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Bone metastases are a frequent and severe complication of advanced-stage cancers. Breast and prostate cancers, the most common malignancies in women and men, respectively, have a particularly high propensity to metastasize to bone. Conceptually, circulating tumour cells (CTCs) in the bloodstream and disseminated tumour cells (DTCs) in the bone marrow provide a snapshot of the dissemination and colonization process en route to clinically apparent bone metastases. Many cell types that constitute the bone microenvironment, including osteoblasts, osteocytes, osteoclasts, adipocytes, endothelial cells, haematopoietic stem cells and immune cells, engage in a dialogue with tumour cells. Some of these cells modify tumour biology, while others are disrupted and out-competed by tumour cells, thus leading to distinct phases of tumour cell migration, dormancy and latency, and therapy resistance and progression to overt bone metastases. Several current bone-protective therapies act by interrupting these interactions, mainly by targeting tumour cell–osteoclast interactions. In this Review, we describe the functional roles of the bone microenvironment and its components in the initiation and propagation of skeletal metastases, outline the biology and clinical relevance of CTCs and DTCs, and discuss established and future therapeutic approaches that specifically target defined components of the bone microenvironment to prevent or treat skeletal metastases.Various cancers can disseminate to the bone, including the most common malignancies in men and women, prostate and breast cancer, respectively. Herein, the authors review the roles of the bone microenvironment in skeletal metastasis, highlighting the biology and clinical relevance of circulating tumour cells and disseminated tumour cells. Notably, bone metastases are associated with considerable morbidity and a poor prognosis, and the authors also discuss established and future therapeutic approaches for targeting components of the bone microenvironment to prevent or treat skeletal metastases.

Background The Cancer Genome Atlas (TCGA) is a pool of molecular data sets publicly accessible and freely available to cancer researchers anywhere around the world. However, wide spread use is limited since an advanced knowledge of statistics and statistical software is required. Results In order to improve accessibility we created Web-TCGA, a web based, freely accessible online tool, which can also be run in a private instance, for integrated analysis of molecular cancer data sets provided by TCGA. In contrast to already available tools, Web-TCGA utilizes different methods for analysis and visualization of TCGA data, allowing users to generate global molecular profiles across different cancer entities simultaneously. In addition to global molecular profiles, Web-TCGA offers highly detailed gene and tumor entity centric analysis by providing interactive tables and views. Conclusions As a supplement to other already available tools, such as cBioPortal (Sci Signal 6:pl1, 2013, Cancer Discov 2:401–4, 2012), Web-TCGA is offering an analysis service, which does not require any installation or configuration, for molecular data sets available at the TCGA. Individual processing requests (queries) are generated by the user for mutation, methylation, expression and copy number variation (CNV) analyses. The user can focus analyses on results from single genes and cancer entities or perform a global analysis (multiple cancer entities and genes simultaneously).

Mice lacking DNase II display a polyarthritis-like disease phenotype that is driven by translocation of self-DNA into the cytoplasm of phagocytic cells, where it is sensed by pattern recognition receptors. While pro-inflammatory gene expression is non-redundantly linked to the presence of STING in these mice, the contribution of the inflammasome pathway has not been explored. To this end, we studied the role of the DNA-sensing inflammasome receptor AIM2 in this self-DNA driven disease model. Arthritis-prone mice lacking AIM2 displayed strongly decreased signs of joint inflammation and associated histopathological findings. This was paralleled with a reduction of caspase-1 activation and pro-inflammatory cytokine production in diseased joints. Interestingly, systemic signs of inflammation that are associated with the lack of DNase II were not dependent on AIM2. Taken together, these data suggest a tissue-specific role for the AIM2 inflammasome as a sensor for endogenous DNA species in the course of a ligand-dependent autoinflammatory condition.

