Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal ( https://cptac-data-portal.georgetown.edu/ ).

Classification of High-Grade Serous Ovarian Carcinoma by Epithelial-to-Mesenchymal Transition Signature and Homologous Recombination Repair Genes.

High-grade serous ovarian cancer (HGSOC) is one of the deadliest cancers that can occur in women. This study aimed to investigate the molecular characteristics of HGSOC through integrative analysis of multi-omics data. We used fresh-frozen, chemotherapy-naïve primary ovarian cancer tissues and matched blood samples of HGSOC patients and conducted next-generation whole-exome sequencing (WES) and RNA sequencing (RNA-seq). Genomic and transcriptomic profiles were comprehensively compared between patients with germline BRCA1/2 mutations and others with wild-type BRCA1/2. HGSOC samples initially divided into two groups by the presence of germline BRCA1/2 mutations showed mutually exclusive somatic mutation patterns, yet the implementation of high-dimensional analysis of RNA-seq and application of epithelial-to-mesenchymal (EMT) index onto the HGSOC samples revealed that they can be divided into two subtypes; homologous recombination repair (HRR)-activated type and mesenchymal type. Patients with mesenchymal HGSOC, characterized by the activation of the EMT transcriptional program, low genomic alteration and diverse cell-type compositions, exhibited significantly worse overall survival than did those with HRR-activated HGSOC (p = 0.002). In validation with The Cancer Genome Atlas (TCGA) HGSOC data, patients with a high EMT index (≥the median) showed significantly worse overall survival than did those with a low EMT index (

Integrative prognostic subtype discovery in high-grade serous ovarian cancer.

OBJECTIVE: We sought to identify novel molecular subtypes of high-grade serous ovarian cancer (HGSC) by the integration of gene expression and proteomics data and to find the underlying biological characteristics of ovarian cancer to improve the clinical outcome. METHODS: The iCluster method was utilized to analysis 131 common HGSC samples between TCGA and Clinical Proteomic Tumor Analysis Consortium databases. Kaplan-Meier survival curves were used to estimate the overall survival of patients, and the differences in survival curves were assessed using the log-rank test. RESULTS: Two novel ovarian cancer subtypes with different overall survival (P = .00114) and different platinum status (P = .0061) were identified. Eighteen messenger RNAs and 38 proteins were selected as differential molecules between subtypes. Pathway analysis demonstrated arrhythmogenic right ventricular cardiomyopathy pathway played a critical role in the discrimination of these two subtypes and desmosomal cadherin DSG2, DSP, JUP, and PKP2 in this pathway were overexpression in subtype I compared with subtype II. CONCLUSION: Our study extended the underlying prognosis-related biological characteristics of high-grade serous ovarian cancer. Enrichment of desmosomal cadherin increased the risk for HGSC prognosis among platinum-sensitive patients, the results guided the revision of the treatment options for platinum-sensitive ovarian cancer patients to improve outcomes.

Molecular profiling and molecular classification of endometrioid ovarian carcinomas.

OBJECTIVE: Endometrioid ovarian carcinomas (EOCs) comprise 5-10% of all ovarian cancers and commonly co-occur with synchronous endometrioid endometrial cancer (EEC). We sought to examine the molecular characteristics of pure EOCs in patients without concomitant EEC. METHODS: EOCs and matched normal samples were subjected to massively parallel sequencing targeting 341-468 cancer-related genes (n = 8) or whole-genome sequencing (n = 28). Mutational frequencies of EOCs were compared to those of high-grade serous ovarian cancers (HGSOCs; n = 224) and EECs (n = 186) from The Cancer Genome Atlas, and synchronous EOCs (n = 23). RESULTS: EOCs were heterogeneous, frequently harboring KRAS, PIK3CA, PTEN, CTNNB1, ARID1A and TP53 mutations. EOCs were distinct from HGSOCs at the mutational level, less frequently harboring TP53 but more frequently displaying KRAS, PIK3CA, PIK3R1, PTEN and CTNNB1 mutations. Compared to synchronous EOCs and pure EECs, pure EOCs less frequently harbored PTEN, PIK3R1 and ARID1A mutations. Akin to EECs, EOCs could be stratified into the four molecular subtypes: 3% POLE (ultramutated), 19% MSI (hypermutated), 17% copy-number high (serous-like) and 61% copy-number low (endometrioid). In addition to microsatellite instability, a subset of EOCs harbored potentially targetable mutations, including AKT1 and ERBB2 hotspot mutations. EOCs of MSI (hypermutated) subtype uniformly displayed a good outcome. CONCLUSIONS: EOCs are heterogeneous at the genomic level and harbor targetable genetic alterations. Despite the similarities in the repertoire of somatic mutations between pure EOCs, synchronous EOCs and EECs, the frequencies of mutations affecting known driver genes differ. Further studies are required to define the impact of the molecular subtypes on the outcome and treatment of EOC patients.

