Integrative Clinical and Molecular Characterization of 665 Next-Generation In Vitro Models from the Human Cancer Models Initiative
October 09, 2025, at 12:00 PM ETAdvancing precision oncology requires a diverse and robust repository of patient-derived cancer models that faithfully reflect the clinical, therapeutic, and molecular attributes of patients’ tumors. To address the limitations of traditional models, the Human Cancer Models Initiative (HCMI) generated 665 next-generation models from thousands of donors across 27 cancer subtypes and comprehensively characterized each model via whole-genome, -exome, -transcriptome, and -methylome profiling. Through cutting-edge integrative analysis, we ensured that most models authentically reflect their parental tumors, and identified subtle differences shaped by culture conditions and the tumor microenvironment. In this webinar, we will feature 47 unique models spanning 16 rare cancers to show you how this unprecedented collection dramatically expands the spectrum of disease diversity. We will explore both treatment-naive and post-treatment models, revealing insightful features tied to therapeutic exposure and powered clinical follow-up data. Several models preserved extrachromosomal DNA amplifications in oncogenes of interest for preclinical studies, setting the stage for transformative preclinical studies. This unparalleled community resource provides a dynamic blueprint for capturing the true breadth of cancer diversity in preclinical systems—empowering researchers, accelerating therapeutic discovery, and driving the future of precision oncology forward.
Key takeways:
- Diverse Cancer Models: The Human Cancer Models Initiative (HCMI) created 665 next-generation models from 27 cancer subtypes, reflecting clinical, therapeutic, and molecular attributes of patients' tumors.
- Authentic Tumor Reflection: Advanced integrative analysis ensured most models accurately reflect their parental tumors, with differences shaped by culture conditions and the tumor microenvironment.
- Unique Preclinical Collection: The collection includes 47 models spanning 16 rare cancers, featuring treatment-naive and post-treatment models, preserving extrachromosomal DNA amplifications in oncogenes for transformative preclinical studies.
Presenters:
Dina ElHarouni, PhD
Postdoctoral Research Fellow, Dana-Farber Cancer Institute
Dina ElHarouni is a postdoctoral research fellow at the Dana-Farber Cancer Institute, and the Broad Institute of MIT and Harvard, specializing in computational oncology and functional precision medicine. She earned her PhD in Clinical Bioinformatics from the German Cancer Research Center (DKFZ), where she focused on therapy response scoring and identifying multi-omics biomarkers to advance precision treatments for pediatric solid tumors. Her work bridges computational models and clinical applications, with the goal of developing personalized treatment strategies through data-driven insights. In recognition of her contributions to computational oncology, Dina was selected to attend the 70th Lindau Nobel Laureates Meeting in 2020/2021 and became a member of the Lindau Alumni Young Scientists community. In 2025, she was awarded the Women in Cancer Research (WICR) Scholar Award from the American Association for Cancer Research (AACR). At Dana-Farber, Dina continues to push the boundaries of precision medicine by integrating computational approaches with functional assays, aiming to transform how patient-derived models inform clinical decision-making. She also co-leads efforts within the Human Cancer Models Initiative (HCMI) to develop, characterize, and apply next-generation patient-derived models as an open resource for the global research community.
Andrew McPherson, PhD
Assistant Attending, Computational Oncologist, Memorial Sloan Kettering Cancer Center
Andrew McPherson is an Assistant Attending Computational Oncologist in the Department of Epidemiology and Biostatistics and a faculty member of Computational Oncology. Andrew’s research focuses on computational methods for analysis of single cell genomic data, with specific focus on understanding genomic instability, mutational processes, clonal evolution and the role of the microenvironment in cancer development and progression. During his doctoral research, Dr. McPherson developed methods for cancer genome sequence analysis, including detection and characterization of genome rearrangements, and inference of clonal phylogenies. Dr. McPhersons post-doctoral research at University of British Columbia focused on the development of computational methods and infrastructure for a novel single cell whole genome sequencing (scWGS) platform, Direct Library Preparation. During his time at MSKCC, Dr. McPherson has led efforts to establish a production implementation of scWGS via DLP within MSKCC, and support use of the platform through collaborations with investigators throughout the institute. In addition, Dr. McPherson has contributed to several large scale single cell genomics studies of Breast and Ovarian cancer.
Abhay U. Andar, PhD
Lead Scientist , Microphysiological Systems
Dr. Abhay U. Andar has over 13 years of experience in translational oncology, microfluidics, and advanced in vitro disease modeling. At ATCC, Dr. Andar leads the Human Cancer Model Initiative (HCMI) portfolio, which includes over 300 patient-derived cancer models spanning 28 indications. His research focuses on developing organoid systems to support therapeutic discovery and translational research in oncology. Dr. Andar earned his Ph.D. and M.Sc. in Biomedical Science and Engineering from the University of Glasgow, and a B.Sc. in Life Sciences from the University of Mumbai. He has authored numerous publications and patents in cancer research, microfluidics, and therapeutic manufacturing, with work featured in Nature Materials, Nature Biomedical Engineering, Lab on Chip, Cancer Research, and Biotechnology and Bioengineering. Dr. Andar’s innovations in Tumor-on-Chip platforms, organoid generation, and immune co-culture systems continue to shape the future of personalized medicine and drug discovery.