Histone lysine methylation is generally performed by SET domain methyltransferases and regulates chromatin structure and gene expression. Here, we identify human C21orf127 (HEMK2, N6AMT1, PrmC), a member of the seven-β-strand family of putative methyltransferases, as a novel histone lysine methyltransferase. C21orf127 functions as an obligate heterodimer with TRMT112, writing the methylation mark on lysine 12 of histone H4 (H4K12) in vitro and in vivo. We characterized H4K12 recognition by solving the crystal structure of human C21orf127–TRMT112, hereafter termed ‘lysine methyltransferase 9’ (KMT9), in complex with S-adenosyl-homocysteine and H4K12me1 peptide. Additional analyses revealed enrichment for KMT9 and H4K12me1 at the promoters of numerous genes encoding cell cycle regulators and control of cell cycle progression by KMT9. Importantly, KMT9 depletion severely affects the proliferation of androgen receptor–dependent, as well as that of castration- and enzalutamide-resistant prostate cancer cells and xenograft tumors. Our data link H4K12 methylation with KMT9-dependent regulation of androgen-independent prostate tumor cell proliferation, thereby providing a promising paradigm for the treatment of castration-resistant prostate cancer.KMT9, a new histone lysine methyltransferase targeting H4K12, is enriched at promoters of genes encoding molecules involved in the cell cycle and controls the growth of androgen receptor–dependent and castration- and enzalutamide-resistant prostate cancer cells and xenograft tumors.

Background Crosstalk between neoplastic and stromal cells fosters prostate cancer (PCa) progression and dissemination. Insight in cell-to-cell communication networks provides new therapeutic avenues to mold processes that contribute to PCa tumor microenvironment (TME) alterations. Here we performed a detailed characterization of PCa tumor endothelial cells (TEC) to delineate intercellular crosstalk between TEC and the PCa TME. Methods TEC isolated from 67 fresh radical prostatectomy (RP) specimens underwent multi-omic ex vivo characterization as well as orthogonal validation of both TEC functions and key markers by immunohistochemistry (IHC) and immunofluorescence (IF). To identify cell–cell interaction targets in TEC, we performed single-cell RNA sequencing (scRNA-seq) in four PCa patients who underwent a RP to catalogue cellular TME composition. Targets were cross-validated using IHC, publicly available datasets, cell culture expriments as well as a PCa xenograft mouse model. Results Compared to adjacent normal endothelial cells (NEC) bulk RNA-seq analysis revealed upregulation of genes associated with tumor vasculature, collagen modification and extracellular matrix remodeling in TEC. PTGIR, PLAC9, CXCL12 and VDR were identified as TEC markers and confirmed by IF and IHC in an independent patient cohort. By scRNA-seq we identified 27 cell (sub)types, including endothelial cells (EC) with arterial, venous and immature signatures, as well as angiogenic tip EC. A focused molecular analysis revealed that arterial TEC displayed highest CXCL12 mRNA expression levels when compared to all other TME cell (sub)populations and showed a negative prognostic role. Receptor-ligand interaction analysis predicted interactions between arterial TEC derived CXCL12 and its cognate receptor CXCR4 on angiogenic tip EC. CXCL12 was in vitro and in vivo validated as actionable TEC target by highlighting the vessel number- and density- reducing activity of the CXCR4-inhibitor AMD3100 in murine PCa as well as by inhibition of TEC proliferation and migration in vitro. Conclusions Overall, our comprehensive analysis identified novel PCa TEC targets and highlights CXCR4/CXCL12 interaction as a potential novel target to interfere with tumor angiogenesis in PCa. Graphical Abstract

Neuroblastomas—the most common tumor type in infants—develop from fetal nerve cells, and their clinical course is highly variable. Some neuroblastomas are fatal despite treatment, whereas others respond well to treatment and some undergo spontaneous regression without treatment. Ackermann et al. sequenced more than 400 pretreatment neuroblastomas and identified molecular features that characterize the three distinct clinical outcomes. Low-risk tumors lack telomere maintenance mechanisms, intermediate-risk tumors harbor telomere maintenance mechanisms, and high-risk tumors harbor telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Science , this issue p. 1165 Neuroblastomas that are positive for telomere maintenance mechanisms are associated with a poorer prognosis. Neuroblastoma is a pediatric tumor of the sympathetic nervous system. Its clinical course ranges from spontaneous tumor regression to fatal progression. To investigate the molecular features of the divergent tumor subtypes, we performed genome sequencing on 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208 of these tumors. We found that patients whose tumors lacked telomere maintenance mechanisms had an excellent prognosis, whereas the prognosis of patients whose tumors harbored telomere maintenance mechanisms was substantially worse. Survival rates were lowest for neuroblastoma patients whose tumors harbored telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Spontaneous tumor regression occurred both in the presence and absence of these mutations in patients with telomere maintenance–negative tumors. On the basis of these data, we propose a mechanistic classification of neuroblastoma that may benefit the clinical management of patients.