Proteomics-Derived Biomarker Panel Improves Diagnostic Precision to Classify Endometrioid and High-grade Serous Ovarian Carcinoma.

PURPOSE: Ovarian carcinomas are a group of distinct diseases classified by histotypes. As histotype-specific treatment evolves, accurate classification will become critical for optimal precision medicine approaches. EXPERIMENTAL DESIGN: To uncover differences between the two most common histotypes, high-grade serous (HGSC) and endometrioid carcinoma, we performed label-free quantitative proteomics on freshly frozen tumor tissues (HGSC, n = 10; endometrioid carcinoma, n = 10). Eight candidate protein biomarkers specific to endometrioid carcinoma were validated by IHC using tissue microarrays representing 361 cases of either endometrioid carcinoma or HGSC. RESULTS: More than 500 proteins were differentially expressed (P < 0.05) between endometrioid carcinoma and HGSC tumor proteomes. A ranked set of 106 proteins was sufficient to correctly discriminate 90% of samples. IHC validated KIAA1324 as the most discriminatory novel biomarker for endometrioid carcinoma. An 8-marker panel was found to exhibit superior performance for discriminating endometrioid carcinoma from HGSC compared with the current standard of WT1 plus TP53 alone, improving the classification rate for HGSC from 90.7% to 99.2%. Endometrioid carcinoma-specific diagnostic markers such as PLCB1, KIAA1324, and SCGB2A1 were also significantly associated with favorable prognosis within endometrioid carcinoma suggesting biological heterogeneity within this histotype. Pathway analysis of proteomic data revealed differences between endometrioid carcinoma and HGSC pertaining to estrogen and interferon signalling. CONCLUSIONS: In summary, these findings support the use of multi-marker panels for the differential diagnosis of difficult cases resembling endometrioid carcinoma and HGSC.

Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer.

Nearly one-third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial treatment with platinum-based therapy. Genomic and clinical characterization of these patients may lead to potential alternative therapies. Here, the objective is to classify non-responders into subsets using clinical and molecular features. Using patients from The Cancer Genome Atlas (TCGA) dataset with platinum-resistant or platinum-refractory HGSC, we performed a genome-wide unsupervised cluster analysis that integrated clinical data, gene copy number variations, gene somatic mutations, and DNA promoter methylation. Pathway enrichment analysis was performed for each cluster to identify the targetable processes. Following the unsupervised cluster analysis, three distinct clusters of non-responders emerged. Cluster 1 had overrepresentation of the stage IV disease and suboptimal debulking, under-expression of miRNAs and mRNAs, hypomethylated DNA, "loss of function" TP53 mutations, and the overexpression of genes in the PDGFR pathway. Cluster 2 had low miRNA expression, generalized hypermethylation, MUC17 mutations, and significant activation of the HIF-1 signaling pathway. Cluster 3 had more optimally cytoreduced stage III patients, overexpression of miRNAs, mixed methylation patterns, and "gain of function" TP53 mutations. However, the survival for all clusters was similar. Integration of genomic and clinical data from patients that do not respond to chemotherapy has identified different subgroups or clusters. Pathway analysis further identified the potential alternative therapeutic targets for each cluster.

High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints.

