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The color variation of hematoxylin and eosin (HE)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between HE images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of HE-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. © 2021 The Pathological Society of Great Britain and Ireland. Published by John WileySons, Ltd.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Endometrial carcinoma, the most common gynaecological cancer, develops from endometrial epithelium which is composed of secretory and ciliated cells. Pathologic classification is unreliable and there is a need for prognostic tools. We used single cell sequencing to study organoid model systems derived from normal endometrial endometrium to discover novel markers specific for endometrial ciliated or secretory cells. A marker of secretory cells (MPST) and several markers of ciliated cells (FAM92B, WDR16, and DYDC2) were validated by immunohistochemistry on organoids and tissue sections. We performed single cell sequencing on endometrial and ovarian tumours and found both secretory-like and ciliated-like tumour cells. We found that ciliated cell markers (DYDC2, CTH, FOXJ1, and p73) and the secretory cell marker MPST were expressed in endometrial tumours and positively correlated with disease-specific and overall survival of endometrial cancer patients. These findings suggest that expression of differentiation markers in tumours correlates with less aggressive disease, as would be expected for tumours that retain differentiation capacity, albeit cryptic in the case of ciliated cells. These markers could be used to improve the risk stratification of endometrial cancer patients, thereby improving their management. We further assessed whether consideration of MPST expression could refine the ProMiSE molecular classification system for endometrial tumours. We found that higher expression levels of MPST could be used to refine stratification of three of the four ProMiSE molecular subgroups, and that any level of MPST expression was able to significantly refine risk stratification of the copy number high subgroup which has the worst prognosis. Taken together, this shows that single cell sequencing of putative cells of origin has the potential to uncover novel biomarkers that could be used to guide management of cancers. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Purpose: Many rare ovarian cancer subtypes, such as small-cell carcinoma of the ovary, hypercalcemic type (SCCOHT), have poor prognosis due to their aggressive nature and resistance to standard platinum- and taxane-based chemotherapy. The development of effective therapeutics has been hindered by the rarity of such tumors. We sought to identify targetable vulnerabilities in rare ovarian cancer subtypes. Experimental Design: We compared the global proteomic landscape of six cases each of endometrioid ovarian cancer (ENOC), clear cell ovarian cancer (CCOC), and SCCOHT to the most common subtype, high-grade serous ovarian cancer (HGSC), to identify potential therapeutic targets. IHC of tissue microarrays was used as validation of arginosuccinate synthase (ASS1) deficiency. The efficacy of arginine-depriving therapeutic ADI-PEG20 was assessed in vitro using cell lines and patient-derived xenograft mouse models representing SCCOHT. Results: Global proteomic analysis identified low ASS1 expression in ENOC, CCOC, and SCCOHT compared with HGSC. Low ASS1 levels were validated through IHC in large patient cohorts. The lowest levels of ASS1 were observed in SCCOHT, where ASS1 was absent in 12 of 31 cases, and expressed in less than 5% of the tumor cells in 9 of 31 cases. ASS1-deficient ovarian cancer cells were sensitive to ADI-PEG20 treatment regardless of subtype in vitro. Furthermore, in two cell line mouse xenograft models and one patient-derived mouse xenograft model of SCCOHT, once-a-week treatment with ADI-PEG20 (30 mg/kg and 15 mg/kg) inhibited tumor growth in vivo. Conclusions: Preclinical in vitro and in vivo studies identified ADI-PEG20 as a potential therapy for patients with rare ovarian cancers, including SCCOHT.

