Dataset Information

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

ABSTRACT: Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide-MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the high-throughput technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103-397, n = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4-18, n = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0-43%, n = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

SUBMITTER: Hansen UK

PROVIDER: S-EPMC7080703 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

Hansen Ulla Kring UK Ramskov Sofie S Bjerregaard Anne-Mette AM Borch Annie A Andersen Rikke R Draghi Arianna A Donia Marco M Bentzen Amalie Kai AK Marquard Andrea Marion AM Szallasi Zoltan Z Eklund Aron Charles AC Svane Inge Marie IM Hadrup Sine Reker SR

Frontiers in immunology 20200312

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally import ...[more]

PMID: 32226429

Similar Datasets

Project description:BACKGROUND:The tumor immune microenvironment has become the focus of research in clear cell renal cell carcinoma (ccRCC) due to its important role in immune surveillance post nephrectomy. This study investigates the correlation of tumor infiltrating immune cell characteristics with rates of recurrence following surgery in localized ccRCC. METHODS:We morphologically identified and scored tumor infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) stained slides of patients with localized ccRCC (stage ?T1b excluding stage IV). The University of Alabama at Birmingham (UAB) dataset (n =?159) was used to discover and the Fox Chase Cancer Center (FCCC) dataset (n =?198) was used to validate the results of morphologic immune cell analysis. We then performed gene expression analysis using the Immune Profile panel by NanoString in the UAB cohort and identified immune cells and pathways associated with recurrence, followed by validation in the Cancer Genome Atlas (TCGA) ccRCC dataset. Infiltrating immune cell types were identified by gene expression deconvolution. RESULTS:The presence of TILs identified by morphology correlated with higher T cell, Th1, CD8+ T and Treg gene signatures. Recurrence was associated with lower T cells and higher neutrophils. Higher Teffector (Teff)/Treg ratio correlated with lower rate of recurrence and was validated in the TCGA dataset. Genes associated with adaptive immune response were downregulated in tumors that recurred. Unsupervised hierarchical clustering identified a subset of patients with over-expression of adaptive response genes including CD8, CD3, GZMA/B, PRF1, IDO1, CTLA4, PDL1, ICOS and TIGIT. These patients had higher morphologic lymphocyte infiltration and T cell gene expression. Higher levels of TILs identified by morphology correlated with higher rates of recurrence in our discovery dataset but not in our validation set. CONCLUSIONS:Recurrence of ccRCC following surgery was associated with lower T cell infiltrate, lower adaptive immune response and higher neutrophil gene expression. Presence of higher Teff/Treg ratio correlated with lower recurrence.

Project description:PURPOSE:To investigate associations between computed tomographic (CT) features of clear cell renal cell carcinoma (RCC) and mutations in VHL, PBRM1, SETD2, KDM5C, or BAP1 genes. MATERIALS AND METHODS:The institutional review board approved this retrospective, hypothesis-generating study of 233 patients with clear cell RCC and waived the informed consent requirement. The study was HIPAA compliant. Three radiologists independently reviewed pretreatment CT images of all clear cell RCCs without knowledge of their genomic profile. One radiologist measured largest diameter and enhancement parameters of each clear cell RCC. Associations between CT features and mutations in VHL, PBRM1, SETD2, KDM5C, and BAP1 genes were tested by using the Fisher exact test. Associations between mutations and size and enhancement were assessed by using the independent t test. Interreader agreement was calculated by using the Fleiss ?. RESULTS:Mutation frequencies among clear cell RCCs were as follows: VHL, 53.2% (124 of 233); PBRM1, 28.8% (67 of 233); SETD2, 7.3% (17 of 233); KDM5C, 6.9% (16 of 233); and BAP1, 6.0% (14 of 233). Mutations of VHL were significantly associated with well-defined tumor margins (P = .013), nodular tumor enhancement (P = .021), and gross appearance of intratumoral vascularity (P = .018). Mutations of KDM5C and BAP1 were significantly associated with evidence of renal vein invasion (P = .022 and .046, respectively). The genotype of solid clear cell RCC differed significantly from the genotype of multicystic clear cell RCC. While mutations of SETD2, KDM5C, and BAP1 were absent in multicystic clear cell RCC, mutations of VHL (P = .016) and PBRM1 (P = .017) were significantly more common among solid clear cell RCC. Interreader agreement for CT feature assessments ranged from substantial to excellent (? = 0.791-0.912). CONCLUSION:This preliminary radiogenomics analysis of clear cell RCC revealed associations between CT features and underlying mutations that warrant further investigation and validation.

Dataset Information

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

Publications

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets