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Immune checkpoint inhibitors have not been effective for immunologically “cold” tumors, such as prostate cancer, which contain scarce tumor infiltrating lymphocytes. We hypothesized that select tissue-specific and immunostimulatory bacteria can potentiate these immunotherapies. Here we show that a patient-derived prostate-specific microbe, CP1, in combination with anti-PD-1 immunotherapy, increases survival and decreases tumor burden in orthotopic MYC- and PTEN -mutant prostate cancer models. CP1 administered intra-urethrally specifically homes to and colonizes tumors without causing any systemic toxicities. CP1 increases immunogenic cell death of cancer cells, T cell cytotoxicity, and tumor infiltration by activated CD8 T cells, Th17 T cells, mature dendritic cells, M1 macrophages, and NK cells. CP1 also decreases intra-tumoral regulatory T cells and VEGF. Mechanistically, blocking CP1-recruited T cells from infiltrating the tumor inhibits its therapeutic efficacy. CP1 is an immunotherapeutic tool demonstrating how a tissue-specific microbe can increase tumor immunogenicity and sensitize an otherwise resistant cancer type to immunotherapy. CP1 is an uropathogenic Escherichia coli previously shown to promote inflammation and progression to prostate cancer. Here the authors show that in the context of a fully developed prostate cancer, CP1 promotes T cell infiltration into the tumour and increases the efficacy of anti-PD1 immunotherapy.

The EPHB4 receptor is implicated in the development of several epithelial tumors and is a promising therapeutic target, including in prostate tumors in which EPHB4 is overexpressed and promotes tumorigenicity. Here, we show that high expression of EPHB4 correlated with poor survival in prostate cancer patients and EPHB4 inhibition induced cell death in both hormone sensitive and castration-resistant prostate cancer cells. EPHB4 inhibition reduced expression of the glucose transporter, GLUT3, impaired glucose uptake, and reduced cellular ATP levels. This was associated with the activation of endoplasmic reticulum stress and tumor cell death with features of immunogenic cell death (ICD), including phosphorylation of eIF2α, increased cell surface calreticulin levels, and release of HMGB1 and ATP. The changes in tumor cell metabolism after EPHB4 inhibition were associated with MYC downregulation, likely mediated by the SRC/p38 MAPK/4EBP1 signaling cascade, known to impair cap-dependent translation. Together, our study indicates a role for EPHB4 inhibition in the induction of immunogenic cell death with implication for prostate cancer therapy.

Deficiencies in DNA repair pathways, including mismatch repair (MMR), have been linked to higher tumor mutation burden and improved response to immune checkpoint inhibitors. However, the significance of MMR mutations in lung cancer has not been well characterized, and the relevance of other processes, including homologous recombination (HR) and polymerase epsilon (POLE) activity, remains unclear. Here, we analyzed a dataset of lung squamous cell carcinoma samples from The Cancer Genome Atlas. Variants in DNA repair genes were associated with increased tumor mutation and neoantigen burden, which in turn were linked with greater tumor infiltration by activated T cells. The subset of tumors with DNA repair gene variants but without T cell infiltration exhibited upregulation of TGF-β and Wnt pathway genes, and a combined score incorporating these genes and DNA repair status accurately predicted immune cell infiltration. Finally, high neoantigen burden was positively associated with genes related to cytolytic activity and immune checkpoints. These findings provide evidence that DNA repair pathway defects and immunomodulatory genes together lead to specific immunophenotypes in lung squamous cell carcinoma and could potentially serve as biomarkers for immunotherapy.

