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Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents’ pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.

Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. Results. The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944–1.000 (median; Q1–Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. Conclusions. The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist’s confidence in the solution with a minor workload for the radiologist.

Purpose Neuroblastoma (NB) is the most frequent extracranial tumor in children. The detection of bone marrow (BM) involvement is crucial for correct staging and risk-adapted treatment. We compared three methods regarding the detection of NB involvement in BM. Methods Eighty-one patients with NB were included in this retrospective study. BM samples were obtained at designated time points at study entry and during treatment or follow-up. The diagnostic tools for BM analysis included cytomorphology (CM), flow cytometry (FCM) and automatic immunofluorescence plus fluorescence in situ hybridization (AIPF). Results We analyzed 369 aspirates in 81 patients in whom AIPF, CM, and FCM were simultaneously available. During the observation period, NB cells were detected in 86/369 (23.3%) cases, by CM in 32/369 (8.7%), by FCM in 52 (14.1%), and by AIPF in 72 (19.5%) samples. AIPF and/or FCM confirmed all positive results obtained in CM and detected 11 additional positive BM aspirates in 294 CM negative samples (p Conclusion The combination of AIPF/FCM yielded the highest detection rate of NB cells in BM. AIPF was the single, most sensitive method in detecting these cells. Although CM did not provide any additional positive results, it is still a useful, readily available and cost-effective tool. The prognostic significance of FCM and AIPF should be confirmed in a prospective study with a larger number of patients.

Neutrophils are evolutionarily conserved innate defense cells implicated in diverse pathological processes. Zebrafish models have contributed substantially to our understanding of neutrophil functions, but similarities to human neutrophil maturation have not been characterized limiting applicability to study human disease.We generated transgenic zebrafish strains to distinguish neutrophil maturation gradesin vivoand established a high-resolution transcriptional profile of neutrophil maturation. We linked gene expression at each stage to characteristic transcription factors, including C/ebpβ, important for late neutrophil maturation. Cross-species comparison of zebrafish, mouse, and human confirmed high molecular similarity in immature stages and discriminated zebrafish-specific from pan-species gene signatures. Applying pan-species neutrophil maturation signatures in RNA-seq data from neuroblastoma patients revealed an association of metastatic tumor cell infiltration in the bone marrow with an increase in mature neutrophils.Our detailed neutrophil maturation atlas provides a valuable resource for studying neutrophil function at different stages across species in health and disease.Graphical abstract

Early childhood tumours are thought to arise from transformed embryonic cells, which often carry large copy number variants (CNVs). However, it remains unclear how CNVs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ human embryonic stem cell (hESC) differentiation to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNVs impair the specification of hESC-derived trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. Overexpression ofMYCN, whose amplification co-occurs with CNVs in NB, exacerbates the differentiation block, and enables tumourigenic cell proliferation. We find links between disrupted cell statesin vitroand tumour cells and connect these states with stepwise disruption of developmental transcription factor networks. Together, our results chart a possible route to NB and provide a mechanistic framework for the CNV-driven initiation of embryonal tumours.

Targeting Anaplastic lymphoma kinase (ALK) is a promising therapeutic strategy for aberrant ALK-expressing malignancies including neuroblastoma (NB), but resistance to ALK inhibitors (ALK TKI) is a distinct possibility necessitating drug combination therapeutic approaches. Using high-throughput, genome-wide CRISPR-Cas9 knockout screens, we identify miR-1304-5p loss as a desensitizer to ALK TKIs in aberrant ALK-expressing NB; inhibition of miR-1304-5p decreased, while mimics increased the sensitivity of NB cells to ALK TKIs. We show that miR-1304-5p targets NRAS, decreasing cell viability via induction of apoptosis. It follows that the farnesyltransferase inhibitor (FTI) lonafarnib in addition to ALK TKIs act synergistically in NB, inducing apoptosis both in vitro and in vivo. In particular, on combined treatment of NB patient derived xenografts with an FTI and an ALK TKI complete regression of tumour growth was observed although tumours rapidly regrew on cessation of therapy. Overall, our data suggests that combined use of ALK TKIs and FTIs, constitutes a therapeutic approach to treat high risk NB although prolonged therapy is likely required to prevent relapse, rendering high-risk NB a chronic rather than a lethal disease.

Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.

The plastic potential of Schwann cells (SCs) is increasingly recognized to play a role after nerve injury and in diseases of the peripheral nervous system. Reports on the interaction between immune cells and SCs indicate their involvement in inflammatory processes. However, the immunocompetence of human SCs has been primarily deduced from neuropathies, but whether after nerve injury SCs directly regulate an adaptive immune response is unknown. Here, we performed comprehensive analysis of immunomodulatory capacities of human repair-related SCs (hrSCs), which recapitulate SC response to nerve injury in vitro. We used our well-established culture model of primary hrSCs from human peripheral nerves and analyzed the transcriptome, secretome, and cell surface proteins for pathways and markers relevant in innate and adaptive immunity, performed phagocytosis assays, and monitored T-cell subset activation in allogeneic co-cultures. Our findings show that hrSCs are phagocytic, which is in line with high MHCII expression. Furthermore, hrSCs express co-regulatory proteins, such as CD40, CD80, B7H3, CD58, CD86, and HVEM, release a plethora of chemoattractants, matrix remodeling proteins and pro- as well as anti-inflammatory cytokines, and upregulate the T-cell inhibiting PD-L1 molecule upon pro-inflammatory stimulation with IFNγ. In contrast to monocytes, hrSC alone are not sufficient to trigger allogenic CD4

