MOTIVATION:Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA-sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. RESULTS:We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigens Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AVAILABILITY AND IMPLEMENTATION:More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).

BackgroundTriple negative breast cancer (TNBC) is an aggressive variant of breast cancer that lacks the expression of estrogen and progesterone receptors (ER and PR) and HER2. Nearly 50% of patients with advanced TNBC will develop brain metastases (BrM), commonly with progressive extracranial disease. Immunotherapy has shown promise in the treatment of advanced TNBC; however, the immune contexture of BrM remains largely unknown. We conducted a comprehensive analysis of TNBC BrM and matched primary tumors to characterize the genomic and immune landscape of TNBC BrM to inform the development of immunotherapy strategies in this aggressive disease.MethodsWhole-exome sequencing (WES) and RNA sequencing were conducted on formalin-fixed, paraffin-embedded samples of BrM and primary tumors of patients with clinical TNBC (n = 25, n = 9 matched pairs) from the LCCC1419 biobank at UNC—Chapel Hill. Matched blood was analyzed by DNA sequencing as a comparison for tumor WES for the identification of somatic variants. A comprehensive genomics assessment, including mutational and copy number alteration analyses, neoantigen prediction, and transcriptomic analysis of the tumor immune microenvironment were performed.ResultsPrimary and BrM tissues were confirmed as TNBC (23/25 primaries, 16/17 BrM) by immunohistochemistry and of the basal intrinsic subtype (13/15 primaries and 16/19 BrM) by PAM50. Compared to primary tumors, BrM demonstrated a higher tumor mutational burden. TP53 was the most frequently mutated gene and was altered in 50% of the samples. Neoantigen prediction showed elevated cancer testis antigen- and endogenous retrovirus-derived MHC class I-binding peptides in both primary tumors and BrM and predicted that single-nucleotide variant (SNV)-derived peptides were significantly higher in BrM. BrM demonstrated a reduced immune gene signature expression, although a signature associated with fibroblast-associated wound healing was elevated in BrM. Metrics of T and B cell receptor diversity were also reduced in BrM.ConclusionsBrM harbored higher mutational burden and SNV-derived neoantigen expression along with reduced immune gene signature expression relative to primary TNBC. Immune signatures correlated with improved survival, including T cell signatures. Further research will expand these findings to other breast cancer subtypes in the same biobank. Exploration of immunomodulatory approaches including vaccine applications and immune checkpoint inhibition to enhance anti-tumor immunity in TNBC BrM is warranted.

Abstract Motivation Splice variant neoantigens are a potential source of tumor-specific antigen (TSA) that are shared between patients in a variety of cancers, including acute myeloid leukemia. Current tools for genomic prediction of splice variant neoantigens demonstrate promise. However, many tools have not been well validated with simulated and/or wet lab approaches, with no studies published that have presented a targeted immunopeptidome mass spectrometry approach designed specifically for identification of predicted splice variant neoantigens. Results In this study, we describe NeoSplice, a novel computational method for splice variant neoantigen prediction based on (i) prediction of tumor-specific k-mers from RNA-seq data, (ii) alignment of differentially expressed k-mers to the splice graph and (iii) inference of the variant transcript with MHC binding prediction. NeoSplice demonstrates high sensitivity and precision (>80% on average across all splice variant classes) through in silico simulated RNA-seq data. Through mass spectrometry analysis of the immunopeptidome of the K562.A2 cell line compared against a synthetic peptide reference of predicted splice variant neoantigens, we validated 4 of 37 predicted antigens corresponding to 3 of 17 unique splice junctions. Lastly, we provide a comparison of NeoSplice against other splice variant prediction tools described in the literature. NeoSplice provides a well-validated platform for prediction of TSA vaccine targets for future cancer antigen vaccine studies to evaluate the clinical efficacy of splice variant neoantigens. Availability and implementation https://github.com/Benjamin-Vincent-Lab/NeoSplice Supplementary information Supplementary data are available at Bioinformatics Advances online.

