Slow integrin-dependent migration organizes networks of tissue-resident mast cells.

Immune cell locomotion is associated with amoeboid migration, a flexible mode of movement, which depends on rapid cycles of actin polymerization and actomyosin contraction1. Many immune cells do not necessarily require integrins, the major family of adhesion receptors in mammals, to move productively through three-dimensional tissue spaces2,3. Instead, they can use alternative strategies to transmit their actin-driven forces to the substrate, explaining their migratory adaptation to changing external environments4-6. However, whether these generalized concepts apply to all immune cells is unclear. Here, we show that the movement of mast cells (immune cells with important roles during allergy and anaphylaxis) differs fundamentally from the widely applied paradigm of interstitial immune cell migration. We identify a crucial role for integrin-dependent adhesion in controlling mast cell movement and localization to anatomical niches rich in KIT ligand, the major mast cell growth and survival factor. Our findings show that substrate-dependent haptokinesis is an important mechanism for the tissue organization of resident immune cells.

Type 1 conventional dendritic cells maintain and guide the differentiation of precursors of exhausted T cells in distinct cellular niches.

Reinvigoration of exhausted CD8+ T (Tex) cells by checkpoint immunotherapy depends on the activation of precursors of exhausted T (Tpex) cells, but the local anatomical context of their maintenance, differentiation, and interplay with other cells is not well understood. Here, we identified transcriptionally distinct Tpex subpopulations, mapped their differentiation trajectories via transitory cellular states toward Tex cells, and localized these cell states to specific splenic niches. Conventional dendritic cells (cDCs) were critical for successful αPD-L1 therapy and were required to mediate viral control. cDC1s were dispensable for Tpex cell expansion but provided an essential niche to promote Tpex cell maintenance, preventing their overactivation and T-cell-mediated immunopathology. Mechanistically, cDC1s insulated Tpex cells via MHC-I-dependent interactions to prevent their activation within other inflammatory environments that further aggravated their exhaustion. Our findings reveal that cDC1s maintain and safeguard Tpex cells within distinct anatomical niches to balance viral control, exhaustion, and immunopathology.

Robust Anti-viral Immunity Requires Multiple Distinct T Cell-Dendritic Cell Interactions.

Host defense against viruses and intracellular parasites depends on effector CD8(+) T cells, whose optimal clonal expansion, differentiation, and memory properties require signals from CD4(+) T cells. Here, we addressed the role of dendritic cell (DC) subsets in initial activation of the two T cell types and their co-operation. Surprisingly, initial priming of CD4(+) and CD8(+) T cells was spatially segregated within the lymph node and occurred on different DCs with temporally distinct patterns of antigen presentation via MHCI versus MHCII molecules. DCs that co-present antigen via both MHC molecules were detected at a later stage; these XCR1(+) DCs are the critical platform involved in CD4(+) T cell augmentation of CD8(+) T cell responses. These findings delineate the complex choreography of cellular interactions underlying effective cell-mediated anti-viral responses, with implications for basic DC subset biology, as well as for translational application to the development of vaccines that evoke optimal T cell immunity.

A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection.

For acutely lethal influenza infections, the relative pathogenic contributions of direct viral damage to lung epithelium versus dysregulated immunity remain unresolved. Here, we take a top-down systems approach to this question. Multigene transcriptional signatures from infected lungs suggested that elevated activation of inflammatory signaling networks distinguished lethal from sublethal infections. Flow cytometry and gene expression analysis involving isolated cell subpopulations from infected lungs showed that neutrophil influx largely accounted for the predictive transcriptional signature. Automated imaging analysis, together with these gene expression and flow data, identified a chemokine-driven feedforward circuit involving proinflammatory neutrophils potently driven by poorly contained lethal viruses. Consistent with these data, attenuation, but not ablation, of the neutrophil-driven response increased survival without changing viral spread. These findings establish the primacy of damaging innate inflammation in at least some forms of influenza-induced lethality and provide a roadmap for the systematic dissection of infection-associated pathology.

T Cell Zone Resident Macrophages Silently Dispose of Apoptotic Cells in the Lymph Node.

In lymph nodes (LNs), dendritic cells (DCs) are thought to dispose of apoptotic cells, a function pertaining to macrophages in other tissues. We found that a population of CX3CR1+ MERTK+ cells located in the T cell zone of LNs, previously identified as DCs, are efferocytic macrophages. Lineage-tracing experiments and shield chimeras indicated that these T zone macrophages (TZM) are long-lived macrophages seeded in utero and slowly replaced by blood monocytes after birth. Imaging the LNs of mice in which TZM and DCs express different fluorescent proteins revealed that TZM-and not DCs-act as the only professional scavengers, clearing apoptotic cells in the LN T cell zone in a CX3CR1-dependent manner. Furthermore, similar to other macrophages, TZM appear inefficient in priming CD4 T cells. Thus, efferocytosis and T cell activation in the LN are uncoupled processes designated to macrophages and DCs, respectively, with implications to the maintenance of immune homeostasis.