Early childhood malignancies are driven by sparse genetic aberrations in oncogenes that often co-occur with large copy number variants (CNVs). The combination of these mutations is thought to transform developmentally pliant embryonic cells to initiate tumorigenesis. However, the mechanistic interactions between CNVs, oncogenes, and differentiation have not been systematically studied due to several obstacles: (i) CNVs cannot be engineered efficiently yet; (ii) transient embryonic progenitors are absent in full-grown tumors; and (iii) inter-species differences in lineage specification limit the applicability of animal models. To overcome these challenges, we used isogenic human embryonic stem cell (hESC) lines carrying gains of chromosome 17q/1q, which are prevalent in the embryonal tumor neuroblastoma (NB). We differentiated these cells toward trunk neural crest (NC) and their sympathoadrenal derivatives, the putative cells-of-origin of NB, and performed single-cell RNA sequencing and cell-biological assays at key differentiation stages. We found that CNVs impaired the specification of sympathoadrenal cell types and instead potentiated early Schwann-cell-precursor-like phenotypes. Additional overexpression of the oncogene MYCN (which is frequently amplified together with CNVs in high-risk NB tumors) exacerbated these differentiation defects, enabled tumourigenic cell proliferation, and generated cell states in vitro that transcriptionally resembled NB tumor cells. Finally, using epigenome analysis, we connected these states to a stepwise disruption of gene-regulatory networks centered on developmental transcription factors. Together, our results chart a mechanistic route to NB tumorigenesis and provide a general framework for the CNV-driven initiation of embryonal tumors, in which CNVs ‘prime’ embryonic cells for oncogenic transformation. The tumor-like cells in our model may serve as proxies to experimentally test therapeutic interventions during tumorigenesis. Citation Format: Ingrid M. Saldana-Guerrero, Luis F. Montano-Gutierrez, Christoph Hafemeister, Dylan Stavish, Lisa E. Shaw, Irfete S. Fetahu, Andrea Wenninger-Weinzierl, Caterina Sturtzel, Celine Souilhol, Sophia Tarelli, Mohamed R. Shoeb, Marie Bernkopf, Polyxeni Bozatzi, Maria Guarini, Eva Bozsaky, Michelle C. Buri, Eva M. Putz, Peter W. Andrews, Ivana Barbaric, Helen E. Bryant, Martin Distel, Sabine Taschner-Mandl, Matthias Farlik, Anestis Tsakiridis, Florian Halbritter. A stem cell model dissects detrimental effects of neuroblastoma-linked chromosomal aberrations on cell differentiation during neural crest development. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3542.

Neuroblastoma (NB) is a pediatric cancer of the sympathetic nervous system and one of the most common solid tumors in infancy. Amplification of MYCN, copy number alterations, numerical and segmental chromosomal aberrations, mutations, and rearrangements on a handful of genes, such as ALK, ATRX, TP53, RAS/MAPK pathway genes, and TERT, are attributed as underlying causes that give rise to NB. However, the heterogeneous nature of the disease—along with the relative paucity of recurrent somatic mutations—reinforces the need to understand the interplay of genetic factors and epigenetic alterations in the context of NB. Epigenetic mechanisms tightly control gene expression, embryogenesis, imprinting, chromosomal stability, and tumorigenesis, thereby playing a pivotal role in physio- and pathological settings. The main epigenetic alterations include aberrant DNA methylation, disrupted patterns of posttranslational histone modifications, alterations in chromatin composition and/or architecture, and aberrant expression of non-coding RNAs. DNA methylation and demethylation are mediated by DNA methyltransferases (DNMTs) and ten-eleven translocation (TET) proteins, respectively, while histone modifications are coordinated by histone acetyltransferases and deacetylases (HATs, HDACs), and histone methyltransferases and demethylases (HMTs, HDMs). This article focuses predominately on the crosstalk between the epigenome and NB, and the implications it has on disease diagnosis and treatment.

Liquid biopsies as a minimally invasive approach have the potential to revolutionize molecular diagnostics. Yet, although protocols for sample handling and the isolation of circulating tumor DNA (ctDNA) are numerous, comprehensive guidelines for diagnostics and research considering all aspects of real-life multicenter clinical studies are currently not available. These include limitations in sample volume, transport, and blood collection tubes. We tested the impact of commonly used (EDTA and heparin) and specialized blood collection tubes and storage conditions on the yield and purity of cell-free DNA for the application in down-stream analysis. Moreover, we evaluated the feasibility of a combined workflow for ctDNA and tumor cell genomic testing and parallel flow cytometric analysis of leukocytes. For genomic analyses, EDTA tubes showed good results if stored for a maximum of 4 hours at room temperature or for up to 24 hours when stored at 4°C. Spike-in experiments revealed that EDTA tubes in combination with density gradient centrifugation allowed the parallel isolation of ctDNA, leukocytes, and low amounts of tumor cells (0.1%) and their immunophenotyping by flow cytometry and down-stream genomic analysis by whole genome sequencing. In conclusion, adhering to time and temperature limits allows the use of routine EDTA blood samples for liquid biopsy analyses. We further provide a workflow enabling the parallel analysis of cell-free and cellular features for disease monitoring and for clonal evolution studies.

