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Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes
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 beni...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738935/ https://www.ncbi.nlm.nih.gov/pubmed/36499142 http://dx.doi.org/10.3390/ijms232314814 |
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author | Gonzalez-Bosquet, Jesus Cardillo, Nicholas D. Reyes, Henry D. Smith, Brian J. Leslie, Kimberly K. Bender, David P. Goodheart, Michael J. Devor, Eric J. |
author_facet | Gonzalez-Bosquet, Jesus Cardillo, Nicholas D. Reyes, Henry D. Smith, Brian J. Leslie, Kimberly K. Bender, David P. Goodheart, Michael J. Devor, Eric J. |
author_sort | Gonzalez-Bosquet, Jesus |
collection | PubMed |
description | 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. Methods: 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. Results: 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. Conclusions: 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. |
format | Online Article Text |
id | pubmed-9738935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97389352022-12-11 Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes Gonzalez-Bosquet, Jesus Cardillo, Nicholas D. Reyes, Henry D. Smith, Brian J. Leslie, Kimberly K. Bender, David P. Goodheart, Michael J. Devor, Eric J. Int J Mol Sci Article 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. Methods: 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. Results: 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. Conclusions: 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. MDPI 2022-11-26 /pmc/articles/PMC9738935/ /pubmed/36499142 http://dx.doi.org/10.3390/ijms232314814 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gonzalez-Bosquet, Jesus Cardillo, Nicholas D. Reyes, Henry D. Smith, Brian J. Leslie, Kimberly K. Bender, David P. Goodheart, Michael J. Devor, Eric J. Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title | Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title_full | Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title_fullStr | Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title_full_unstemmed | Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title_short | Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes |
title_sort | using genomic variation to distinguish ovarian high-grade serous carcinoma from benign fallopian tubes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738935/ https://www.ncbi.nlm.nih.gov/pubmed/36499142 http://dx.doi.org/10.3390/ijms232314814 |
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