Prostate cancer is a hormone-driven disease and its tumor cell growth highly relies on increased androgen receptor (AR) signaling. Therefore, targeted therapy directed against androgen synthesis or AR activation is broadly used and continually improved. However, a subset of patients eventually progresses to castration-resistant disease. To date, various mechanisms of resistance have been identified including the development of AR-independent aggressive variant prostate cancer based on neuroendocrine transdifferentiation (NED). Here, we review the highly complex processes contributing to NED. Genetic, epigenetic, transcriptional aberrations and posttranscriptional modifications are highlighted and the potential interplay of the different factors is discussed. Background Aggressive variant prostate cancer (AVPC) with traits of neuroendocrine differentiation emerges in a rising number of patients in recent years. Among others, advanced therapies targeting the androgen receptor axis have been considered causative for this development. Cell growth of AVPC often occurs completely independent of the androgen receptor signal transduction pathway and cells have mostly lost the typical cellular features of prostate adenocarcinoma. This complicates both diagnosis and treatment of this very aggressive disease. We believe that a deeper understanding of the complex molecular pathological mechanisms contributing to transdifferentiation will help to improve diagnostic procedures and develop effective treatment strategies. Indeed, in recent years, many scientists have made important contributions to unravel possible causes and mechanisms in the context of neuroendocrine transdifferentiation. However, the complexity of the diverse molecular pathways has not been captured completely, yet. This narrative review comprehensively highlights the individual steps of neuroendocrine transdifferentiation and makes an important contribution in bringing together the results found so far.

The mechanisms behind the evolution of complex genomic amplifications in cancer have remained largely unclear. Using whole-genome sequencing data of the pediatric tumor neuroblastoma, we here identified a type of amplification, termed ‘seismic amplification’, that is characterized by multiple rearrangements and discontinuous copy number levels. Overall, seismic amplifications occurred in 9.9% (274 of 2,756) of cases across 38 cancer types, and were associated with massively increased copy numbers and elevated oncogene expression. Reconstruction of the development of seismic amplification showed a stepwise evolution, starting with a chromothripsis event, followed by formation of circular extrachromosomal DNA that subsequently underwent repetitive rounds of circular recombination. The resulting amplicons persisted as extrachromosomal DNA circles or had reintegrated into the genome in overt tumors. Together, our data indicate that the sequential occurrence of chromothripsis and circular recombination drives oncogene amplification and overexpression in a substantial fraction of human malignancies. Seismic amplifications arise from several cycles of circular recombination of circular extrachromosomal DNA formed as a result of chromothripsis. The process provides a mechanism for oncogene amplification in a number of different human tumor types.

When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen’s Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen’s Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen’s Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen’s Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.

In this retrospective study, we analyzed the association between tumor budding and perineural invasion as well as their prognostic role in pancreatic ductal adenocarcinoma. A total of N = 119 patients resected for pancreatic ductal carcinoma from 1996 to 2015 were included. Clinical and standard histopathological parameters were retrieved from the patient’s records. One representative hematoxylin and eosin section from the tumor region was examined for perineural invasion and tumor budding using light microscopy. Tumor budding was assessed independently using two different methods: in the first approach, the number of buds was counted over three fields of 0.237 mm2 at 40-fold magnification; in the second approach, tumor budding was quantified according to the recommendation of the International Tumor Budding Consensus Conference (ITBCC) over a field of 0.785 mm2 at 20-fold magnification. Linear and logistic regression was applied to delineate association between perineural invasion, tumor budding, and other parameters; Kaplan-Meier and Cox regression were used in the survival analysis. Regardless of the quantification approach, high tumor budding was a significant negative prognostic factor in the univariable Cox regression (> 5 buds/0.237 mm2, hazard ratio (HR) 1.66, 95% confidence interval (CI) 1.06–2.61, p = 0.027; ≥ 10 buds/0.785 mm2, HR 1.68, 95% CI 1.07–2.64, p = 0.024). In the multivariable model adjusting for stage and standard histopathological parameters, lymph vessel invasion (HR = 2.43, 95% CI 1.47–4.03, p = 0.001) and tumor budding > 5 buds/0.237 mm2 (HR = 1.70, 95% CI 1.07–2.7, p = 0.026) were independent negative prognostic factors, while adjuvant therapy was a positive prognostic factor (HR = 0.54, 95% CI 0.33–0.86, p = 0.009). No significant prognostic value could be delineated for perineural invasion. In conclusion, tumor budding is an independent negative prognostic factor in pancreatic ductal adenocarcinoma associated with lymph node metastasis. The prognostic role of perineural invasion remains uncertain.