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.
Our aim was to characterize the pathological, molecular and clinical outcomes of clear cell carcinoma of the endometrium (CCC).
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.
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.
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.
•There is molecular heterogeneity within clear cell carcinoma (CCC) of the endometrium.•P53wt CCC of endometrium show aggressive features and are distinct from other p53wt endometrial cancers.•Molecular classification of CCC of endometrium elicits prognostic parameters that are not apparent with histology alone.
Abstract 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.
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.
•Proteomics analysis has the potential to uncover new biomarkers for difficult to recognize tumors.•Mesonephric carcinoma and mesonephric-like carcinomas have similar proteomes.•Euchromatic histone lysine methyltransferase 2, eukaryotic translation elongation factor 1 alpha 2, and glutathione S-transferase Mu 3 may play a role in mesonephric cancers of the gynecologic tract.
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.
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.
•LINE-1 retrotransposon open reading frame 1 (L1ORF1p) is expressed in many cancers.•L1ORF1p associates with advanced stage and serous histotype in gynecological cancers.•L1ORF1p is expressed in STIC lesions, including in p53 null lesions.
Abstract 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.
Abstract
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.
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