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FGFR1 expression in cancer cell lines.

Detailed description of STS Cohort 1 including the clinical data and FGFR1 status.

Soft-tissue sarcoma cohorts.

Detailed description of STS Cohort 2 including the clinical data and FGFR1 status.

Purpose: Altered FGFR1 signaling has emerged as a therapeutic target in epithelial malignancies. In contrast, the role of FGFR1 in soft-tissue sarcoma (STS) has not been established. Prompted by the detection and subsequent therapeutic inhibition of amplified FGFR1 in a patient with metastatic leiomyosarcoma, we investigated the oncogenic properties of FGFR1 and its potential as a drug target in patients with STS.Experimental Design: The frequency of FGFR1 amplification and overexpression, as assessed by FISH, microarray-based comparative genomic hybridization and mRNA expression profiling, SNP array profiling, and RNA sequencing, was determined in three patient cohorts. The sensitivity of STS cell lines with or without FGFR1 alterations to genetic and pharmacologic FGFR1 inhibition and the signaling pathways engaged by FGFR1 were investigated using viability assays, colony formation assays, and biochemical analysis.Results: Increased FGFR1 copy number was detected in 74 of 190 (38.9%; cohort 1), 13 of 79 (16.5%; cohort 2), and 80 of 254 (31.5%; cohort 3) patients. FGFR1 overexpression occurred in 16 of 79 (20.2%, cohort 2) and 39 of 254 (15.4%; cohort 3) patients. Targeting of FGFR1 by RNA interference and small-molecule inhibitors (PD173074, AZD4547, BGJ398) revealed that the requirement for FGFR1 signaling in STS cells is dictated by FGFR1 expression levels, and identified the MAPK–ERK1/2 axis as critical FGFR1 effector pathway.Conclusions: These data identify FGFR1 as a driver gene in multiple STS subtypes and support FGFR1 inhibition, guided by patient selection according to the FGFR1 expression and monitoring of MAPK–ERK1/2 signaling, as a therapeutic option in this challenging group of diseases. Clin Cancer Res; 23(4); 962–73. ©2016 AACR.

Radiographic assessment of target lesions following BGJ398 treatment.

Therapies that utilize immune checkpoint inhibition work by leveraging mutation-derived neoantigens and have shown greater clinical efficacy in tumors with higher mutational burden. Whether tumors with a low mutational burden are susceptible to neoantigen-targeted therapy has not been fully addressed. To examine the feasibility of neoantigen-specific adoptive T-cell therapy, the authors studied the T-cell response against somatic variants in five patients with myelodysplastic syndrome (MDS), a malignancy with a very low tumor mutational burden. DNA and RNA from tumor (CD34+) and normal (CD3+) cells isolated from the patients' blood were sequenced to predict patient-specific MDS neopeptides. Neopeptides representing the somatic variants were used to induce and expand autologous T cells ex vivo, and these were systematically tested in killing assays to determine the proportion of neopeptides yielding neoantigen-specific T cells. The authors identified a total of 32 somatic variants (four to eight per patient) and found that 21 (66%) induced a peptide-specific T-cell response and 19 (59%) induced a T-cell response capable of killing autologous tumor cells. Of the 32 somatic variants, 11 (34%) induced a CD4+ response and 11 (34%) induced a CD8+ response that killed the tumor. These results indicate that in vitro induction of neoantigen-specific T cells is feasible for tumors with very low mutational burden and that this approach warrants investigation as a therapeutic option for such patients.

Summary Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co‐expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions., We addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQ2.5 however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively.

The availability of MHC-binding prediction tools has been useful in guiding studies aimed at identifying candidate target Ags to generate reactive T cells and to characterize viral and tumor-reactive T cells. Nevertheless, prediction algorithms appear to function poorly for epitopes containing cysteine (Cys) residues, which can oxidize and form disulfide bonds with other Cys residues under oxidizing conditions, thus potentially interfering with their ability to bind to MHC molecules. Analysis of the results of HLA-A*02:01 class I binding assays carried out in the presence and absence of the reducing agent 2-ME indicated that the predicted affinity for 25% of Cys-containing epitopes was underestimated by a factor of 3 or more. Additional analyses were undertaken to evaluate the responses of human CD8+ tumor-reactive T cells against 10 Cys-containing HLA class I–restricted minimal determinants containing substitutions of α-aminobutyric acid (AABA), a cysteine analogue containing a methyl group in place of the sulfhydryl group present in Cys, for the native Cys residues. Substitutions of AABA for Cys at putative MHC anchor positions often significantly enhanced T cell recognition, whereas substitutions at non-MHC anchor positions were neutral, except for one epitope where this modification abolished T cell recognition. These findings demonstrate the need to evaluate MHC binding and T cell recognition of Cys-containing peptides under conditions that prevent Cys oxidation, and to adjust current prediction binding algorithms for HLA-A*02:01 and potentially additional class I alleles to more accurately rank peptides containing Cys anchor residues.