Vitamin E increased prostate cancer risk in the Selenium and Vitamin E Cancer Prevention Trial (SELECT) through unknown mechanisms while Selenium showed no efficacy. We determined the effects of the SELECT supplements on benign (primary), premalignant ( RWPE-1) and malignant (LNCaP) prostate epithelial organoids. While the supplements decreased proliferation and induced cell death in cancer organoids, they had no effect on the benign organoids. In contrast, Vitamin E enhanced cell proliferation and survival in the premalignant organoids in a manner that recapitulated the SELECT results. Indeed, while Vitamin E induced a pro-proliferative gene expression signature, Selenium alone or combined with Vitamin E produced an anti-proliferative signature. The premalignant organoids also displayed significant downregulation of glucose transporter and glycolytic gene expression pointing to metabolic alterations. Detached RWPE-1 cells had low ATP levels due to diminished glucose uptake and glycolysis which was rescued by Vitamin E through the activation of fatty acid oxidation (FAO). FAO inhibition abrogated the ATP rescue, diminished survival of the inner matrix detached cells, restoring the normal hollow lumen morphology in Vitamin E treated organoids. Organoid models therefore clarify the paradoxical findings from SELECT and demonstrate that Vitamin E promotes tumorigenesis in the early stages of prostate cancer evolution.

Immunotherapy has revolutionized the treatment landscape for many cancer types. The treatment for renal cell carcinoma (RCC) has especially evolved in recent years, from cytokine-based immunotherapies to immune checkpoint inhibitors. Although clinical benefit from immunotherapy is limited to a subset of patients, many combination-based approaches have led to improved outcomes. The success of such approaches is a direct result of the tumor immunology knowledge accrued regarding the RCC microenvironment, which, while highly immunogenic, demonstrates many unique characteristics. Ongoing translational work has elucidated some of the mechanisms of response, as well as primary and secondary resistance, to immunotherapy. Here, we provide a comprehensive review of the RCC immunophenotype with a specific focus on how preclinical and clinical data are shaping the future of immunotherapy.

Features of constrained adaptive immunity and high neoantigen burden have been correlated with response to immune checkpoint inhibitors (ICIs). In an attempt to stimulate antitumor immunity, we evaluated atezolizumab (anti-programmed cell death protein 1 ligand 1) in combination with PGV001, a personalized neoantigen vaccine, in participants with urothelial cancer. The primary endpoints were feasibility (as defined by neoantigen identification, peptide synthesis, vaccine production time and vaccine administration) and safety. Secondary endpoints included objective response rate, duration of response and progression-free survival for participants treated in the metastatic setting, time to progression for participants treated in the adjuvant setting, overall survival and vaccine-induced neoantigen-specific T cell immunity. A vaccine was successfully prepared (median 20.3 weeks) for 10 of 12 enrolled participants. All participants initiating treatment completed the priming cycle. The most common treatment-related adverse events were grade 1 injection site reactions, fatigue and fever. At a median follow-up of 39 months, three of four participants treated in the adjuvant setting were free of recurrence and two of five participants treated in the metastatic setting with measurable disease achieved an objective response. All participants demonstrated on-treatment emergence of neoantigen-specific T cell responses. Neoantigen vaccination plus ICI was feasible and safe, meeting its endpoints, and warrants further investigation. ClinicalTrials.gov registration: NCT03359239 . Galsky and colleagues report the results of a phase 1 clinical trial of anti-programmed cell death protein 1 ligand 1 atezolizumab in combination with PGV001, a personalized neoantigen vaccine, in participants with urothelial cancer.

