Abstract 70: SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors.

Abstract Metastasis accounts for over 90% of mortality in colorectal cancer (CRC), yet predicting whether, when, and where it will occur remains a major clinical challenge. Existing tools, including TNM staging, ctDNA, and mutational profiling, cannot anticipate metastatic tropism or guide site-specific surveillance and therapy decisions. Consequently, many patients with stage II-III CRC receive suboptimal postoperative treatment. To address this gap, we established SPOT-Met (Spatial Predictors Of Tropism and Metastasis), a foundation-scale initiative that integrates subcellular-resolution spatial multi-omics with AI-driven modeling to infer the molecular and architectural rules governing organ-specific metastasis. We spatially profiled 1,000 CRC primary tumors and ∼100 matched metastatic and adjacent normal tissues using the Singular Genomics G4X platform, generating >300 million same-sample transcriptomic, proteomic, and morphologic cell profiles at submicron resolution. Each case is linked to bulk RNA-seq, qPCR-based mutational data, and detailed clinical metadata encompassing metastasis site, timing, therapy response, and survival. SPOT-Met also incorporates a focused cohort of patients treated with pembrolizumab, enabling the spatial dissection of immunotherapy responses. Comparative analyses of responders and non-responders are underway, revealing emerging spatial differences in immune and stromal architecture. Preliminary data suggest that immune organization and cell-cell topology, rather than total immune content, may distinguish therapeutic outcomes, highlighting the potential of spatial context as a predictive biomarker beyond PD-L1 expression or tumor mutational burden. Early findings further indicate that liver-tropic tumors preferentially form perivascular stromal hubs enriched for metabolic and extracellular matrix signatures, whereas non-metastatic tumors retain compact, immune-regulated crypt structures. Integrating these spatial and molecular features enhances the retrospective classification of metastatic organotropism compared to histopathology alone. SPOT-Met is being developed as a biopsy-compatible diagnostic assay to predict metastatic potential and organotropism at the time of diagnosis, aiming to double current prognostic precision for stage II-III CRC. By uniting spatial multi-omics and AI at unprecedented scale, SPOT-Met transitions metastasis research from retrospective observation to prospective prediction, advancing precision oncology and immunotherapy stratification. Citation Format: Jiwoon Park, Ryan Shultzaberger, Braulio Banuelos, McKenzie Pavlich, Sabrina Shore, Daan Witters, Kyung-A Kim, Taeyul K. Kim, Minsun Jung, Han Sang Kim, Christopher E. Mason. SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors [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 70.

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Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-70
Primary Topic
Single-cell and spatial transcriptomics
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article
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article

Abstract 70: SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors.

Jiwoon Park, Han Sang Kim, M. Katherine Jung, McKenzie Pavlich et al.
Cancer Research
Single-cell and spatial transcriptomics
article

Abstract 70: SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors.

Jiwoon Park, Han Sang Kim, M. Katherine Jung, McKenzie Pavlich, Sabrina Shore, Daan Witters, Ryan Shultzaberger, Kyung‐A Kim, Taeyul K. Kim, Christopher E. Mason, Braulio Banuelos
article en

Abstract

Abstract Metastasis accounts for over 90% of mortality in colorectal cancer (CRC), yet predicting whether, when, and where it will occur remains a major clinical challenge. Existing tools, including TNM staging, ctDNA, and mutational profiling, cannot anticipate metastatic tropism or guide site-specific surveillance and therapy decisions. Consequently, many patients with stage II-III CRC receive suboptimal postoperative treatment. To address this gap, we established SPOT-Met (Spatial Predictors Of Tropism and Metastasis), a foundation-scale initiative that integrates subcellular-resolution spatial multi-omics with AI-driven modeling to infer the molecular and architectural rules governing organ-specific metastasis. We spatially profiled 1,000 CRC primary tumors and ∼100 matched metastatic and adjacent normal tissues using the Singular Genomics G4X platform, generating >300 million same-sample transcriptomic, proteomic, and morphologic cell profiles at submicron resolution. Each case is linked to bulk RNA-seq, qPCR-based mutational data, and detailed clinical metadata encompassing metastasis site, timing, therapy response, and survival. SPOT-Met also incorporates a focused cohort of patients treated with pembrolizumab, enabling the spatial dissection of immunotherapy responses. Comparative analyses of responders and non-responders are underway, revealing emerging spatial differences in immune and stromal architecture. Preliminary data suggest that immune organization and cell-cell topology, rather than total immune content, may distinguish therapeutic outcomes, highlighting the potential of spatial context as a predictive biomarker beyond PD-L1 expression or tumor mutational burden. Early findings further indicate that liver-tropic tumors preferentially form perivascular stromal hubs enriched for metabolic and extracellular matrix signatures, whereas non-metastatic tumors retain compact, immune-regulated crypt structures. Integrating these spatial and molecular features enhances the retrospective classification of metastatic organotropism compared to histopathology alone. SPOT-Met is being developed as a biopsy-compatible diagnostic assay to predict metastatic potential and organotropism at the time of diagnosis, aiming to double current prognostic precision for stage II-III CRC. By uniting spatial multi-omics and AI at unprecedented scale, SPOT-Met transitions metastasis research from retrospective observation to prospective prediction, advancing precision oncology and immunotherapy stratification. Citation Format: Jiwoon Park, Ryan Shultzaberger, Braulio Banuelos, McKenzie Pavlich, Sabrina Shore, Daan Witters, Kyung-A Kim, Taeyul K. Kim, Minsun Jung, Han Sang Kim, Christopher E. Mason. SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors [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 70.

Cancer ResearchVol. 86(7_Supplement)
Yonsei University (KR), Cornell University (US), ID Genomics (United States) (US), Weill Cornell Medicine (US)
Good health and well-being
Openalex Percentile: Top 8%
Single-cell and spatial transcriptomics
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