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MotivationMany computational tools attempt to infer gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data. One recent advance is DeepSEM, a deep generative model generalizing the Linear Structural Equation Model (SEM) that improves benchmark performance over popular GRN inference methods. While DeepSEM is promising, its results are not stable over multiple runs. To overcome the instability and resolve dropout handling concerns, we propose GRN-VAE.ResultsGRN-VAE improves stability and efficiency while maintaining accuracy by delayed introduction of the sparse loss term. To minimize the negative impact of dropout in single-cell data, GRN-VAE trains on non-zero data. Most importantly, we introduce a novel idea, Dropout Augmentation, to improve model robustness by adding a small amount of simulated dropout to the data. GRN-VAE compares favorably to other methods on the BEELINE benchmark data sets, using several collections of “ground truth” regulatory relationships, and on a real-world data set, where it efficiently provides stable results consistent with literature-based findings.ConclusionsThe stability and robustness of GRN-VAE make it a practical and valuable addition to the toolkit for GRN inference from single-cell data. Dropout Augmentation may have wider applications beyond the GRN-inference problem.Availability and implementationSource code is available athttps://bcb.cs.tufts.edu/GRN-VAEContacthao.zhu@tufts.edu;donna.slonim@tufts.edu

Introduction RNA-binding proteins (RBPs) play an important role in skeletal muscle development and disease by regulating RNA splicing. In myotonic dystrophy type 1 (DM1), the RBP MBNL1 (muscleblind-like) is sequestered by toxic CUG repeats, leading to missplicing of MBNL1 targets. Mounting evidence from the literature has implicated other factors in the pathogenesis of DM1. Herein we sought to evaluate the functional role of the splicing factor hnRNP L in normal and DM1 muscle cells. Methods Co-immunoprecipitation assays using hnRNPL and MBNL1 expression constructs and splicing profiling in normal and DM1 muscle cell lines were performed. Zebrafish morpholinos targeting hnrpl and hnrnpl2 were injected into one-cell zebrafish for developmental and muscle analysis. In human myoblasts downregulation of hnRNP L was achieved with shRNAi. Ascochlorin administration to DM1 myoblasts was performed and expression of the CUG repeats, DM1 splicing biomarkers, and hnRNP L expression levels were evaluated. Results Using DM1 patient myoblast cell lines we observed the formation of abnormal hnRNP L nuclear foci within and outside the expanded CUG repeats, suggesting a role for this factor in DM1 pathology. We showed that the antiviral and antitumorigenic isoprenoid compound ascochlorin increased MBNL1 and hnRNP L expression levels. Drug treatment of DM1 muscle cells with ascochlorin partially rescued missplicing of established early biomarkers of DM1 and improved the defective myotube formation displayed by DM1 muscle cells. Discussion Together, these studies revealed that hnRNP L can modulate DM1 pathologies and is a potential therapeutic target.

SummaryMaternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many of which are mediated by in utero microglial programming. Microglia remain inaccessible at birth and throughout development, thus identification of noninvasive biomarkers that can reflect fetal brain microglial programming may permit screening and intervention during critical developmental windows. Here we used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells). Single-cell RNA sequencing of murine fetal brain and placental macrophages demonstrated shared transcriptional programs. Comparison with human datasets demonstrated that placental resident macrophage signatures are highly conserved between mice and humans. Single-cell RNA-seq identified sex differences in fetal microglial and Hofbauer cell programs, and robust differences between placenta-associated maternal macrophage/monocyte (PAMM) populations in the context of a male versus a female fetus. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, potentially facilitating the early identification of offspring most vulnerable to neurodevelopmental disorders.

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. Using pathway centrality, we identify mediating pathways in three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)). We systematically evaluate the significance of all identified central pathways using genetic interactions. Mediating pathways shared by all three pulmonary disorders favor innate immune and inflammation-related processes, including toll-like receptor (TLR) signaling, PDGF- and angiotensin-regulated airway remodeling, the JAK-STAT signaling pathway, and interferon gamma. Disease-specific mediators, such as neurodevelopmental processes in BPD or adhesion molecules in COPD, are also highlighted. Some of our findings implicate pathways already in development as drug targets, while others may suggest new therapeutic approaches.