Among a litany of malignancies affecting the female reproductive tract, that of the ovary is the most frequently fatal. Moreover, while the steady pace of scientific discovery has fuelled recent ameliorations in the outcomes of many other cancers, the rates of mortality for ovarian cancer have been stagnant since around 1980. Yet despite the grim outlook, progress is being made towards better understanding the fundamental biology of this disease and how its biology in turn influences clinical behaviour. It has long been evident that ovarian cancer is not a unitary disease but rather a multiplicity of distinct malignancies that share a common anatomical site upon presentation. Of these, the high-grade serous subtype predominates in the clinical setting and is responsible for a disproportionate share of the fatalities from all forms of ovarian cancer. This review aims to provide a detailed overview of the clinical-pathological features of ovarian cancer with a particular focus on the high-grade serous subtype. Along with a description of the relevant clinical aspects of this disease, including novel trends in treatment strategies, this text will inform the reader of recent updates to the scientific literature regarding the origin, aetiology and molecular-genetic basis of high-grade serous ovarian cancer (HGSOC).

A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas.

BACKGROUND: Copy Number Alternations (CNAs) is defined as somatic gain or loss of DNA regions. The profiles of CNAs may provide a fingerprint specific to a tumor type or tumor grade. Low-coverage sequencing for reporting CNAs has recently gained interest since successfully translated into clinical applications. Ovarian serous carcinomas can be classified into two largely mutually exclusive grades, low grade and high grade, based on their histologic features. The grade classification based on the genomics may provide valuable clue on how to best manage these patients in clinic. Based on the study of ovarian serous carcinomas, we explore the methodology of combining CNAs reporting from low-coverage sequencing with machine learning techniques to stratify tumor biospecimens of different grades. RESULTS: We have developed a data-driven methodology for tumor classification using the profiles of CNAs reported by low-coverage sequencing. The proposed method called Bag-of-Segments is used to summarize fixed-length CNA features predictive of tumor grades. These features are further processed by machine learning techniques to obtain classification models. High accuracy is obtained for classifying ovarian serous carcinoma into high and low grades based on leave-one-out cross-validation experiments. The models that are weakly influenced by the sequence coverage and the purity of the sample can also be built, which would be of higher relevance for clinical applications. The patterns captured by Bag-of-Segments features correlate with current clinical knowledge: low grade ovarian tumors being related to aneuploidy events associated to mitotic errors while high grade ovarian tumors are induced by DNA repair gene malfunction. CONCLUSIONS: The proposed data-driven method obtains high accuracy with various parametrizations for the ovarian serous carcinoma study, indicating that it has good generalization potential towards other CNA classification problems. This method could be applied to the more difficult task of classifying ovarian serous carcinomas with ambiguous histology or in those with low grade tumor co-existing with high grade tumor. The closer genomic relationship of these tumor samples to low or high grade may provide important clinical value.

Factors that influence survival in high-grade serous ovarian cancer: A complex relationship between molecular subtype, disease dissemination, and operability.

OBJECTIVE: To investigate the relationship between molecular subtype, intraperitoneal (IP) disease dissemination patterns, resectability, and overall survival (OS) in advanced high-grade serous ovarian cancer (HGSOC). METHODS: Patients undergoing primary surgery for stage III-IV HGSOC at Mayo Clinic from 1994 to 2011 were categorized into three IP disease dissemination patterns: upper abdominal or miliary; lower abdominal; and pelvic. Residual disease was defined as 0 (RD0), 0.1-0.5, 0.6-1.0, or >1 cm. Molecular subtypes were derived from Agilent 4x44k tumor mRNA expression profiles and categorized as mesenchymal (MES) or non-mesenchymal (non-MES). RESULTS: Operative and molecular data was available for 334 patients. Median OS was shorter in patients with MES compared to non-MES subtypes (34.2 vs 44.6 months; P = 0.009). Patients with MES subtype were more likely to have upper abdominal/miliary disease compared to non-MES subtype (90% vs. 72%, P < 0.001). For patients with upper abdominal/miliary disease, complete resection (RD0) was less common in MES compared to non-MES subtypes (11% vs. 27%, P = 0.004). On multivariable analysis, RD was the only factor associated with OS (P < 0.001). In patients with upper abdominal/miliary disease, though less commonly achieved, RD0 improved survival irrespective of molecular subtype (median OS of 69.2 and 57.9 months for MES and non-MES subtype). CONCLUSIONS: Our results support a paradigm in which molecular subtype is an important driver of dissemination pattern; this in turn impacts resectability and ultimately survival. Consequently mesenchymal subtype is associated with much lower rates of complete resection, though RD0 remains the most important independent predictor of survival.

