Search

To examine the hypothesis that colorectal carcinomas with and without TP53 mutations may be characterised by aetiological heterogeneity, we analysed a group of 107 patients with primary Dukes' C colorectal cancer seen at the Memorial Sloan-Kettering Cancer Center (MSKCC) from 1986 to 1990. We assessed p53 overexpression using the monoclonal antibody PAb 1801, and identified 42 (39%) patients displaying p53-positive phenotype, defined as > or = 25% of positive cells. Patients with two or more first-degree relatives with cancer had an odds ratio (OR) of 2.9 (95% CI 1.0-8.3) for p53 overexpression in comparison with those without a family history of cancer (trend test, P = 0.11). A possible association between body weight and p53 overexpression was observed. The ORs were 1.9 for the second quartile, 1.9 for the third quartile and 3.4 for the highest quartile in comparison with the lowest quartile (trend test, P = 0.06). No association between occupational physical activity, smoking, drinking, parity and p53 overexpression was identified. The results suggest that p53 overexpression may be related to genetic predisposition to colorectal cancer, and p53-positive and p53-negative colorectal cancers may be controlled by different aetiological pathways.

Artificial intelligence (AI) systems can improve cancer diagnosis, yet their development often relies on subjective histologic features as ground truth for training. Herein, we developed an AI model applied to histologic whole-slide images using CDH1 biallelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 biallelic mutations (accuracy = 0.95) and diagnosed ILC (accuracy = 0.96). A total of 74% of samples classified by the AI model as having CDH1 biallelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and noncoding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI models applied to whole-slide image. Significance: Genetic alterations linked to strong genotypic-phenotypic correlations can be utilized to develop AI systems applied to pathology that facilitate cancer diagnosis and biologic discoveries., (©2024 American Association for Cancer Research.)

Acinar cell carcinoma (ACC) and pancreatoblastoma (PBL) are rare pancreatic malignancies with acinar differentiation. Proteogenomic profiling of ACC and PBL revealed distinct protein expression patterns compared to pancreatic ductal adenocarcinoma (PDAC) and benign pancreas. ACC and PBL exhibited similarities, with enrichment in proteins related to RNA processing, chromosome organization, and the mitoribosome, while PDACs overexpressed proteins associated with actin-based processes, extracellular matrix, and immune-active stroma. Pathway activity differences in metabolic adaptation, epithelial-to-mesenchymal transition, and DNA repair were characterized between these diseases. PBL showed upregulation of Wnt-CTNNB1 and IGF2 pathways. Seventeen ACC-specific proteins suggested connections to metabolic diseases with mitochondrial dysfunction, while 34 PBL-specific proteins marked this pediatric cancer with an embryonic stem cell phenotype and alterations in chromosomal proteins and the cell cycle. This study provides novel insights into the proteomic landscapes of ACC and PBL, offering potential targets for diagnostic and therapeutic development., (© 2024. The Author(s).)

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data., (© 2024. The Author(s).)

