Retrospective identification of patients with SRRM2-related neurodevelopmental disorder in a single tertiary children's hospital.

SRRM2-related neurodevelopmental disorder is a recently described genetic diagnosis caused by loss-of-function variants in SRRM2. In order to understand the clinical spectrum of SRRM2-related neurodevelopmental disorder, we performed a retrospective exome data and clinical chart review at a single tertiary children's hospital, Children's Hospital of Philadelphia (CHOP). Among approximately 3100 clinical exome sequencing cases performed at CHOP, we identified three patients with SRRM2 loss-of-function pathogenic variants, in addition to one patient previously described in the literature. Common clinical features include developmental delay, attention deficit hyperactivity disorder, macrocephaly, hypotonia, gastroesophageal reflux, overweight/obesity, and autism. While developmental disabilities are commonly seen in all individuals with SRRM2 variants, the degree of developmental delay and intellectual disability is variable. Our data suggest that SRRM2-related neurodevelopmental disorder can be identified in 0.3% of individuals with developmental disabilities receiving exome sequencing.

Nuclear speckleopathies: developmental disorders caused by variants in genes encoding nuclear speckle proteins.

Nuclear speckles are small, membrane-less organelles that reside within the nucleus. Nuclear speckles serve as a regulatory hub coordinating complex RNA metabolism steps including gene transcription, pre-mRNA splicing, RNA modifications, and mRNA nuclear export. Reflecting the importance of proper nuclear speckle function in regulating normal human development, an increasing number of genetic disorders have been found to result from mutations in the genes encoding nuclear speckle proteins. To denote this growing class of genetic disorders, we propose "nuclear speckleopathies". Notably, developmental disabilities are commonly seen in individuals with nuclear speckleopathies, suggesting the particular importance of nuclear speckles in ensuring normal neurocognitive development. In this review article, a general overview of nuclear speckle function, and the current knowledge of the mechanisms underlying some nuclear speckleopathies, such as ZTTK syndrome, NKAP-related syndrome, TARP syndrome, and TAR syndrome, are discussed. These nuclear speckleopathies represent valuable models to understand the basic function of nuclear speckles and how its functional defects result in human developmental disorders.

Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma.

Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.

Tissue microRNA expression profiling in hepatic and pulmonary metastatic melanoma.

Malignant melanoma has a propensity for the development of hepatic and pulmonary metastases. MicroRNAs (miRs) are small, noncoding RNA molecules containing about 22 nucleotides that mediate protein expression and can contribute to cancer progression. We aim to identify clinically useful differences in miR expression in metastatic melanoma tissue. RNA was extracted from formalin-fixed, paraffin-embedded samples of hepatic and pulmonary metastatic melanoma, benign, nevi, and primary cutaneous melanoma. Assessment of miR expression was performed on purified RNA using the NanoString nCounter miRNA assay. miRs with greater than twofold change in expression when compared to other tumor sites (P value ≤ 0.05, modified t-test) were identified as dysregulated. Common gene targets were then identified among dysregulated miRs unique to each metastatic site. Melanoma metastatic to the liver had differential expression of 26 miRs compared to benign nevi and 16 miRs compared to primary melanoma (P < 0.048). Melanoma metastatic to the lung had differential expression of 19 miRs compared to benign nevi and 10 miRs compared to primary melanoma (P < 0.024). Compared to lung metastases, liver metastases had greater than twofold upregulation of four miRs, and 4.2-fold downregulation of miR-200c-3p (P < 0.0081). These findings indicate that sites of metastatic melanoma have unique miR profiles that may contribute to their development and localization. Further investigation of the utility of these miRs as diagnostic and prognostic biomarkers and their impact on the development of metastatic melanoma is warranted.

Neuroblastoma RAS viral oncogene homolog mRNA is differentially spliced to give five distinct isoforms: implications for melanoma therapy.

Neuroblastoma RAS viral oncogene homolog is a commonly mutated oncogene in melanoma, and therapeutic targeting of neuroblastoma RAS viral oncogene homolog has proven difficult. We characterized the expression and phenotypic functions of five recently discovered splice isoforms of neuroblastoma RAS viral oncogene homolog in melanoma. Canonical neuroblastoma RAS viral oncogene homolog (isoform-1) was expressed to the highest degree and its expression was significantly increased in melanoma metastases compared to primary lesions. Isoform-5 expression in metastases showed a significant, positive correlation with survival and tumours over-expressing isoform-5 had significantly decreased growth in a xenograft model. In contrast, over-expression of any isoform resulted in enhanced proliferation, and invasiveness was increased with over-expression of isoform-1 or isoform-2. Downstream signalling analysis indicated that the isoforms signalled differentially through the mitogen-activated protein kinase and PI3K pathways and A375 cells over-expressing isoform-2 or isoform-5 showed resistance to vemurafenib treatment in vitro. The neuroblastoma RAS viral oncogene homolog isoforms appear to play varying roles in melanoma phenotype and could potentially serve as biomarkers for therapeutic response and disease prognosis.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies.

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network "impact matrix", which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced "omics" profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.