Tumor-specific Neoantigens Research Articles

1028P Clinical results of a pilot trial of GEN-009, a neoantigen vaccine containing immunogenic tumour specific neoantigens, in combination with PD-1 inhibitors in advanced cancers

1028P Clinical results of a pilot trial of GEN-009, a neoantigen vaccine containing immunogenic tumour specific neoantigens, in combination with PD-1 inhibitors in advanced cancers

Endogenous Retrovirus Transcript Levels Are Associated with Immunogenic Signatures in Multiple Metastatic Cancer Types.

Next-generation sequencing of solid tumors has revealed variable signatures of immunogenicity across tumors, but underlying molecular characteristics driving such variation are not fully understood. Although expression of endogenous retrovirus (ERV)-containing transcripts can provide a source of tumor-specific neoantigen in some cancer models, associations between ERV levels and immunogenicity across different types of metastatic cancer are not well established. We performed bioinformatics analysis of genomic, transcriptomic, and clinical data across an integrated cohort of 199 patients with metastatic breast, colorectal, and pancreatic ductal adenocarcinoma tumors. Within each cancer type, we identified a subgroup of viral mimicry tumors in which increased ERV levels were coupled with transcriptional signatures of autonomous antiviral response and immunogenicity. In addition, viral mimicry colorectal and pancreatic tumors showed increased expression of DNA demethylation gene TET2 Taken together, these data demonstrate the existence of an ERV-associated viral mimicry phenotype across three distinct metastatic cancer types, while indicating links between ERV abundance, epigenetic dysregulation, and immunogenicity.

Tumor-Specific CD8+ T-Cell Responses Induced by DNA Vaccination.

DNA vaccines assisted by electroporation efficiently trigger antitumor cytotoxic CD8+ T cell responses in preclinical cancer models and hold potential for human use. They can be easily engineered to express either tumor-associated self-antigens, which are broadly expressed among tumor patients but also in healthy tissue, or tumor-specific neoantigens, which are uniquely expressed in tumors and differ among patients. Recently, it has been demonstrated that DNA vaccination generates both circulating and tissue-resident compartments of CD8+ T cells, which act concertedly against tumors. Here we describe the steps to obtain and test DNA vaccines against models of self-antigens and neoantigens in mice. It includes the evaluation of effector and memory CD8+ Tcell responses, as well as assessing the antitumor potential in vivo using transplantable syngeneic tumor models.

Synthetic High-density Lipoprotein Nanodiscs for Personalized Immunotherapy Against Gliomas.

Gliomas are brain tumors with dismal prognoses. The standard-of-care treatments for gliomas include surgical resection, radiation, and temozolomide administration; however, they have been ineffective in providing significant increases in median survival. Antigen-specific cancer vaccines and immune checkpoint blockade may provide promising immunotherapeutic approaches for gliomas. We have developed immunotherapy delivery vehicles based on synthetic high-density lipoprotein (sHDL) loaded with CpG, a Toll-like receptor-9 agonist, and tumor-specific neoantigens to target gliomas and elicit immune-mediated tumor regression. We demonstrate that vaccination with neoantigen peptide-sHDL/CpG cocktail in combination with anti-PD-L1 immune checkpoint blocker elicits robust neoantigen-specific T-cell responses against GL261 cells and eliminated established orthotopic GL261 glioma in 33% of mice. Mice remained tumor free upon tumor cell rechallenge in the contralateral hemisphere, indicating the development of immunologic memory. Moreover, in a genetically engineered murine model of orthotopic mutant IDH1 (mIDH1) glioma, sHDL vaccination with mIDH1 neoantigen eliminated glioma in 30% of animals and significantly extended the animal survival, demonstrating the versatility of our approach in multiple glioma models. Overall, our strategy provides a general roadmap for combination immunotherapy against gliomas and other cancer types.

Abstract 3210: Strategies to improve the sensitivity and ranking ability of neoantigen prediction methods: Report on the results of the Tumor nEoantigen SeLection Alliance (TESLA)

