Specific Mechanisms of Chromosomal Instability Indicate Therapeutic Sensitivities in High-Grade Serous Ovarian Carcinoma
Abstract
Chromosomal instability (CIN) comprises continual gain and loss of chromosomes or parts of chromosomes and occurs in the majority of cancers, often conferring poor prognosis. Because of a scarcity of functional studies and poor understanding of how genetic or gene expression landscapes connect to specific CIN mechanisms, causes of CIN in most cancer types remain unknown. High-grade serous ovarian carcinoma (HGSC), the most common subtype of ovarian cancer, is the major cause of death due to gynecologic malignancy in the Western world, with chemotherapy resistance developing in almost all patients. HGSC exhibits high rates of chromosomal aberrations and knowledge of causative mechanisms would represent an important step toward combating this disease. Here we perform the first in-depth functional characterization of mechanisms driving CIN in HGSC in seven cell lines that accurately recapitulate HGSC genetics. Multiple mechanisms coexisted to drive CIN in HGSC, including elevated microtubule dynamics and DNA replication stress that can be partially rescued to reduce CIN by low doses of paclitaxel and nucleoside supplementation, respectively. Distinct CIN mechanisms indicated relationships with HGSC-relevant therapy including PARP inhibition and microtubule-targeting agents. Comprehensive genomic and transcriptomic profiling revealed deregulation of various genes involved in genome stability but were not directly predictive of specific CIN mechanisms, underscoring the importance of functional characterization to identify causes of CIN. Overall, we show that HGSC CIN is complex and suggest that specific CIN mechanisms could be used as functional biomarkers to indicate appropriate therapy.
Significance:
These findings characterize multiple deregulated mechanisms of genome stability that lead to CIN in ovarian cancer and demonstrate the benefit of integrating analysis of said mechanisms into predictions of therapy response.