Objective Our aim was to characterize the pathological, molecular and clinical outcomes of clear cell carcinoma of the endometrium (CCC). Methods CCC underwent ProMisE (Proactive Molecular Risk Classifier for Endometrial Cancer) classification identifying four molecular subtypes: i) ‘POLEmut’ for ECs with pathogenic POLE mutations, ii) ‘MMRd’, if there is loss of mismatch repair proteins by immunohistochemistry (IHC), iii) ‘p53wt’ or iv) ‘p53abn’ based on p53 IHC staining. Clinicopathologic parameters, immune markers (CD3, CD8, CD79a, CD138, PD-1), ER, L1CAM, and outcomes were assessed. Results 52 CCCs were classified, including 1 (2%) POLEmut, 5 (10%) MMRd, 28 (54%) p53wt and 18 (35%) p53abn. Women with p53abn and p53wt CCCs were older than women with MMRd and POLEmut subtypes. p53wt CCC were distinct from typical p53wt endometrioid carcinomas; more likely to arise in older, thinner women, with advanced stage disease, LVSI and lymph node involvement, and a higher proportion ER negative, L1CAM overexpressing tumors with markedly worse outcomes. High levels of immune infiltrates (TILhigh) were observed in 75% of the CCC cohort. L1CAM overexpression was highest within p53abn and p53wt subtypes of CCC. Conclusion CCC is a heterogeneous disease encompassing all four molecular subtypes and a wide range of clinical outcomes. Outcomes of patients with POLEmut, MMRd and p53abn CCC are not distinguishable from those of other patients with these respective subtypes of EC; p53wt CCC, however, differ from endometrioid p53wt EC in clinical, pathological, molecular features and outcomes. Thus, p53wt CCC of endometrium appear to be a distinct clinicopathological entity within the larger group of p53wt ECs.

Hereditary diffuse gastric cancer (HDGC) is a cancer syndrome caused by germline variants in CDH1, the gene encoding the cell-cell adhesion molecule E-cadherin. Loss of E-cadherin in cancer is associated with cellular dedifferentiation and poor prognosis, but the mechanisms through which CDH1 loss initiates HDGC are not known. Using single-cell RNA sequencing, we explored the transcriptional landscape of a murine organoid model of HDGC to characterize the impact of CDH1 loss in early tumourigenesis. Progenitor populations of stratified squamous and simple columnar epithelium, characteristic of the mouse stomach, showed lineage-specific transcriptional programs. Cdh1 inactivation resulted in shifts along the squamous differentiation trajectory associated with aberrant expression of genes central to gastrointestinal epithelial differentiation. Cytokeratin 7 (CK7), encoded by the differentiation-dependent gene Krt7, was a specific marker for early neoplastic lesions in CDH1 carriers. Our findings suggest that deregulation of developmental transcriptional programs may precede malignancy in HDGC. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Mesonephric carcinomas (MEs) and female adnexal tumors of probable Wolffian origin (FATWO) are derived from embryologic remnants of Wolffian/mesonephric ducts. Mesonephric-like carcinomas (MLCs) show identical morphology to ME of the cervix but occur in the uterus and ovary without convincing mesonephric remnants. ME, MLC, and FATWO are challenging to diagnose due to their morphologic similarities to Müllerian/paramesonephric tumors, contributing to a lack of evidence-based and tumor-specific treatments. We performed whole-proteomic analysis on 9 ME/MLC and 56 endometrial carcinomas (ECs) to identify potential diagnostic biomarkers. Although there were no convincing differences between ME and MLC, 543 proteins showed increased expression in ME/MLC relative to EC. From these proteins, euchromatic histone lysine methyltransferase 2 (EHMT2), glutathione S-transferase Mu 3 (GSTM3), eukaryotic translation elongation factor 1 alpha 2 (EEF1A2), and glycogen synthase kinase 3 beta were identified as putative biomarkers. Immunohistochemistry was performed on these candidates and GATA3 in 14 ME/MLC, 8 FATWO, 155 EC, and normal tissues. Of the candidates, only GATA3 and EHMT2 were highly expressed in mesonephric remnants and mesonephric-derived male tissues. GATA3 had the highest sensitivity and specificity for ME/MLC versus EC (93% and 99%) but was absent in FATWO. EHMT2 was 100% sensitive for ME/MLCFATWO but was not specific (65%). Similarly, EEF1A2 was reasonably sensitive to ME/MLC (92%) and FATWO (88%) but was the least specific (38%). GSTM3 performed intermediately (sensitivity for ME/MLC and FATWO: 83% and 38%, respectively; specificity 67%). Although GATA3 remained the best diagnostic biomarker for ME/MLC, we have identified EHMT2, EEF1A2, and GSTM3 as proteins of interest in these cancers. FATWO's cell of origin is uncertain and remains an area for future research.