Inactivating alterations in the SWItch/Sucrose NonFermentable (SWI/SNF) Chromatin Remodeling Complex subunits have been described in multiple tumor types. Recent studies focused on SMARC subunits of this complex to understand their relationship with tumor characteristics and therapeutic opportunities. To date, pancreatic cancer with these alterations has not been well studied, although isolated cases of undifferentiated carcinomas have been reported. Herein, we screened 59 pancreatic undifferentiated carcinomas for alterations in SWI/SNF complex-related (SMARCB1 [BAF47/INI1], SMARCA4 [BRG1], SMARCA2 [BRM]) proteins and/or genes using immunohistochemistry and/or next-generation sequencing. Cases with alterations in SWI/SNF complex-related proteins/genes were compared with cases without alterations, as well as with 96 conventional pancreatic ductal adenocarcinomas (PDAC). In all tumor groups, mismatch repair and PD-L1 protein expression were also evaluated. Thirty of 59 (51%) undifferentiated carcinomas had a loss of SWI/SNF complex-related protein expression or gene alteration. Twenty-seven of 30 (90%) SWI-/SNF-deficient undifferentiated carcinomas had rhabdoid morphology (vs 9/29 [31%] SWI-/SNF-retained undifferentiated carcinomas; P < .001) and all expressed cytokeratin, at least focally. Immunohistochemically, SMARCB1 protein expression was absent in 16/30 (53%) cases, SMARCA2 in 4/30 (13%), and SMARCA4 in 4/30 (13%); both SMARCB1 and SMARCA2 protein expressions were absent in 1/30 (3%). Five of 8 (62.5%) SWI-/SNF-deficient undifferentiated carcinomas that displayed loss of SMARCB1 protein expression by immunohistochemistry were found to have corresponding SMARCB1 deletions by next-generation sequencing. Analysis of canonical driver mutations for PDAC in these cases showed KRAS (2/5) and TP53 (2/5) abnormalities. Median combined positive score for PD-L1 (E1L3N) was significantly higher in the undifferentiated carcinomas with/without SWI/SNF deficiency compared with the conventional PDACs (P < .001). SWI-/SNF-deficient undifferentiated carcinomas were larger (P < .001) and occurred in younger patients (P < .001). Patients with SWI-/SNF-deficient undifferentiated carcinoma had worse overall survival compared with patients with SWI-/SNF-retained undifferentiated carcinoma (P = .004) and PDAC (P < .001). Our findings demonstrate that SWI-/SNF-deficient pancreatic undifferentiated carcinomas are frequently characterized by rhabdoid morphology, exhibit highly aggressive behavior, and have a negative prognostic impact. The ones with SMARCB1 deletions appear to be frequently KRAS wild type. Innovative developmental therapeutic strategies targeting this genomic basis of the SWI/SNF complex and the therapeutic implications of EZH2 inhibition (NCT03213665), SMARCA2 degrader (NCT05639751), or immunotherapy are currently under investigation., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

Pancreatic neuroendocrine tumors (PanNETs) represent well-differentiated endocrine neoplasms with variable clinical outcomes. Predicting patient outcomes using the current tumor grading system is challenging. In addition, traditional systemic treatment options for PanNETs, such as somatostatin analogs or cytotoxic chemotherapies, are very limited. To address these issues, we characterized PanNETs using integrated proteogenomics and identified four subtypes. Two proteomic subtypes showed high recurrence rates, suggesting clinical aggressiveness that was missed by current classification. Hypoxia and inflammatory pathways were significantly enriched in the clinically aggressive subtypes. Detailed analyses revealed metabolic adaptation via glycolysis upregulation and oxidative phosphorylation downregulation under hypoxic conditions. Inflammatory signature analysis revealed that immunosuppressive molecules were enriched in immune hot tumors and might be immunotherapy targets. In this study, we characterized clinically aggressive proteomic subtypes of well-differentiated PanNETs and identified candidate therapeutic targets., Competing Interests: D.S.K. is now an employee of Paige.AI. M.H.R. is a member of the Scientific Advisory Boards of Azenta Life Sciences and Universal Diagnostics (UDX). None of these companies had any role in the support, design, execution, data analysis, or any other aspect of this study., (© 2024 Published by Elsevier Inc.)

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate., Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background & Aims: Cytologic and histopathologic diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice, whereas it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms., Methods: DNA methylation data were obtained for 301 primary tumors spanning 6 primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and extreme gradient boosting machine learning models were trained to distinguish between tumor types. Methylation data of 29 nonpancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin., Results: After benchmarking 3 state-of-the-art machine learning models, the random forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold improved the random forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of nonpancreatic neoplasms achieved an area under the curve of >0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample., Conclusions: Pancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Metastatic progression of colorectal adenocarcinoma (CRC) remains poorly understood and poses significant challenges for treatment. To overcome these challenges, we performed multiomics analyses of primary CRC and liver metastases. Genomic alterations, such as structural variants or copy number alterations, were enriched in oncogenes and tumor suppressor genes and increased in metastases. Unsupervised mass spectrometry-based proteomics of 135 primary and 123 metastatic CRCs uncovered distinct proteomic subtypes, three each for primary and metastatic CRCs, respectively. Integrated analyses revealed that hypoxia, stemness, and immune signatures characterize these 6 subtypes. Hypoxic CRC harbors high epithelial-to-mesenchymal transition features and metabolic adaptation. CRC with a stemness signature shows high oncogenic pathway activation and alternative telomere lengthening (ALT) phenotype, especially in metastatic lesions. Tumor microenvironment analysis shows immune evasion via modulation of major histocompatibility complex (MHC) class I/II and antigen processing pathways. This study characterizes both primary and metastatic CRCs and provides a large proteogenomics dataset of metastatic progression., Competing Interests: Declaration of interests D.S.K. was a consultant for and is now employed by Paige.AI. P.G.B. and J.G. are employees of and hold equity interests in Genuity Science. J.Y.W. is the founder of Curandis. M.H.A.R. is a member of the scientific advisory boards of Azenta Life Sciences and Universal DX. None of these companies had any role in design, execution, data analysis, or any other aspect of this study., (Copyright © 2024. Published by Elsevier Inc.)

