Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes

By S. Shukla, Michael S. Rooney, M. Rajasagi, G. Tiao, P. Dixon, Mike S. Lawrence, Jonathan D. Stevens, William J. Lane, Jamie L. Dellagatta, S. Steelman and 6 more. Nature Biotechnology, 2015

596 citations recorded by the source corpus.

Detection of somatic mutations in human leukocyte antigen (HLA) genes using whole-exome sequencing (WES) is hampered by the high polymorphism of the HLA loci, which prevents alignment of sequencing reads to the human reference genome. We describe a computational pipeline that enables accurate inference of germline alleles of class I HLA-A, B and C genes and subsequent detection of mutations in these genes using the inferred alleles as a reference. Analysis of WES data from 7,930 pairs of tumor and healthy tissue from the same patient revealed 298 nonsilent HLA mutations in tumors from 266 patients. These 298 mutations are enriched for likely functional mutations, including putative loss-of-function events. Recurrence of mutations suggested that these 'hotspot' sites were positively selected. Cancers with recurrent somatic HLA mutations were associated with upregulation of signatures of cytolytic activity characteristic of tumor infiltration by effector lymphocytes, supporting immune evasion by altered HLA function as a contributory mechanism in cancer.

Subfields: Cancer Research, Immunology, Oncology

View Paper: https://www.semanticscholar.org/paper/364ec7f8d1e24fd1104421e0e0f02b8f3301d90b

Citations in the corpus

• Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance (2017, unknown): “Next, we compared the frequency of human leukocyte antigen (HLA) somatic mutations (44) in pretreatment samples and found that HLA somatic mutations were found in only one pretreatment biopsy from a CTLA-4 blockade nonresponder (table S5).”
• Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, unknown): “Class I HLA mutation and neoantigen analysis Class I HLA typing and mutation detection: HLA typing and detection of mutations in class I HLA genes (HLA-A/B/C) was performed using Polysolver (Shukla et al., 2015).”
• HLA variation and disease (2018, unknown): “…study of HLA genetics has not only focused on disease risk but has also found that certain tumours display recurrent somatic mutations in HLA genes 88 , including mutations that promote a loss of HLA allele heterozygosity 89 ; consequently, such tumours are more likely to undergo immune eva­ sion…”
• The Immune Landscape of Cancer (2018, unknown): “In order to validate the typing results from OptiType, we compared the HLA class I four-digit types obtained from the software PolySolver on TCGA Whole Exome Sequencing data samples (Shukla et al., 2015).”
• Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients (2018, unknown): “…set of alternative splicing events This Paper https://gdc.cancer.gov/about-data/ publications/PanCanAtlas-Splicing-2018 HLA types Shukla et al., 2015 https://www.synapse.org/#!Synapse: syn5974638 Neoepitopes This paper https://www.synapse.org/#!Synapse: syn12180140 Tumor…”
• Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas (2016, unknown): “HLA alleles were called with POLYSOLVER 46 for all lung cancer exomes.”
• Integrative molecular and clinical modeling of clinical outcomes to PD-1 blockade in metastatic melanoma patients (2019, unknown): “Briefly, HLA-type was inferred using POLYSOLVER 65 , which uses a Bayesian classifier to determine genotype for each patient.”
• Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma (2016, unknown): “each sample (Shukla et al., 2015) and then used mutations to predict neopeptides that bind with high affinity (<500 nM) to personal HLA molecules (Rajasagi et al.”
• Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer (2016, unknown): “Given the high polymorphism of the HLA loci which can prevent appropriate alignment of sequencing reads to the reference genome, we performed a separate bioinformatic analysis using POLYSOLVER to independently detect somatic Class I HLA mutations (14).”
• Next-Generation Sequencing Technology: Current Trends and Advancements (2023, unknown): “As technology progresses, NGS is expected to play a pivotal role in areas such as single-cell genomics, long-read sequencing, epigenomics, and multi-omics integration, enabling a deeper understanding of cellular processes, disease mechanisms, and personalized treatment strategies.”
• SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions (2018, unknown): “These segments include known germline deletions such as GSTT1 (36), BTNL8-BTNL3 (37) together with germline hyper-polymorphic regions such as the Human Leukocyte Antigen (HLA) loci (38).”