Abstract Neoantigen-based therapies hold the promise of being safe, personalized anti-cancer therapies for a broad range of cancers. A crucial step for the development of effective neoantigen therapies is the identification of putative tumor specific neoantigens typically achieved through in silico analyses. In silico-based approaches have the benefit of being fast, modular and reproducible. A wide range of tools are available for variant calling, determining peptide-HLA binding affinity, peptide foreignness, agritopicity and more, and researchers are continuously adding to the lot. However, there does not currently exist a common reference data set with which these different approaches can be compared, and the key parameters for effective neoantigen identification remain elusive. Here we introduce a consortium-based initiative, the Tumor nEoantigen SeLection Alliance (TESLA), to address these needs and describe straight-forward strategies through which the sensitivity and ranking ability of neoantigen prediction methods can be improved. TESLA participants were provided with whole exome and RNA sequencing from six tumors (3 melanoma and 3 NSCLC) and in turn submitted predictions for immunogenic peptides. Twenty-five groups from academia, pharma and biotech participated to this challenge. Peptides identification and ranking across the teams were assessed and showed little overlap. The limited overlap could not be explained simply by differences in variant calling. From these predictions, 608 peptides regrouping the teams' highly ranked epitopes plus epitopes frequently called and ranked across the teams, were assessed for T-cell recognition in patient-matched samples as well as for in-vitro MHC binding affinity. Analyses showed that peptides recognized by T-cells had significantly stronger MHC binding affinity and were derived from genes with significantly higher gene expression. Concomitantly, neoantigen pipelines which prioritized epitopes with strong binding affinity and/or which filtered out those originating from genes with low tumor variant allele fraction or low gene expression were associated with improved ability to identify and rank neoantigens. Direct interventions on participant predictions using these identified traits demonstrated substantial improvement in the performance of the neoantigen predictions. Pipeline analysis indicated that there are a range of approaches to neoantigen prediction and improving upon an existing neoantigen pipeline requires assessing that pipeline for a range of characteristics, including variant detection ability, peptide filtering ability, and peptide ranking ability, and improving those areas where the pipeline performance is sub-optimal. TESLA data will be continually available and serve as a living benchmark to improve neoantigen prediction. Citation Format: Daniel K. Wells, Kristen Dang, Vanessa M. Hubbard-Lucey, Kathleen C. Sheehan, Andrew Lamb, Jeffrey P. Ward, John Sidney, Ana B. Blazquez, Andrew J. Rech, Jesse Zaretsky, Begonya Comin-Anduix, Alphonsus H. Ng, William Chour, Thomas V. Yu, Hira Rizvi, Jia Chen, Patrice Manning, Taha Merghoub, Justin Guinney, Adam Kolom, Cheryl Selinsky, Antoni Ribas, Matthew D. Hellmann, Ton N. Schumacher, Nir Hacohen, Pia Kvistborg, Alessandro Sette, James R. Heath, Nina Bhardwaj, Fred Ramsdell, Robert D. Schreiber, Nadine A. Defranoux, TESLA Consortium. Strategies to improve the sensitivity and ranking ability of neoantigen prediction methods: Report on the results of the Tumor nEoantigen SeLection Alliance (TESLA) [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 3210.

Abstract 2085: Precision neoantigen discovery using a pan-allelic machine learning model for enabling the development of composite biomarkers and personalized immunotherapy

Abstract Background: Technologies for neoantigen discovery are critical for developing more advanced, composite biomarkers for immunotherapy as well as personalized cancer therapies. Precision neoantigen discovery entails comprehensive detection of tumor specific genomic variants and accurate prediction of MHC presentation of epitopes originating from such variants. Here we present our pan-allelic machine learning model for predicting MHC class I presentation and identifying potentially immunogenic patient-specific neoantigens. We then apply our predictions to develop composite biomarkers that can stratify patients by response to immunotherapy. Methods: Mono-allelic cell lines were generated by transfecting a single allele of interest into HLA-null K562 cell lines. Immunoprecipitation mass spectrometry (IPMS) was performed as follows: W6/32 antibody was used for immunoprecipitation of HLA complexes, followed by elution of bound peptides and identification using liquid chromatography-mass spectrometry. Machine learning models were implemented to predict neoantigen presentation. Our prediction model, integrated into the ImmunoID NeXT platform, was used with other genomic features to generate composite neoantigen-based biomarkers. Results: We generated a high quality and unambiguous immunopeptidomics training dataset by performing IPMS on ~60 mono-allelic cell lines, with ongoing efforts to expand it to ~100 alleles. These alleles, selected to optimize both allelic diversity and population coverage, enable accurate and comprehensive modeling of MHC ligand processing and presentation. Our advanced prediction model combines multiple modelling strategies, including deep neural networks, convolutional neural networks and gradient boosted decision trees. New features that model antigen-processing were implemented using large scale public and private datasets to improve presentation specificity (60% PPV in dataset containing 999-fold decoys). As a result, our pan-allelic model has significantly higher specificity across a range of sensitivity values in comparison to NetMHCPan 4.0 and generalizes to both trained and untrained alleles. Our comprehensive validation strategy includes: evaluation of overall performance of the model on an independent, multi-allelic immunopeptidomics dataset generated from tumor samples; validation of top ranking tumor specific neoantigens nominated using an integrated patient-centric model that incorporates HLA loss of heterozygosity using targeted proteomics (Parallel Reaction Monitoring); and evaluation of our model's utility to predict neoantigens that drive immunogenic responses to tumors. Finally, a composite neoantigen-based biomarker score calculated using our model stratifies patients by response to immunotherapy. Conclusions: In summary, we present here a pan-allelic MHC presentation prediction model trained on a large mono-allelic data set and evaluated using tumor samples and known immunogenic peptides. Through integration with ImmunoID NeXT, our platform enables precision neoantigen discovery by comprehensively surveying neoantigens and accurately predicting MHC presentation. These methods can significantly enhance the creation of composite biomarkers and applications in personalized immunotherapy. Citation Format: DATTATREYA MELLACHERUVU, Rachel Marty Pyke, Charles Abbott, Nick Phillips, Rena McClory, John West, Richard Chen, Sean Michael Boyle. Precision neoantigen discovery using a pan-allelic machine learning model for enabling the development of composite biomarkers and personalized immunotherapy [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 2085.