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance—random identity by descent—is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.

Telomere maintenance by telomerase activation or alternative lengthening of telomeres (ALT) is a major determinant of poor outcome in neuroblastoma. Here, we screen for ALT in primary and relapsed neuroblastomas (n = 760) and characterize its features using multi-omics profiling. ALT-positive tumors are molecularly distinct from other neuroblastoma subtypes and enriched in a population-based clinical sequencing study cohort for relapsed cases. They display reduced ATRX/DAXX complex abundance, due to either ATRX mutations (55%) or low protein expression. The heterochromatic histone mark H3K9me3 recognized by ATRX is enriched at the telomeres of ALT-positive tumors. Notably, we find a high frequency of telomeric repeat loci with a neuroblastoma ALT-specific hotspot on chr1q42.2 and loss of the adjacent chromosomal segment forming a neo-telomere. ALT-positive neuroblastomas proliferate slowly, which is reflected by a protracted clinical course of disease. Nevertheless, children with an ALT-positive neuroblastoma have dismal outcome., Alternative lengthening of telomeres (ALT) is associated with a poor outcome in neuroblastoma. Here, the authors find that ALT is associated with mutated ATRX and/or reduced protein abundance, frequent telomeric repeat loci and heterochromatic telomeric chromatin.

In neuroblastoma, MYCN amplification and 11q-deletion are important, although incomplete, markers of high-risk disease. It is therefore relevant to characterize additional alterations that can function as prognostic and/or predictive markers. Using SNP-microarrays, a group of neuroblastoma patients showing amplification of one or multiple 12q loci was identified. Two loci containing CDK4 and MDM2 were commonly co-amplified, although amplification of either locus in the absence of the other was observed. Pharmacological inhibition of CDK4/6 with ribociclib or abemaciclib decreased proliferation in a broad set of neuroblastoma cell lines, including CDK4/MDM2-amplified, whereas MDM2 inhibition by Nutlin-3a was only effective in p53wild-type cells. Combined CDK4/MDM2 targeting had an additive effect in p53wild-type cell lines, while no or negative additive effect was observed in p53mutated cells. Most 12q-amplified primary tumors were of abdominal origin, including those of intrarenal origin initially suspected of being Wilms’ tumor. An atypical metastatic pattern was also observed with low degree of bone marrow involvement, favoring other sites such as the lungs. Here we present detailed biological data of an aggressive neuroblastoma subgroup hallmarked by 12q amplification and atypical clinical presentation for which our in vitro studies indicate that CDK4 and/or MDM2 inhibition also could be beneficial.

In neuroblastoma (NB), the ALK receptor tyrosine kinase can be constitutively activated through activating point mutations or genomic amplification. We studiedDiagnostic tumor samples were available from 1,092 HR-NBL1/SIOPEN patients to determineGenomicGenetic alterations of

Clinical decision-making in cancer and other diseases relies on timely and cost-effective genome-wide testing. Classical bioinformatic algorithms, such as Rawcopy, can support genomic analysis by calling genomic breakpoints and copy-number variations (CNVs), but often require manual data curation, which is error prone, time-consuming, and thus substantially increasing costs of genomic testing and hampering timely delivery of test results to the treating physician. We aimed to investigate whether deep learning algorithms can be used to learn from genome-wide single-nucleotide polymorphism array (SNPa) data and improve state-of-the-art algorithms. We developed, applied, and validated a novel deep neural network (DNN), DeepSNP. A manually curated data set of 50 SNPa analyses was used as truth-set. We show that DeepSNP can learn from SNPa data and classify the presence or absence of genomic breakpoints within large genomic windows with high precision and recall. DeepSNP was compared with well-known neural network models as well as with Rawcopy. Moreover, the use of a localization unit indicates the ability to pinpoint genomic breakpoints despite their exact location not being provided while training. DeepSNP results demonstrate the potential of DNN architectures to learn from genomic SNPa data and encourage further adaptation for CNV detection in SNPa and other genomic data types.

