The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-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 H&E 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 H&E-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 Wiley & Sons, Ltd.

The proteome of clear cell ovarian carcinoma.

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.

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

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.

Modelling hereditary diffuse gastric cancer initiation using transgenic mouse-derived gastric organoids and single-cell sequencing.

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.

Single cell transcriptomes of normal endometrial derived organoids uncover novel cell type markers and cryptic differentiation of primary tumours.

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.

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

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.

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies.

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.

Molecular subtypes of clear cell carcinoma of the endometrium: Opportunities for prognostic and predictive stratification.

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.

Learning generalizable AI models for multi-center histopathology image classification.

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA's potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers.

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 employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed 'p53abn-like NSMP'), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the 'p53abn-like NSMP' group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study's findings are applicable exclusively to females.

Liver biochemical flare with immune checkpoint therapy in metastatic Merkel cell carcinoma: A liver biopsy is always necessary.

Merkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine carcinoma of the skin. Treatment for locoregional MCC includes local excision with regional lymphadenectomy, followed by adjuvant radiotherapy. Immune checkpoint inhibitors (ICI) have emerged as a breakthrough treatment of metastatic MCC. Nevertheless, T-cell immune response is triggered against self-antigens resulting in immune-mediated toxicities, including ICI-mediated hepatotoxicity. We report a case of recurrent metastatic MCC treated with avelumab, a PD-L1 inhibitor, with subsequent significant liver biochemical flare. The initial clinical diagnosis was ICI-mediated hepatotoxicity. Workup to rule out competing causes of liver injury came back negative. Hence, avelumab was discontinued, and the patient was initiated on steroid therapy with stepwise escalation. Owing to clinical and laboratory deterioration, it was then decided to perform a percutaneous liver biopsy to document steroid-refractory ICI-mediated hepatotoxicity and/or rule out other causes of potential liver injury. The liver biopsy showed MCC tumor cells almost entirely infiltrating the hepatic parenchyma, confirmed by immunohistochemistry. At that point, steroid therapy was discontinued, and the patient was transitioned into palliative care. In conclusion, patients with apparent ICI-related hepatotoxicity should always be considered for a liver biopsy to exclude massive infiltrative malignancy as the true cause of liver dysfunction.

Well-Differentiated Grade 3 Neuroendocrine Tumors: Characteristics, Treatments, and Outcomes From a Population-Based Study.

OBJECTIVES: 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. METHODS: The British Columbia provincial database was queried for G3 NETs diagnosed 2004 to 2021, and charts were reviewed to describe clinical features and outcomes. RESULTS: 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). CONCLUSIONS: 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.

Spray coagulation with snare-tip versus argon plasma coagulation: An ex vivo study evaluating tissue effects.

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 ±â€Š159 µm and 825 ±â€Š467 µm for SC60 (n = 9) and SC80 (n = 8), respectively. This was compared to 539 ±â€Š151 µm for APC20 (n = 8) and 779 ±â€Š267 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 ±â€Š0.852 mm² for SC60 and 2.54 ±â€Š1.83 mm² for SC80 compared to 1.22 ±â€Š0.569 mm² for APC20 and 1.99 ±â€Š0.769 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.

Isolated Liver Rejection After Lung and Combined Kidney-Liver Transplantation: A Case Report.

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.

Incidental alpha-1-antitrypsin deficiency found in post-transplant liver allografts: Report of two cases.

Alpha-1 antitrypsin deficiency is an autosomal recessive disease most commonly caused by misfolding of the Alpha-1-antitrypsin protein, which prevents its release from hepatocytes into the systemic circulation. This results in increased lifetime risk of liver and lung disease. Due to its variable penetrance, presentation and natural history, patients with alpha-1 antitrypsin deficiency are often underdiagnosed. In this report, we present two cases of alpha-1 antitrypsin deficiency in deceased-donor liver transplant allografts diagnosed post-transplant. There is currently no known adverse outcome directly linked to alpha-1 antitrypsin deficiency in the immediate post-transplant follow-up period. Thus, these allografts should not be excluded from transplantation.

Modeling High-Grade Serous Ovarian Carcinoma Using a Combination of In Vivo Fallopian Tube Electroporation and CRISPR-Cas9-Mediated Genome Editing.

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.

The significance of histological activity measurements in immune checkpoint inhibitor colitis.

BACKGROUND: Colitis is a significant complication of immune checkpoint inhibitors (ICI). Currently, clinical and endoscopic severity are used to guide therapy. AIMS: 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). RESULTS: 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). CONCLUSION: 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.

Whole-proteome analysis of mesonephric-derived cancers describes new potential biomarkers.

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/MLC & FATWO 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.

Establishment and characterization of VOA1066 cells: An undifferentiated endometrial carcinoma cell line.

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.

Arginine Depletion Therapy with ADI-PEG20 Limits Tumor Growth in Argininosuccinate Synthase-Deficient Ovarian Cancer, Including Small-Cell Carcinoma of the Ovary, Hypercalcemic Type.

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.