Background Elimination of cancer cells by T cells is a critical mechanism of antitumor immunity and cancer immunotherapy response. T cells recognize cancer cells via engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex (MHC) molecules on the cancer cell surface. Discovery of the full landscape of tumor antigens in any given individual will be important for understanding response to immunotherapy and optimizing strategies for antigen-specific immunotherapy development. Although T cell epitopes can be derived from antigen proteins coded for by multiple genomic sources, bioinformatics tools used to identify tumor-specific epitopes using DNA and RNA sequencing data have largely focused on epitopes derived from somatic variants. Methods We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigen Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants (SNVs), insertions and deletions (InDels), gene fusions, splice variants, cancer testis antigens (CTAs), overexpressed self-antigens, viruses, and human endogenous retroviruses (hERVs). The main advantage of LENS is that it extends the breadth of genomic sources of tumor antigens that may be discovered using genomics data. Other advantages include modularity, extensibility, ease of use, incorporation of phasing and germline variant information in epitope identification, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources (table 1). LENS is open-source and freely available for academic use (https://gitlab.com/bgv-lens). Results To demonstrate the utility of LENS, we present an analysis of the predicted antigen landscape in 115 acute myeloid leukemia (AML) samples. AML was chosen due its low somatic mutation rate and dearth of classical (SNV-derived) neoantigens. Predicted tumor antigens were distributed unevenly across genomic sources (figure 1). The highest degree of antigen sharing was found in those derived from aberrantly expressed endogenous retroviral genes, which were also highly-expressed in the RNA-seq data (figure 2). A small number of gene fusion-derived antigens were also highly expressed with high predicted binding affinity (figure 3). Conclusions We expect that LENS will be a valuable platform and resource for analysis of polyepitopic T cell responses across the full immunodominance hierarchy, antigen selection for therapeutic neoantigen vaccination, and T cell epitope discovery, especially in cancers with few somatic mutations and increased importance of tumor-specific epitopes from genomic sources beyond single nucleotide variants. In AML specifically, our results support further development of hERV and gene fusion-based tools for immune monitoring and therapeutics going forward.

Abstract BACKGROUND Triple negative breast cancer (TNBC) lacks expression of hormone receptors (estrogen and progesterone receptors, ER and PR) and HER2. Almost 50% of patients with metastatic TNBC will develop brain metastases (BrM), often with concurrent progressive extracranial disease. While immunotherapy has shown promise in the treatment of advanced TNBC, the immune profile of BrM remains largely unknown. To inform the development of immunotherapy strategies in this aggressive disease, we characterized the genomic and immune landscape of TNBC BrM and matched primary tumors. METHODS Formalin-fixed, paraffin-embedded samples of BrM and primary tumors of patients with clinical TNBC (n=25, n=9 matched pairs) from the LCCC1419 biobank at UNC-Chapel Hill were analyzed by whole exome (WES) and RNA sequencing, with matched blood DNA sequenced for identification of somatic variants. Mutational and copy number alteration analyses, neoantigen prediction, and transcriptomic analysis of the tumor immune microenvironment were performed. RESULTS Primary and BrM tissues were confirmed as TNBC and of the basal intrinsic subtype. Compared to primary tumors, BrM demonstrated higher tumor mutational burden. Neoantigen prediction showed elevated cancer testis antigen- and endogenous retrovirus-derived MHC class I-binding peptides in both primary tumors and BrM, and predicted single nucleotide variants (SNV)-derived peptides were significantly higher in BrM. BrM demonstrated reduced immune gene signature expression, although a signature associated with fibroblast-associated wound healing was elevated in BrM. Metrics of T and B cell receptor diversity were also reduced in BrM. CONCLUSIONS BrM harbored higher mutational burden and SNV-derived neoantigen expression along with reduced immune gene signature expression relative to primary TNBC. Further research will expand these findings to other breast cancer subtypes. Exploration of immunomodulatory approaches including vaccine applications and immune checkpoint inhibition to enhance anti-tumor immunity in TNBC BrM are warranted.

A Correction to this paper has been published: https://doi.org/10.1038/s41565-021-00864-w.

Abstract Purpose: Despite promising advances in breast cancer immunotherapy, augmenting T-cell infiltration has remained a significant challenge. Although neither individual vaccines nor immune checkpoint blockade (ICB) have had broad success as monotherapies, we hypothesized that targeted vaccination against an oncogenic driver in combination with ICB could direct and enable antitumor immunity in advanced cancers. Experimental Design: Our models of HER2+ breast cancer exhibit molecular signatures that are reflective of advanced human HER2+ breast cancer, with a small numbers of neoepitopes and elevated immunosuppressive markers. Using these, we vaccinated against the oncogenic HER2Δ16 isoform, a nondriver tumor-associated gene (GFP), and specific neoepitopes. We further tested the effect of vaccination or anti–PD-1, alone and in combination. Results: We found that only vaccination targeting HER2Δ16, a driver of oncogenicity and HER2-therapeutic resistance, could elicit significant antitumor responses, while vaccines targeting a nondriver tumor-specific antigen or tumor neoepitopes did not. Vaccine-induced HER2-specific CD8+ T cells were essential for responses, which were more effective early in tumor development. Long-term tumor control of advanced cancers occurred only when HER2Δ16 vaccination was combined with αPD-1. Single-cell RNA sequencing of tumor-infiltrating T cells revealed that while vaccination expanded CD8 T cells, only the combination of vaccine with αPD-1 induced functional gene expression signatures in those CD8 T cells. Furthermore, we show that expanded clones are HER2-reactive, conclusively demonstrating the efficacy of this vaccination strategy in targeting HER2. Conclusions: Combining oncogenic driver targeted vaccines with selective ICB offers a rational paradigm for precision immunotherapy, which we are clinically evaluating in a phase II trial (NCT03632941).