A common strategy for predicting candidate human leukocyte antigen class I T-cell epitopes is to use an affinity-based threshold of 500 nM. Although a 500 nM threshold across alleles effectively identifies most epitopes across alleles, findings showing that major histocompatibility complex repertoire sizes vary by allele indicate that using thresholds specific to individual alleles may improve epitope identification. In this work, we compare different strategies utilizing common and allele-specific thresholds to identify an optimal approach for T-cell epitope prediction. First, we confirmed previous observations that different human leukocyte antigen class I alleles correspond with varying repertoire sizes. Here, we define general and allele-specific thresholds that capture 80% of eluted ligands and a different set of thresholds associated with capturing 9-mer T-cell epitopes at 80% sensitivity. Our analysis revealed that allele-specific threshold performance was roughly equivalent to that of a common threshold when considering a relatively large number of alleles. However, when predicting epitopes for only a few alleles, the use of allele-specific thresholds would be preferable. Finally, we present here for public use a set of allele-specific thresholds that may be used to identify T-cell epitopes at 80% sensitivity., Graphical Abstract, Highlights • Confirmed findings that different HLA class I alleles have varying repertoire sizes. • Defined common and allele-specific thresholds that capture 80% of eluted ligands. • Defined common and allele-specific thresholds that capture 80% of T-cell epitopes. • Allele-specific thresholds perform more consistently when analyzing a few alleles., In Brief Candidate HLA class I T-cell epitopes are frequently identified using a single threshold across multiple alleles. However, previous studies have demonstrated that different HLA class I alleles have varying repertoire sizes. In this study, we investigated the performance of different strategies to perform epitope-binding prediction for multiple HLA class I alleles simultaneously. We identified and present here a set of general and allele-specific thresholds that may be used to identify eluted ligands and T-cell epitopes at 80% sensitivity.

Through somatic mutations, cancer cells accumulate cancer-specific mutated peptides, commonly referred to as neoepitopes or neoantigens, that can be recognized by T cells. Due to their cancer-specific nature, neoantigens are a highly attractive area of study for developing cancer immunotherapies. By comparing whole-exome sequencing of patient tumor tissue and matched normal tissue, researchers have been able to identify cancer-specific neoepitopes. However, the filters that can accurately delineate immunogenic neoepitopes from non-immunogenic remain elusive. Current methods, such as the pipeline developed by the Tumor Neoantigen Selection Alliance (TESLA), typically select short peptides based on hypothesized rules of immunogenicity. Importantly, only a small percentage of these epitopes have been reported to be recognized by T cells through experimental validation assays. In this study, we systematically tested the neoantigen prediction capabilities of our in-house pipeline, Identify-Prioritize-Validate (IPV), and the TESLA pipeline. Whole-exome sequencing from 11 head and neck squamous cell carcinoma patients was used to identify somatic variants. With IPV, variants were filtered and ranked primarily based on variant allele frequencies in RNA and DNA and 20mer peptides were generated from each mutation. In parallel, the TESLA pipeline was used to rank 8- 12-mer peptides based on a combination of HLA binding and stability predictions as well as expression levels of source antigens. Pools of the top 10 peptides from both methods were generated. To enable the direct comparison of TESLA and IPV while accounting for the disparate peptide lengths produced by the pipelines, two additional pools were generated: one pool of 20mers from the TESLA pipeline and one pool of 8-12mers from IPV. The four peptide pools were individually cultured in vitro with patient-matched PBMCs and immune recognition was measured through IFNg and IL5 using fluorospot assays. The IPV pipeline consistently outperformed the TESLA pipeline in predicting neoantigens that elicited an immune response. 5 of the 11 patients showed significant immune recognition of the IPV 20mer peptide pool, only 2 patients responded to the 20mer TESLA pool, while no patients responded to the TESLA and IPV 8-12mer pools. Through this data, we prove the efficacy of IPV, and its filters, and highlight the importance of utilizing 20mer peptides over shorter peptides. Finally, we deconvoluted the positive IPV responses and were able to provide the sequences of novel immunogenic neoepitopes. Our work underscores the improvement in the predictive ability of IPV in comparison to typical pipelines that output short peptides based on parameters derived from a priori knowledge and demonstrates the ability of IPV to be utilized as a tool to identify novel neoantigens. Citation Format: Leila Yasmine Chihab, Zeynep Kosaloglu-Yalcin, Jason Greenbaum, Aaron Miller, Stephen Schoenberger, Bjoern Peters. Experimental comparison of two pipelines to identify neo-epitopes in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2719.