Introduction: Neuroblastoma is the most common solid tumor in infants and arises from progenitor cells during sympathoadrenal development. While the majority of primary tumor cells resembles adrenergic neurons, more undifferentiated mesenchymal or neural-crest cell-like phenotypes have been found, especially in pretreated and high-risk cases. In most high-risk neuroblastoma patients, tumor cells have disseminated to the bone marrow. Problem Statement and Aim: However, the bone marrow, as a key regulator of hematopoietic and mesenchymal stem cell quiescence, is a frequent site of dissemination not only of neuroblastoma but also of other solid cancers such as breast and prostate cancer. Still, comprehensive single-cell analyses of bone marrow metastases, i.e. disseminated tumor cells (DTCs) and cells of their microenvironment, have not yet been undertaken. Herein, we aimed to capture tumor heterogeneity and investigate microenvironmental changes in a solid cancer with bone marrow involvement. Methods: To that end, we applied a multi-omics data mining approach to define a multiplex imaging panel and designed DeepFLEX, a computational pipeline for subsequent multiplex image analysis. Thereby, we obtained a single-cell map of over 35,000 DTCs and cells of their microenvironment in the metastatic bone marrow niche. In addition, we independently profiled the transcriptome of 38 patients with and without bone marrow metastasis. Results: We revealed vast diversity among DTCs and suggest that FAIM2 can act as a complementary marker to capture DTC heterogeneity. However, DTCs in this study mainly expressed markers of the adrenergic lineage, such as GD2, but did not show a mesenchymal phenotype. Interestingly, we demonstrate that malignant bone marrow infiltration is associated with an inflammatory response and at the same time the presence of immuno-suppressive cell types, most significantly an immature neutrophil/granulocyte-myeloid derived suppressor-like cell type. Conclusion: Our findings demonstrate a high level of heterogeneity among DTCs and suggest that the latter shape the bone marrow microenvironment, warranting deeper investigations of spatio-temporal dynamics at the single-cell level and their clinical relevance. Citation Format: Daria Lazic, Florian Kromp, Fikret Rifatbegovic, Peter Repiscak, Filip Mivalt, Florian Halbritter, Marie Bernkopf, Andrea Bileck, Marek Ussowicz, Inge M. Ambros, Peter F. Ambros, Christopher Gerner, Ruth Ladenstein, Christian Ostalecki, Sabine Taschner-Mandl. Mapping bone marrow metastasis in neuroblastoma by deep multiplex imaging and transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 977.

Purpose Neuroblastoma (NB) is the most frequent solid malignancy in children outside the central nervous system. Detection of bone marrow (BM) involvement is crucial for correct staging and risk-adapted treatment. We compared three different methods regarding the detection of NB involvement in BM. Methods Eighty-one patients with NB were included in this retrospective study. BM samples were obtained at designated time points at study entry and during treatment or follow-up. The diagnostic tools for BM analysis included cytomorphology (CM), flow cytometry (FCM) and automatic immunofluorescence plus fluorescence in situ hybridization (AIPF). Results We analyzed 369 aspirates in 81 patients in whom AIPF, CM, and FCM were simultaneously available. During the observation period, NB cells were detected in 86/369 (23.3%) cases, by CM in 32/369 (8.7%) samples, by FCM in 52 (14.1%) samples, and by AIPF in 72 (19.5%) samples. AIPF and/or FCM confirmed all positive results obtained in CM and detected 11 additional BM aspirates with NB cells in 294 CM negative samples (p

Separating and labeling each nuclear instance (instance-aware segmentation) is the key challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been demonstrated to solve nuclear image segmentation tasks across different imaging modalities, but a systematic comparison on complex immunofluorescence images has not been performed. Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity. In this work, we evaluate and compare the segmentation effectiveness of multiple deep learning architectures (U-Net, U-Net ResNet, Cellpose, Mask R-CNN, KG instance segmentation) and two conventional algorithms (Iterative h-min based watershed, Attributed relational graphs) on complex fluorescence nuclear images of various types. We propose and evaluate a novel strategy to create artificial images to extend the training set. Results show that instance-aware segmentation architectures and Cellpose outperform the U-Net architectures and conventional methods on complex images in terms of F1 scores, while the U-Net architectures achieve overall higher mean Dice scores. Training with additional artificially generated images improves recall and F1 scores for complex images, thereby leading to top F1 scores for three out of five sample preparation types. Mask R-CNN trained on artificial images achieves the overall highest F1 score on complex images of similar conditions to the training set images while Cellpose achieves the overall highest F1 score on complex images of new imaging conditions. We provide quantitative results demonstrating that images annotated by under-graduates are sufficient for training instance-aware segmentation architectures to efficiently segment complex fluorescence nuclear images.

