cIAPs control RIPK1 kinase activity-dependent and -independent cell death and tissue inflammation.

Cellular inhibitor of apoptosis proteins (cIAPs) are RING-containing E3 ubiquitin ligases that ubiquitylate receptor-interacting protein kinase 1 (RIPK1) to regulate TNF signalling. Here, we established mice simultaneously expressing enzymatically inactive cIAP1/2 variants, bearing mutations in the RING domains of cIAP1/2 (cIAP1/2 mutant RING, cIAP1/2MutR ). cIap1/2MutR/MutR mice died during embryonic development due to RIPK1-mediated apoptosis. While expression of kinase-inactive RIPK1D138N rescued embryonic development, Ripk1D138N/D138N /cIap1/2MutR/MutR mice developed systemic inflammation and died postweaning. Cells expressing cIAP1/2MutR and RIPK1D138N were still susceptible to TNF-induced apoptosis and necroptosis, implying additional kinase-independent RIPK1 activities in regulating TNF signalling. Although further ablation of Ripk3 did not lead to any phenotypic improvement, Tnfr1 gene knock-out prevented early onset of systemic inflammation and premature mortality, indicating that cIAPs control TNFR1-mediated toxicity independent of RIPK1 and RIPK3. Beyond providing novel molecular insights into TNF-signalling, the mouse model established in this study can serve as a useful tool to further evaluate ongoing therapeutic protocols using inhibitors of TNF, cIAPs and RIPK1.

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 (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in 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 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set 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 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 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, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Enhancing Prostate Cancer Diagnosis: Artificial intelligence-Driven Virtual Biopsy for Optimal Magnetic Resonance Imaging-Targeted Biopsy Approach and Gleason Grading Strategy.

An optimal approach to magnetic resonance imaging fusion targeted prostate biopsy (PBx) remains unclear (number of cores, intercore distance, Gleason grading [GG] principle). The aim of this study was to develop a precise pixel-wise segmentation diagnostic artificial intelligence (AI) algorithm for tumor detection and GG as well as an algorithm for virtual prostate biopsy that are used together to systematically investigate and find an optimal approach to targeted PBx. Pixel-wise AI algorithms for tumor detection and GG were developed using a high-quality, manually annotated data set (slides n = 442) after fast-track annotation transfer into segmentation style. To this end, a virtual biopsy algorithm was developed that can perform random biopsies from tumor regions in whole-mount whole-slide images with predefined parameters. A cohort of 115 radical prostatectomy (RP) patient cases with clinically significant, magnetic resonance imaging-visible tumors (n = 121) was used for systematic studies of the optimal biopsy approach. Three expert genitourinary (GU) pathologists (Y.T., A.P., A.Q.) participated in the validation. The tumor detection algorithm (aware version sensitivity/specificity 0.99/0.90, balanced version 0.97/0.97) and GG algorithm (quadratic kappa range vs pathologists 0.77-0.78) perform on par with expert GU pathologists. In total, 65,340 virtual biopsies were performed to study different biopsy approaches with the following results: (1) 4 biopsy cores is the optimal number for a targeted PBx, (2) cumulative GG strategy is superior to using maximal Gleason score for single cores, (3) controlling for minimal intercore distance does not improve the predictive accuracy for the RP Gleason score, (4) using tertiary Gleason pattern principle (for AI tool) in cumulative GG strategy might allow better predictions of final RP Gleason score. The AI algorithm (based on cumulative GG strategy) predicted the RP Gleason score of the tumor better than 2 of the 3 expert GU pathologists. In this study, using an original approach of virtual prostate biopsy on the real cohort of patient cases, we find the optimal approach to the biopsy procedure and the subsequent GG of a targeted PBx. We publicly release 2 large data sets with associated expert pathologists' GG and our virtual biopsy algorithm.

PITX2 as a Sensitive and Specific Marker of Midgut Neuroendocrine Tumors: Results from a Cohort of 1157 Primary Neuroendocrine Neoplasms.

As neuroendocrine tumors (NETs) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathologic assessment. Because a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 neuroendocrine carcinomas (NEC) according to the immunoreactive score (IRS) and correlated PITX2 expression groups with general tumor groups and primary localization. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), whereas CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of neuroendocrine neoplasms. Therefore, our data argue toward implementation into diagnostic panels applied for NET as a firstline midgut marker.

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.

Treatment of local recurrence of esophageal cancer following Ivor-Lewis esophagectomy-Experiences of a high-volume center.

