Mutational Analysis and Predicting Response to Immunotherapy最新文献

{"title":"Abstract B096: Dissecting the flames from the fire: Distribution of immune checkpoints in hot and cold tumors","authors":"S. Warren, T. Hood, P. Danaher, A. Cesano","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B096","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B096","url":null,"abstract":"Introduction: Numerous immune checkpoint inhibitors are being developed for the clinic, but identifying the population of patients most likely to respond remains a significant challenge. PD-(L)1 blocking antibodies have been approved for multiple indications, but even in those indications the majority of patients fail to respond to PD-(L)1 monotherapy. Consequently, diagnostic assays have been developed to identify patients with a higher likelihood of response. PD-L1 immunohistochemistry is the platform for multiple assays currently being used in the clinical as companion and complementary diagnostics for the PD-(L)1 checkpoint inhibitors, but those assays have limited sensitivity and selectivity and have inherent risk of subjective interpretation bias. Tumor mutation burden is in development as a proxy readout for a tumor’s potential to prime immune responses, but it does not measure the actual presence of an immune response, and it is not able to inform treatment decisions if there is the option of more than one immunomodulatory intervention. Gene expression assays have the advantage of being a sensitive, selective, and quantitative assay which can directly measure immune biology, and may overcome many of the limitations of the other assay platforms. The Tumor Inflammation Signature (TIS) has been developed on the NanoString® platform as an 18-gene signature of a suppressed immune response within the tumor and has been developed as a clinically validated assay which enriches for response to anti-PD-1 (Ayers, JCI 2017). We have recently evaluated the distribution of TIS in The Cancer Genome Atlas (TCGA) database to understand the prevalence and distribution of immune “hot” vs “cold” tumors by indication (Danaher, JITC 2018). We now extend that work to evaluate the expression of individual immune checkpoint molecules after segregating tumors by TIS to understand the distribution of immune checkpoints across indications and within the context of a preexisting immune response. Methods: We leverage biostatistical analysis of the RNA-seq data in the TCGA database to evaluate the expression of the TIS signature and individual immune checkpoints. Results: We observe that the expression of many immune checkpoint molecules is directly proportional to the degree of immune infiltrate within the tumor as measured by TIS. As such, there is a distribution of IO targets across indications, with inflamed tumors expressing greater median levels of immune checkpoints vs noninflamed tumors. Within individual indication, we also see a distribution of hot and cold tumors, and a corresponding distribution of checkpoint molecules, indicating that there may be some subpopulations of patients with the potential to respond to immune checkpoint blockade even in an indication that is nonresponsive in an unselected population. Furthermore, we also observe increased expression of particular immune checkpoints in subpopulations of certain tumors. For example, certain bladder ","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134138725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B080: Improved neoantigen vaccine selection by combining prediction of pMHC presentation and T-cell epitopes","authors":"Julia Kodysh, Tim O’Donnell, A. Blázquez, J. Finnigan, N. Bhardwaj, A. Rubinsteyn","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B080","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B080","url":null,"abstract":"The OpenVax group has helped initiate two neoantigen vaccine clinical trials (NCT02721043, NCT02721043) at Mount Sinai based on a simple multiplicative ranking criterion which assigns equal weight to expression and predicted Class I MHC binding affinity of mutated peptides (1). This poster seeks to better ground our ranking method for selecting the contents of neoantigen vaccines in several sources of immunological data. We built a better model of MHC-I presentation on the cell surface by relating RNA expression and MHC affinity to pMHC ligands identified with mass spectrometry (2). Secondly, we trained a model of overall T-cell immunogenicity whose primary input is the predicted pMHC presentation score of any peptide-MHC combination, alongside other features such as similarity to the self proteome. This model is trained on T-cell response data deposited in the Immune Epitope Database (3). Lastly, we assembled a small dataset of peptide sequences used in neoantigen vaccine trials (1,4,5), which are labeled by whether they achieved a CD8+ or CD4+ T-cell response. This dataset allows us to explore several hypotheses about the relationship between immunogenic response and sequence similarity to both the self proteome and pathogenic proteomes. References: 1. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018. 2. Abelin JG, Keskin DB,..., Wu CJ. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017. 3. Vita R, Overton JA, …, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2014. [4. Sahin U, Derhovanessian E, …, Tureci O. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 2017. 