Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment

Rojas, Verónica; Hirshfield, Kim M.; Ganesan, Shridar; Rodrı́guez-Rodrı́guez, Lorna

doi:10.3390/ijms17122113

Cited by 185 publications

(186 citation statements)

References 125 publications

(231 reference statements)

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“…3,4 In up to 60% of cases with sporadic high-grade serous carcinoma of ovary, serous tubal intraepithelial carcinomas were found. 5 Extensive evaluation protocols of fallopian tubes have shown the fimbriated end to be the most common site of serous tubal intraepithelial carcinoma.…”

Section: Discussionmentioning

confidence: 99%

Unilateral minimal ovarian cancer with peritoneal implant and an intraepithelial carcinoma in the contralateral fallopian tube

Terada

Honda

Nakagawa

et al. 2017

Int J Reprod Contracept Obstet Gynecol

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Here we present postoperative pathology of an 82-year-old woman who presented with massive ascites, and an implant-like adenocarcinoma on her intrapelvic peritoneum, which revealed a minimal (<5mm) serous adenocarcinoma on her left ovary and an intraepithelial carcinoma on inner surface of her right Fallopian tube. The left ovarian serous adenocarcinoma may have originated as an intraepithelial carcinoma on contralateral Fallopian tube.

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Section: Discussionmentioning

confidence: 99%

Unilateral minimal ovarian cancer with peritoneal implant and an intraepithelial carcinoma in the contralateral fallopian tube

Terada

Honda

Nakagawa

et al. 2017

Int J Reprod Contracept Obstet Gynecol

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“…High‐ and low‐grade serous carcinomas originate in the fimbriated end of the fallopian tube and, subsequently, involve the ovary. However, these tumors show a very different genetic landscape and behavior: high‐grade serous ovarian carcinomas generally bear TP53 mutations and BRCA1/2 epi‐mutations and are chemosensitive, while low‐grade serous ovarian carcinomas generally bear Ki‐RAS and B‐RAF mutations and are chemoresistant …”

Section: The Prognostic Impact Of Stroma In Ovarian Cancermentioning

confidence: 99%

The metabolic cross‐talk between epithelial cancer cells and stromal fibroblasts in ovarian cancer progression: Autophagy plays a role

Thuwajit

Ferraresi

Titone

et al. 2017

Medicinal Research Reviews

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Cancer and stromal cells, which include (cancer‐associated) fibroblasts, adipocytes, and immune cells, constitute a mixed cellular ecosystem that dynamically influences the behavior of each component, creating conditions that ultimately favor the emergence of malignant clones. Ovarian cancer cells release cytokines that recruit and activate stromal fibroblasts and immune cells, so perpetuating a state of inflammation in the stroma that hampers the immune response and facilitates cancer survival and propagation. Further, the stroma vasculature impacts the metabolism of the cells by providing or limiting the availability of oxygen and nutrients. Autophagy, a lysosomal catabolic process with homeostatic and prosurvival functions, influences the behavior of cancer cells, affecting a variety of processes such as the survival in metabolic harsh conditions, the invasive growth, the development of immune and chemo resistance, the maintenance of stem‐like properties, and dormancy. Further, autophagy is involved in the secretion and the signaling of promigratory cytokines. Cancer‐associated fibroblasts can influence the actual level of autophagy in ovarian cancer cells through the secretion of pro‐inflammatory cytokines and the release of autophagy‐derived metabolites and substrates. Interrupting the metabolic cross‐talk between cancer cells and cancer‐associated fibroblasts could be an effective therapeutic strategy to arrest the progression and prevent the relapse of ovarian cancer.

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“…Novel classification guidelines and molecular profiling have been developed recently. However, clinical routine typically relies on histology and extensive immunohistochemistry of tumor biopsies.…”

Section: Introductionmentioning

confidence: 99%

MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

Klein

Kanter

Kulbe

et al. 2018

Proteomics Clinical Apps

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Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin‐fixed‐paraffin‐embedded tissue of 20 patients with ovarian clear‐cell, 14 low‐grade serous, 19 high‐grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI‐Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM‐lin) and radial basis function kernels (SVM‐rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI‐Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM‐lin, 74% SVM‐rbf, 83% NN, and 85% CNN. Based on sensitivity (69–100%) and specificity (90–99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI‐Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

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Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment

Cited by 185 publications

References 125 publications

Unilateral minimal ovarian cancer with peritoneal implant and an intraepithelial carcinoma in the contralateral fallopian tube

Unilateral minimal ovarian cancer with peritoneal implant and an intraepithelial carcinoma in the contralateral fallopian tube

The metabolic cross‐talk between epithelial cancer cells and stromal fibroblasts in ovarian cancer progression: Autophagy plays a role

MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

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