Cisplatin-based chemotherapy has been associated with durable disease control in a small subset of patients with metastatic urothelial cancer. However, the mechanistic basis for this phenomenon has remained elusive. Antitumor immunity may underlie these exceptional responders. In a phase II trial evaluating a phased schedule of gemcitabine and cisplatin followed by gemcitabine and cisplatin with ipilimumab for metastatic urothelial cancer, 4 of 36 patients achieved durable disease-free treatment-free survival (DDFTFS) and remain in remission over 5 years after enrolment on the study. We sought to identify the genomic and immunological mechanisms associated with functional cures of such patients. Whole exome sequencing was performed on pretreatment archival tumor tissue. Neoantigen prediction and ranking were performed using a novel pipeline. For a subset of patients with available biospecimens, selected peptides were tested for neoantigen-specific T cell reactivity in peripheral blood CD4 + and CD8 + T cells cultured with autologous antigen-presenting cells at baseline, postchemotherapy, and postchemotherapy and ipilimumab timepoints. Multiplex assays of serum protein analytes were also assessed at each time point. Serum proteomic analysis revealed that pretreatment, patients achieving DDFTFS demonstrated an immune activated phenotype with elevations in T H 1 adaptive immunity, costimulatory molecules, and immune checkpoint markers. After combination cisplatin-based chemotherapy and ipilimumab treatment, DDFTFS patients again displayed enrichment for markers of adaptive immunity, as well as T cell cytotoxicity. CD27 was uniquely enriched in DDFTFS patients at all timepoints. Neoantigen reactivity was not detected in any patient at baseline or post two cycles of chemotherapy. Both CD4 + and CD8 + neoantigen-specific T cell reactivity was detected in two of two DDFTFS patients in comparison to zero of five non-DDFTFS patients after combination cisplatin-based chemotherapy and ipilimumab treatment. Antitumor immunity may underlie functional cures achieved in patients with metastatic urothelial cancer treated with cisplatin-based chemotherapy and immune checkpoint blockade. Probing the mechanistic basis for DDFTFS may facilitate the identification of biomarkers, therapeutic components, and optimal treatment sequences necessary to extend this ultimate goal to a larger subset of patients.

BackgroundWhile there has been tremendous promise of immunotherapy in treating cancer, most patients do not respond to treatment.1 Biomarker development has grown as the field attempts to better select patients that may benefit from immunotherapy as well as to further understanding of effective use of immunotherapy in cancer.2–4 Here, we utilize a deep learning approach to leverage single-cell information from spatial immunohistochemistry data to query predictive immune signatures of response to immunotherapy.MethodsIn this work, we adapt a previously described multiple instance learning (MIL) approach5–7 to analyze single cell tabular data in a supervised machine learning approach, allowing us to not only create predictive models of response but interrogate the specific correlates of response learned by the underlying machine learning model. We utilize this newly described MIL deep learning approach (figure 1a) to analyze single cell data obtained from multiplexed spatial immunohistochemistry data obtained from pre-treatment tumor samples in CheckMate 275, a phase 2 clinical trial of checkpoint inhibition in metastatic urothelial carcinoma, to predict response (via RECIST) in this cohort and reveal insights into an effective immune response at the single cell level and their spatial relationships.ResultsOur model was most predictive of response (figure 1b, AUC = 0.81) when applied solely to cells in the extra-tumoral tissue (outside of the tumor bed). When looking at the predictive signature of response, we noted an association of the predictive signature in the extra-tumoral space to key immune markers including CD3/CD8, CD11b, CD68, DC-LAMP, and PDL1 (figure 2a,b), suggesting the importance of this immune signature’s presence in the extra-tumoral tissue as being predictive of response prior to the initiation of immunotherapy. Finally, we interrogated the spatial organization of these predictive cells. By quantifying the level of co-localization of predictive cells via Moran’s Index, we noted that the predictive signature was more co-localized within responders vs non-responder (figure 3a,b), and was an independent correlate of response (figure 3c), suggesting an effective immune response not only requires an immune infiltrated tumor but co-localization of these key immune cells in the extra-tumoral space.ConclusionsThese findings highlight the utility of deep learning at the single cell level to identify predictive immune signatures of response and note that while the quantity of the immune infiltration is predictive of response, the spatial organization of this immune response is an independent correlate of response and a hallmark of clinical benefit.ReferencesTopalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, ... Sznol M. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. New England Journal of Medicine, 2012;366(26):2443–2454Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, ... Chan TA. Genetic basis for clinical response to CTLA-4 blockade in melanoma. New England Journal of Medicine, 2014;371(23):2189–2199Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, ... Chan TA. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science, 2015;348(6230):124–128Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, ... Garraway LA. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science, 2015;350(6257):207–211Sidhom JW, Larman HB, Pardoll DM, Baras AS. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires. Nature communications, 2021;12(1):1605.Sidhom JW, Oliveira G, Ross-MacDonald P, Wind-Rotolo M, Wu CJ, Pardoll DM, Baras AS. Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy. Science Advances, 2022;8(37):eabq5089.Sidhom JW, Siddarthan IJ, Lai BS, Luo A, Hambley BC, Bynum J, ... Shenderov E. Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features. NPJ Precision Oncology, 2021;5(1):38.Ethics ApprovalCheckMate 275 (NCT02387996) is a BMS-sponsored, multi-center, institutional-review-board-approved, phase 2 single arm clinical trial of nivolumab in patients with metastatic or unresectable urothelial cancer who have progressed or recurred following treatment with a platinum agent.Abstract 1309 Figure 1Multiple-instance deep learning model. (A) Architecture of multiple-instance deep learning model. Model takes as an input a feature vector for each cell or instance (i.e. gene expression, antibody staining, etc). Each cell’s set of features passes through multiple fullyconnected dense layers to reach a learned latent representation before being aggregated across all cells in a given sample (i.e. patient). This aggregate latent representation is then passed through a second fully-connected set of layers before being used to make a sample-level (patient-level) classification. (B) When fit in Monte-Carlo cross-validation, this model achieved a classification performance characterized by receiver operating characteristics (ROC) curve with an Area Under the Curve (AUC) of 0.81. This model was also able to stratify survival by (C) OS and (D) PFS.Abstract 1309 Figure 2Single-cell UMAP representations. (A) UMAP dimensionality reduction was applied to single cell features (antibody staining from multiplexed IHC) from the cells from the extra-tumoral space. Per-cell assignments of probability of response were derived from the previously fit predictive model (blue = responder signature, red = non-responder signature). (B) UMAP dimensionality reduction colored by antibody expression (red = high expression, blue = low expression). (C) UMAP dimensionality reduction shown for each patient where color of point corresponds to predictive signature from model (as in (A)). Above each plot is the patient identifier and the corresponding probability of response as determined by the model in parenthesis. Plot edge colors are denoted given the patient’s response status as determined by RECIST (blue = CRPR, red = SDPD).Abstract 1309 Figure 3Spatial characteristics of predictive immune signature. (A) Moran’s index was computed given the predicted probability of each cell (features) and spatial coordinates in each sample. A high Moran’s Index suggests a high co-localization of predictive cells within a sample while a low Moran’s Index suggests a low co-localization of predictive cells within a sample. A higher Moran’s Index in responders therefore suggests higher co-localization of predictive cells within responders than in non-responders. (B) Spatial visual representation of predictive cells is shown for all patients within cohort, sorted by Moran’s Index in decreasing order. Above each plot is the patient identifier and the corresponding Moran’s Index in parenthesis. Color of cell corresponds with probability of response (red = high probability of response, blue = low probability of response). Plot edge colors are denoted given the patient’s response status as determined by RECIST (blue = CRPR, red = SDPD). (C) Predictive Signature per sample vs Moran’s Index is plotted for each sample in the cohort. (D) When the predicted probability of response (Pred) and Moran’s Index (Moran) were used in multivariate logistic regression modeling, they remain as two independent predictors as evidenced by the fact that the 95% bootstrapped confidence intervals of the model coefficients do not cross 0 (95% CI: Pred = [0.420, 1.472], Moran = [0.029, 0.721])

