Identification of nephropathy predictors in urine from children with a recent diagnosis of type 1 diabetes.

Despite research progresses, the chance to accurately predict the risk for diabetic nephropathy (DN) is still poor. So far, the first evidence of DN is micro-albuminuria, which is detected only 10-20 years after the onset of diabetes. Our goal is to develop new predictive tools of nephropathy starting from urine, which can be easily obtained using noninvasive procedures and it is directly related to kidney. Since it is reasonable to suppose that, in predisposed patients, the mechanisms leading to nephropathy start acting since the diabetes onset, urine from children with recent diagnosis of type 1 diabetes was subjected to proteomic analysis in comparison to age-matched controls. Targeted confirmation was performed on children with a longer history of diabetes using Western Blotting and applying a urinary lipidomic approach. To definitively understand whether the observed alterations could be related to diabetic nephropathy, urine from diabetic adults with or without albuminuria was also examined. For the first time, lipid metabolisms of prostaglandin and ceramide, which are significantly and specifically modified in association with DN, are shown to be already altered in children with a recent diabetes diagnosis. Future studies on larger cohorts are needed to improve the validity and generalizability of these findings. Data are available via ProteomeXchange with identifier PXD011183 Submission details: Project Name: Urinary proteomics by 2DE and LC-MS/MS. Project accession: PXD011183 Project DOI: https://doi.org/10.6019/PXD011183 SIGNIFICANCE: Nephropathy is a very common diabetic complication. Once established, its progression can only be slowed down but full control or remission is achieved in very few cases, thus posing a large burden on worldwide health. The first evidence of diabetic nephropathy (DN) is micro-albuminuria, but only 30% of patients with micro-albuminuria progress to proteinuria, while in some patients it spontaneously reverts to normo-albuminuria. Thus, there is clear need for biomarkers that can accurately predict the risk to develop DN. Herein, by applying proteomic and lipidomic approaches on urine samples, we show that alteration of prostaglandin and ceramide metabolisms specifically occurs in association with DN. Interestingly, we demonstrate that the modification of these metabolic pathways is an early event in diabetic patients, suggesting the identified changed proteins as possible predictive biomarkers of diabetes-induced renal function decline.

An Aggressive Subtype of Stage I Lung Adenocarcinoma with Molecular and Prognostic Characteristics Typical of Advanced Lung Cancers.

PURPOSE: The National Lung Cancer Screening Trial has confirmed that lung cancer mortality can be reduced if tumors are diagnosed early, that is, at stage I. However, a substantial fraction of stage I lung cancer patients still develop metastatic disease within 5 years from surgery. Prognostic biomarkers are therefore needed to identify patients at risk of an adverse outcome, who might benefit from multimodality treatment. EXPERIMENTAL DESIGN: We extensively validated a 10-gene prognostic signature in a cohort of 507 lung adenocarcinoma patients using formalin-fixed paraffin-embedded samples. Furthermore, we performed an integrated analysis of gene expression, methylation, somatic mutations, copy number variations, and proteomic profiles on an independent cohort of 468 patients from The Cancer Genome Atlas (TCGA). RESULTS: Stage I lung cancer patients (N = 351) identified as high-risk by the 10-gene signature displayed a 4-fold increased risk of death [HR = 3.98; 95% confidence interval (CI), 1.73-9.14], with a 3-year overall survival of 84.2% (95% CI, 78.7-89.7) compared with 95.6% (92.4-98.8) in low-risk patients. The analysis of TCGA cohort revealed that the 10-gene signature identifies a subgroup of stage I lung adenocarcinomas displaying distinct molecular characteristics and associated with aggressive behavior and poor outcome. CONCLUSIONS: We validated a 10-gene prognostic signature capable of identifying a molecular subtype of stage I lung adenocarcinoma with characteristics remarkably similar to those of advanced lung cancer. We propose that our signature might aid the identification of stage I patients who would benefit from multimodality treatment. Clin Cancer Res; 23(1); 62-72. ©2016 AACR.

Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case.

BACKGROUND: The identification of circulating microRNAs (miRNAs) in the blood has been recently exploited for the development of minimally invasive tests for the early detection of cancer. Nevertheless, the clinical transferability of such tests is uncertain due to still-insufficient standardization and optimization of methods to detect circulating miRNAs in the clinical setting. METHODS: We performed a series of tests to optimize the quantification of serum miRNAs that compose the miR-Test, a signature for lung cancer early detection, and systematically analyzed variables that could affect the performance of the test. We took advantage of a large-scale (>1000 samples) validation study of the miR-Test that we recently published, to evaluate, in clinical samples, the effects of analytical and preanalytical variables on the quantification of circulating miRNAs and the clinical output of the signature (risk score). RESULTS: We developed a streamlined and standardized pipeline for the processing of clinical serum samples that allows the isolation and analysis of circulating miRNAs by quantitative reverse-transcription PCR, with a throughput compatible with screening trials. The major source of analytical variation came from RNA isolation from serum, which could be corrected by use of external (spike-in) or endogenous miRNAs as a reference for normalization. We also introduced standard operating procedures and QC steps to check for unspecific fluctuations that arise from the lack of standardized criteria in the collection or handling of the samples (preanalytical factors). CONCLUSIONS: We propose our methodology as a reference for the development of clinical-grade blood tests on the basis of miRNA detection.

miR-Test: a blood test for lung cancer early detection.