PURPOSE: Patients with local recurrence of esophageal cancer have a highly decreased overall survival. There is currently no standardized treatment algorithm for this group. This retrospective cohort study aimed to evaluate the survival of patients with local recurrence, despite receiving individualized treatment options. METHODS: 241 of 1791 patients were diagnosed with a local recurrence following Ivor-Lewis esophagectomy at the University Hospital of Cologne. 59 patients, who were diagnosed only with a local recurrence of adeno- or squamous cell carcinoma and received their individualized therapy regimes at our high-volume center, were included. RESULTS: The study included 52 patients with adenocarcinoma and 7 with squamous cell carcinoma. Among these, 6 patients underwent resection, 19 received solely chemotherapy, 29 received chemoradiotherapy, and 5 were provided with best supportive care. Patients who underwent resection showed a better survival outcome compared to patients without resection (median OS: not reached vs. 15.1 months, p = 0.012). Best supportive care and palliative care were found to be independent risk factors for shorter overall survival compared to curative intended treatment options like local resection or chemoradiotherapy. CONCLUSION: In this study, different treatment strategies for patients with local recurrence of esophageal cancer were depicted. Resection as well as chemoradiotherapy could play a role in selected patients. Further prospective studies are needed to improve the selection of eligible patients.

LH supplementation in IVF: human nature, politics, and elephants in the room.

Luteinizing hormone (LH) is present throughout the natural follicular phase. However, the debate is still not settled on whether LH is needed during ovarian stimulation in IVF. This commentary looks at the evolution of this debate, mentioning three elephants in the room that were ignored by the Pharma industry, professional organizations, and clinicians alike: 1. The different endocrinology between the long agonist and the antagonist protocols. 2. The fixed dose of the two most widely commercially available antagonist preparations, namely cetrorelix and ganirelix. 3. The fact that most research in this area uses population-based criteria, ignoring endocrine parameters. Individual genetics of the LH receptor gene may also serve to individualize LH needs during stimulation; however, the jury is still out regarding this approach. CONCLUSIONS: Individual endocrine and genetics parameters may shed meaningful light on the question of LH supplemental during ovarian stimulation.

Monitoring of controlled ovarian stimulation in IVF.

Since the inception of in vitro fertilization (IVF), monitoring of controlled ovarian stimulation (COS) has traditionally involved numerous appointments for ultrasound and laboratory testing to guide medication use and dosing, determine trigger timing, and allow for measures to reduce the risk of ovarian hyperstimulation syndrome (OHSS). Recent advances in the field of assisted reproductive technology (ART) have called into question the timing and frequency of COS monitoring appointments, as discussed in this commentary.

The extent of androgen receptor and HER2 expression allows for targeted therapy in most cases of salivary duct carcinoma: analysis of clinical and histopathological data in a tertiary care center.

PURPOSE: The incidence of salivary duct carcinoma (SDC) seems to be underestimated due to inaccurate classification. Further, the frequency of SDC patients with targeted therapy options according to current guidelines is unclear. Therefore, this study aimed at (a) describing the proportion of SDC among salivary gland carcinoma (SGC) before and after reclassification of cases initially classified as adenocarcinoma, not otherwise specified (ANOS); and (b) quantifying the frequency of SDC patients with targeted therapy options. METHODS: All patients with SDC or ANOS treated in a tertiary care center between 1996 and 2023 were identified. Histopathological diagnosis was verified for patients primarily diagnosed with SDC and reviewed for patients initially diagnosed with ANOS. Clinical data for SDC patients were retrieved from clinical charts. Immunohistochemical (IHC) androgen receptor (AR) and HER2 staining was performed. RESULTS: Among 46 SDC, 34 were primarily diagnosed as SDC and 12 had initially been classified as ANOS. The proportion of SDC among SGC was 12.1% and was rising when comparing the time periods 2000-2015 (7.1-11.5%) versus 2016-2023 (15.4-18.1%). Nuclear AR staining in > 70% of tumor cells was found in 56.8% and HER2 positivity (IHC 3 +) in 36.4% of cases. 70.5% of patients showed AR staining in > 70% of tumor cells and/or HER2 positivity and therefore at least one molecular target. 5-year overall and disease-free survival (DFS) were 62.8% and 41.0%. Multivariate Cox regression revealed positive resection margins (HR = 4.0, p = 0.03) as independent negative predictor for DFS. CONCLUSIONS: The results suggest a rising SDC incidence and show that the extent of the AR and HER2 expression allows for targeted therapy in most SDC cases.

Differentially expressed messenger RNA/proteins can distinguish teratoma from necrosis in postchemotherapy retroperitoneal lymph node dissection tissue.

