Risk stratification and molecular targeting have been key to increasing cure rates for pediatric cancers in high-income countries. In contrast, precise diagnosis in low-resource settings is hindered by insufficient pathology infrastructure. The Global HOPE program aims to improve outcomes for pediatric cancer in Sub-Saharan Africa (SSA) by building local clinical care and diagnostic capacity. This study aimed to assess the feasibility of implementing molecular assays to improve leukemia diagnoses in SSA. Custom NanoString nCounter gene fusion assays, previously validated in the US, were used to test samples from suspected leukemia patients. The NanoString platform was chosen due to relatively low cost, minimal technical and bioinformatics expertise required, ability to test sub-optimal RNA, and rapid turnaround time. Fusion results were analyzed blindly, then compared to morphology and flow cytometry results. Of 117 leukemia samples, 74 were fusion-positive, 30 were negative, 7 were not interpretable, and 6 failed RNA quality. Nine additional samples were negative for leukemia by flow cytometry and negative for gene fusions. All 74 gene fusions aligned with the immunophenotype determined by flow cytometry. Fourteen samples had additional information available to further confirm the accuracy of the gene fusion results. The testing provided a more precise diagnosis in >60% of cases, and 9 cases were identified that could be treated with an available tyrosine kinase inhibitor, if detected at diagnosis. As risk-stratified and targeted therapies become more available in SSA, implementing this testing in real-time will enable the treatment of pediatric cancer to move toward incorporating risk stratification for optimized therapy.

UNASSIGNED: Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3.
UNASSIGNED: Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34).
UNASSIGNED: Several genes known to be pro-fibrotic (e.g. CD206, TREM2, CD163, and ARG1) showed either no significant differences or significantly decreased with advanced fibrosis. Although marked variability in gene expression was observed in individual patients with cirrhosis, several druggable targets and their ligands (e.g. CCR2, CCR5, CCL2, CCL5, and LGALS3) were significantly increased when compared to patients with minimal fibrosis. Antibody panels identified populations that were significantly increased (e.g. Mac387+), decreased (e.g. CD14+), or enriched (e.g. interactions of Mac387) in patients that had progression of disease or advanced fibrosis. Despite heterogeneity in patients with SLD, several macrophage phenotypes and druggable targets showed a positive correlation with increasing NAFLD activity scores and fibrosis stages.
UNASSIGNED: Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels.
UNASSIGNED: Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.

Global warming is leading to an increase in the frequency and intensity of extreme weather events, magnifying the breadth of temperatures faced by ectotherms across days and seasons. Despite the importance and ecological relevance of diurnal thermal variability, the vast majority of knowledge on gene expression patterns and physiology stems from animals acclimated to constant temperatures or in the early stages of exposure to a new temperature regime. If heterothermal environments modulate responses differently from constant thermal environments, our existing capacity to forecast impacts of climate warming may be compromised. To address this knowledge gap, we acclimated barramundi (Lates calcarifer) to 23 °C, 29 °C (optimal), 35 °C and to thermal cycling conditions (23-35 °C daily with a mean of 29 °C) and sampled liver and white muscle tissue before acclimation and after 2 and 17 weeks of acclimation. NanoString nCounter technologies were used to measure expression of 20 genes related to metabolism, growth and maintenance of cellular homeostasis. Acclimation to cool and warm conditions caused predictable changes in whole-animal performance (metabolism and growth) and the underlying gene expression patterns. Acclimation to a cycling temperature regime did not change the molecular regulation of metabolism or growth compared with barramundi acclimated to constant 29 °C, nor did it cause any discernible effects on whole-animal performance. However, the heat shock response was higher in the former group, suggesting that barramundi under a daily temperature cycle have an increased need for cellular chaperoning to minimise detrimental effects of temperature on proteins. We conclude that the genetic regulation of metabolism and growth may be more dependent on the mean daily temperature than on the daily temperature range.

Cells experience increased genome instability through the course of disease development including cancer initiation and progression. Point mutations, insertion/deletions, translocations, and amplifications of both coding and noncoding regions all contribute to cancer phenotypes. Copy number variation (CNV), i.e., changes of the number of copies of nuclear DNA, occurs in the genome of even normal somatic cells. Studies to understand the effects of CNV on tumor development, especially aspects concerning tumor aggressiveness and the influence on outcomes of therapeutic modalities, have been reignited by the breakthrough technologies of the molecular genomics. This section discusses the significance of analyzing CNVs that cause simultaneous increase/decrease of clusters of genes, using the expression profile of XRCC1 with its neighbor genes LIG1, PNKP, and POLD1 as an example. Methods for CNV assay at the individual gene level on formalin-fixed, paraffin-embedded (FFPE) tissues using the NanoString nCounter technology will then be described.

Triple-negative breast cancer (TNBC) is a rare and aggressive breast cancer subtype. Unlike the estrogen receptor-positive subtype, whose recurrence risk can be predicted by gene expression-based signature, TNBC is more heterogeneous, with diverse drug sensitivity levels to standard regimens. This study explored the benefit of gene expression-based profiling for classifying the molecular subtypes of Thai TNBC patients.
The nCounter-based Breast 360 gene expression was used to classify Thai TNBC retrospective cohort subgroups. Their expression profiles were then compared against the previously established TNBC classification system. The differential characteristics of the tumor microenvironment and DNA damage repair signatures across subgroups were also explored.
Thai TNBC cohort could be classified into four main subgroups, corresponding to the LAR, BL-2, and M subtypes based on Lehmann\'s TNBC classification. The PAM50 gene set classified most samples as basal-like subtypes except for Group 1. Group 1 exhibited similar enrichment of the metabolic and hormone response pathways to the LAR subtype. Group 2 shared pathway activation with the BL-2 subtype. Group 3 showed an increase in the EMT pathway, similar to the M subtype. Group 4 showed no correlation with Lehmann\'s TNBC. The tumor microenvironment (TME) analysis showed high TME cell abundance with increased expression of immune blockade genes in Group 2. Group 4 exhibited low TME cell abundance and reduced immune blockade gene expressions. We also observed distinct signatures of the DNA double-strand break repair genes in Group 1.
Our study reported unique characteristics between the four TNBC subgroups and showed the potential use of immune checkpoint and PARP inhibitors in subsets of Thai TNBC patients. Our findings warrant further clinical investigation to validate TNBC\'s sensitivity to these regimens.

Formalin-fixed paraffin-embedded (FFPE) tissue is the most common source of archived material for genomic medicine. However, FFPE tissue is suboptimal for high-throughput analyses, such as RNA sequencing, because the quality of nucleic acids in FFPE tissues is low. We compared RNA-seq with the nCounter system to evaluate use of FFPE tissue for genomic medicine. Twelve fresh frozen bladder cancer samples were analyzed by both RNA sequencing and nCounter, and matched FFPE samples, by nCounter. Gene-expression values obtained by these two platforms were compared by calculating Pearson correlation coefficients for each sample (across the set of matched genes) and for each matched gene (across the set of samples). For each sample, gene-expression levels measured by RNA sequencing highly correlated with those measured by nCounter (all Pearson\'s R > 0.8, P < 0.0001), as seen by hierarchical clustering. RNA sequencing results for fresh frozen tissues positively correlated with nCounter results for FFPE tissues (R ranged from 0.675 to 0.873, all P < 0.0001). Correlation and hierarchical-clustering analyses of nCounter data from the two specimens demonstrated a strong positive correlation between each group (R ranged from 0.779 to 0.977, all P < 0.0001). Our findings suggest that the nCounter system is useful for assaying archived-FFPE samples and that the gene-expression signatures obtained from FFPE samples represent those from fresh frozen tissues.