The adaptive immune system in vertebrates has evolved to recognize non-self-antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared six metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

MHC class I antigen processing consists of multiple steps that result in the presentation of MHC bound peptides that can be recognized as T cell epitopes. Many of the pathway steps can be predicted using computational methods, but one is often neglected: mRNA expression of the epitope source proteins. In this study, we improve epitope prediction by taking into account both peptide-MHC binding affinities and expression levels of the peptide’s source protein. Specifically, we utilized biophysical principles and existing MHC binding prediction tools in concert with RNA expression to derive a function that estimates the likelihood of a peptide being presented on a given MHC class I molecule. Our combined model of Antigen eXpression based Epitope Likelihood-Function (AXEL-F) outperformed predictions based only on binding or based only on antigen expression for discriminating eluted ligands from random background peptides as well as in predicting neoantigens that are recognized by T cells. We also showed that in cases where cancer patient-specific RNA-Seq data is not available, cancer-type matched expression data from TCGA can be used to accurately estimate patient-specific gene expression. Using AXEL-F together with TGCA expression data we were able to more accurately predict neoantigens that are recognized by T cells. The method is available in the IEDB Analysis Resource and free to use for the academic community.Significance statementEpitope prediction tools have been used to call epitopes in viruses and other pathogens for almost 30 years, and more recently, to call cancer neoantigens. Several such tools have been developed, however most of them ignore the mRNA expression of the epitope source proteins. In the present study, we have, to our knowledge for the first time, developed a biophysically motivated model to combine peptide-MHC binding and abundance of the peptide’s source protein to improve epitope predictions. Our novel tool AXEL-F is freely available on the IEDB and presents a clear opportunity for predicting and selecting epitopes more efficiently.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

2615 Background: Neoantigens (NeoAg) are key targets for personalized immunotherapy but efficient methods for their systematic identification and therapeutic targeting remain elusive. We developed a methodology to reliably identify and verify somatic alteration-derived neoantigens based on natural T cell responses against them which formed the basis of an individualized therapeutic vaccine strategy. Methods: This is a phase Ib study to assess the immunogenicity, safety and early clinical activity of personalized synthetic long peptides (PSLP) cancer vaccines in combination with pembrolizumab for patients with treatment refractory metastatic solid tumors or PSLP vaccine alone as an adjuvant treatment with patients with no evidence of disease (NED) that incorporates patient-specific NeoAg identified by an HLA-agnostic, functional T-cell assay (see table). Results: At the time of data cutoff, a total of 5 patients had been treated on ARM-A, 5 patients on ARM-C and 2 patients on ARM-D. AES possibly attributed to personalized vaccine (PSLP), or pembrolizumab, or both include: Grade 1: Arthralgia (1); Diarrhea (1); Fever (4); Fatigue (7); Generalized muscle weakness (1); Headache (2); Nausea (1); Confusion (1); Injection site reaction (5); Rash maculo-papular (3); Flu like symptoms (5); Myalgia (1); and Grade 2: Diarrhea (1); Fatigue (1); Hyperhidrosis (1); Hypothyroidism (1); Injection site reaction (1); Proteinuria (1); Renal and Urinary – other (1); and Grade 3: Colitis (1). For the 9 patients with at least 1 radiographic assessment at the time of analysis 6 had a best response of stable disease (SD) and 3 had progressive disease (PD). Immune monitoring of peripheral blood specimens consistently demonstrated that NeoAg-specific T cell responses were enhanced following administration of PSLP vaccine. On-treatment biopsies demonstrated immune-editing with the variant allele frequency of targeted mutations decreasing following administration of the PSLP vaccine. Conclusions: Taken together, these data meet the trial primary endpoint by demonstrating PSLP vaccines targeting NeoAg identified using the HLA-agnostic pipeline augment effector T cell function against these targets. Clinical trial information: NCT02287428. [Table: see text]