Tandem mass spectrometry (MS/MS) is a highly sensitive and selective method for the detection of tumor-associated peptide antigens. These short, nontryptic sequences may lack basic residues, resulting in the formation of predominantly [peptide + H]+ ions in electrospray. These singly charged ions tend to undergo inefficient dissociation, leading to issues in sequence determination. Addition of alkali metal salts to the electrospray solvent can drive the formation of [peptide + H + metal]2+ ions that have enhanced dissociation characteristics relative to [peptide + H]+ ions. Both previously identified tumor-associated antigens and predicted neoantigen sequences were investigated. The previously reported rearrangement mechanism in MS/MS of sodium-cationized peptides is applied here to demonstrate complete C-terminal sequencing of tumor-associated peptide antigens. Differential ion mobility spectrometry (DIMS) is shown to selectively enrich [peptide + H + metal]2+ species by filtering out singly charged interferences at relatively low field strengths, offsetting the decrease in signal intensity associated with the use of alkali metal cations.

BackgroundMeasures of the adaptive immune response have prognostic and predictive associations in melanoma and other cancer types. Specifically, intratumoral T cell density and function have considerable prognostic and predictive value in skin cutaneous melanoma (SKCM). Less is known about the significance of tumor-infiltrating B cells in SKCM. Our goal was to understand the prognostic and predictive value of B cell phenotypic subsets in SKCM using RNA sequencing.MethodsWe used our previously published algorithm, V'DJer, to assemble B cell receptor (BCR) repertoires and estimate diversity from short-read RNA sequencing (RNA-seq). We applied machine learning-based cellular phenotype classifiers to measure relative similarity of bulk tumor sample gene expression profiles and different B cell phenotypes. We assessed these aspects of B cell biology in 473 SKCM from the Cancer Genome Atlas Project (TCGA) as well as in RNA-seq data corresponding to tumor samples procured from patients who received CTLA-4 and PD-1 inhibitors for metastatic SKCM.ResultsWe found that the BCR repertoire was associated with different clinical factors, such as tumor tissue site and sex. However, increased clonality of the BCR repertoire was favorably prognostic in SKCM and was prognostic even after first conditioning on various clinical factors. Mutation burden was not correlated with any BCR measurement, and no specific mutation had an altered BCR repertoire. Lack of an assembled BCR in pre-treatment tumor tissues was associated with a lack of anti-tumor response to a CTLA-4 inhibitor in metastatic SKCM.ConclusionsThese findings suggest an important prognostic and predictive role for B cell characteristics in SKCM. This has implications for melanoma immunobiology and potential development of immunogenomics features to predict survival and response to immunotherapy.

Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclassic out-of-frame antigens capable of driving antitumor immunity.

The study of tumour-specific antigens (TSAs) as targets for antitumour therapies has accelerated within the past decade. The most commonly studied class of TSAs are those derived from non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens. However, to increase the repertoire of available therapeutic TSA targets, 'alternative TSAs', defined here as high-specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated. Among these alternative TSAs are antigens derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other processes. Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the advantage of being widely shared by multiple tumours, allowing for universal, off-the-shelf therapies. In this Opinion article, we will outline the biology, available computational tools, preclinical and/or clinical studies and relevant cancers for each alternative TSA class, as well as discuss both current challenges preventing the therapeutic application of alternative TSAs and potential solutions to aid in their clinical translation.