TFF3 Expression as Stratification Marker in Borderline Epithelial Tumors of the Ovary.

Borderline tumors (BOT) of the ovary account for 10% to 20% of ovarian neoplasms. Like ovarian cancer, BOT encompass several different histological subtypes (serous, mucinous, endometrioid, clear cell, transitional cell and mixed) with serous (SBOT) and mucinous (MBOT) the most common. Current hypotheses suggest low-grade serous carcinoma may develop in a stepwise fashion from SBOT whereas the majority of high grade serous carcinomas develop rapidly presumably from inclusion cysts or ovarian surface epithelium. The pathogenesis of mucinous ovarian tumors is still puzzling. Molecular markers could help to better define relationships between such entities. Trefoil factor-3 (TFF3) is an estrogen-regulated gene associated with prognosis in different types of cancer. It has also been included in a recent marker panel predicting subtypes of ovarian carcinoma. We analyzed the expression of TFF3 by immunohistochemistry in a cohort of 137 BOT and its association with histopathological features. Overall expression rate of TFF3 was 21.9%. None of the BOT with serous and endometrioid histology displayed strong TFF3 expression. On the other hand, TFF3 was highly expressed in 61.4% of MBOT cases and 33.3% of BOT with mixed histology (P < 0.001) suggesting a potential function of the protein in that subtypes. Associations of TFF3 expression with FIGO stage and micropapillary pattern were significant in the overall cohort but confounded by their correlation with histological subtypes. The highly specific expression of TFF3 in MBOT may help to further clarify potential relationships of tumors with mucinous histology and warrants further studies.

[New features in the 2014 WHO classification of uterine neoplasms]. / Neues in der WHO-Klassifikation 2014 der Tumoren des Corpus uteri.

The 2014 World Health Organization (WHO) classification of uterine tumors revealed simplification of the classification by fusion of several entities and the introduction of novel entities. Among the multitude of alterations, the following are named: a simplified classification for precursor lesions of endometrial carcinoma now distinguishes between hyperplasia without atypia and atypical hyperplasia, the latter also known as endometrioid intraepithelial neoplasia (EIN). For endometrial carcinoma a differentiation is made between type 1 (endometrioid carcinoma with variants and mucinous carcinoma) and type 2 (serous and clear cell carcinoma). Besides a papillary architecture serous carcinomas may show solid and glandular features and TP53 immunohistochemistry with an "all or null pattern" assists in the diagnosis of serous carcinoma with ambiguous features. Neuroendocrine neoplasms are categorized in a similar way to the gastrointestinal tract into well differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas (small cell and large cell types). Leiomyosarcomas of the uterus are typically high grade and characterized by marked nuclear atypia and lively mitotic activity. Low grade stromal neoplasms frequently show gene fusions, such as JAZF1/SUZ12. High grade endometrial stromal sarcoma is newly defined by cyclin D1 overexpression and the presence of the fusion gene YWHAE/FAM22 and must be distinguished from undifferentiated uterine sarcoma. Carcinosarcomas (malignant mixed Mullerian tumors MMMT) show biological and molecular similarities to high-grade carcinomas.

Validation of Revised FIGO Staging Classification for Cancer of the Ovary, Fallopian Tube, and Peritoneum Based on a Single Histological Type.

