing, Selection, extension and Ligation (DASL), a novel technology that allows for gene expression analysis with partially degraded mRNAs obtained from FFPE specimens. Differentially expressed genes were submitted to the NIH functional analysis tool DAVID to search for enrichment in biologically related groups. Multiplex quantitative (q)RT-PCR was used to validate differential expression of genes of interest and to examine other candidate genes in new set of PD-L1(+) vs.(-) melanomas (n=11). Results: DASL/DAVID analysis of PD-L1(+) vs. PD-L1(-) melanomas uncovered genes of interest in several immunologically relevant pathways, including “Defense Response” (p Conclusions: These experiments identified groups of functionally related, differentially expressed immuno-regulatory genes in PD-L1(+) melanomas. These factors may coordinately create an immunosuppressive tumor microenvironment, by synergizing to enhance PD-L1 expression on tumor cells and to inhibit T cell function. Immunohistochemical and in vitro functional validation assays of candidate gene products are currently underway in order to identify new ways to overcome adaptive immune resistance in synergistic treatment combinations with PD-1/PD-L1 blockade. Citation Format: Geoffrey D. Young, Tracee L. McMiller, Haiying Xu, Shuming Chen, Alan E. Berger, Jinshui Fan, Robert A. Anders, Christopher Cheadle, Drew M. Pardoll, Suzanne L. Topalian, Janis M. Taube. Differential expression of immuno-regulatory genes associated with PD-L1 display: Implications for clinical blockade of the PD-1/PD-L1 pathway in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 446. doi:10.1158/1538-7445.AM2013-446