5. Ott P, Hu Z, …, Wu CJ. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 2017. Citation Format: Julia Kodysh, Tim O9Donnell, Ana B. Blazquez, John Finnigan, Nina Bhardwaj, Alex Rubinsteyn. Improved neoantigen vaccine selection by combining prediction of pMHC presentation and T-cell epitopes [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B080.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130689032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA31: Measuring the emergence of non-self in tumors","authors":"M. Luksza, Alexander Solovyov, N. Vabret, V. Balachandran, N. Riaz, V. Makarov, M. Hellmann, A. Snyder, S. Funt, R. Remark, M. Merad, S. Gnjatic, D. Bajorin, J. Rosenberg, S. Leach, A. Levine, T. Chan, N. Bhardwaj, J. Wolchock, B. Greenbaum","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-IA31","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-IA31","url":null,"abstract":"Molecules generated by mutational and epigenetic processes in tumors have been associated with recognition of tumors by the innate and adaptive immune system. For example, neoantigens have been implicated in response to checkpoint blockade therapies. Likewise, the display of pathogen-associated patterns by nucleic acids unsilenced by epigenetic alterations have been implicated in activation of the innate immune system. Here we determine molecular features which place a tumor at a selective advantage or disadvantage, and how these selective pressures depend on the tumor’s environment. We have proposed general frameworks to address these questions. In the case of tumor neoantigens, we present a fitness model of candidate immunogenic neoantigens distributed across a tumor’s subclonal structure in a given microenvironment. We show how our approach can be used to characterize response to checkpoint-blockade therapies and apply it to the general problem of immune-driven tumor evolution in a unique cohort of long-term survivors of pancreatic cancer. In the case of immunostimulatory RNA, we proposed a method of calculating entropic forces for determining the likelihood of tumoral RNA being recognized as pathogen-associated and characterizing classes of pathogen mimicry. Citation Format: Marta Luksza, Alexander Solovyov, Nicolas Vabret, Vinod Balachandran, Nadeem Riaz, Vladimir Makarov, Matthew D. Hellmann , Alexandra Snyder, Samuel Funt, Romain Remark, Miriam Merad, Sacha Gnjatic, Dean F. Bajorin, Jonathan Rosenberg, Steven Leach, Arnold J. Levine, Timothy A. Chan, Nina Bhardwaj, Jedd Wolchock, Benjamin D. Greenbaum. Measuring the emergence of non-self in tumors [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr IA31.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"52 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129615267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B095: Mapping the genetic features of immune checkpoint responsiveness using AAV-CRISPR mediated in vivo screen","authors":"Guangchuan Wang, Ryan D. Chow, Z. Bai, Lupeng Ye, Sidi Chen","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B095","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B095","url":null,"abstract":"Immune checkpoint blockade has achieved tremendous clinical success across many tumor types, but fails to induce clinical responses in many patients. The mechanisms underlying checkpoint resistance remain poorly characterized. Recent studies have applied next generation sequencing techniques to catalog the mutational burden of patient tumors, which provides a wealth of data to determine common mutations. To map the genetic features of response to checkpoint blockade immunotherapy as well as correlating the clinical efficacy with certain mutations, we developed a novel direct in vivo CRISPR screening approach for high-throughput profiling of functional cancer drivers in an autochthonous manner by injecting AAVs carrying an sgRNA library targeting the top 50 TCGA pan-cancer recurrently mutated tumor suppressor genes (mTSG) into the immunocompetent Cas9 transgenic mice. All mice that received the AAV-mTSG library developed liver cancer and died within four months. We then utilized MIP sequencing of sgRNA target sites to chart the mutational landscape of these tumors, revealing the functional consequence of multiple variants in driving liver tumorigenesis as well as identifying specific gene pairs that were co-occurring across mice. Using this approach, we also mapped the mutation landscape changes under the pressures of immune checkpoint inhibitors, anti-PD1 or anti-CTLA4. We monitored liver tumor growth in AAV-mTSG injected LSL-Cas9;LSL-Fluc mice by using intravital bioluminescent imaging system (IVIS) in combination with dissection check before drug administration. Using IVIS data, we grouped them into 3 size-matched cohorts to receive anti-PD1 or anti-CTLA4 treatments or PBS control. According to the survival data, the mice with mTSG-induced liver tumor benefit from anti-PD1 or anti-CLTA4 treatment. By comparing the mutation frequencies of liver tumors in the mice receiving either checkpoint inhibitors or PBS treatment, we mapped the mutation landscape changes associated with anti-PD1 or anti-CTLA4 treatment. We are performing validation studies on top targets such as Arid1a, Stk11, and B2M. Using this approach, we systematically mapped the correlation of these top 50 driver mutations with cancer immune evasion and immunotherapy responsiveness, providing a valuable reference for patient stratification when considering immunotherapy as well as novel targets for synergistic interventions. Citation Format: Guangchuan Wang, Ryan Chow, Zhigang Bai, Lupeng Ye, Sidi Chen. Mapping the genetic features of immune checkpoint responsiveness using AAV-CRISPR mediated in vivo screen [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B095.