Purpose: Endometrioid ovarian carcinoma (ENOC) is generally associated with a more favorable prognosis compared with other ovarian carcinomas. Nonetheless, current patient treatment continues to follow a “one-size-fits-all” approach. Even though tumor staging offers stratification, personalized treatments remain elusive. As ENOC shares many clinical and molecular features with its endometrial counterpart, we sought to investigate The Cancer Genome Atlas–inspired endometrial carcinoma (EC) molecular subtyping in a cohort of ENOC. Experimental Design: IHC and mutation biomarkers were used to segregate 511 ENOC tumors into four EC-inspired molecular subtypes: low-risk POLE mutant (POLEmut), moderate-risk mismatch repair deficient (MMRd), high-risk p53 abnormal (p53abn), and moderate-risk with no specific molecular profile (NSMP). Survival analysis with established clinicopathologic and subtype-specific features was performed. Results: A total of 3.5% of cases were POLEmut, 13.7% MMRd, 9.6% p53abn, and 73.2% NSMP, each showing distinct outcomes (P < 0.001) and survival similar to observations in EC. Median OS was 18.1 years in NSMP, 12.3 years in MMRd, 4.7 years in p53abn, and not reached for POLEmut cases. Subtypes were independent of stage, grade, and residual disease in multivariate analysis. Conclusions: EC-inspired molecular classification provides independent prognostic information in ENOC. Our findings support investigating molecular subtype–specific management recommendations for patients with ENOC; for example, subtypes may provide guidance when fertility-sparing treatment is desired. Similarities between ENOC and EC suggest that patients with ENOC may benefit from management strategies applied to EC and the opportunity to study those in umbrella trials.

Dedifferentiated endometrial carcinoma (DDEC) is a rare but highly aggressive type of endometrial cancer, in which an undifferentiated carcinoma arises from a low-grade endometrioid endometrial carcinoma. The low-grade component is often eclipsed, likely due to an outgrowth of the undifferentiated component, and the tumor may appear as a pure undifferentiated endometrial carcinoma (UEC). We and others have recently identified inactivating mutations of SMARCA4, SMARCB1 or ARID1B, subunits of the SWI/SNF chromatin-remodeling complex, that are unique to the undifferentiated component and are present in a large portion of DDEC and UEC. However, the understanding of whether and how these mutations drive cancer progression and histologic dedifferentiation is hindered by lack of cell line models of DDEC or UEC. Here, we established the first UEC cell line, VOA1066, which is highly tumorigenic in vivo. This cell line has a stable genome with very few somatic mutations, which do include inactivating mutations of ARID1A and ARID1B (2 mutations each), and a heterozygous hotspot DICER1 mutation in its RNase IIIb domain. Immunohistochemistry staining confirmed the loss of ARID1B, but ARID1A staining was retained due to the presence of a truncating non-functional ARID1A protein. The heterozygous DICER1 hotspot mutation has little effect on microRNA biogenesis. No additional DICER1 hotspot mutations have been identified in a cohort of 33 primary tumors. Therefore, we have established the first UEC cell line with dual inactivation of both ARID1A and ARID1B as the main genomic feature. This cell line will be useful for studying the roles of ARID1A and ARID1B mutations in the development of UEC.

Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pathology, the way in which we view and interpret tissue morphology has remained largely unchanged since the inception of our profession. In this review, we discuss new technological advances that promise to transform the way in which we access tissue morphology and how we use it to guide patient care.

