Identification Of Biomarkers For Early Detection Research Articles

Interrogating the precancerous evolution of pathway dysfunction in lung squamous cell carcinoma using XTABLE.

Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low- and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression. To facilitate this research, we have designed XTABLE (Exploring Transcriptomes of Bronchial Lesions), an open-source application that integrates the most extensive transcriptomic databases of PMLs published so far. With this tool, users can stratify samples using multiple parameters and interrogate PML biology in multiple manners, such as two- and multiple-group comparisons, interrogation of genes of interests, and transcriptional signatures. Using XTABLE, we have carried out a comparative study of the potential role of chromosomal instability scores as biomarkers of PML progression and mapped the onset of the most relevant LUSC pathways to the sequence of LUSC developmental stages. XTABLE will critically facilitate new research for the identification of early detection biomarkers and acquire a better understanding of the LUSC precancerous stages.

Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance.

Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers. We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90. This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms. Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.

Intracranial Aneurysm Biomarker Candidates Identified by a Proteome-Wide Study.

The scientific basis of intracranial aneurysm (IA) formation, its rupture and further development of cerebral vasospasm is incompletely understood. Aberrant protein expression may drive structural alterations of vasculature found in IA. Deciphering the molecular mechanisms underlying these events will lead to identification of early detection biomarkers and in turn, improved treatment outcomes. To unravel differential protein expression in three clinical subgroups of IA patients: (1) unruptured aneurysm, (2) ruptured aneurysm without vasospasm, (3) ruptured aneurysm who developed vasospasm, we performed untargeted quantitative proteomic analysis of aneurysm tissue and serum samples from three subgroups of IA patients and control subjects. Candidate molecules were then validated in a larger cohort of patients using enzyme-linked immunosorbent assay. A total of 937 and 294 proteins were identified from aneurysm tissue and serum samples, respectively. Several proteins that are known to maintain structural integrity of vasculature were found to be dysregulated in the context of aneurysm. ORM1, a glycoprotein, was significantly upregulated in both tissue and serum samples of unruptured aneurysm patients. We employed a larger cohort of subjects (n = 26) and validated ORM1 as a potential biomarker for screening of unruptured aneurysms. Samples from ruptured aneurysms with vasospasm showed significant upregulation of MMP9, a protease, compared with ruptured aneurysms without vasospasm. We validated MMP9 as a potential biomarker for vasospasm in a larger cohort (n = 52). This study reports the first global proteomic analysis of the entire clinical spectrum of IA. Furthermore, this study suggests ORM1 and MMP9 as potential biomarkers for unruptured aneurysm and cerebral vasospasm, respectively.

Abstract B23: Plasma basigin as an early detection biomarker for pancreatic adenocarcinoma

Abstract Poor survival rates for pancreatic adenocarcinoma (PDAC) are in part due to late presentation. Earlier diagnosis is recognized as a means to improve outcomes by increasing the possibility for potentially curative treatment. Identification of early detection biomarkers is a research priority. The stromal compartment is a potential source for biomarkers as a strong desmoplastic response is a hallmark of PDAC. The interaction of mesenchymal, cancer, and inflammatory cells in the tumor induce secretion of autocrine and paracrine effectors and extracellular matrix remodeling enzymes that contribute to tumor growth, invasion, angiogenesis, and metastasis. Basigin (BSG), a cell surface glycoprotein, plays a role in the interaction of cancer and mesenchymal cells through the stimulation of extracellular matrix remodeling enzymes. Circulating BSG has also been demonstrated as a diagnostic/prognostic biomarker for other cancers. We evaluated the potential utility of plasma BSG as a PDAC diagnostic and prognostic biomarker. BSG levels were measured by ELISA in plasma from 106 healthy control subjects (mean = 3.29 ng/ml, 95% CI =3.0 – 3.6), 77 patients with chronic pancreatitis (3.72 ng/ml, 3.3 – 4.1 CI), and 112 pre-treatment samples from patients with PDAC (4.19 ng/ml, 3.9 – 4.5 CI). Nonparametric analysis revealed a significant difference for the model (P = 0.02, Wilcoxon). By ANOVA and Tukey-Kramer post-hoc tests, the distributions of BSG levels in PDAC and healthy control subjects were significantly different (P = 0.0003), but the comparison of PDAC to chronic pancreatitis cases was not significant (P = 0.147). A striking difference was noted after separating the PDAC cases into early stage (I-II) and late stage (III-IV) groups. ANOVA indicated that early stage disease (N = 70, 4.61 ng/ml, 4.2 – 5.0 CI) had significantly elevated BSG levels compared to healthy controls (P < 0.0001), chronic pancreatitis cases (P = 0.006), and late stage PDAC cases (P = 0.003, N = 42, 3.48 ng/ml, 2.9 – 3.9 CI). Increased plasma BSG levels were also significantly related to decreased survival in univariate Cox proportional hazards models (P = 0.01) indicating a 14% increase in risk per 1 ng/ml. This relationship remained significant in multivariate models adjusting for stage and post-diagnosis treatments. The results suggest that circulating BSG is an early stage phenomenon during PDAC development and would apparently be unique in its ability to specifically identify early stage PDAC cases. If validated, this biomarker should substantially contribute to the accuracy of an early detection biomarker panel. Further investigations are warranted into the usefulness of circulating BSG for early detection, selection of patients for surgical resection, identification of recurrence, screening high-risks groups and/or inclusion in panel screens. Citation Format: Samaa Kamal, Matthew A. Firpo, Courtney L. Scaife, Adler G. Douglas, Kenneth M. Boucher, Sean J. Mulvihill. Plasma basigin as an early detection biomarker for pancreatic adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr B23.

A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000–5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273–283, FIBA 5–16, and LBN 306–313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens.