Fast-track development and multi-institutional clinical validation of an artificial intelligence algorithm for detection of lymph node metastasis in colorectal cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer. The aim of this study was to develop of a clinical-grade digital pathology tool for LNM detection in colorectal cancer (CRC) using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from five pathology departments digitized by four different scanning systems. A high-quality, large training dataset was generated within 7 days, and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980-1.000, 0.997-1.000, and 0.913-0.990, correspondingly. Only 5 of 14460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multi-scanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test datasets to facilitate academic research.

Structure-guided design of a selective inhibitor of the methyltransferase KMT9 with cellular activity.

Inhibition of epigenetic regulators by small molecules is an attractive strategy for cancer treatment. Recently, we characterised the role of lysine methyltransferase 9 (KMT9) in prostate, lung, and colon cancer. Our observation that the enzymatic activity was required for tumour cell proliferation identified KMT9 as a potential therapeutic target. Here, we report the development of a potent and selective KMT9 inhibitor (compound 4, KMI169) with cellular activity through structure-based drug design. KMI169 functions as a bi-substrate inhibitor targeting the SAM and substrate binding pockets of KMT9 and exhibits high potency, selectivity, and cellular target engagement. KMT9 inhibition selectively downregulates target genes involved in cell cycle regulation and impairs proliferation of tumours cells including castration- and enzalutamide-resistant prostate cancer cells. KMI169 represents a valuable tool to probe cellular KMT9 functions and paves the way for the development of clinical candidate inhibitors as therapeutic options to treat malignancies such as therapy-resistant prostate cancer.

An inducible Cd79b mutation confers ibrutinib sensitivity in mouse models of Myd88-driven diffuse large B-cell lymphoma.

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma and constitutes a highly heterogenous disease. Recent comprehensive genomic profiling revealed the identity of numerous molecularly defined DLBCL subtypes, including a cluster which is characterized by recurrent aberrations in MYD88, CD79B, and BCL2, as well as various lesions promoting a block in plasma cell differentiation, including PRDM1, TBL1XR1, and SPIB. Here, we generated a series of autochthonous mouse models to mimic this DLBCL cluster and specifically focused on the impact of Cd79b mutations in this setting. We show that canonical Cd79b immunoreceptor tyrosine-based activation motif (ITAM) mutations do not accelerate Myd88- and BCL2-driven lymphomagenesis. Cd79b-mutant murine DLBCL were enriched for IgM surface expression, reminiscent of their human counterparts. Moreover, Cd79b-mutant lymphomas displayed a robust formation of cytoplasmic signaling complexes involving MYD88, CD79B, MALT1, and BTK. These complexes were disrupted upon pharmacological BTK inhibition. The BTK inhibitor-mediated disruption of these signaling complexes translated into a selective ibrutinib sensitivity of lymphomas harboring combined Cd79b and Myd88 mutations. Altogether, this in-depth cross-species comparison provides a framework for the development of molecularly targeted therapeutic intervention strategies in DLBCL.

Risk and protective factors for postpartum depression among Polish women - a prospective study.

This prospective study conducted at a single center in 2022 aims to identify risk and protective factors for postpartum depression (PPD) in Polish women and to assess the impact of pregnancy, delivery, the postpartum period, and psychosocial factors on PPD. After delivery and 4 weeks later, 311 women filled out two questionnaires of our design related to risk factors for PPD. Immune Power Personality Questionnaire, Walsh Family Resilience Questionnaire, and Edinburg Postnatal Depression Scale were also applied. The predictors of PPD identified at two time points included: use of antidepressants, previous depressive episodes, family history of depression, risk of preterm delivery, anxiety about child's health, and breastfeeding and sleep problems. Risk factors for PPD found only after delivery were: suicidal ideation before pregnancy, stressful life events, premature rupture of the membranes, and cesarean section. Inhalation analgesia during labor reduced the PPD frequency. At 4 weeks' postpartum, regular physical activity was also predictive of PPD, while breastfeeding, financial satisfaction, and sufficient sleep duration were protective factors. PPD after delivery was negatively correlated with capacity to confide, hardiness, assertiveness, self-complexity, and communication. PPD at 4 weeks postpartum decreased belief systems, organization patterns, and communication. Two proposed self-designed questionnaires can be useful for effectively screening PPD in the Polish population.

