Abstract 1287: A spatiotemporal proteome map of chemotherapy response in metastatic breast cancer

Abstract Cells are not simply aggregates of molecules; they are highly organized entities with dynamic structures and functions that evolve in response to external stimuli, such as drug treatments. Understanding how tumor cells reorganize these structures and functions, both spatially and temporally, is essential for unraveling the mechanisms underlying tumor behaviors including treatment response. Through the NIH Bridge2AI Cell Maps for AI (CM4AI) data generation project, we profiled spatiotemporal proteomics in metastatic breast cancer cells across untreated and chemotherapy-treated conditions using complementary proteomics technologies: immunofluorescence imaging (∼11,000 images covering >100,000 single cells to map subcellular localization of >500 proteins), mass spectrometry (protein interactions of >500 proteins and complex organization across >5000 proteins), and single-cell CRISPR sequencing (>120,000 single-cell transcriptomes across 200 gene perturbations). Using self-supervised contrastive learning, we fuse protein embeddings across modalities while preserving treatment-induced protein dynamics. Our analyses reveal that chemotherapy triggers substantial protein reorganization. For example, in untreated cells, HDAC8 forms an assembly in close proximity to TET1, SETDB2, PHF6 and YWHAG. However, after paclitaxel treatment, its expression increases substantially and relocates from cytosol to nucleus. It forms a new seven-protein assembly with PARP1, KAT6B, DNMT3A, HDAC9, HDAC2, and BRD4. Building on this foundation, we will construct an integrated map that (a) delineates how individual proteins assemble into complexes and higher-order structures; (b) annotates these subcellular components with their functional states; and (c) models the dynamics of specific cellular components across drug treatment contexts. Citation Format: Xiaoyu Zhao, Jiahao Gao, Gege (Scarlett) Qian, Richa Tiwari, Jan N. Hansen, Antoine Forget, Sami Nourreddine, Yesh Doctor, Jillian Parker, Prashant Mali, Nevan Krogan, Emma Lundburg, Trey Ideker. A spatiotemporal proteome map of chemotherapy response in metastatic breast cancer [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 1287.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-1287
Primary Topic
14-3-3 protein interactions
Type
article
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article

Abstract 1287: A spatiotemporal proteome map of chemotherapy response in metastatic breast cancer

Antoine Forget, Jillian A. Parker, Sami Nourreddine, Jiahao Gao et al.
Cancer Research
14-3-3 protein interactions
article

Abstract 1287: A spatiotemporal proteome map of chemotherapy response in metastatic breast cancer

Antoine Forget, Jillian A. Parker, Sami Nourreddine, Jiahao Gao, Jan N. Hansen, Xiaoyu Zhao, Gege Qian, Yesh Doctor, Prashant Mali, Richa Tiwari, Nevan Krogan, Trey Ideker, Emma Lundburg
article en

Abstract

Abstract Cells are not simply aggregates of molecules; they are highly organized entities with dynamic structures and functions that evolve in response to external stimuli, such as drug treatments. Understanding how tumor cells reorganize these structures and functions, both spatially and temporally, is essential for unraveling the mechanisms underlying tumor behaviors including treatment response. Through the NIH Bridge2AI Cell Maps for AI (CM4AI) data generation project, we profiled spatiotemporal proteomics in metastatic breast cancer cells across untreated and chemotherapy-treated conditions using complementary proteomics technologies: immunofluorescence imaging (∼11,000 images covering >100,000 single cells to map subcellular localization of >500 proteins), mass spectrometry (protein interactions of >500 proteins and complex organization across >5000 proteins), and single-cell CRISPR sequencing (>120,000 single-cell transcriptomes across 200 gene perturbations). Using self-supervised contrastive learning, we fuse protein embeddings across modalities while preserving treatment-induced protein dynamics. Our analyses reveal that chemotherapy triggers substantial protein reorganization. For example, in untreated cells, HDAC8 forms an assembly in close proximity to TET1, SETDB2, PHF6 and YWHAG. However, after paclitaxel treatment, its expression increases substantially and relocates from cytosol to nucleus. It forms a new seven-protein assembly with PARP1, KAT6B, DNMT3A, HDAC9, HDAC2, and BRD4. Building on this foundation, we will construct an integrated map that (a) delineates how individual proteins assemble into complexes and higher-order structures; (b) annotates these subcellular components with their functional states; and (c) models the dynamics of specific cellular components across drug treatment contexts. Citation Format: Xiaoyu Zhao, Jiahao Gao, Gege (Scarlett) Qian, Richa Tiwari, Jan N. Hansen, Antoine Forget, Sami Nourreddine, Yesh Doctor, Jillian Parker, Prashant Mali, Nevan Krogan, Emma Lundburg, Trey Ideker. A spatiotemporal proteome map of chemotherapy response in metastatic breast cancer [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 1287.

Cancer ResearchVol. 86(7_Supplement)
Palo Alto University (US), University of California, San Francisco (US), University of California San Diego (US), Stanford University (US)
Good health and well-being
Openalex Percentile: Top 7%
14-3-3 protein interactions
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