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Simple Summary: Neuroblastoma is a rare type of cancer that usually affects children. The high-risk patients' expected survival rate is less than 50%. One reason is the lack of precision in the standard treatment protocol: a one-size-fits-all multi-modal therapy. The study presented in this paper was designed to address this deficit by optimising the use of two chemotherapeutic agents—vincristine and cyclophosphamide—during induction chemotherapy—the part of the protocol that shrinks the primary tumour before surgical removal. We combined a mathematical model and an optimisation algorithm to identify the best chemotherapy schedules for a cohort of virtual patients with different initial tumour compositions. Our results reveal novel strategies to exploit a pair of drugs with different levels of efficacy, provide a platform on which to individualise induction chemotherapy, and lay the foundation for a personalised therapy that leverages targeted therapies, multi-region sequencing, liquid biopsies, and modern computational methods to improve today's multi-modal therapy. 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. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor segmentation is a key step in oncologic imaging processing. We have recently developed a model to detect and segment neuroblastic tumors on MR images based on deep learning architecture nnU-Net. In this work, we performed an independent validation of the automatic segmentation tool with a large heterogeneous dataset. We reviewed the automatic segmentations and manually edited them when necessary. We were able to show that the automatic network was able to locate and segment the primary tumor on the T2 weighted images in the majority of cases, with an extremely high agreement between the automatic tool and the manually edited masks. The time needed for manual adjustment was very low. 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. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. 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%. [ABSTRACT FROM AUTHOR]

Neuroblastoma is the most common extracranial solid tumor in children. The chromatin remodeler ATRX is frequently mutated in high‐risk patients with a poor prognosis. Although many studies have reported ATRX aberrations and the associated clinical characteristics in neuroblastoma, a comprehensive overview is currently lacking. In this study, we extensively characterize the mutational spectrum of ATRX aberrations in neuroblastoma tumors reported in previous studies and present an overview of patient and tumor characteristics. We collected the data of a total of 127 neuroblastoma patients and three cell lines with ATRX aberrations originating from 20 papers. We subdivide the ATRX aberrations into nonsense, missense, and multiexon deletions (MEDs) and show that 68% of them are MEDs. Of these MEDs, 75% are predicted to be in‐frame. Furthermore, we identify a missense mutational hotspot region in the helicase domain. We also confirm that all three ATRX mutation types are more often identified in patients diagnosed at an older age, but still approximately 40% of the patients are aged 5 years or younger at diagnosis. Surprisingly, we found that 11q deletions are enriched in neuroblastomas with ATRX deletions compared to a reference cohort, but not in neuroblastomas with ATRX point mutations. Taken together, our data emphasizes a distinct ATRX mutation spectrum in neuroblastoma, which should be considered when studying molecular phenotypes and therapeutic strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Metastases in the bone marrow (BM) in form of disseminated tumor cells (DTCs) are frequent events at diagnosis and also at relapse in high-risk neuroblastoma patients. The frequently highly diluted occurrence of DTCs requires adequate enrichment strategies to enable their detailed characterization. However, to avoid methodical artifacts we tested whether pre-analytical processing steps—including transport duration, temperature and, importantly, tumor cell enrichment techniques—are confounding factors for gene expression analysis in DTCs. Methods: LAN-1 neuroblastoma cells were spiked into tumor free BM and/or peripheral blood and: i) kept at room temperature or at 4°C for 24, 48 and 72 hours; ii) frozen down at -80°C and thawed; iii) enriched via magnetic beads. The effect on the gene expression signature of LAN-1 cells was analyzed by qPCR arrays and gene expression microarrays. Results: Neither storage at –80°C in DMSO and subsequent thawing nor enrichment of spiked-in neuroblastoma cells changed the expression of the analyzed genes significantly. Whereas storage at 4°C altered the expression of analyzed genes (14.3%) only at the 72h-timepoint in comparison to the 0h-timepoint, storage at room temperature had a much more profound effect on gene expression by affecting 20% at 24h, 26% at 48h and 43% at 72h of the analyzed genes. Conclusion: Using neuroblastoma as a model, we show that tumor cell enrichment by magnetic bead separation has virtually no effect on gene expression in DTCs. However, transport time and temperature can influence the expression profile remarkably. Thus, the expression profile of routinely collected BM samples can be analyzed without concern as long as the transport conditions are monitored. [ABSTRACT FROM AUTHOR]

Simple Summary: Bone marrow metastasis frequently occurs in patients with solid cancers and most often leads to poor outcome. Yet, the composition of bone marrow metastases, including tumor and surrounding cells, has so far not been characterized. Herein, we aimed to investigate the diversity of tumor and surrounding cells, i.e., the microenvironment, in bone marrow metastases, using the childhood tumor neuroblastoma as a model. To this end, we screened genome-wide datasets to define a panel of cell-specific markers for multiplex microscopy of metastatic bone marrow samples, and developed DeepFLEX, a computational pipeline for subsequent image analysis. Thereby, we identified 35,000 single cells covering metastasized tumor cells, and various types of developing immune and bone marrow cells. In parallel, we analyzed the transcriptome, i.e., all genes that are expressed as mRNA, of 38 patients with and without bone marrow metastasis. We found vast tumor cell diversity and identified a marker protein, FAIM2, which can help to identify a broader range of tumor cell variants. In addition we showed that tumor cell metastasis in the bone marrow is associated with an immune response resembling inflammation, and the presence of cells that can repress an immune attack against cancer cells. Our study suggests that metastatic tumor cells are shaping the bone marrow microenvironment and builds the basis to further investigate its clinical relevance. While the bone marrow attracts tumor cells in many solid cancers leading to poor outcome in affected patients, comprehensive analyses of bone marrow metastases have not been performed on a single-cell level. We here set out to capture tumor heterogeneity and unravel microenvironmental changes in neuroblastoma, a solid cancer with bone marrow involvement. To this end, we employed a multi-omics data mining approach to define a multiplex imaging panel and developed DeepFLEX, a pipeline for subsequent multiplex image analysis, whereby we constructed a single-cell atlas of over 35,000 disseminated tumor cells (DTCs) and cells of their microenvironment in the metastatic bone marrow niche. Further, we independently profiled the transcriptome of a cohort of 38 patients with and without bone marrow metastasis. Our results revealed vast diversity among DTCs and suggest that FAIM2 can act as a complementary marker to capture DTC heterogeneity. Importantly, 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 prominently an immature neutrophil/granulocytic myeloid-derived suppressor-like cell type. The presented findings indicate that metastatic tumor cells shape the bone marrow microenvironment, warranting deeper investigations of spatio-temporal dynamics at the single-cell level and their clinical relevance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]