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We evaluated a population-based cohort of metastatic well-differentiated grade 3 gastroenteropancreatic neuroendocrine tumors (G3 NETs) to describe their characteristics, prognosis, and treatment outcomes.The British Columbia provincial database was queried for G3 NETs diagnosed 2004 to 2021, and charts were reviewed to describe clinical features and outcomes.Forty-one patients were identified, most were diagnosed with pancreatic (58.5%) or midgut (26.8%) primary tumor and Ki-67 was less than 55% in 68.3%. The primary was resected in 19 (46.3%) with median disease-free survival of 25.2 months. Once metastatic, patients received a median of one line of systemic therapy. Median overall survival with metastatic disease was 33.8 months. Median progression-free survival was longest in patients treated with capecitabine-temozolomide (20.6 months) or somatostatin analogs (7.9 months), while etoposide-platinum provided little benefit (2.4 months). Limited data of efficacy for targeted therapies and radionuclide therapy was available. Seven patients (17.1%) were also treated with local therapies, which were associated with improved overall survival (median not reached, hazard ratio, 0.23; P = 0.012).Capecitabine-temozolomide and somatostatin analogs were associated with clinically meaningful benefit, and use of local therapies provided benefits in selected patients. Multidisciplinary discussion is essential to optimize individual outcomes in this heterogeneous population.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employed artificial intelligence (AI)-powered histopathology image analysis to identify a novel sub-group of NSMP EC patients that had markedly inferior progression free and disease free survival in a discovery cohort of 368 patients and an independent validation cohort of 290 patients from another center. Shallow whole genome sequencing revealed a higher burden of copy number abnormalities in the identified group, compared to other NSMP EC, in our discovery and validation cohorts. Taken together, our work demonstrates the power of AI to discover new knowledge, identifying a prognostically relevant subset of EC that is unrecognizable with conventional histopathological assessment, refining image-based tumor classification.

Supplementary Table

Summary of tumour genotyping

Supplementary Figure1

Ovarian cancer is the most lethal gynecologic cancer to date. High-grade serous ovarian carcinoma (HGSOC) accounts for most ovarian cancer cases, and it is most frequently diagnosed at advanced stages. Here, we developed a novel strategy to generate somatic ovarian cancer mouse models using a combination of in vivo electroporation and CRISPR-Cas9–mediated genome editing. Mutation of tumor suppressor genes associated with HGSOC in two different combinations (Brca1, Tp53, Pten with and without Lkb1) resulted in successfully generation of HGSOC, albeit with different latencies and pathophysiology. Implementing Cre lineage tracing in this system enabled visualization of peritoneal micrometastases in an immune-competent environment. In addition, these models displayed copy number alterations and phenotypes similar to human HGSOC. Because this strategy is flexible in selecting mutation combinations and targeting areas, it could prove highly useful for generating mouse models to advance the understanding and treatment of ovarian cancer.Significance:This study unveils a new strategy to generate genetic mouse models of ovarian cancer with high flexibility in selecting mutation combinations and targeting areas.

IC50 curves for ADI-PEG20 in ovarian cancer cell lines. A, ASS1-deficient ovarian cancer cell lines and B, ASS1-proficient ovarian cancer cell lines.

Package with all supplemental figures and tables

Table containing proteins differentially expressed in at least two rarer ovarian cancer subtypes when compared to HGSC.

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.

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.

Supplemental material and methods

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.

Clear cell ovarian carcinoma (CCOC) is the second most common subtype of epithelial ovarian carcinoma. Late-stage CCOC is not responsive to gold-standard chemotherapy and results in suboptimal outcomes for patients. In-depth molecular insight is urgently needed to stratify the disease and drive therapeutic development. We conducted global proteomics for 192 cases of CCOC and compared these with other epithelial ovarian carcinoma subtypes. Our results showed distinct proteomic differences in CCOC compared with other epithelial ovarian cancer subtypes including alterations in lipid and purine metabolism pathways. Furthermore, we report potential clinically significant proteomic subgroups within CCOC, suggesting the biologic plausibility of stratified treatment for this cancer. Taken together, our results provide a comprehensive understanding of the CCOC proteomic landscape to facilitate future understanding and research of this disease. © 2022 The Pathological Society of Great Britain and Ireland.