Mouse models of diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is a genetically highly heterogeneous disease. Yet, to date, the vast majority of patients receive standardized frontline chemo-immune-therapy consisting of an anthracycline backbone. Using these regimens, approximately 65% of patients can be cured, whereas the remaining 35% of patients will face relapsed or refractory disease, which, even in the era of CAR-T cells, is difficult to treat. To systematically tackle this high medical need, it is important to design, generate and deploy suitable in vivo model systems that capture disease biology, heterogeneity and drug response. Recently published, large comprehensive genomic characterization studies, which defined molecular sub-groups of DLBCL, provide an ideal framework for the generation of autochthonous mouse models, as well as an ideal benchmark for cell line-derived or patient-derived mouse models of DLBCL. Here we discuss the current state of the art in the field of mouse modelling of human DLBCL, with a particular focus on disease biology and genetically defined molecular vulnerabilities, as well as potential targeting strategies.

Incidence, risk factors and outcome of acute kidney injury in critically ill COVID-19 patients in Tyrol, Austria: a prospective multicenter registry study.

INTRODUCTION: Acute kidney injury is a frequent complication in critically ill patients with and without COVID-19. The aim of this study was to evaluate the incidence of, and risk factors for, acute kidney injury and its effect on clinical outcomes of critically ill COVID-19 patients in Tyrol, Austria. METHODS: This multicenter prospective registry study included adult patients with a SARS-CoV-2 infection confirmed by polymerase chain reaction, who were treated in one of the 12 dedicated intensive care units during the COVID-19 pandemic from February 2020 until May 2022. RESULTS: In total, 1042 patients were included during the study period. The median age of the overall cohort was 66 years. Of the included patients, 267 (26%) developed acute kidney injury during their intensive care unit stay. In total, 12.3% (n = 126) required renal replacement therapy with a median duration of 9 (IQR 3-18) days. In patients with acute kidney injury the rate of invasive mechanical ventilation was significantly higher with 85% (n = 227) compared to 41% (n = 312) in the no acute kidney injury group (p < 0.001). The most important risk factors for acute kidney injury were invasive mechanical ventilation (OR = 4.19, p < 0.001), vasopressor use (OR = 3.17, p < 0.001) and chronic kidney disease (OR = 2.30, p < 0.001) in a multivariable logistic regression analysis. Hospital and intensive care unit mortality were significantly higher in patients with acute kidney injury compared to patients without acute kidney injury (Hospital mortality: 52.1% vs. 17.2%, p < 0.001, ICU-mortality: 47.2% vs. 14.7%, p < 0.001). CONCLUSION: As in non-COVID-19 patients, acute kidney injury is clearly associated with increased mortality in critically ill COVID-19 patients. Among known risk factors, invasive mechanical ventilation has been identified as an independent and strong predictor of acute kidney injury.

Collagen XVII regulates tumor growth in pancreatic cancer through interaction with the tumor microenvironment.

Expression of the gene for collagen XVII (COL17A1) in tumor tissue is positively or negatively associated with patient survival depending on cancer type. High COL17A1 expression is thus a favorable prognostic marker for breast cancer but unfavorable for pancreatic cancer. This study explored the effects of COL17A1 expression on pancreatic tumor growth and their underlying mechanisms. Analysis of published single-cell RNA-sequencing data for human pancreatic cancer tissue revealed that COL17A1 was expressed predominantly in cancer cells rather than surrounding stromal cells. Forced expression of COL17A1 did not substantially affect the proliferation rate of the mouse pancreatic cancer cell lines KPC and AK4.4 in vitro. However, in mouse homograft tumor models in which KPC or AK4.4 cells were injected into syngeneic C57BL/6 or FVB mice, respectively, COL17A1 expression promoted or suppressed tumor growth, respectively, suggesting that the effect of COL17A1 on tumor growth was influenced by the tumor microenvironment. RNA-sequencing analysis of tumor tissue revealed effects of COL17A1 on gene expression profiles (including the expression of genes related to cell proliferation, the immune response, Wnt signaling, and Hippo signaling) that differed between C57BL/6-KPC and FVB-AK4.4 tumors. Our data thus suggest that COL17A1 promotes or suppresses cancer progression in a manner dependent on the interaction of tumor cells with the tumor microenvironment.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients.

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90-100%]; HPV testing: five-year survival rate: 80% [95% CI = 71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05-0.44], intermediate HR = 0.58 [95% CI = 0.34-0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15-0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.

[Hyponatremia : Etiology, diagnosis and acute therapy]. / Hyponatriämie : Ätiologie, Diagnostik und Akuttherapie.

