The Cancer Exome Generated by Alternative mRNA Splicing Dilutes Predicted HLA Class I Epitope Density

Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I supertype representatives consistently found to contain fewer predicted epitopes compared to normal tissue. We observed a significant difference in amino acid composition between protein sequences associated with normal versus cancer tissue, as transcripts found in cancer are enriched with hydrophilic amino acids. This variation contributes to the observed significant lower likelihood of cancer-specific peptides to be predicted epitopes compared to peptides found in normal tissue.

[1]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[2]  Tyson A. Clark,et al.  Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array , 2006, BMC Genomics.

[3]  Sherif Abou Elela,et al.  Cancer-associated regulation of alternative splicing , 2009, Nature Structural &Molecular Biology.

[4]  R. Skotheim,et al.  Alternative splicing in cancer: noise, functional, or systematic? , 2007, The international journal of biochemistry & cell biology.

[5]  Thomas J Hudson,et al.  Survey of allelic expression using EST mining. , 2005, Genome research.

[6]  J. Berzofsky,et al.  Evidence for selection against human lung cancers bearing p53 missense mutations which occur within the HLA A*0201 peptide consensus motif. , 1994, Cancer research.

[7]  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.

[8]  Charles R. Cantor,et al.  Annual Review of Biophysics and Bioengineering , 1972 .

[9]  Hanhua Cheng,et al.  Genome-wide detection of testis- and testicular cancer-specific alternative splicing. , 2007, Carcinogenesis.

[10]  T. Ndung’u,et al.  HLArestrictor—a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides , 2010, Immunogenetics.

[11]  Morten Nielsen,et al.  Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods , 2009, Bioinform..

[12]  D. van Baarle,et al.  A Comparative Study of HLA Binding Affinity and Ligand Diversity: Implications for Generating Immunodominant CD8+ T Cell Responses1 , 2009, The Journal of Immunology.

[13]  O. Lund,et al.  NetMHCpan, a method for MHC class I binding prediction beyond humans , 2008, Immunogenetics.

[14]  Stephen H. White,et al.  Experimentally determined hydrophobicity scale for proteins at membrane interfaces , 1996, Nature Structural Biology.

[15]  Richard J. Simpson,et al.  Proteins and proteomics : a laboratory manual , 2003 .

[16]  J.,et al.  The New England Journal of Medicine , 2012 .

[17]  C. V. Jongeneel,et al.  eVOC: a controlled vocabulary for unifying gene expression data. , 2003, Genome research.

[18]  W. Kauzmann Some factors in the interpretation of protein denaturation. , 1959, Advances in protein chemistry.

[19]  F. Garrido,et al.  Coordinated downregulation of the antigen presentation machinery and HLA class I/β2‐microglobulin complex is responsible for HLA‐ABC loss in bladder cancer , 2005, International journal of cancer.

[20]  Rasmus Wernersson,et al.  Virtual Ribosome—a comprehensive DNA translation tool with support for integration of sequence feature annotation , 2006, Nucleic Acids Res..

[21]  H. Morreau,et al.  MUTYH‐associated polyposis carcinomas frequently lose HLA class I expression—a common event amongst DNA‐repair‐deficient colorectal cancers , 2009, Journal of Pathology.

[22]  J. Manley,et al.  Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. , 2010, Genes & development.

[23]  F M Richards,et al.  Areas, volumes, packing and protein structure. , 1977, Annual review of biophysics and bioengineering.

[24]  C Sander,et al.  Polarity as a criterion in protein design. , 1989, Protein engineering.

[25]  D. Wetlaufer Protein structure. , 1986, Science.

[26]  T. D. Schneider,et al.  Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.

[27]  K. R. Woods,et al.  Prediction of protein antigenic determinants from amino acid sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[28]  K. Nakachi,et al.  Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: an 11-year follow-up study of a general population , 2000, The Lancet.

[29]  T. Cooper,et al.  Pre-mRNA splicing and human disease. , 2003, Genes & development.

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

[31]  O. Lund,et al.  Definition of supertypes for HLA molecules using clustering of specificity matrices , 2004, Immunogenetics.

[32]  Manfred Spraul,et al.  Discrimination of metabolic profiles of pancreatic cancer from chronic pancreatitis by high‐resolution magic angle spinning 1H nuclear magnetic resonance and principal components analysis , 2007, Cancer science.

[33]  E. B. Wilson Probable Inference, the Law of Succession, and Statistical Inference , 1927 .

[34]  B. Seliger,et al.  Frequent loss of HLA-A2 expression in metastasizing ovarian carcinomas associated with genomic haplotype loss and HLA-A2-restricted HER-2/neu-specific immunity. , 2006, Cancer research.

[35]  R. Ferris,et al.  Immune Escape Associated with Functional Defects in Antigen-Processing Machinery in Head and Neck Cancer , 2006, Clinical Cancer Research.

[36]  L. Blankenship,et al.  Missense mutations in the BRCT domain of BRCA-1 from high-risk women frequently perturb strongly hydrophobic amino acids conserved among mammals. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[37]  Anne-Mette K. Hein,et al.  Alternative Splicing in Colon, Bladder, and Prostate Cancer Identified by Exon Array Analysis*S , 2008, Molecular & Cellular Proteomics.

[38]  P. Chomez,et al.  A gene encoding an antigen recognized by cytolytic T lymphocytes on a human melanoma. , 1991, Science.

[39]  Oliver Hofmann,et al.  ASTD: The Alternative Splicing and Transcript Diversity database. , 2009, Genomics.

[40]  A M Lesk,et al.  Interior and surface of monomeric proteins. , 1987, Journal of molecular biology.

[41]  I. Ellis,et al.  Immunosurveillance is active in colorectal cancer as downregulation but not complete loss of MHC class I expression correlates with a poor prognosis , 2006, International journal of cancer.

[42]  Søren Brunak,et al.  Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach , 2004, Bioinform..

[43]  O. Gotoh,et al.  Cancer-associated splicing variants of the CDCA1 and MSMB genes expressed in cancer cell lines and surgically resected gastric cancer tissues. , 2009, Surgery.

[44]  Wei Zou,et al.  Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. , 2010, Rapid communications in mass spectrometry : RCM.

[45]  B. Seliger Different regulation of MHC Class I antigen processing components in human tumors , 2008, Journal of immunotoxicology.

[46]  Y. Sanejouand,et al.  Which effective property of amino acids is best preserved by the genetic code? , 1998, Protein engineering.