Purpose: In neuroblastoma (NB), the ALK receptor tyrosine kinase can be constitutively activated through activating point mutations or genomic amplification. We studied ALK genetic alterations in high-risk (HR) patients on the HR-NBL1/SIOPEN trial to determine their frequency, correlation with clinical parameters, and prognostic impact. Materials and methods: Diagnostic tumor samples were available from 1,092 HR-NBL1/SIOPEN patients to determine ALK amplification status (n = 330), ALK mutational profile (n = 191), or both (n = 571). Results: Genomic ALK amplification (ALKa) was detected in 4.5% of cases (41 out of 901), all except one with MYCN amplification (MNA). ALKa was associated with a significantly poorer overall survival (OS) (5-year OS: ALKa [n = 41] 28% [95% CI, 15 to 42]; no-ALKa [n = 860] 51% [95% CI, 47 to 54], [P < .001]), particularly in cases with metastatic disease. ALK mutations (ALKm) were detected at a clonal level (> 20% mutated allele fraction) in 10% of cases (76 out of 762) and at a subclonal level (mutated allele fraction 0.1%-20%) in 3.9% of patients (30 out of 762), with a strong correlation between the presence of ALKm and MNA (P < .001). Among 571 cases with known ALKa and ALKm status, a statistically significant difference in OS was observed between cases with ALKa or clonal ALKm versus subclonal ALKm or no ALK alterations (5-year OS: ALKa [n = 19], 26% [95% CI, 10 to 47], clonal ALKm [n = 65] 33% [95% CI, 21 to 44], subclonal ALKm (n = 22) 48% [95% CI, 26 to 67], and no alteration [n = 465], 51% [95% CI, 46 to 55], respectively; P = .001). Importantly, in a multivariate model, involvement of more than one metastatic compartment (hazard ratio [HR], 2.87; P < .001), ALKa (HR, 2.38; P = .004), and clonal ALKm (HR, 1.77; P = .001) were independent predictors of poor outcome. Conclusion: Genetic alterations of ALK (clonal mutations and amplifications) in HR-NB are independent predictors of poorer survival. These data provide a rationale for integration of ALK inhibitors in upfront treatment of HR-NB with ALK alterations. Key Objective: High risk neuroblastoma (HR-NB) is one of the most difficult childhood cancers to cure. This study examined whether the presence of an ALK alteration (amplification or mutation) was associated with a poor prognosis in a large patient series treated on the prospective European high-risk neuroblastoma trial (HR-NBL1). Knowledge Generated: We found that ALK amplification or clonal mutation was associated with inferior prognosis in patients with HR-NB and both are independent prognostic variables on multivariate analysis. To our knowledge, this is the first study to report the highly prognostic significance of ALK amplification in HR-NB. Relevance: As ALK can be targeted therapeutically, this study convincingly argues for the introduction of ALK inhibitors for upfront management of patients with HR-NB with ALK aberrations. Importantly, the prognostic significance of ALK alterations included a subgroup of trial patients treated with the current standard of care for HR-NB including anti-GD2 immunotherapy. info:eu-repo/semantics/publishedVersion

Adult Schwann cells (SCs) possess an inherent plastic potential. This plasticity allows SCs to acquire repair-specific functions essential for peripheral nerve regeneration. Here, we investigate whether stromal SCs in benign-behaving peripheral neuroblastic tumors adopt a similar cellular state. We profile ganglioneuromas and neuroblastomas, rich and poor in SC stroma, respectively, and peripheral nerves after injury, rich in repair SCs. Indeed, stromal SCs in ganglioneuromas and repair SCs share the expression of nerve repair-associated genes. Neuroblastoma cells, derived from aggressive tumors, respond to primary repair-related SCs and their secretome with increased neuronal differentiation and reduced proliferation. Within the pool of secreted stromal and repair SC factors, we identify EGFL8, a matricellular protein with so far undescribed function, to act as neuritogen and to rewire cellular signaling by activating kinases involved in neurogenesis. In summary, we report that human SCs undergo a similar adaptive response in two patho-physiologically distinct situations, peripheral nerve injury and tumor development., Schwann cells (SCs) can acquire a repair phenotype following nerve injury. Here, the authors show that stromal SCs in ganglioneuromas express nerve-repair genes. Importantly, neuroblastoma cells respond to repair-related SCs increasing neuronal differentiation and reducing proliferation via EGFL8.

Sequencing of cell-free DNA in the blood of cancer patients (liquid biopsy) provides attractive opportunities for early diagnosis, assessment of treatment response, and minimally invasive disease monitoring. To unlock liquid biopsy analysis for pediatric tumors with few genetic aberrations, we introduce an integrated genetic/epigenetic analysis method and demonstrate its utility on 241 deep whole-genome sequencing profiles of 95 patients with Ewing sarcoma and 31 patients with other pediatric sarcomas. Our method achieves sensitive detection and classification of circulating tumor DNA in peripheral blood independent of any genetic alterations. Moreover, we benchmark different metrics for cell-free DNA fragmentation analysis, and we introduce the LIQUORICE algorithm for detecting circulating tumor DNA based on cancer-specific chromatin signatures. Finally, we combine several fragmentation-based metrics into an integrated machine learning classifier for liquid biopsy analysis that exploits widespread epigenetic deregulation and is tailored to cancers with low mutation rates. Clinical associations highlight the potential value of cfDNA fragmentation patterns as prognostic biomarkers in Ewing sarcoma. In summary, our study provides a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations, and it renders the benefits of liquid biopsy more readily accessible for childhood cancers., Liquid biopsies enable minimally invasive applications for diagnosis and treatment monitoring. Here the authors analyse fragmentation patterns of circulating tumour DNA on multiple levels and develop a bioinformatic tool, LIQUORICE, to accurately detect and classify paediatric cancers with low mutational burden.

