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BRIEF RESEARCH REPORT article
Front. Oncol.
Sec. Cancer Molecular Targets and Therapeutics
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1549237
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Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome. We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumora well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.
Keywords: Spatial transcriptomics, Cholesterol, MC38, colorectal cancer, Visium, PD-1, immunotherapy resistance. (Min.5-Max. 8
Received: 20 Dec 2024; Accepted: 24 Feb 2025.
Copyright: © 2025 Kavran, Bai, Rabe, Kreshock, Fisher, Cheng, Lewin, Dai, Meyer, Mavrakis, Lyubetskaya and Drokhlyansky. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Anna Lyubetskaya, Informatics and Predictive Sciences, Bristol Myers Squibb, Cambridge, United States
Eugene Drokhlyansky, Mechanisms of Cancer Resistance TRC, Bristol Myers Squibb, Cambridge, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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