Abstract INTRODUCTION: Approximately 50% of patients with metastatic triple negative breast cancer (TNBC) will develop brain metastases (BM). Routinely treated with radiotherapy and/or surgery, survival is generally less than one year. There are no approved systemic therapies to treat TNBC BM. We characterized the genomic and immune landscape of TNBC BM to foster the development of effective brain permeable anti-cancer agents, including immunotherapy. EXPERIMENTAL PROCEDURES: A clinically-annotated BCBM biobank of archival tissues was created under IRB approval. DNA (tumor/normal) and RNA (tumor) were extracted from TNBC primaries and BM; whole exome (WES) and RNA sequencing (RNASeq) was performed. Mutations were determined from WES as those co-identified by two variant callers (Strelka|Cadabra). Immune gene signature expression, molecular subtype identification, and T cell receptor repertoires were inferred from RNAseq. RESULTS: 32 TNBC patient tissues (14 primaries, 18 BCBM, 6 primary-BCBM matched), characterized as basal-like by PAM50, were analyzed. Top exome mutation calls included ten genes in ≥19% of BCBMs including TP53, ATM, and PIK3R1, and four genes in ≥18% of primaries including TP53 and PIK3R1. Many immune gene signatures were lower in BM compared to primaries including B cell, dendritic cell, regulatory T cell, and IgG cluster (p< 0.05). A signature of PD-1 inhibition responsiveness was higher in BM compared with primaries (p< 0.05). BCBM T cell receptor repertoires showed higher evenness and lower read count (both p < 0.01) compared to primaries. CONCLUSIONS: TNBC BM compared to primaries that metastasize to the brain show lower immune gene signature expression, higher PD-1 inhibition response signature expression, and T cell receptor repertoire features less characteristic of an active antigen-specific response. Mutations common to TNBC BM and primaries include TP53 and PIK3R1. Given that non-BCBM (i.e. lung and melanoma) show response to checkpoint inhibitors, these findings collectively support further study of immunotherapy for TNBC BM.

Abstract Background Allogeneic stem cell transplantation (aSCT) remains the most effective form of immunotherapy for the treatment of high risk acute myeloid leukemia (AML). The alloreactivity that induces the so-called “graft versus leukemia” (GvL) effect is mediated by minor histocompatibility antigens (mHA) presented by recipient class I or II HLA that induce a donor derived T cell response. Methods To identify candidate GvL mHA, we mapped RNA-Seq data from HLA-A*02:01 transfected U937 cells to the human reference genome to identify high frequency SNPs that are expressed in hematopoietic tissue, leukemia or testis and not in GvHD target organs such as skin, colon or liver. All 8 to 11-mer amino acids containing identified SNPs were computationally evaluated for binding affinity to HLA-A*02:01, and we validated predictions using genotyping data from 101 AML patients who underwent aSCT. Peptides from highly expressed genes and with predicted high affinity to HLA-A*02:01 were synthesized. Targeted mass spectrometry using parallel reaction monitoring (PRM) compared the MS/MS spectra of a peptide epitope pool to those of the synthesized peptides. Results Our computational analysis predicted 63 candidate mHA, of which the 48 most promising candidates were selected for targeted MS based upon gene expression and predicted HLA-binding affinity. Of the 48 best candidates, 4 HLA-A*02:01 restricted peptide epitopes were identified by mass spectrometry: 3 different epitopes derived from CD33 and 1 derived from CD300a. Conclusion Combined computational prediction and targeted mass spectrometry is an efficient method for the identification of candidate GvL antigens that can be further tested for immunogenicity.

For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.