OBJECTIVE: This study aimed to evaluate the prognostic significance of revised International Federation of Gynecology and Obstetrics (FIGO2013) staging classification for cancer of the ovary, fallopian tube, and peritoneum in patients exhibiting high-grade serous histology. METHODS: Clinical records of patients with high-grade serous carcinoma who underwent primary surgery between 2007 and 2012 were reviewed retrospectively. Patients were reclassified according to the FIGO2013 criteria. Progression-free survival (PFS) and overall survival (OS) were calculated for each stage using Kaplan-Meier estimates and compared with the log-rank test. RESULTS: In total, 125 patients were included in the analysis. The distribution of the study cohort according to the revised classification was as follows; stage I, 6 patients; stage II, 9 patients; stage III, 85 patients; and stage IV, 25 patients. Median follow-up time was 36 months (95% confidence interval [CI], 3-110). The median PFS and OS were 14 months (95% CI, 12.4-15.6) and 60 months (95% CI, 47.0-72.9), respectively. Both PFS and OS were significantly different among stages I, II, III, and IV (P < 0.01). Subgroup analyses for stage III disease also revealed significant differences in survival. The median PFS for stages IIIA1, IIIB, and IIIC was 56, 46, and 16 months, respectively (P < 0.01), and the median OS was 104, 95, and 60 months, respectively (P = 0.03). The outcomes of patients with stage IV disease differed slightly but nonsignificantly according to new substages. The median PFS for stages IVA and IVB was 12 and 6 months, respectively (hazard ratio, 1.16; 95% CI, 0.48-2.79; P = 0.72), and the median OS was 41 and 24 months, respectively (hazard ratio, 1.62; 95% CI, 0.58-4.55; P = 0.35). The study sample was insufficient in size for subgroup analyses in stages I and II. CONCLUSIONS: The revised FIGO2013 staging system is highly prognostic for discriminating outcomes of patients with high-grade serous carcinoma across stages I to IV, in subgroups of stage III, but not in subgroups of stage IV.

Establishment of a Novel Histopathological Classification of High-Grade Serous Ovarian Carcinoma Correlated with Prognostically Distinct Gene Expression Subtypes.

Recently, The Cancer Genome Atlas data revealed four molecular subtypes of high-grade serous ovarian carcinoma (HGSOC) exhibiting distinct prognoses. We developed four novel HGSOC histopathological subtypes by focusing on tumor microenvironment: mesenchymal transition, defined by a remarkable desmoplastic reaction; immune reactive by lymphocytes infiltrating the tumor; solid and proliferative by a solid growth pattern; and papilloglandular by a papillary architecture. Unsupervised hierarchical clustering revealed four clusters correlated with histopathological subtypes in both Kyoto and Niigata HGSOC transcriptome data sets (P < 0.001). Gene set enrichment analysis revealed pathways enriched in our histopathological classification significantly overlapped with the four molecular subtypes: mesenchymal, immunoreactive, proliferative, and differentiated (P < 0.0001, respectively). In 132 HGSOC cases, progression-free survival and overall survival were best in the immune reactive, whereas overall survival was worst in the mesenchymal transition (P < 0.001, respectively), findings reproduced in 89 validation cases (P < 0.05, respectively). The CLOVAR_MES_UP single-sample gene set enrichment analysis scores representing the mesenchymal molecular subtype were higher in paclitaxel responders than nonresponders (P = 0.002) in the GSE15622 data set. Taxane-containing regimens improved survival of cases with high MES_UP scores compared with nontaxane regimens (P < 0.001) in the GSE9891 data set. Our novel histopathological classification of HGSOC correlates with distinct prognostic transcriptome subtypes. The mesenchymal transition subtype might be particularly sensitive to taxane.

The new WHO classification of ovarian, fallopian tube, and primary peritoneal cancer and its clinical implications.

INTRODUCTION: Molecular pathological research has contributed to improving the knowledge of different subtypes of ovarian cancer. In parallel with the implementation of the new FIGO staging classification, the WHO classification was revised. The latter is mainly based on the histopathological findings and defines the actual type of tumor. It has, therefore, also an important impact on prognosis and therapy of the patient. MATERIALS AND METHODS: The new WHO Classification of Ovarian Cancer published 2014 by Robert Kurman and co-authors is summarized. The major changes compared to the hitherto existing classification are presented. RESULTS: The new classification eliminates the previous focus of mesothelial origin of ovarian cancer. Instead, it features a discussion of tubal carcinogenesis of hereditary and some other high-grade serous carcinomas. The previously assumed pathogenesis pathway may be correct for some, but not for all, serous cancers. The new classification was established to classify ovarian cancer in a more consistent way. The earlier transitional cell type of ovarian cancer has been removed while seromucinous tumors have been added as a new entity. The role of some borderline tumors as one possible step in the progression from benign to invasive lesions is incorporated. The article summarizes the essential updates concerning serous, mucinous, seromucinous, endometrioid, clear-cell, and Brenner tumors. CONCLUSION: The new WHO classification takes into account the recent findings on the origin, pathogenesis, and prognosis of different ovarian cancer subtypes. The tubal origin of hereditary and some non-hereditary high-grade serous cancers is mentioned in contrast to the hitherto theory of mesothelial origin of tumors. Seromucinous tumors represent a new entity.

