Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19.

The transition from analogue to digital pathology (DP) in Switzerland has coincided with the COVID-19 crisis. The Swiss Digital Pathology Consortium conducted a national survey to assess the experience of pathologists in dealing with the challenges of the pandemic and how this has influenced the outlook and adoption of DP. A survey containing 20 questions relating to DP, personal experiences and challenges during the pandemic was addressed to Swiss pathologists at different experience stages in private practice, community and university hospitals. All 74 respondents were pathologists, with 81.1% reporting more than 5 years of diagnostic service experience. 32.5% reported having read 100 digital slides or more in a diagnostic context. 39.2% reported using whole slide imaging systems at their primary workplace. Key DP use cases before the COVID-19 lockdown were tumour boards (39.2%), education (60.8%) and research (44.6%), with DP used for primary diagnosis in 13.5%. During the COVID-19 crisis, the use of DP for primary diagnostics more than doubled (30% vs 13.5%), with internal consults as important drivers (22.5% vs 16.5%), while research use (25% vs 44.6%) and external consults (17.5% vs 41.9%) strongly decreased. Key challenges identified included a lack of established standard operating procedures and availability of specialised hardware and software. This survey indicates that the crisis acted as a catalyst in promoting DP adoption in centres where basic workflows were already established while posing major technical and organisational challenges in institutions that were at an early stage of DP implementation

A transcriptionally and functionally distinct PD-1⁺ CD8⁺ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade.

Evidence from mouse chronic viral infection models suggests that CD8 <sup>+</sup> T cell subsets characterized by distinct expression levels of the receptor PD-1 diverge in their state of exhaustion and potential for reinvigoration by PD-1 blockade. However, it remains unknown whether T cells in human cancer adopt a similar spectrum of exhausted states based on PD-1 expression levels. We compared transcriptional, metabolic and functional signatures of intratumoral CD8 <sup>+</sup> T lymphocyte populations with high (PD-1 <sup>T</sup> ), intermediate (PD-1 <sup>N</sup> ) and no PD-1 expression (PD-1 <sup>-</sup> ) from non-small-cell lung cancer patients. PD-1 <sup>T</sup> T cells showed a markedly different transcriptional and metabolic profile from PD-1 <sup>N</sup> and PD-1 <sup>-</sup> lymphocytes, as well as an intrinsically high capacity for tumor recognition. Furthermore, while PD-1 <sup>T</sup> lymphocytes were impaired in classical effector cytokine production, they produced CXCL13, which mediates immune cell recruitment to tertiary lymphoid structures. Strikingly, the presence of PD-1 <sup>T</sup> cells was strongly predictive for both response and survival in a small cohort of non-small-cell lung cancer patients treated with PD-1 blockade. The characterization of a distinct state of tumor-reactive, PD-1-bright lymphocytes in human cancer, which only partially resembles that seen in chronic infection, provides potential avenues for therapeutic intervention

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem

Pharmacological prevention of type 2 diabetes.

audience: researcher, professional, studen

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Molecular tumour pathology - and tumour geneticsMTG8 - Moleculaire pathologie van gynecologische tumore

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients

Geographic analysis of RKIP expression and its clinical relevance in colorectal cancer

Background:This study evaluates the geographic expression pattern of Raf-1 Kinase Inhibitor Protein (RKIP) in colorectal cancer (CRC) in correlation with clinicopathological and molecular features, markers of epithelial-mesenchymal transition (EMT) and survival outcome.Methods:Whole-tissue sections of 220 well-characterised CRCs were immunostained for RKIP. NF-κB and E-Cadherin expression was assessed using a matched multi-punch tissue microarray. Analysis of mismatch repair (MMR) protein expression, B-Raf and KRAS mutations was performed. RKIP expression in normal mucosa, tumour centre, invasion front and tumour buds was each assessed for clinical relevance.Results:RKIP was diffusely expressed in normal mucosa and progressively lost towards tumour centre and front (P<0.0001). Only 0.9% of tumour buds were RKIP-positive. In the tumour centre, RKIP deficiency predicted metastatic disease (P=0.0307), vascular invasion (P=0.0506), tumour budding (P=0.0112) and an invasive border configuration (P=0.0084). Loss of RKIP correlated with NF-κB activation (P=0.0002) and loss of E-Cadherin (P<0.0001). Absence of RKIP was more common in MMR-deficient cancers (P=0.0191), while no impact of KRAS and B-Raf mutation was observed. RKIP in the tumour centre was identified as a strong prognostic indicator (HR (95% CI): 2.13 (1.27-3.56); P=0.0042) independently of TNM classification and therapy (P=0.0474).Conclusion:The clinical relevance of RKIP expression as an independent prognostic factor is restricted to the tumour centre. Loss of RKIP predicts features of EMT and correlates with frequent distant metastasis. © 2013 Cancer Research UK. All rights reserved

