Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study.

The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma). METHODS: Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis). RESULTS: aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts. CONCLUSION: CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

SCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions.

MOTIVATION: The association of splicing signatures with disease is a leading area of study for prognosis, diagnosis and therapy. We present a novel fast-performing annotation-dependent tool called SCANVIS for scoring and annotating splice junctions (SJs), with an efficient visualization tool that highlights SJ details such as frame-shifts and annotation support for individual samples or a sample cohort. RESULTS: Using publicly available samples, we show that the tissue specificity inherent in splicing signatures is maintained with the Relative Read Support scoring method in SCANVIS, and we showcase some visualizations to demonstrate the usefulness of incorporating annotation details into sashimi plots. AVAILABILITY AND IMPLEMENTATION: https://github.com/nygenome/SCANVIS and https://bioconductor.org/packages/SCANVIS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sequencing and curation strategies for identifying candidate glioblastoma treatments.

BACKGROUND: Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. METHODS: A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. RESULTS: WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. CONCLUSION: These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Immunogenic neoantigens derived from gene fusions stimulate T cell responses.

Anti-tumor immunity is driven by self versus non-self discrimination. Many immunotherapeutic approaches to cancer have taken advantage of tumor neoantigens derived from somatic mutations. Here, we demonstrate that gene fusions are a source of immunogenic neoantigens that can mediate responses to immunotherapy. We identified an exceptional responder with metastatic head and neck cancer who experienced a complete response to immune checkpoint inhibitor therapy, despite a low mutational load and minimal pre-treatment immune infiltration in the tumor. Using whole-genome sequencing and RNA sequencing, we identified a novel gene fusion and demonstrated that it produces a neoantigen that can specifically elicit a host cytotoxic T cell response. In a cohort of head and neck tumors with low mutation burden, minimal immune infiltration and prevalent gene fusions, we also identified gene fusion-derived neoantigens that generate cytotoxic T cell responses. Finally, analyzing additional datasets of fusion-positive cancers, including checkpoint-inhibitor-treated tumors, we found evidence of immune surveillance resulting in negative selective pressure against gene fusion-derived neoantigens. These findings highlight an important class of tumor-specific antigens and have implications for targeting gene fusion events in cancers that would otherwise be less poised for response to immunotherapy, including cancers with low mutational load and minimal immune infiltration.

Histologic and Transcriptional Evidence of Subclinical Synovial Inflammation in Patients With Rheumatoid Arthritis in Clinical Remission.

OBJECTIVE: Patients with rheumatoid arthritis (RA) in clinical remission may have subclinical synovial inflammation. This study was undertaken to determine the proportion of patients with RA in remission or with low disease activity at the time of arthroplasty who had histologic or transcriptional evidence of synovitis, and to identify clinical features that distinguished patients as having subclinical synovitis. METHODS: We compared Disease Activity Score in 28 joints (DAS28) to synovial histologic features in 135 patients with RA undergoing arthroplasty. We also compared DAS28 scores to RNA-Seq data in a subset of 35 patients. RESULTS: Fourteen percent of patients met DAS28 criteria for clinical remission (DAS28 <2.6), and another 15% met criteria for low disease activity (DAS28 <3.2). Histologic analysis of synovium revealed synovitis in 27% and 31% of samples from patients in remission and patients with low disease activity, respectively. Patients with low disease activity and synovitis also exhibited increased C-reactive protein (CRP) (P = 0.0006) and increased anti-cyclic citrullinated peptide (anti-CCP) antibody levels (P = 0.03) compared to patients without synovitis. Compared to patients with a "low inflammatory synovium" subtype, 183 genes were differentially expressed in the synovium of patients with subclinical synovitis. The majority of these genes (86%) were also differentially expressed in the synovium of patients with clinically active disease (DAS28 ≥3.2). CONCLUSION: Thirty-one percent of patients with low clinical disease activity exhibited histologic evidence of subclinical synovitis, which was associated with increased CRP and anti-CCP levels. Our findings suggest that synovial gene expression signatures of clinical synovitis are present in patients with subclinical synovitis.

Unexpected similarities between C9ORF72 and sporadic forms of ALS/FTD suggest a common disease mechanism.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) represent two ends of a disease spectrum with shared clinical, genetic and pathological features. These include near ubiquitous pathological inclusions of the RNA-binding protein (RBP) TDP-43, and often the presence of a GGGGCC expansion in the C9ORF72 (C9) gene. Previously, we reported that the sequestration of hnRNP H altered the splicing of target transcripts in C9ALS patients (Conlon et al., 2016). Here, we show that this signature also occurs in half of 50 postmortem sporadic, non-C9 ALS/FTD brains. Furthermore, and equally surprisingly, these 'like-C9' brains also contained correspondingly high amounts of insoluble TDP-43, as well as several other disease-related RBPs, and this correlates with widespread global splicing defects. Finally, we show that the like-C9 sporadic patients, like actual C9ALS patients, were much more likely to have developed FTD. We propose that these unexpected links between C9 and sporadic ALS/FTD define a common mechanism in this disease spectrum.

Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.

OBJECTIVE: In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning. METHODS: Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes. RESULTS: Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor ß, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others. CONCLUSION: Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.

miR-193b-Regulated Signaling Networks Serve as Tumor Suppressors in Liposarcoma and Promote Adipogenesis in Adipose-Derived Stem Cells.

Well-differentiated and dedifferentiated liposarcomas (WDLS/DDLS) account for approximately 13% of all soft tissue sarcoma in adults and cause substantial morbidity or mortality in the majority of patients. In this study, we evaluated the functions of miRNA (miR-193b) in liposarcoma in vitro and in vivo Deep RNA sequencing on 93 WDLS, 145 DDLS, and 12 normal fat samples demonstrated that miR-193b was significantly underexpressed in DDLS compared with normal fat. Reintroduction of miR-193b induced apoptosis in liposarcoma cells and promoted adipogenesis in human adipose-derived stem cells (ASC). Integrative transcriptomic and proteomic analysis of miR-193b-target networks identified novel direct targets, including CRK-like proto-oncogene (CRKL) and focal adhesion kinase (FAK). miR-193b was found to regulate FAK-SRC-CRKL signaling through CRKL and FAK. miR-193b also stimulated reactive oxygen species signaling by targeting the antioxidant methionine sulfoxide reductase A to modulate liposarcoma cell survival and ASC differentiation state. Expression of miR-193b in liposarcoma cells was downregulated by promoter methylation, resulting at least in part from increased expression of the DNA methyltransferase DNMT1 in WDLS/DDLS. In vivo, miR-193b mimetics and FAK inhibitor (PF-562271) each inhibited liposarcoma xenograft growth. In summary, miR-193b not only functions as a tumor suppressor in liposarcoma but also promotes adipogenesis in ASC. Furthermore, this study reveals key tyrosine kinase and DNA methylation pathways in liposarcoma, some with immediate implications for therapeutic exploration. Cancer Res; 77(21); 5728-40. ©2017 AACR.

Integrin-α10 Dependency Identifies RAC and RICTOR as Therapeutic Targets in High-Grade Myxofibrosarcoma.

Myxofibrosarcoma is a common mesenchymal malignancy with complex genomics and heterogeneous clinical outcomes. Through gene-expression profiling of 64 primary high-grade myxofibrosarcomas, we defined an expression signature associated with clinical outcome. The gene most significantly associated with disease-specific death and distant metastasis was ITGA10 (integrin-α10). Functional studies revealed that myxofibrosarcoma cells strongly depended on integrin-α10, whereas normal mesenchymal cells did not. Integrin-α10 transmitted its tumor-specific signal via TRIO and RICTOR, two oncoproteins that are frequently co-overexpressed through gene amplification on chromosome 5p. TRIO and RICTOR activated RAC/PAK and AKT/mTOR to promote sarcoma cell survival. Inhibition of these proteins with EHop-016 (RAC inhibitor) and INK128 (mTOR inhibitor) had antitumor effects in tumor-derived cell lines and mouse xenografts, and combining the drugs enhanced the effects. Our results demonstrate the importance of integrin-α10/TRIO/RICTOR signaling for driving myxofibrosarcoma progression and provide the basis for promising targeted treatment strategies for patients with high-risk disease. SIGNIFICANCE: Identifying the molecular pathogenesis for myxofibrosarcoma progression has proven challenging given the highly complex genomic alterations in this tumor type. We found that integrin-α10 promotes tumor cell survival through activation of TRIO-RAC-RICTOR-mTOR signaling, and that inhibitors of RAC and mTOR have antitumor effects in vivo, thus identifying a potential treatment strategy for patients with high-risk myxofibrosarcoma. Cancer Discov; 6(10); 1148-65. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1069.

Near universal detection of alterations in CTNNB1 and Wnt pathway regulators in desmoid-type fibromatosis by whole-exome sequencing and genomic analysis.

CTNNB1 mutations or APC abnormalities have been observed in ∼85% of desmoids examined by Sanger sequencing and are associated with Wnt/ß-catenin activation. We sought to identify molecular aberrations in "wild-type" tumors (those without CTNNB1 or APC alteration) and to determine their prognostic relevance. CTNNB1 was examined by Sanger sequencing in 117 desmoids; a mutation was observed in 101 (86%) and 16 were wild type. Wild-type status did not associate with tumor recurrence. Moreover, in unsupervised clustering based on U133A-derived gene expression profiles, wild-type and mutated tumors clustered together. Whole-exome sequencing of eight of the wild-type desmoids revealed that three had a CTNNB1 mutation that had been undetected by Sanger sequencing. The mutation was found in a mean 16% of reads (vs. 37% for mutations identified by Sanger). Of the other five wild-type tumors sequenced, two had APC loss, two had chromosome 6 loss, and one had mutation of BMI1. The finding of low-frequency CTNNB1 mutation or APC loss in wild-type desmoids was validated in the remaining eight wild-type desmoids; directed miSeq identified low-frequency CTNNB1 mutation in four and comparative genomic hybridization identified APC loss in one. These results demonstrate that mutations affecting CTNNB1 or APC occur more frequently in desmoids than previously recognized (111 of 117; 95%), and designation of wild-type genotype is largely determined by sensitivity of detection methods. Even true CTNNB1 wild-type tumors (determined by next-generation sequencing) may have genomic alterations associated with Wnt activation (chromosome 6 loss/BMI1 mutation), supporting Wnt/ß-catenin activation as the common pathway governing desmoid initiation.

Sequence and chromatin determinants of cell-type-specific transcription factor binding.

Gene regulatory programs in distinct cell types are maintained in large part through the cell-type-specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type-specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line-specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type-specific binding patterns, four display distinct cell-type-specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type-specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type-specific binding profiles.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans.

Mirtrons are intronic hairpin substrates of the dicing machinery that generate functional microRNAs. In this study, we describe experimental assays that defined the essential requirements for entry of introns into the mirtron pathway. These data informed a bioinformatic screen that effectively identified functional mirtrons from the Drosophila melanogaster transcriptome. These included 17 known and six confident novel mirtrons among the top 51 candidates, and additional candidates had limited read evidence in available small RNA data. Our computational model also proved effective on Caenorhabditis elegans, for which the identification of 14 cloned mirtrons among the top 22 candidates more than tripled the number of validated mirtrons in this species. A few low-scoring introns generated mirtron-like read patterns from atypical RNA structures, but their paucity suggests that relatively few such loci were not captured by our model. Unexpectedly, we uncovered examples of clustered mirtrons in both fly and worm genomes, including a <8-kb region in C. elegans harboring eight distinct mirtrons. Altogether, we demonstrate that discovery of functional mirtrons, unlike canonical miRNAs, is amenable to computational methods independent of evolutionary constraint.

High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Comparing RNA secondary structures using a relaxed base-pair score.

The use of free energy-based algorithms to compute RNA secondary structures produces, in general, large numbers of foldings. Recent research has addressed the problem of grouping structures into a small number of clusters and computing a representative folding for each cluster. At the heart of this problem is the need to compute a quantity that measures the difference between pairs of foldings. We introduce a new concept, the relaxed base-pair (RBP) score, designed to give a more biologically realistic measure of the difference between structures than the base-pair (BP) metric, which simply counts the number of base pairs in one structure but not the other. The degree of relaxation is determined by a single relaxation parameter, t. When t = 0, (no relaxation) our method is the same as the BP metric. At the other extreme, a very large value of t will give a distance of 0 for identical structures and 1 for structures that differ. Scores can be recomputed with different values of t, at virtually no extra computation cost, to yield satisfactory results. Our results indicate that relaxed measures give more stable and more meaningful clusters than the BP metric. We also use the RBP score to compute representative foldings for each cluster.

Bayesian unsupervised learning with multiple data types.

We propose Bayesian generative models for unsupervised learning with two types of data and an assumed dependency of one type of data on the other. We consider two algorithmic approaches, based on a correspondence model, where latent variables are shared across datasets. These models indicate the appropriate number of clusters in addition to indicating relevant features in both types of data. We evaluate the model on artificially created data. We then apply the method to a breast cancer dataset consisting of gene expression and microRNA array data derived from the same patients. We assume partial dependence of gene expression on microRNA expression in this study. The method ranks genes within subtypes which have statistically significant abnormal expression and ranks associated abnormally expressing microRNA. We report a genetic signature for the basal-like subtype of breast cancer found across a number of previous gene expression array studies. Using the two algorithmic approaches we find that this signature also arises from clustering on the microRNA expression data and appears derivative from this data.

Ranking genes by their co-expression to subsets of pathway members.

Cellular processes are often carried out by intricate systems of interacting genes and proteins. Some of these systems are rather well studied and described in pathway databases, while the roles and functions of the majority of genes are poorly understood. A large compendium of public microarray data is available that covers a variety of conditions, samples, and tissues and provides a rich source for genome-scale information. We focus our study on the analysis of 35 curated biological pathways in the context of gene co-expression over a large variety of biological conditions. By defining a global co-expression similarity rank for each gene and pathway, we perform exhaustive leave-one-out computations to describe existing pathway memberships using other members of the corresponding pathway as reference. We demonstrate that while successful in recovering biological base processes such as metabolism and translation, the global correlation measure fails to detect gene memberships in signaling pathways where co-expression is less evident. Our results also show that pathway membership detection is more effective when using only a subset of corresponding pathway members as reference, supporting the existence of more tightly co-expressed subsets of genes within pathways. Our study assesses the predictive power of global gene expression correlation measures in reconstructing biological systems of various functions and specificity. The developed computational network has immediate applications in detecting dubious pathway members and predicting novel member candidates.