Abstract Introduction: Triple negative breast cancer (TNBC) is an aggressive subset of BC with high metastatic potential. Once metastatic, half of patients (pts) with TNBC will develop brain metastases (BM), commonly with progressive extracranial disease. While TNBC BM are routinely treated with radiotherapy, survival is generally less than one year. There are no approved systemic therapies to treat TNBC BM. Both the blood brain barrier and paucity of data on the biologic underpinnings and immune response of BCBM contribute to inadequate therapies for this disease. We sought to characterize the genomic and immune landscape of TNBC BM to foster the development of effective brain permeable anti-cancer agents. Experimental Procedures: A clinically-annotated BCBM biobank of archival brain metastases and primary BC that eventually metastasize to the brain was created under IRB approval (LCCC1419). DNA (tumor/normal) and RNA (tumor only) was extracted. Following library preparation, whole exome (WES) and RNA sequencing (RNASeq) was performed. Common mutations were determined from WES as those co-identified by two variant callers (Strelka, Cadabra), while immune gene signature expression, molecular subtype identification, and B and T cell receptor repertoires were inferred from RNAseq data. Results: Of the 26 enrolled pts, median age at BCBM diagnosis was 52 years (35-72); 23% were African American and 57% Caucasian. Additional non-brain metastatic sites included bone (23%), liver (20%), and lung (35%); 23% had a solitary BCBM. 35% of pts received chemotherapy after BCBM diagnosis, while 92% received radiation to the brain (21% WBRT, 5% SRS, 25% SRS plus WBRT and resection). The median survival from BCBM was 6 months. 40 (93%) of the tissues (21 primaries, 22 BCBM) were characterized as basal-like by PAM50; 1 HER2-enriched and 2 Luminal A. 34 genes were mutated in ≥20% of BCBMs, while only 8 were mutated in at least 25% of primaries. Commonly mutated BCBM genes included TP53, ATM and MYH9; in primaries only TP53. Many immune gene signatures were lower in BCBM compared to primary BC including B cell, dendritic cell, regulatory T cell, and IgG cluster (p<0.05). A signature of responsiveness to PD1 inhibition in melanoma was higher in BCBM compared with primary BC (p<0.05). Globally, the BCBM T cell receptor repertoires showed higher diversity (TCR evenness, p=0.006) and lower read count (TCR total read count, p=0.0267) compared to primary BC. Conclusions: TNBC BM compared to primary BC that metastasize to the brain had more significantly mutated genes, lower immune gene signature expression, higher PD1 inhibition response signature expression, and T cell receptor repertoire features less characteristic of an active antigen-specific response. Given that checkpoint inhibitors are showing response in non-BCBM (i.e. lung and melanoma), these findings indicate that immunotherapy to treat patients with TNBC BM is worthy of exploration. Citation Format: Benjamin G. Vincent, Maria Sambade, Shengjie Chai, Marni B. Siegel, Luz Cuaboy, Alan Hoyle, Joel Parker, Charles M. Perou, Carey K. Anders. Genomic and immune characterization of triple-negative breast cancer brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4055.

T-cell responses to minor histocompatibility antigens (mHAs) mediate both antitumor immunity (graft-versus-leukemia [GVL]) and graft-versus-host disease (GVHD) in allogeneic stem cell transplant. Identifying mHAs with high allele frequency, tight binding affinity to common HLA molecules, and narrow tissue restriction could enhance immunotherapy against leukemia. Genotyping and HLA allele data from 101 HLA-matched donor-recipient pairs (DRPs) were computationally analyzed to predict both class I and class II mHAs likely to induce either GVL or GVHD. Roughly twice as many mHAs were predicted in HLA-matched unrelated donor (MUD) stem cell transplantation (SCT) compared with HLA-matched related transplants, an expected result given greater genetic disparity in MUD SCT. Computational analysis predicted 14 of 18 previously identified mHAs, with 2 minor antigen mismatches not being contained in the patient cohort, 1 missed mHA resulting from a noncanonical translation of the peptide antigen, and 1 case of poor binding prediction. A predicted peptide epitope derived from GRK4, a protein expressed in acute myeloid leukemia and testis, was confirmed by targeted differential ion mobility spectrometry-tandem mass spectrometry. T cells specific to UNC-GRK4-V were identified by tetramer analysis both in DRPs where a minor antigen mismatch was predicted and in DRPs where the donor contained the allele encoding UNC-GRK4-V, suggesting that this antigen could be both an mHA and a cancer-testis antigen. Computational analysis of genomic and transcriptomic data can reliably predict leukemia-associated mHA and can be used to guide targeted mHA discovery.

Abstract High-grade urothelial cancer contains intrinsic molecular subtypes that exhibit differences in underlying tumor biology and can be divided into luminal-like and basal-like subtypes. We describe here the first subtype-specific murine models of bladder cancer and show that Upk3a-CreERT2; Trp53L/L; PtenL/L; Rosa26LSL-Luc (UPPL, luminal-like) and BBN (basal-like) tumors are more faithful to human bladder cancer than the widely used MB49 cells. Following engraftment into immunocompetent C57BL/6 mice, BBN tumors were more responsive to PD-1 inhibition than UPPL tumors. Responding tumors within the BBN model showed differences in immune microenvironment composition, including increased ratios of CD8+:CD4+ and memory:regulatory T cells. Finally, we predicted and confirmed immunogenicity of tumor neoantigens in each model. These UPPL and BBN models will be a valuable resource for future studies examining bladder cancer biology and immunotherapy. Significance: This work establishes human-relevant mouse models of bladder cancer. Cancer Res; 78(14); 3954–68. ©2018 AACR.