BACKGROUND: Before postchemotherapy retroperitoneal lymph node dissection (pcRPLND), in patients with metastasized germ cell tumors (GCTs), those harboring necrosis (NEC) cannot be distinguished from those who have teratoma (TER), resulting in relevant overtreatment, whereas microRNA-371a-3p may be predictive for viable GCT. The purpose of this study was to explore messenger RNA (mRNA) and proteins to distinguish TER from NEC in pcRPLND tissue. METHODS: The discovery cohort consisted in total of 48 patients, including 16 each with TER, viable GCT, and NEC. Representative areas were microdissected. A NanoString panel and proteomics were used to analyze 770 genes and >5000 proteins. The most significantly and differentially expressed combination of both parameters, mRNA and its associated protein, between TER and NEC was validated using immunohistochemistry (IHC) in an independent validation cohort comprising 66 patients who were not part of the discovery cohort. RESULTS: The authors observed that anterior gradient protein 2 homolog (AGR2) and keratin, type I cytoskeletal 19 (KRT19) were significantly differentially expressed in TER versus NEC in mRNA and protein analyses (proteomics). The technical validation using IHC was successful in the same patients. These proteins were further validated by IHC in the independent patient cohort and exhibited significantly higher levels in TER versus NEC (p < .0001; area under the curve, 1.0; sensitivity and specificity, 100% each). CONCLUSIONS: The current study demonstrated that KRT19 and AGR2 mRNA and protein are overexpressed in TER versus NEC in pcRPLND tissue and might serve as a future diagnostic target to detect TER, for instance, by functional imaging, to avoid overtreatment. PLAIN LANGUAGE SUMMARY: The proteins and the corresponding genes called AGR2 and KRT19 can differentiate between teratoma and necrosis in remaining tumor masses after chemotherapy in patients who have metastasized testicular cancer. This may be a way to improve presurgical diagnostics and to reduce the current overtreatment of patients with necrosis only, who could be treated sufficiently by surveillance.

Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks.

BACKGROUND: Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. METHODS: Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN's generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. RESULTS: We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. CONCLUSIONS: We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

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.

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.

The tight junction protein claudin 6 is a potential target for patient-individualized treatment in esophageal and gastric adenocarcinoma and is associated with poor prognosis.

BACKGROUND: The prognosis of esophageal adenocarcinoma (EAC) and gastric adenocarcinoma (GAC) remains poor, and new therapeutic approaches are urgently needed. Claudin 6 (CLDN6) is an oncofetal antigen that is largely absent in healthy tissues and upregulated in several cancers, making it a promising therapeutical target. In this study, the expression of CLDN6 was assessed in an large Caucasian EAC and GAC cohort. METHODS: RNA-Seq data from 89 EACs and 371 GACs were obtained from The Cancer Genome Atlas project and EAC/GAC cases were stratified by CLDN6 mRNA expression based on a survival-associated cutoff. For groups with CLDN6 expression above or below this cutoff, differential gene expression analyses were performed using DESeq, and dysregulated biological pathways were identified using the Enrichr tool. Additionally, CLDN6 protein expression was assessed in more than 800 EACs and almost 600 GACs using a CLDN6-specific immunohistochemical antibody (clone 58-4B-2) that is currently used in Phase I/II trials to identify patients with CLDN6-positive tumors (NCT05262530; NCT04503278). The expression of CLDN6 was also correlated with histopathological parameters and overall survival (OS). RESULTS: EACs and GACs with high CLDN6 mRNA levels displayed an overexpression of pathways regulating the cell cycle, DNA replication, and receptor / extracellular matrix interactions. CLDN6 protein expression was associated with shorter OS in EAC and GAC, both in treatment-naïve subgroups and cohorts receiving neoadjuvant therapy. In multivariate analysis, CLDN6 protein expression was an independent adverse prognostic factor in EAC associated with a shorter OS (HR: 1.75; p = 0.01) and GAC (HR: 2.74; p = 0.028). CONCLUSIONS: High expression of CLDN6 mRNA is associated with the dysregulation of distinct biological pathways regulating cell growth, proliferation, and cell-matrix interactions. Clinically, the expression of CLDN6 protein is a valuable adverse prognostic marker in EAC and GAC.

Long-Term Postsurgical Outcomes of Neoadjuvant Chemoradiation (CROSS) Versus Chemotherapy (FLOT) for Multimodal Treatment of Adenocarcinoma of the Esophagus and the Esophagogastric Junction.

