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.

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.

Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View.

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells.

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens.

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients.

Manifold medicine: A schema that expands treatment dimensionality.

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called 'Manifold Medicine', in which disease states are described by vectorial positions on several body-wide axes.

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies.

Modular network mechanism of CCN1-associated resistance to HSV-1-derived oncolytic immunovirotherapies for glioblastomas.

Glioblastomas (GBMs) are the most common and lethal primary brain malignancy in adults. Oncolytic virus (OV) immunotherapies selectively kill GBM cells in a manner that elicits antitumor immunity. Cellular communication network factor 1 (CCN1), a protein found in most GBM microenvironments, expression predicts resistance to OVs, particularly herpes simplex virus type 1 (HSV-1).

Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells.

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored.

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.

Regulostat Inferelator: a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes.

With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes.

Reducing the RNA binding protein TIA1 protects against tau-mediated neurodegeneration in vivo.

Emerging studies suggest a role for tau in regulating the biology of RNA binding proteins (RBPs). We now show that reducing the RBP T-cell intracellular antigen 1 (TIA1) in vivo protects against neurodegeneration and prolongs survival in transgenic P301S Tau mice. Biochemical fractionation shows co-enrichment and co-localization of tau oligomers and RBPs in transgenic P301S Tau mice.

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.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context.

Mosaic zebrafish transgenesis for functional genomic analysis of candidate cooperative genes in tumor pathogenesis.

Comprehensive genomic analysis has uncovered surprisingly large numbers of genetic alterations in various types of cancers. To robustly and efficiently identify oncogenic "drivers" among these tumors and define their complex relationships with concurrent genetic alterations during tumor pathogenesis remains a daunting task. Recently, zebrafish have emerged as an important animal model for studying human diseases, largely because of their ease of maintenance, high fecundity, obvious advantages for in vivo imaging, high conservation of oncogenes and their molecular pathways, susceptibility to tumorigenesis and, most importantly, the availability of transgenic techniques suitable for use in the fish.

The evolutionary strata of DARPP-32 tail implicates hierarchical functional expansion in higher vertebrates.

DARPP-32 (dopamine and adenosine 3', 5'-monophosphate-regulated phosphoprotein of 32 kDa), which belongs to PPP1R1 gene family, is known to act as an important integrator in dopamine-mediated neurotransmission via the inhibition of protein phosphatase-1 (PP1). Besides its neuronal roles, this protein also behaves as a key player in pathological and pharmacological aspects. Use of bioinformatics and phylogenetics approaches to further characterize the molecular features of DARPP-32 can guide future works.

Molecular insights into 4-nitrophenol-induced hepatotoxicity in zebrafish: transcriptomic, histological and targeted gene expression analyses.

Background: 4-Nitrophenol (4-NP) is a prioritized environmental pollutant and its toxicity has been investigated using zebrafish, advocated as an alternative toxicological model. However, molecular information of 4-NP induced hepatotoxicity is still limited. This study aimed to obtain molecular insights into 4-NP-induced hepatotoxicity using zebrafish as a model.

Inverted expression profiles of sex-biased genes in response to toxicant perturbations and diseases.

The influence of sex factor is widely recognized in various diseases, but its molecular basis, particularly how sex-biased genes, those with sexually dimorphic expression, behave in response to toxico-pathological changes is poorly understood. In this study, zebrafish toxicogenomic data and transcriptomic data from human pathological studies were analysed for the responses of male- and female-biased genes. Our analyses revealed obvious inverted expression profiles of sex-biased genes, where affected males tended to up-regulate genes of female-biased expression and down-regulate genes of male-biased expression, and vice versa in affected females, in a broad range of toxico-pathological conditions.