ArticleiScience2025
Integrative multi-omics characterization of 12 syngeneic mouse models.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
Mouse syngeneic models serve as indispensable tools for elucidating tumor-immune interactions and assessing immunotherapy efficacy. In this study, we first conducted a comprehensive evaluation of six label-free protein quantification pipelines across 12 mouse syngeneic models, revealing that data-independent acquisition (DIA) significantly outperforms data-dependent acquisition (DDA) in terms of data coverage, reproducibility, and inter-model discrimination. We next performed an integrative multi-omics analysis to uncover molecular mechanisms associated with treatment response. Our analysis identified Dnmt3a and Igf2r, which are correlated with resistance to immune checkpoint inhibitors (ICIs), and highlighted key pathways including interferon signaling and oxidative phosphorylation that distinguish responders from non-responders. To facilitate broader research applications, we have developed an interactive web resource that shares our multi-omics datasets and analytical results, equipped with user-friendly tools for further exploration. This resource aims to accelerate preclinical research and contribute to the development of personalized cancer therapies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.