Predicting neuroblastoma using developmental signals and a logic-based model

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning

Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughput technology in the biomedical field has made it possible to obtain multi-omics data, whose integration can compensate for missing or unreliable information in a single data source. In this study, we integrated clinical data and two omics data, i.e., gene expression and DNA methylation data, to study the prognosis of neuroblastoma. Since the features in omics data are redundant, it is crucial to conduct feature selection on them. We proposed a two-step feature selection (TSFS) method to quickly and accurately select the optimal features, where the first step aims at selecting candidate features and the second step is to remove redundant features among them using our proposed maximal association coefficient (MAC). Our goal is to predict composite clinical outcomes for neuroblastoma patients, i.e., their survival time and vital status at the last follow-up, which was validated to be two inter-correlated tasks. We conducted a series of experiments and evaluated the experimental results using accuracy and AUC (area under the ROC curve) evaluation metrics, which indicated that by the combination of the integration of the three types of data, our proposed TSFS method and a multi-task learning method can synergistically improve the reliability and accuracy of the prediction models.

Preclinical Models of Neuroblastoma-Current Status and Perspectives

Preclinical in vitro and in vivo models remain indispensable tools in cancer research. These classic models, including two- and three-dimensional cell culture techniques and animal models, are crucial for basic and translational studies. However, each model has its own limitations and typically does not fully recapitulate the course of the human disease. Therefore, there is an urgent need for the development of novel, advanced systems that can allow for efficient evaluation of the mechanisms underlying cancer development and progression, more accurately reflect the disease pathophysiology and complexity, and effectively inform therapeutic decisions for patients. Preclinical models are especially important for rare cancers, such as neuroblastoma, where the availability of patient-derived specimens that could be used for potential therapy evaluation and screening is limited. Neuroblastoma modeling is further complicated by the disease heterogeneity. In this review, we present the current status of preclinical models for neuroblastoma research, discuss their development and characteristics emphasizing strengths and limitations, and describe the necessity of the development of novel, more advanced and clinically relevant approaches.

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.

Single-cell reconstruction with spatial context of migrating neural crest cells and their microenvironments during vertebrate head and neck formation

The dynamics of multipotent neural crest cell differentiation and invasion as cells travel throughout the vertebrate embryo remain unclear. Here, we preserve spatial information to derive the transcriptional states of migrating neural crest cells and the cellular landscape of the first four chick cranial to cardiac branchial arches (BA1-4) using label-free, unsorted single-cell RNA sequencing. The faithful capture of branchial arch-specific genes led to identification of novel markers of migrating neural crest cells and 266 invasion genes common to all BA1-4 streams. Perturbation analysis of a small subset of invasion genes and time-lapse imaging identified their functional role to regulate neural crest cell behaviors. Comparison of the neural crest invasion signature to other cell invasion phenomena revealed a shared set of 45 genes, a subset of which showed direct relevance to human neuroblastoma cell lines analyzed after exposure to the in vivo chick embryonic neural crest microenvironment. Our data define an important spatio-temporal reference resource to address patterning of the vertebrate head and neck, and previously unidentified cell invasion genes with the potential for broad impact.

The embryonic trunk neural crest microenvironment regulates the plasticity and invasion of human neuroblastoma via TrkB signaling

Mistakes in trunk neural crest (NC) cell migration may lead to birth defects of the sympathetic nervous system (SNS) and neuroblastoma (NB) cancer. Receptor tyrosine kinase B (TrkB) and its ligand BDNF critically regulate NC cell migration during normal SNS development and elevated expression of TrkB is correlated with high-risk NB patients. However, in the absence of a model with in vivo interrogation of human NB cell and gene expression dynamics, the mechanistic role of TrkB in NB disease progression remains unclear. Here, we study the functional relationship between TrkB, cell invasion and plasticity of human NB cells by taking advantage of our validated in vivo chick embryo transplant model. We find that LAN5 (high TrkB) and SHSY5Y (moderate TrkB) human NB cells aggressively invade host embryos and populate typical NC targets, however loss of TrkB function significantly reduces cell invasion. In contrast, NB1643 (low TrkB) cells remain near the transplant site, but over-expression of TrkB leads to significant cell invasion. Invasive NB cells show enhanced expression of genes indicative of the most invasive host NC cells. In contrast, transplanted human NB cells down-regulate known NB tumor initiating and stem cell markers. Human NB cells that remain within the dorsal neural tube transplant also show enhanced expression of cell differentiation genes, resulting in an improved disease outcome as predicted by a computational algorithm. These in vivo data support TrkB as an important biomarker and target to control NB aggressiveness and identify the chick embryonic trunk neural crest microenvironment as a source of signals to drive NB to a less aggressive state, likely acting at the dorsal neural tube.