MotivationDrug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease’s impact on expression in disease-relevant tissues. The high throughput LINCS project has expanded the universe of compounds and cell types for which data are available, but even with this effort, many potentially clinically useful combinations are missing. To evaluate the possibility of repurposing drugs this way despite missing data, we compared collaborative filtering with either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation.ResultsMethods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood-based collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation, and we identified connections between related compounds even when many were not measured in the relevant cells. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.Contactdonna.slonim@tufts.edu

Dynamic processes are inherently important in disease, and identifying disease-related disruptions of normal dynamic processes can provide information about individual patients. We have previously characterized individuals' disease states via pathway-based anomalies in expression data, and we have identified disease-correlated disruption of predictable dynamic patterns by modeling a virtual time series in static data. Here we combine the two approaches, using an anomaly detection model and virtual time series to identify anomalous temporal processes in specific disease states. We demonstrate that this approach can informatively characterize individual patients, suggesting personalized therapeutic approaches.

© 2019 Society for the Study of Addiction Worldwide consumption of opioids remains at historic levels. Preclinical studies report intergenerational effects on the endogenous opioid system of future progeny following preconception morphine exposure. Given the role of endogenous opioids in energy homeostasis, such effects could impact metabolism in the next generation. Thus, we examined diet-induced modifications in F1 male progeny of morphine-exposed female rats (MORF1). When fed a high fat-sugar diet (FSD) for 6 weeks, MORF1 males display features of emerging metabolic syndrome; they consume more food, gain more weight, and develop fasting-induced hyperglycemia and hyperinsulinemia. In the hypothalamus, proteins involved in energy homeostasis are modified and RNA sequencing revealed down-regulation of genes associated with neuronal plasticity, coupled with up-regulation of genes associated with immune, inflammatory, and metabolic processes that are specific to FSD-maintained MORF1 males. Thus, limited preconception morphine exposure in female rats increases the risk of metabolic syndrome/type 2 diabetes in the next generation.

Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. Not all of these claims can be correct. Equally, for this same reason, developers struggle to demonstrate convincingly that they created a new and superior algorithm or implementation. Moreover, the developer community often has difficulty discerning which new approaches constitute true scientific advances for the field. The obvious answer to this conundrum is to develop benchmarks—meaning standard points of reference that facilitate evaluating the performance of different tools—allowing both users and developers to compare multiple tools in an unbiased fashion.

While many transcriptional profiling experiments measure dynamic processes that change over time, few include enough time points to adequately capture temporal changes in expression. This is especially true for data from human subjects, for which relevant samples may be hard to obtain, and for developmental processes where dynamics are critically important. Although most expression data sets sample at a single time point, it is possible to use accompanying temporal information to create a virtual time series by combining data from different individuals.We introduce TEMPO, a pathway-based outlier detection approach for finding pathways showing significant temporal changes in expression patterns from such combined data. We present findings from applications to existing microarray and RNA-seq data sets. TEMPO identifies temporal dysregulation of biologically relevant pathways in patients with autism spectrum disorders, Huntington’s disease, Alzheimer’s disease, and COPD. Its findings are distinct from those of standard temporal or gene set analysis methodologies.Overall, our experiments demonstrate that there is enough signal to overcome the noise inherent in such virtual time series, and that a temporal pathway approach can identify new functional, temporal, or developmental processes associated with specific phenotypes.AvailabilityAn R package implementing this method and full results tables are available at bcb.cs.tufts.edu/tempo/.

Human fetuses with Down syndrome demonstrate abnormal brain growth and reduced neurogenesis. Despite the prenatal onset of the phenotype, most therapeutic trials have been conducted in adults. Here, we present evidence for fetal brain molecular and neonatal behavioral alterations in the Ts1Cje mouse model of Down syndrome. Embryonic day 15.5 brain hemisphere RNA from Ts1Cje embryos (n = 5) and wild type littermates (n = 5) was processed and hybridized to mouse gene 1.0 ST arrays. Bioinformatic analyses were implemented to identify differential gene and pathway regulation during Ts1Cje fetal brain development. In separate experiments, the Fox scale, ultrasonic vocalization and homing tests were used to investigate behavioral deficits in Ts1Cje pups (n = 29) versus WT littermates (n = 64) at postnatal days 3–21. Ts1Cje fetal brains displayed more differentially regulated genes (n = 71) than adult (n = 31) when compared to their age-matched euploid brains. Ts1Cje embryonic brains showed up-regulation of cell cycle markers and down-regulation of the solute-carrier amino acid transporters. Several cellular processes were dysregulated at both stages, including apoptosis, inflammation, Jak/Stat signaling, G-protein signaling, and oxidoreductase activity. In addition, early behavioral deficits in surface righting, cliff aversion, negative geotaxis, forelimb grasp, ultrasonic vocalization, and the homing tests were observed. The Ts1Cje mouse model exhibits abnormal gene expression during fetal brain development, and significant neonatal behavioral deficits in the pre-weaning period. In combination with human studies, this suggests that the Down syndrome phenotype manifests prenatally and provides a rationale for prenatal therapy to improve perinatal brain development and postnatal neurocognition. © 2015 Wiley Periodicals, Inc.

Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine-learning problem. However, due to the dimensionality and variability of expression data, existing methods based on feature space analysis or individual anomalously expressed genes are insufficient. We present a novel approach, CSAX, that identifies pathways in an individual sample in which the normal expression relationships are disrupted. To evaluate our approach, we have compiled and released a compendium of public expression data sets, reformulated to create a test bed for anomaly detection. We demonstrate the accuracy of CSAX on the data sets in our compendium, compare it to other leading methods, and show that CSAX aids in both identifying anomalies and explaining their underlying biology. We describe an approach to characterizing the difficulty of specific expression anomaly detection tasks. We then illustrate CSAX's value in two developmental case studies. Confirming prior hypotheses, CSAX highlights disruption of platelet activation pathways in a neonate with retinopathy of prematurity and identifies, for the first time, dysregulated oxidative stress response in second trimester amniotic fluid of fetuses with obese mothers. Our approach provides an important step toward identification of individual disease patterns in the era of precision medicine.

The advent of high throughput technologies contributes to the rapid growth of molecular-level knowledge about human disease. However, existing disease taxonomies tend to focus on either physiological characterizations of disease or the organizational and billing needs of hospitals. Most fail to fully incorporate our rapidly increasing knowledge about molecular causes of disease. More modern disease taxonomies would presumably be built based on the combination of clinical, physiological, and molecular data. In this study, we analyzed our ability to infer disease relationships from molecular data alone. This approach may provide insights into how to ultimately build more modern taxonomies of disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We introduce the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. We find mediating pathways for three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)) using pathway centrality. Mediating pathways shared by all three pulmonary disorders heavily favor inflammatory or immune responses and include specific pathways such as cytokine production, NF Kappa B, and JAK/STAT signaling. Disease-specific mediators, such as insulin signaling in BPD or homeostasis in COPD, are also highlighted. We support our findings, some of which suggest new treatment approaches, both with anecdotal evidence from the literature and via systematic evaluation using genetic interactions.

Turner syndrome is a sex chromosome aneuploidy with characteristic malformations. Amniotic fluid, a complex biological material, could contribute to the understanding of Turner syndrome pathogenesis. In this pilot study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was utilized to identify specific genes/organ systems that may play a role in Turner syndrome pathophysiology. Cell-free RNA from amniotic fluid of five mid-trimester Turner syndrome fetuses and five euploid female fetuses matched for gestational age was extracted, amplified, and hybridized onto Affymetrix(®) U133 Plus 2.0 arrays. Significantly differentially regulated genes were identified using paired t tests. Biological interpretation was performed using Ingenuity Pathway Analysis and BioGPS gene expression atlas. There were 470 statistically significantly differentially expressed genes identified. They were widely distributed across the genome. XIST was significantly down-regulated (p < 0.0001); SHOX was not differentially expressed. One of the most highly represented organ systems was the hematologic/immune system, distinguishing the Turner syndrome transcriptome from other aneuploidies we previously studied. Manual curation of the differentially expressed gene list identified genes of possible pathologic significance, including NFATC3, IGFBP5, and LDLR. Transcriptomic differences in the amniotic fluid of Turner syndrome fetuses are due to genome-wide dysregulation. The hematologic/immune system differences may play a role in early-onset autoimmune dysfunction. Other genes identified with possible pathologic significance are associated with cardiac and skeletal systems, which are known to be affected in females with Turner syndrome. The discovery-driven approach described here may be useful in elucidating novel mechanisms of disease in Turner syndrome.

Trisomies 18 and 21 are the two most common live born autosomal aneuploidies in humans. While the anatomic abnormalities in affected fetuses are well documented, the dysregulated biological pathways associated with the development of the aneuploid phenotype are less clear. Amniotic fluid (AF) cell-free RNA is a valuable source of biological information obtainable from live fetuses. In this study, we mined gene expression data previously produced by our group from mid-trimester AF supernatant samples. We identified the euploid, trisomy 18 and trisomy 21 AF transcriptomes, and analyzed them with a particular focus on the nervous system. We used multiple bioinformatics resources, including DAVID, Ingenuity Pathway Analysis, and the BioGPS Gene Expression Atlas. Our analyses confirmed that AF supernatant from aneuploid fetuses is enriched for nervous system gene expression and neurological disease pathways. Tissue analysis showed that fetal brain cortex and Cajal-Retzius cells were significantly enriched for genes contained in the AF transcriptomes. We also examined AF transcripts known to be dysregulated in aneuploid fetuses compared with euploid controls and identified several brain-specific transcripts among them. Many of these genes play critical roles in nervous system development. NEUROD2, which was downregulated in trisomy 18, induces neurogenic differentiation. SOX11, downregulated in trisomy 21, is a transcription factor that is essential for pan-neuronal protein expression and axonal growth of sensory neurons. Our results show that whole transcriptome analysis of cell-free RNA in AF from live pregnancies permits discovery of biomarkers of abnormal human neurodevelopment and advances our understanding of the pathophysiology of aneuploidy.

