This study will obtain tumor samples from nephrectomy specimens in a multi-regional fashion and subject them to integrated genomics, proteomics, pathological, and radiological assessment. The goal is to better understand the the molecular basis for how various cells within the tumor microenvironment act in a coordinated manner to facilitate tumor progression and therapy resistance. Our ultimate aim is to leverage this data resource to identify novel therapeutic targets and biomarkers to improve the clinical management of this disease.
This study is an observational prospective trial where renal cell carcinoma samples are isolated from nephrectomy specimens in a multi-regional manner and subjected to an integrated genomic, proteomic, pathological, radiological assessment to provide a detailed morphological and molecular view of the RCC microenvironment. The major genomics technologies used to examine tumour specimens include single cell RNA sequencing including nuclear (NUCseq) and epitope sequencing (CITEseq). Additionally, bulk RNA/DNA sequencing, targeted mutational profiling, and imaging mass cytometry will be performed. These technologies will determine mutational and gene/protein expression profiles of various heterogeneous cell populations within the tumour microenvironment, including cancer, immune, and supporting cells. This will allow identification of mutations and abnormal expression patterns within individual cells, that will be used to infer targetable vulnerabilities and biomarker signatures.
Study Type
OBSERVATIONAL
Enrollment
146
University Health Network, Princess Margaret Hospital
Toronto, Ontario, Canada
Molecular profiling of various cell populations within tumour by using single cell RNAseq.
The RNA transcripts of tumour cells will be sequenced and subjected to informatics and descriptive statistical methods to generate comprehensive data sets that aid in better understanding of the tumour microenvironment in various patients and disease types. These data sets will provide insights into drug target discovery, and used as hypothesis-generating guidance for future study aims.
Time frame: 10 years
Determination of the radiological and pathological features associated with the derived tumor microenvironment molecular data
The radiological and pathological assessment on each tumour case will be paired with the gene- and protein-level data to provide a comprehensive picture of the disease. These datasets will be used as training sets to guide machine learning technologies with the intention of aiding diagnosis, prognosis prediction, and treatment plans in the future.
Time frame: 10 years
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