A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC.
"However, one limitation of this approach was that the area under the curve (AUC) for LRG1 and the AUC of the combined markers (LRG1 + CA125) were not statistically different from the AUC of CA125 alone. Recently, LRG1 was found to be enriched in the urine of patients with appendicitis , as well as the serum of individuals with lung cancer , and heart failure . It has also been suggested as a biomarker of ulcerative colitis . "
[Show abstract][Hide abstract] ABSTRACT: Background
Ovarian cancer (OvCa) is the most lethal gynecological malignancy. The emergence of high-throughput technologies, such as mass spectrometry, has allowed for a paradigm shift in the way we search for novel biomarkers. Urine-based peptidomic profiling is a novel approach that may result in the discovery of noninvasive biomarkers for diagnosing patients with OvCa. In this study, the peptidome of urine from 6 ovarian cancer patients and 6 healthy controls was deciphered.
Urine samples underwent ultrafiltration and the filtrate was subjected to solid phase extraction, followed by fractionation using strong cation exchange chromatography. These fractions were analyzed using an Orbitrap mass spectrometer. Over 4600 unique endogenous urine peptides arising from 713 proteins were catalogued, representing the largest urine peptidome reported to date. Each specimen was processed in triplicate and reproducibility at the protein (69-76%) and peptide (58-63%) levels were noted. More importantly, over 3100 unique peptides were detected solely in OvCa specimens. One such promising biomarker was leucine-rich alpha-2-glycoprotein (LRG1), where multiple peptides were found in all urines from OvCa patients, but only one peptide was found in one healthy control urine sample.
Mining the urine peptidome may yield highly promising novel OvCa biomarkers.
"Some of these are as follows: 34 miRNA signatures , expression profiles of 11 miRNAs (miR-106a, miR-15b, miR-27b, miR-142-3p, miR-26b, miR-182, miR-126, let7g, let-7i and miR-30e-5p) from serum , 7 miRNA signatures , overexpression of six snoRNAs , and expression of 3 miRs (miR-205, miR-210 and miR-708) in sputum . Additional signatures and markers have also been reported from the plasma proteome [11,12], the salivary proteome , the serum epigenome , sputum-based genomics , and blood-based gene expression studies . However, none of these have progressed sufficiently to provide the necessary specificity and sensitivity required for clinical implementation. "
[Show abstract][Hide abstract] ABSTRACT: Lung cancer accounts for the highest number of cancer-related deaths worldwide. Early diagnosis significantly
increases the disease-free survival rate and a large amount of effort has been expended in screening trials and the
development of early molecular diagnostics. However, a gold standard diagnostic strategy is not yet available.
Here, based on miRNA expression profile in lung cancer and using a novel in silico reverse-transcriptomics
approach, followed by analysis of the interactome; we have identified potential transcription factor (TF) markers
that would facilitate diagnosis of subtype specific lung cancer. A subset of seven TF markers has been used in a
microarray screen and was then validated by blood-based qPCR using stage-II and IV non-small cell lung
carcinomas (NSCLC). Our results suggest that overexpression of HMGA1, E2F6, IRF1, and TFDP1 and downregulation
or no expression of SUV39H1, RBL1, and HNRPD in blood is suitable for diagnosis of lung adenocarcinoma and
squamous cell carcinoma sub-types of NSCLC. Here, E2F6 was, for the first time, found to be upregulated in NSCLC
blood samples. The miRNA-TF-miRNA interaction based molecular mechanisms of these seven markers in NSCLC
revealed that HMGA1 and TFDP1 play vital roles in lung cancer tumorigenesis. The strategy developed in this work
is applicable to any other cancer or disease and can assist in the identification of potential biomarkers.
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