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet the clinical needs. In this study, we developed an in silico method with a deep convolutional neural network (CNN) model, iConMHC, to predict peptide MHC binding affinity. Unlike other in silico methods that only learn from properties of amino acid in neoantigen peptides alone and/or MHCs alone, iConMHC learns from physical and chemical interaction properties between pairwise amino acids from the two molecules. These properties, such as contact potentials and distances in folded proteins, directly affect neoantigen-MHC binding affinity. In addition, IConMHC is a pan-allele model that is capable of making predictions for all the MHC alleles. Even for those rare MHC alleles without training data, iConMHC can make predictions with reasonable accuracy. We benchmarked iConMHC with other commonly used MHC-I binding predictors and found our model performs better than most of the pan-allele models.

Abstract PR05: APOBEC mutagenesis is tightly linked to the immune landscape and immunotherapy biomarkers in head and neck squamous cell carcinoma

Abstract Significant interest is focused on the immune microenvironment of head and neck squamous cell carcinoma (HNSCC) due in part to the recent success of immune modulating therapeutics. HNSCC is known to be an immunologically active tumor with immune cell infiltration levels among the highest of all cancers, high mutational burden, and a subset of patients who respond to immune checkpoint blockade. One of the primary sources of mutations in HNSCC is the cytidine deaminase APOBEC3, which is also a known factor in innate immunity. Why particular HNSCCs have higher rates of APOBEC mutations and how these mutations relate to the immune microenvironment and immune activation remain unknown. We utilized whole-exome and RNA-Seq datasets from 276 HNSCC patients from TCGA to annotate APOBEC mutations, immune cell populations, activating and end effectors of immunity and predict neoantigens. In combination with FFPE tissue sections from matched patients, patient demographic data and germline APOBEC polymorphism status, we interrogated the relationship between APOBEC mutations and the immune landscape. Immune cell populations and composite scores of immune activation and infiltration (IFNy score, CYT score, Estimate Immune score) are tightly associated with APOBEC mutational burden, both univariately and after controlling for HPV status (p = 0.013-0.014). HNSCC has the highest levels of IFNy across cancer types with high APOBEC mutational burden, with the highest IFNy scores in HPV mediated HNSCC (HPVmHNSCC) (p = 0.05). Similarly, PD-L1 expression is positively associated with APOBEC mutational burden (p = 0.04). Mutation-induced tumor-specific neoantigen burden is strongly associated with APOBEC mutational burden while other sources of neoantigens are not associated (p = 8e-12). The presence of a germline APOBEC gene polymorphism is more prevalent in non-white, non-black race patients and within this group, patients with the polymorphism have higher APOBEC mutational burden (p= 0.002). Taken together, these data suggest that multiple mechanisms may exist within HNSCC that lead to APOBEC mutations. Immune upregulation, potentially in response to mutation-induced neoantigens, appears to be the primary driver, with viral infection inducing additional IFNy production in the subset of tumors caused by HPV. These mechanisms may be additive and not mutually exclusive, which could explain the higher levels of APOBEC mutations in HPVmHNSCC. The tight interconnectedness of APOBEC mutations and the immune landscape in HNSCC, including numerous known markers of response to immunotherapy, highlights the importance of the immune microenvironment in mutation acquisition in HNSCC. Citation Format: Yan Lin, Shuyan Zhai, Fengshen Kuo, Timothy A. Chan, Luc G. Morris, Robert L. Ferris, Daniel L. Faden, Fei Ding. APOBEC mutagenesis is tightly linked to the immune landscape and immunotherapy biomarkers in head and neck squamous cell carcinoma [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr PR05.

Whole-exome sequencing in a patient with synchronous triple primary malignancies involving lung cancer: a case report.