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126182720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B084: Methylation landscape of tumors associated with antitumor immune signature","authors":"C. Ock, Changhee Park, Kyeonghun Jeong, Sohee Jung, J. Bae, Kwangsoo Kim","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B084","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B084","url":null,"abstract":"The most reliable predictive biomarker of cancer immunotherapy is gene expression profile (GEP) of tumor microenvironment. GEPs such as local immune cytolytic activity, interferon-gamma signature, and immune signature score have been reported to represent anti-tumor immune signature. Previously, we reported that immune signature score was positively correlated with tumor mutational burden, but negatively correlated with chromosomal instability (CIN) score, since tumors with high CIN score had significantly low neoantigen burden. However, methylation signature or burden of tumor would also affect antitumor immunogenicity, there has been no analysis reported so far. In the current study, we investigated if methylation landscape of tumor would be associated with GEPs of anti-tumor immune signature using The Cancer Genome Atlas (TCGA) pan-cancer database. In TCGA, 8269 pan-cancer samples had both RNA sequencing data and methylation data using Infinium HumanMethylation450K BeadChip, which were included in the main analysis. Although tumors with high mutational burden (Mu-type) and high CIN burden (C-type) were exclusively classified with negative correlation, methylation burden was not correlated with mutational burden or CIN burden in any pattern. Interestingly, antitumor immune signature measured by local immune cytolytic activity (CytAct) was clearly decreased with high methylational burden, as seen in high CIN burden. Hypermethylation of promoter of genes related to tumor antigen recognition by T-cell such as HLA family, B2M, CD74, and CD274 (PD-L1) were negatively associated with CytAct in pan-cancer analysis. In conclusion, methylation signature of tumor is also associated with antitumor immunogenicity with a negative correlation in general. Further study of whether specific methylation pattern would be associated with anti-PD-1/PD-L1 inhibitors in clinical study would be warranted. Citation Format: Chan-Young Ock, Changhee Park, Kyeonghun Jeong, Sohee Jung, Jeong Mo Bae, Kwangsoo Kim. Methylation landscape of tumors associated with antitumor immune signature [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B084.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115035367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B079: Evaluation of tools for predicting mutated tumor antigens from exome and RNA sequencing","authors":"Julia Kodysh, J. Finnigan, A. Rubinsteyn","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B079","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B079","url":null,"abstract":"Neoantigen vaccination is an emerging modality of cancer immunotherapy with many ongoing trials. One central question of neoantigen vaccination is the method for selecting which mutated tumor-specific antigens to include in a patient’s vaccine. Many in-silico pipelines for neoantigen selection have been published in the past few years, but no comprehensive evaluation has compared them directly on the same tumor/normal sequencing data. We evaluate several publicly available commonly used neoantigen pipelines (pVACtools [1], MuPeXI [2], TIminer [3], OpenVax [4]) on both murine and human cancer samples. Our evaluation highlights the salient differences between these pipelines and shows the divergent results they achieve. References: 1. Kiwala S, Hundal J, …, Griffith M. pVACtools: Computational selection and visualization of neoantigens for personalized cancer vaccine design. Cancer Genetics 2018. 2. Bjerregaard A-M, Nielsen M, ..., Eklund AC. MuPeXI: Prediction of neo-epitopes from tumor sequencing data. Cancer Immunology Immunotherapy 2018. 3. Tappeiner E, Finotello F, ..., Trajanoski Z. TIminer: NGS data mining pipeline for cancer immunology and immunotherapy. Bioinformatics 2017. 4. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018. Citation Format: Julia Kodysh, John P. Finnigan, Alex Rubinsteyn. Evaluation of tools for predicting mutated tumor antigens from exome and RNA sequencing [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B079.