Modelling Cell Invasion: A Review of What JD Murray and the Embryo Can Teach Us

Cell invasion and cell plasticity are critical to human development but are also striking features of cancer metastasis. By distributing a multipotent cell type from a place of birth to distal locations, the vertebrate embryo builds organs. In comparison, metastatic tumor cells often acquire a de-differentiated phenotype and migrate away from a primary site to inhabit new microenvironments, disrupting normal organ function. Countless observations of both embryonic cell migration and tumor metastasis have demonstrated complex cell signaling and interactive behaviors that have long confounded scientist and clinician alike. James D. Murray realized the important role of mathematics in biology and developed a unique strategy to address complex biological questions such as these. His work offers a practical template for constructing clear, logical, direct and verifiable models that help to explain complex cell behaviors and direct new experiments. His pioneering work at the interface of development and cancer made significant contributions to glioblastoma cancer and embryonic pattern formation using often simple models with tremendous predictive potential. Here, we provide a brief overview of advances in cell invasion and cell plasticity using the embryonic neural crest and its ancestral relationship to aggressive cancers that put into current context the timeless aspects of his work.

Construction of a prognostic model based on nine immune-related genes and identification of small molecule drugs for hepatocellular carcinoma (HCC)

This study aimed to develop a prognostic model for hepatocellular carcinoma (HCC) based on immune-related genes and to identify new potential small-molecule drugs. A differential gene expression analysis of high-throughput microarray data from The Cancer Genome Atlas (TCGA) was performed to identify immune-related genes. By comparison with an immune-related genome, nine genes with important prognostic value for HCC were identified. The prognostic characteristics were established based on univariate and multivariate COX and Lasso regression analyzes. Subsequently, immune-related HCC risk signatures were constructed based on these identified nine immune-related genes and patients were classified as being at high or low risk according to these signatures. The overall survival (OS) time of high-risk patients was significantly shorter than that of low-risk patients. When studied as an independent prognostic factor of HCC, the significant prognostic value of this feature can be seen in the stratified cohorts. For clinical application, it was developed a nomogram that includes nine clinical risk factors and the prognostic model built based on the identified immune-related genes. Internal and external verification on 243 HCC tissues through International Cancer Genome Consortium (ICGC) database were performed to make this model more accurate and reliable. In addition, it was observed a positive regulation between the identified immune-related genes and their transcription factors found in HCC patients. Moreover, physiological function and signaling pathway of identified immune-related genes were studied by GO and KEGG enrichment analysis. Finally, several new small molecular drugs with potential for the treatment of HCC have been identified in the CMap database.

Alterations in viability and CYP1A1 expression in SH SY5Y cell line by pollutants present in Madín Dam, Mexico

Currently one of the problems facing global development is the availability of water. Although water is abundant the planet only a small portion is for human use and consumption. The problem is exacerbated due to different factors, mainly: meteorological phenomena, the presence of contaminants in the water and the increase in the number of inhabitants. Potential effects of pollutants not only can affect freshwater biota but also can be implicated in cancer development and neurodegenerative diseases in humans. The study was conducted in the Madín Dam, a reservoir of economic importance for the geographical area in which it is located, as well as catering to the population of nearby areas, and is a place where recreational activities such as fishing and kayaking are carried out. The aim of this study was to identify the toxic effects that the pollutants present in the water of the Madín Dam can generate on a human cell line (SH SY5Y) evaluating the cell viability and the participation of the Aril Hydrocarbon Receptor (AhR) and Pregnane X receptor (PXR) through of the expression of the CYP1A1 and CYP3A4 (canonical genes). In one of the five sites analyzed, cell viability was up to 50%, in this site a decrease in the normal expression of CYP1A1 was observed (p < 0.05) and the CYP3A4 gene was not expressed in the cells SH SY5Y. These results show that the SH SY5Y cell line is a good biomarker for assessing the human toxicity of environmental pollutants and relating it to neurodegenerative diseases.

Noninvasive Identification of Immune-Related Biomarkers in Hepatocellular Carcinoma

Primary liver carcinoma is one of the most common malignant tumors with a poor prognosis. This study aims to uncover the potential mechanisms and identify core biomarkers of hepatocellular carcinoma (HCC). The HCC gene expression profile GSE49515 was chosen to analyze the differentially expressed genes from purified RNA of peripheral blood mononuclear cells, including 10 HCC patients and 10 normal individuals. GO and KEGG pathway analysis and PPI network were used, and the enrichment of the core genes out of 15 hub genes was evaluated using GEPIA and GSEA, respectively. We employed flow cytometry to count mononuclear cells to verify our opinions. In this analysis, 344 DEGs were captured, including 188 upregulated genes and 156 downregulated genes; besides that, 15 hub genes were identified. GO analysis and KEGG analysis showed the DEGs were particularly involved in immune response, antigen processing and presentation, and infection and inflammation. The PPI network uncovered 2 modules were also mainly involved in immune response. In conclusion, our analysis disclosed the immune subversion was the major signature of HCC associated closely with JUN, VEGFA, TNFSF10, and TLR4, which could be novel noninvasive biomarkers in peripheral blood and targets for early diagnosis and therapy of HCC.