ミャンマーと中国の国境貿易 -- 「特区」と新ビルマ・ロード (バンコク研究センター プロジェクトII)

Regulatory T cells (Treg) mediate tolerance towards self-antigens by suppression of innate and adaptive immunity. In cancer patients, tumor-infiltrating FoxP3+ Treg suppress local anti-tumor immune responses and are often associated with poor prognosis. Markers that are selectively expressed on tumor-infiltrating Treg may serve as targets for immunotherapy of cancer. Here we show that CD103, an integrin mediating lymphocyte retention in epithelial tissues, is expressed at high levels on tumor-infiltrating FoxP3+ Treg in several types of murine cancer. In the CT26 model of colon cancer up to 90% of the intratumoral FoxP3+ cells expressed CD103 compared to less than 20% in lymphoid organs. CD103+ Treg suppressed T effector cell activation more strongly than CD103(neg) Treg. Expression of CD103 on Treg closely correlated with intratumoral levels of transforming growth factor β (TGF-β) and could be induced in a TGF-β-dependent manner by tumor cell lines. In vivo, gene silencing of TGF-β reduced the frequency of CD103+ Treg, demonstrating that CD103 expression on tumor-infiltrating Treg is driven by intratumoral TGF-β. Functional blockade of CD103 using a monoclonal antibody did however not reduce the number of intratumoral Treg, indicating that CD103 is not involved in homing or retention of FoxP3+ cells in the tumor tissue. In conclusion, expression of CD103 is a hallmark of Treg that infiltrate TGF-β-secreting tumors. CD103 thus represents an interesting target for selective depletion of tumor-infiltrating Treg, a strategy that may help to improve anti-cancer therapy

Tumor infiltrating lymphocytes in lymph node metastases of stage III melanoma correspond to response and survival in nine patients treated with ipilimumab at the time of stage IV disease.

Prognosis of metastatic melanoma improved with the development of checkpoint inhibitors. The role of tumor infiltrating lymphocytes (TILs) in lymph node metastases of stage III melanoma remains unclear. We retrospectively characterized TILs in primary melanomas and matched lymph node metastases (stage III melanoma) of patients treated with the checkpoint inhibitor ipilimumab. Tumor infiltrating lymphocytes were characterized for CD3, CD4, and CD8 expressions by immunohistochemistry. 4/9 patients (44%) responded to treatment with ipilimumab (1 complete and 2 partial remissions, 1 stable disease). All responders exhibited CD4 and CD8 T-cell infiltration in their lymph node metastases, whereas all non-responders did not show an infiltration of the lymph node metastasis with TILs. The correlation between the presence and absence of TILs in responders vs. non-responders was statistically significant (p = 0.008). Median distant metastases free survival, i.e., progression from stage III to stage IV melanoma, was similar in responders and non-responders (22.1 vs. 19.3 months; p = 0.462). Median progression free and overall survival show a trend in favor of the patients having TIL rich lymph node metastases (6.8 vs. 3.3 months, p = 0.09; and all alive at last follow-up vs. 8.2 months, respectively, p = 0.08). Our data suggest a correlation between the T-cell infiltration of the lymph node metastases in stage III melanoma and the response to ipilimumab once these patients progress to stage IV disease

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices