Phased Genomics for Improved Noninvasive Diagnosis and Risk Stratification of Kidney Cancers Based on Genomic Instability and Associated CpG Hypermethylation
Abstract
Background: Cancer results from the clonal expansion of genetically aberrant cells. The genetic aberrations range from mutations of a single base in the genome to large-scale abnormalities such as gains and losses of whole chromosomes (aneuploidy). Based on cytogenetic methods, it was established early on that aneuploidy is an almost universal hallmark of renal cell carcinoma (RCC). In addition, it has been found that distinct histological RCC subtypes, such as clear cell RCC (ccRCC) or papillary RCC (pRCC), harbor highly specific cytogenetic aberrations. These findings gave rise to a comprehensive oncogenetic model of RCC. However, not all genetic defects can be easily detected using conventional cytogenetic methods such as karyotyping. In particular, abnormalities involving the gain, loss, or rearrangement of genetic material within segments of a single chromosome, commonly referred to as structural and copy-number variants (CNVs) remained notoriously difficult to study before the advent of next-generation sequencing (NGS). Conversely, the unprecedented fidelity of sequencing-based techniques and increasingly powerful algorithms enable us to detect and quantify these events even in heterogeneous tissues and low-purity specimens. Most recently, careful examination of such genomic sequencing data demonstrated that an increase in genomic instability is a common characteristic of cancer progression. Our own preliminary data strongly suggests that ccRCC patients with highly aneuploid and genetically unstable tumors are significantly more likely to relapse and metastasize. Objective: Therefore, the overarching goal of our translational project is to translate these methodological advances and genomic findings into clinical tools for the early detection and risk assessment (prognostication) of patients with RCC. Toward this goal we propose to first improve our understanding of chromosomal instability in ccRCC, and to develop computational approaches to accurately detect and quantify it. Next, we will determine whether the degree of chromosomal instability is associated with adverse outcomes. Finally, we will develop and validate methods to detect these high-risk genomically unstable tumors from minimally invasive liquid biopsies. KCRP FY20 Areas of Emphasis (AE): This proposal is responsive to the Early Detection Studies option and addresses several AEs. Our clinical goal is to improve early detection of high-risk kidney cancer from tissue biopsies and blood, which overlaps the following AEs: Prognosis, Biomarker Development, Liquid Biopsy, and Early Detection. Our translational goal is to enhance the understanding of RCC chromosomal instability, which aligns with the AE Genetics. Impact: While many RCCs are detected incidentally and most of them are cured through surgery, 25% of patients are diagnosed with evidence of metastatic disease, and 30% of ccRCCs will recur. These patients represent a high-risk population that is most likely to benefit from earlier detection and adjuvant therapy. At present, there are no diagnostic or prognostic tests that would select this high-risk patient population. Our goal is to change clinical practice by providing clinicians with tools to detect and stratify aggressive kidney cancers as early as possible. Innovation: Our research is innovative both at the scientific and methodological level. While recurrent mutations and aneuploidy have been widely recognized as hallmarks of RCC, the role of genomic instability in RCC progression remains elusive. We have developed innovative approaches to detect chromosomal abnormalities and estimate whether individual genetic lesions are present in all or only a subset of cells. This approach uniquely enables us to test the association between ploidy, chromosomal instability, and clinical outcomes. The identification of a high-risk stratum of ccRCC will open up research avenues into their optimal clinical managemen
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110824
Entities
People
- Marcin Cieslik
Organizations
- United States Army
- University of Michigan