Reversion analysis reveals the immunogenicity in vivo of a poorly MHC I-binding cancer neoepitope
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ABSTRACT:
PROVIDER: PRJNA718324 | ENA |
REPOSITORIES: ENA
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Project description:Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.
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Project description:Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.
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Project description:The appended raw files, csv files and other documents were deposited in the public domain in support for the publication
Enabling Routine MHC-II Associated Peptide ProteomicS (MAPPs) for Risk Assessment of Drug-Induced Immunogenicity
Guido Steiner, Celine Marban-Doran, Jessica Langer, Tatiana Pimenova, Gonzalo Duran-Pacheco; Denise Sauter; Anja Langenkamp, Corinne Solier, Thomas Singer, Katharine Bray-French, and Axel Ducret
The abstract is appended below:
MHC-II Associated Peptide ProteomicS (MAPPs) is a mass spectrometry-based approach to identify and relatively quantitate naturally processed and presented MHC-II associated peptides that can potentially activate T cells and contribute to the immunogenicity of a drug. Acceptance of the MAPPs technology as an appropriate pre-clinical (and potentially clinical) immunogenicity risk assessment tool depends on its technical stability and robustness but also on the ability to compare results across experiments and donors. To this end, we developed a specialized MAPPs data processing pipeline, dataMAPPs, which presents complex mass spectrometric datasets in the form of heat maps (heatMAPPs) enabling rapid and convenient comparison between conditions and donors. A customized normalization procedure based on identified endogenous peptides standardizes signal intensities within and between donors and enable cross-experimental comparison. We evaluated the technical reproducibility of the MAPPs platform using tool compounds with respect to the most prominent experimental factors and found that the systematic biological differences across donors by far outweighed any technical source of variation. We illustrate the capability of the MAPPs platform to generate data that may be used for pre-clinical risk assessment of drug-induced immunogenicity and discuss its applicability in the clinics.
The raw files and associated data for publicly available compounds are appended in the depository. The database (dataMAPPS publication db.FASTA) that was used to search the raw files is also included.
The current dataset is divided in three sections, each with explaining documentation:
Folder Fig. 2_4_5 contains the raw files, the PEAKS identification files and the quantification files that were used to generate the Fig. 2, 4 and 5 of the publication. The raw files were also used to generate the test files deposited on http://SourceForge.net/projects/datamapps/ which is alsothe source to download the R source code for dataMAPPs.
Folder Fig. 6BC contains the raw files, the PEAKS identification files and the quantification files that were used to generate the Fig. 6 BC. The raw files used to generate Fig. 6A were not included; they have been generated using a compound that is proprietary.
Folder Fig. 8_KLH contains the raw files, the PEAKS identification files and the quantification files that were used to generate the Fig. 8.
2020-05-05 | MSV000085375 | MassIVE