Context.—: Tumoral (grossly visible) intraductal neoplasms of the bile ducts are still being characterized., Objective.—: To investigate their morphologic, immunohistochemical, and molecular features., Design.—: Forty-one cases were classified as gastric-, intestinal-, pancreatobiliary-type intraductal papillary neoplasm (IPN), intraductal oncocytic papillary neoplasm (IOPN), or intraductal tubulopapillary neoplasm (ITPN) on the basis of histology. All neoplasms were subjected to targeted next-generation sequencing., Results.—: The mean age at diagnosis was 69 years (42-81 years); male to female ratio was 1.3. Most neoplasms (n = 23, 56%) were extrahepatic/large (mean size, 4.6 cm). The majority (n = 32, 78%) contained high-grade dysplasia, and 68% (n = 28) revealed invasion. All gastric-type IPNs (n = 9) and most ITPNs/IOPNs showed consistent colabeling for CK7/MUC6, which was less common among others (P = .004). Intestinal-type IPNs (n = 5) showed higher rates of CK20 expression than others (P < .001). Overall, the most commonly mutated genes included TP53 and APC, while copy number variants affected ELF3 and CDKN2A/B. All gastric-type IPNs contained an alteration affecting the Wnt signaling pathway; 7 of 9 (78%) showed aberrations in the MAPK pathway. Mutations in APC and KRAS were common in gastric-type IPNs as compared with others (P = .01 for both). SMAD4 was more frequently mutated in intestinal-type IPNs (P = .02). Pancreatobiliary-type IPNs (n = 14) exhibited frequent alterations in tumor suppressor genes including TP53, CDKN2A/B, and ARID2 (P = .04, P = .01 and P = .002, respectively). Of 6 IOPNs analyzed, 3 (50%) revealed ATP1B1-PRKACB fusion. ITPNs (n = 6) showed relatively few recurrent genetic aberrations. Follow-up information was available for 38 patients (median, 58.5 months). The ratio of disease-related deaths was higher for the cases with invasion (56% versus 10%)., Conclusions.—: Tumoral intraductal neoplasms of the bile ducts, similar to their counterparts in the pancreas, are morphologically and genetically heterogeneous., (© 2023 College of American Pathologists.)

Solitary fibrous tumor (SFT) has been increasingly reported in various anatomic sites. However, it is still extremely rare in the pancreas. Herein, we present the first series of primary pancreatic SFTs. Nine cases of primary pancreatic SFTs were analyzed. The mean age was 60 years (36 to 76 y) with no sex predilection. Six tumors were in the head, 3 were in the tail. On imaging studies, tumors were described as a hypervascular mass, 2 revealed cystic areas, and 3 were favored to be neuroendocrine tumors. On biopsy, 2 cases were diagnosed as atypical spindle cell tumor; one was misdiagnosed as suspicious for sarcoma, and another case as metastatic renal cell carcinoma. Two were diagnosed as low-grade sarcoma and low-grade stromal tumor on frozen sections. Grossly, tumors were well-demarcated with a median size of 4 cm (0.9 to 15 cm). Microscopically, they were composed of ovoid to spindle tumor cells with no significant mitotic activity and were arranged in alternating hypercellular and hypocellular areas. Staghorn-like vessels and entrapped pancreatic parenchyma were also detected within all tumors. Tumor cells revealed diffuse/strong nuclear STAT6 expression in 7 of 8, CD34 in 7 of 9, and bcl-2 in 4 of 4 tested cases. One tested tumor harbored NAB2 - STAT6 fusion. Eight patients with available follow-up data were free of disease at a mean follow-up of 76 months (3 to 189 mo). SFT should be considered in the differential diagnoses of mesenchymal neoplasms of the pancreas. Immunohistochemical nuclear STAT6 expression is a characteristic feature of SFT. Primary pancreatic SFTs seem to have favorable biological behavior in our series., Competing Interests: Conflicts of Interest and Source of Funding: Funded in part by the Melamed Family Foundation and by the Cancer Center Support Grant of the National Institutes of Health/National Cancer Institute under award number P30CA008748. J.S. is supported in part by the National Cancer Institute of the National Institutes of Health under award number R37CA262209. For the remaining authors, none were declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Context.—: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking., Objective.—: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance., Design.—: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads., Results.—: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase., Conclusions.—: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology., (© 2023 College of American Pathologists.)

Understanding the timing and spectrum of genetic alterations that contribute to the development of pancreatic cancer is essential for effective interventions and treatments. The aim of this study was to characterize somatic ATM alterations in noninvasive pancreatic precursor lesions and invasive pancreatic adenocarcinomas from patients with and without pathogenic germline ATM variants. DNA was isolated and sequenced from the invasive pancreatic ductal adenocarcinomas and precursor lesions of patients with a pathogenic germline ATM variant. Tumor and precursor lesions from these patients as well as colloid carcinoma from patients without a germline ATM variant were immunolabeled to assess ATM expression. Among patients with a pathogenic germline ATM variant, somatic ATM alterations, either mutations and/or loss of protein expression, were identified in 75.0% of invasive pancreatic adenocarcinomas but only 7.1% of pancreatic precursor lesions. Loss of ATM expression was also detected in 31.0% of colloid carcinomas from patients unselected for germline ATM status, significantly higher than in pancreatic precursor lesions [pancreatic intraepithelial neoplasms (p = 0.0013); intraductal papillary mucinous neoplasms, p = 0.0040] and pancreatic ductal adenocarcinoma (p = 0.0076) unselected for germline ATM status. These data are consistent with the second hit to ATM being a late event in pancreatic tumorigenesis. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