CD8+ T Cells Orchestrate pDC-XCR1+ Dendritic Cell Spatial and Functional Cooperativity to Optimize Priming.

Adaptive cellular immunity is initiated by antigen-specific interactions between T lymphocytes and dendritic cells (DCs). Plasmacytoid DCs (pDCs) support antiviral immunity by linking innate and adaptive immune responses. Here we examined pDC spatiotemporal dynamics during viral infection to uncover when, where, and how they exert their functions. We found that pDCs accumulated at sites of CD8+ T cell antigen-driven activation in a CCR5-dependent fashion. Furthermore, activated CD8+ T cells orchestrated the local recruitment of lymph node-resident XCR1 chemokine receptor-expressing DCs via secretion of the XCL1 chemokine. Functionally, this CD8+ T cell-mediated reorganization of the local DC network allowed for the interaction and cooperation of pDCs and XCR1+ DCs, thereby optimizing XCR1+ DC maturation and cross-presentation. These data support a model in which CD8+ T cells upon activation create their own optimal priming microenvironment by recruiting additional DC subsets to the site of initial antigen recognition.

Clonal Proliferation and Stochastic Pruning Orchestrate Lymph Node Vasculature Remodeling.

Lymph node (LN) expansion during an immune response relies on the transient remodeling of its vasculature. Although the mechanisms driving LN endothelial cell division are beginning to be understood, a comprehensive view of LN endothelial cell dynamics at the single-cell level is lacking. Here, we used multicolored fluorescent fate-mapping models to track the behavior of blood endothelial cells during LN expansion upon inflammation and subsequent return to homeostasis. We found that expansion of the LN vasculature relied on the sequential assembly of endothelial cell proliferative units. This segmented growth was sustained by the clonal proliferation of high endothelial venule (HEV) cells, which act as local progenitors to create capillaries and HEV neo-vessels at the periphery of the LN. Return to homeostasis was accompanied by the stochastic death of pre-existing and neo-synthesized LN endothelial cells. Thus, our fate-mapping studies unravel-at a single-cell level-the complex dynamics of vascular-tree remodeling during LN expansion and contraction.

Single-cell gene regulatory network prediction by explainable AI.

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification.

Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor-specific mutations or gene fusions, there is an unmet need to identify additional diagnostic biomarkers for entities lacking specific alterations. In this study, we collected a comprehensive cohort of 363 cases encompassing 20 different salivary gland tumor entities and explored the potential of DNA methylation to classify these tumors. We were able to show that most entities show specific epigenetic signatures and present a machine learning algorithm that achieved a mean balanced accuracy of 0.991. Of note, we showed that cribriform adenocarcinoma is epigenetically distinct from classical polymorphous adenocarcinoma, which could support risk stratification of these tumors. Myoepithelioma and pleomorphic adenoma form a uniform epigenetic class, supporting the theory of a single entity with a broad but continuous morphologic spectrum. Furthermore, we identified a histomorphologically heterogeneous but epigenetically distinct class that could represent a novel tumor entity. In conclusion, our study provides a comprehensive resource of the DNA methylation landscape of salivary gland tumors. Our data provide novel insight into disputed entities and show the potential of DNA methylation to identify new tumor classes. Furthermore, in future, our machine learning classifier could support the histopathologic diagnosis of salivary gland tumors.

PPARG activation promotes the proliferation of colorectal cancer cell lines and enhances the antiproliferative effect of 5-fluorouracil.