Purpose: Altered FGFR1 signaling has emerged as a therapeutic target in epithelial malignancies. In contrast, the role of FGFR1 in soft-tissue sarcoma (STS) has not been established. Prompted by the detection and subsequent therapeutic inhibition of amplified FGFR1 in a patient with metastatic leiomyosarcoma, we investigated the oncogenic properties of FGFR1 and its potential as a drug target in patients with STS. Experimental Design: The frequency of FGFR1 amplification and overexpression, as assessed by FISH, microarray-based comparative genomic hybridization and mRNA expression profiling, SNP array profiling, and RNA sequencing, was determined in three patient cohorts. The sensitivity of STS cell lines with or without FGFR1 alterations to genetic and pharmacologic FGFR1 inhibition and the signaling pathways engaged by FGFR1 were investigated using viability assays, colony formation assays, and biochemical analysis. Results: Increased FGFR1 copy number was detected in 74 of 190 (38.9%; cohort 1), 13 of 79 (16.5%; cohort 2), and 80 of 254 (31.5%; cohort 3) patients. FGFR1 overexpression occurred in 16 of 79 (20.2%, cohort 2) and 39 of 254 (15.4%; cohort 3) patients. Targeting of FGFR1 by RNA interference and small-molecule inhibitors (PD173074, AZD4547, BGJ398) revealed that the requirement for FGFR1 signaling in STS cells is dictated by FGFR1 expression levels, and identified the MAPK–ERK1/2 axis as critical FGFR1 effector pathway. Conclusions: These data identify FGFR1 as a driver gene in multiple STS subtypes and support FGFR1 inhibition, guided by patient selection according to the FGFR1 expression and monitoring of MAPK–ERK1/2 signaling, as a therapeutic option in this challenging group of diseases. Clin Cancer Res; 23(4); 962–73. ©2016 AACR.

OncoImmunology 8(9), e1626193 (2019). doi:10.1080/2162402X.2019.1626193, Published by Taylor & Franics, Abingdon

Background: In the field of breast cancer tumor biology, triple-negative breast cancer patients are the main focus of current clinical trials exploring the use of immune checkpoint inhibitors due to higher frequencies of somatic mutations, neoantigens, and resulting tumor-specific T-cell reactivity. Case Report: Here, we present the case of a 66-year-old woman with metastatic luminal breast cancer that rapidly responded to monotherapy with pembrolizumab, a monoclonal anti-PD-1 antibody. This patient obtained a partial clinical response within the first cycle of treatment and an ongoing durable complete remission after 12 weeks. Except for a transient immune-related thyreoiditis, there were no side effects observed offering remarkable quality of life to the patient. To evaluate the underlying mechanisms, we performed immunohistochemistry, explored the mutational landscape by whole-exome sequencing, and identified potential T-cell epitopes by prediction of neoantigens with high affinity binding to one of the patient's HLA. Briefly, we found a strong infiltration of CD8+ T cells without staining for PD-L1 in the tumor stroma. Exome sequencing revealed an enormous frequency of somatic and tumor-specific alterations, mainly C>T/G>A transitions. The mutational pattern was further linked to genome instability and deficient mismatch repair supported by the loss of MSH6 protein expression and therefore leading to susceptibility to immune checkpoint blockade. Conclusion: Within the overall goal to establish operating procedures for breast cancer immunotherapy, we propose to re-evaluate testing for deficient mismatch repair and to further intensify the search for biomarkers predictive for the success of immune checkpoint modulation including all tumor biologic subtypes of breast cancer.

Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

Purpose: Determine the prognostic and predictive significance of tumor associated antigen (TAA)-specific serum antibodies in melanoma patients of a large adjuvant vaccination phase III trial. Patients and methods: Serum IgG antibodies were measured against a panel of 43 antigens by a bead-based multiplex assay in 970 stage II melanoma patients of the EORTC18961 trial, evaluating adjuvant ganglioside GM2-KLH/QS-21 vaccination versus observation. Primary end point was relapse-free survival (RFS). Patients' sera at baseline, after 12 and 48 weeks of study treatment and at the last available time point (at recurrence/remission) were evaluated. Results: Prognostic clinical variables are gender, surgical confirmation of lymph node-negative status, Breslow thickness and ulceration of the primary. Prognostic spontaneous antibody responses were associated with a significant dismal (GM2, Rhod_E2, SSX2) or good prognosis (CyclinB1, SCYE1v1) for RFS, distant metastasis-free (DMFS) or overall survival (OS). Predictive spontaneous antibody responses based on significant interaction with treatment were RhodN p = 0.02, Rab38 p = 0.04 for RFS, RhodE2 p = 0.006, Recoverin p = 0.04 for DMFS and RhodE2 p = 0.003; Recoverin p = 0.04, NA17.A p = 0.04, for OS respectively. The subgroups of patients according to antibody responses for RFS were determined for RhodN sero-negative (n = 849, HR = 1.07, p = 0.6); RhodN sero-positive (n = 121,HR = 0.42, p = 0.01) and Rab38 sero-negative (n = 682, HR = 1.12, p = 0.42), Rab38 sero-positive (n = 288, HR = 0.65, p = 0.04) patients respectively. Conclusion: We identified prognostic serum antibody responses against TAA in stage II melanoma patients. A set of antibody responses correlated with a beneficial outcome for GM2 vaccination.