Bone marrow commonly serves as a metastatic niche for disseminated tumor cells (DTCs) of solid cancers in patients with unfavorable clinical outcome. Single-cell assessment of bone marrow metastases is essential to decipher the entire spectrum of tumor heterogeneity in these cancers, however, has previously not been performed.Here we used multi-epitope-ligand cartography (MELC) to spatially profile 20 biomarkers and assess morphology in DTCs as well as hematopoietic and mesenchymal cells of eight bone marrow metastases from neuroblastoma patients. We developed DeepFLEX, a single-cell image analysis pipeline for MELC data that combines deep learning-based cell and nucleus segmentation and overcomes frequent challenges of multiplex imaging methods including autofluorescence and unspecific antibody binding.Using DeepFLEX, we built a single-cell atlas of bone marrow metastases comprising more than 35,000 single cells. Comparisons of cell type proportions between samples indicated that microenvironmental changes in the metastatic bone marrow are associated with tumor cell infiltration and therapy response. Hierarchical clustering of DTCs revealed multiple phenotypes with highly diverse expression of markers such as FAIM2, an inhibitory protein in the Fas apoptotic pathway, which we propose as a complementary marker to capture DTC heterogeneity in neuroblastoma.The presented single-cell atlas provides first insights into the heterogeneity of human bone marrow metastases and is an important step towards a deeper understanding of DTCs and their interactions with the bone marrow niche.

The remarkable plasticity of Schwann cells (SCs) enables the acquisition of repair-specific functions essential for peripheral nerve regeneration. We hypothesized that this plastic potential is manifested in stromal SCs found within mostly benign-behaving peripheral neuroblastic tumors. To shed light on the cellular state and impact of stromal SCs, we combined transcriptome and proteome profiling of human ganglioneuromas and neuroblastomas, rich and poor in SC-stroma, respectively, as well as human injured nerve explants, rich in repair SCs. The results revealed a nerve repair-characteristic gene expression signature of stromal SCs. In turn, primary repair SCs had a pro-differentiating and anti-proliferative effect on aggressive neuroblastoma cell lines after direct and trans-well co-culture. Within the pool of secreted stromal/repair SC factors, we identified EGFL8, a matricellular protein with so far undescribed function, to induce neuronal differentiation of neuroblastoma cell lines. This study indicates that human SCs undergo a similar adaptive response in two patho-physiologically distinct situations, peripheral nerve injury and tumor development. This response is mediated by EGFL8 and other SC derived factors, which might be of therapeutic value for neuroblastic tumors and nerve regeneration.SYNOPSISIn order to investigate the nature of stromal Schwann cells in benign peripheral neuroblastic tumors (ganglioneuromas), we compared the cellular state of stromal Schwann cells with repair-associated Schwann cells emerging in peripheral nerves after injury.Stromal Schwann cells in ganglioneuromas and repair Schwann cells in injured nerves share the expression of nerve repair-associated genes.Neuroblastoma cell lines, derived from high-risk metastatic peripheral neuroblastic tumors (neuroblastomas), respond to primary repair Schwann cells and their secretome with increased neuronal differentiation and reduced proliferation.Stromal and repair Schwann cells express the matricellular protein EGFL8, which is capable to induce neuronal differentiation of neuroblastoma cell lines in recombinant form.THE PAPER EXPLAINEDProblemIn response to peripheral nerve damage, Schwann cells (SCs) are able to transform into specialized repair cells essential for nerve cell regeneration. Our previous studies indicated that this reactive/adaptive potential of human SCs is not restricted to injured nerve cells but also emerges in response to peripheral neuroblastic tumor cells. The usually benign subtypes of peripheral neuroblastic tumors, i.e. ganglioneuroblastomas and ganglioneuromas, contain neuronal differentiating tumor cells and are pervaded by various portions of stromal SCs. Of note, the amount of stromal SCs correlates with a favorable tumor behavior and increased patient survival, whereas aggressive subtypes of peripheral neuroblastic tumors, i.e. neuroblastomas, usually lack stromal SCs and have bad prognosis. This enigma prompted us to investigate the molecular wiring and functional state of stromal SCsversusinjury-associated repair SCs and how SC signals could be leveraged as therapeutics.ResultOur study revealed that the cellular state of stromal SCs in ganglioneuromas is in many aspects very similar to human repair SCs in injured nerves as both, stromal SCs and repair SCs, are equipped with distinct nerve repair-associated functions. Hence, we exposed different cell lines, derived from high-risk metastatic neuroblastomas, to primary repair SCs or their secretome. The results demonstrated that repair SCs had a pro-differentiating and anti-proliferative effect of on neuroblastoma cell lines upon direct and/or indirect contact. Searching for secreted anti-tumor factors by transcriptome and proteome analyses identified that the matricellular protein EGFL8 was highly expressed in injured nerves and ganglioneuromas. EGFL8 gene expression in peripheral neuroblastic tumors further correlated with increased patient survival. Indeed, treatment of neuroblastoma cell lines with recombinant EGFL8 promoted neuronal differentiation and present EGFL8 as a novel neuritogen.ImpactThese findings demonstrate that stromal SCs are equipped with the tools to exert nerve repair-associated functions on peripheral neuroblastic tumor cells and the tumor microenvironment. We further show that the pool of secreted stromal/repair SC molecules contains yet uncharacterized factors with a therapeutic potential for aggressive neuroblastomas. We conclude that the inherent plasticity (reactive/adaptive potential) of SCs is responsible for the development of usually benign ganglioneuroblastomas and ganglioneuromas and, thus, is of utmost interest to be exploited in future treatment approaches for aggressive neuroblastoma subtypes.