Low-Stage High-Grade Serous Ovarian Carcinomas: Support for an Extraovarian Origin.

Many adnexal high-grade serous carcinomas (HGSCs) may derive from microscopic precursors in the fallopian tube. By studying a series of low-stage ovarian carcinomas, we anticipated that HGSCs would be distributed in a pattern suggesting secondary involvement, helping to indirectly validate the fallopian tube origin theory, and that most ovarian carcinomas other than serous carcinomas would demonstrate features consistent with derivation from precursors located in or transplanted to the ovary. Seventy-six patients with low-stage (FIGO I/II) sporadic ovarian carcinoma who underwent primary surgical management at Memorial Sloan Kettering Cancer Center from 1980 to 2000 were included in the study. Histologic type was assigned using Gilks' criteria. Similar to the approach taken when distinguishing primary and metastatic mucinous or endometrioid carcinoma involving ovary, cases interpreted as showing a "primary" pattern of ovarian involvement had ≥3 of the following features: unilateral tumor, size >12 cm, no surface involvement, no multinodularity, and no destructive stromal invasion. All other cases were considered to show a "metastatic" pattern of ovarian involvement. Cases were evaluated for p53 and WT-1 expression, using standard techniques on a tissue microarray. TP53 gene sequencing was also performed. Cases comprised HGSC (n=22), endometrioid carcinoma (n=30), clear cell carcinoma (n=13), and mucinous carcinoma (n=11). HGSCs displayed substantially more "metastatic features" than the non-HGSC group and a mean overall size that was smaller (8.85 vs. 14.1 cm). Statistically significant differences were found for bilaterality (63% vs. 7.3%), P=0.0001; multinodularity (55% vs. 7.3%), P=0.0001; tumor size, P=0.003; and surface involvement (50% vs. 13%), P=0.002. Five of 22 (23%) of HGSCs showed a "primary pattern" of ovarian involvement. There were no significant differences between these cases and "metastatic pattern" HGSCs when comparing morphology, immunophenotype, TP53 mutational status, and clinical outcomes. Most low-stage HGSCs demonstrate patterns of ovarian involvement that suggest metastasis from another source, such as the fallopian tube. Both metastatic pattern HGSCs and unilateral, low-stage HGSCs can behave aggressively.

Molecular Alterations of TP53 are a Defining Feature of Ovarian High-Grade Serous Carcinoma: A Rereview of Cases Lacking TP53 Mutations in The Cancer Genome Atlas Ovarian Study.

The Cancer Genome Atlas has reported that 96% of ovarian high-grade serous carcinomas (HGSCs) have TP53 somatic mutations suggesting that mutation of this gene is a defining feature of this neoplasm. In the current study, 5 gynecologic pathologists independently evaluated hematoxylin and eosin slides of 14 available cases from The Cancer Genome Atlas classified as HGSC that lacked a TP53 mutation. The histologic diagnoses rendered by these pathologists and the accompanying molecular genetic data are the subject of this report. Only 1 case (Case 5), which contained a homozygous deletion of TP53, had unanimous interobserver agreement for a diagnosis of pure HGSC. In 1 case (Case 3), all 5 observers (100%) rendered a diagnosis of HGSC; however, 3 observers (60%) noted that the histologic features were not classic for HGSC and suggested this case may have arisen from a low-grade serous carcinoma (arisen from an alternate pathway compared with the usual HGSC). In 2 cases (Cases 4 and 12), only 3 observers (60%) in each case, respectively, interpreted it as having a component of HGSC. In the remaining 10 (71%) of tumors (Cases 1, 2, 6-11, 13, and 14), the consensus diagnosis was not HGSC, with individual diagnoses including low-grade serous carcinoma, high-grade endometrioid carcinoma, HGSC, metastatic carcinoma, clear cell carcinoma, atypical proliferative (borderline) serous tumor, and adenocarcinoma, not otherwise specified. Therefore, 13 (93%) of the tumors (Cases 1-4 and 6-14) were either not a pure HGSC or represented a diagnosis other than HGSC, all with molecular results not characteristic of HGSC. Accordingly, our review of the TP53 wild-type HGSCs reported in The Cancer Genome Atlas suggests that 100% of de novo HGSCs contain TP53 somatic mutations or deletions, with the exception of the rare HGSCs that develop from a low-grade serous tumor precursor. We, therefore, propose that lack of molecular alterations of TP53 are essentially inconsistent with the diagnosis of ovarian HGSC and that tumors diagnosed as such should be rigorously reassessed to achieve correct classification.

