Diverse Engraftment Capability of Neuroblastoma Cell Lines in Zebrafish Larvae.

Xenotransplantation of neuroblastoma cells into larval zebrafish allows the characterization of their tumorigenic abilities and high-throughput treatment screening. This established preclinical model traditionally relies on microinjection into the yolk or perivitelline space, leaving the engraftment ability of cells at the hindbrain ventricle (HBV) and pericardial space (PCS), sites valuable for evaluating metastasis, angiogenesis, and the brain microenvironment, unknown. To address this gap in knowledge, Casper zebrafish at 48 h postfertilization were microinjected with approximately 200 Kelly, Be(2)-C, SK-N-AS, or SY5Y cells into either the HBV or PCS.

MRI Manifestations of Breast Cancer Stroma and their Role in Predicting Molecular Subtype: A Case-control Study.

Objective: This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction.

Methods: 57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body.

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms.

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies.

Manifold epigenetics: A conceptual model that guides engineering strategies to improve whole-body regenerative health.

Despite the promising advances in regenerative medicine, there is a critical need for improved therapies. For example, delaying aging and improving healthspan is an imminent societal challenge. Our ability to identify biological cues as well as communications between cells and organs are keys to enhance regenerative health and improve patient care.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction.

Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma.

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Although only a few recurrent somatic mutations have been identified, chromosomal abnormalities, including the loss of heterozygosity (LOH) at the chromosome 1p and gains of chromosome 17q, are often seen in the high-risk cases. The biological basis and evolutionary forces that drive such genetic abnormalities remain enigmatic.

Metabolic Enzyme DLST Promotes Tumor Aggression and Reveals a Vulnerability to OXPHOS Inhibition in High-Risk Neuroblastoma.

High-risk neuroblastoma remains therapeutically challenging to treat, and the mechanisms promoting disease aggression are poorly understood. Here, we show that elevated expression of dihydrolipoamide S-succinyltransferase (DLST) predicts poor treatment outcome and aggressive disease in patients with neuroblastoma. DLST is an E2 component of the α-ketoglutarate (αKG) dehydrogenase complex, which governs the entry of glutamine into the tricarboxylic acid cycle (TCA) for oxidative decarboxylation.

Zebrafish as a Neuroblastoma Model: Progress Made, Promise for the Future.

For nearly a decade, researchers in the field of pediatric oncology have been using zebrafish as a model for understanding the contributions of genetic alternations to the pathogenesis of neuroblastoma (NB), and exploring the molecular and cellular mechanisms that underlie neuroblastoma initiation and metastasis. In this review, we will enumerate and illustrate the key advantages of using the zebrafish model in NB research, which allows researchers to: monitor tumor development in real-time; robustly manipulate gene expression (either transiently or stably); rapidly evaluate the cooperative interactions of multiple genetic alterations to disease pathogenesis; and provide a highly efficient and low-cost methodology to screen for effective pharmaceutical interventions (both alone and in combination with one another). This review will then list some of the common challenges of using the zebrafish model and provide strategies for overcoming these difficulties.

Zebrafish Model of Neuroblastoma Metastasis.

Zebrafish has emerged as an important animal model to study human diseases, especially cancer. Along with the robust transgenic and genome editing technologies applied in zebrafish modeling, the ease of maintenance, high-yield productivity, and powerful live imaging altogether make the zebrafish a valuable model system to study metastasis and cellular and molecular bases underlying this process in vivo. The first zebrafish neuroblastoma (NB) model of metastasis was developed by overexpressing two oncogenes, MYCN and LMO1, under control of the dopamine-beta-hydroxylase (dβh) promoter.

A novel combination therapy targeting ubiquitin-specific protease 5 in MYCN-driven neuroblastoma.

