Date of Award
Doctor of Philosophy (PhD)
Cancer is a highly complex disease with aberrations at the genetic, epigenetic, transcriptomic, and protein levels that drove its phenotypic diversity. Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, comprising roughly 80% of cases. To define the epigenetic and transcriptomic regulation of ccRCC at the single nucleus (sn) level, we performed snRNA-seq and snATAC-seq in 34 and 28 samples respectively, including primary tumors and normal adjacent tissues, and matched them with bulk proteogenomics data. We identified tumor-specific markers and tumor subpopulations using snRNA-seq, which demonstrated diverse pathway activity within and across patients. PBRM1 and BAP1 are two of the most frequently mutated genes in ccRCC, and both encode epigenetic regulators. However, the consequences of BAP1 and PBRM1 mutations on chromatin accessibility and downstream transcriptional networks remain largely unknown. Utilizing the combined analysis of snATAC-seq and snRNA-seq, we dissected chromatin accessibility and transcriptome changes associated with BAP1 and PBRM1 mutations, illuminating molecular alterations underlying differential phenotypes between BAP1- and PBRM1-mutant patients.
For the treatment of RCC, patients with metastatic or inoperable tumors typically receive systemic treatment with targeted therapy and/or immunotherapy. Although these drugs have been proven effective to some extent, resistance eventually develops, and combinational therapy will be necessary to overcome such resistance. Patient-derived xenograft (PDX) models have proven valuable in studying treatment mechanisms and novel therapeutics for cancer, including renal cell carcinoma. Hence, we performed a series of drug tests on a set of RCC PDX models, in which cabozantinib and sapanisertib are the two most effective drugs, and found the combination of two drugs is effective for all six models. We collected PDX tumors at baseline and under treatments and performed bulk whole-exome sequencing, bulk RNA-seq, bulk proteomics and phosphoproteomics, and snRNA-seq. We revealed the pathways affected by the combination therapy and identified treatment-affected proteins that are associated with patient survival. We also identified baseline protein markers that may serve to predict treatment response, such as MET, with support from snRNA-seq data. This study proposed a potential new combination for RCC patients and revealed potential molecular alterations underlying tumor reduction induced by the combination treatment.
Chair and Committee
Table S2 Marker genes used for cell-type annotation.xlsx (23 kB)
Table S3 Tumor_specific_and_Tumor_vs_PT_markers.xlsx (10047 kB)
Table S5 BAP1_PBRM1-associated differentially expressed genes and accessible peaks.xlsx (894 kB)
Table S4 Selected pathways, epithelial, and EMT scores among tumor clusters.xlsx (3657 kB)
Wu, Yige, "Multi-Omics Investigation of Tumor Heterogeneity, Oncogenic Signaling, and Treatment Response in Human Cancers" (2022). Arts & Sciences Electronic Theses and Dissertations. 2730.