Blood-based Gene Expression Signature Research Articles

Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson’s disease

Blood-based gene expression signatures could potentially be used as biomarkers for PD. However, it is unclear whether genetically-regulated transcriptomic signatures can provide novel gene candidates for use as PD biomarkers. We leveraged on the Genotype-Tissue Expression (GTEx) database to impute whole-blood transcriptomic expression using summary statistics of three large-scale PD GWAS. A random forest classifier was used with the consensus whole-blood imputed gene signature (IGS) to discriminate between cases and controls. Outcome measures included Area under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve. We demonstrated that the IGS (n = 37 genes) is conserved across PD GWAS studies and brain tissues. IGS discriminated between cases and controls in an independent whole-blood RNA-sequencing study (1176 PD, 254 prodromal, and 860 healthy controls) with mean AUC and accuracy of 64.8% and 69.4% for PD cohort, and 78.8% and 74% for prodromal cohort. PATL2 was the top-performing imputed gene in both PD and prodromal PD cohorts, whose classifier performance varied with biological sex (higher performance for males and females in the PD and prodromal PD, respectively). Single-cell RNA-sequencing studies (scRNA-seq) of healthy humans and PD patients found PATL2 to be enriched in terminal effector CD8+ and cytotoxic CD4+ cells, whose proportions are both increased in PD patients. We demonstrated the utility of GWAS transcriptomic imputation in identifying novel whole-blood transcriptomic signatures which could be leveraged upon for PD biomarker derivation. We identified PATL2 as a potential biomarker in both clinical and prodromic PD.

NETest: serial liquid biopsies in gastroenteropancreatic NET surveillance.

ObjectiveUp to now, serial NETest measurements in individuals assessing the disease course of gastroenteropancreatic neuroendocrine tumors (GEPNETs) at long-term follow-up and treatment response were not studied.DesignThe study was a longitudinal validation study of serial NETest measurements – a blood-based gene expression signature – in 132 patients with GEPNETs on therapy or watch-and-wait strategy.MethodsSerial samples were collected during 46 (range: 6–71) months of follow-up. NETest scores were compared with Response Evaluation Criteria in Solid Tumors version 1.1-defined treatment response (e.g. no evidence of disease (NED), stable disease (SD) or progressive disease (PD)).ResultsConsecutive NETest scores fluctuated substantially (range: 0–100) over time in individuals with SD (n = 28) and NED (n = 30). Follow-up samples were significantly higher in SD (samples 3–5) and NED subgroups (samples 2–5) compared with baseline results, without changes in imaging. In 82% of untreated patients with PD, consecutive NETest scores consistently remained high. In patients undergoing systemic treatment, the median pre-treatment NETest score in treatment-responders was 76.5 (n = 22) vs 33 (n = 12) in non-responders (P = 0.001). Patients with low pre-treatment scores had 21 months reduced progression-free survival (10 vs 31 months; P = 0.01). The accuracy of the NETest for treatment response prediction was 0.73 (P = 0.009).ConclusionIn patients not undergoing treatment, consecutive low NETest scores are associated with indolent behavior. Patients who develop PD exhibit elevated scores. Elevated results have important predictive value for treatment responsiveness and could be used for individualizing decisions on systemic therapy. The clinical value of follow-up NETest scores for patients who choose to watch and wait requires further study.

A 20-Gene Expression Diagnostic Signature of Bovine Respiratory Disease in Cattle

Bovine Respiratory Disease (BRD) is a prevalent disease in cattle rearing systems globally with significant health and economic costs. Current diagnostic methods of BRD rely on subjective visual signs and physical examination, which are suboptimal. This study, therefore, aims to find a blood-based gene expression signature for the diagnostic identification of BRD in cattle. The Gene Expression Omnibus dataset, GSE152959, was downloaded and used for analysis. The analyses performed included differential gene expression (DGE), clustering and machine learning prediction. Ninety genes were differentially expressed in BRD samples compared to controls. The GSE150706 dataset was used as the test dataset for machine learning prediction. The DEGs identified clustered the GSE150706 samples with good accuracy. For the machine learning prediction, 92 % of correctly predicted samples were obtained using twenty genes as features. Therefore, the identified 20-gene expression signature has BRD diagnostic utility in cattle. This signature could potentially be used to develop standardized and reliable diagnostic tests of Bovine Respiratory Disease in cattle. Improved diagnostics will lead to early detection and treatment, reducing the health and economic costs associated with the disease. Further validation in larger cattle cohorts is required.

