Dataset Information

A pipeline for identification and validation of tumor-specific antigens in a mouse model of metastatic breast cancer.

ABSTRACT: Cancer immunotherapy continues to make headway as a treatment for advanced stage tumors, revealing an urgent need to understand the fundamentals of anti-tumor immune responses. Noteworthy is a scarcity of data pertaining to the breadth and specificity of tumor-specific T cell responses in metastatic breast cancer. Autochthonous transgenic models of breast cancer display spontaneous metastasis in the FVB/NJ mouse strain, yet a lack of knowledge regarding tumor-bound MHC/peptide immune epitopes in this mouse model limits the characterization of tumor-specific T cell responses, and the mechanisms that regulate T cell responses in the metastatic setting. We recently generated the NetH2pan prediction tool for murine class I MHC ligands by building an FVB/NJ H-2q ligand database and combining it with public information from six other murine MHC alleles. Here, we deployed NetH2pan in combination with an advanced proteomics workflow to identify immunogenic T cell epitopes in the MMTV-PyMT transgenic model for metastatic breast cancer. Five unique MHC I/PyMT epitopes were identified. These tumor-specific epitopes were confirmed to be presented by the class I MHC of primary MMTV-PyMT tumors and their T cell immunogenicity was validated. Vaccination using a DNA construct encoding a truncated PyMT protein generated CD8 + T cell responses to these MHC class I/peptide complexes and prevented tumor development. In sum, we have established an MHC-ligand discovery pipeline in FVB/NJ mice, identified and tracked H-2D^q/PyMT neoantigen-specific T cells, and developed a vaccine that prevents tumor development in this metastatic model of breast cancer.

SUBMITTER: DeVette CI

PROVIDER: S-EPMC6959440 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Tumor-specific antigens or neoantigens are peptides that are expressed only in cancer cells and not in healthy cells. Some of these molecules can induce an immune response, and therefore, their use in immunotherapeutic strategies based on cancer vaccines has been extensively explored. Studies based on these approaches have been triggered by the current high-throughput DNA sequencing technologies. However, there is no universal nor straightforward bioinformatic protocol to discover neoantigens using DNA sequencing data. Thus, we propose a bioinformatic protocol to detect tumor-specific antigens associated with single nucleotide variants (SNVs) or "mutations" in tumoral tissues. For this purpose, we used publicly available data to build our model, including exome sequencing data from colorectal cancer and healthy cells obtained from a single case, as well as frequent human leukocyte antigen (HLA) class I alleles in a specific population. HLA data from Costa Rican Central Valley population was selected as an example. The strategy included three main steps: (1) pre-processing of sequencing data; (2) variant calling analysis to detect tumor-specific SNVs in comparison with healthy tissue; and (3) prediction and characterization of peptides (protein fragments, the tumor-specific antigens) derived from the variants, in the context of their affinity with frequent alleles of the selected population. In our model data, we found 28 non-silent SNVs, present in 17 genes in chromosome one. The protocol yielded 23 strong binders peptides derived from the SNVs for frequent HLA class I alleles for the Costa Rican population. Although the analyses were performed as an example to implement the pipeline, to our knowledge, this is the first study of an in silico cancer vaccine using DNA sequencing data in the context of the HLA alleles. It is concluded that the standardized protocol was not only able to identify neoantigens in a specific but also provides a complete pipeline for the eventual design of cancer vaccines using the best bioinformatic practices.Supplementary informationThe online version contains supplementary material available at 10.1007/s43657-022-00084-9.

Project description:BackgroundCancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.MethodsOrgan-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.ResultsComparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.ConclusionsWe performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.

Dataset Information

A pipeline for identification and validation of tumor-specific antigens in a mouse model of metastatic breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets