Abstract 148: Multiomic sequencing reveals diversity in clonal landscape and genomic alterations in a lung metastatic PDX osteosarcoma model

Abstract Introduction: Osteosarcoma is a primary malignant bone tumor characterized by the production of spindle cells resulting in immature bone formation. The lung is the most frequent site of metastatic disease and relapse occurs in more than 30 % of patients. Recent results show that relapse is due to a subset of cells with different phenotypic and genetic signatures conferring advantage to drive progression and drug resistance within the intra-tumorally heterogeneous population. Thus, developing a defined in vivo model that can identify the rare genetic subpopulation and recapitulate clonal evolution is crucial. In this study, we used whole exome, barcode and RNA sequencing to characterize the landscape of genomic alterations and also track and identify the clonal subpopulations, clonal and genetic drivers of lungs metastasis. Methods: We injected barcoded PDX (OS17) cells intravenously in ten SCID mice to track the clonal subpopulation of cells that metastasize to the lungs. Metastasized lung nodules were collected following death or euthanasia. All samples collected were snap frozen in liquid nitrogen and stored at -80°C. DNA and RNA was extracted and analyzed by PCR, NGS, WES and RNA sequencing to map clonality, mutational and evolutionary profiles. Results: Four (40%) of the mice developed lung metastases with three mice having multiple metastatic lung nodules. Surgery to remove the metastatic nodules was performed 216 ± 55. 5 days following tumor cell injection. Nine metastatic lung nodules from three mice were used for sequencing and analysis. A Shannon-Weaver and Jaccard similarity index show a diversity in clonal architecture between the lung nodules. A total of 15, 394 somatic variants was identified including 12, 547 single nucleotide variants (SNV), 4054 synonymous, 2662 missense, 28 nonsense mutations and 1639 somatic indels. The SNVs accounted for 81% of all variants followed by CNV 11 % and indels 8% respectively. Synonymous and missense mutations were the most frequently observed in all samples. Copy number loss was two-fold compared to copy number gain across all tumors. Furthermore, the deletions span longer genomic distances compared to amplifications. The mutational spectrum showed that the highest substitutions were CT/GA followed by TC/AG; the least substitutions were TA/AT. Also, we identified a large set of genes with genomic aberrations. These include amplifications in VEGF, RB1, RUNX, PARP4, ICAM3, EGFR, BRCA2, COLA6A1, COLA6A2, CCND3, CDKN2D etc. Conclusions: In the current model, we identified multiple tumorigenic seeding clones and the diverse set of genes mutated in osteosarcoma. Further analysis of the mutational signature resulted in the identification of potential genes that act as drivers in the lung metastatic process. Acknowledgments: Swim Across America, the Foster Foundation and the Barbara Epstein Foundation. Citation Format: Sylvester Jusu, Wendong Zhang, Zhongting Zhang, Xu Zhaohui, Sankaranarayanan Kannan, Yifei Wang, Zhou Xin, Yi Yanhua, Michael Roth, Jonathan Gill, Richard Gorlick. Multiomic sequencing reveals diversity in clonal landscape and genomic alterations in a lung metastatic PDX osteosarcoma model abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 148.