The utility of autologous induced pluripotent stem cell (iPSC) therapies for tissue regeneration depends on reliable production of immunologically silent functional iPSC derivatives. However, rejection of autologous iPSC-derived cells has been reported, although the mechanism underlying rejection is largely unknown. We hypothesized that de novo mutations in mitochondrial DNA (mtDNA), which has far less reliable repair mechanisms than chromosomal DNA, might produce neoantigens capable of eliciting immune recognition and rejection. Here we present evidence in mice and humans that nonsynonymous mtDNA mutations can arise and become enriched during reprogramming to the iPSC stage, long-term culture and differentiation into target cells. These mtDNA mutations encode neoantigens that provoke an immune response that is highly specific and dependent on the host major histocompatibility complex genotype. Our results reveal that autologous iPSCs and their derivatives are not inherently immunologically inert for autologous transplantation and suggest that iPSC-derived products should be screened for mtDNA mutations.

Neoantigens (NeoAg) offer attractive therapeutic targets for directing a patient’s immune response to the immunogenic subset of mutations expressed exclusively by their cancer cells. Despite the specificity with which NeoAg enable tumor recognition, the majority of approaches for their identification rely on purely predictive methods such as calculating the ability of mutated peptides to bind to a patient’s set of HLA molecules, and have met with limited success in revealing natural targets present on tumor cells. We have developed a novel HLA-agnostic functional approach to NeoAg identification which combines genomic sequencing with bioinformatic analysis to nominate mutations for subsequent functional analysis using patient’s own T cells in an effort to identify natural responses generated under physiologic conditions. Using this, we identified a missense mutation (V205>I) in the ribosomal protein RPS2 that is recognized by CD8+ T cells from tumor-infiltrating lymphocytes (TIL) of a metastatic HPV-16+ Head and Neck Squamous Cell Carcinoma (HNSCC) lesion. We then performed adoptive cellular therapy (ACT) using either unseparated TIL or those enriched for RPS2 V205>I-specific CD8+ T cells and found the latter to be superior in controlling outgrowth of tumor of a PDX cell line generated from this lesion in NSG mice. Finally, we used single-cell transcriptomics to isolate the genes encoding the RPS2-specific TCR and show that it recognizes the mutated peptide bound to HLA-B*07.02 as well as the tumor cell line expressing the RPS2 protein. These results demonstrate that high-affinity NeoAg-specific T cell responses can be identified in cancer patients, that ACT of these cells can control tumor growth, and that the relevant TCR can be isolated for use in TCR engineering-based immunotherapy. Citation Format: Martin S. Naradikian, Leslie Montero, Samantha Hall, Milad Bahmanof, Rukman Thota, Luise Sternberg, Jerome Lane, Zeynep Kosaloglu-Yalcin, Manasa Lanka, Aaron Miller, Bjoern Peters, Ezra Cohen, Stephen Schoenberger. Identification and characterization of neoantigen specific T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1439.

Mutations acquired during oncogenesis can result in so-called neoantigens, i.e. mutation-bearing peptides that have the potential to be bound by HLA molecules and recognized by T cells. Neoantigens were suggested to be crucial for the outcome of immune checkpoint therapies and accordingly, the neoantigen load of a tumor is often being used to predict therapy outcome. Current approaches utilize HLA binding predictions to rank peptides, assuming that neoepitopes are ranked towards the top. The neoantigen load of a given tumor is then usually defined as the number of peptides binding above a certain threshold. Using this approach some reports found a very strong correlation of neoantigen load and response to checkpoint blockade therapy while others found a better correlation when mutational load was used instead. However, comparing results between studies is hard because they used different tools and thresholds to identify mutations, and applied different criteria to prioritize their set of mutated peptides. We sought to overcome this lack of uniformity in re-analyzing data from several published checkpoint blockade therapy studies to consistently define the mutational burden and neoantigen load, and correlate them with therapy outcomes We obtained sequencing data from checkpoint blockade studies deposited in NCBI dbGaP and run all datasets through our in-house mapping and variant calling pipelines. Neoepitope candidates were predicted and scored using our previously published pipeline. We applied different scoring schemes to define the neoantigen load which were based on HLA binding predictions with varying thresholds and different metrics used (i.e. predicted IC50 vs. percentile rank). We found that the exact definition of neoantigen load has a major impact on how well this metric performs in predicting checkpoint blockade therapy outcome. Furthermore, we implemented a likelihood score which is based on data of known immunogenic neoantigens and describes the likelihood of a predicted neoantigen to be immunogenic. Based on our preliminary analysis to date, we found that the cumulative likelihood score of a patient is a good predictor of checkpoint blockade therapy outcome, outperforming tumor mutational burden and predicted neoantigen load in several cohorts. Citation Format: Zeynep Kosaloglu-Yalcin, Angela Frentzen, Ashmitaa Logandha Ramamoorthy Premlal, Jason Greenbaum, Alessandro Sette, Bjoern Peters. Assessing the impact of neoantigen load on checkpoint blockade efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3374.

