Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.

BACKGROUND: Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS: This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS: Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION: These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.

Patients with cancer often need support for their mental health. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This study trained a type of artificial intelligence (AI) called natural language processing to read the consultation report an oncologist writes after they first see a patient to predict which patients will see a counsellor or psychiatrist. The AI predicted this with performance similar to other uses of AI in mental health, and used different words and phrases to predict who would see a psychiatrist compared to seeing a counsellor. We believe this is the first use of AI to predict mental health outcomes from medical documents written by clinicians outside of mental health. This study suggests this type of AI can predict the mental health needs of patients with cancer from this widely-available document.

Microbial dysbiosis and the host airway epithelial response: insights into HIV-associated COPD using multi'omics profiling.

BACKGROUND: People living with HIV (PLWH) are at increased risk of developing Chronic Obstructive Pulmonary Disease (COPD) independent of cigarette smoking. We hypothesized that dysbiosis in PLWH is associated with epigenetic and transcriptomic disruptions in the airway epithelium. METHODS: Airway epithelial brushings were collected from 18 COPD + HIV + , 16 COPD - HIV + , 22 COPD + HIV - and 20 COPD - HIV - subjects. The microbiome, methylome, and transcriptome were profiled using 16S sequencing, Illumina Infinium Methylation EPIC chip, and RNA sequencing, respectively. Multi 'omic integration was performed using Data Integration Analysis for Biomarker discovery using Latent cOmponents. A correlation > 0.7 was used to identify key interactions between the 'omes. RESULTS: The COPD + HIV -, COPD -HIV + , and COPD + HIV + groups had reduced Shannon Diversity (p = 0.004, p = 0.023, and p = 5.5e-06, respectively) compared to individuals with neither COPD nor HIV, with the COPD + HIV + group demonstrating the most reduced diversity. Microbial communities were significantly different between the four groups (p = 0.001). Multi 'omic integration identified correlations between Bacteroidetes Prevotella, genes FUZ, FASTKD3, and ACVR1B, and epigenetic features CpG-FUZ and CpG-PHLDB3. CONCLUSION: PLWH with COPD manifest decreased diversity and altered microbial communities in their airway epithelial microbiome. The reduction in Prevotella in this group was linked with epigenetic and transcriptomic disruptions in host genes including FUZ, FASTKD3, and ACVR1B.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing.

Importance: Predicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer. Objective: To investigate whether natural language processing can predict survival of patients with general cancer from a patient's initial oncologist consultation document. Design, Setting, and Participants: This retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded. Exposures: Initial oncologist consultation documents were analyzed using traditional and neural language models. Main Outcomes and Measures: The primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used. Results: Of the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found. Conclusions and Relevance: These findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.

Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system.

BACKGROUND: Manually extracted data points from health records are collated on an institutional, provincial, and national level to facilitate clinical research. However, the labour-intensive clinical chart review process puts an increasing burden on healthcare system budgets. Therefore, an automated information extraction system is needed to ensure the timeliness and scalability of research data. METHODS: We used a dataset of 100 synoptic operative and 100 pathology reports, evenly split into 50 reports in training and test sets for each report type. The training set guided our development of a Natural Language Processing (NLP) extraction pipeline system, which accepts scanned images of operative and pathology reports. The system uses a combination of rule-based and transfer learning methods to extract numeric encodings from text. We also developed visualization tools to compare the manual and automated extractions. The code for this paper was made available on GitHub. RESULTS: A test set of 50 operative and 50 pathology reports were used to evaluate the extraction accuracies of the NLP pipeline. Gold standard, defined as manual extraction by expert reviewers, yielded accuracies of 90.5% for operative reports and 96.0% for pathology reports, while the NLP system achieved overall 91.9% (operative) and 95.4% (pathology) accuracy. The pipeline successfully extracted outcomes data pertinent to breast cancer tumor characteristics (e.g. presence of invasive carcinoma, size, histologic type), prognostic factors (e.g. number of lymph nodes with micro-metastases and macro-metastases, pathologic stage), and treatment-related variables (e.g. margins, neo-adjuvant treatment, surgical indication) with high accuracy. Out of the 48 variables across operative and pathology codebooks, NLP yielded 43 variables with F-scores of at least 0.90; in comparison, a trained human annotator yielded 44 variables with F-scores of at least 0.90. CONCLUSIONS: The NLP system achieves near-human-level accuracy in both operative and pathology reports using a minimal curated dataset. This system uniquely provides a robust solution for transparent, adaptable, and scalable automation of data extraction from patient health records. It may serve to advance breast cancer clinical research by facilitating collection of vast amounts of valuable health data at a population level.

The transition from normal lung anatomy to minimal and established fibrosis in idiopathic pulmonary fibrosis (IPF).

BACKGROUND: The transition from normal lung anatomy to minimal and established fibrosis is an important feature of the pathology of idiopathic pulmonary fibrosis (IPF). The purpose of this report is to examine the molecular and cellular mechanisms associated with this transition. METHODS: Pre-operative thoracic Multidetector Computed Tomography (MDCT) scans of patients with severe IPF (n = 9) were used to identify regions of minimal(n = 27) and established fibrosis(n = 27). MDCT, Micro-CT, quantitative histology, and next-generation sequencing were used to compare 24 samples from donor controls (n = 4) to minimal and established fibrosis samples. FINDINGS: The present results extended earlier reports about the transition from normal lung anatomy to minimal and established fibrosis by showing that there are activations of TGFBI, T cell co-stimulatory genes, and the down-regulation of inhibitory immune-checkpoint genes compared to controls. The expression patterns of these genes indicated activation of a field immune response, which is further supported by the increased infiltration of inflammatory immune cells dominated by lymphocytes that are capable of forming lymphoid follicles. Moreover, fibrosis pathways, mucin secretion, surfactant, TLRs, and cytokine storm-related genes also participate in the transitions from normal lung anatomy to minimal and established fibrosis. INTERPRETATION: The transition from normal lung anatomy to minimal and established fibrosis is associated with genes that are involved in the tissue repair processes, the activation of immune responses as well as the increased infiltration of CD4, CD8, B cell lymphocytes, and macrophages. These molecular and cellular events correlate with the development of structural abnormality of IPF and probably contribute to its pathogenesis.

HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft Patients.

BACKGROUND: Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT. METHODS: Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]). RESULTS: ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81). CONCLUSIONS: HEARTBiT's diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Blood biomarkers to predict short-term pulmonary exacerbation risk in children and adolescents with CF: A pilot study.

In CF, pulmonary exacerbations (PEx) can lead to permanent loss in lung function and thus should be prevented. Previously, we identified a blood protein biosignature consisting of 6 proteins capable of predicting short-term PEx events in CF adults. In this study, we utilized blood samples from the placebo arm of a randomized controlled trial to assess whether this candidate protein biosignature was also capable of predicting short-term PEx events in CF children and adolescents. This pilot study provides preliminary evidence that blood inflammation can be monitored to predict short-term PEx risk in CF children and adolescents.

Proteomic Profiling to Identify Blood Biomarkers Predictive of Response to Azithromycin in Children and Adolescents With Cystic Fibrosis.

BACKGROUND: Azithromycin reduces pulmonary exacerbation (PEx) risk in cystic fibrosis (CF), but not all individuals benefit. The goal of this study was to discover blood protein biomarkers predictive of clinical response to azithromycin treatment in children and adolescents with CF. METHODS: Novel proteomic technologies were applied to examine 188 serum and plasma protein samples from 40 patients with CF who were randomized to receive azithromycin in the AZ0004 trial. Early changes in blood protein levels from day 0 to day 28 of treatment were examined in relation to changes in FEV1 percent predicted and weight by days 28 and 168, and to predict PEx risk by day 168. RESULTS: Early changes in the levels of 15 plasma proteins following 28 days of azithromycin significantly correlated with changes in FEV1 percent predicted from day 0 to day 28 (Q value < 0.10), but this finding was not sustained to day 168. Early changes in serum calprotectin levels following 28 days of azithromycin were predictive of PEx risk by day 168 of treatment (area under the curve = 0.76; 95% CI, 0.57-0.95). Based on a calprotectin cutoff to maximize test sensitivity (88%) and specificity (68%), 40% of subjects who had a calprotectin reduction less than the cutoff experienced at least one PEx compared with only 8% of subjects with calprotectin reduction greater than the cutoff. CONCLUSIONS: Early changes in blood protein biomarkers following azithromycin treatment were associated with short-term changes, but not longer term changes, in lung function. Early change in serum calprotectin level was predictive of response to azithromycin in terms of modifying PEx risk.

Multiple reaction monitoring mass spectrometry to identify novel plasma protein biomarkers of treatment response in cystic fibrosis pulmonary exacerbations.

