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Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic...
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Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic Cancers: Utility and Limitations

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: closed (22 July 2022) | Viewed by 23871

Special Issue Editors

Dr. Mohamed Desouki

E-Mail Website
Guest Editor
Roswell Park Comprehensive Cancer Center, Department of Pathology, Buffalo, NY 14203, USA
Interests: endometrial cancer; ovarian cancer; breast cancer; pathology
Special Issues, Collections and Topics in MDPI journals
Dr. Oluwole Fadare

E-Mail Website
Guest Editor
Department of Pathology, Anatomic Pathology Division, University of California San Diego Health, 9300 Campus Point Drive, Suite 1-200, MC 7723, La Jolla, CA 92037, USA
Interests: gynaecologic malignancies; ovarian; endometrial; pathology; molecular testing

Special Issue Information

Dear colleagues,

The tremendous advances in the field of molecular testing, especially in archival material, have affected the field of gynecologic (GYN) malignancies. This has a great extent the understanding of tumor biology and determined clinical behavior in most instances. The proposed aim of this Special Issue is to cover the most recent advances in molecular testing of GYN malignancies, especially small-cell and conventional carcinomas. A special emphasis will be placed on newly characterized molecular entities such as SMARCA4-deficient undifferentiated carcinoma, NTRK, RET, and COL1A1-PDGFB rearranged gyn malignancies. In addition to the newly described entities, newly established testing such as FOXL2 and DICER1 mutational analysis on ovarian sex cord stromal tumors will be also included. Genotyping of gestational trophoblastic diseases has become an integral part of testing to identify the origin of a particular gestational trophoblastic disease that will assist in rendering the correct diagnosis and predicting the clinical outcome of this heterogeneous group of diseases.

Topics:

  1. Updates on FOXL2 testing on ovarian sex cord stromal tumors;
  2. Updates on DICER1 testing on ovarian sex cord stromal tumors;
  3. Updates on genotyping of gestational trophoblastic diseases;
  4. SMARCA4-deficient undifferentiated carcinoma of the female genital system;
  5. Updates on molecular alterations on ovarian small cell carcinoma of the hypercalcemic type;
  6. Utilization of molecular subclassification in endometrial cancers in clinical practice;
  7. Mismatch repair proteins and microsatellite instability testing in gynecologic malignancies;
  8. NTRK, RET, and COL1A1-PDGFB rearranged gyn sarcomas;
  9. P53 testing in gynaecologic malignancies.

Dr. Mohamed Desouki
Dr. Oluwole Fadare
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • FOXL2
  • DICER1
  • SMARCA4
  • ovarian small cell carcinoma
  • NTRK
  • RET and COL1A1-PDGFB
  • gynaecologic malignancies

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Published Papers (4 papers)