Neoantigens (NeoAg) offer attractive therapeutic targets for directing a patient’s immune response to the immunogenic subset of mutations expressed exclusively by their cancer cells. Despite the specificity with which NeoAg enable tumor recognition, the majority of approaches for their identification rely on purely predictive methods such as calculating the ability of mutated peptides to bind to a patient’s set of HLA molecules. These methods have met with limited success in revealing natural targets present on tumor cells. We have developed a novel HLA-agnostic functional approach to NeoAg identification which combines genomic sequencing with bioinformatic analysis to nominate mutations for subsequent functional analysis using patient’s own T cells in an effort to identify natural responses generated under physiologic conditions. Using this, we identified a missense mutation (V205I) in the ribosomal protein RPS2 that is recognized by CD8+ T cells from tumor-infiltrating lymphocytes (TIL) of a metastatic HPV16+ Head and Neck Squamous Cell Carcinoma lesion. We then performed adoptive cellular therapy (ACT) using either unseparated TIL or those enriched for RPS2 V205I-specific CD8+ T cells and found the latter to be superior in controlling outgrowth of tumor of a PDX cell line generated from this lesion in NSG mice. Finally, we used single-cell transcriptomics to isolate the genes encoding the RPS2-specific TCR and show that it recognizes the mutated peptide bound to HLA-B*07:02. These results demonstrate that high-affinity NeoAg-specific T cell responses can be identified in cancer patients, that ACT of these cells can control tumor growth, and that the relevant TCR can be isolated for use in TCR engineering-based immunotherapy.

Neoantigens were suggested to be crucial for the outcome of immune checkpoint blockade (ICB) therapies and accordingly, the neoantigen load of a tumor is often being used to predict therapy outcome. Current approaches utilize HLA binding predictions to rank peptides, and the neoantigen load of a given tumor is then usually defined as the number of peptides binding above a certain threshold. Using this approach some reports found a strong correlation of neoantigen load and response to ICB while others found a better correlation when mutational load was used instead. However, comparing results between studies is hard because they used different tools and thresholds to identify mutations. We sought to overcome this lack of uniformity in re-analyzing data from several published studies to consistently define the mutational burden and neoantigen load, and correlate them with therapy outcomes. We obtained sequencing data from several ICB studies and run all datasets through our in-house mapping and variant calling pipelines. We applied different scoring schemes to define the neoantigen load which were based on HLA binding predictions with varying thresholds and different metrics used. We found that the exact definition of neoantigen load has a major impact on how well this metric performs in predicting checkpoint blockade therapy outcome. We also implemented a likelihood score which is based on data of known immunogenic neoantigens and describes the likelihood of a predicted neoantigen to be immunogenic. Based on our preliminary analysis to date, we found that the cumulative likelihood score of a patient is a good predictor of ICB outcome, outperforming tumor mutational burden and predicted neoantigen load in several cohorts.

Background: Breast cancer is one of the most common malignancies with increasing incidences every year and a leading cause of death among women. Although early stage breast cancer can be effectively treated, there are limited numbers of treatment options available for patients with advanced and metastatic disease. The novel breast cancer associated antigen NY-BR-1 was identified by SEREX analysis and is expressed in the majority (>70%) of breast tumors as well as metastases, in normal breast tissue, in testis and occasionally in prostate tissue. The biological function and regulation of NY-BR-1 is up to date unknown. Methods: We performed an in silico analysis on the genetic variations of the NY-BR-1 gene using data available in public SNP databases and the tools SIFT, Polyphen and Provean to find possible functional SNPs. Additionally, we considered the allele frequency of the found damaging SNPs and also analyzed data from an in-house sequencing project of 55 breast cancer samples for recurring SNPs, recorded in dbSNP. Results: Over 2800 SNPs are recorded in the dbSNP and NHLBI ESP databases for the NY-BR-1 gene. Of these, 65 (2.07%) are synonymous SNPs, 191 (6.09%) are non-synoymous SNPs, and 2430 (77.48%) are noncoding intronic SNPs. As a result, 69 non-synoymous SNPs were predicted to be damaging by at least two, and 16 SNPs were predicted as damaging by all three of the used tools. The SNPs rs200639888, rs367841401 and rs377750885 were categorized as highly damaging by all three tools. Eight damaging SNPs are located in the ankyrin repeat domain (ANK), a domain known for its frequent involvement in protein-protein interactions. No distinctive features could be observed in the allele frequency of the analyzed SNPs. Conclusion: Considering these results we expect to gain more insights into the variations of the NY-BR-1 gene and their possible impact on giving rise to splice variants and therefore influence the function of NY-BR-1 in healthy tissue as well as in breast cancer.

Neuroendocrine tumors (NETs) of the gastrointestinal tract are a rare and heterogeneous group of neoplasms with unique tumor biology and clinical management issues. While surgery is the only curative treatment option in patients with early stage NETs, the optimal management strategy for patients with advanced metastatic NETs is unknown. Based on the tremendous success of immunotherapeutic approaches, we sought to investigate such approaches in a case of metastatic rectal NET. Here, we apply an integrative approach using various computational and experimental methods to explore several aspects of the tumor-host immune interactions for immunotherapeutic options. Sequencing of six different liver metastases revealed a quite homogenous set of mutations, and further analysis of these mutations for immunogenicity revealed few neo-epitopes with pre-existing T cell reactivity, which can be used in therapeutic vaccines. Staining for immunomodulatory proteins and cytokine profiling showed that the immune setting is surprisingly different, when compared to liver metastases of colorectal cancer for instance. Taken together, our results highlight the broad range and complexity of tumor-host immune interaction and underline the value of an integrative approach.

Epitopes that arise from a somatic mutation, so called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. It has however become evident that the vast majority of these neoepitope candidates do not induce a T cell response when tested in vivo or in vitro, i.e. they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. Several studies have used different combinations of immunoinformatic tools such as MHC binding predictions to prioritize the initial set of neoepitopes candidates. The tools to use and thresholds to apply for this prioritization has so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. To establish the appropriate tools and thresholds in the cancer setting, we here curated a set of immunogenic neoepitopes from the published literature and performed detailed analyses to detect what features discriminate immunogenic neoepitopes from a background set of mutated peptides. We experimentally measured the HLA binding affinity of all curated immunogenic neoepitopes. In doing so, we aimed to identify the optimal affinity threshold to effectively identify immunogenic neoepitopes. As a next step, we sought to assess the added value of different immunoinformatics tools, including various HLA binding prediction algorithms, processing prediction, stability prediction, and immunogenicity prediction, to most effectively detect immunogenic neoepitopes. The obtained results are now going to be used to facilitate the development of more accurate prediction algorithms. Citation Format: Zeynep Kosaloglu-Yalcin, Manasa Lanka, John Sidney, Kerrie Vaughan, Jason Greenbaum, Alessandro Sette, Bjoern Peters. Optimizing immunogenicity prediction of neoepitopes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-002. doi:10.1158/1538-7445.AM2017-LB-002

Many cancers appear to have an increased somatic mutation rate. Intriguingly, increased numbers of tumor-infiltrating T cells are often found in cancers with defective DNA repair, which especially accumulate high numbers of somatic mutations. These observations indicate that an accumulation of mutations could generate a number of neo-antigens that could be recognized by the immune system. Many studies have focused on identifying such neo-antigens recognized by tumor-infiltrating lymphocytes (TILs), and a number of point mutations in a wide variety of genes have already been shown to be recognized by CD4+ and CD8+ TILs. It has been further shown that TILs play an important role for tumor progression. In primary colorectal cancer (CRC) for example, high densities of TILs have been repeatedly associated with an improved survival rate.While primary CRC tumors have already been analyzed extensively, not much is known about the immune setting of CRC liver metastases. We intended to gain better insight and to eventually provide new therapeutic approaches for patients with metastatic CRC.We have collected colorectal cancer liver metastases from a cohort of patients, where complete clinical data is available. We additionally performed whole-exome sequencing of tumor and control samples and analyzed the somatic variations in each tumor. We detected somatic single nucleotide variants (SNVs) and used computational epitope prediction to assess the immunogenicity of the mutated peptides.Analysis of the somatic mutation profile revealed that the wellknown tumor suppressor genes APC and TP53, as well as the oncogene KRAS are frequently mutated in our patient cohort. Besides from these frequent mutations, the somatic mutation profiles are quite heterogeneous, with low overlap in mutations between single patients. We furthermore performed computational epitope prediction for HLA*A2 for each mutated peptide in each patient and analyzed the resulting epitope landscapes. The results show, that the epitope distribution is also quite heterogeneous with low overlap in predicted epitopes between single patients.This data now provides the basis for further analyses. The tumor microenvironment will be further analyzed, and the impact of each mutation, as well as clinical data will be correlated.