Lung cancer is the leading cause of cancer death worldwide. Low-dose computed tomography screening (LDCT) was recently shown to anticipate the time of diagnosis, thus reducing lung cancer mortality. However, concerns persist about the feasibility and costs of large-scale LDCT programs. Such concerns may be addressed by clearly defining the target "high-risk" population that needs to be screened by LDCT. We recently identified a serum microRNA signature (the miR-Test) that could identify the optimal target population. Here, we performed a large-scale validation study of the miR-Test in high-risk individuals (n = 1115) enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program. The overall accuracy, sensitivity, and specificity of the miR-Test are 74.9% (95% confidence interval [CI] = 72.2% to 77.6%), 77.8% (95% CI = 64.2% to 91.4%), and 74.8% (95% CI = 72.1% to 77.5%), respectively; the area under the curve is 0.85 (95% CI = 0.78 to 0.92). These results argue that the miR-Test might represent a useful tool for lung cancer screening in high-risk individuals.

Calcium signaling-related proteins are associated with broncho-pulmonary dysplasia progression.

Broncho-pulmonary dysplasia (BPD) is a chronic pulmonary disorder that follows premature birth. It is preceded by respiratory distress syndrome (RDS), characterized by acute respiratory failure due to deficiency of surfactant at birth. Clinical characteristics of infants affected by BPD have widely changed in the last decades: they are extraordinarly immature, with impaired alveolar and vascular lung development. To build up new therapeutic strategies for BPD babies, it is necessary to understand the pathogenic mechanisms, which are complicated by environmental risk factors and genetic predisposition. Therefore, the aim of this study was to highlight protein changes in the broncho-alveolar lavage fluid (BALF), thus providing an appropriate picture on what is happening in the locus of injury. We analyzed BALF samples from preterm babies, born at different stages of lung development. We confirmed that gestational age is relevant for BPD progression, but we also detected few de-regulated proteins in the younger babies; we discovered less abundant calcium signaling-related proteins, consistent with BPD severity, comparing severe to mild BPD babies with matched gestational age. In conclusion, this study suggests a subset of proteins to be investigated to better treat BPD babies and facilitate the definition of potential drug targets for novel therapies. BIOLOGICAL SIGNIFICANCE: Pulmonary biomarkers are needed to predict the clinical course of lung disease, status, progression and response to treatment. A key aspect in biomarker discovery is uncovering molecules that appear early during disease initiation, when the natural history of the disease can be modified. Using a proteomic-based approach we compared broncho-alveolar lavage fluid (BALF) protein profile from preterm neonates at different postmenstrual ages, to have a molecular description of broncho-pulmonary dysplasia (BPD) progression. BALF provided a snapshot of local molecular changes, which are relevant for early diagnosis, assessment and characterization of lung disorders. We showed that even if the studied patients had similar clinical phenotype (they all developed severe BPD and they were all cured in the same way in terms of mechanical ventilation, surfactant administration, antenatal steroid treatment and ibuprofen treatment for patent ductus arteriosus), however their BALF protein profiling displayed significant differences in a subset of proteins, which could be exploited to facilitate the development of novel effective therapies, distinct for age and severity of the disease.

Comparison of different depletion strategies for improving resolution of the human urine proteome.

BACKGROUND: Urine, being an ultrafiltrate of plasma, is a rich source for biomarker discovery. Since potential new disease markers are often present in low concentrations, a prefractionation/enrichment step could be useful in the discovery process. To enhance the detection of low-abundance proteins, three immuno-affinity depletion approaches were evaluated. METHODS: To remove the most abundant proteins from a human urine sample, GenWay Spin IgY-12 kit, HPLC Agilent Hu-PL7 and a home-made column vs. human serum albumin [immuno-affinity column (IAC)] were compared. Quantification of total proteins, 2-D gel electrophoresis (2-DE), Progenesis gel images analysis and mass spectrometric proteins identification were applied to evaluate these strategies. RESULTS: Reproducibility of depletion columns, by estimating protein content of unbound fractions, were: 343+/-20.0 microg, 5.8%; 186.3+/-13.3 microg, 7.2%; 292+/-20.6 microg, 8.8% [mean+/- standard deviation (SD), CV%], for GenWay, Agilent and IAC methods, respectively. To isolate urinary protein after depletion, ethanol precipitation provided the highest recovery (80%). Applying 2-DE and Progenesis analysis, the number of spots visualized on the gels was 468+/-21, 331+/-7, 368+/-22 and 304+/-7 (mean+/-SD) for GenWay, Agilent, IAC, and the undepleted urine pool sample, respectively, with a significant difference p<0.001 compared to the GenWay procedure. CONCLUSIONS: The sequential procedure of urine samples using multi-protein immuno-affinity depletion represents a valid tool for simplifying 2-DE analysis of the urine proteome. Particularly, the GenWay kit followed by ethanol precipitation was found to be the most efficient method for exploring the urine proteome.