Diagnosis of Ovarian Carcinoma Histotype Based on Limited Sampling: A Prospective Study Comparing Cytology, Frozen Section, and Core Biopsies to Full Pathologic Examination

Cell-free DNA from ascites identifies clinically relevant variants and tumour evolution in patients with advanced ovarian cancer

The emergence of targeted therapies has transformed ovarian cancer treatment. However, biomarker profiling for precision medicine is limited by access to quality, tumour-enriched tissue samples. The use of cell-free DNA (cfDNA) in ascites presents a potential solution to this challenge. In this study, next-generation sequencing was performed on ascites-derived cfDNA samples (26 samples from 15 human participants with ovarian cancer), with matched DNA from ascites-derived tumour cells (n = 5) and archived formalin-fixed paraffin-embedded (FFPE) tissue (n = 5). Similar tumour purity and variant detection were achieved with cfDNA compared to FFPE and ascites cell DNA. Analysis of large-scale genomic alterations, loss of heterozygosity and tumour mutation burden identified six cases of high genomic instability (including four with pathogenic BRCA1 and BRCA2 mutations). Copy number profiles and subclone prevalence changed between sequential ascites samples, particularly in a case where deletions and chromothripsis in Chr17p13.1 and Chr8q resulted in changes in clinically relevant TP53 and MYC variants over time. Ascites cfDNA identified clinically actionable information, concordant to tissue biopsies, enabling opportunistic molecular profiling. This advocates for analysis of ascites cfDNA in lieu of accessing tumour tissue via biopsy.

High-grade serous ovarian carcinoma with a sertoliform pattern associated with BRCA mutation: a clinicopathological and molecular analysis

Herein, we report a clinicopathological and molecular analysis of a case of tubo-ovarian high-grade serous carcinoma (HGSC) with a sertoliform pattern. A 45-year-old woman underwent surgery due to an advanced bilateral adnexal carcinoma with peritoneal and appendiceal metastases. Histological examination revealed an HGSC exhibiting a distinct sertoliform component. Such component showed diffuse PAX8, p53 (mutation-type), and p16 (block-type) expression, increased vimentin and decreased WT1 expression compared to the conventional HGSC component, membrane β-catenin positivity, heterogeneous estrogen, and progesterone positivity, and retained PTEN and mismatch repair expression and negativity for GATA3, TTF1, inhibin, calretinin, CD10, CDX2, chromogranin, and synaptophysin. Molecular analysis showed a germline BRCA2 mutation; no mutations were detected in POLE, POLD1, MLH1, MSH2, MSH6, PMS2, APC, CTNNB1, MUTYH, and EPCAM. In conclusion, a sertoliform pattern can be part of the morphological spectrum of BRCA-related HGSC.

A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma

OBJECTIVES

To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from non-OCCC and to develop a nomogram to preoperatively predict the probability of OCCC.

METHODS

This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis ≤90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC.

RESULTS

The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)].

CONCLUSIONS

A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach.

Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer

OBJECTIVE

To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.

MATERIAL AND METHODS

We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.

RESULTS

The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000).

CONCLUSION

Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.

Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification

BACKGROUND

Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs).

MATERIALS AND METHODS

A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2-6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review).

RESULTS

Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance.

CONCLUSIONS

The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.

CT texture analysis in histological classification of epithelial ovarian carcinoma

OBJECTIVES

The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC).

METHODS

Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features.

RESULTS

HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464).

CONCLUSION

CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features.

KEY POINTS

• A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

[Borderline ovarian tumours: CNGOF Guidelines for clinical practice - Biopathology of ovarian borderline tumors]

OBJECTIVES

Ovarian borderline tumors (OBT) represent a heterogeneous group of lesions with specific management for each histological subtype. Thus, the correct histological diagnosis is mandatory.

MATERIAL AND METHODS

References were searched by PubMed from January 2000 to January 2018 and original articles in French and English literature were selected.

RESULTS AND CONCLUSIONS

OBT should be classified according to the last WHO classification. Any micro-invasion (foci<5mm) or microcarcinoma (foci<5mm with nuclear atypia and desmoplastic stromal reaction) should be indicated in the pathology report. In case of serous OBT, variants (classical or the micropapillary/cribriform) should be indicated (grade C). The peritoneal implants associated with OBT, should be classified as invasive or noninvasive, according to the extension into the underlying adipous tissue. If no adipous tissue is seen the term undetermined should be used (grade B). In case of mucinous OBT bilateral and/or with peritoneal implants or peritoneal pseudomyxoma a search for primitive gastrointestinal, appendiceal or biliopancreatic tumor should be performed (grade C). In case of OBT, a thorough sampling of the tumor is recommended, with 1 block/cm and 2 blocks/cm in case of mucinous OBT, serous OBT micropapillary variant, OBT with intraepithelial carcinoma or/and micro-invasion. Peritoneal implants should be examined in toto. Omentum without macroscopic lesion should be sampled in 4 to 6 blocks (grade C). In case of ovarian cyst suspicious for OBT, fine needle aspiration is not recommended (grade C). In case of ovarian tumor suspicious for OBT, intraoperative examination should be performed by a gynecological pathologist (grade C).

