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Saliva to predict risk of disease using transcriptomics and epigenetics

ABSTRACT: Background and study aims There are numerous lifestyle-altering diseases in the UK for which patients undergo multiple invasive tests before they can be properly diagnosed. These tests are often uncomfortable and inconvenient for patients, in addition to being very costly for the National Health Service (NHS). They typically involve a degree of risk to patients (e.g. bleeding and bowel rupture during endoscopy, or harmful radiation exposure from CT scanning). Many of these tests also tend to have normal results, since only a small fraction of patients are eventually diagnosed with the disease being sought. The aim of this study is to analyse symptoms, risk factors and genetic changes detected in saliva samples to predict patients’ risk of developing diseases. Who can participate? Patients aged over 18 who are already known to have oesophageal or colorectal disease including Crohn's disease, and patients who are awaiting tests to diagnose or exclude these diseases. What does the study involve? Participants complete a questionnaire to obtain information regarding their symptoms and risk factors for the disease. Saliva and blood samples are collected and, when appropriate, blood and tissue samples are taken during any investigations that they are already scheduled to undergo as part of their treatment process. No additional procedures or interventions are performed on these patients, and their clinical treatment is not affected in any way. Genetic analysis is performed on these samples to see if the characteristics of the patients’ saliva in combination with symptoms and other risk factors can accurately predict their disease. The saliva test results are compared with the blood and, where possible, tissue test results.

DISEASE(S): Crohn's Disease,Oesophageal Cancer,Colorectal Cancer

PROVIDER: 2409128 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Sjogren’s syndrome (SS) is characterized by dysfunctional mucous membranes and dysregulated moisture-secreting glands resulting in various symptoms, including dry mouth and dry eyes. Here, we wanted to profile and compare the tears and saliva proteomes of SS patients to healthy controls. Tear and saliva samples were collected and subjected to an isotopic dimethylation labeling shotgun proteomics workflow to identify alterations in protein levels. In tear samples, we identified 83 upregulated and 112 downregulated proteins. Altered pathways in SS patients’ tears included leukocyte transendothelial migration, neutrophil degranulation, and post-translation protein phosphorylation. In healthy controls’ tears, enriched proteins for glycolysis, amino acid metabolism and apoptotic signaling pathway were identified. In saliva, we identified 108 upregulated and 45 downregulated proteins. Altered pathways in SS patients’ saliva included cornification, sensory perception to taste and neutrophil degranulation. In healthy controls’ saliva, enriched proteins for JAK-STAT signaling after interleukin-12 stimulation, phagocytosis and glycolysis in senescence were identified. Dysregulated protease activity is implicated in the initiation of inflammation and immune cell recruitment in SS. We identified 20 proteases and protease inhibitors in tears and 18 in saliva which are differentially expressed between SS patients and healthy controls. Next, we quantified endogenous proteoglycan 4 (PRG4), a mucin-like glycoprotein previously shown to be present in tears and never studied in saliva, in tear wash and saliva samples via a bead-based immune assay. We identified decreased levels of PRG4 in SS patients’ tear wash compared to normal samples. Conversely, in saliva, we found elevated levels of PRG4 concentration and visualized PRG4 expression in human parotid gland via immunohistological staining. These findings improve our mechanistic understanding of the disease and changes in SS patients’ protein expression will help identify new potential drug targets. PRG4 is among the promising targets, which we identified here, in saliva, for the first time.

Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora This study, which was approved by the UCLA Institutional Review Board, started sample collection in February 2006 and ended in April 2008. It had a discovery and verification phase, followed by an independent validation phase. All patients with clinically diagnosed early or locally advanced pancreatic cancer, chronic pancreatitis and matched healthy control were recruited from the UCLA Medical Center. The controls were matched for gender, age, ethnicity and smoking history. We chose early and locally advanced pancreatic cancers because they were considered early stage diseases. All patients were recently diagnosed with primary disease, and had not received any prior treatment in the form of chemotherapy, radiotherapy, surgery, or alternative remedies. No subjects had a history of prior malignancy, immunodeficiency, autoimmune disorders, hepatitis, or HIV infection. Written informed consent was obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were collected and processed as previously described. The saliva bank of pancreatic disease at the UCLA Dental Research Institute has collected 283 saliva samples since 2006. Of these, 114 samples, from 42 pancreatic cancer patients, 30 chronic pancreatitis patients and 42 healthy control subjects, met the following eligibility criteria: the disease subjectsâ?? samples were obtained immediately after primary diagnosis from patients without evidence of metastasis; the saliva sample was free of blood (Table 1 in accompanying manuscript). Of the 114 samples, 12 pancreatic cancer samples and 12 healthy control samples were chosen for the discovery and verification phase. The transcriptomic approach profiled the saliva supernatant samples from 12 pancreatic cancer patients and 12 healthy control subjects using the Affymetrix Human Genome U133 Plus 2.0 Array platform.

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Saliva to predict risk of disease using transcriptomics and epigenetics

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