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Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

Cell death represents a basic biological paradigm that governs outcomes and long-term sequelae in almost every hepatic disease condition. Acute liver failure is characterized by massive loss of parenchymal cells but is usually followed by restitution ad integrum. By contrast, cell death in chronic liver diseases often occurs at a lesser extent but leads to long-term alterations in organ architecture and function, contributing to chronic hepatocyte turnover, the recruitment of immune cells and activation of hepatic stellate cells. These chronic cell death responses contribute to the development of liver fibrosis, cirrhosis and cancer. It has become evident that, besides apoptosis, necroptosis is a highly relevant form of programmed cell death in the liver. Differential activation of specific forms of programmed cell death might not only affect outcomes in liver diseases but also offer novel opportunities for therapeutic intervention. Here, we summarize the underlying molecular mechanisms and open questions about disease-specific activation and roles of programmed cell death forms, their contribution to response signatures and their detection. We focus on the role of apoptosis and necroptosis in acute liver injury, nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) and liver cancer, and possible translations into clinical applications.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.A deep residual learning framework identifies microsatellite instability in histology slides from patients with cancer and can be used to guide immunotherapy.

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a \"deep stroma score,\" which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the \"Darmkrebs: Chancen der Verhütung durch Screening\" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

ObjectiveBone marrow-derived myeloid cells accumulate in the liver as monocytes and macrophages during the progression of obesity-related non-alcoholic fatty liver disease (NAFLD) to steatohepatitis (NASH). Myeloid cells comprise heterogeneous subsets, and dietary overnutrition may affect macrophages in the liver and bone marrow. We therefore aimed at characterising in depth the functional adaptations of myeloid cells in fatty liver.DesignWe employed single-cell RNA sequencing to comprehensively assess the heterogeneity of myeloid cells in the liver and bone marrow during NAFLD, by analysing C57BL/6 mice fed with a high-fat, high-sugar, high-cholesterol ‘Western diet’ for 16 weeks. We also characterised NAFLD-driven functional adaptations of macrophages in vitro and their functional relevance during steatohepatitis in vivo.ResultsSingle-cell RNA sequencing identified distinct myeloid cell clusters in the liver and bone marrow. In both compartments, monocyte-derived populations were largely expanded in NASH-affected mice. Importantly, the liver myeloid compartment adapted a unique inflammatory phenotype during NAFLD progression, exemplarily characterised by downregulated inflammatory calprotectin (S100A8/A9) in macrophage and dendritic cell subsets. This distinctive gene signature was also found in their bone marrow precursors. The NASH myeloid phenotype was principally recapitulated by in vitro exposure of bone marrow-derived macrophages with fatty acids, depended on toll-like receptor 4 signalling and defined a characteristic response pattern to lipopolysaccharide stimulation. This imprinted and stable NASH myeloid immune phenotype functionally determined inflammatory responses following acute liver injury (acetaminophen poisoning) in vivo.ConclusionLiver myeloid leucocytes and their bone marrow precursors adapt a common and functionally relevant inflammatory signature during NAFLD progression.

Cholangiocarcinoma (CCA) is the second most common primary liver cancer and associated with a dismal prognosis due to the lack of an efficient systemic therapy. In contrast to other cancers, new immunotherapies have demonstrated unsatisfactory results in clinical trials, underlining the importance of a deeper understanding of the special tumor microenvironment of CCA and the role of immune cells interacting with the tumor. Tumor-infiltrating lymphocytes (TILs) are an important component of the adaptive immune system and the foundation of current immunotherapy. Therefore, the aim of this systemic review is to summarize the current literature focusing on the proportions and distribution, molecular pathogenesis, prognostic significance of TILs and their role in immunotherapy for CCA patients. In CCA, CD8+ and CD4+ T lymphocytes represent the majority of TILs and are mostly sequestered around the cancer cells. CD20+ B lymphocytes and Natural Killer (NK) cells are less frequent. In contrast, Foxp3+ cells (regulatory T cells, Tregs) are observed to infiltrate into the tumor. In the immune microenvironment of CCA, cancer cells and stromal cells such as TAMs, TANs, MSDCs and CAFs inhibit the immune protection function of TILs by secreting factors like IL-10 and TGF-β. With respect to molecular pathogenesis, the Wnt/-catenin, TGF-signaling routes, aPKC-i/P-Sp1/Snail Signaling, B7-H1/PD-1Pathway and Fas/FasL signaling pathways are connected to the malignant potential and contributed to tumor immune evasion by increasing TIL apoptosis. Distinct subtypes of TILs show different prognostic implications for the long-term outcome in CCA. Although there are occasionally conflicting results, CD8+ and CD4+ T cells, and CD20+ B cells are positively correlated with the oncological prognosis of CCA, while a high number of Tregs is very likely associated with worse overall survival. TILs also play a major role in immunotherapy for CCA. In summary, the presence of TILs may represent an important marker for the prognosis and a potential target for novel therapy, but more clinical and translational data is needed to fully unravel the importance of TILs in the treatment of CCA.

Primary liver cancer represents a major health problem. It comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which differ markedly with regards to their morphology, metastatic potential and responses to therapy. However, the regulatory molecules and tissue context that commit transformed hepatic cells towards HCC or ICC are largely unknown. Here we show that the hepatic microenvironment epigenetically shapes lineage commitment in mosaic mouse models of liver tumorigenesis. Whereas a necroptosis-associated hepatic cytokine microenvironment determines ICC outgrowth from oncogenically transformed hepatocytes, hepatocytes containing identical oncogenic drivers give rise to HCC if they are surrounded by apoptotic hepatocytes. Epigenome and transcriptome profiling of mouse HCC and ICC singled out Tbx3 and Prdm5 as major microenvironment-dependent and epigenetically regulated lineage-commitment factors, a function that is conserved in humans. Together, our results provide insight into lineage commitment in liver tumorigenesis, and explain molecularly why common liver-damaging risk factors can lead to either HCC or ICC. The tumour microenvironment determines which type of liver cancer develops, with transformed hepatocytes giving rise to intrahepatic cholangiocarcinoma or hepatocellular carcinoma depending or whether they are surrounded by cells undergoing necroptosis or apoptosis.

