Danute Bankaitis-Davis

Transcriptome Profiling of Whole Blood Cells Identifies PLEK2 and C1QB in Human Melanoma

Background Developing analytical methodologies to identify biomarkers in easily accessible body fluids is highly valuable for the early diagnosis and management of cancer patients. Peripheral whole blood is a “nucleic acid-rich” and “inflammatory cell-rich” information reservoir and represents systemic processes altered by the presence of cancer cells. Methodology/Principal Findings We conducted transcriptome profiling of whole blood cells from melanoma patients. To overcome challenges associated with blood-based transcriptome analysis, we used a PAXgene™ tube and NuGEN Ovation™ globin reduction system. The combined use of these systems in microarray resulted in the identification of 78 unique genes differentially expressed in the blood of melanoma patients. Of these, 68 genes were further analyzed by quantitative reverse transcriptase PCR using blood samples from 45 newly diagnosed melanoma patients (stage I to IV) and 50 healthy control individuals. Thirty-nine genes were verified to be differentially expressed in blood samples from melanoma patients. A stepwise logit analysis selected eighteen 2-gene signatures that distinguish melanoma from healthy controls. Of these, a 2-gene signature consisting of PLEK2 and C1QB led to the best result that correctly classified 93.3% melanoma patients and 90% healthy controls. Both genes were upregulated in blood samples of melanoma patients from all stages. Further analysis using blood fractionation showed that CD45− and CD45+ populations were responsible for the altered expression levels of PLEK2 and C1QB, respectively. Conclusions/Significance The current study provides the first analysis of whole blood-based transcriptome biomarkers for malignant melanoma. The expression of PLEK2, the strongest gene to classify melanoma patients, in CD45− subsets illustrates the importance of analyzing whole blood cells for biomarker studies. The study suggests that transcriptome profiling of blood cells could be used for both early detection of melanoma and monitoring of patients for residual disease.

Combined anti-tumor necrosis factor-α therapy and DMARD therapy in rheumatoid arthritis patients reduces inflammatory gene expression in whole blood compared to DMARD therapy alone

Periodic assessment of gene expression for diagnosis and monitoring in rheumatoid arthritis (RA) may provide a readily available and useful method to detect subclinical disease progression and follow responses to therapy with disease modifying anti-rheumatic agents (DMARDs) or anti-TNF-α therapy. We used quantitative real-time PCR to compare peripheral blood gene expression profiles in active (“unstable”) RA patients on DMARDs, stable RA patients on DMARDs, and stable RA patients treated with a combination of a disease-modifying anti-rheumatoid drug (DMARD) and an anti-TNF-α agent (infliximab or etanercept) to healthy human controls. The expression of 48 inflammatory genes were compared between healthy controls (N = 122), unstable DMARD patients (N = 18), stable DMARD patients (N = 26), and stable patients on combination therapy (N = 20). Expression of 13 genes was very low or undetectable in all study groups. Compared to healthy controls, patients with unstable RA on DMARDs exhibited increased expression of 25 genes, stable DMARD patients exhibited increased expression of 14 genes and decreased expression of five genes, and combined therapy patients exhibited increased expression of six genes and decreased expression of 10 genes. These findings demonstrate that active RA is associated with increased expression of circulating inflammatory markers whereas increases in inflammatory gene expression are diminished in patients with stable disease on either DMARD or anti-TNF-α therapy. Furthermore, combination DMARD and anti-TNF-α therapy is associated with greater reductions in circulating inflammatory gene expression compared to DMARD therapy alone. These results suggest that assessment of peripheral blood gene expression may prove useful to monitor disease progression and response to therapy.