PurposeMany rare ovarian cancer subtypes such as small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) have poor prognosis due to their aggressive nature and resistance to standard platinum and taxane based chemotherapy. The development of effective therapeutics has been hindered by the rarity of such tumors. We sought to identify targetable vulnerabilities in rare ovarian cancer subtypes.Experimental DesignWe compared the global proteomic landscape of six cases each of endometrioid ovarian cancer (ENOC), clear cell ovarian cancer (CCOC), and SCCOHT to the most common subtype high grade serous ovarian cancer (HGSC) to identify potential therapeutic targets. Immunohistochemistry of tissue microarrays were used as validation of ASS1 deficiency. The efficacy of arginine-depriving therapeutic ADI-PEG20 was assessed in vitro using cell lines and patient derived xenograft mouse models representing SCCOHT.ResultsGlobal proteomic analysis identified low ASS1 expression in ENOC, CCOC, and SCCOHT compared to HGSC. Low ASS1 levels were validated through IHC in a large patient cohort. The lowest levels of ASS1 were observed in SCCOHT, where ASS1 was absent in 2/15 cases, and expressed in less than 5% of the tumor cells in 8/15 cases. ASS1 deficient ovarian cancer cells were sensitive to ADI-PEG20 treatment regardless of subtype in vitro. Furthermore, in two cell line mouse xenograft models and one patient derived mouse xenograft model of SCCOHT, once a week treatment of ADI-PEG20 (30mg/kg and 15mg/kg) inhibited tumor growth in vivo.ConclusionsPreclinical in vitro and in vivo studies identified ADI-PEG20 as a potential therapy for patients with rare ovarian cancers including SCCOHT.Translational relevanceMany rare ovarian cancers lack effective management strategies and are resistant to the standard platinum- and taxane-based chemotherapy. Thus, for a rare ovarian cancer subtype like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) - an aggressive malignancy affecting young women in their twenties, effective targeted therapeutics are urgently needed. We used global proteomics to identify a deficiency in arginosuccinate synthase (ASS1) as a common feature among some rare ovarian cancer subtypes. Using in-vitro and in-vivo models, we demonstrated that the arginine-depriving investigational agent ADI-PEG20 effectively inhibited cell growth in ASS1 deficient ovarian cancers including SCCOHT, establishing it as a potential therapeutic agent for rare ovarian cancer subtypes deficient in ASS1. Further clinical investigation is warranted.

Summary LINE-1 (L1) retrotransposons are mobile genetic elements capable of “copy-and-pasting” their own sequences into random genomic loci, and one of the proteins it uses to achieve mobility is LINE-1 open reading frame 1 protein (L1ORF1p). L1ORF1p expression is found across many epithelial cancers, including small cohorts of ovarian and endometrial cancers, and is highly expressed in cancers with mutant p53 expressions. Here we aimed to gain insights into L1ORF1p expression levels within specific histotypes of ovarian cancers: high-grade serous (n = 585), low-grade serous (n = 26), clear cell (n = 132), endometrioid (n = 148), and mucinous (n = 32) ovarian cancers, as well as endometrial cancers (n = 607) using tissue microarray (TMA's). We demonstrated that L1ORF1p expression is associated with advanced stage and serous histotype in gynecological cancers. Like previous studies, we found a higher proportion of L1ORF1p expression in cases with aberrant p53 expression. We evaluated the expression of L1ORF1p in serous tubal intraepithelial carcinomas (STICs) (n = 6) and p53 signature lesions (n = 2) in fallopian tubes. Three STIC cases displayed aberrant p53 overexpression with corresponding L1ORF1p expression in the same tissues, but such correlation was not seen in the two p53 signature lesions, suggesting that L1 protein may be expressed after dysplastic transformation. The remaining three STIC cases have TP53 nonsense mutations with absent p53 expression but a strong and clear L1ORF1p expression within the STIC lesions. While L1ORF1p may not be prognostic in gynecological cancers, it may be useful clinically as a diagnostic IHC marker for p53 null STIC lesions and this warrants further investigation.