Ovarian cancer is the most lethal gynecologic cancer to date. High-grade serous ovarian carcinoma (HGSOC) accounts for most ovarian cancer cases, and it is most frequently diagnosed at advanced stages. Here, we developed a novel strategy to generate somatic ovarian cancer mouse models using a combination of in vivo electroporation and CRISPR-Cas9–mediated genome editing. Mutation of tumor suppressor genes associated with HGSOC in two different combinations (Brca1, Tp53, Pten with and without Lkb1) resulted in successfully generation of HGSOC, albeit with different latencies and pathophysiology. Implementing Cre lineage tracing in this system enabled visualization of peritoneal micrometastases in an immune-competent environment. In addition, these models displayed copy number alterations and phenotypes similar to human HGSOC. Because this strategy is flexible in selecting mutation combinations and targeting areas, it could prove highly useful for generating mouse models to advance the understanding and treatment of ovarian cancer. Significance: This study unveils a new strategy to generate genetic mouse models of ovarian cancer with high flexibility in selecting mutation combinations and targeting areas.

Background and study aims Argon plasma coagulation (APC) is an effective and safe modality for many gastrointestinal conditions requiring hemostasis and/or ablation. However, it can be quite costly. A potentially more cost-effective alternative is snare-tip spray coagulation (SC). This study aimed to determine whether SC would be a safe and effective alternative to APC using an ex-vivo model. Methods Using two resected porcine stomach, 36 randomized gastric areas were ablated for 2 seconds with either APC at 1.0 L/min 20 W (APC20) and 1.4 L/min 40 W (APC40) or SC with Effect 2 60 W (SC60) and 80 W (SC80) from 3 mm. Extent of tissue injury was then analyzed histopathologically. Results The mean coagulation depth was 790 ± 159 µm and 825 ± 467 µm for SC60 (n = 9) and SC80 (n = 8), respectively. This was compared to 539 ± 151 µm for APC20 (n = 8) and 779 ± 267 µm for APC40 (n = 9). Mean difference (MD) in coagulation depth between SC60 and APC40 was 12 µm (95 % confidence interval [CI], –191 to 214 µm; P = 0.91) and was 47 µm (95 %CI, –162 to 255 µm; P = 0.81) between SC80 and APC40. There was a greater depth of injury with APC40 (MD, 240 µm; 95 %CI, 62 to 418 µm; P = 0.04) and with SC60 (MD, 252 µm; 95 %CI, 141 to 362 µm; P = 0.004) when compared to APC20. Mean cross-sectional area of coagulation was 2.39 ± 0.852 mm² for SC60 and 2.54 ± 1.83 mm² for SC80 compared to 1.22 ± 0.569 mm² for APC20 and 1.99 ± 0.769 mm² for APC40. Seventy-eight percent reached the muscularis mucosa (MM) and 11 % the submucosa in the SC60 group compared to 50 % and 38 % in SC80 and 56 % and 11 % in APC40, respectively. Thirty-eight percent of APC20 specimens reached the MM. The muscularis propria was unaffected. Conclusions This small ex-vivo study suggests that SC60 and SC80 may be safe alternatives to APC40 with comparable coagulation depths and area effects.

Liver allografts are unique in solid organ transplantation as they are less susceptible to both acute and chronic rejection. Operational tolerance, defined as prolonged graft survival in the absence of immunosuppression, is also achieved more frequently with liver allografts. It is unknown if the presence of multiple allografts in the same individual, levels of immunosuppression, or the presence of cystic fibrosis (CF) impacts the livers ability to ward off rejection or achieve operational tolerance. We describe an unsensitized, ABO-compatible patient with CF who underwent double lung transplantation and several years later a combined liver-kidney transplant. He developed isolated late acute T-cell mediated rejection of his liver allograft despite a high level of immunosuppression (IS) required for his lung and kidney allografts. To our knowledge, this is the first case of isolated liver rejection in a patient with 3 separate organ allografts, or in a patient with CF, to be reported in the literature. This isolated liver rejection is out of keeping with typically accepted ideas about orthotopic liver tolerance.