Objectives: To identify factors associated with concordance between World Health Organization (WHO) grade on cytological analysis (c-grade) and histopathological analysis (h-grade) of surgical specimen in patients with PanNETs and examine trends in utilization and accuracy of EUS-FNA in preoperatively predicting grade., Background: WHO grading system is prognostic in pancreatic neuroendo-crine tumors (PanNETs). The concordance between c-grade and h-grade is reported to be between 50% and 92%., Methods: A multicenter retrospective study was performed on patients undergoing resection for PanNETs at four high-volume centers between 2010 and 2019. Patients with functional or syndrome-associated tumors, and those receiving neoadjuvant therapy were excluded. Factors associated with concordance between c-grade and h-grade and trends of utilization of EUS-FNA were assessed., Results: Of 869 patients included, 517 (59.5%) underwent EUS-FNA; 452 (87.4%) were diagnostic of PanNETs and WHO-grade was reported for 270 (59.7%) patients. The concordance between c-grade and h-grade was 80.4% with moderate concordance ( Kc = 0.52, 95% CI: 0.41-0.63). Significantly higher rates of concordance were observed in patients with smaller tumors (<2 vs. ≥2cm, 81.1% vs. 60.4%, P = 0.005). Highest concordance (98.1%) was observed in patients with small tumors undergoing assessment between 2015-2019 with a near-perfect concordance ( Kc = 0.88, 95% CI: 0.61-1.00). An increase in the utilization of EUS-FNA (56.1% to 64.1%) was observed over the last 2 decades ( P = 0.017) and WHO-grade was more frequently reported (44.2% vs. 77.6%, P < 0.001). However, concordance between c-grade and h-grade did not change significantly (P = 0.118)., Conclusion: Recently, a trend towards increasing utilization and improved diagnostic accuracy of EUS-FNA has been observed in PanNETs. Concordance between c-grade and h-grade is associated with tumor size with near-perfect agreement when assessing PanNETs <2cm in size., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Nonfunctional pancreatic neuroendocrine tumors display a wide range of biological behavior, and nodal disease is associated with metastatic disease and poorer survival. The aim of this study was to develop a tool to predict nodal disease in patients with small (≤2 cm) nonfunctional pancreatic neuroendocrine tumors., Methods: A multicenter retrospective study was performed on patients undergoing resection for small nonfunctional pancreatic neuroendocrine tumors. Patients with genetic syndromes, metastatic disease at diagnosis, neoadjuvant therapy, or positive resection margin were excluded. Factors associated with nodal disease were identified to develop a predictive model. Internal validation was performed using bootstrap with 1,000 resamples., Results: Nodal disease was observed in 39 (11.1%) of the 353 patients included. Presence of nodal disease was significantly associated with lower 5-year disease-free survival (71.6% vs 96.2%, P < .001). Two predictors were strongly associated with nodal disease: G2 grade (odds ratio: 3.51, 95% confidence interval: 1.71-7.22, P = .001) and tumor size (per mm increase, odds ratio: 1.14, 95% confidence interval: 1.03-1.25, P = .009). Adequate discrimination was observed with an area under the curve of 0.71 (95% confidence interval: 0.63-0.80). Based on risk distribution, 3 risk groups of nodal disease were identified; low (<5%), intermediate (≥5% to <20%), and high (≥20%) risk. The observed mean risk of nodal disease was 3.7% in the low-risk patients, 9.6% in the intermediate-risk patients, and 30.4% in the high-risk patients (P < .001). The 10-year disease-free survival in the low, intermediate, and high-risk groups was 100%, 88.8%, and 50.1%, respectively., Conclusion: Our model using tumor grade and size can predict nodal disease in small nonfunctional pancreatic neuroendocrine tumors. Integration of this tool into clinical practice could help guide management of these patients., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Context.—: Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined., Objective.—: To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers., Design.—: With a universal slide viewer used in clinical routine diagnostics, we evaluated the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels., Results.—: Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression., Conclusions.—: This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.

Objective: To determine the associations of pancreatobiliary maljunction (PBM) in the West., Background: PBM (anomalous union of common bile duct and pancreatic duct) is mostly regarded as an Asian-only disorder, with 200X risk of gallbladder cancer (GBc), attributed to reflux of pancreatic enzymes. Methods: Radiologic images of 840 patients in the US who underwent pancreatobiliary resections were reviewed for PBM and contrasted with 171 GBC cases from Japan., Results: Eight % of the US GBCs (24/300) had PBM (similar to Japan; 15/ 171, 8.8%), in addition to 1/42 bile duct carcinomas and 5/33 choledochal cysts. None of the 30 PBM cases from the US had been diagnosed as PBM in the original work-up. PBM was not found in other pancreatobiliary disorders. Clinicopathologic features of the 39 PBM-associated GBCs (US:24, Japan:15) were similar; however, comparison with non-PBM GBCs revealed that they occurred predominantly in females (F/M = 3); at younger (<50-year-old) age (21% vs 6.5% in non-PBM GBCs; P = 0.01); were uncommonly associated with gallstones (14% vs 58%; P < 0.001); had higher rate of tumor-infiltrating lymphocytes (69% vs 44%; P = 0.04); arose more often through adenoma-carcinoma sequence (31% vs 12%; P = 0.02); and had a higher proportion of nonconventional carcinomas (21% vs 7%; P = 0.03). Conclusions: PBM accounts for 8% of GBCs also in the West but is typically undiagnosed. PBM-GBCs tend to manifest in younger age and often through adenoma-carcinoma sequence, leading to unusual carcinoma types. If PBM is encountered, cholecystectomy and surveillance of bile ducts is warranted. PBM-associated GBCs offer an invaluable model for variant anatomy-induced chemical (reflux-related) carcinogenesis., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Pancreatic neoplasms are heterogenous and have traditionally been classified by assessing their lines of cellular differentiation using histopathologic methods, particularly morphologic and immunohistochemical evaluation. These methods frequently identify overlapping differentiation along ductal, acinar, and neuroendocrine lines, raising diagnostic challenges as well as questions regarding the relationship of these neoplasms. Neoplasms with acinar differentiation, in particular, frequently show more than one line of differentiation based on immunolabeling. Genome methylation signatures, in contrast, are better conserved within cellular lineages, and are increasingly used to support the classification of neoplasms. We characterized the epigenetic relationships between pancreatoblastomas, acinar cell carcinomas (including mixed variants), pancreatic neuroendocrine tumors, solid pseudopapillary neoplasms, and pancreatic ductal adenocarcinomas using a genome-wide array platform. Using unsupervised learning approaches, pancreatic neuroendocrine tumors, solid pseudopapillary neoplasms, ductal adenocarcinomas, and normal pancreatic tissue samples all localized to distinct clusters based on their methylation profiles, whereas all neoplasms with acinar differentiation occupied a broad overlapping region located between the predominantly acinar normal pancreatic tissue and ductal adenocarcinoma clusters. Our data provide evidence to suggest that acinar cell carcinomas and pancreatoblastomas are similar at the epigenetic level. These findings are consistent with genomic and clinical observations that mixed acinar neoplasms are closely related to pure acinar cell carcinomas rather than to neuroendocrine tumors or ductal adenocarcinomas., (© 2021. The Author(s), under exclusive licence to United States & Canadian Academy of Pathology.)