Amniotic fluid is a complex biological material that provides a unique window into the developing human. Residual amniotic fluid supernatant contains cell-free fetal RNA. The objective of this study was to develop an understanding of the amniotic fluid core transcriptome by analyzing the transcripts ubiquitously present in the amniotic fluid supernatant of euploid midtrimester fetuses.This was an in silico (computational) investigation using publicly available gene expression data previously produced by our group from 12 euploid midtrimester amniotic fluid samples. Functional analyses were performed using a web-based software analysis tool. Organ specificity was examined for each transcript using a gene expression atlas. For fetal organs not represented in the atlas, manual literature searching and the web-based software analysis tool were used to generate fetal organ-associated gene lists.There were 476 well-annotated genes present in 12 of 12 amniotic fluid samples. Functional analysis identified six physiologic systems represented in the amniotic fluid core transcriptome, including musculoskeletal and nervous system development and function and embryonic and organismal development. Mammalian target of rapamycin signaling was identified as a key canonical pathway. Twenty-three highly organ-specific transcripts were identified; six of these are known to be highly expressed in the fetal brain.Amniotic fluid cell-free fetal RNA can provide biological information on multiple fetal organ systems. The presence of fetal-brain specific transcripts in amniotic fluid suggests novel approaches to the study of developmental disorders that involve the central nervous system. The finding that the mammalian target of rapamycin signaling is enriched in midtrimester fetuses may have future applications in the study of fetal growth disorders.

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Trisomy 18 is a common human aneuploidy that is associated with significant perinatal mortality. Unlike the well-characterized "critical region" in trisomy 21 (21q22), there is no corresponding region on chromosome 18 associated with its pathogenesis. The high morbidity and mortality of affected individuals has limited extensive investigations. In order to better understand the molecular mechanisms underlying the congenital anomalies observed in this condition, we investigated the in utero gene expression profile of second trimester fetuses affected with trisomy 18. Total RNA was extracted from cell-free amniotic fluid supernatant from aneuploid fetuses and euploid controls matched for gestational age and hybridized to Affymetrix U133 Plus 2.0 arrays. Individual differentially expressed transcripts were obtained by two-tailed t tests. Over-represented functional pathways among these genes were identified with DAVID and Ingenuity(®) Pathways Analysis. Results show that three hundred and fifty-two probe sets representing 251 annotated genes were statistically significantly differentially expressed between trisomy 18 and controls. Only 7 genes (2.8% of the annotated total) were located on chromosome 18, including ROCK1, an up-regulated gene involved in valvuloseptal and endocardial cushion formation. Pathway analysis indicated disrupted function in ion transport, MHCII/T cell mediated immunity, DNA repair, G-protein mediated signaling, kinases, and glycosylation. Significant down-regulation of genes involved in adrenal development was identified, which may explain both the abnormal maternal serum estriols and the pre and postnatal growth restriction in trisomy 18. Comparison of this gene set to one previously generated for trisomy 21 fetuses revealed only six overlapping differentially regulated genes. This study contributes novel information regarding functional developmental gene expression differences in fetuses with trisomy 18.

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data--microarray gene expression data from knockout experiments--allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.

To characterize the differences between second trimester Down syndrome (DS) and euploid fetuses, we used Affymetrix microarrays to compare gene expression in uncultured amniotic fluid supernatant samples. Functional pathway analysis highlighted the importance of oxidative stress, ion transport, and G protein signaling in the DS fetuses. Further evidence supporting these results was derived by correlating the observed gene expression patterns to those of small molecule drugs via the Connectivity Map. Our results suggest that there are secondary adverse consequences of DS evident in the second trimester, leading to testable hypotheses about possible antenatal therapy for DS.

