Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

• Published in: International journal of molecular sciences Vol. 23; no. 23; p. 14814
• Main Authors: Gonzalez-Bosquet, Jesus, Cardillo, Nicholas D, Reyes, Henry D, Smith, Brian J, Leslie, Kimberly K, Bender, David P, Goodheart, Michael J, Devor, Eric J
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.11.2022; MDPI
• Subjects: Algorithms; Benign; Copy number; Cystadenocarcinoma, Serous - diagnosis; Cystadenocarcinoma, Serous - genetics; Cystadenocarcinoma, Serous - pathology; Datasets; Deoxyribonucleic acid; DNA; Fallopian Tube Neoplasms - pathology; Fallopian tubes; Fallopian Tubes - pathology; Female; genetic variation; Genome; Genomics; Humans; Machine learning; Metastasis; Multivariate analysis; Mutation; Nucleotides; Ovarian cancer; Ovarian Neoplasms - diagnosis; Ovarian Neoplasms - genetics; Ovarian Neoplasms - pathology; Pilot Projects; prediction model; Prediction models; Retrospective Studies; RNA sequencing; Tumors; Variation; whole exome sequencing; RNA sequencing; genetic variation; prediction model; ovarian cancer; whole exome sequencing
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms232314814

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.