The prognosis of heart failure (HF) patients is determined to a decisive extent by comorbidities. The present study investigates the association between a broad spectrum of diseases and the occurrence of HF in a large collective of outpatients. This retrospective case control study assessed the prevalence of 37 cardiac and extracardiac diseases in patients with an initial diagnosis of heart failure (ICD-10: I50) in 1,274 general practices in Germany between January 2005 and December 2019. The study is based on the Disease Analyzer database (IQVIA), which contains drug prescriptions, diagnoses, and basic medical and demographic data. Patients with and without heart failure were matched by sex, age, and index year. Hazard regression models were conducted to evaluate the association between different disease entities and heart failure. The present study included 162,246 patients with heart failure and 162,246 patients without heart failure. Mean age [SD] was 73.7 [12.1] years; 52.6% were women. Out of 37 predefined diagnoses, 36 were more prevalent in HF patients. The highest prevalence was primary hypertension (63.4% in HF patients vs. 53.3% in controls, p  < 0.001) followed by lipid metabolism disorders (34.6% in HF patients vs. 29.1% in HF patients p  < 0.001) and diabetes mellitus type II (32.2% in HF patients vs. 25.2% in controls, p  < 0.001). In the regression analysis, 19 diseases were significantly associated with heart failure. Non-cardiovascular diagnoses strongly associated with HF were obesity (HR = 1.46), chronic bronchitis and COPD (HR = 1.41), gout (HR: 1.41), and chronic kidney disease (HR = 1.27). In the present study, we identified a variety of cardiac and extracardiac diseases associated with heart failure. Our data underscore the immense importance of comorbidities, even as early as at the stage of initial diagnosis of heart failure.

Non‐alcoholic fatty liver disease (NAFLD) represents the most common liver disease in Western countries and often progresses to non‐alcoholic steatohepatitis (NASH) leading ultimately to liver fibrosis and liver cancer. The occurrence of hepatocyte cell death—so far characterized as hepatocyte apoptosis—represents a fundamental step from benign steatosis toward progressive steatohepatitis. In contrast, the function of RIP3‐dependent “necroptosis” in NASH and NASH‐induced fibrosis is currently unknown. We show that RIP3 is upregulated in human NASH and in a dietary mouse model of steatohepatitis. RIP3 mediates liver injury, inflammation, induction of hepatic progenitor cells/activated cholangiocytes, and liver fibrosis through a pathway suppressed by Caspase‐8. This function of RIP3 is mediated by a positive feedback loop involving activation of Jun‐(N)‐terminal Kinase (JNK). Furthermore, RIP3‐dependent JNK activation promotes the release of pro‐inflammatory mediators like MCP‐1, thereby attracting macrophages to the injured liver and further augmenting RIP3‐dependent signaling, cell death, and liver fibrosis. Thus, RIP3‐dependent necroptosis controls NASH‐induced liver fibrosis. This pathway might represent a novel and specific target for pharmacological strategies in patients with NASH. Synopsis RIP3‐dependent necroptosis mediates NASH‐induced liver fibrosis via activation of JNK, MCP‐1‐mediated recruitment of monocytes, and an expansion of intrahepatic biliary/progenitor cells. Caspase‐8 appears to suppress the deleterious effect of RIP3. RIP3 mediates liver injury in MCD‐diet‐induced NASH. RIP3—similar to Caspase‐8—does not affect CCl4‐induced liver fibrosis and thus might be a specific target in metabolic liver disease. Human NASH livers strongly express RIP3. Targeting RIP3 might represent a novel‐specific approach in human NASH. Graphical Abstract RIP3‐dependent necroptosis mediates NASH‐induced liver fibrosis via activation of JNK, MCP‐1‐mediated recruitment of monocytes, and an expansion of intrahepatic biliary/progenitor cells. Caspase‐8 appears to suppress the deleterious effect of RIP3.

Abdominal infections including cholangitis represent a major problem in patients with perihilar cholangiocarcinoma (pCCA). Thus, we investigated bacterial colonization of the bile ducts and determined its impact on postoperative outcome focusing on abdominal infections. A cohort of 95 pCCA patients who underwent surgery between 2010 and 2019 with available intraoperative microbial bile cultures were analyzed regarding bile duct colonization and postoperative abdominal infection by group comparisons and logistic regressions. 84.2% (80/95) showed bacterial colonization of the bile ducts and 54.7% (52/95) developed postoperative abdominal infections. Enterococcus faecalis (38.8%, 31/80), Enterococcus faecium (32.5%, 26/80), Enterobacter cloacae (16.3%, 13/80) and Escherichia coli (11.3%, 9/80) were the most common bacteria colonizing the bile ducts and Enterococcus faecium (71.2%, 37/52), Enterococcus faecalis (30.8%, 16/52), Enterobacter cloacae (25.0%, 13/52) and Escherichia coli (19.2%, 10/52) the most common causes of postoperative abdominal infection. Further, reduced susceptibility to perioperative antibiotic prophylaxis (OR = 10.10, p  = .007) was identified as independent predictor of postoperative abdominal infection. Bacterial colonization is common in pCCA patients and reduced susceptibility of the bacteria to the intraoperative antibiotic prophylaxis is an independent predictor of postoperative abdominal infections. Adapting antibiotic prophylaxis might therefore have the potential to improve surgical outcome pCCA patients.