The discovery of fetal mRNA transcripts in the maternal circulation holds great promise for noninvasive prenatal diagnosis. To identify potential fetal biomarkers, we studied whole blood and plasma gene transcripts that were common to 9 term pregnant women and their newborns but absent or reduced in the mothers postpartum. RNA was isolated from peripheral or umbilical blood and hybridized to gene expression arrays. Gene expression, paired Student's t test, and pathway analyses were performed. In whole blood, 157 gene transcripts met statistical significance. These fetal biomarkers included 27 developmental genes, 5 sensory perception genes, and 22 genes involved in neonatal physiology. Transcripts were predominantly expressed or restricted to the fetus, the embryo, or the neonate. Real-time RT-PCR amplification confirmed the presence of specific gene transcripts; SNP analysis demonstrated the presence of 3 fetal transcripts in maternal antepartum blood. Comparison of whole blood and plasma samples from the same pregnant woman suggested that placental genes are more easily detected in plasma. We conclude that fetal and placental mRNA circulates in the blood of pregnant women. Transcriptional analysis of maternal whole blood identifies a unique set of biologically diverse fetal genes and has a multitude of clinical applications.

'Omics analysis of large datasets has an increasingly important role in perinatal research, but understanding gene expression analyses in the fetal context remains a challenge. We compared the interpretation provided by a widely used systems biology resource (ingenuity pathway analysis [IPA]) with that from gene set enrichment analysis (GSEA) with functional annotation curated specifically for the fetus (Developmental FunctionaL Annotation at Tufts [DFLAT]).Using amniotic fluid supernatant transcriptome datasets previously produced by our group, we analyzed 3 different developmental perturbations: aneuploidy (Trisomy 21 [T21]), hemodynamic (twin-twin transfusion syndrome [TTTS]), and metabolic (maternal obesity) vs sex- and gestational age-matched control subjects. Differentially expressed probe sets were identified with the use of paired t-tests with the Benjamini-Hochberg correction for multiple testing (P.05). Functional analyses were performed with IPA and GSEA/DFLAT. Outputs were compared for biologic relevance to the fetus.Compared with control subjects, there were 414 significantly dysregulated probe sets in T21 fetuses, 2226 in TTTS recipient twins, and 470 in fetuses of obese women. Each analytic output was unique but complementary. For T21, both IPA and GSEA/DFLAT identified dysregulation of brain, cardiovascular, and integumentary system development. For TTTS, both analytic tools identified dysregulation of cell growth/proliferation, immune and inflammatory signaling, brain, and cardiovascular development. For maternal obesity, both tools identified dysregulation of immune and inflammatory signaling, brain and musculoskeletal development, and cell death. GSEA/DFLAT identified substantially more dysregulated biologic functions in fetuses of obese women (1203 vs 151). For all 3 datasets, GSEA/DFLAT provided more comprehensive information about brain development. IPA consistently provided more detailed annotation about cell death. IPA produced many dysregulated terms that pertained to cancer (14 in T21, 109 in TTTS, 26 in maternal obesity); GSEA/DFLAT did not.Interpretation of the fetal amniotic fluid supernatant transcriptome depends on the analytic program, which suggests that1 resource should be used. Within IPA, physiologic cellular proliferation in the fetus produced many "false positive" annotations that pertained to cancer, which reflects its bias toward adult diseases. This study supports the use of gene annotation resources with a developmental focus, such as DFLAT, for 'omics studies in perinatal medicine.

Purpose: Given their accessibility, surrogate tissues, such as peripheral blood mononuclear cells (PBMC), may provide potential predictive biomarkers in clinical pharmacogenomic studies. In leukemias and lymphomas, the prognostic value of peripheral blast expression profiles is clear; however, it is unclear whether circulating mononuclear cells of patients with solid tumors might yield profiles with similar prognostic associations. Experimental Design: In this study, we evaluated the association of expression profiles in PBMCs with clinical outcomes in patients with advanced renal cell cancer. Transcriptional patterns in PBMCs of 45 renal cell cancer patients were compared with clinical outcome data at the conclusion of a phase II study of the mTOR kinase inhibitor CCI-779 to determine whether pretreatment transcriptional patterns in PBMCs were correlated with eventual patient outcomes. Results: Unsupervised hierarchical clustering of the PBMC profiles using all expressed genes identified clusters of patients with significant differences in survival. Cox proportional hazards modeling showed that the expression levels of many PBMC transcripts were predictors for the patient outcomes of time to progression and overall survival (time to death). Supervised class prediction approaches identified multivariate expression patterns in PBMCs capable of assigning favorable outcomes of time to death and time to progression in a test set of renal cancer patients, with overall performance accuracies of 72% and 85%, respectively. Conclusions: The present study provides the first example of gene expression profiling in peripheral blood, a clinically accessible surrogate tissue, for identifying patterns of gene expression associated with higher likelihoods of positive outcome in patients with a solid tumor.

A new theoretical model helps to evaluate the tradeoffs between running technical replicates in high-throughput experiments and sampling at more time points.