The Genomic Heterogeneity of FIGO Grade 3 Endometrioid Carcinoma Impacts Diagnostic Accuracy and Reproducibility.

The Cancer Genome Atlas (TCGA) identified 4 groups of endometrial carcinomas based on an integrated genomic characterization: POLE ultramutated (POLE), microsatellite instability-high, copy number-low (CN-L), and copy number-high (CN-H). In that study, CN-H comprised all of the serous carcinoma cases and 25% of all International Federation of Gynecology and Obstetrics (FIGO) Grade 3 endometrioid carcinoma cases. In this study, 2 expert gynecologic pathologists undertook a morphologic reassessment of the FIGO Grade 3 endometrioid carcinoma subset of the TCGA study cohort, including an analysis for evidence of serous differentiation. Interobserver variability κvalues are reported for the histologic evaluation of all 4 genomic clusters, and diagnostic discrepancies are discussed. Overall, there were 55 agreements, 6 disagreements, and 14 deferrals. Of the 75 cases analyzed, 6 cases had a consensus morphologic diagnosis of serous carcinoma, but only 2 of these cases had a serous carcinoma genotype, whereas the remaining 4 cases were genotypically endometrioid carcinoma. For the CN-H group, 2 of 15 cases were serous carcinoma by morphology and genotype, whereas at least 1 pathologist interpreted the remaining 13 cases as endometrioid carcinoma. The interobserver agreement rate was highest in the CN-L group (90%; κ=0.9), compared with the other genomic groups (POLE: 62%, κ=0.55; microsatellite instability-high: 78%, κ=0.74; and CN-H: 53%, κ=0.48). Our review confirms that most high-grade endometrial carcinomas diagnosed by TCGA as FIGO Grade 3 endometrioid carcinoma are indeed endometrioid carcinomas by morphology and genotype, and that the reproducibility of histologic diagnosis between pathologists varies between the TCGA-integrated genomic clusters.

Is Invasive Micropapillary Serous Carcinoma a Low-grade Carcinoma?

"Invasive micropapillary serous carcinoma" has been proposed as a synonym for low-grade serous carcinoma by some expert pathologists. In contrast, Singer and colleagues reported that some serous carcinomas with conspicuous invasive micropapillary pattern (SC-IMPs) can show high-grade nuclear atypia. However, the molecular features of such tumors have not been well documented. The aim of this study was to demonstrate and emphasize the fact that high-grade serous carcinoma confirmed by immunohistochemistry and molecular analysis can show conspicuous invasive micropapillary pattern. We selected 24 "SC-IMPs" and investigated: (1) their morphologic features; (2) the immunostaining pattern of p53 protein; and (3) KRAS/BRAF/TP53 gene mutations. The 24 SC-IMPs were subdivided into low-grade and high-grade tumors based primarily on the nuclear atypia, with the mitotic rate used as a secondary feature: low grade (n=5) and high grade (n=19). Low-grade SC-IMPs were characterized by low-mitotic activity, absence of abnormal mitosis, presence of serous borderline tumor, occasional BRAF mutation, and infrequent TP53 mutation. High-grade SC-IMPs were characterized by high-mitotic activity, presence of abnormal mitosis, conventional high-grade serous carcinoma, frequent TP53 mutation, and lack of KRAS/BRAF mutation. We demonstrated that high-grade serous carcinoma confirmed by aberrant p53 immunostaining and molecular analysis can show conspicuous invasive micropapillary pattern, validating Singer and colleague's report. Serous carcinoma with conspicuous invasive micropapillary pattern should not be readily regarded as low-grade serous carcinoma. Nuclear grade is the most important diagnostic feature in the SC-IMPs.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

OBJECTIVES: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. MATERIALS AND METHODS: We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. RESULTS: The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. CONCLUSIONS: We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.