UCSF

Intra-tumor heterogeneity is a significant barrier to successful cancer therapy. Tumor subpopulations have distinct transcriptional drivers. In this study, I tested whether targeting state-specific transcription drivers could be a strategy to improve therapy. Like many other cancers, Osteosarcoma (OS) is highly heterogeneous and has proven difficult to treat with targeted therapies. Recent single-cell studies have explored the heterogeneity of this disease and found that metastatic tumors contain a transcriptomic state enriched for the TNF-α, PI3K, TGFß and mTOR pathways. I identified similar transcriptomic states in OS PDX-derived cell lines. I then used several computational methods to identify critical drivers of these cellular states. Perturb-seq was then used to knock down expression of these putative drivers and identify which genes altered the transcriptomic state towards a more computational population. I found that knockdown of NFE2L3 increased the proportion of cells sensitive to targeted therapy. This approach, which can be used as a target discovery pipeline in other cancers, could be used to increase the sensitivity of tumors to established or novel cancer therapies.