BackgroundICIs increase survival in mUC1 but only a subset (~15–25%) of patients experience durable disease control.2 Differences in the tumor microenvironment (TME) might underlie such differential responses. However, the complex network of cellular interactions within the TME that associate with response and resistance to ICIs remains underexplored.MethodsMultiplex Immunohistochemical Consecutive Staining on a Single Slide (MICSSS)3 was performed on UC specimens (N=40) from CheckMate 2754 prior to treatment with nivolumab. 9 immunohistochemical stains (PD-L1, CD8, CD3, pan-cytokeratin, fibronectin, CD68, FAP, DC-LAMP,5 CD11b) were sequentially performed on a single slide per patient. Image processing, whole slide annotation (median 464,554 cells/slide), and intra- and extra-tumoral compartment training, were performed using QuPath (figure 1).6 Responders (CR, PR) and non-responders (SD, PD) were defined per RECIST v1.1. Immunophenotypic designations of ‘inflamed’, ‘excluded’, and ‘desert’ were defined via tumor margin CD8 analysis.7 Lymphoid aggregates were identified morphologically with dense CD3 positivity. Single-cell spatial analysis was performed defining neighborhoods as the 25 nearest neighboring cells.ResultsTME characterization demonstrated inter-tumoral heterogeneity, both in the intra- and extra-tumoral compartments (figure 2). Responders contained >2-fold increased intra-tumoral CD8 cells, though no cell types were significantly altered in comparison to non-responders. In contrast, extra-tumoral CD3, CD8, CD3CD8-, DC-LAMP (PD-L1- and PD-L1+), and PD-L1+ CD11b cells were significantly enriched in responders (figure 3). Inflamed tumors were more prevalent and excluded/desert tumors less prevalent in responders, with inflamed tumors containing increased intra-tumoral T cell and DC-LAMP infiltration. There were no significant differences in infiltrate composition between inflamed responders and inflamed non-responders, while excluded/desert responders demonstrated enrichment for extra-tumoral DC-LAMP cells (PD-L1- and PD-L1+) and intra-tumoral PD-L1- DC-LAMP cells as compared to excluded/desert non-responders (figure 4). Responder tumors also contained an increased density of lymphoid aggregates, which were found in closer proximity to tumor regions, were associated with increased survival, and were comprised of a greater degree of DC-LAMP cells (figure 5). Spatial analysis of extra-tumoral cells identified a unique immune and tumor-enriched PD-L1+ neighborhood (cluster 0) predominant in responders, and a distinct CD11b and tumor-based neighborhood devoid of PD-L1 and other immune cells (cluster 2) predominant in non-responders (figure 6).ConclusionsMultiplex immunohistochemistry identified unique immunophenotypic and spatial TME features specific to mUC responders to ICI. Both increased infiltration and the geographic arrangement of T cells, dendritic cells, and PD-L1 positivity, particularly in the extra-tumoral compartment, may prove key in identifying responders to ICI.Trial RegistrationThis is a secondary translational analysis from NCT02387996 (CheckMate 275, CA209–275)ReferencesBellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee J-L, Fong L, et al. Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma. New England Journal of Medicine. 2017;376:1015–26.Galsky MD, Arija JÁA, Bamias A, Davis ID, Santis MD, Kikuchi E, et al. Atezolizumab with or without chemotherapy in metastatic urothelial cancer (IMvigor130): a multicentre, randomised, placebo-controlled phase 3 trial. The Lancet. 2020;395:1547–57.Akturk G, Sweeney R, Remark R, Merad M, Gnjatic S. Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS): Multiplexed Chromogenic IHC Assay for High-Dimensional Tissue Analysis. Methods Mol Biol. 2020;2055:497–519.Sharma P, Retz M, Siefker-Radtke A, Baron A, Necchi A, Bedke J, et al. Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275): a multicentre, single-arm, phase 2 trial. The Lancet Oncology. 2017;18:312–22.Maier B, Leader AM, Chen ST, Tung N, Chang C, LeBerichel J, et al. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature. 2020;580:257–62.Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017;7:16878.Braun DA, Hou Y, Bakouny Z, Ficial M, Sant’ Angelo M, Forman J, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020;26:909–18.Ethics ApprovalThe initial clinical trial NCT02387996 obtained appropriate ethics approval, and was conducted in accordance with Good Clinical Practice guidelines defined by the International Conference on Harmonisation. All participants provided informed consent before taking part in the study based on the principles of the Declaration of Helsinki. Approval was granted from local institutional review boards or ethics committees at each center (as published).Abstract 1465 Figure 1MICSSS analysis pipeline. 