NEW YORK (GenomeWeb) – A team led by researchers at Johns Hopkins University analyzed whole-genome sequence data, DNA methylation profiles, drug sensitivity information, and gene expression information for dozens of cell lines generated from serous, clear cell, mucinous, undifferentiated, endometrioid, mixed, or unclassified forms of ovarian cancer, finding that sensitivity to certain targeted inhibitors may be informed by genome-wide rearrangement patterns in the tumors.
"Our analyses identified molecular alterations not previously reported in ovarian cancer, delineated genes modulated by genetic and epigenetic changes, and highlighted specific sequence, structural, and epigenetic alterations associated with sensitivity and resistance to common pathway inhibitors," co-corresponding authors Robert Scharpf and Victor Velculescu, researchers with the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, and their colleagues wrote in a paper published online today in Cell Reports.
In particular, the researchers observed that cell lines marked by PIK3CA or PPP2R1A mutations appeared more prone to respond to the PI3-kinase inhibitor GNE-493, and that those with amplifications involving the MYC gene or widespread rearrangements showed increased sensitivity to the BMN673 PARP inhibitor. They also noted that sensitivity to the MEK inhibitor MEK162 seemed to increase in ovarian cancer lines with SMAD3 or SMAD4 mutations or deletions.
The researchers' analyses relied on Illumina HiSeq2000 whole-genome sequence data for 45 ovarian cancer cell lines sequenced to an average depth of 32-fold, identifying somatic and germline mutations, copy number changes, and rearrangements through comparisons to sequences from unmatched normal blood samples or lymphoblastoid cell lines generated for individuals from a range of ethnicities.
The team also did targeted high-coverage sequencing, array-based DNA methylation profiling, and an in vitro assay aimed at gauging response to PI3K, PARP, and MEK inhibitors applied at different concentration for one week. It further incorporated array-based gene expression profiles to search for potential ties between specific driver gene alterations, tumor subtypes, and/or drug response profiles.
Among the nearly 41,800 rare germline or somatic mutations detected, the researchers narrowed in on 659 suspected somatic driver mutations, including recurrent mutations affecting genes such as TP53, ARID1A, and PIK3CA.
The data made it possible to pick up mutation signatures associated with ovarian cancer subtypes or age at diagnosis, along with suspected driver genes. The team also identified structural variants and fusions that appeared to influence ovarian cancer cell line responses to the PI3K, PARP, and MEK inhibitors it considered.
The researchers suggested that the current findings can serve as a resource for future studies of ovarian cancer, though they noted that the results will likely be enhanced with additional types of clinical and genomic data.
"In the future, proteomic, metabolomic, and carbohydrate changes can be added to the compendium of genomic, epigenomics, and transcriptomic information for these cell lines," the authors concluded. "Additionally, further efforts will be needed to demonstrate that these observations can be translated broadly to ovarian cancer patients."