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

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.

Colitis is a significant complication of immune checkpoint inhibitors (ICI). Currently, clinical and endoscopic severity are used to guide therapy.To investigate associations between clinical, endoscopic, and histological features with outcomes METHODS: We identified 149 patients from seven institutions with biopsy-proven ICI colitis. Biopsies were evaluated for histological features including the Geboes score, and the Robarts histopathological index (RHI) was calculated. Clinical, endoscopic, and histological data were tested for associations with biological use and adverse colitis outcomes (biological-refractory colitis, colectomy or death from colitis).Three mutually exclusive histological patterns were identified: acute colitis, chronic active colitis and microscopic colitis. Microscopic colitis was associated with older age (68.5 vs 61 years for acute colitis pattern, P = 0.02) and longer time to colitis (5.5 vs 3 months for the other patterns, P = 0.05). Biological use was associated with earlier time to colitis (2 vs 3 months, P = 0.04) and higher RHI (18 vs 12, P = 0.007). On multivariate analysis, RHI ≥14 was associated with biological use with an odds ratio of 4.5 (95% CI 1.4-13.8; P = 0.01). Adverse colitis outcomes were associated with shorter time to colitis (2 vs 3 months, P = 0.008) and higher RHI (24 vs 14, P = 0.001). On multivariate analysis, RHI ≥24 was associated with adverse colitis outcomes with an odds ratio 9.5 (95% CI 2.1-42.3 P = 0.003).Histological activity as measured by RHI is the only factor independently associated with biological use and adverse colitis outcomes. Prospective studies are needed to validate these findings to determine if histological activity should be incorporated into therapeutic algorithms.

Ovarian cancer is the most lethal gynecologic cancer to date. High-grade serous ovarian carcinoma (HGSOC) accounts for most ovarian cancer cases, and it is most frequently diagnosed at advanced stages. Here, we developed a novel strategy to generate somatic ovarian cancer mouse models using a combination of

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.

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 (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) 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 (CNNs) 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 at http://gan.aimlab.ca/.

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.

Objectives Approximately 15% of ECs are diagnosed in women before the natural age of menopause. Fertility-sparing conservative management options are increasingly utilized, however biomarkers to inform prognosis or direct therapy are lacking. We sought to determine the value of additional immunohistochemical biomarkers in young women with EC in the context of modern TCGA-based molecular classification. Methods Allred scores for estrogen/progesterone/androgen receptor and Ki67 in addition to immune characterization measuring stromal and epithelial expression of CD3/CD8/CD79a/CD138/PD1 and TILhigh vs. TILlow clusters was performed in a cohort of previously characterized(n=257) young women ( Results Young women had a high proportion of immune-rich ECs: 80% TILhigh compared with 60% TILhigh in non-age stratified cohorts. Expression of all immune biomarkers was enriched within POLE and MMRd subtypes. Within MMRd and p53wt ECs TILhigh immune cluster was associated with improved overall-(OS)and disease-specific survival (DSS)(p Conclusions Molecular classification with additional selective biomarker testing can provide additional prognostic information and may help stratify young women with ECs for targeted therapies.

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.