Hyponatremia is one of the most common electrolyte disorders in emergency departments and hospitalized patients. Serum sodium concentration is controlled by osmoregulation and volume regulation. Both pathways are regulated via the release of antidiuretic hormone (ADH). Syndrome of inappropriate release of ADH (SIADH) may be caused by neoplasms or pneumonia but may also be triggered by drug use or drug abuse. Excessive fluid intake may also result in a decrease in serum sodium concentration. Rapid alteration in serum sodium concentration leads to cell swelling or cell shrinkage, which primarily causes neurological symptoms. The dynamics of development of hyponatremia and its duration are crucial. In addition to blood testing, a clinical examination and urine analysis are essential in the differential diagnosis of hyponatremia.

An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.

Intratumoral Abundance of M2-Macrophages is Associated With Unfavorable Prognosis and Markers of T-Cell Exhaustion in Small Cell Lung Cancer Patients.

Small cell lung cancer (SCLC) accounts for about 10% to 15% of lung cancer cases. Unlike non-SCLC, therapy options for SCLC are limited, reflected by a 5-year survival rate of about 7%. At the same time, the rise of immunotherapeutic approaches in cancer therapy has rationalized to account for inflammatory phenotypes in tumors. However, the composition of the inflammatory microenvironment in human SCLC is poorly understood to date. In our study, we used in-depth image analysis of virtual whole-slide-images of 45 SCLC tumors and evaluated different markers of M2-macrophages (CD163 and CD204) together with global immunologic markers (CD4, CD8, CD68, CD38, FOXP3, and CD20) and characterized their abundance intratumorally using quantitative image analysis, combined with a deep-learning model for tumor segmentation. In addition, independent scoring, blinded to the results of the computational analysis, was performed by an expert pathologist (A.Q.) of both CD163/CD204 and PD-L1. To this end, we evaluated the prognostic relevance of the abundance of these cell types to overall survival. Given a 2-tier threshold of the median of the M2 marker CD163 within the study population, there was a 12-month overall survival rate of 22% (95% CI, 10%-47%) for patients with high CD163 abundance and 41% (95% CI, 25%-68%) for patients with low CD163 counts. Patients with increased CD163 had a median overall survival of 3 months compared to 8.34 months for patients with decreased CD163 counts (P = .039), which could be confirmed by an expert pathologist (A.Q., P = .018). By analyzing cases with increased CD163 cell infiltrates, a trend for higher FOXP3 counts and PD-L1 positive cells, together with increased CD8 T-cell infiltrates, was observed, which could be confirmed using an independent cohort at the transcriptional level. Together, we showed that markers of M2 were associated with unfavorable outcome in our study cohort.

Individual Epitope-Specific CD8+ T Cell Immune Responses Are Shaped Differently during Chronic Viral Infection.

A hallmark in chronic viral infections are exhausted antigen-specific CD8+ T cell responses and the inability of the immune system to eliminate the virus. Currently, there is limited information on the variability of epitope-specific T cell exhaustion within one immune response and the relevance to the T cell receptor (TCR) repertoire. The aim of this study was a comprehensive analysis and comparison of three lymphocytic choriomeningitis virus (LCMV) epitope-specific CD8+ T cell responses (NP396, GP33 and NP205) in a chronic setting with immune intervention, e.g., immune checkpoint inhibitor (ICI) therapy, in regard to the TCR repertoire. These responses, though measured within the same mice, were individual and independent from each other. The massively exhausted NP396-specific CD8+ T cells revealed a significantly reduced TCR repertoire diversity, whereas less-exhausted GP33-specific CD8+ T cell responses were rather unaffected by chronicity in regard to their TCR repertoire diversity. NP205-specific CD8+ T cell responses showed a very special TCR repertoire with a prominent public motif of TCR clonotypes that was present in all NP205-specific responses, which separated this from NP396- and GP33-specific responses. Additionally, we showed that TCR repertoire shifts induced by ICI therapy are heterogeneous on the epitope level, by revealing profound effects in NP396-, less severe and opposed effects in NP205-, and minor effects in GP33-specific responses. Overall, our data revealed individual epitope-specific responses within one viral response that are differently affected by exhaustion and ICI therapy. These individual shapings of epitope-specific T cell responses and their TCR repertoires in an LCMV mouse model indicates important implications for focusing on epitope-specific responses in future evaluations for therapeutic approaches, e.g., for chronic hepatitis virus infections in humans.

Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study.

BACKGROUND: Oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction are among the most common malignant epithelial tumours. Most patients receive neoadjuvant therapy before complete tumour resection. Histological assessment after resection includes identification of residual tumour tissue and areas of regressive tumour, data which are used to calculate a clinically relevant regression score. We developed an artificial intelligence (AI) algorithm for tumour tissue detection and tumour regression grading in surgical specimens from patients with oesophageal adenocarcinoma or adenocarcinoma of the oesophagogastric junction. METHODS: We used one training cohort and four independent test cohorts to develop, train, and validate a deep learning tool. The material consisted of histological slides from surgically resected specimens from patients with oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction from three pathology institutes (two in Germany, one in Austria) and oesophageal cancer cohort of The Cancer Genome Atlas (TCGA). All slides were from neoadjuvantly treated patients except for those from the TCGA cohort, who were neoadjuvant-therapy naive. Data from training cohort and test cohort cases were extensively manually annotated for 11 tissue classes. A convolutional neural network was trained on the data using a supervised principle. First, the tool was formally validated using manually annotated test datasets. Next, tumour regression grading was assessed in a retrospective cohort of post-neoadjuvant therapy surgical specimens. The grading of the algorithm was compared with that of a group of 12 board-certified pathologists from one department. To further validate the tool, three pathologists processed whole resection cases with and without AI assistance. FINDINGS: Of the four test cohorts, one included 22 manually annotated histological slides (n=20 patients), one included 62 sides (n=15), one included 214 slides (n=69), and the final one included 22 manually annotated histological slides (n=22). In the independent test cohorts the AI tool had high patch-level accuracy for identifying both tumour and regression tissue. When we validated the concordance of the AI tool against analyses by a group of pathologists (n=12), agreement was 63·6% (quadratic kappa 0·749; p<0·0001) at case level. The AI-based regression grading triggered true reclassification of resected tumour slides in seven cases (including six cases who had small tumour regions that were initially missed by pathologists). Use of the AI tool by three pathologists increased interobserver agreement and substantially reduced diagnostic time per case compared with working without AI assistance. INTERPRETATION: Use of our AI tool in the diagnostics of oesophageal adenocarcinoma resection specimens by pathologists increased diagnostic accuracy, interobserver concordance, and significantly reduced assessment time. Prospective validation of the tool is required. FUNDING: North Rhine-Westphalia state, Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation.

Diabetes mellitus is associated with 90-day mortality in old critically ill COVID-19 patients: a multicenter prospective observational cohort study.

BACKGROUND: Several studies have found an association between diabetes mellitus, disease severity and outcome in COVID-19 patients. Old critically ill patients are particularly at risk. This study aimed to investigate the impact of diabetes mellitus on 90-day mortality in a high-risk cohort of critically ill patients over 70 years of age. METHODS: This multicentre international prospective cohort study was performed in 151 ICUs across 26 countries. We included patients ≥ 70 years of age with a confirmed SARS-CoV-2 infection admitted to the intensive care unit from 19th March 2020 through 15th July 2021. Patients were categorized into two groups according to the presence of diabetes mellitus. Primary outcome was 90-day mortality. Kaplan-Meier overall survival curves until day 90 were analysed and compared using the log-rank test. Mixed-effect Weibull regression models were computed to investigate the influence of diabetes mellitus on 90-day mortality. RESULTS: This study included 3420 patients with a median age of 76 years were included. Among these, 37.3% (n = 1277) had a history of diabetes mellitus. Patients with diabetes showed higher rates of frailty (32% vs. 18%) and several comorbidities including chronic heart failure (20% vs. 11%), hypertension (79% vs. 59%) and chronic kidney disease (25% vs. 11%), but not of pulmonary comorbidities (22% vs. 22%). The 90-day mortality was significantly higher in patients with diabetes than those without diabetes (64% vs. 56%, p < 0.001). The association of diabetes and 90-day mortality remained significant (HR 1.18 [1.06-1.31], p = 0.003) after adjustment for age, sex, SOFA-score and other comorbidities in a Weibull regression analysis. CONCLUSION: Diabetes mellitus was a relevant risk factor for 90-day mortality in old critically ill patients with COVID-19. STUDY REGISTRATION: NCT04321265, registered March 19th, 2020.

Photoreductive ß-aminoalkylation with amino acids affords functionalized γ-aminoketones for nucleoside mimics.