DNA sequencing assays have been used to characterize the vaginal microbiome and to identify associations with clinical outcomes. The purpose of this study was to evaluate the utility of the NanoString nCounter platform, a more efficient assay compared to sequencing, for the characterization of vaginal microbial communities. A panel of NanoString nCounter probes was designed to detect common vaginal bacteria and viruses with relevance to reproductive health. A defined synthetic community of microbes and 43 clinical samples were interrogated with NanoString nCounter assays and compared to known compositions or metagenomic shotgun sequencing (MSS) results. The NanoString nCounter platform and MSS were able to distinguish closely related microbes. In clinical samples, the relative abundance of bacterial species was similar between the two assays. The assays sometimes disagreed when targets were present at low abundance. More viruses were detected by MSS than by nCounter. However, the nCounter assays are able to provide results in about 30 h with minimal hands-on time, whereas MSS requires at least 138 to 178 h with extensive hands-on time. The reagent cost for the two assays was similar, but the overall cost of the nCounter was lower due to the minimal hands-on time. MSS can be used to inform the design of a targeted multiplex panel for the assessment of vaginal microbial communities, thereby allowing for more cost-effective and rapid screening of patient samples for research studies. The sensitivity for low abundance microbes could be improved, possibly by adding additional target amplification cycles before nCounter assessment. This approach has potential as an assay with both research and clinical applications. IMPORTANCE Metagenomic shotgun sequencing can inform the design of a targeted multiplex panel by which the NanoString nCounter platform can assess vaginal microbial communities, thereby allowing for more cost-effective and rapid screening of patient samples.

Treatment failures of glioblastoma (GBM) occur within high-dose radiation fields. We hypothesized that this is due to increased capacity for DNA damage repair in GBM. We identified 24 adult GBM patients treated with maximal safe resection followed by radiation with concurrent and adjuvant temozolomide. The mRNA from patients was quantified using NanoString Technologies\' nCounter platform and compared with 12 non-neoplastic temporal lobe tissue samples as a control. Differential expression analysis identified seven DNA repair genes significantly upregulated in GBM tissues relative to controls (>4-fold difference, adjusted p values < 0.001). Among these seven genes, Cox proportional hazards models identified RAD51 to be associated with an increased risk of death (HR = 3.49; p = 0.03). Kaplan-Meier (KM) analysis showed that patients with high RAD51 expression had significantly shorter OS compared to low levels (median OS of 10.6 mo. vs 20.1 mo.; log-rank p = 0.03). Our findings were validated in a larger external dataset of 162 patients using publicly available gene expression data quantified by the same NanoString technology (median OS of 13.8 mo. vs. 17.4 mo; log-rank p = 0.006). Within this uniformly treated GBM population, RAD51, in the homologous recombination pathway, was overexpressed (vs. normal brain) and inversely correlated with OS. High RAD51 expression may be a prognostic biomarker and a therapeutic target in GBM.

Chronic wounds are a significant health problem worldwide. However, nothing is known about how chronic wounds initiate and develop. Here we use a chronic wound model in diabetic mice and a Systems Biology Approach using nanoString nCounter technology and weighted gene correlation network analysis (WGCNA), with tissues collected at 6, 12, 24 and 48 h post-wounding, to identify metabolic signalling pathways involved in initiation of chronicity. Normalized counts obtained from the nanoString nCounter Mouse Metabolic Panel were used for the WGCNA, which groups genes into co-expression modules to visualize the correlation network. Genes with significant module membership and gene trait significance (p < 0.05) were used to identify signalling pathways that are important for the development of chronicity. The pathway analysis using the Reactome database showed stabilization of PTEN, which down-regulates PI3K/AKT1, which in turn down-regulates Nrf2, as shown by ELISA, thus disabling antioxidant production, resulting in high oxidative stress levels. We find that pathways involved in inflammation, including those that generate pro-inflammatory lipids derived from arachidonic acid metabolism, IFNγ and catecholamines, occur. Moreover, HIF3α is over-expressed, potentially blocking Hif1α and preventing activation of growth factors and cytokines that promote granulation tissue formation. We also find that FGF1 is under-expressed, while thrombospondin-1 is over-expressed, resulting in decreased angiogenesis, a process that is critical for healing. Finally, enzymes involved in glycolysis are down-regulated, resulting in decreased production of pyruvate, a molecule critical for ATP production, leading to extensive cell death and wound paralysis. These findings offer new avenues of study that may lead to the development of novel treatments of CW to be administered right after debridement.

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (prediction analysis of microarray 50 gene set, PAM50). The present study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction (SSP)) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair κ statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, human epidermal growth factor receptor II (HER2)-enriched, luminal A and luminal B subtypes predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It is not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross-platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.