Chromosomal aneuploidies are prognostic markers across a wide variety of tumor types, and recent literature suggests that pancreatic neuroendocrine tumors are no different. In this study 214 patients with grade 1, 2, or 3 pancreatic neuroendocrine tumors had their tissue examined for chromosomal copy number alterations using next-generation sequencing. Univariate and multivariate statistical analyses were performed with all-cause mortality and disease-specific mortality as the end comparators. As such, the cohort stratified into 3 different clinically relevant chromosomal subgroups: an indolent subgroup characterized by loss of chromosome 11 in relative isolation, an aggressive subgroup characterized by losses of chromosomes 1, 2, 3, 6, 10, 11, 16, and 22 and with no loss of chromosomes 4, 5, 7, 12, 14, 17, 19, and 20, and finally a heterogeneous third group with a subset of cases that behave even more aggressively than the aforementioned., Competing Interests: Conflicts of Interest and Source of Funding: D.C. work is supported by the Ovarian Cancer Research Alliance [Ann Schreiber Mentored Investigator Award; grant number 650320]. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Currently, there are no internationally accepted consensus guidelines for pathologic evaluation of posttherapy pancreatectomy specimens. The Neoadjuvant Therapy Working Group of Pancreatobiliary Pathology Society was formed in 2018 to review grossing protocols, literature, and major issues and to develop recommendations for pathologic evaluation of posttherapy pancreatectomy specimens. The working group generated the following recommendations: (1) Systematic and standardized grossing and sampling protocols should be adopted for pancreatectomy specimens for treated pancreatic ductal adenocarcinoma (PDAC). (2) Consecutive mapping sections along the largest gross tumor dimension are recommended to validate tumor size by histology as required by the College of American Pathologists (CAP) cancer protocol. (3) Tumor size of treated PDACs should be measured microscopically as the largest dimension of tumor outer limits that is bound by viable tumor cells, including intervening stroma. (4) The MD Anderson grading system for tumor response has a better correlation with prognosis and better interobserver concordance among pathologists than does the CAP system. (5) A case should not be classified as a complete response unless the entire pancreas, peripancreatic tissues, ampulla of Vater, common bile duct, and duodenum adjacent to the pancreas are submitted for microscopic examination. (6) Future studies on tumor response of lymph node metastases, molecular and/or immunohistochemical markers, as well as application of artificial intelligence in grading tumor response of treated PDAC are needed. In summary, systematic, standardized pathologic evaluation, accurate tumor size measurement, and reproducible tumor response grading to neoadjuvant therapy are needed for optimal patient care. The criteria and discussions provided here may provide guidance towards these goals., Competing Interests: Conflicts of Interest and Source of Funding: H.W. received the National Institutes of Health grants (1R01CA196941, 1R01CA195651, U01CA196403, P01CA117969, and P50CA221707). J.S. received the National Institutes of Health grant (K08CA234222). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Cancer immunotherapy can result in lasting tumor regression, but predictive biomarkers of treatment response remain ill-defined. Here, we performed single-cell proteomics, transcriptomics, and genomics on matched untreated and IL2 injected metastases from patients with melanoma. Lesions that completely regressed following intralesional IL2 harbored increased fractions and densities of nonproliferating CD8+ T cells lacking expression of PD-1, LAG-3, and TIM-3 (PD-1-LAG-3-TIM-3-). Untreated lesions from patients who subsequently responded with complete eradication of all tumor cells in all injected lesions (individuals referred to herein as "extreme responders") were characterized by proliferating CD8+ T cells with an exhausted phenotype (PD-1+LAG-3+TIM-3+), stromal B-cell aggregates, and expression of IFNγ and IL2 response genes. Loss of membranous MHC class I expression in tumor cells of untreated lesions was associated with resistance to IL2 therapy. We validated this finding in an independent cohort of metastatic melanoma patients treated with intralesional or systemic IL2. Our study suggests that intact tumor-cell antigen presentation is required for melanoma response to IL2 and describes a multidimensional and spatial approach to develop immuno-oncology biomarker hypotheses using routinely collected clinical biospecimens., (©2022 American Association for Cancer Research.)