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Research

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17 pages, 2064 KiB  
Article
Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer
by Nicholas Cardillo, Eric J. Devor, Silvana Pedra Nobre, Andreea Newtson, Kimberly Leslie, David P. Bender, Brian J. Smith, Michael J. Goodheart and Jesus Gonzalez-Bosquet
Cancers 2022, 14(14), 3554; https://doi.org/10.3390/cancers14143554 - 21 Jul 2022
Cited by 4 | Viewed by 2186
Abstract
Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an [...] Read more.
Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes. Full article
(This article belongs to the Special Issue Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic Cancers: Utility and Limitations)
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7 pages, 1091 KiB  
Article
The Clinical Utility and Impact of Next Generation Sequencing in Gynecologic Cancers
by Vijaya Kadam Maruthi, Mahyar Khazaeli, Devi Jeyachandran and Mohamed Mokhtar Desouki
Cancers 2022, 14(5), 1352; https://doi.org/10.3390/cancers14051352 - 7 Mar 2022
Cited by 5 | Viewed by 2533
Abstract
Next generation sequencing (NGS) has facilitated the identification of molecularly targeted therapies. However, clinical utility is an emerging challenge. Our objective was to identify the clinical utility of NGS testing in gynecologic cancers. A retrospective review of clinico-pathologic data was performed on 299 [...] Read more.
Next generation sequencing (NGS) has facilitated the identification of molecularly targeted therapies. However, clinical utility is an emerging challenge. Our objective was to identify the clinical utility of NGS testing in gynecologic cancers. A retrospective review of clinico-pathologic data was performed on 299 gynecological cancers where NGS testing had been performed to identify (1) recognition of actionable targets for therapy, (2) whether the therapy changed based on the findings, and (3) the impact on survival. High grade serous carcinoma was the most common tumor (52.5%). The number of genetic alterations ranged from 0 to 25 with a mean of 2.8/case. The most altered genes were TP53, PIK3CA, BRCA1 and BRCA2. Among 299 patients, 100 had actionable alterations (79 received a targeted treatment (Group1), 29 did not receive treatment (Group 2), and there were no actionable alterations in 199 (Group3). The death rate in groups 1, 2 and 3 was 54.4%, 42.8% and 50.2%, with an average survival of 18.6, 6.6 and 10.8 months, respectively (p = 0.002). In summary, NGS testing for gynecologic cancers detected 33.4% of actionable alterations with a high clinical action rate. Along with the high clinical utility of NGS, testing also seemed to improve survival for patients who received targeted treatment. Full article
(This article belongs to the Special Issue Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic Cancers: Utility and Limitations)
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13 pages, 1062 KiB  
Article
Familial Occurrence of Adult Granulosa Cell Tumors: Analysis of Whole-Genome Germline Variants
by Joline F. Roze, Joachim Kutzera, Wouter Koole, Margreet G. E. M. Ausems, Kristi Engelstad, Jurgen M. J. Piek, Cor D. de Kroon, René H. M. Verheijen, Gijs van Haaften, Ronald P. Zweemer and Glen R. Monroe
Cancers 2021, 13(10), 2430; https://doi.org/10.3390/cancers13102430 - 18 May 2021
Cited by 3 | Viewed by 4029
Abstract
Adult granulosa cell tumor (AGCT) is a rare ovarian cancer subtype, with a peak incidence around 50–55 years. Although AGCT can occur in specific syndromes, a genetic predisposition for AGCT has not been identified. The aim of this study is to identify a [...] Read more.
Adult granulosa cell tumor (AGCT) is a rare ovarian cancer subtype, with a peak incidence around 50–55 years. Although AGCT can occur in specific syndromes, a genetic predisposition for AGCT has not been identified. The aim of this study is to identify a genetic variant in families with AGCT patients, potentially contributing to tumor evolution. We identified four families, each including two women diagnosed with AGCT. Whole-genome sequencing was performed to identify overlapping germline variants or affected genes. Familial relationship was evaluated using genealogy and genomic analyses. Patient characteristics, medical (family) history, and pedigrees were collected. Findings were compared to a reference group of 33 unrelated AGCT patients. Mean age at diagnosis was 38 years (range from 17 to 60) versus 51 years in the reference group, and seven of eight patients were premenopausal. In two families, three first degree relatives were diagnosed with breast cancer. Furthermore, polycystic ovary syndrome (PCOS) and subfertility was reported in three families. Predicted deleterious variants in PIK3C2G, BMP5, and LRP2 were identified. In conclusion, AGCTs occur in families and could potentially be hereditary. In these families, the age of AGCT diagnosis is lower and cases of breast cancer, PCOS, and subfertility are present. We could not identify an overlapping genetic variant or affected locus that may explain a genetic predisposition for AGCT. Full article
(This article belongs to the Special Issue Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic Cancers: Utility and Limitations)
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Review

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16 pages, 1073 KiB  
Review
The Evolution of Ovarian Carcinoma Subclassification
by Martin Köbel and Eun Young Kang
Cancers 2022, 14(2), 416; https://doi.org/10.3390/cancers14020416 - 14 Jan 2022
Cited by 49 | Viewed by 14293
Abstract
The phenotypically informed histotype classification remains the mainstay of ovarian carcinoma subclassification. Histotypes of ovarian epithelial neoplasms have evolved with each edition of the WHO Classification of Female Genital Tumours. The current fifth edition (2020) lists five principal histotypes: high-grade serous carcinoma (HGSC), [...] Read more.
The phenotypically informed histotype classification remains the mainstay of ovarian carcinoma subclassification. Histotypes of ovarian epithelial neoplasms have evolved with each edition of the WHO Classification of Female Genital Tumours. The current fifth edition (2020) lists five principal histotypes: high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC) and clear cell carcinoma (CCC). Since histotypes arise from different cells of origin, cell lineage-specific diagnostic immunohistochemical markers and histotype-specific oncogenic alterations can confirm the morphological diagnosis. A four-marker immunohistochemical panel (WT1/p53/napsin A/PR) can distinguish the five principal histotypes with high accuracy, and additional immunohistochemical markers can be used depending on the diagnostic considerations. Histotypes are further stratified into molecular subtypes and assessed with predictive biomarker tests. HGSCs have recently been subclassified based on mechanisms of chromosomal instability, mRNA expression profiles or individual candidate biomarkers. ECs are composed of the same molecular subtypes (POLE-mutated/mismatch repair-deficient/no specific molecular profile/p53-abnormal) with the same prognostic stratification as their endometrial counterparts. Although methylation analyses and gene expression and sequencing showed at least two clusters, the molecular subtypes of CCCs remain largely elusive to date. Mutational and immunohistochemical data on LGSC have suggested five molecular subtypes with prognostic differences. While our understanding of the molecular composition of ovarian carcinomas has significantly advanced and continues to evolve, the need for treatment options suitable for these alterations is becoming more obvious. Further preclinical studies using histotype-defined and molecular subtype-characterized model systems are needed to expand the therapeutic spectrum for women diagnosed with ovarian carcinomas. Full article
(This article belongs to the Special Issue Molecular Testing for Pathologic Diagnosis, Prediction and Prognostication in Gynecologic Cancers: Utility and Limitations)
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