A network-based phenotype mapping approach to identify genes that modulate drug response phenotypes

One-stop local and whole-body staging of children with cancer

Accurate staging and re-staging of cancer in children is crucial for patient management. Currently, children with a newly diagnosed cancer must undergo a series of imaging tests, which are stressful, time-consuming, partially redundant, expensive, and can require repetitive anesthesia. New approaches for pediatric cancer staging can evaluate the primary tumor and metastases in a single session. However, traditional one-stop imaging tests, such as CT and positron emission tomography (PET)/CT, are associated with considerable radiation exposure. This is particularly concerning for children because they are more sensitive to ionizing radiation than adults and they live long enough to experience secondary cancers later in life. In this review article we discuss child-tailored imaging tests for tumor detection and therapy response assessment - tests that can be obtained with substantially reduced radiation exposure compared to traditional CT and PET/CT scans. This includes diffusion-weighted imaging (DWI)/MRI and integrated [F-18]2-fluoro-2-deoxyglucose (18F-FDG) PET/MRI scans. While several investigators have compared the value of DWI/MRI and 18F-FDG PET/MRI for staging pediatric cancer, the value of these novel imaging technologies for cancer therapy monitoring has received surprisingly little attention. In this article, we share our experiences and review existing literature on this subject.

Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early-Stage Breast Cancer

Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor-positive early breast cancer in postmenopausal women. None of the third-generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single-nucleotide polymorphism (SNP)-treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial-to-mesenchymal transition and growth pathways, in both cases with SNP-dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

In the burgeoning field of personalized medicine, genetic variation is recognized as a major contributor to patients’ differential responses to drugs. Lymphoblastoid cell lines (LCLs) are a consistent and convenient representation of cells used for in vitro research. Human genome sequencing with LCLs can identify new genes that influence individuals’ drug responses, including the dose-response relationship, which describes the relationship between physiological response and the amount of exposure to a substance. In this work, we conduct high-throughput screening and genome-wide association mapping using 680 LCLs from the 1000 Genomes Project to identify new genes that influence individual response to 44 widely used anticancer drugs. We found the NQO1 gene to be associated with the dose-response of several drugs, namely arsenic trioxide, erlotinib, trametinib, and the paclitaxel + epirubicin combination, and performed follow-up analyses to better understand its functional role in drug response. Our results indicate NQO1 expression is correlated with increased drug resistance and provide some evidence that SNP rs1800566 influences drug response by altering protein activity for these four treatments. With further research, NQO1 has potential use as a therapeutic target, for example, suppressing NQO1 expression to increase sensitivity to particular drugs.

Understanding breast cancer heterogeneity through non-genetic heterogeneity

Intricacy in treatment and diagnosis of breast cancer has been an obstacle due to genotype and phenotype heterogeneity. Understanding of non-genetic heterogeneity mechanisms along with considering role of genetic heterogeneity may fill the gaps in landscape painting of heterogeneity. The main factors contribute to non-genetic heterogeneity including: transcriptional pulsing/bursting or discontinuous transcriptions, stochastic partitioning of components at cell division and various signal transduction from tumor ecosystem. Throughout this review, we desired to provide a conceptual framework focused on non-genetic heterogeneity, which has been intended to offer insight into prediction, diagnosis and treatment of breast cancer.

Artificial intelligence applications for pediatric oncology imaging

Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community.

Pathogenesis of thrombosis: cellular and pharmacogenetic contributions

Our understanding of thrombosis formation has evolved significantly ever since physician Rudolf Virchow proposed his "triad" theory in 1856. Modern science has elucidated the mechanisms of stasis, hypercoagulability, and endothelial dysfunction. Today, we have a firm understanding of the key molecular factors involved in the coagulation cascade and fibrinolytic system, as well as the underlying genetic influences. This knowledge of cellular and genetic contributors has been translated into diverse pharmaceutical interventions. Here, we examine the molecular and cellular mechanisms of thrombosis and its associated pathologies. We also review the current state of pharmacologic interventions, including pro- and anti-thrombotics, direct oral anticoagulants, and anti-platelet therapies. The pharmacogenetic factors that guide clinical decision making and prognosis are described in detail. Finally, we explore new approaches to thrombosis drug discovery, repurposing, and diagnostics. We argue that network biology tools will enable a systems pharmacology revolution in the next generation of interventions, facilitating precision medicine applications and ultimately leading to improved patient outcomes.

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. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.