[Next generation tissue biobanking: quality assured, financed, and integrated?].

Biobanks are essential for biomedical research, particularly in the era of personalized medicine. In Germany, 36 biobanks have been established over the past decade that are connected under the German Biobank Alliance (GBA). These biobanks store high-quality biological samples along with clinical data to support research projects.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

Background: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient.

Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities.

The redirection of T cells has emerged as an attractive therapeutic principle in B cell non-Hodgkin lymphoma (B-NHL). However, a detailed characterization of lymphoma-infiltrating T cells across B-NHL entities is missing. Here we present an in-depth T cell reference map of nodal B-NHL, based on cellular indexing of transcriptomes and epitopes, T cell receptor sequencing, flow cytometry and multiplexed immunofluorescence applied to 101 lymph nodes from patients with diffuse large B cell, mantle cell, follicular or marginal zone lymphoma, and from healthy controls.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements.

Differential Immunoexpression of Inhibitory Immune Checkpoint Molecules and Clinicopathological Correlates in Keratoacanthoma, Primary Cutaneous Squamous Cell Carcinoma and Metastases.

Beyond established anti-programmed cell death protein 1/programmed cell death ligand 1 immunotherapy, T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) and its ligand CD155 are promising novel inhibitory immune checkpoint targets in human malignancies. Yet, in cutaneous squamous cell carcinoma, evidence on the collective expression patterns of these inhibitory immune checkpoints is scarce. Complete tumour sections of 36 cutaneous squamous cell carcinoma, 5 cutaneous metastases and 9 keratoacanthomas, a highly-differentiated, squamoproliferative tumour, with disparately benign biologic behaviour, were evaluated by immunohistochemistry for expression of programmed cell death ligand 1 (Tumor Proportion Score, Immune Cell Score), TIGIT, CD155 and CD8+ immune infiltrates.

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.

N-cadherin: A diagnostic marker to help discriminate primary liver carcinomas from extrahepatic carcinomas.

Distinguishing primary liver cancer (PLC), namely hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), from liver metastases is of crucial clinical importance. Histopathology remains the gold standard, but differential diagnosis may be challenging. While absent in most epithelial, the expression of the adherens junction glycoprotein N-cadherin is commonly restricted to neural and mesenchymal cells, or carcinoma cells that undergo the phenomenon of epithelial-to-mesenchymal transition (EMT).

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

Background: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer.

CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study.

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival.

High-throughput electron tomography identifies centriole over-elongation as an early event in plasma cell disorders.

Plasma cell disorders are clonal outgrowths of pre-malignant or malignant plasma cells, characterized by extensive chromosomal aberrations. Centrosome abnormalities are a major driver of chromosomal instability in cancer but their origin, incidence, and composition in primary tumor cells is poorly understood. Using cutting-edge, semi-automated high-throughput electron tomography, we characterized at nanoscale 1386 centrioles in CD138 plasma cells from eight healthy donors and 21 patients with plasma cell disorders, and 722 centrioles from different control populations.

Functionally distinct cancer-associated fibroblast subpopulations establish a tumor promoting environment in squamous cell carcinoma.

Cutaneous squamous cell carcinoma (cSCC) is a serious public health problem due to its high incidence and metastatic potential. It may progress from actinic keratosis (AK), a precancerous lesion, or the in situ carcinoma, Bowen's disease (BD). During this progression, malignant keratinocytes activate dermal fibroblasts into tumor promoting cancer-associated fibroblasts (CAFs), whose origin and emergence remain largely unknown.

Deep-learning based classification of a tumor marker for prognosis on Hodgkin's disease.

Purpose: Hodgkin's disease is a common malignant disorder in adolescent patients. Although most patients are cured, approximately 10%-15% of patients experience a relapse or have resistant disease. Furthermore, there are no definitive molecular predictors for early identification of patients at high risk of treatment failure to first line therapy.

Standardizing lymphangiography and lymphatic interventions: a preclinical in vivo approach with detailed procedural steps.

Purpose: To present a preclinical in vivo approach for standardization and training of lymphangiography and lymphatic interventions using a pictorial review.

Materials And Methods: Different lipiodol- and gadolinium-based lymphangiography and lymphatic interventions were performed in twelve (12) landrace pigs with a mean bodyweight of 34 ± 2 kg using various imaging and guiding modalities, similar to the procedures used in humans. The techniques used were explicitly introduced and illustrated.

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies.

Bovine meat and milk factor protein expression in tumor-free mucosa of colorectal cancer patients coincides with macrophages and might interfere with patient survival.

Bovine milk and meat factors (BMMFs) are plasmid-like DNA molecules isolated from bovine milk and serum, as well as the peritumor of colorectal cancer (CRC) patients. BMMFs have been proposed as zoonotic infectious agents and drivers of indirect carcinogenesis of CRC, inducing chronic tissue inflammation, radical formation and increased levels of DNA damage. Data on expression of BMMFs in large clinical cohorts to test an association with co-markers and clinical parameters were not previously available and were therefore assessed in this study.

Structural expression of bovine milk and meat factors in tissues of colorectal, lung and pancreatic cancer patients.

Chronic inflammation, linked to the presence of bovine milk and meat factors (BMMFs) and specific subsets of macrophages, results in oxygen radical synthesis and induction of mutations in DNA of actively replicating cells and replicating single stranded DNA. Cancers arising from this process have been characterized as indirect carcinogenesis by infectious agents (without persistence of genes of the agent in premalignant or cancers cells). Here, we investigate structural properties of pleomorphic vesicles, regularly identified by staining peritumor tissues of colorectal, lung and pancreatic cancer for expression of BMMF Rep.

iPLA2β-Null Mice Show HCC Protection by an Induction of Cell-Cycle Arrest after Diethylnitrosamine Treatment.

Group VIA phospholipase A2 (iPLA2β) play diverse biological functions in epithelial cells and macrophages. Global deletion in iPLA2β-null (KO) mice leads to protection against hepatic steatosis in non-alcoholic fatty liver disease, in part, due to the replenishment of the loss of hepatocellular phospholipids. As the loss of phospholipids also occurs in hepatocellular carcinoma (HCC), we hypothesized that global deletion in KO mice may lead to protection against HCC.

Validation of the prognostic value of CD3 and CD8 cell densities analogous to the Immunoscore® by stage and location of colorectal cancer: an independent patient cohort study.

In addition to the traditional staging system in colorectal cancer (CRC), the Immunoscore® has been proposed to characterize the level of immune infiltration in tumor tissue and as a potential prognostic marker. The aim of this study was to examine and validate associations of an immune cell score analogous to the Immunoscore® with established molecular tumor markers and with CRC patient survival in a routine setting. Patients from a population-based cohort study with available CRC tumor tissue blocks were included in this analysis.

IgE type multiple myeloma exhibits hypermutated phenotype and tumor reactive T cells.

Multiple myeloma (MM) is a hematological malignancy originating from malignant and clonally expanding plasma cells. MM can be molecularly stratified, and its clonal evolution deciphered based on the Ig heavy and light chains of the respective malignant plasma cell clone. Of all MM subtypes, IgE type MM accounts for only <0.