9 sequential immunohistocliemical stains were performed on a single slide tot each patient. Images were then co-registered to the single cell level, confirmed at each quadrant of the specimen. Tissue annotation arid cell segmentation were performed on whole tissue specimens (median 464,554, range 134,796–1,255,791 cells per patient). Stain positivity was confirmed at multiple regions for each stain and patient. Intra- and extra-tumoral compartment training was performed on multiple regionsAbstract 1465 Figure 2Whole cohort TME characterization. (A) Waterfall plot demonstrating the TME composition breakdown from each cell type, each column repesents an individual patient (B) Waterfall plots (top), scatter plots (bottom) demonstrating the percentage of each cell type within the intra-tumoral (left) or extra-tumoral (right) campartrnentsAbstract 1465 Figure 3Compartment-specific TME infiltrate in responders and non-responders. Comparison of lymphoid, myeloid (including PD-L1 positive and negative subsets), and matrisome cell types between responders (blue) and non-responders (gray) in the (A) intra-tumoral and (B) extra-tumoral compartments. Bar plots (right) represent the ratio of the median cell concentration between responders and non-responders, dotted lines indicating ≥2-fold difference. * P<0.05, ** P<0.01, *** P<0.001Abstract 1465 Figure 4TME immunophenotypes in responders and non-responders. (A) Example images of inflamed, excluded, and desert TMEs, CD8 in pink, CK in brown (left) Breakdown of irnmunophenotype designation by CD8 analysis at the tumor outer margin and tumor inner margin (center), Breakdown of immunophenotype designation in responders and non-responders (right). Bar plots represent the ratio of the median cell concentration between (B) inflamed and desert/excluded patients, (C) inflamed responders and inflamed non-responders, and (D) excluded/desert responders and excluded/desert non-responders (right). Dotted lines indicating ≥2-fold difference. *P<0.05, **P<0.01, ***P<0.001, P>0.05 and <0.1 listedAbstract 1465 Figure 5Lymphoid aggregate analysis in responders and non-responders. (A) Density of lymphoid aggregates per patitent. (B) Distance from each lymphoid aggregate to the nearest tumor region. Black bar respresents the median (left). Distribution of distances (right). (C) Kaplan-Meier curve for overall survival of patitents separated by median density of lymphoid aggregates. (D) Bar plot represents the ratio of the median cell concentration within lymphoid aggregates between responders and non-responders, dotted line indicates ≥2-fold difference. * P<0.05, ** P<0.01, *** P<0.001, *** P<0.0001Abstract 1465 Figure 6Neighborhood Analysìs of Extra-Tumoral Tissue. (A) Neighborhood characterization was completed by applyrig K-Nearest Neighbors (k = 25) to single-cell expression dala. where the mean expression value over 25 nearest neighbors (including center cell) was averaged over each stain to create a mean expression vector for each neighborhood. UMAP dimensionality reduction was applied on neighborhoods. where the centre cell is classified as within the extra-tumoral tissue, to visualize distribution of neighborhoods along with corresponding antibody expressions. (B) K-means clustering was applied on neighborhood expression vectors (n-clusters = 4) and is visual-ized in UMAP space. (C) Composition of each sample by clusters was conducted and stratified between responders/non-responders by each cluster (** P<0.01). (D) Cluster charaterization visualized via clustermap where each row represents a cluster and columns represent average expression of given marker for each cluster (z-scores shown, red = high expression, blue = low expression)

Epithelial-mesenchymal transition (EMT) is able to drive metastasis during progression of multiple cancer types, including non-small cell lung cancer (NSCLC). As resistance to immunotherapy has been associated with EMT and immune exclusion in melanoma, it is important to understand alterations to T-cell infiltration and the tumor microenvironment during EMT in lung adenocarcinoma and squamous cell carcinoma. We conducted an integrated analysis of the immune landscape in NSCLCs through EMT scores derived from a previously established 16 gene signature of canonical EMT markers. EMT was associated with exclusion of immune cells critical in the immune response to cancer, with significantly lower infiltration of CD4 T-cells in lung adenocarcinoma and CD4/CD8 T-cells in squamous cell carcinoma. EMT was also associated with increased expression of multiple immunosuppressive cytokines, including IL-10 and TGF-β. Furthermore, overexpression of targetable immune checkpoints, such as CTLA-4 and TIM-3 were associated with EMT in both NSCLCs. An association may exist between immune exclusion and EMT in NSCLC. Further investigation is merited as its mechanism is not completely understood and a better understanding of this association could lead to the development of biomarkers that could accurately predict response to immunotherapy.