Background: Low survival rates in metastatic high-grade osteosarcoma (HGOS) have remained stagnant for the last three decades. This study aims to investigate the role of aminopeptidase N (ANPEP) in HGOS progression and its targeting with a novel lipophilic peptidase-enhanced cytotoxic compound melphalan flufenamide (melflufen) in HGOS. Methods: Meta-analysis of publicly available gene expression datasets was performed to determine the impact of ANPEP gene expression on metastasis-free survival of HGOS patients. The efficacy of standard-of-care anti-neoplastic drugs and a lipophilic peptidase-enhanced cytotoxic conjugate melflufen was investigated in patient-derived HGOS ex vivo models and cell lines. The kinetics of apoptosis and necrosis induced by melflufen and doxorubicin were compared. Anti-neoplastic effects of melflufen were investigated in vivo. Results: Elevated ANPEP expression in diagnostic biopsies of HGOS patients was found to significantly reduce metastasis-free survival. In drug sensitivity assays, melflufen has shown an anti-proliferative effect in HGOS ex vivo samples and cell lines, including those resistant to methotrexate, etoposide, doxorubicin, and PARP inhibitors. Further, HGOS cells treated with melflufen displayed a rapid induction of apoptosis and this sensitivity correlated with high expression of ANPEP. In combination treatments, melflufen demonstrated synergy with doxorubicin in killing HGOS cells. Finally, Melflufen displayed anti-tumor growth and anti-metastatic effects in vivo. Conclusion: This study may pave the way for use of melflufen as an adjuvant to doxorubicin in improving the therapeutic efficacy for the treatment of metastatic HGOS.

Pediatric cancers often mimic fetal tissues and express proteins normally silenced postnatally that could serve as immune targets. We developed T cells expressing chimeric antigen receptors (CARs) targeting glypican-2 (GPC2), a fetal antigen expressed on neuroblastoma (NB) and several other solid tumors. CARs engineered using standard designs control NBs with transgenic GPC2 overexpression, but not those expressing clinically relevant GPC2 site density (∼5,000 molecules/cell, range 1-6 × 10

Neuroblastoma is the most common extracranial solid tumor in childhood. The vast majority of metastatic (M) stage patients present with disseminated tumor cells (DTCs) in the bone marrow (BM) at diagnosis and relapse. Although these cells represent a major obstacle in the treatment of neuroblastoma patients, insights into their expression profile remained elusive. The present RNA-Seq study of stage 4/M primary tumors, enriched BM-derived diagnostic and relapse DTCs, as well as the corresponding BM-derived mononuclear cells (MNCs) from 53 patients revealed 322 differentially expressed genes in DTCs as compared to the tumors (q 2). Particularly, the levels of transcripts encoded by mitochondrial DNA were elevated in DTCs, whereas, for example, genes involved in angiogenesis were downregulated. Furthermore, 224 genes were highly expressed in DTCs and only slightly, if at all, in MNCs (q 6). Interestingly, we found the transcriptome of relapse DTCs largely resembling those of diagnostic DTCs with only 113 differentially expressed genes under relaxed cut-offs (q 0.5). Notably, relapse DTCs showed a positional enrichment of 31 downregulated genes on chromosome 19, including five tumor suppressor genes: SIRT6, BBC3/PUMA, STK11, CADM4 and GLTSCR2. This first RNA-Seq analysis of neuroblastoma DTCs revealed their unique expression profile in comparison to the tumors and MNCs, and less pronounced differences between diagnostic and relapse DTCs. The latter preferentially affected downregulation of genes encoded by chromosome 19. As these alterations might be associated with treatment failure and disease relapse, further functional studies on DTCs should be considered.

Fully-automated nuclear image segmentation is the prerequisite to ensure statistically significant, quantitative analyses of tissue preparations,applied in digital pathology or quantitative microscopy. The design of segmentation methods that work independently of the tissue type or preparation is complex, due to variations in nuclear morphology, staining intensity, cell density and nuclei aggregations. Machine learning-based segmentation methods can overcome these challenges, however high quality expert-annotated images are required for training. Currently, the limited number of annotated fluorescence image datasets publicly available do not cover a broad range of tissues and preparations. We present a comprehensive, annotated dataset including tightly aggregated nuclei of multiple tissues for the training of machine learning-based nuclear segmentation algorithms. The proposed dataset covers sample preparation methods frequently used in quantitative immunofluorescence microscopy. We demonstrate the heterogeneity of the dataset with respect to multiple parameters such as magnification, modality, signal-to-noise ratio and diagnosis. Based on a suggested split into training and test sets and additional single-nuclei expert annotations, machine learning-based image segmentation methods can be trained and evaluated., Measurement(s) nucleus • Annotation • Frozen Section • Neuroblastoma • Touch Prep Slide • Centrifuged Smear Slide • cells grown on slide • Ganglioneuroblastoma • Wilms Tumor • HaCaT cell Technology Type(s) Fluorescence Imaging • machine learning Factor Type(s) nucleus segmentation Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12570854