Blood transcriptional signatures of gallstone pathogenesis in patients with cholelithiasis and choledocholithiasis Running Title: Blood gene expression gallstone

Objective Disease-specific molecular signalling in peripheral blood has the potential to inform on pathophysiological mechanisms of diseases. Here, we aimed to investigate the blood-based gene expression profiles that reflect disease-specific pathogenic mechanisms of gallstones, such as antioxidant defence, heat-shock responses, DNA damage and repair, ABC pump mechanisms, mucin signals, mitochondrial dysfunction and apoptosis in patients with Cholelithiasis (CHL) and Choledocholithiasis (CHDL) Materials and Methods The relative fold-change in the mRNA expression levels of 73 genes were analysed using a gallstone-related qRT-PCR array in 10 control individuals, 24 CHL patients and 23 CHDL patients. Serum malondialdehyde levels was determined by thiobarbituric-acid reactive substance assays, and 8-hydroxy-2’-deoxyguanosine levels was determined by ELISA assays. Results Our results showed that peripheral whole blood gene expression profile strongly reflects tissue specific molecular signalling in gallstone pathogenesis. The present findings of altered gene expressions including antioxidant defence (CuZn-SOD, CAT), heat shock protein (HSP70), DNA repair (MLH1 and RAD18), pro-apoptotic (P53, BAX, Cyt-c and Caspase 3), ABC transporter (ABCB1, ABCC2, and LRP1) and Mucin signals (MUCIN4, MUCIN5AC and MUCIN5B) point out to DNA damages via oxidative stress as well as deteriorating ABC types pump mechanisms and mucin signals in CHL and CHDL patients. Our findings may also suggest that activation of mitophagy activator, DRP1/hFIS1/PINK1 axis induced by oxidative stress and DNA damage may have a role in the pathogenesis of CHL and CHDL. Conclusions Our results indicate that a blood-based gene expression signature has promising accuracy for monitorize pathogenesis of disease in CHL patients, CHDL patients and unaffected controls.

Blood-based gene expression signature associated with metastatic castrate-resistant prostate cancer patient response to abiraterone plus prednisone or enzalutamide.

Precision medicine approaches for managing patients with metastatic castrate-resistant prostate cancer (mCRPC) are lacking. Non-invasive approaches for molecular monitoring of disease are urgently needed, especially for patients suffering from bone metastases for whom tissue biopsy is challenging. Here we utilized baseline blood samples to identify mCRPC patients most likely to benefit from abiraterone plus prednisone (AAP) or enzalutamide. Baseline blood samples were collected for circulating tumor cell (CTC) enumeration and qPCR-based gene expression analysis from 51 men with mCRPC beginning treatment with abiraterone or enzalutamide. Of 51 patients (median age 68 years [51-82]), 22 received AAP (abiraterone 1000 mg/day plus prednisone 10 mg/day) and 29 received enzalutamide (160 mg/day). The cohort was randomly divided into training (n = 37) and test (n = 14) sets. Baseline clinical variables (Gleason score, PSA, testosterone, and hemoglobin), CTC count, and qPCR-based gene expression data for 141 genes/isoforms in CTC-enriched blood were analyzed with respect to overall survival (OS). Genes with expression most associated with OS included MSLN, ARG2, FGF8, KLK3, ESRP2, NPR3, CCND1, and WNT5A. Using a Cox-elastic net model for our test set, the 8-gene expression signature had a c-index of 0.87(95% CI [0.80, 0.94]) and was more strongly associated with OS than clinical variables or CTC count alone, or a combination of the three variables. For patients with a low-risk vs. high-risk gene expression signature, median OS was not reached vs. 18 months, respectively (HR 5.32 [1.91-14.80], p = 0.001). For the subset of 41 patients for whom progression-free survival (PFS) data was available, the median PFS for patients with a low-risk vs high-risk gene expression signature was 20 vs. 5 months, respectively (HR 2.95 [1.46-5.98], p = 0.003). If validated in a larger prospective study, this test may predict patients most likely to benefit from second-generation antiandrogen therapy.

RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation

BackgroundA gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC.MethodsGenome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis.ResultsIn the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation.LimitationsIdentical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis.ConclusionsUsing a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.

Prediction of metastatic castrate-resistant prostate cancer response to abiraterone or enzalutamide by a baseline blood-based CTC gene expression signature.

e16529 Background: Prostate cancer is the most common cancer in men in the U.S., with 30% 5-year overall survival (OS) for patients (pts) with metastases. To take a precision medicine approach to the management of metastatic castrate-resistant prostate cancer (mCRPC), we developed a blood circulating tumor cell (CTC)-based test to identify mCRPC pts most likely to benefit from abiraterone (abi) or enzalutamide (enza). Methods: In this multi-institution prospective study, men with mCRPC were enrolled prior to starting abi (1,000 mg/d plus prednisone 10 mg/d) or enza (160 mg/d). At baseline (BL), 12 w, and progression, blood samples were collected for CellSearch-based CTC enumeration and qPCR-based gene expression analysis. Results: 69 pts (median age 68 y [50-82]) received abi (n = 25) or enza (n = 44) and had evaluable blood samples. Consistent with prior publications, among 43 pts with BL CTC &gt; 0, clearance of detectable CTCs (BL CTCs &gt; 0 and 12 w CTCs = 0), was achieved in 24 patients (55.8%), and was associated with greater median OS (31 mo vs. 18 mo, log-rank p = 0.03). The 43 pts with BL CTC &gt; 0 were then randomly divided into training (n = 31) and validation (n = 12) sets. Baseline gene expression data for the training set was used to develop a model to predict CTC clearance, starting with a panel of 141 expressed genes/isoforms including those associated with prostate cancer. Of the models tested, random forest yielded the best performance, with respective training and validation set sensitivity of 0.7 and 1, specificity 0.75 and 0.71, AUC 0.88 and 0.91. Top genes identified include those previously associated with disease – HOXB13, ESRP2, KLK3, GRHL2, and KRT19, among others. Conclusions: A gene expression signature from a baseline blood sample with CellSearch-enriched CTCs can predict clearance of detectable CTCs in response to abi/enza with high AUC and may give insight into molecular mechanisms of response. A prospective study with a larger number of patients will be required to further validate our findings. Ultimately, this blood test has the potential to select the patients most likely to benefit from second-generation antiandrogen vs. non-hormonal systemic treatment.

P.7.a.004 - Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning

P.7.a.004 - Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning

Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning

ObjectiveThe aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.MethodsWe used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value <0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms.ResultsHierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.ConclusionThe results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product.

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses.

Several microarray-based studies have investigated gene expression profiles in major depressive disorder (MDD), yet with highly variable findings. We examined blood-based genome-wide expression signatures of MDD, focusing on molecular pathways and networks underlying differentially expressed genes (DEGs) and behaviours of hypothesis-driven, evidence-based candidate genes for depression. Agilent human whole-genome arrays were used to measure gene expression in 14 medication-free outpatients with MDD who were at least moderately ill and 14 healthy controls matched pairwise for age and sex. After filtering, we compared expression of entire probes between patients and controls and identified DEGs. The DEGs were evaluated by pathway and network analyses. For the candidate gene analysis, we utilized 169 previously prioritized genes and examined their case-control separation efficiency and correlational co-expression network in patients relative to controls. The 317 screened DEGs mapped to a significantly over-represented pathway, the “synaptic transmission” pathway. The protein-protein interaction network was also significantly enriched, in which a number of key molecules for depression were included. The co-expression network of candidate genes was markedly disrupted in patients. This study provided evidence for an altered molecular network along with several key molecules in MDD and confirmed that the candidate genes are worthwhile targets for depression research.

