Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade

The Cancer Genome Atlas revealed the genomic landscapes of common human cancers. In parallel, immunotherapy with checkpoint blockers is transforming the treatment of advanced cancers. As only a minority of the patients is responsive to checkpoint blockers, the identification of predictive markers and the mechanisms of resistance is a subject of intense research. To facilitate understanding of the tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers, and created The Cancer Immunome Atlas (http://tcia.at). Cellular characterization of the immune infiltrates revealed a role of cancer-germline antigens in spontaneous immunity and showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was superior predictor of response to anti-CTLA-4 and anti-PD-1 antibodies in two independent validation cohorts. Our findings and the developed resource may help informing cancer immunotherapy and facilitate the development of precision immune-oncology.

[1]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer-associated genes , 2013 .

[2]  G. Getz,et al.  Accurate estimation of homologue-specific DNA concentration-ratios in cancer samples allows long-range haplotyping , 2011 .

[3]  Günter P. Wagner,et al.  A model based criterion for gene expression calls using RNA-seq data , 2013, Theory in Biosciences.

[4]  Michael A. Choti,et al.  Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets , 2015, Nature Communications.

[5]  A. McKenna,et al.  Evolution and Impact of Subclonal Mutations in Chronic Lymphocytic Leukemia , 2012, Cell.

[6]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[7]  John Quackenbush,et al.  Genesis: cluster analysis of microarray data , 2002, Bioinform..

[8]  R. deLeeuw,et al.  The Prognostic Value of FoxP3+ Tumor-Infiltrating Lymphocytes in Cancer: A Critical Review of the Literature , 2012, Clinical Cancer Research.

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

[10]  C. Blank,et al.  Immune resistance orchestrated by the tumor microenvironment , 2006, Immunological reviews.

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

[12]  Nuno A. Fonseca,et al.  Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants , 2015, Nucleic Acids Res..

[13]  C. Sautès-Fridman,et al.  The immune contexture in human tumours: impact on clinical outcome , 2012, Nature Reviews Cancer.

[14]  A. Bouchard-Côté,et al.  PyClone: statistical inference of clonal population structure in cancer , 2014, Nature Methods.

[15]  David T. W. Jones,et al.  Signatures of mutational processes in human cancer , 2013, Nature.

[16]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[17]  D. Schadendorf,et al.  Pooled Analysis of Long-Term Survival Data From Phase II and Phase III Trials of Ipilimumab in Unresectable or Metastatic Melanoma. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  Pornpimol Charoentong,et al.  Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy , 2015, Genome Biology.

[19]  Andrew J. G. Simpson,et al.  CTdatabase: a knowledge-base of high-throughput and curated data on cancer-testis antigens , 2008, Nucleic Acids Res..

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

[21]  Ben S. Wittner,et al.  Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 , 2009, Nature.

[22]  C. Melief,et al.  Cancer immunology. , 2011, Current opinion in immunology.

[23]  E. Lander,et al.  Lessons from the Cancer Genome , 2013, Cell.

[24]  S. Ganesan,et al.  Biomarkers for Immunotherapy: Current Developments and Challenges. , 2016, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.

[25]  M. Maio,et al.  Antitumor activity of epigenetic immunomodulation combined with CTLA-4 blockade in syngeneic mouse models , 2015, Oncoimmunology.

[26]  S. Gabriel,et al.  Genomic correlates of response to CTLA-4 blockade in metastatic melanoma , 2015, Science.

[27]  Juan Carlos Fernández,et al.  Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms , 2014, Ann. Oper. Res..

[28]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[29]  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.

[30]  Noemi Andor,et al.  EXPANDS: expanding ploidy and allele frequency on nested subpopulations , 2013, Bioinform..

[31]  J. Sosman,et al.  Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma , 2016, Cell.

[32]  Jun S. Liu,et al.  Comprehensive analyses of tumor immunity: implications for cancer immunotherapy , 2016, Genome Biology.

[33]  J. Wolchok PD-1 Blockers , 2015, Cell.

[34]  Jimmy Lin,et al.  Mining Exomic Sequencing Data to Identify Mutated Antigens Recognized by Adoptively Transferred Tumor-reactive T cells , 2013, Nature Medicine.

[35]  C. Perou,et al.  Allele-specific copy number analysis of tumors , 2010, Proceedings of the National Academy of Sciences.

[36]  Steven J. M. Jones,et al.  Genomic Classification of Cutaneous Melanoma , 2015, Cell.

[37]  Chris Sander,et al.  An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma. , 2016, Cancer cell.

[38]  Joseph T. Glessner,et al.  PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. , 2007, Genome research.

[39]  Zlatko Trajanoski,et al.  Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  Michael P. Schroeder,et al.  In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. , 2015, Cancer cell.

[41]  Z. Trajanoski,et al.  Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome , 2006, Science.

[42]  Pia Kvistborg,et al.  The cancer antigenome , 2012, The EMBO journal.

[43]  E. Oikonomou,et al.  BRAF vs RAS oncogenes: are mutations of the same pathway equal? differential signalling and therapeutic implications , 2014, Oncotarget.

[44]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[45]  M. Stratton,et al.  Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[46]  Z. Trajanoski,et al.  Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. , 2013, Immunity.

[47]  F. Garrido,et al.  “Hard” and “soft” lesions underlying the HLA class I alterations in cancer cells: Implications for immunotherapy , 2010, International journal of cancer.

[48]  L. Spinelli,et al.  BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses , 2015, BMC Genomics.

[49]  Scott D. Brown,et al.  Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival , 2014, Genome research.

[50]  Benjamin J. Raphael,et al.  Mutational landscape and significance across 12 major cancer types , 2013, Nature.

[51]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[52]  Pornpimol Charoentong,et al.  Computational genomics tools for dissecting tumour–immune cell interactions , 2016, Nature Reviews Genetics.

[53]  Nicolai J. Birkbak,et al.  Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade , 2016, Science.

[54]  O. Lund,et al.  NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence , 2007, PloS one.

[55]  Pornpimol Charoentong,et al.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks , 2009, Bioinform..

[56]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[57]  P. Coulie,et al.  Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy , 2014, Nature Reviews Cancer.

[58]  M. Vetizou,et al.  Resistance Mechanisms to Immune-Checkpoint Blockade in Cancer: Tumor-Intrinsic and -Extrinsic Factors. , 2016, Immunity.

[59]  J. Gartner,et al.  Immunogenicity of somatic mutations in human gastrointestinal cancers , 2015, Science.

[60]  C. Perou,et al.  Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types. , 2016, Journal of the National Cancer Institute.

[61]  Steven J. M. Jones,et al.  Integrated Genomic Characterization of Papillary Thyroid Carcinoma , 2014, Cell.

[62]  N. Hacohen,et al.  Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity , 2015, Cell.

[63]  I. Mellman,et al.  Oncology meets immunology: the cancer-immunity cycle. , 2013, Immunity.

[64]  Trevor J Pugh,et al.  Oncotator: Cancer Variant Annotation Tool , 2015, Human mutation.

[65]  A. McKenna,et al.  Absolute quantification of somatic DNA alterations in human cancer , 2012, Nature Biotechnology.