Abstract 6358: Single cell transcriptional dynamics in the HCMI cancer model collection
Authors: Dina ElHarouni, Mushriq Al-Jazrawe, Seongmin Choi, Merve Dede, Toshinori Hinoue, Sean A. Misek, Heeju Noh, Luca Zanella, Moony Tseng, Hayley E. Francies, Priya Sridevi, Rachana Agarwal, Cindy W. Kyi, Julyann Perez-Mayoral, Megan J. Stine, Eva Tonsing-Carter, James M. Clinton, The HCMI Network, Peter W. Laird, Calvin J. Kuo, Olivier Elemento, David L. Spector, Andrew D. Cherniack, Kyle Elrott, Martin L. Ferguson, Rameen Beroukhim, Katherine A. Hoadley, Nicolas Robine, Andrew McPherson, Matthew J. Garnett, David A. Tuveson, Andrea Califano, Paul T. Spellman, Keith L. Ligon, Daniela S. Gerhard, Louis M. Staudt, Jesse Boehm
Journal: Cancer Research (2025)

Next generation cancer models, including organoids, neurospheres and cell lines, are significantly advancing our ability to study individual human tumors in vitro and to develop novel cancer therapeutics in settings that resemble in vivo conditions. However, recent questions have emerged as to the degree to which in vitro models may faithfully represent intra- and inter-patient cell state heterogeneity at the transcriptional level of single cells. The Human Cancer Models Initiative (HCMI), a global collaboration involving the NCI (NIH), Cancer Research UK, the Wellcome Sanger Institute and the Hubrecht Organoid Technology foundation, has generated a large repertoire of 665 patient-derived models of human cancers, including organoids, neurospheres and conditionally reprogrammed cells, with matching parental tumor and clinical annotations. We conducted a tumor-model fidelity analysis using bulk profiles from Whole Genome Sequencing (WGS) and RNA-seq and confirmed strong cell-state concordance between each model and its parental tumor across most HCMI tumor-model pairs, thus showcasing models’ ability to retain key genetic, transcriptional and epigenetic attributes of their parental tumors. However, our analysis also identified divergent features in a subset (&amp;lt;8%) of models, potentially resulting in a loss of fidelity and challenging their translational use. To identify whether this discordance might be driven by ex vivo adaptations or selective evolutionary pressures from in vitro culture, we profiled a representative subset (n=13 pairs) of glioblastoma (GBM, n=7) and pancreatic cancer (PAAD, n=6) tumor-model pairs using single-nucleus RNA-seq (snRNA-seq). Analysis of 132,593 nuclei (tumors: 66,054; models: 66,539) identified outlier GBM models undergoing proneural-to-mesenchymal (PTM) transition and highlighted serum culture media as a major associated factor of this process. Additionally, we found that divergences in selected PAAD (72,688 nuclei: tumors: 30,254; models: 42,434) pairs were linked to the complete loss of the rich tumor microenvironment cells in the original tumors and the in vitro expansion of select malignant subpopulations, as identified by network-based analysis of VIPER-inferred protein activity profiles. The observed divergences suggest that in vitro culture conditions can occasionally drive cellular reprogramming and alter phenotypic fidelity, resulting in varying degrees of state-specific transitions. However, the majority of the models (even those classified as outliers) are preserved for state-frequency in cellular subpopulations between tumors and models, and most if not all of the cellular states found in the parental tumor, thus faithfully representing intra-tumor heterogeneity. Taken together, these findings highlight the translational potential of the HCMI model repository and its translational relevance as an invaluable tool to support precision oncology. Citation Format: Dina ElHarouni, Mushriq Al-Jazrawe, Seongmin Choi, Merve Dede, Toshinori Hinoue, Sean A. Misek, Heeju Noh, Luca Zanella, Moony Tseng, Hayley E. Francies, Priya Sridevi, Rachana Agarwal, Cindy W. Kyi, Julyann Perez-Mayoral, Megan J. Stine, Eva Tonsing-Carter, James M. Clinton, The HCMI Network, Peter W. Laird, Calvin J. Kuo, Olivier Elemento, David L. Spector, Andrew D. Cherniack, Kyle Elrott, Martin L. Ferguson, Rameen Beroukhim, Katherine A. Hoadley, Nicolas Robine, Andrew McPherson, Matthew J. Garnett, David A. Tuveson, Andrea Califano, Paul T. Spellman, Keith L. Ligon, Daniela S. Gerhard, Louis M. Staudt, Jesse Boehm. Single cell transcriptional dynamics in the HCMI cancer model collection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6358.