Abstract 6608: Integrating HCMI models and tumors with CCLE and TCGA: Advancing cancer modeling and precision oncology
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 Ellrott, Martin L. Ferguson, Rameen Beroukhim, Katherine A. Hoadley, Nicolas Robine, Andrew McPherson, Mathew J. Garnett, David A. Tuveson, Andrea Califano, Paul T. Spellman, Keith L. Ligon, Daniela S. Gerhard, Louis M. Staudt, Jesse S. Boehm
Journal: Cancer Research (2025)

Abstract The Human Cancer Models Initiative (HCMI) has developed 665 novel cancer models, including organoids and matched parental tumors, providing a significant addition to existing cancer model resources. To evaluate the transcriptional relatedness of HCMI models and tumors to the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA), we used the Celligner algorithm to align transcriptomic profiles across datasets, removing systematic biases while preserving intrinsic biological variability. HCMI models and tumors both exhibited high transcriptional fidelity to TCGA tumors, clustering closely with their respective tumor types in the Celligner-aligned space. Pairwise Euclidean distances showed complementary strengths between HCMI and CCLE models. For example, HCMI models demonstrated significantly closer alignment to TCGA tumors in glioblastoma, breast cancer, and ovarian cancer, while CCLE models performed comparably or better in colorectal cancer. Notably, aligning HCMI tumors to TCGA confirmed their strong transcriptional relatedness, validating the fidelity of these models to their original tumor states. Combining HCMI and CCLE datasets further enhanced the total transcriptional representation across the diversity of TCGA tumors, underscoring the complementary roles of these resources. The HCMI collection uniquely includes rare cancer types such as nephroblastoma, desmoid tumors, and ampulla of Vater carcinoma, which are absent in CCLE. Celligner analysis confirmed that these rare HCMI models faithfully retained transcriptional features of their corresponding TCGA tumors. This expands opportunities to study rare and clinically challenging cancers that have been underrepresented in preclinical models. These findings demonstrate the value of integrating HCMI, CCLE, and TCGA datasets. Together, they form a complementary and robust compendium for studying tumor biology, enabling improved cancer modeling and advancing 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 Ellrott, Martin L. Ferguson, Rameen Beroukhim, Katherine A. Hoadley, Nicolas Robine, Andrew McPherson, Mathew J. Garnett, David A. Tuveson, Andrea Califano, Paul T. Spellman, Keith L. Ligon, Daniela S. Gerhard, Louis M. Staudt, Jesse S. Boehm. Integrating HCMI models and tumors with CCLE and TCGA: Advancing cancer modeling and precision oncology [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 6608.