Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival

Brown, Scott D.; Warren, René L.; Gibb, Ewan A.; Martin, Spencer D.; Spinelli, John J.; Nelson, Brad H.; Holt, Robert A.

doi:10.1101/gr.165985.113

Cited by 534 publications

(437 citation statements)

References 65 publications

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“…5,20,22 However, most of these studies used different combinations of tools and applied different criteria to prioritize their set of mutated epitopes. 6,23–29 This lack of uniformity in defining ideal criteria for neoepitope prioritization shows the need for a study like ours to determine an optimal approach that combines the main factors influencing immunogenicity within the specific context of cancer.…”

Section: Introductionmentioning

confidence: 98%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

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Section: Introductionmentioning

confidence: 98%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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“…1 –5 Increasing tumor mutational burden (TMB) and the generation of neoepitopes have been associated with immunogenicity in several tumor types. 6,7 TMB is associated with clinical benefit to immune checkpoint blockade in patients with melanoma, lung, and colon cancer. 8–11 The role of TMB in tumor immunogenicity is less clear in breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Tumor mutational burden is a determinant of immune-mediated survival in breast cancer

et al. 2018

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Mounting evidence supports a role for the immune system in breast cancer outcomes. The ability to distinguish highly immunogenic tumors susceptible to anti-tumor immunity from weakly immunogenic or inherently immune-resistant tumors would guide development of therapeutic strategies in breast cancer. Genomic, transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohorts were used to examine statistical associations between tumor mutational burden (TMB) and the survival of patients whose tumors were assigned to previously-described prognostic immune subclasses reflecting favorable, weak or poor immune-infiltrate dispositions (FID, WID or PID, respectively). Tumor immune subclasses were associated with survival in patients with high TMB (TMB-Hi, P < 0.001) but not in those with low TMB (TMB-Lo, P = 0.44). This statistical relationship was confirmed in the METABRIC cohort (TMB-Hi, P = 0.047; TMB-Lo, P = 0.39), and also found to hold true in the more-indolent Luminal A tumor subtype (TMB-Hi, P = 0.011; TMB-Lo, P = 0.91). In TMB-Hi tumors, the FID subclass was associated with prolonged survival independent of tumor stage, molecular subtype, age and treatment. Copy number analysis revealed the reproducible, preferential amplification of chromosome 1q immune-regulatory genes in the PID immune subclass. These findings demonstrate a previously unappreciated role for TMB as a determinant of immune-mediated survival of breast cancer patients and identify candidate immune-regulatory mechanisms associated with immunologically cold tumors. Immune subtyping of breast cancers may offer opportunities for therapeutic stratification.

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“…Robbins and colleagues (Robbins et al, 2013) and Van and colleagues (van Rooij et al, 2013) analyzed whole exome sequences data to predict mutated antigens without considering HLA allele of patient's nor any epitope filtering criteria and docking studies. Brown and colleagues (Brown et al, 2014) showed epitope prediction on RNA-Seq analysis. All these contributions exemplify the independent use of several of the methods included in our workflow.…”

Section: Resultsmentioning

confidence: 99%

An integrative computational framework for personalized detection of tumor epitopes in melanoma immunotherapy

Jaitly

Schaft

Doerrie

et al. 2016

Preprint

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In aggressive solid tumors like melanoma, a strategy for therapy personalization can be achieved by combining high-throughput data on the patient's specific tumor mutation and expression profiles. A remarkable case is dendritic cell-based immunotherapy, where tumor epitopes identified from the patient's specific mutation profiles are loaded on patient-derived mature dendritic cells to stimulate cytotoxic T cell mediated anticancer immunity. Here we present a personalized computational pipeline for the selection of tumor-specific epitopes based on 1) patient specific haplotype; 2) cancer associated mutations; and 3) expression profiles of mutation carrying genes. We applied our workflow to one melanoma patient. Specifically, we analyzed tumor whole exome sequencing and RNA sequencing data to first detect tumor-specific mutations followed by epitope prediction based on the patient's HLA haplotype and filtering of epitopes using expression profile and binding affinity. We performed docking studies to predict the best set of epitopes targeting the patient's alleles. The proposed workflow enables us to find personalized tumor-specific epitopes for stimulating cytotoxic T-cell responses.

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Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival

Cited by 534 publications

References 65 publications

Predicting T cell recognition of MHC class I restricted neoepitopes

Predicting T cell recognition of MHC class I restricted neoepitopes

Tumor mutational burden is a determinant of immune-mediated survival in breast cancer

An integrative computational framework for personalized detection of tumor epitopes in melanoma immunotherapy

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