The incidence of multiple primary malignancies (MPMs) has been increasing rapidly in recent years, however, the genetic pathogenesis is largely unknown on account of rare cases, especially for those patients who are diagnosed with three or more tumors. Under these circumstances, whole-exome sequencing (WES) may help to provide more comprehensive genomic information and guidance to proper therapeutic strategies. Here, we presented a rare case of a 66-year-old Chinese male patient who was diagnosed with synchronous triple primary malignancies: esophageal squamous cell carcinoma (ESCC), lung adenocarcinoma (LA), and hepatocellular carcinoma (HCC). Tumors were surgically removed within 3 months. WES was performed when the patient suffered from cancer recurrence and tumor-specific neoantigens were predicted. Each tumor displayed a distinct somatic mutation profile, providing direct evidence of independent origins. No shared driver gene mutation or neoantigen was detected among the three tumors. Two germline alterations of cancer susceptibility genes—SPINK1 c.194 + 2T>C and JAK3 c.425G>A were identified. This case is the first report of synchronous primary triple cancers covering the esophagus, lung, and liver. Our findings highlight the complexities of MPMs that even when under identical germline genetic backgrounds, the occurrence of MPMs can be a random event and driven by distinct somatic gene mutations. Synchronous multiple primary cancers that originated from different organs may not have common therapeutic gene targets, and it can be difficult to find a treatment to cover all the tumors.

GEN-009, a neoantigen vaccine containing ATLAS selected neoantigens, to generate broad sustained immunity against immunogenic tumor mutations and avoid inhibitory peptides.

3107 Background: Tumor-specific neoantigens provide personalized targets for immunotherapy. Vaccines against epitopes predicted by in silico approaches very rarely induce CD4+ and CD8+ ex vivo T cell responses regardless of formulation. ATLAS selects neoantigens for vaccine inclusion using ex vivo screening of all patient-specific mutations to identify pre-existing CD4+ or CD8+ T cell responses and to exclude Inhibigens, which are inhibitory peptides that suppress immunity and accelerate tumor progression. The Inhibigen burden correlates with patient outcomes in observational studies and rapid tumor progression in mouse models. Methods: GEN-009-101 is a phase 1/2a study testing safety, immunogenicity and clinical activity in immune responsive tumors. After next-generation tumor sequencing and ATLAS testing of autologous leukocytes, up to 20 stimulatory synthetic long peptides adjuvanted with poly-ICLC comprise each personalized vaccine. Eight vaccinated patients have been followed for sustained immunological responses and clinical outcomes. Results: The 40 doses given across patients have induced only mild local discomfort and no DLT. Vaccination has generated immune responses against 99% of administered peptides, with both CD8+ and CD4+ responses in ex vivo fluorospot assays. To date, no patients have developed recurrent disease. Broad immunity develops as early as Day 29 and is sustained for over 12 months. Immune response against individual peptides is correlated with peptide concentration (OR = 1.26, p≤0.0001) but not with other classifiers such as GRAVY index (Grand Average of Hydropathy), tumor type, injection site or sex. The Inhibigen burden prior to treatment again correlates with disease progression. Conclusions: GEN-009 identifies tumor specific immune targets from the individual patient’s tumor mutagens. Initial clinical data show that ATLAS antigen selection may be critical to the induction of broad, rapid and sustained immunity against tumor specific neoantigens. Clinical vaccination with PD-1 blockade is in process. Clinical trial information: NCT03633110 . [Table: see text]

Unsuccessful checkpoint blockade and vaccination therapy despite expansion of tumor neoantigen-specific CD8 T cells in the Lewis Lung Carcinoma (LLC) model

Abstract The most promising cancer immunotherapies to date target immune checkpoint receptors, such as PD-1 and CTLA-4. However, T cell checkpoint blockade has no inherent tumor specificity and shows uneven response, with up to 70–80% of patients resistant to these therapies. Here, we use a spontaneous mouse model resistant to immunotherapy to explore how immune checkpoint blockade (ICB) affects tumor-specific T cells and understand why these cells fail to eradicate tumor cells after treatment. Using combinatorial tetramer staining by CyTOF, we identified a new tumor-specific neoantigen in Lewis Lung Carcinoma (LLC). These tumor-specific CD8+ tumor-infiltrating lymphocytes (TILs) strongly exhibit an activation phenotype and can also be detected in different peripheral compartments at lower frequency. Surprisingly, despite an absence of tumor regression, we observed a remarkable expansion of tumor-specific CD8+ TILs after ICB with anti-PD1, anti-CTLA4, or neoantigen peptide vaccination. This expansion is associated with phenotypic changes, including enrichment of exhaustion markers and CD39 expression indicating chronic TCR activation and confirming recognition of tumor cells. Current single-cell RNA sequencing and TCR profiling analysis will correlate transcriptomics and clonality of these tumor-specific T cells after cancer therapies with anti-tumor response and elucidate their functionalities within the tumor microenvironment. Overall this work provides a new model for studying neoantigens in tumors and revisits the role of neoantigen-specific T cells in the context of ICB, which will have important implications in designing future immunotherapeutic strategies.