We developed a facile photoreductive and stereoselective ß-aminoalkylation of a crowded enone by blue LED light irradiation using a wide variety of α-amino acids in order to access 5'-amino substituted carbasugar nucleosides for SAM-based methyltransferase inhibitors. This photochemical method provides highly functionalized carbasugar mimics for nucleoside analogue synthesis.

Inhibition of CXCR4 Enhances the Efficacy of Radiotherapy in Metastatic Prostate Cancer Models.

Radiotherapy (RT) is a standard treatment for patients with advanced prostate cancer (PCa). Previous preclinical studies showed that SDF1α/CXCR4 axis could mediate PCa metastasis (most often to the bones) and cancer resistance to RT. We found high levels of expression for both SDF1α and its receptor CXCR4 in primary and metastatic PCa tissue samples. In vitro analyses using PCa cells revealed an important role of CXCR4 in cell invasion but not radiotolerance. Pharmacologic inhibition of CXCR4 using AMD3100 showed no efficacy in orthotopic primary and bone metastatic PCa models. However, when combined with RT, AMD3100 potentiated the effect of local single-dose RT (12 Gy) in both models. Moreover, CXCR4 inhibition also reduced lymph node metastasis from primary PCa. Notably, CXCR4 inhibition promoted the normalization of bone metastatic PCa vasculature and reduced tissue hypoxia. In conclusion, the SDF1α/CXCR4 axis is a potential therapeutic target in metastatic PCa patients treated with RT.

The extracellular matrix landscape in salivary gland carcinomas is defined by cellular differentiation via expression of three distinct protein modules.

The extracellular matrix (ECM) is an integral part of the tumor microenvironment of carcinomas. Even though salivary gland carcinomas (SGCs) display a range of tumor cell differentiation and distinct extracellular matrices, their ECM landscape has not been characterized in depth. The ECM composition of 89 SGC primaries, 14 metastases, and 25 normal salivary gland tissues was assessed using deep proteomic profiling. Machine learning algorithms and network analysis were used to detect tumor groups and protein modules that explain specific ECM landscapes. Multimodal in situ studies to validate exploratory findings and to infer a putative cellular origin of ECM components were applied. We revealed two fundamental SGC ECM classes which align with the presence or absence of myoepithelial tumor differentiation. We describe the SGC ECM through three biologically distinct protein modules that are differentially expressed across ECM classes and cell types. The modules have a distinct prognostic impact on different SGC types. Since targeted therapy is rarely available for SGC, we used the proteomic expression profile to identify putative therapeutic targets. In summary, we provide the first extensive inventory of ECM components in SGC, a difficult-to-treat disease that encompasses tumors with distinct cellular differentiation. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Distinct Genetically Determined Origins of Myd88/BCL2-Driven Aggressive Lymphoma Rationalize Targeted Therapeutic Intervention Strategies.

Genomic profiling revealed the identity of at least 5 subtypes of diffuse large B-cell lymphoma (DLBCL), including the MCD/C5 cluster characterized by aberrations in MYD88, BCL2, PRDM1, and/or SPIB. We generated mouse models harboring B cell-specific Prdm1 or Spib aberrations on the background of oncogenic Myd88 and Bcl2 lesions. We deployed whole-exome sequencing, transcriptome, flow-cytometry, and mass cytometry analyses to demonstrate that Prdm1- or Spib-altered lymphomas display molecular features consistent with prememory B cells and light-zone B cells, whereas lymphomas lacking these alterations were enriched for late light-zone and plasmablast-associated gene sets. Consistent with the phenotypic evidence for increased B cell receptor signaling activity in Prdm1-altered lymphomas, we demonstrate that combined BTK/BCL2 inhibition displays therapeutic activity in mice and in five of six relapsed/refractory DLBCL patients. Moreover, Prdm1-altered lymphomas were immunogenic upon transplantation into immuno-competent hosts, displayed an actionable PD-L1 surface expression, and were sensitive to antimurine-CD19-CAR-T cell therapy, in vivo. SIGNIFICANCE: Relapsed/refractory DLBCL remains a major medical challenge, and most of these patients succumb to their disease. Here, we generated mouse models, faithfully recapitulating the biology of MYD88-driven human DLBCL. These models revealed robust preclinical activity of combined BTK/BCL2 inhibition. We confirmed activity of this regimen in pretreated non-GCB-DLBCL patients. See related commentary by Leveille et al., p. 8. This article is highlighted in the In This Issue feature, p. 1.