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

Bosquet, Jesús González; Cardillo, Nicholas; Pineda, Henry Reyes; Smith, Brian J.; Leslie, Kimberly K.; Bender, David; Goodheart, Michael J.; Devor, Eric J.

doi:10.3390/ijms232314814

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…Furthermore, we describe a multivariate lasso regression model that distinguishes HGSC and EEC with the determination of SNVs in a single gene with high performance (measured in AUC). Previous work from our laboratory has confirmed that high-performance models for detection of HGSC are possible with SNV analysis [13]. However, the overarching goal of this study was not to detect HGSC and EEC SNVs but to determine the association between this genomic variation and changes in microbial communities in neoplastic tissues.…”

Section: Discussionmentioning

confidence: 92%

“…SNVs are some of the most common sources of genomic variation in cancer. SNVs can also be distinctive of each cancer, so much so that patterns of SNVs could be used to create models that would discriminate cancerous tissue from benign tissue [ 13 ]. In the present study, we characterized patterns of somatic SNVs, detected with the best recommended practices of genome sequencing [ 35 ], from HGSC and EEC.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, all cancers harbor a myriad of genomic variations, either in the form of single nucleotide variation (SNV), copy number variation (CNV) or structural variation. The pattern of these variations are characteristics of each neoplasia [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation

Bosquet

McDonald

Bender

et al. 2023

Cancers

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There are strong correlations between the microbiome and human disease, including cancer. However, very little is known about potential mechanisms associated with malignant transformation in microbiome-associated gynecological cancer, except for HPV-induced cervical cancer. Our hypothesis is that differences in bacterial communities in upper genital tract epithelium may lead to selection of specific genomic variation at the cellular level of these tissues that may predispose to their malignant transformation. We first assessed differences in the taxonomic composition of microbial communities and genomic variation between gynecologic cancers and normal samples. Then, we performed a correlation analysis to assess whether differences in microbial communities selected for specific single nucleotide variation (SNV) between normal and gynecological cancers. We validated these results in independent datasets. This is a retrospective nested case-control study that used clinical and genomic information to perform all analyses. Our present study confirms a changing landscape in microbial communities as we progress into the upper genital tract, with more diversity in lower levels of the tract. Some of the different genomic variations between cancer and controls strongly correlated with the changing microbial communities. Pathway analyses including these correlated genes may help understand the basis for how changing bacterial landscapes may lead to these cancers. However, one of the most important implications of our findings is the possibility of cancer prevention in women at risk by detecting altered bacterial communities in the upper genital tract epithelium.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation

Bosquet

McDonald

Bender

et al. 2023

Cancers

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“…In the work by Erickson et al, 28 p53 mutations were determined in blood isolated from tampons of patients with or without high-grade serous carcinoma, and although all eight high-grade serous carcinoma tumors harbored a p53 mutation in the tumor, only three of the eight blood samples from tampons showed the mutation. In the Gonzalez-Bosquet et al report, 29 a model of 49 single nucleotide variants had excellent performance with an AUC of 1.0 in distinguishing high-grade serous carcinoma from benign fallopian tube; models with 11 copy number variants (AUC 0.87) and 17 structural variants (AUC 0.73) performed more poorly. In the study by Vanderstichele et al, 30 chromosomal copy number from cell-free DNA was examined in a total of 112 patients (44 healthy control group, 57 high-grade serous carcinomas or borderline, 11 benign), and they reported a specificity of 99.6% and sensitivity of 78% when benign tumors were compared with high-grade serous carcinoma tumors.…”

Section: Resultsmentioning

confidence: 98%

Early Ovarian Cancer Detection in the Age of Fallopian Tube Precursors

Greenwood,

Woodruff,

Nguyen

et al. 2024

Obstetrics &Amp; Gynecology

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OBJECTIVE: To determine biomarkers other than CA 125 that could be used in identifying early-stage ovarian cancer. DATA SOURCES: Ovid MEDLINE ALL, EMBASE, Web of Science Core Collection, ScienceDirect, Clinicaltrials.gov, and CAB Direct were searched for English-language studies between January 2008 and April 2023 for the concepts of high-grade serous ovarian cancer, testing, and prevention or early diagnosis. METHODS OF STUDY SELECTION: The 5,523 related articles were uploaded to Covidence. Screening by two independent reviewers of the article abstracts led to the identification of 245 peer-reviewed primary research articles for full-text review. Full-text review by those reviewers led to the identification of 131 peer-reviewed primary research articles used for this review. TABULATION, INTEGRATION, AND RESULTS Of 131 studies, only 55 reported sensitivity, specificity, or area under the curve (AUC), with 36 of the studies reporting at least one biomarker with a specificity of 80% or greater specificity or 0.9 or greater AUC. CONCLUSION: These findings suggest that although many types of biomarkers are being tested in ovarian cancer, most have similar or worse detection rates compared with CA 125 and have the same limitations of poor detection rates in early-stage disease. However, 27.5% of articles (36/131) reported biomarkers with better sensitivity and an AUC greater than 0.9 compared with CA 125 alone and deserve further exploration.

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Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction

Gandotra,

Kumar,

Modi

et al. 2024

Artif Intell Rev

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Gynaecological cancers encompass a spectrum of malignancies affecting the female reproductive system, comprising the cervix, uterus, ovaries, vulva, vagina, and fallopian tubes. The significant health threat posed by these cancers worldwide highlight the crucial need for techniques for early detection and prediction of gynaecological cancers. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published from 2013 up to 2023 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and encompass AI technique for the early detection and prediction of gynaecological cancers. Based on the study of different articles on gynaecological cancer, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. This work highlights the impact of gynaecological cancer on women belonging to different age groups and regions of the world. A detailed categorization of the traditional techniques like physical-radiological, bio-physical and bio-chemical used to detect gynaecological cancer by health organizations is also presented in the study. Besides, this work also explores the methodology used by different researchers in which AI plays a crucial role in identifying cancer symptoms at earlier stages. The paper also investigates the pivotal study years, highlighting the periods when the highest number of research articles on gynaecological cancer are published. The challenges faced by researchers while performing AI-based research on gynaecological cancers are also highlighted in this work. The features and representations such as Magnetic Resonance Imaging (MRI), ultrasound, pap smear, pathological, etc., which proficient the AI algorithms in early detection of gynaecological cancer are also explored. This comprehensive review contributes to the understanding of the role of AI in improving the detection and prognosis of gynaecological cancers, and provides insights for future research directions and clinical applications. AI has the potential to substantially reduce mortality rates linked to gynaecological cancer in the future by enabling earlier identification, individualised risk assessment, and improved treatment techniques. This would ultimately improve patient outcomes and raise the standard of healthcare for all individuals.

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

Cited by 3 publications

References 43 publications

Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation

Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation

Early Ovarian Cancer Detection in the Age of Fallopian Tube Precursors

Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction

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