Chambers, K. (BMEB) – Using Genomics and Artificial Intelligence to improve prognosis for osteosarcoma patients
Transcriptomic profiling has been transformative in pediatric oncology. Pediatric cancers arise from disrupted developmental programs. Their impaired transcriptional states reflect cell lineage infidelity, aberrant differentiation, and immune-microenvironment interactions distinct from those of adult tumors(Gröbner et al., 2018; X. Ma et al., 2018). Within the osteosarcoma (OS) landscape, despite being the most common bone tumor of childhood, it remains one of the least genomically characterized pediatric cancers. Advancements in survival for localized disease, outcomes for metastatic or recurrent OS have remained stagnant for decades. Transcriptomics characterization of OS has facilitated the exposure of the unique chromothripsis patterns associated with the disease (Sayles et al., 2019; Schott et al., 2023). Largely, progress in OS genomics is still limited by the lack of harmonized, cross-study datasets accessible to researchers. I detail my contributions to OS research, beginning with the curation of the largest publicly available and harmonized RNA-sequencing osteosarcoma dataset (Chapter 2). A continuous part of my research involved the systematic democratization, aggregation, harmonization, and open sharing of pediatric cancer transcriptomic datasets within the Treehouse Childhood Cancer Initiative (Beale et al., 2025). This dataset provided a foundation for the analyses and discoveries presented in this dissertation. I utilize the multi-cohort and transcriptomic multi-omic public OS dataset to discover and define biologically meaningful subtypes that may explain differences in progression and treatment response (Chapter 3). Finally, I expand these advanced computational approaches into the realm of diagnostic pathology by evaluating strategies for integrating generative AI into rare cancer classification. I leverage both general and domain-specific diffusion models alongside GPT-4o–generated pathology prompts to guide histologic image synthesis (Chapter 4). In summary, my work advances transcriptional subtyping in OS by leveraging transcriptomic data to identify molecular subtypes of OS that could inform treatment strategies.
Host: Krizia Chambers, Ph.D. Candidate, Biomolecular Engineering & Bioinformatics
Advisor: Olena Vaske
Zoom- https://ucsc.zoom.us/j/93569812001?pwd=RWBuZUdQq2Yo1K4kQ75WRmP0uKjYAH.1&jst=3
Passcode- 915392