BACKGROUND: Systemic inflammation decreases with IV antibiotics during the treatment of CF pulmonary exacerbations (PEx). We used multiple reaction monitoring mass spectrometry and immunoassays to monitor blood proteins during PEx treatment to determine if early changes could be used to predict PEx outcomes following treatment. METHODS: Blood samples from 25 PEx (22 unique adults) were collected within 24h of admission, day 5, day 10, and at IV antibiotic completion. Ninety-two blood proteins involved in host immunity and inflammation were measured. RESULTS: Levels of several blood proteins changed from admission to end of IV antibiotics, most increasing with treatment. Early changes (admission to day 5) in fibrinogen levels had the strongest correlation with overall improvement in CFRSD-CRISS and FEV1% predicted by the end of treatment. CONCLUSIONS: Several plasma proteins changed significantly with IV antibiotics. Future studies will evaluate fibrinogen as an early biomarker of PEx treatment response in CF.

The Effect of Different Case Definitions of Current Smoking on the Discovery of Smoking-Related Blood Gene Expression Signatures in Chronic Obstructive Pulmonary Disease.

INTRODUCTION: Smoking is the number one modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD). Clinical, epidemiological and increasingly "omics" studies assess or adjust for current smoking status using only self-report, which may be inaccurate. Objective measures such as exhaled carbon monoxide (eCO) may also be problematic owing to limitations in the measurements and the relatively short half life of the molecule. In this study, we determined the impact of different case definitions of current cigarette smoking on gene expression in peripheral blood of patients with COPD. METHODS: Peripheral blood gene expression from 573 former- and current-smokers with COPD in the ECLIPSE study was used to find genes whose expression was associated with smoking status. Current smoking was defined using self-report, eCO concentrations, or both. Linear regression was used to determine the association of current smoking status with gene expression adjusting for age, sex and propensity score. Pathway enrichment analyses were performed on genes with P < .001. RESULT: Using self-report or eCO, only two genes were differentially expressed between current and ex-smokers, with no enrichment in biological processes. When current smoking was defined using both eCO and self-report, four genes were differentially expressed (LRRN3, PID1, FUCA1, GPR15) with enrichment in 40 biological pathways related to metabolic processes, response to hypoxia and hormonal stimulus. Additionally, the combined definition provided better distributions of test statistics for differential gene expression. CONCLUSION: A combined phenotype of eCO and self report allows for better discovery of genes and pathways related to current smoking. IMPLICATIONS: Studies relying only on self report of smoking status to assess or adjust for the impact of smoking may not fully capture its effect and will lead to residual confounding of results.

Discovery of novel plasma protein biomarkers to predict imminent cystic fibrosis pulmonary exacerbations using multiple reaction monitoring mass spectrometry.

BACKGROUND: Despite the significant morbidity and mortality related to pulmonary exacerbations in cystic fibrosis (CF), there remains no reliable predictor of imminent exacerbation. OBJECTIVE: To identify blood-based biomarkers to predict imminent (<4 months from stable blood draw) CF pulmonary exacerbations using targeted proteomics. METHODS: 104 subjects provided plasma samples when clinically stable and were randomly split into discovery (n=70) and replication (n=34) cohorts. Multiple reaction monitoring mass spectrometry (MRM-MS) was used to measure 117 peptides (79 proteins) from plasma. Plasma proteins with differential abundance between subjects who did versus did not develop an imminent exacerbation were analysed and proteins with fold difference >1.5 between the groups were included in an MRM-MS classifier model to predict imminent exacerbations. Performance characteristics were compared with clinical predictors and candidate plasma protein biomarkers. RESULTS: Six proteins were included in the final MRM-MS protein panel. The area under the curve (AUC) for the prediction of imminent exacerbations was highest for the MRM-MS protein panel (AUC 0.74) in comparison to FEV1% predicted (AUC 0.55) and the top candidate plasma protein biomarkers, including C-reactive protein (AUC 0.61) and interleukin-6 (AUC 0.60). The MRM-MS protein panel performed similarly in the replication cohort (AUC 0.73). CONCLUSIONS: Using MRM-MS, a six-protein panel measured from plasma can distinguish individuals with versus without an imminent exacerbation. With further replication and assay development, this biomarker panel may be clinically applicable for prediction of exacerbations in individuals with CF.

The Effect of Statins on Blood Gene Expression in COPD.

BACKGROUND: COPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown. OBJECTIVE: Identify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD. METHODS: Whole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. RESULTS: 25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington's disease, Parkinson's disease and acute myeloid leukemia gene signatures. CONCLUSION: The blood gene signature of statins' use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology.

Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection.

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.

BACKGROUND: Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. METHODS: Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. RESULTS: The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. CONCLUSIONS: Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.

Long non-coding RNAs are expressed in oral mucosa and altered in oral premalignant lesions.

Oral epithelial dysplasias are believed to progress through a series of histopathological stages; from mild to severe dysplasia, to carcinoma in situ, and finally to invasive OSCC. Underlying this change in histopathological grade are gross chromosome alterations and changes in gene expression of both protein-coding genes and non-coding RNAs. Recent papers have described associations of aberrant expression of microRNAs, one class of non-coding RNAs, with oral cancer. However, expression profiling of long non-coding RNAs (lncRNAs) has not been reported. Long non-coding RNAs are a novel class of mRNA-like transcripts with no protein coding capacity, but with a variety of functions including roles in epigenetics and gene regulation. In recent reports, the aberrant expression of lncRNAs has been associated with human cancers, suggesting a critical role in tumorigenesis. Here, we present the first long non-coding RNA expression map for the human oral mucosa. We describe the expression of 325 long non-coding RNAs, suggesting lncRNA expression contributes significantly to the oral transcriptome. Intriguingly, ∼60% of the detected lncRNAs show aberrant expression in oral premalignant lesions. A number of these lncRNAs have been previously associated with other human cancers.

Effects of sample timing and treatment on gene expression in early acute renal allograft rejection.

BACKGROUND: We have shown that genomic biomarkers in peripheral blood provide evidence of early graft rejection and may offer an important option for posttransplant monitoring, and we are working to improve this signature to maximize assay performance. METHODS: This clinical refinement study (n=79) used gene expression profiling in a case-control design to compare whole blood samples between normal subjects (n=20) and patients with (n=20) or without (n=39) biopsy-confirmed acute rejection (BCAR). RESULTS: Gene expression in peripheral blood from subjects with BCAR before treatment differed significantly from that of normal subjects and transplant recipients without BCAR. Hierarchical clustering and principal component analysis showed that samples obtained 1 to 5 days after the start of treatment of BCAR were segregated across both groups before treatment or without BCAR and that this was closely related to the time lag between treatment and sampling. Genes differentially expressed during BCAR included FKSG49, LMAN2, NFYC, LIMK2, JUNB, NASP, MALAT1, ITGAX, HLA-J, FKBP1A, and RBMS1, and gene ontology analysis highlighted changes in networks related to cytoskeletal reorganization, apoptosis, and immune signaling, whereas after treatment change highlighted pathways of cellular metabolism, cell-cycle regulation, DNA damage, and apoptosis. CONCLUSION: Gene expression in the peripheral blood is associated with BCAR, and the pattern of expression changes rapidly after treatment. This may offer a potential tool for diagnosis of rejection and immunologic monitoring of response to treatment, which is now being evaluated in a large multicenter international study.

Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection.

Signal transduction networks can be perturbed biochemically, genetically, and pharmacologically to unravel their functions. But at the systems level, it is not clear how such perturbations are best implemented to extract molecular mechanisms that underlie network function. Here, we combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling network response of cardiomyocytes to coxsackievirus B3 (CVB3) infection. Using all possible pairs of six kinase inhibitors, we assembled a dynamic nine-protein phosphorylation signature of perturbed CVB3 infectivity. Cluster analysis of the resulting dataset showed repeatedly that paired inhibitor data were required for accurate data-driven predictions of kinase substrate links in the host network. With pairwise data, we also derived a high-confidence network based on partial correlations, which identified phospho-IκBα as a central "hub" in the measured phosphorylation signature. The reconstructed network helped to connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF and IL-1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host cell viability, implicating TNF and IL-1 as cell autonomous components of CVB3-induced myocardial damage. We conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants.

A sequence-based approach to identify reference genes for gene expression analysis.

BACKGROUND: An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer) may not be suitable in another (e.g. breast cancer). Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate. METHODS: Serial analysis of gene expression (SAGE) profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR), and their impact on differential expression analysis of microarray data was evaluated. RESULTS: We show that (i) conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii) reference genes identified for lung cancer do not perform well for other cancer types (breast and brain), (iii) reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv) normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung cancer exhibit higher statistical significance using a dataset normalized with our reference genes relative to normalization without using our reference genes. CONCLUSIONS: Our analyses found NDUFA1, RPL19, RAB5C, and RPS18 to occupy the top ranking positions among 15 suitable reference genes optimal for normalization of lung tissue expression data. Significantly, the approach used in this study can be applied to data generated using new generation sequencing platforms for the identification of reference genes optimal within diverse contexts.

Proteomic signatures in plasma during early acute renal allograft rejection.

Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic monitoring.