While endometrial cancer (EC) has an overall good prognosis, some patients do poorly and there is room for refinement within current classification systems. Using the TCGA prognostic grouping of EC, our group developed the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), which reliably and reproducibly stratifies ECs into four prognostic groups: POLEmut, p53wt/NSMP, MMRd (mismatch repair deficient), and p53abn, with the best prognosis for POLEmut to worst prognosis for p53abn. In the current study, global proteomic analysis was performed using the clinical SP3-CTP workflow on archival tissues from 151 patients including 40 MMRd, 34 POLEmut, 34 p53abn and 43 p53wt/NSMP, with clinical follow up data. Included in the cohort were 11 replicate samples (different parts of the same tumor) to examine spatial heterogeneity in the proteomic profiles. Replicate samples were highly correlated to each other, with the exception of three POLEmut cases with very poor correlation in the proteome in different parts of the tumor. As POLEmut tumors have an exceedingly high mutation burden, it is not surprising that this translates to heterogeneity at the proteomic level. Disease specific survival was examined to determine prognostic significance within the whole cohort and within individual molecular subgroups. High TOMM34, PLTP or TSFM expression was correlated to poor disease specific survival in the whole cohort and independently prognostic when molecular subtype, grade and histotype are considered. High MGST, NCL or XPNPEP3 were associated with poor outcomes within the p53wt/NSMP group. POLD2 and ENAH were prognostic within the MMRd group. Within the p53abn group, ACADVL and BABAM1 were found to be prognostic, and GRB7 was found to be enriched in the p53abn group compared to other molecular subtypes. As the group with the worst prognosis, p53abn group could benefit from novel therapeutic avenues. ACADVL, BABAM1 and GRB7 all lie within pathways that are potentially targetable. Our proteomic analysis has identified prognostic markers that may be useful in further refining current molecular classification to help guide treatment decisions. Furthermore, new therapeutic interventions could be developed to target proteins and pathways identified by this proteomics screen. Citation Format: Dawn R. Cochrane, Gian Negri, Jutta Huvila, David A. Farnell, Emily Thompson, Winnie Yang, Genny Trigo-Gonzales, Amy Lum, Sandra Spencer, Ryan Riley, Samuel Leung, Christine Chow, Jamie Lim, Martin Koebel, Stefan Kommoss, Friedrich Kommoss, Lien Hoang, David G. Huntsman, Gregg Morin, Jessica N. McAlpine. Global proteomic profiling of endometrial carcinomas identify prognostic markers [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR002.

Background: High-grade serous ovarian carcinoma (HGSOC) accounts for >70% of ovarian cancer-related deaths and is the most common ovarian cancer histotype, most originating from precancerous p53 lesions in the fallopian tube (FT) fimbria. Use of oral contraceptive pills (OCPs) for 5 years or more is associated with a >40% reduction in risk of HGSOC, but the mechanism is unknown. We hypothesize that OCP use reduces the incidence of p53 lesions. Our preliminary data show higher incidence of p53 lesions in post- compared to premenopausal women; therefore we aim to quantify p53 lesions in postmenopausal women who previously did or did not use OCPs. This will provide insight into the protective effects of OCPs against HGSOC. Preliminary Results: We determined the presence of p53 lesions by immunohistochemistry (IHC) in FT of women up to 40 years old (n=27) and >60 years old (n=24) who underwent salpingectomies for noncancer reasons. p53 lesions were identified in 3/27 cases of the younger cohort (11%) and in 10/24 of the older cohort (42%). Thus, we conclude an increased incidence of p53 lesions in older compared to younger women. Proposed Design: IHC for p53 will be performed on FT fimbria of women >55 years old who received salpingectomy for noncancer reasons. Based on an assumed reduction in p53 lesions of 35% in women who used OCPs for 5 years or more compared to nonusers (25 vs. 42%), analysis of 190 cases from each group will provide >80% power (p Conclusion: Our preliminary study found that 42% of postmenopausal women had p53 lesions, informing this study design. The study registered through this abstract will be the first to examine the impact of OCPs on the earliest known precursors of HGSOC. Citation Format: Kendall Greening, Anthony Karnezis, Dawn Cochrane, David Farnell, Lien Hoang, Gillian Hanley, David Huntsman. The effect of OCP use on the incidence of precancerous p53 lesions in fallopian tube fimbria [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B30.