Competing Interests: The authors have no relevant financial interest in the products or companies described in this article.

In this review, we detail the changes and the relevant features that are applied to neuroendocrine neoplasms (NENs) in the 2022 WHO Classification of Endocrine and Neuroendocrine Tumors. Using a question-and-answer approach, we discuss the consolidation of the nomenclature that distinguishes neuronal paragangliomas from epithelial neoplasms, which are divided into well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). The criteria for these distinctions based on differentiation are outlined. NETs are generally (but not always) graded as G1, G2, and G3 based on proliferation, whereas NECs are by definition high grade; the importance of Ki67 as a tool for classification and grading is emphasized. The clinical relevance of proper classification is explained, and the importance of hormonal function is examined, including eutopic and ectopic hormone production. The tools available to pathologists for accurate classification include the conventional biomarkers of neuroendocrine lineage and differentiation, INSM1, synaptophysin, chromogranins, and somatostatin receptors (SSTRs), but also include transcription factors that can identify the site of origin of a metastatic lesion of unknown primary site, as well as hormones, enzymes, and keratins that play a role in functional and structural correlation. The recognition of highly proliferative, well-differentiated NETs has resulted in the need for biomarkers that can distinguish these G3 NETs from NECs, including stains to determine expression of SSTRs and those that can indicate the unique molecular pathogenetic alterations that underlie the distinction, for example, global loss of RB and aberrant p53 in pancreatic NECs compared with loss of ATRX, DAXX, and menin in pancreatic NETs. Other differential diagnoses are discussed with recommendations for biomarkers that can assist in correct classification, including the distinctions between epithelial and non-epithelial NENs that have allowed reclassification of epithelial NETs in the spine, in the duodenum, and in the middle ear; the first two may be composite tumors with neuronal and glial elements, and as this feature is integral to the duodenal lesion, it is now classified as composite gangliocytoma/neuroma and neuroendocrine tumor (CoGNET). The many other aspects of differential diagnosis are detailed with recommendations for biomarkers that can distinguish NENs from non-neuroendocrine lesions that can mimic their morphology. The concepts of mixed neuroendocrine and non-neuroendocrine (MiNEN) and amphicrine tumors are clarified with information about how to approach such lesions in routine practice. Theranostic biomarkers that assist patient management are reviewed. Given the significant proportion of NENs that are associated with germline mutations that predispose to this disease, we explain the role of the pathologist in identifying precursor lesions and applying molecular immunohistochemistry to guide genetic testing., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)