Bottom-up Pediatric Sarcoma Modeling Using Genetic Engineering and Induced Pluripotent Stem Cell Technologies
2022-12
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Bottom-up Pediatric Sarcoma Modeling Using Genetic Engineering and Induced Pluripotent Stem Cell Technologies
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2022-12
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From the onset, induced pluripotent stem cell (iPSC) technology radically changed how we study human disease and it continues to improve our understanding of disease ontology. Continual advances in cell culture techniques, genome engineering, and the -omics fields have expanded the use of iPSCs into our daily disease modeling toolkit. Using iPSCs to model cancer is not especially new; in fact, the first iPSC-derived from cancer cells was around 2011. But, the dedifferentiation process is largely inhibited in solid tumor cells and these cancer-derived iPSCs only capture a single cancer cell genome, losing all heterogeneity. Sarcomas are rare, heterogenous tumors arising from the mesenchymal lineage. Many sarcomas, such as Osteosarcoma (OSA) and Ewing sarcoma (ES), have had limited therapeutic advancements since the advent of chemotherapy. To improve therapeutic outcomes for these patients it is becoming clear that we need to identify tumor-promoting molecular profiles and gain a better understanding of tumor evolution. To do this, we implemented the use of genetic engineering strategies in karyotypically normal iPSC to generate bottom-up models of OSA and ES. OSA is the most common pediatric cancer of the bone and is characterized by a complex genome, but few bona fide OSA-dependent mutations have been identified. To make our iPSC-derived iOSA model, we installed OSA-associated mutations in TP53 and RB1 using the CRISPR/Cas9 system to generate knockout iPSC. After differentiation into mesenchymal stromal cells (iMSC) and osteoblasts (iOB) we then used retrovirus to overexpress constitutively active cMYCT58A and/or hRASG12V. The mutated iMSC and iOB cells had differential proliferation rates, colony forming ability, and tumor formation potential in immunodeficient mice, as well as evidence of large karyotype level mutations similar to those seen in human OSA genomics. Additionally, tumors from the iOSA model had RNA-seq profiles resembling primary OSA. This model demonstrates that using iPSCs for cancer modeling in genomically complex cancers is possible and can illuminate how these cancers initiate and evolve. In addition to OSA, I used iPSCs to model ES, a translocation driven cancer, with a 9-fold higher incidence rate in children of European (EUR) ancestry compared to African (AFR). Using iPSC-derived from individuals spanning the polymorphic spectrum of ES diagnosis, I initiated expression of the ES driving alteration, a EWSR1-ETS translocation. Specifically, I used lentivirus to express the EWSR1-FLI1 fusion protein (EWS/FLI) in iPSC-derived neural crest cells (iNCC). Cells of increasing AFR ancestry had lower tolerance to EWS/FLI expression, a result in line with the aforementioned differences in incidence of ES seen in EUR and AFR children. To investigate the molecular basis of this observation, we used RNA-seq and CUT&TAG to determine the gene expression and global occupancy differentials across ancestries driven by EWS/FLI. Genetic loci that were both differentially expressed and bound were nominated as our ancestry-linked differential Ewing sarcoma response (ALDER) loci. To this end we have identified 80 ALDER loci containing established and novel EWS/FLI target genes for further analysis. This study demonstrates the feasibility and utility of ancestry-informed iPSC modeling to identify novel and potentially targetable pathways to treat ES. In this work we applied genetic engineering tools in iPSC to generate novel models of the gnomically complex OSA, and the gnomically quiet, translocation driven ES. Collectively, the models described here provide a baseline system to study how OSA and ES initiate and the early stages of cancer development.
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University of Minnesota Ph.D. dissertation. December 2022. Major: Comparative and Molecular Biosciences. Advisors: Branden Moriarity, Beau Webber. 1 computer file (PDF); xix, 174 pages.
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Becklin, Kelsie. (2022). Bottom-up Pediatric Sarcoma Modeling Using Genetic Engineering and Induced Pluripotent Stem Cell Technologies. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/252557.
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