Identification of 13 blood-based gene expression signatures to accurately distinguish tuberculosis from other pulmonary diseases and healthy controls

Tuberculosis (TB), caused by infection with mycobacterium tuberculosis, is still a major threat to human health worldwide. Current diagnostic methods encounter some limitations, such as sample collection problem or unsatisfied sensitivity and specificity issue. Moreover, it is hard to identify TB from some of other lung diseases without invasive biopsy. In this paper, the logistic models with three representative regularization approaches including Lasso (the most popular regularization method), and L1/2 (the method that inclines to achieve more sparse solution than Lasso) and Elastic Net (the method that encourages a grouping effect of genes in the results) adopted together to select the common gene signatures in microarray data of peripheral blood cells. As the result, 13 common gene signatures were selected, and sequentially the classifier based on them is constructed by the SVM approach, which can accurately distinguish tuberculosis from other pulmonary diseases and healthy controls. In the test and validation datasets of the blood gene expression profiles, the generated classification model achieved 91.86% sensitivity and 93.48% specificity averagely. Its sensitivity is improved 6%, but only 26% gene signatures used compared to recent research results. These 13 gene signatures selected by our methods can be used as the basis of a blood-based test for the detection of TB from other pulmonary diseases and healthy controls.

PD-0023 - Validation Study of a Blood-Based Gene Expression Signature for Colorectal Cancer Detection

Introduction: Colorectal cancer (CRC) is the leading cause of cancer related deaths worldwide. In 2008, more than 1 million cases were newly diagnosed and more than 600,000 people died from the disease. The early detection of CRC is crucial for successful treatment and patient survival. However, compliance with current screening methods remains poor and there is a clear need for an accurate in vitro blood test to increase participation in CRC screening.

Leveraging next-generation phenotyping and pan-omics to identify novel biomarkers and pharmaceutical targets for cardiovascular disease.

Leveraging next-generation phenotyping and pan-omics to identify novel biomarkers and pharmaceutical targets for cardiovascular disease.

Identification and Validation of a Blood-Based 18-Gene Expression Signature in Colorectal Cancer

The early detection of colorectal cancer (CRC) is crucial for successful treatment and patient survival. However, compliance with current screening methods remains poor. This study aimed to identify an accurate blood-based gene expression signature for CRC detection. Gene expression in peripheral blood samples from 216 patients with CRC tumors and 187 controls was investigated in the study. We first conducted a microarray analysis to select candidate genes that were significantly differentially expressed between patients with cancer and controls. A quantitative reverse transcription PCR assay was then used to evaluate the expression of selected genes. A gene expression signature was identified using a training set (n = 200) and then validated using an independent test set (n = 160). We identified an 18-gene signature that discriminated the patients with CRC from controls with 92% accuracy, 91% sensitivity, and 92% specificity. The signature performance was further validated in the independent test set with 86% accuracy, 84% sensitivity, and 88% specificity. The area under the receiver operating characteristics curve was 0.94. The signature was shown to be enriched in genes related to immune functions. This study identified an 18-gene signature that accurately discriminated patients with CRC from controls in peripheral blood samples. Our results prompt the further development of blood-based gene expression biomarkers for the diagnosis and early detection of CRC.

A blood-based gene expression signature of early-stage non-small cell lung cancer.

69 Background: Accurate blood-based biomarker for early cancer detection could be an easier and more convenient screening option than monitoring the target organ via tissue or imaging. We recently identified and validated eight genetic biomarkers of early-stage lung adenocarcinoma detectable in both peripheral whole blood (PWB) and lung tissue of smokers. Since biomarkers distinguishing benign disease versus lung malignancy across all cell types are needed in the diagnostic clinical setting, it is important to test the identified biomarkers in other lung cancer histologies, particularly in squamous cell carcinoma (SQCC), the second most common lung cancer histology after adenocarcinoma (AD). Methods: Using Real-Time Quantitative PCR (qRT-PCR), we measured mRNA levels for the eight candidate genes in PWB of 48 randomly sampled stage I SQCC cases, in addition to previously analyzed 82 AD cases and 130 age, sex, and smoking frequency matched healthy controls from the Environment And Genetics in Lung cancer Etiology (EAGLE) case-control study. The qRT-PCR data were analyzed using the 2-ΔΔCtmethod to compare SQCC cases with controls. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the candidate biomarkers in SQCC separately, and in SQCC and AD together. Results: Expression of TGFBR3, RUNX3, TRGC2, TRGV9, TARP, and TSTA3 genes, significantly differentiated SQCC cases versus controls, while ACP1 and VCAN gene expression did not. The eight genes combined discriminated patients with lung cancer from healthy controls with similarly high accuracy in SQCC and overall (AUC = 0.80 ± 0.1). RUNX3 showed the highest single gene accuracy for SQCC (AUC = 0.78). Conclusions: We showed that the previously identified gene expression signature of early-stage lung AD also differentiated early stage SQCC from healthy controls and demonstrated its sensitivity and specificity as a potential diagnostic lung cancer biomarker. Since lung cancer is the most common cause of cancer mortality worldwide and current smokers are at very high risk, our smoking-specific findings, if confirmed and translated into screening approaches, have the potential to impact public health.

Whole blood transcriptome correlates with treatment response in nasopharyngeal carcinoma

BackgroundTreatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response.MethodsRNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n = 28) of a pre-intervention case cohort of patients whom we followed post-treatment.ResultsA blood-based gene expression signature composed of three genes — LDLRAP1, PHF20, and LUC7L3 — is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0·90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.ConclusionWe have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.

Blood-Based Gene Expression Signatures of Infants and Toddlers With Autism

Autism spectrum disorders (ASDs) are highly heritable neurodevelopmental disorders that onset clinically during the first years of life. ASD risk biomarkers expressed early in life could significantly impact diagnosis and treatment, but no transcriptome-wide biomarker classifiers derived from fresh blood samples from children with autism have yet emerged. Using a community-based, prospective, longitudinal method, we identified 60 infants and toddlers at risk for ASDs (autistic disorder and pervasive developmental disorder), 34 at-risk for language delay, 17 at-risk for global developmental delay, and 68 typically developing comparison children. Diagnoses were confirmed via longitudinal follow-up. Each child's mRNA expression profile in peripheral blood mononuclear cells was determined by microarray. Potential ASD biomarkers were discovered in one-half of the sample and used to build a classifier, with high diagnostic accuracy in the remaining half of the sample. The mRNA expression abnormalities reliably observed in peripheral blood mononuclear cells, which are safely and easily assayed in infants, offer the first potential peripheral blood-based, early biomarker panel of risk for autism in infants and toddlers. Future work should verify these biomarkers and evaluate whether they may also serve as indirect indices of deviant molecular neural mechanisms in autism.

Blood signatures for early liver cancer detection in patients with chronic hepatitis B.

4106 Background: Some 20-30% of patients with chronic hepatitis B (CHB) eventually develop hepatocellular carcinoma (HCC), a disease with poor prognosis and high mortality unless detected early. Ultrasound and/or alpha-fetoprotein (AFP) are widely used for HCC surveillance. However these technologies are operator-dependent, have low sensitivity and are considered inadequate for screening. This study describes a blood-based gene-expression signature prognostic for HCC in CHB patients. Methods: PaxGene was used to extract RNA from whole blood samples taken from more than 1,000 Malaysian patients (matched HCC, CHB, healthy controls). Complementary DNA was generated from isolated RNA via reverse transcription and analyzed using Affymetrix U133Plus 2.0 with MAS 5.0 normalization. To minimize bias we developed a novel algorithm based on SentinelPair regression analysis to identify biomarker panels. A Monte Carlo search identified pair combinations that could differentiate between cancer and controls. Twelve genes were selected for qRT-PCR Cohort 1 verification (n=139). Six genes were then short-listed for qRT-PCR Cohort 2 verification using independent samples (n=150). Results: Multivariate quantitative microarray analysis of 147 samples (HCC=47; CHB=50; controls=50) identified 12 probe sets differentially expressed between HCC patients and controls and CHB (ROC AUC=0.90). These genes were evaluated using qRT-PCR on the same cohort (ROC AUC=0.86). Six genes were subsequently short listed for qRT-PCR Cohort 2 verification. The AUC of the 6-gene-combination of Cohort 2 (HCC=50; CHB=50; controls=50) was 0.80. Importantly, of the thirteen HCC with low AFP (&lt;15ng/ml), 9 were predicted as “positive.” Conclusions: Blood RNA biomarkers identified in this study may form the basis of a test that can be used in patients with CHB for detection of early HCC, potentially improving prognosis for this cancer. This blood-based test would represent a significant clinical alternative to ultrasound and/or AFP.

Analysis of blood-based gene expression signature in first-episode psychosis

Blood-based transcriptomic signature in psychosis is an alternative to a brain-based signature. In this study, we profiled whole-blood RNA from 26 patients with first-episode psychosis, and 26 matching controls. We identified a 400-gene signature that classified patients from controls accurately and tested the robustness using other modelling methods.