Ovarian Carcinoma Histotype: Strengths and Limitations of Integrating Morphology With Immunohistochemical Predictions

Ovarian carcinoma histotypes are critical for research and patient management and currently assigned by a combination of histomorphology +/- ancillary immunohistochemistry (IHC). We aimed to validate the previously described IHC algorithm (Calculator of Ovarian carcinoma Subtype/histotype Probability version 3, COSPv3) in an independent population-based cohort, and to identify problem areas for IHC predictions. Histotype was abstracted from cancer registries for eligible ovarian carcinoma cases diagnosed from 2002 to 2011 in Alberta and British Columbia, Canada. Slides were reviewed according to World Health Organization 2014 criteria, tissue microarrays were stained with and scored for the 8 COSPv3 IHC markers, and COSPv3 histotype predictions were calculated. Discordant cases for review and COSPv3 prediction were arbitrated by integrating morphology with IHC results. The integrated histotype (N=880) was then used to identify areas of inferior COSPv3 performance. Review histotype and integrated histotype demonstrated 93% agreement suggesting that IHC information revises expert review in up to 7% of cases. There was also 93% agreement between COSPv3 prediction and integrated histotype. COSPv3 errors predominated in 4 areas: endometrioid carcinoma (EC) versus clear cell (N=23), EC versus low-grade serous (N=15), EC versus high-grade serous (N=11), and high-grade versus low-grade serous (N=6). Most problems were related to Napsin A-negative clear cell, WT1-positive EC, and p53 IHC wild-type high-grade serous carcinomas. Although 93% of COSPv3 prediction accuracy was validated, some histotyping required integration of morphology with ancillary test results. Awareness of these limitations will avoid overreliance on IHC and misclassification of histotypes for research and clinical management.

[Biopathology of ovarian carcinomas early and advanced-stages: Article drafted from the French guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

OBJECTIVES

Ovarian carcinomas represent a heterogeneous group of lesions with specific therapeutic management for each histological subtype. Thus, the correct histological diagnosis is mandatory.

MATERIAL AND METHODS

References were searched by PubMed from January 2000 to January 2018 and original articles in French and English literature were selected.

RESULTS AND CONCLUSIONS

In case of ovarian mass suspicious for cancer, a frozen section analysis may be proposed, if it could impact the surgical management. A positive histological diagnosis of ovarian carcinoma (type and grade) has to be rendered on histological (and not cytological) material before any chemotherapy with multiples and large sized biopsies. In case of needle biopsy, at least three fragments with needles>16G are needed. Histological biopsies need to be formalin-fixed (4% formaldehyde) less than 1h after resection and at least 6hours fixation is mandatory for small size biopsies. Tissue transfer to pathological labs up to 48hours under vacuum and at +4°C (in case of large surgical specimens) may be an alternative. Gross examination should include the description of all specimens and their integrity, the site of the tumor and the dimension of all specimens and nodules. Multiples sampling is needed, including the capsule, the solid areas, at least 1 to 2 blocks per cm of tumor for mucinous lesions, the Fallopian tube in toto, at least 3 blocks on grossly normal omentum and one block on the largest omental nodule. WHO classification should be used to classify the carcinoma (type and grade), with the use of a panel of immunohistochemical markers. High-grade ovarian carcinomas (serous and endometrioid) should be tested for BRCA mutation and in case of a detectable tumor mutation, the patient should be referred to an oncogenetic consultation.

An Immunohistochemical Algorithm for Ovarian Carcinoma Typing

There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials.

High-grade serous carcinoma of tubo-ovarian origin: recent developments

Extra-uterine high-grade serous carcinoma (HGSC) accounts for most of the morbidity and mortality associated with ovarian carcinoma, and is one of the leading causes of cancer death in women. Until recently our understanding of HGSC was very limited compared to other common cancers, and it has only been during the last 15 years that we have learned how to diagnose this ovarian carcinoma histotype accurately. Since then, however, there has been rapid progress, with identification of a precursor lesion in the fallopian tube, development of prevention strategies for both those with inherited susceptibility (hereditary breast and ovarian cancer syndrome) and without the syndrome, and elucidation of the molecular events important in oncogenesis. This molecular understanding has led to new treatment strategies for HGSC, with the promise of more to come in the near future. In this review we focus on these recent changes, including diagnostic criteria/differential diagnosis, primary site assignment, precursor lesions and the molecular pathology of HGSC.