Combining Immunomics and Genomics for the Analysis of the Microenvironment of Colorectal Cancer Liver Metastases

Cancer immunotherapies have recently shown outstanding clinical results in a number of patients across various tumor types. However, currently only a fraction of patients responds to immunotherapy, and it is a major concern to understand the underlying mechanisms. The composition of the tumor microenvironment has been shown to have an important impact on tumor growth and progression, as well as on response to therapy. It has been reported that the type and density of tumor-infiltrating immune cells are highly predictive for disease outcome in various cancers. These studies have also suggested that a high density of tumor-infiltrating lymphocytes is strongly correlated with mutational load. One hypothesis in this context is that somatic mutations found in cancer cells may give rise to novel epitopes, so-called neoepitopes, which attract and keep lymphocytes at the tumor site. Neoepitopes have also been suggested to be crucial for the outcome of immune checkpoint therapies, as it was reported that cancers with a high mutational load respond best to checkpoint therapy. An explanation for this is that mutations give rise to neoepitopes that can be targeted by specific T cells following their release from inhibitory signals. It has now become evident that effective immunotherapies have to be tailored to the specific immune setting of each tumor. The complex interplay between the tumor and the immune system has to be systematically analyzed for characterizing patients and identifying therapies they will most likely benefit from. This highly personalized approach requires the integrated analysis of numerous tumor and host factors. Accordingly, the main aim of this PhD project was the establishment of an integrated analysis pipeline to obtain detailed data about tumor-host interactions, including analysis of the mutational and neoepitope load, the type and densities of tumor-infiltrating immune cells, the expression of immunological markers, and the expression of specific cytokines. This analysis pipeline combines available genomic and immunomic resources and adds further depth into the analysis by additional computational pipelines. The already well established sequencing and somatic mutation detection pipelines that have been developed in the DKFZ bioinformatics departments (Prof. Roland Eils and Prof. Benedikt Brors) were integrated with the cytokine profiling and histological analysis workflows in Professor Jäger's group (NCT, Medical Oncology). Additional computational pipelines for HLA genotyping from sequencing data, as well as for epitope predictions for HLA class I and class II were implemented and included. Taken together these pipelines provide a broad picture of tumor-host interactions. The established analysis pipeline allows the rapid and systematic analysis of large patient cohorts. Professor Jäger's group has been collecting colorectal cancer (CRC) liver metastases and systematically characterizing their immune cell infiltration and cytokine profiles, as well as the correlation to clinical outcome. In these studies it was shown that in general, there are at least two patient groups for each CRC stage: patients with high infiltrate density and patients with low infiltrate density, with the latter having a much worse prognosis. A patient cohort including 10 patients with high densities of infiltrating lymphocytes (TIL-high) and 10 patients with low densities (TIL-low) was assembled and provided for analysis in this PhD project. The described integrated analysis pipeline was developed using this patient cohort. The established analysis pipeline was then used to systematically investigate TIL-high versus TIL-low CRC metastases in order to assess the correlation of mutational and neoepitope load to lymphocyte infiltration and whether additional factors distinguishing the two groups can be discovered. The results show that the mutational and neoepitope load is not significantly different between patients with high and patients with low lymphocyte density in the analyzed patient cohort. Although a trend can be observed in a way that the TIL-high group seems to be enriched for mutations and neoepitopes, no statistical significance was detectable. Instead, the cytokine expression profiles are clearly distinct between the two subgroups: CXCL12, CXCL9, CCL7, CCL27, IL-17, IL-13, IL-7, IL-4, IFNg, GM-CSF, HGF, and TRAIL are significantly overexpressed in the TIL-high group. Interestingly both, pro-tumorigenic as well as anti-tumorigenic factors are overexpressed in the TIL-high group. Histological analysis additionally revealed that the TIL-high samples are enriched for macrophages. Furthermore, PD-L1, the ligand for the inhibitory immune checkpoint protein PD-1, is overexpressed in the majority of TIL-high samples when compared to the TIL-low samples. These results indicate that the immune contexture at the metastatic lesion seems to be a stronger factor for lymphocyte infiltration than the mutational and neoepitope landscape. The established integrated analysis pipeline has already been applied in the clinic to conduct case studies with several patients being treated at the NCT. Patients with refractory and rare cancers were extensively analyzed for their genomic and immunomic features, which enabled the exploration of additional immunotherapeutic strategies. In doing so, a working logistics for the clinical setting was established, and the results provided insights into the feasibility of the approach. Based on these findings, clinical studies with neoepitope-based vaccines are currently under development in Professor Jäger's group, and the predictive impact of the newly established integrated analysis pipeline will be evaluated in prospective clinical trials

In silico SNP analysis of the breast cancer antigen NY-BR-1

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

A meta-analysis of epitopes in prostate-specific antigens identifies opportunities and knowledge gaps

Background: The Cancer Epitope Database and Analysis Resource (CEDAR) is a newly developed repository of cancer epitope data from peer-reviewed publications, which includes epitope-specific T cell, antibody, and MHC ligand assays. Here we focus on prostate cancer as our first cancer category to demonstrate the capabilities of CEDAR, and to shed light on the advances of epitope-related prostate cancer research.Results: The meta-analysis focused on a subset of data describing epitopes from 8 prostate-specific (PS) antigens. A total of 460 epitopes were associated with these proteins, 187 T cell, 109B cell, and 271 MHC ligand epitopes. The number of epitopes was not correlated with the length of the protein; however, we found a significant positive correlation between the number of references per specific PS antigen and the number of reported epitopes. Forty-four different class I and 27 class II restrictions were found, with the most epitopes described for HLA-A*02:01 and HLA-DRB1*01:01. Cytokine assays were mostly limited to IFNg assays and a very limited number of tetramer assays were performed. Monoclonal and polyclonal B cell responses were balanced, with the highest number of epitopes studied in ELISA/Western blot assays. Additionally, epitopes were generically described as associated with prostate cancer, with little granularity specifying diseases state. We found that in vivo and tumor recognition assays were sparse, and the number of epitopes with annotated B/T cell receptor information were limited. Potential immunodominant regions were identified by the use of the ImmunomeBrowser tool. Conclusion: CEDAR provides a comprehensive repository of epitopes related to prostate-specific antigens. This inventory of epitope data with its wealth of searchable T cell, B cell and MHC ligand information provides a useful tool for the scientific community. At the same time, we identify significant knowledge gaps that could be addressed by experimental analysis.</p

The Cancer Epitope Database and Analysis Resource (CEDAR)

We established The Cancer Epitope Database and Analysis Resource (CEDAR) to catalog all epitope data in the context of cancer. The specific molecular targets of adaptive T cell and B cell immune responses are referred to as epitopes. Epitopes derived from cancer antigens are of high relevance as they are recognized by anti-cancer immune cells. Detailed knowledge of the molecular characteristic of cancer epitopes and associated metadata is relevant to understanding and planning prophylactic and therapeutic applications and accurately characterizing naturally occurring immune responses and cancer immunopathology. CEDAR provides a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and serves as a companion site to the Immune Epitope Database (IEDB), which is focused on infectious, autoimmune, and allergic diseases. CEDAR is freely accessible at https://cedar.iedb.org/.</p

Targeting Fibroblast Growth Factor Receptor 1 for Treatment of Soft-Tissue Sarcoma

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. (C)2016 AACR

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

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

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

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