HLA class-I and class-II restricted neoantigen loads predict overall survival in breast cancer

ABSTRACT Tumors acquire numerous mutations during development and progression. When translated into proteins, these mutations give rise to neoantigens that can be recognized by T cells and generate antibodies, representing an exciting direction of cancer immunotherapy. While neoantigens have been reported in many cancer types, the profiling of neoantigens often focused on the class-I subtype that are presented to CD8 + T cells, and the relationship between neoantigen load and clinical outcomes was often inconsistent among cancer types. In this study, we described an informatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the detected neoantigens. We found recurrent HLA class-I and class-II restricted neoantigens across breast cancer cases, and uncovered associations between neoantigen load and clinical traits. Both class-I and class-II neoantigen loads from SNM and INDEL were found to predict overall survival independent of tumor mutational burden (TMB), breast cancer subtypes, tumor-infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis. Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer.

[1]  D. Berry,et al.  Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. , 1997, Journal of the National Cancer Institute.

[2]  F. Couch,et al.  BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. , 1997, The New England journal of medicine.

[3]  M. King,et al.  Breast and Ovarian Cancer Risks Due to Inherited Mutations in BRCA1 and BRCA2 , 2003, Science.

[4]  C. Huber,et al.  The response of autologous T cells to a human melanoma is dominated by mutated neoantigens. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[5]  K. Kinzler,et al.  Epitope landscape in breast and colorectal cancer. , 2008, Cancer research.

[6]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[7]  A. Børresen-Dale,et al.  Characterization of BRCA1 and BRCA2 deleterious mutations and variants of unknown clinical significance in unilateral and bilateral breast cancer: the WECARE study , 2010, Human mutation.

[8]  Ying Li,et al.  TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data , 2011, Bioinform..

[9]  Heng Li Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[10]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer genes , 2014 .

[11]  John N. Weinstein,et al.  PRADA: pipeline for RNA sequencing data analysis , 2014, Bioinform..

[12]  Benjamin Schubert,et al.  OptiType: precision HLA typing from next-generation sequencing data , 2014, Bioinform..

[13]  Steven N. Hart,et al.  The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation , 2014, Bioinform..

[14]  T. Schumacher,et al.  Neoantigens in cancer immunotherapy , 2015, Science.

[15]  Martin L. Miller,et al.  Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer , 2015, Science.

[16]  Thomas D. Wu,et al.  Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations , 2016, Nature Genetics.

[17]  T. Schumacher,et al.  Neoantigen landscape dynamics during human melanoma–T cell interactions , 2016, Nature.

[18]  Lauren L. Ritterhouse,et al.  Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer , 2016, Oncotarget.

[19]  M. Nielsen,et al.  NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets , 2016, Genome Medicine.

[20]  R. Yamada,et al.  HLA‐HD: An accurate HLA typing algorithm for next‐generation sequencing data , 2017, Human mutation.

[21]  F. Kühnel,et al.  CD4 and CD8 T lymphocyte interplay in controlling tumor growth , 2017, Cellular and Molecular Life Sciences.

[22]  Y. Asmann,et al.  High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma , 2017, Blood Cancer Journal.

[23]  F. Kühnel,et al.  Neoantigen Targeting—Dawn of a New Era in Cancer Immunotherapy? , 2017, Front. Immunol..

[24]  E. Jaffee,et al.  Targeting neoantigens to augment antitumour immunity , 2017, Nature Reviews Cancer.

[25]  M. Ringnér,et al.  Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma , 2017, Nature Communications.

[26]  Yusuke Nakamura,et al.  Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors , 2016, Journal of Human Genetics.

[27]  Rob Patro,et al.  Salmon provides fast and bias-aware quantification of transcript expression , 2017, Nature Methods.

[28]  Adrian V. Lee,et al.  An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics , 2018, Cell.

[29]  David L. Gibbs,et al.  Integrative Molecular Characterization of Malignant Pleural Mesothelioma. , 2018, Cancer discovery.

[30]  J. Greenbaum,et al.  Improved methods for predicting peptide binding affinity to MHC class II molecules , 2018, Immunology.

[31]  Steven J. M. Jones,et al.  The Immune Landscape of Cancer , 2018, Immunity.

[32]  Bent Petersen,et al.  Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios , 2018, BMC Bioinformatics.

[33]  Sarah H. Johnson,et al.  Neoantigenic Potential of Complex Chromosomal Rearrangements in Mesothelioma , 2019, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[34]  Melissa A. Wilson,et al.  The neoepitope landscape of breast cancer: implications for immunotherapy , 2019, BMC Cancer.

[35]  M. Guan,et al.  The Landscape of Tumor Fusion Neoantigens: A Pan-Cancer Analysis , 2019, iScience.

[36]  C. Leong,et al.  Association of BRCA1- and BRCA2-deficiency with mutation burden, expression of PD-L1/PD-1, immune infiltrates, and T cell-inflamed signature in breast cancer , 2019, PloS one.

[37]  C. Brennan,et al.  Tumor mutational load predicts survival after immunotherapy across multiple cancer types , 2019, Nature Genetics.

[38]  J. Castle,et al.  Mutation-Derived Neoantigens for Cancer Immunotherapy , 2019, Front. Immunol..

[39]  Jennifer G. Abelin,et al.  Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. , 2019, Immunity.

[40]  Flavia E. Popescu,et al.  Identification of candidate neoantigens produced by fusion transcripts in human osteosarcomas , 2019, Scientific Reports.