BACKGROUND: The question of the ideal neoadjuvant therapy for locally advanced esophagogastric adenocarcinoma has not been answered to date. Multimodal treatment has become a standard treatment for these adenocarcinomas. Currently, perioperative chemotherapy (FLOT) or neoadjuvant chemoradiation (CROSS) is recommended. METHODS: A monocentric retrospective analysis compared long-term survival after CROSS versus FLOT. The study enrolled patients with adenocarcinoma of the esophagus (EAC) or the esophagogastric junction type I or II undergoing oncologic Ivor-Lewis esophagectomy between January 2012 and December 2019. The primary objective was to determine the long-term outcome in terms of overall survival. The secondary objectives were to determine differences regarding the histopathologic categories after neoadjuvant treatment and the histomorphologic regression. RESULTS: The findings showed no survival advantage for one or the other treatment in this highly standardized cohort. All the patients underwent open (CROSS: 9.4% vs. FLOT: 22%), hybrid (CROSS: 82% vs. FLOT: 72%), or minimally invasive (CROSS: 8.9% vs. FLOT: 5.6%) thoracoabdominal esophagectomy. The median post-surgical follow-up period was 57.6 months (95% confidence interval [CI] 23.2-109.7 months), and the median survival was longer for the CROSS patients (54 months) than for the FLOT patients (37.2 months) (p = 0.053). The overall 5-years survival was 47% for the entire cohort (48% for the CROSS and 43% for the FLOT patients). The CROSS patients showed a better pathologic response and fewer advanced tumor stages. CONCLUSION: The improved pathologic response after CROSS cannot be translated into longer overall survival. To date, the choice of which neoadjuvant treatment to use can be made only on the basis of clinical parameters and the patient's performance status.

High NANOG expression correlates with worse patients' survival in esophageal adenocarcinoma.

BACKGROUND: Patients diagnosed with esophageal cancer demonstrate a low overall survival even despite the established multimodal therapy as the current standard of care. Therefore, further biomarkers for patients with high-risk and additional therapy options are needed. NANOG is a transcription factor, which can be found in stem cells and is known to support tumorigenesis. METHODS: Six hundred sixty patients with esophageal adenocarcinoma, who were operated at the University of Cologne with a curative intent, were included. Immunohistochemical stainings for NANOG were performed. The study population was divided into NANOG-positive and -negative subgroups. RESULTS: Positive NANOG expression correlates significantly with worse overall survival (p = 0.002) and could be confirmed as an independent risk factor for worse patient survival in multivariate analysis (HR = 1.40, 95%CI = 1.09-1.80, p = 0.006). This effect could be detected in the subgroup of primarily operated patients, but not in patients after neoadjuvant therapy. CONCLUSIONS: We describe a NANOG-positive subgroup of patients with esophageal cancer, who exhibit worse overall survival in a large patient cohort. This discovery suggests the potential use of NANOG as a biomarker for both intensified therapy and stricter follow-up regimes. Additionally, NANOG-positive stem cell-like cancer cells could be used as a new antitumoral treatment target if validated in mechanistic and clinical studies.

Sex-specific aspects in patients with oropharyngeal squamous cell carcinoma: a bicentric cohort study.

BACKGROUND: Oropharyngeal squamous cell carcinoma (OPSCC) is the only subgroup of head neck cancer that presents with an increased incidence. Gender-specific studies in other cancer entities have revealed differences in treatment response and prognosis. However, only limited data in OPSCC according to gender and human papillomavirus (HPV) status exist. Therefore, we aimed to investigate sex-specific differences in OPSCC and how these may be distributed in relation to HPV and other risk factors. METHODS: This retrospective, bicentric study included 1629 patients with OPSCC diagnosed between 1992 and 2020. We formed subgroups based on TNM status, American Joint Cancer Committee 8th edition (AJCC8), HPV status, treatment modality (surgery (± radio(chemo)therapy (RCT) vs. definitive RCT) and patient-related risk factors and investigated gender differences and their impact on patients survival via descriptive-,uni- and multivariate analysis. RESULTS: With the exception of alcohol abuse, no significant differences were found in risk factors between men and women. Females presented with better OS than males in the subgroup T1-2, N + , independent of risk factors (p = 0.008). Males demonstrated significant stratification through all AJCC8 stages (all p < 0.050). In contrast, women were lacking significance between stage II and III (p = 0.992). With regard to therapy (surgery (± R(C)T) - vs. definitive RCT) women treated with surgery had better OS than men in the whole cohort (p = 0.008). Similar results were detected in the HPV-negative OPSCC sub-cohort (p = 0.042) and in high-risk groups (AJCC8 stage III and IV with M0, p = 0.003). CONCLUSION: Sex-specific differences in OPSCC represent a health disparity, particularly according to staging and treatment, which need to be addressed in future studies.

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin-eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The DIY IVF cycle-harnessing the power of deeptech to bring ART to the masses.

The emergence of telehealth including telemedicine, at-home testing, and mobile health applications has enabled patients to self-manage their reproductive care, especially during the COVID-19 pandemic. Reproduction is rapidly changing and embracing deeptech initiatives that can improve outcomes and facilitate personalized fertility solutions in the near future. This so-called DIY IVF informed by deeptech and moderated by femtech not only holds a tremendous amount of promise, but also challenges and possible pitfalls. This review discusses the current status of deeptech and femtech for IVF care in a post-Roe v. Wade environment.

Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine.

Microscopy has long played a pivotal role in the field of assisted reproductive technology (ART). The advent of artificial intelligence (AI) has opened the door for new approaches to sperm and oocyte assessment and selection, with the potential for improved ART outcomes.