The Influence of Optical Coherence Tomography Measurements of Retinal Nerve Fiber Layer on Decision-Making in Glaucoma Diagnosis.

PURPOSE: To quantify the influence of spectral domain optical coherence tomography (SDOCT) on decision-making in patients with suspected glaucoma. METHODS: A prospective cross-sectional study involving 40 eyes of 20 patients referred by community optometrists due to suspected glaucoma. All patients had disc photographs and standard automated perimetry (SAP), and results were presented to 13 ophthalmologists who estimated pre-test probability of glaucoma (0-100%) for a total of 520 observations. Ophthalmologists were then permitted to modify probabilities of disease based on SDOCT retinal nerve fiber layer (RNFL) measurements (post-test probability). The effect of information from SDOCT on decision to treat, monitor, or discharge was assessed. Agreement among graders was assessed using intraclass correlation coefficients (ICC) and correlated component regression (CCR) was used to identify variables influencing management decisions. RESULTS: Patients had an average age of 69.0 ± 10.1 years, SAP mean deviation of 2.71 ± 3.13 dB, and RNFL thickness of 86.2 ± 16.7 µm. Average pre-test probability of glaucoma was 37.0 ± 33.6% with SDOCT resulting in a 13.3 ± 18.1% change in estimated probability. Incorporating information from SDOCT improved agreement regarding probability of glaucoma (ICC = 0.50 (95% CI 0.38 to 0.64) without SDOCT versus 0.64 (95% CI 0.52 to 0.76) with SDOCT). SDOCT led to a change from decision to "treat or monitor" to "discharge" in 22 of 520 cases and a change from "discharge" to "treat or monitor" in 11 of 520 cases. Pre-test probability and RNFL thickness were predictors of post-test probability of glaucoma, contributing 69 and 31% of the variance in post-test probability, respectively. CONCLUSIONS: Information from SDOCT altered estimated probability of glaucoma and improved agreement among clinicians in those suspected of having the disease.

Blood mRNA expression profiling predicts survival in patients treated with tremelimumab.

PURPOSE: Tremelimumab (ticilimumab, Pfizer), is a monoclonal antibody (mAb) targeting cytotoxic T lymphocyte-associated antigen-4 (CTLA-4). Ipilimumab (Yervoy, BMS), another anti-CTLA-4 antibody, is approved by the U.S. Federal Drug Administration (FDA). Biomarkers are needed to identify the subset of patients who will achieve tumor control with CTLA-4 blockade. EXPERIMENTAL DESIGN: Pretreatment peripheral blood samples from 218 patients with melanoma who were refractory to prior therapy and receiving tremelimumab in a multicenter phase II study were measured for 169 mRNA transcripts using reverse transcription polymerase chain reaction (RT-PCR). A two-class latent model yielded a risk score based on four genes that were highly predictive of survival (P < 0.001). This signature was validated in an independent population of 260 treatment-naïve patients with melanoma enrolled in a multicenter phase III study of tremelimumab. RESULTS: Median follow-up was 297 days for the training population and 386 days for the test population. Expression levels of the 169 genes were closely correlated across the two populations (r = 0.9939). A four-gene model, including cathepsin D (CTSD), phopholipase A2 group VII (PLA2G7), thioredoxin reductase 1 (TXNRD1), and interleukin 1 receptor-associated kinase 3 (IRAK3), predicted survival in the test population (P = 0.001 by log-rank test). This four-gene model added to the predictive value of clinical predictors (P < 0.0001). CONCLUSIONS: Expression levels of CTSD, PLA2G7, TXNRD1, and IRAK3 in peripheral blood are predictive of survival in patients with melanoma treated with tremelimumab. Blood mRNA signatures should be further explored to define patient subsets likely to benefit from immunotherapy.

A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: a prospective study.

BACKGROUND: Survival for patients with castration-resistant prostate cancer is highly variable. We assessed the effectiveness of a whole-blood RNA transcript-based model as a prognostic biomarker in castration-resistant prostate cancer. METHODS: Peripheral blood was prospectively collected from 62 men with castration-resistant prostate cancer on various treatment regimens who were enrolled in a training set at the Dana-Farber Cancer Institute (Boston, MA, USA) from August, 2006, to June, 2008, and from 140 patients with castration-resistant prostate cancer in a validation set from Memorial Sloan-Kettering Cancer Center (New York, NY, USA) from August, 2006, to February, 2009. A panel of 168 inflammation-related and prostate cancer-related genes was assessed with optimised quantitative PCR to assess biomarkers predictive of survival. FINDINGS: A six-gene model (consisting of ABL2, SEMA4D, ITGAL, and C1QA, TIMP1, CDKN1A) separated patients with castration-resistant prostate cancer into two risk groups: a low-risk group with a median survival of more than 34·9 months (median survival was not reached) and a high-risk group with a median survival of 7·8 months (95% CI 1·8-13·9; p<0·0001). The prognostic utility of the six-gene model was validated in an independent cohort. This model was associated with a significantly higher area under the curve compared with a clinicopathological model (0·90 [95% CI 0·78-0·96] vs 0·65 [0·52-0·78]; p=0·0067). INTERPRETATION: Transcriptional profiling of whole blood yields crucial prognostic information about men with castration-resistant prostate cancer. The six-gene model suggests possible dysregulation of the immune system, a finding that warrants further study. FUNDING: Source MDX.

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.