325 CellSARCTx: a single-cell transcriptomics atlas of adoptive cellular therapy targets in soft tissue sarcomas

20240 citationsJournal Articlehybrid Open Access

Authors

Stefano Testa · Beth Israel Deaconess Medical Center

Aastha Pal · Stanford University

Ajay Subramanian · Stanford University

Minggui Pan · Stanford University

Nam Q. Bui · Stanford University

Sushama Varma · Stanford University

Matt van de Rijn ·

Abstract

<h3>Background</h3> Soft tissue sarcomas (STS) are aggressive neoplasms with limited treatments. Afami-cel, a T-cell receptor engineered T cell (TCR-T), showed efficacy in MAGE-A4+synovial sarcoma (SS) and myxoid round cell liposarcoma,1 demonstrating the potential of adoptive T-cell therapies (ACTs) in STS. However, the lack of STS-specific targets with low-normal tissues expression limits the applicability of ACTs in STS.2 Hence, we developed a pipeline leveraging single cell RNA sequencing (scRNA-seq) to discover TCR-T and chimeric antigen receptor T (CAR-T) cells targets, including intracellular and membrane oncoproteins (figure 1). <h3>Methods</h3> We built an atlas of ACT STS targets, CellSARCTx, by screening scRNA-seq datasets for STS-specific genes using differential expression testing on 32 STS3 4 and 30 normal tissue samples,5–7 with 986749 cells total. Our cohort included SS, leiomyosarcomas (LMS), undifferentiated pleomorphic sarcomas (UPS), myxofibrosarcomas, and well-differentiated/dedifferentiated liposarcomas. Target off-tumor toxicity was assessed using multiomics data in normal tissues, including scRNA-seq,5 bulk RNA-seq,8 immunohistochemistry, and proteomics,9 10 and was compared to that of known targets.11 Target relevance to STS was assessed using bulk RNA-seq TCGA data,12 by analyzing recurrent genomics events in STS, through STS proteomics datasets,13 14 and using CRISPR KO cell lines screens.15 To target surface antigens, we focused on monospecific and bispecific ‘AND’-gated and ‘AND-NOT’-gated CAR-Ts. To target intracellular oncoproteins with TCR-T and peptide-centric CAR-Ts (PC-CAR-Ts), we identified tumoral peptide-MHC (pairs, using in silico immunogenicity and HLA presentation predictions.16 17 Risk of T-cell cross-reactivity was assessed with peptide similarity and cross-reactivity algorithms leveraging a manually curated dataset of 46x106 normal, using afami-cel’s target GVYDGREHTV–A*02:01 as benchmark. <h3>Results</h3> We identified 449 monospecific and 14192 bispecific CAR-T targets, as well as 5683 (figure 2). Transmembrane proteases (MMP14, FAP), glypicans (GPC6, GPC3), semaphorins (SEMA4A, SEMA4B), cell adhesion (CLMP, NEO1), and immune checkpoint molecules (NECTIN2, BTN3A2) were the most frequent targets. Targets as TSPAN31 and GLIPR1L1 were amplified and involved by fusions in STS, indicating an oncogenic driver role. Many targets were highly expressed in STS proteomics samples, including PLAUR (UPS 69%), PTK7 (SS 72%), MMP14 (UPS 83%), and CD99 (LMS 71%). ADAM32 and WBP2NL were the antigens with the highest number of targetable (133 and 128). Several ‘promiscuous’ peptides formed with multiple HLA alleles, like YVVDGQIIIY from PSG8/PSG11, while others bound to few prevalent alleles, like YYWPRPRRY from GAGE1/GAGE12H binding to A*24:02. <h3>Conclusions</h3> We provide a large repository that can propel development of ACTs in STS, while offering a framework for similar analyses in other solid tumors. <h3>References</h3> D’Angelo SP, Araujo DM, Abdul Razak AR, et al. Afamitresgene autoleucel for advanced synovial sarcoma and myxoid round cell liposarcoma (SPEARHEAD-1): an international, open-label, phase 2 trial. The Lancet, 2024;403:1460–1471. Larson RC, Maus MV. Recent advances and discoveries in the mechanisms and functions of CAR T cells. Nat Rev Cancer 2021;21:145–161. Subramanian A, Nemat-Gorgani N, Ellis-Caleo TJ, et al. Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy. Nat Cancer 2024;5:642–658. Jerby-Arnon L, Neftel C, Shore ME, et al. Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma. Nat Med 2021;27:289–300. Karlsson M, Zhang C, Méar L, et al. A single–cell type transcriptomics map of human tissues. Sci. Adv. 2021;7:eabh2169. Regev A, Teichmann SA, Lander ES, et al. The Human Cell Atlas. Elife 2017;6:e27041. The Tabula Sapiens Consortium, Jones RC, Karkanias J, et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 2022;376:eabl4896. GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, et al. Genetic effects on gene expression across human tissues. Nature 2017;550:204–213. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science 2015;347:1260419. Kim M-S, Pinto SM, Getnet D, et al. A draft map of the human proteome. Nature 2014;509:575–581. MacKay M, Afshinnekoo E, Rub J, et al. The therapeutic landscape for cells engineered with chimeric antigen receptors. Nat Biotechnol 2020;38:233–244. Abeshouse A, Adebamowo C, Adebamowo SN, et al. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell 2017;171:950–965.e28. Burns J, Wilding CP, Krasny L, et al. The proteomic landscape of soft tissue sarcomas. Nat Commun 2023;14:3834. Tang S, Wang Y, Luo R, et al. Proteomic characterization identifies clinically relevant subgroups of soft tissue sarcoma. Nat Commun 2024;15:1381. Pacini C, Dempster JM, Boyle I, et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun 2021;12:1661. Sarkizova S, Klaeger S, Le PM, et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat Biotechnol 2020;38:199–209. Li G, Iyer B, Prasath VBS, et al. DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics 2021;22:bbab160. <h3>Ethics Approval</h3> All samples analyzed in this study were collected with informed consent from subjects enrolled on protocols approved by the Institutional Review Boards at Stanford University and University of California Los Angeles.

Topics & Keywords

CAR-T cell therapy research

UN Sustainable Development Goals

Gender equality

Publication Details

Published in: Regular and Young Investigator Award Abstracts

DOI: 10.1136/jitc-2024-sitc2024.0325

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