Introduction: An underlying basis of cancer immunotherapy is the activation of the patient's own immune effector cells to specifically eradicate tumor cells. Different strategies of cancer immunotherapy have shown outstanding success in a number of patients across various tumor types. However, currently only a fraction of patients respond to immunotherapy, and therapy works only in a subset of tumors. To understand why, an integrated analysis of host and tumor factors is required. Fundamental issues in the analysis of key prognostic and predictive components include elucidating the complex interaction between tumors and their microenvironment, determining the type and densities of tumor-infiltrating immune cells, and understanding how the mutational load influences therapy outcome. Sophisticated analysis is required for patients with refractory or rare cancers to explore new therapeutic strategies. We established a systems medicine approach to obtain detailed data about tumor-host interactions, which may provide information on clinical outcome and help identify patients who are most likely to benefit from immunotherapy. Methods and Results: Whole-exome sequencing of tumor and control samples is performed from freshly obtained patient samples. In addition, whole-transcriptome sequencing of tumor tissue is performed if possible. Single nucleotide variations (SNVs) are detected in each tumor. Sequence-based HLA typing and computational epitope prediction methods are used to assess the immunogenicity of each SNV. The grade of immune cell infiltration and the expression of selected immunomodulatory proteins, e.g. PD-1 and PD-L1, is assessed by immunohistochemical stainings of tissue samples. Additionally, cytokine and chemokine profiles are determined in the tumor microenvironment. The resulting data can aid in treatment decision, e.g. PD-1 blocking antibodies can be administered when elevated PD-1 expression is detected. Alternatively, peptide vaccination is considered if a sufficient number of immunogenic mutations is detected. Based on the mutational profiles, mutation specific 29mer peptides and corresponding wild type peptides are produced for ELIspot assays with patient PBMCs to assess mutation-specific T cell reactivity. To assess the changes over time, blood and tissue are obtained during therapy to monitor therapy-induced changes in the periphery and the tumor microenvironment. Conclusion: Here we present our established workflow to systematically analyze tumor-host interactions in cancer patients and show preliminary data of different facets and clinical implications of these complex interactions. To evaluate and validate immunological and pathological characteristics of a tumor and the methods to assess its immunogenicity as well as its complex interplay with the microenvironment, comprehensive translational studies have to be conducted. Eventually, the obtained information will help to identify important subgroups and patients who are most likely to benefit from a certain type of immunotherapy. Citation Format: Zeynep Kosaloglu, Inka Zoernig, Niels Halama, Bruno Koehler, Anne Katrin Berger, Christoph Springfeld, Alexis Ulrich, Martin Schneider, Felix Lasitschka, Hanno Glimm, Stefan Froehling, Benedikt Brors, Dirk Jaeger. Combining immunomics and genomics for immunotherapy of refractory and rare cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-287.

Breast cancer is one of the most common malignancies with increasing incidence every year and a leading cause of death among women. Although early stage breast cancer can be effectively treated, there are limited numbers of treatment options available for patients with advanced and metastatic disease. The breast cancer associated antigen NY-BR-1 was identified by a serological screening strategy (SEREX). NY-BR-1 is expressed in the majority (>70%) of breast tumors as well as metastases, in normal breast tissue, in testis, and occasionally in prostate tissue. NY-BR-1 was confirmed as an immunogenic antigen as we could detect NY-BR-1 specific spontaneous humoral and cellular immune responses in several breast cancer patients. However, biological function, regulatory mechanisms, and interaction partners of NY-BR-1 are still unknown. Therefore, we combined integrative functional analysis (genomics, transcriptomics and epigenetics) and experiments to unravel the transcriptional regulation of NY-BR-1 and to detect its function within the mammary gland. As numerous binding sites for nuclear hormone receptors (e.g. Estrogen Receptor alpha, Progesterone receptor and Glucocorticoid receptor) and CpG islands were predicted within the promoter sequence of NY-BR-1, primary-tissues from breast reductions or tumor cells obtained from pleural effusions were treated with different hormones (Estradiol, Dexamethasone, Progesterone, Retinoic acid, Vitamin D) or with the HDAC inhibitors Valproic acid, Trichostatin A and the demethylating agent 5´Aza-Deoxycytidine in order to analyze the transcriptional regulation of NY-BR-1. Additionally, we analyzed the methylation status at various sites within the promoter region of NY-BR-1 using EpiTyper MassARRAY technology and publicly available resources such as the TCGA database. Here, we present preliminary results of our analysis and evaluate their implication for further investigation of NY-BR-1. Citation Format: Julia Bitzer, Zeynep Kosaloglu, Niels Halama, Claudia Ziegelmeier, Tina Lerchl, Anna Spille, Maria Pudenz, Eva Koellensperger, Stefan Eichmueller, Wolfram Osen, Andreas Schneeweiss, Frederik Marmé, Inka Zoernig, Dirk Jaeger. Molecular characterization of the breast cancer-associated antigen NY-BR-1. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1987. doi:10.1158/1538-7445.AM2015-1987