BACKGROUND: Peroxisome proliferator-activated receptor gamma (PPARG) is a member of the nuclear receptor family. It is involved in the regulation of adipogenesis, lipid metabolism, insulin sensitivity, vascular homeostasis and inflammation. In addition, PPARG agonists, known as thiazolidinediones, are well established in the treatment of type 2 diabetes mellitus. PPARGs role in cancer is a matter of debate, as pro- and anti-tumour properties have been described in various tumour entities. Currently, the specific role of PPARG in patients with colorectal cancer (CRC) is not fully understood. MATERIAL AND METHODS: The prognostic impact of PPARG expression was investigated by immunohistochemistry in a case-control study using a matched pair selection of CRC tumours (n = 246) with either distant metastases to the liver (n = 82), lung (n = 82) or without distant metastases (n = 82). Its effect on proliferation as well as the sensitivity to the chemotherapeutic drug 5-fluorouracil (5-FU) was examined after activation, inhibition, and transient gene knockdown of PPARG in the CRC cell lines SW403 and HT29. RESULTS: High PPARG expression was significantly associated with pulmonary metastasis (p = 0.019). Patients without distant metastases had a significantly longer overall survival with low PPARG expression in their tumours compared to patients with high PPARG expression (p = 0.045). In the pulmonary metastasis cohort instead, a trend towards longer survival was observed for patients with high PPARG expression in their tumour (p = 0.059). Activation of PPARG by pioglitazone and rosiglitazone resulted in a significant dose-dependent increase in proliferation of CRC cell lines. Inhibition of PPARG by its specific inhibitor GW9662 and siRNA-mediated knockdown of PPARG significantly decreased proliferation. Activating PPARG significantly increased the CRC cell lines sensitivity to 5-FU while its inhibition decreased it. CONCLUSION: The prognostic effect of PPARG expression depends on the metastasis localization in advanced CRC patients. Activation of PPARG increased malignancy associated traits such as proliferation in CRC cell lines but also increases sensitivity towards the chemotherapeutic agent 5-FU. Based on this finding, a combination therapy of PPARG agonists and 5-FU-based chemotherapy constitutes a promising strategy which should be further investigated.

Genetics and beyond: Precision Medicine Real-World Data for Patients with Cervical, Vaginal or Vulvar Cancer in a Tertiary Cancer Center.

Advances in molecular tumor diagnostics have transformed cancer care. However, it remains unclear whether precision oncology has the same impact and transformative nature across all malignancies. We conducted a retrospective analysis of patients with human papillomavirus (HPV)-related gynecologic malignancies who underwent comprehensive molecular profiling and subsequent discussion at the interdisciplinary Molecular Tumor Board (MTB) of the University Hospital, LMU Munich, between 11/2017 and 06/2022. We identified a total cohort of 31 patients diagnosed with cervical (CC), vaginal or vulvar cancer. Twenty-two patients (fraction: 0.71) harbored at least one mutation. Fifteen patients (0.48) had an actionable mutation and fourteen (0.45) received a recommendation for a targeted treatment within the MTB. One CC patient received a biomarker-guided treatment recommended by the MTB and achieved stable disease on the mTOR inhibitor temsirolimus for eight months. Factors leading to non-adherence to MTB recommendations in other patient cases included informed patient refusal, rapid deterioration, stable disease, or use of alternative targeted but biomarker-agnostic treatments such as antibody-drug conjugates or checkpoint inhibitors. Despite a remarkable rate of actionable mutations in HPV-related gynecologic malignancies at our institution, immediate implementation of biomarker-guided targeted treatment recommendations remained low, and access to targeted treatment options after MTB discussion remained a major challenge.

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.

Precision cancer medicine: Concepts, current practice, and future developments.

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.

AIM: Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and neuropathologists, which is time-consuming, labour-intensive and subjective. METHODS: We, therefore, developed an image analysis approach based on expert annotations of 123,181 digitised CSF objects from 78 patients corresponding to 15 clinically relevant categories and trained a multiclass convolutional neural network (CNN). RESULTS: The CNN classified the 15 categories with high accuracy (mean AUC 97.3%). By using explainable artificial intelligence (XAI), we demonstrate that the CNN identified meaningful cellular substructures in CSF cells recapitulating human pattern recognition. Based on the evaluation of 511 cells selected from 12 different CSF samples, we validated the CNN by comparing it with seven board-certified neuropathologists blinded for clinical information. Inter-rater agreement between the CNN and the ground truth was non-inferior (Krippendorff's alpha 0.79) compared with the agreement of seven human raters and the ground truth (mean Krippendorff's alpha 0.72, range 0.56-0.81). The CNN assigned the correct diagnostic label (inflammatory, haemorrhagic or neoplastic) in 10 out of 11 clinical samples, compared with 7-11 out of 11 by human raters. CONCLUSIONS: Our approach provides the basis to overcome current limitations in automated cell classification for routine diagnostics and demonstrates how a visual explanation framework can connect machine decision-making with cell properties and thus provide a novel versatile and quantitative method for investigating CSF manifestations of various neurological diseases.

T-cell-receptor-dependent signal intensity dominantly controls CD4(+) T cell polarization In Vivo.