Histone deacetylase (HDAC) inhibitors are effective in MYCN-driven cancers, because of a unique need for HDAC recruitment by the MYCN oncogenic signal. However, HDAC inhibitors are much more effective in combination with other anti-cancer agents. To identify novel compounds which act synergistically with HDAC inhibitor, such as suberanoyl hydroxamic acid (SAHA), we performed a cell-based, high-throughput drug screen of 10,560 small molecule compounds from a drug-like diversity library and identified a small molecule compound (SE486-11) which synergistically enhanced the cytotoxic effects of SAHA.

GAS7 Deficiency Promotes Metastasis in MYCN-Driven Neuroblastoma.

One of the greatest barriers to curative treatment of neuroblastoma is its frequent metastatic outgrowth prior to diagnosis, especially in cases driven by amplification of the oncogene. However, only a limited number of regulatory proteins that contribute to this complex -mediated process have been elucidated. Here we show that the () gene, located at chromosome band 17p13.

Ablation Behavior of a Carbon Fabric Reinforced Phenolic Composite Modified by Surface-Decorated ZrB/SiC.

Carbon fabric reinforced phenolic composites were widely used as TPSs (thermal protection system) material in the aerospace industry. However, their limited oxidative ablation resistance restricted their further utility in more serious service conditions. In this study, the surface-decorated ZrB/SiC and its modified carbon fabric reinforced phenolic composites have been successfully prepared.

Drugging MYCN Oncogenic Signaling through the MYCN-PA2G4 Binding Interface.

MYCN is a major driver for the childhood cancer, neuroblastoma, however, there are no inhibitors of this target. Enhanced MYCN protein stability is a key component of MYCN oncogenesis and is maintained by multiple feedforward expression loops involving MYCN transactivation target genes. Here, we reveal the oncogenic role of a novel MYCN target and binding protein, proliferation-associated 2AG4 (PA2G4).

Drives a Subset of High-Risk Pediatric Neuroblastomas and Is Activated through Mechanisms Including Enhancer Hijacking and Focal Enhancer Amplification.

The amplified gene serves as an oncogenic driver in approximately 20% of high-risk pediatric neuroblastomas. Here, we show that the family member is a potent transforming gene in a separate subset of high-risk neuroblastoma cases (∼10%), based on (i) its upregulation by focal enhancer amplification or genomic rearrangements leading to enhancer hijacking, and (ii) its ability to transform neuroblastoma precursor cells in a transgenic animal model. The aberrant regulatory elements associated with oncogenic activation include focally amplified distal enhancers and translocation of highly active enhancers from other genes to within topologically associating domains containing the gene locus.

LMO1 Synergizes with MYCN to Promote Neuroblastoma Initiation and Metastasis.

A genome-wide association study identified LMO1, which encodes an LIM-domain-only transcriptional cofactor, as a neuroblastoma susceptibility gene that functions as an oncogene in high-risk neuroblastoma. Here we show that dβh promoter-mediated expression of LMO1 in zebrafish synergizes with MYCN to increase the proliferation of hyperplastic sympathoadrenal precursor cells, leading to a reduced latency and increased penetrance of neuroblastomagenesis. The transgenic expression of LMO1 also promoted hematogenous dissemination and distant metastasis, which was linked to neuroblastoma cell invasion and migration, and elevated expression levels of genes affecting tumor cell-extracellular matrix interaction, including loxl3, itga2b, itga3, and itga5.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e.

Critical Role for GAB2 in Neuroblastoma Pathogenesis through the Promotion of SHP2/MYCN Cooperation.

Growing evidence suggests a major role for Src-homology-2-domain-containing phosphatase 2 (SHP2/PTPN11) in MYCN-driven high-risk neuroblastoma, although biologic confirmation and a plausible mechanism for this contribution are lacking. Using a zebrafish model of MYCN-overexpressing neuroblastoma, we demonstrate that mutant ptpn11 expression in the adrenal gland analog of MYCN transgenic fish promotes the proliferation of hyperplastic neuroblasts, accelerates neuroblastomagenesis, and increases tumor penetrance. We identify a similar mechanism in tumors with wild-type ptpn11 and dysregulated Gab2, which encodes a Shp2 activator that is overexpressed in human neuroblastomas.

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development.