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"13 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133077137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PR12: Functional identification and therapeutic targeting of tumor neoantigens","authors":"S. Schoenberger, Aaron M. Miller, Luise Sternberg, Leslie Montero Cuencac, Milad Bahmanof, Zeynep Koasaloglu-Yalcin, Manasa Lanka, A. Premlal, P. Vijayanand, J. Greenbaum, Allesandro Seatte, Ezra E. W. Cohen, Bjoern Peters","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-PR12","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-PR12","url":null,"abstract":"Accurate identification of tumor-specific neoantigens (NeoAg) is essential for the development of effective personalized cancer vaccines and cellular immunotherapies. The success rates for purely computational approaches which rely on predicted HLA-binding have been disappointing, as these generally ignore 85-90% of total mutations and find less than 5% of those selected can be confirmed as T-cell targets. We have developed a novel NeoAg identification platform in which WES and RNAseq metadata is used to nominate mutations for subsequent functional T-cell analysis using autologous PBMC and/or TIL. Applying this platform to tumors of low mutational burden including PDAC, HNSCC, and MSS-CRC, we report that an average of 35% of expressed mutations selected for functional testing can be verified as neoantigens, and that a significant number of these would be missed by HLA-binding algorithms. Responses comprise both type I and type 2 CD4+ and CD8+ effector T-cells recognizing both “passenger” mutations and known activating mutations in driver oncogenes such as KRAS, PIK3CA, and NRAS. Additionally, we have established a single-cell platform for isolation of T-cell receptors (TCR) against these shared recurrent mutations, and have opened a phase 1b clinical trial to evaluate the efficacy of personalized NeoAg vaccination in solid tumors. Citation Format: Stephen Phillip Schoenberger, Aaron M. Miller, Luise A. Sternberg, Leslie Montero Cuencac, Milad Bahmanof, Zeynep Koasaloglu-Yalcin, Manasa Lanka, Ashmitaa Premlal, Pandurangan Vijayanand, Jason Greenbaum, Allesandro Seatte, Ezra E.W. Cohen, Bjoern Peters. Functional identification and therapeutic targeting of tumor neoantigens [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr PR12.","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133683027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B089: Application of precision cancer immunotherapy design tools to bladder cancer: Non-self-like neoepitopes as a prognostic biomarker","authors":"Guilhem Richard, R. Sweis, L. Moise, M. Ardito, W. Martin, Gad Berdugo, G. Steinberg, A. Groot","doi":"10.1158/2326-6074.CRICIMTEATIAACR18-B089","DOIUrl":"https://doi.org/10.1158/2326-6074.CRICIMTEATIAACR18-B089","url":null,"abstract":"Precision cancer immunotherapy targeting mutations expressed by cancer cells has proven to effectively control the tumor of patients in multiple clinical trials (Sahin et al., Nature 2017; Ott et al., Nature 2017). However, the selection of immunogenic T-cell neo-epitopes remains challenging and many epitopes selected using traditional methodologies fail to induce effector T-cell responses. Poor performance may partially be due to inclusion of mutated epitopes cross-conserved with self-epitopes recognized by regulatory (Treg), anergic, or deleted T-cells. Vaccination with self-epitopes can lead to weak effector responses, active immune suppression, and toxicity due to immune-mediated adverse effects. In addition, most cancer vaccine studies focus on the selection of CD8 T-cell neo-epitopes due to an apparent lack of robust and accurate CD4 T-cell epitope prediction tools. We have developed Ancer, an integrated and streamlined neo-epitope selection pipeline, that accelerates the selection of both CD4 and CD8 T-cell neo-epitopes from next-generation sequencing (NGS) data. Ancer leverages EpiMatrix and JanusMatrix, predictive algorithms that have been extensively validated in prospective vaccine studies for infectious diseases (Moise et al., Hum Vaccines Immunother 2015; Wada et al., Sci Rep 2017). Distinctive features of Ancer are its ability to accurately predict Class II HLA ligands, or CD4 epitopes, with EpiMatrix, and to identify tolerated or Treg epitopes with JanusMatrix. In addition, screening candidate sequences with JanusMatrix enables to the removal of neo-epitopes that may trigger off-target events, which have in some cases abruptly halted the development of promising cancer therapies. Ancer was applied to NGS data derived from the BLCA bladder cancer cohort from The Cancer Genome Atlas (TCGA) database. On average, 55 out of 204 missense mutations in bladder cancer patients’ tumors met Ancer’s quality control standards, in an initial analysis carried out for a representative set of 11 patients. This subset of high-quality missense variants was then screened using Ancer settings defined by the unique HLA of each patient, to derive the best vaccine candidate sequences encompassing these mutations. A median number of 24 (interquartile range: 15-64) candidate sequences were generated for each patient under study. The time required to select sequences for all of the patients in this study was less than two days. This initial analysis of eleven BLCA bladder cancer cohort patients demonstrates the capacity of Ancer to define a sufficient number of candidate sequences for vaccinating bladder cancer patients in a precision immunotherapy setting. We also assessed Ancer’s ability to predict patient outcomes on a larger subset of 58 individuals. While the disease-free status of BLCA patients could not be explained by their tumor mutational burden (AUC = 0.55, p-value = 0.1328), nor by their load of missense mutations (AUC = 0.54, p-value = 0.1740), ","PeriodicalId":433681,"journal":{"name":"Mutational Analysis and Predicting Response to Immunotherapy","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116458793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}