Polarization of effector CD4(+) T cells can be influenced by both antigen-specific signals and by pathogen- or adjuvant-induced cytokines, with current models attributing a dominant role to the latter. Here we have examined the relationship between these factors in shaping cell-mediated immunity by using intravital imaging of CD4(+) T cell interactions with dendritic cells (DCs) exposed to polarizing adjuvants. These studies revealed a close correspondence between strength of T cell receptor (TCR)-dependent signaling and T helper 1 (Th1) versus Th2 cell fate, with antigen concentration dominating over adjuvant in controlling T cell polarity. Consistent with this finding, at a fixed antigen concentration, adjuvants inducing Th1 cells operated by affecting DC costimulation that amplified TCR signaling. TCR signal strength controlled downstream cytokine receptor expression, linking the two components in a hierarchical fashion. These data reveal how quantitative integration of antigen display and costimulation regulates downstream checkpoints responsible for cytokine-mediated control of effector differentiation.

Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation.

In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location-dependent patterns of genetic and epigenetic alterations.

Cutaneous, ocular, and mucosal melanomas are histologically indistinguishable tumors that are driven by a different spectrum of genetic alterations. With current methods, identification of the site of origin of a melanoma metastasis is challenging. DNA methylation profiling has shown promise for the identification of the site of tumor origin in various settings. Here we explore the DNA methylation landscape of melanomas from different sites and analyze if different melanoma origins can be distinguished by their epigenetic profile. We performed DNA methylation analysis, next generation DNA panel sequencing, and copy number analysis of 82 non-cutaneous and 25 cutaneous melanoma samples. We further analyzed eight normal melanocyte cell culture preparations. DNA methylation analysis separated uveal melanomas from melanomas of other primary sites. Mucosal, conjunctival, and cutaneous melanomas shared a common global DNA methylation profile. Still, we observed location-dependent DNA methylation differences in cancer-related genes, such as low frequencies of RARB (7/63) and CDKN2A promoter methylation (6/63) in mucosal melanomas, or a high frequency of APC promoter methylation in conjunctival melanomas (6/9). Furthermore, all investigated melanomas of the paranasal sinus showed loss of PTEN expression (9/9), mainly caused by promoter methylation. This was less frequently seen in melanomas of other sites (24/98). Copy number analysis revealed recurrent amplifications in mucosal melanomas, including chromosomes 4q, 5p, 11q and 12q. Most melanomas of the oral cavity showed gains of chromosome 5p with TERT amplification (8/10), while 11q amplifications were enriched in melanomas of the nasal cavity (7/16). In summary, mucosal, conjunctival, and cutaneous melanomas show a surprisingly similar global DNA methylation profile and identification of the site of origin by DNA methylation testing is likely not feasible. Still, our study demonstrates tumor location-dependent differences of promoter methylation frequencies in specific cancer-related genes together with tumor site-specific enrichment for specific chromosomal changes and genetic mutations. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

The other colon cancer: a population-based cohort study of appendix tumour trends and prognosis.

AIM: Appendiceal neoplasms are rare subtypes of colorectal tumours that mainly affect younger patients some 20 years earlier than other colon tumours. The aim of this study was to gain more insight into the histological subtypes of this rare disease and include cases previously excluded, such as mucinous neoplasia. METHOD: The cohort study included 1097 patients from the Munich Cancer Registry (MCR) diagnosed between 1998 and 2020. Joinpoint analysis was used to determine trend in incidence. Baseline demographic comparisons and survival analyses using competing risk and univariate/multivariate methods were conducted according to tumour histology: adenocarcinoma (ADENO), neuroendocrine neoplasia (NEN), mixed adeno-neuroendocrine carcinoma (MANEC), and low- (LAMN) and high-grade mucinous neoplasia (HAMN). RESULTS: Up to 2016 the number of cases increased significantly [annual per cent change (APC) = 6.86, p < 0.001] followed by a decline in the following years (APC = -14.82, p = 0.014; average APC = 2.5, p = 0.046). Comparison of all patients showed that NEN (48.4%) and mucinous neoplasms (11.6%) had a considerably better prognosis than ADENO (36.0%) and MANEC (3.0%, p < 0.0001). A multivariate analysis within the NEN and ADENO subgroups revealed that further histological classification was not prognostically relevant, while older age and regional tumour spread at diagnosis were associated with a poor prognosis. ADENO histology with high tumour grade and appendectomy only was also associated with poorer survival. CONCLUSION: Appendiceal neoplasms are histologically heterogeneous; however, this diversity becomes less relevant compared with the marked difference from cancers of the remaining colon. The previously observed increase in cases appears to be abating; fewer cases of appendicitis and/or appendectomies or changes in histopathological assessment may be behind this trend.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.