Abstract 1425: Identifying tumor-specific membrane targets, therapeutics, and matching cell lines for experimental validation with the Multiomics2Targets2 workflow.

Abstract Rapid advancements in the multi-omics profiling of tumors from cancer patients have opened up a range of possibilities for personalized target identification. To this end, we previously developed Multiomics2Targets, a platform that identifies tumor-specific membrane proteins. The tumor-specific membrane targets are highly expressed in the input RNA-seq tumor samples while lowly expressed in hundreds of normal tissues and cell types based on transcriptomics profiling atlases created from GTEx, Tabula Sapiens, and ARCHS4. To further prioritize the identified targets for conducting in-vitro and in-vivo experiments, we upgraded the Multiomics2Targets workflow with data from the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Dependency Map (DepMap). From CCLE we identify correlated cell lines that also express the identified targets, and from DepMap we examine the effects of the identified targets on cell line proliferation when knocked down. We also added to the workflow drug predictions by integrating Connectivity Mapping resources from six databases. Additionally, the updated Multiomics2Targets workflow can be applied to single cell RNA-seq data, or bulk RNA-seq samples can be deconvoluted into single-cell-like profiles. The upgraded workflow also applies the tools ChEA-KG and Enrichr to identify enriched transcription factor regulatory subnetworks, pathways, associated phenotypes, and other enriched terms. The resulting figures and tables are compiled into an automatically generated report which includes introduction, methods, results, and conclusions. We demonstrate the utility of the upgraded workflow by discovering tumor subtype-specific cell-surface targets, enriched pathways, down-regulating chemical perturbations, and most similar cell lines for pancreatic adenocarcinoma tumors from the Clinical Proteomic Tumor Analysis Consortium 3 project. We validate several highly ranked targets and chemical perturbations by literature search, as well as suggest novel targets, therapeutics, and cell lines for future in-vitro and in-vivo validation experiments. The updated drug and target discovery workflow is encoded into a new software application called Multiomics2Targets2 (M2T2). Citation Format: Anna I. Byrd, Lily D. Taub, Avi Ma'ayan, . Identifying tumor-specific membrane targets, therapeutics, and matching cell lines for experimental validation with the Multiomics2Targets2 workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1425.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-1425
Primary Topic
Single-cell and spatial transcriptomics
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article
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article

Abstract 1425: Identifying tumor-specific membrane targets, therapeutics, and matching cell lines for experimental validation with the Multiomics2Targets2 workflow.

Lily Taub, Avi Ma'ayan, Anna I. Byrd
Cancer Research
Single-cell and spatial transcriptomics
article

Abstract 1425: Identifying tumor-specific membrane targets, therapeutics, and matching cell lines for experimental validation with the Multiomics2Targets2 workflow.

Lily Taub, Avi Ma'ayan, Anna I. Byrd
article en

Abstract

Abstract Rapid advancements in the multi-omics profiling of tumors from cancer patients have opened up a range of possibilities for personalized target identification. To this end, we previously developed Multiomics2Targets, a platform that identifies tumor-specific membrane proteins. The tumor-specific membrane targets are highly expressed in the input RNA-seq tumor samples while lowly expressed in hundreds of normal tissues and cell types based on transcriptomics profiling atlases created from GTEx, Tabula Sapiens, and ARCHS4. To further prioritize the identified targets for conducting in-vitro and in-vivo experiments, we upgraded the Multiomics2Targets workflow with data from the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Dependency Map (DepMap). From CCLE we identify correlated cell lines that also express the identified targets, and from DepMap we examine the effects of the identified targets on cell line proliferation when knocked down. We also added to the workflow drug predictions by integrating Connectivity Mapping resources from six databases. Additionally, the updated Multiomics2Targets workflow can be applied to single cell RNA-seq data, or bulk RNA-seq samples can be deconvoluted into single-cell-like profiles. The upgraded workflow also applies the tools ChEA-KG and Enrichr to identify enriched transcription factor regulatory subnetworks, pathways, associated phenotypes, and other enriched terms. The resulting figures and tables are compiled into an automatically generated report which includes introduction, methods, results, and conclusions. We demonstrate the utility of the upgraded workflow by discovering tumor subtype-specific cell-surface targets, enriched pathways, down-regulating chemical perturbations, and most similar cell lines for pancreatic adenocarcinoma tumors from the Clinical Proteomic Tumor Analysis Consortium 3 project. We validate several highly ranked targets and chemical perturbations by literature search, as well as suggest novel targets, therapeutics, and cell lines for future in-vitro and in-vivo validation experiments. The updated drug and target discovery workflow is encoded into a new software application called Multiomics2Targets2 (M2T2). Citation Format: Anna I. Byrd, Lily D. Taub, Avi Ma'ayan, . Identifying tumor-specific membrane targets, therapeutics, and matching cell lines for experimental validation with the Multiomics2Targets2 workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1425.

Cancer ResearchVol. 86(7_Supplement)
Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 8%
Single-cell and spatial transcriptomics
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