Inhibigens, personal neoantigens that drive suppressive T cell responses, abrogate protection of therapeutic anti-tumor vaccines

Abstract Therapeutic cancer vaccine efficacy is reliant on tumor-specific neoantigens. Empirical methods to identify relevant neoantigens may overcome poor predictive values of current in silico approaches. The ATLAS™ platform is a personalized bioassay that uses high-throughput screening of autologous APCs and T cells against a patient’s mutanome to identify neoantigens that are characterized as stimulatory or inhibitory (inhibigens) based on changes in T cell cytokine secretion. In a B16F10 murine model, therapeutic vaccination with ATLAS-identified neoantigens resulted in arrested tumor growth with durable immune responses that protected mice from re-challenge. Mice that were vaccinated with an inhibigen and neoantigen combination had significantly abrogated tumor protection relative to controls. IFNγ ELISpot analysis revealed that inhibigen vaccination suppressed all antigen-specific immune responses, suggesting that inhibigens can be immunodominant. Inhibigen vaccinated mice had multiple tumor microenvironment changes including reduced T cell infiltration, altered CD8+/CD4+ T cell ratios and increased inhibitory marker expression relative to controls. These results indicate that the presence of inhibigens in an otherwise protective vaccine can alter the tumor microenvironment and abolish T cell-mediated protection. Epitope mapping of neoantigens and inhibigens and tetramer design for differential gene signature analysis of inhibigen-specific T cells is being performed. These data suggest that identification and exclusion of inhibigens from cancer vaccines is critical to prevent unintended pro-tumor responses.

A tumor-specific neoepitope expressed in homologous/self or heterologous/viral antigens induced comparable effector CD8+ T-cell responses by DNA vaccination

Somatic mutations in tumors often generate neoproteins that contain MHC-I-binding neoepitopes. Little is known if and how efficient tumor-specific neoantigens activate CD8+ T cells. Here, we asked whether a de novo generated neoepitope, encoded either within an otherwise conserved and ubiquitously expressed self-antigen or in a chimeric HBV core antigen expression platform, providing heterologous helper functions, induces CD8+ T cells in C57Bl/6J mice by DNA immunization. For it, we chose an established Db/Sp244-252/R251H neoepitope generated in the murine Endophilin-B2/SH3GLB2 (EndoB2-Sp) protein by a single amino acid exchange. We showed that a single injection of EndoB2-Sp expression vectors efficiently primed dimer/pentamer+, IFN-γ+ and cytolytic Db/Sp244-252/R251H-specific effector CD8+ T cells in C57Bl/6J mice. Priming of Db/Sp244-252/R251H-specific CD8+ T cells proceeded independent from CD4+ T-cell help in MHC-II-deficient Aα-/- mice. As compared to the homologous EndoB2-Sp vaccine, the selective expression of the Db/Sp244-252/R251H neoepitope in chimeric particle-forming and assembly-deficient HBV core antigens induced comparable frequencies Db/Sp244-252/R251H-specific CD8+ T cells with the same cytolytic effector phenotype. The homologous EndoB2 carrier, but not the nine-residue neoepitope presented on chimeric HBV core particles, induced EndoB2-specific IgG antibody responses. The HBV core expression platform is thus an attractive option to selectively induce neoepitope-specific effector CD8+ T cells by DNA vaccination. These novel findings have practical implications for the design of heterologous/self and heterologous/viral cancer vaccines that prime and/or activate neoepitope-specific CD8+ T cells.

Abstract IA07: Hitting the target: T-cell receptors, cross-reactivity, and tumors

Abstract Cancer arises due to an accumulation of genetic alterations, which can lead to the production of mutant proteins that are not expressed by normal cells. Some of these mutant proteins can be processed into short peptides and presented on the cell surface by major histocompatibility complex molecules as neoepitopes and CD8+ T cells can effectively recognize and mount responses against these neoepitopes across various cancer types. However, tumor-specific lymphocytes are generally characterized by an exhausted phenotype, which can in some circumstances be reversed with checkpoint blockade therapies that restore antitumor activity, with those cancer types and individual tumors that bear relatively high mutation burdens showing more robust responses. Using predictive algorithms to identify putative endogenous antitumor T-cell responses across solid tumor types has resulted in an average 2% of predicted neoepitopes associated with a detectable response. This has led to the assumption that low mutation burden tumors, which include many pediatric tumors, are poorly immunogenic. Here, we report that pediatric patients with acute lymphocytic leukemia (ALL) possess broad pools of tumor-associated neoepitope-specific CD8+ T cells, responding to 86% of tested neoantigens and recognizing 68% of the tested neoepitopes. We characterized phenotypic and transcriptional profiles of these epitope-specific antitumor CD8+ T cells at the single-cell level and found a heterogeneous population that included many highly functional effector cells. Moreover, we observed immunodominance hierarchies among the neoepitope-specific CD8+ TILs, whereby a majority of tetramer-bound CD8+ T cells were restricted to one or two putative neoepitopes. Our results indicate that robust antitumor immune responses are induced in these pediatric ALL tumors despite their low mutation burdens and emphasize the importance of immunodominance in shaping cellular immune responses. These data suggest that pediatric cancers may be amenable to immunotherapies aimed at enhancing immune recognition of tumor-specific neoantigens. Citation Format: Paul G. Thomas. Hitting the target: T-cell receptors, cross-reactivity, and tumors [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr IA07.

Abstract A19: HLA allele-specific loss of heterozygosity detection using augmented exome capture approach

Abstract Loss of human leukocyte antigen (HLA) is of increasing interest as a mechanism of cancer immune evasion and biomarker for cancer immunotherapy response. Each cancer patient has six class I HLA alleles that are capable of presenting a set of tumor-specific neoantigens. However, HLA alleles are often deleted in tumors, resulting in a loss of heterozygosity (LOH). When LOH occurs, neoantigen presentation is significantly impaired, potentially facilitating tumor immune evasion. Given the biologic impact of HLA LOH, there is a need for robust algorithms that can detect allele-specific HLA LOH in tumor samples. Here, we describe a novel computational approach to detect HLA LOH from exome sequencing, demonstrate the robustness of the method, and apply the method to calculate the frequencies of HLA LOH in key cancer types. We performed exome sequencing with augmented HLA region capture on the ImmunoID NeXT platform for tumor and normal samples of 184 patients across several cancer types and identified 430 nonhomozygous HLA genes. Next, we extracted the reads mapping to a custom HLA database and mapped them on to the patient-specific HLA alleles. For each allele, we calculated two key features: 1) the tumor b-allele frequency normalized by the native b-allele frequency and 2) the allele-specific tumor to normal coverage ratio. Using these two features, along with tumor purity and ploidy values, we trained a random forest model on a subset of the HLA genes (n=300). While standard copy-number variant (CNV) tools are unable to detect LOH in the polymorphic HLA genes, they can accurately measure deletions in their flanking regions, which we used to validate the accuracy of our allele-specific HLA LOH algorithm. In our test set (n=130), we found a high concordance between our allele-specific deletion calls and the generic deletion calls (94% accuracy, 0.85 F1 Score). When we constrained our test set to samples with high tumor content (>50%, n=40), we saw even stronger concordance (98% accuracy, 0.95 F1 Score). The only discordant call was a focal deletion within an HLA gene that was detected by our algorithm but missed by the CNV tool. Next, we ran our algorithm on patient samples of different cancer indications. For non-small cell lung cancer, we found a high frequency of patients affected by LOH (35%, 9 of 26), which is similar to frequencies previously reported in the literature. Furthermore, we found a lower frequency of melanoma tumors with LOH (15%, 7 of 48). In conclusion, we developed a novel algorithm to call allele-specific HLA LOH on the ImmunoID NeXT exome sequencing platform that augments coverage in the polymorphic HLA locus and demonstrated overall robust performance. The relatively high frequency of LOH events we detected in the melanoma and lung cancer samples suggests the importance of LOH analysis to inform cancer immunotherapy biomarker studies and personalized cancer therapies that depend on neoantigen presentation. Citation Format: Rachel Marty Pyke, Charles Abbott, Simo V. Zhang, Datta Mellacheruvu, John West, Richard Chen, Sean Michael Boyle. HLA allele-specific loss of heterozygosity detection using augmented exome capture approach [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A19.

Genomic landscape and immune microenvironment features of FH-deficient renal cell carcinoma.

717 Background: Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare but aggressive subtype of RCC, and an understanding of their genomic landscape is lacking. Methods: Thirteen FH-deficient RCC and matched normal samples from our medical center were used for whole-exome sequencing and molecular profiling. Transcriptomic sequencing data were analyzed from The Cancer Genome Atlas (TCGA) RCC samples and two independent datasets of FH-mutated RCC. For each patient in our cohort, clinical outcomes were recorded, with median follow-up until March, 2019. Results: We identified FH-deficient RCC had high mutation burden, frequent somatic mutations in MUC4, MUC16, ANKRD6 and PRDM16 and germline pathologic FANCD2 mutation. In contrast to mainly RCC subtypes, FH-deficient RCC exhibited elevated mutational signatures of homologous recombination deficiency (7/13, 53%) and microsatellite instability (4/13, 30%). Moreover, we revealed that FH-deficient RCC was characteristic of an immunogenic tumor type, with high tumor-specific neoantigen burdens, enhanced immune cells infiltration but immunosuppressive microenvironment. In our cohort, 11 patients received systematic treatments; 63% (5/8) responded poorly to antiangiogenic or anti-mammalian target of rapamycin agents, but all (6/6) achieved benefit from immune checkpoint blockade monotherapy or combination with antiangiogenic agents. Conclusions: Our study provides a better understanding of the mutational landscape of FH-deficient RCC, highlighting the potential of immunotherapy for treating this patient populations.

Characterizing MHC-I Genotype Predictive Power for Oncogenic Mutation Probability in Cancer Patients.

MHC class I proteins present intracellular peptides on the cell's surface, enabling the immune system to recognize tumor-specific neoantigens of early neoplastic cells and eliminate them before the tumor develops further. However, variability in peptide-MHC-I affinity results in variable presentation of oncogenic peptides, leading to variable likelihood of immune evasion across both individuals and mutations. Since the major determinant of peptide-MHC-I affinity in patients is individual MHC-I genotype, we developed a residue-centric presentation score taking both mutated residues and MHC-I genotype into account and hypothesized that high scores (which correspond to poor presentation) would correlate to high mutation frequencies within tumors. We applied our scoring system to 9176 tumor samples from TCGA across 1018 recurrent mutations and found that, indeed, presentation scores predicted mutation probability. These findings open the door to more personalized treatment plans based on simple genotyping. Here, we outline the computational tools and statistical methods used to arrive at this conclusion.

Anti-TIF1-γ autoantibodies: warning lights of a tumour autoantigen.

Anti-transcription intermediary factor 1 (TIF1)-γ autoantibodies are robustly linked with cancer-associated DM in adults. This review aims to give an overview of the physiological context of TIF1-γ and to determine whether there is a pathophysiological link between anti-TIF1-γ autoantibodies and the occurrence of cancer. Detection of anti-TIF1-γ autoantibodies has a high sensitivity and specificity for cancer-associated DM in adults and is therefore useful for both diagnosis and cancer risk stratification. The function of the autoantigen, TIF1-γ, may provide insight into the mechanism behind this association. TIF1-γ is a ubiquitously present protein involved in various biological pathways, including TGF-β signalling. In cancer, it can act either as a tumour suppressor or promoter, depending on the cellular context and cancer stage. Evolving data provide pathophysiological insights, linking anti-TIF1-γ autoantibodies to both the anti-tumour response and to muscle and skin damage. TIF1-γ expression is increased in muscle and skin tissue of patients with DM. Mutations or loss-of-heterozygosity in TIF1-γ alleles in malignant tissue may result in the expression of tumour-specific neo-antigens stimulating autoantibody production. The newly formed autoantibodies are hypothesized to cross-react with antigens in muscle and skin, driving the development of DM. Based on the current evidence, anti-TIF1-γ autoantibodies should be considered warning lights of a potential tumour autoantigen and should alert the physician to the possibility of an underlying cancer.

A cocktail of polyclonal affinity enriched antibodies against melanoma mutations increases binding and inhibits tumor growth

BackgroundAntibodies that target a single tumor antigen fail to cure stage IV cancer patients due to tumor heterogeneity and variable expression of antigen. Tumor cells with insufficient binding of antibody will not undergo antibody induced cytotoxicity. We describe targeting multiple tumor-specific antigens that resulted in homogeneous dense binding to mouse melanoma cells and significant tumor growth inhibition. MethodsSurface-related tumor-specific mutations on B16-F10 cells were identified. Peptides containing the single amino acid mutation were synthesized for 9 different neoantigens. Rabbits were vaccinated with each of these peptides and high affinity polyclonal antibodies to each peptide were obtained. The 9 antibodies were combined as a cocktail and mice with implanted B16-F10 cells were treated with and without PD1 inhibitor. ResultsEven a single dose of the antibody cocktail inhibited tumor growth and prolonged survival. PD1 inhibitor alone had little effect on tumor growth. The antibody cocktail plus PD1 inhibition increased tumor response and 4 doses of the cocktail completely prevented tumor growth in 50% of the mice. Complete responses were durable. The complete responders were highly resistant to tumor re-challenge at 6 months. No adverse events were identified in the antibody treated mice. ConclusionsMultiple tumor-specific cell surface-related neoantigens were abundant in B16-F10 cells. Antibodies to 9 of these neoantigens had variable binding but when combined had dense homogeneous binding. Even one dose of this cocktail of 9 antibodies improved survival and when multiple doses were combined with PD1 inhibition 50% of the mice were rendered permanently tumor free.