Endometrial epithelium gives rise to both endometrial and ovarian cancers (of clear-cell and endometrioid subtypes), the latter arising from ectopic endometrium (endometriosis). Endometrial epithelium comprises mainly secretory cells, with a minor ciliated cell population. Due to their scarcity, little is known about the biology or function of endometrial ciliated cells. To understand the biology of endometrial epithelium, and by extension the cancers that arise from it, organoids derived from normal endometrial tissue were cultured. Notch signaling inhibitors were used to induce ciliated cell differentiation. Through single-cell RNA sequencing, distinct secretory and ciliated cell populations were observed, with the ciliated cell population increasing with Notch signaling inhibition. Many novel markers of ciliated cells were observed, but no highly specific markers of secretory cell differentiation. A marker of secretory cells (MST) and several markers of ciliated cells (FAM92B, WDR16 and DYDC2) were validated by immunohistochemistry on organoids and tissue sections. In endometrial tumors, both MST and FAM92B exhibited diffuse staining and were markers of better prognosis. This suggests that tumors expressing differentiation markers have better prognosis, whether it is a marker of secretory or ciliated cells. Interestingly, a small number of endometrial tumors stained positive for DYDC2; however, these tumors exhibited a variable staining pattern with 25-50% tumor cells staining intensely, and the remaining tumor cells not staining at all. A similar variable staining pattern had been observed previously with CTH, another ciliated cell marker. Endometrial and ovarian tumor tissue microarrays were stained with DYDC2, CTH and two ciliated cell markers, FOXJ1 and p73. For all these markers, a subset of tumors displayed a variable staining pattern and for endometrial cancers, the variable staining was a good prognostic indicator. Single-cell sequencing of endometrial tumors has been able to capture these two populations of tumor cells. In ovarian tumors, only variable CTH staining was a significant prognostic indicator. Normal endometrial secretory cells are able to differentiate into ciliated cells, and the variable staining pattern suggests that a subset of tumors retains this ability, and these are clinically less aggressive. Using single-cell sequencing technology on normal tissues to guide development of prognostic markers and provide insight into the biology of the tumors arising from these tissues may be useful for many other tumor types. Citation Format: Dawn R. Cochrane, Kieran R. Campbell, Kendall Greening, Germain C. Ho, James Hopkins, Minh Bui, Vassilena Sharlandjieva, Daniel Lai, Maya DeGrood, Evan W. Gibbard, Samuel Leung, Angela S. Cheng, Jamie L.P. Lim, Samantha Neilson, David Farnell, Friedrich Kommoss, Jessica N. McAlpine, Sohrab P. Shah, David G. Huntsman. Single-cell RNA sequencing of normal endometrial organoids uncovers novel cell-type markers for prognostication of primary tumor samples [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B09.

Obliterative portal venopathy (OPV) is a rare hepatic vascular disorder that has only recently become well recognized, in part from explanted liver of transplant patients. OPV often presents as a sudden onset of portal hypertension with no significant liver enzyme derangement. Histopathology remains the only means of diagnosis. We report a case of OPV in …

The EPODE Canadian Obesity Forum: Game Changer JEAN-MICHEL BORYS*, DENNIS EDELL*, EMILE LEVY*, JAN VINCK*, PAULINE HARPER*, MIREILLE ROILLET*, INGRID BAKKAR*, LULU COHEN-FARNELL*, DAVID FARNELL*, JULIE MAYER*, ZSOFIA ZAMBO* 1 EPODE International Network, Brussels, Belgium 2 EPODE European Network, Proteines, Paris, France 3 EPODE Canada, Toronto, ON, Canada University of Montreal, Montreal, QC, Canada Hepatology and Nutrition Unit, Sainte-Justine Mother-Child University Hospital, Montreal, QC, Canada Hasselt University, Hasselt, Belgium Viasano, Hasselt, Belgium Windesheim University of Applied Sciences, Zwolle, Netherlands 9 Jongeren Op Gezong Gewicht, Den Haag, Netherlands Real Food for Real Kids, Toronto, ON, Canada