The remarkable feature of Schwann cells (SCs) to transform into a repair phenotype turned the spotlight on this powerful cell type. SCs provide the regenerative environment for axonal re-growth after peripheral nerve injury (PNI) and play a vital role in differentiation of neuroblastic tumors into a benign subtype of neuroblastoma, a tumor originating from neural crest-derived neuroblasts. Hence, understanding their mode-of-action is of utmost interest for new approaches in regenerative medicine, but also for neuroblastoma therapy. However, literature on human SCs is scarce and it is unknown to which extent human SC cultures reflect the SC repair phenotype developing after PNI in patients. We performed high-resolution proteome profiling and RNA-sequencing on highly enriched human SC and fibroblast cultures, control and ex vivo degenerated nerve explants to identify novel molecules and functional processes active in repair SCs. In fact, we found cultured SCs and degenerated nerves to share a similar repair SC-associated expression signature, including the upregulation of JUN, as well as two prominent functions, i.e., myelin debris clearance and antigen presentation via MHCII. In addition to myelin degradation, cultured SCs were capable of actively taking up cell-extrinsic components in functional phagocytosis and co-cultivation assays. Moreover, in cultured SCs and degenerated nerve tissue MHCII was upregulated at the cellular level along with high expression of chemoattractants and co-inhibitory rather than -stimulatory molecules. These results demonstrate human SC cultures to execute an inherent program of nerve repair and support two novel repair SC functions, debris clearance via phagocytosis-related mechanisms and type II immune-regulation. GLIA 2016;64:2133-2153.

Poor prognosis and frequent relapses are major challenges for patients with high-risk neuroblastoma (NB), especially when tumors show MYCN amplification. High-dose chemotherapy triggers apoptosis, necrosis and senescence, a cellular stress response leading to permanent proliferative arrest and a typical senescence-associated secretome (SASP). SASP components reinforce growth-arrest and act immune-stimulatory, while others are tumor-promoting. We evaluated whether metronomic, i.e. long-term, repetitive low-dose, drug treatment induces senescence in vitro and in vivo. And importantly, by using the secretome as a discriminator for beneficial versus adverse effects of senescence, drugs with a tumor-inhibiting SASP were identified. We demonstrate that metronomic application of chemotherapeutic drugs induces therapy-induced senescence, characterized by cell cycle arrest, p21WAF/CIP1 up-regulation and DNA double-strand breaks selectively in MYCN-amplified NB. Low-dose topotecan (TPT) was identified as an inducer of a favorable SASP while lacking NFKB1/p50 activation. In contrast, Bromo-deoxy-uridine induced senescent NB-cells secret a tumor-promoting SASP in a NFKB1/p50-dependent manner. Importantly, TPT-treated senescent tumor cells act growth-inhibitory in a dose-dependent manner on non-senescent tumor cells and MYCN expression is significantly reduced in vitro and in vivo. Furthermore, in a mouse xenotransplant-model for MYCN-amplified NB metronomic TPT leads to senescence selectively in tumor cells, complete or partial remission, prolonged survival and a favorable SASP. This new mode-of-action of metronomic TPT treatment, i.e. promoting a tumor-inhibiting type of senescence in MYCN-amplified tumors, is clinically relevant as metronomic regimens are increasingly implemented in therapy protocols of various cancer entities and are considered as a feasible maintenance treatment option with moderate adverse event profiles.

Chimeric antigen receptor (CAR) T cells have proven to be a powerful cellular therapy for B cell malignancies. Massive efforts are now being undertaken to reproduce the high efficacy of CAR T cells in the treatment of other malignancies. Here, predictive preclinical model systems are important, and the current gold standard for preclinical evaluation of CAR T cells are mouse xenografts. However, mouse xenograft assays are expensive and slow. Therefore, an additional vertebrate in vivo assay would be beneficial to bridge the gap from in vitro to mouse xenografts. Here, we present a novel assay based on embryonic zebrafish xenografts to investigate CAR T cell-mediated killing of human cancer cells. Using a CD19-specific CAR and Nalm-6 leukemia cells, we show that live observation of killing of Nalm-6 cells by CAR T cells is possible in zebrafish embryos. Furthermore, we applied Fiji macros enabling automated quantification of Nalm-6 cells and CAR T cells over time. In conclusion, we provide a proof-of-principle study that embryonic zebrafish xenografts can be used to investigate CAR T cell-mediated killing of tumor cells. This assay is cost-effective, fast, and offers live imaging possibilities to directly investigate CAR T cell migration, engagement, and killing of effector cells.

Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at predicting the presence or absence of genomic breakpoints, an indicator of structural chromosomal variations, in windows of 40,000 probes. We compare our results with well-known neural network models as well as Rawcopy though this tool is designed to predict breakpoints and in addition genomic segments with high sensitivity. We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models. Qualitative examples suggest that integration of a localization unit may enable breakpoint detection and prediction of genomic segments, even if the breakpoint coordinates were not provided for network training. These results warrant further evaluation of DeepSNP for breakpoint localization and subsequent calling of genomic segments., Accepted at the Joint ICML and IJCAI 2018 Workshop on Computational Biology