T Cells Educated By DC/AML Fusions in the Context of 4-1BB Costimulation As a Potent Strategy for Adoptive Cellular Therapy

Introduction: Our group has developed a novel vaccine using patient-derived acute myeloid leukemia (AML) cells and autologous dendritic cells (DCs), capable of presenting a broad array of leukemia antigens. In a phase I/II clinical trial DC/AML vaccination led to an expansion of leukemia-specific T cells. We hypothesized that the fusion vaccine offered a unique platform for ex vivo expansion of functionally potent leukemia specific T cells with broad specificity targeting shared and tumor specific neoantigens. We postulated that incorporating 4-1BB (CD137) mediated co-stimulation would further enhance activation of antigen specific T cells and the development of a crucial memory response as well as promote survival and persistence. Here we describe therapeutic exploration of the use of 4-1BB to augment vaccine-educated T cells for adoptive cellular therapy in an immunocompetent murine model. Methods: DC/AML fusion vaccine was generated using DCs obtained from C57BL/6J mice and syngeneic C1498 AML cells as previously described. T cells were obtained from splenocytes after magnetic bead isolation and cultured with irradiated DC/AML fusion vaccine in the presence of IL-15 and IL-7. Following co-culture, 4-1BB positive T cells were ligated using agonistic 4-1BB antibody (3H3 clone, BioXCell) and further selected with RatIgG2a magnetic beads (Easy Sep). Subsequently T cells were expanded with anti-CD3/CD28 activation beads (Dynabeads). In vivo, mice underwent retro-orbital inoculation with C1498 and vaccination with irradiated fusion cells the following day. Agonistic mouse anti-4-1BB antibody was injected intraperitoneally on day 4 and day 7. In addition, C1498 cells were transduced with Mcherry/luciferase and a reproducible model of disease progression was established. Results: DC/fusion stimulated T cells showed increased immune activation as measured by multichannel flow cytometric analysis. Compared to unstimulated T cells, there was 5-fold increase in CD4+CD25+CD69+, and a 10-fold and 7-fold increase in 4-1BB and intracellular IFNƔ expression on CD8+ cells respectively. Following agonistic 4-1BB ligation and bead isolation, the proliferation rate was increased in the 4-1BB positive fraction as compared to both 4-1BB negative cells and unstimulated T cells. In addition, the 4-1BB positive fraction demonstrated increased cytotoxicity, as measured by a CTL assay detecting granzyme B with 1:10 tumor to effector cells. A shift from naïve to memory T cell phenotype was also observed. Following DC/fusion stimulation, CD44+CD62L- cells comprised 67% of CD8+ cells versus 20% without stimulation, the latter reflecting the effect of cytokines alone. Following 4-1BB ligation and anti-CD3/CD28 bead expansion, this phenotype was retained with the CD4+ and CD8+ effector memory and central memory compartments comprising the majority of T cells. Such findings are significant as presence of memory T cell populations are a critical component for successful adoptive cell transfer. The effect of agonistic 4-1BB antibody following vaccination was evaluated in vivo in an aggressive immunocompetent murine AML model. The combination of DC/AML fusion vaccine with 4-1BB antibody was associated with increased long-term survival (>120 days) of 40% versus 20% of mice treated with vaccine alone while all controls required euthanasia by 40 days. Conclusion: In the current study we have demonstrated the ability of DC/AML fusion vaccine to stimulate T cells ex-vivo as demonstrated by both early-activation (CD25,CD69), upregulation of antigen-specific markers (CD137) and cytokine secretion. Further enhancement of the cellular product using agonistic 4-1BB ligation and isolation simultaneously enriches for antigen-activated cells, as demonstrated by more potent cytotoxicity, as well as promoting memory phenotype and survival. Use of 4-1BB ligation for antigen-specific selection while providing an agonistic co-stimulatory signal is a potentially novel approach for development of non-engineered T cells. Ongoing experiments evaluating the efficacy of 4-1BB selected vaccine educated T cells using bioluminescence monitoring will be reported as well as in vitro use of patient-derived T cells. Disclosures Kufe: Canbas: Consultancy, Honoraria; Victa BioTherapeutics: Consultancy, Equity Ownership, Honoraria, Membership on an entity's Board of Directors or advisory committees; Genus Oncology: Equity Ownership; Hillstream BioPharma: Equity Ownership; Reata Pharmaceuticals: Consultancy, Equity Ownership, Honoraria; Nanogen Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Rosenblatt:Dava Oncology: Other: Education; Partner Tx: Other: Advisory Board; Parexel: Consultancy; Celgene: Research Funding; BMS: Research Funding; Amgen: Other: Advisory Board; Merck: Other: Advisory Board; BMS: Other: Advisory Board ; Imaging Endpoint: Consultancy. Avigan:Takeda: Consultancy; Parexel: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partners Tx: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy.