Blood-based Gene Expression Signature Research Articles

Blood-Based Gene Expression Signatures in Non–Small Cell Lung Cancer

Blood-based surrogate markers would be attractive biomarkers for early detection, diagnosis, prognosis, and prediction of therapeutic outcome in cancer. Disease-associated gene expression signatures in peripheral blood mononuclear cells (PBMC) have been described for several cancer types. However, RNA-stabilized whole blood-based technologies would be clinically more applicable and robust. We evaluated the applicability of whole blood-based gene expression profiling for the detection of non-small cell lung cancer (NSCLC). Expression profiles were generated from PAXgene-stabilized blood samples from three independent groups consisting of NSCLC cases and controls (n = 77, 54, and 102), using the Illumina WG6-VS2 system. Several genes are consistently differentially expressed in whole blood of NSCLC patients and controls. These expression profiles were used to build a diagnostic classifier for NSCLC, which was validated in an independent validation set of NSCLC patients (stages I-IV) and hospital-based controls. The area under the receiver operator curve was calculated to be 0.824 (P < 0.001). In a further independent dataset of stage I NSCLC patients and healthy controls the AUC was 0.977 (P < 0.001). Specificity of the classifier was validated by permutation analysis in both validation cohorts. Genes within the classifier are enriched in immune-associated genes and show specificity for NSCLC. Our results show that gene expression profiles of whole blood allow for detection of manifest NSCLC. These results prompt further development of gene expression-based biomarker tests in peripheral blood for the diagnosis and early detection of NSCLC.

Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures

Gene expression profiling with microarray technology suggests that peripheral blood cells might be a surrogate for postmortem brain tissue in studies of schizophrenia. The development of an accessible peripheral biomarker would substantially help in the diagnosis of this disease. We used a bioinformatics approach to examine whether the gene expression signature in whole blood contains enough information to make a specific diagnosis of schizophrenia. Unpaired t-tests of gene expression datasets from 52 antipsychotics-free schizophrenia patients and 49 normal controls identified 792 differentially expressed probes. Functional profiling with DAVID revealed that eleven of these genes were previously reported to be associated with schizophrenia, and 73 of them were expressed in the brain tissue. We analyzed the datasets with one of the supervised classifiers, artificial neural networks (ANNs). The samples were subdivided into training and testing sets. Quality filtering and stepwise forward selection identified 14 probes as predictors of the diagnosis. ANNs were then trained with the selected probes as the input and the training set for known diagnosis as the output. The constructed model achieved 91.2% diagnostic accuracy in the training set and 87.9% accuracy in the hold-out testing set. On the other hand, hierarchical clustering, a standard but unsupervised classifier, failed to separate patients and controls. These results suggest analysis of a blood-based gene expression signature with the supervised classifier, ANNs, might be a diagnostic tool for schizophrenia.

Peripheral-blood gene expression profiling in bladder cancer (BC) patients (pts)

5076 Background: There is a compelling need for biomarkers in the diagnosis and surveillance of bladder cancer (BC). In a pilot study, we previously reported that gene expression profile signature in circulating blood cells (CBC) can distinguish BC pts from other genitourinary cancers and from healthy controls. Here, we take our hypothesis-generating study to the hypothesis-testing level in a larger independent cohort of pts that: 1) includes more stringent control subjects, i.e., bladder pathology other than cancer; and 2) seeks to develop a gene-signature from CBC that can distinguish the presence and extent of BC. Methods: 85 pts (28 superficial BC (sBC), 27 metastatic BC (mBC), and 30 pts with benign bladder pathology) were studied. RNA from CBC was isolated and purified total RNA was labeled and hybridized onto Illumina Human-6 v2 Expression BeadChips (48,000 transcript probes/sample). Expression data were log base 2 transformed and within-array standardized. Unsupervised hierarchical clustering of 4438 high variance genes was used to group samples. T-tests were used to compare the 3 groups of pts. The Benjamini-Hochberg method was used to adjust the resulting p-values for multiple comparisons. Genes were selected based on adjusted p-value (≤ 0.05) and fold change (≥1.5). Results: Clustering led to almost complete separation of BC and benign pts and partial separation of sBC and mBC pts. A subset of 600 genes (230 significantly over-expressed genes and 370 under-expressed genes) were strikingly different between mBC pts compared to benign pts. The top-ranking overexpressed genes include genes related to inflammatory responses such as: peptidoglycan recognition protein 1 (p=2.9×10−8), CD177 molecule (p=5.4×10−8) and haptoglobin (p=5.6×10−7). Top-ranking underexpressed genes included genes related to cell cycle regulation and molecular chaperones such as: CDC like kinase 1 (p=1.7×10−9), Purinergic receptor P2Y (p=1.7×10−9), and Chaperonin Subunit 6a (p=2.3×10−9). Conclusions: Our study reveals a distinct blood-based gene expression signature that differentiates between metastatic BC (pts) and those with benign bladder pathology conditions. Efforts are ongoing to validate a smaller set of high-ranking genes as a BC signature that can be used as a diagnostic/prognostic tool. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Expert Testimony Other Remuneration Bristol-Myers Squibb, GPC Biotech Bristol-Myers Squibb, Eli Lilly, Genentech™ BioOncology, Pfizer

Employing a blood based gene expression signature to detect early stage breast cancer

21117 Background: Existing methods to detect breast cancer (BC) in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. We recently demonstrated the potential use of gene expression profiling in peripheral blood cells (PBC) for early detection of BC (1) and repeated this with a larger study using the Agilent platform with an accuracy of 75± 7%. Objective: 2 studies are presented that investigate: i) whether effective normalization of experimental conditions can improve diagnostic accuracy, ii) whether a blood based signature developed for BC can discriminate other forms of cancer, and iii) whether an expression signature developed using stage 0 patients can be used to predict BC in stage I disease, and vice versa. Material and Methods: Study I enrolled 60 females with BC and 60 healthy females. Study II enrolled 20 females with early stage BC (10 stage 0 and 10 stage I), 20 healthy females, and 8 females with colon cancer. Gene expression analysis was conducted using the ABI HGSM v2.0 with 32,878 oligo probes. Expression data were analyzed by PLSR for model building and results validated using cross-validation and test set validation. Results: Effective normalization of the data led to improved diagnostic performance. The signature developed using 20 BC and 20 non-BC samples classified 7/8 colon cancer patients as non-BC. The signature developed using stage 0 vs non-BC detected cancer in stage I patients, and the signature developed for stage 1 detected cancer in stage 0 patients. Conclusion: A blood-based gene expression signature can be developed for early stage breast cancer, which is specific and able to distinguish between other forms of malignancy such as colon cancer. The gene expression pattern is systemically affected in early stage BC patients in which there is typically no direct contact of blood cells with cancer cells.

O3-01-01: Employing blood-based gene expression signature to detect Alzheimer’s disease

O3-01-01: Employing blood-based gene expression signature to detect Alzheimer’s disease