Distinctive profile of monomeric and polymeric anti-SSA/Ro52 immunoglobulin A1 isoforms in saliva of patients with primary Sjögren's syndrome and Sicca.

OBJECTIVE: Primary Sjögren's syndrome (pSS) is the second most common chronic autoimmune connective tissue disease. Autoantibodies, immunoglobulin (IgG) anti-SSA/Ro, in serum is a key diagnostic feature of pSS. Since pSS is a disease of the salivary gland, we investigated anti-SSA/Ro52 in saliva. METHODS: Using a novel electrochemical detection platform, Electric Field-Induced Release and Measurement, we measured IgG/M/A, IgG, IgA, IgA isotypes (IgA1 and IgA2) and IgA1 subclasses (polymeric and monomeric IgA1) to anti-SSA/Ro52 in saliva supernatant of 34 pSS, 35 dry eyes and dry mouth (patients with Sicca) and 41 health controls. RESULTS: Saliva IgG/M/A, IgG, IgA, IgA isotypes and IgA1 subclasses to anti-SSA/Ro52 differed significantly between pSS, non-pSS Sicca and healthy subjects. Elevated monomeric IgA1 was observed in patients with non-pSS Sicca while elevated polymeric IgA1 (pIgA1) was observed in patients with pSS. Salivary polymeric but not monomeric IgA1 (mIgA1) isoform correlated with focus score (r2=0.467, p=0.001) CONCLUSIONS: Salivary anti-Ro52 polymeric IgA1 isoform is associated with glandular inflammation in pSS, while salivary monomeric IgA1 is associated with Sicca. Whether IgA1 isotope switching plays a role in the progression of the Sicca to pSS warrants further investigation.

Identification of salivary metabolites for oral squamous cell carcinoma and oral epithelial dysplasia screening from persistent suspicious oral mucosal lesions.

OBJECTIVE: To identify salivary metabolite biomarkers to differentiate patients with oral squamous cell carcinoma and oral epithelial dysplasia (OSCC/OED) from those with persistent suspicious oral mucosal lesions (PSOML). SUBJECTS AND METHODS: Whole unstimulated saliva samples were collected from age-, sex-, and race-matched patients who had a lesion in the oral cavity and for whom open biopsies were performed. The patients included OSCC (n = 6), OED (n = 10), and PSOML (n = 32). Hydrophilic metabolites in saliva samples were comprehensively analyzed using capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate OSCC/OED from PSOML. RESULTS: Six metabolites were significantly different in OSCC/OED compared with PSOML. From these six metabolites, ornithine, o-hydroxybenzoate, and ribose 5-phosphate (R5P) were used to develop the MLR model, which resulted in a high value for the area under receiver operating characteristic curve (AUC 0.871, 95% confidential interval (CI) 0.760-0.982; p < 0.001) to discriminate OSCC/OED from PSOML. CONCLUSIONS: This is the first study to identify salivary metabolites that discriminate OSCC/OED from PSOML rather than from healthy controls. The profiles of salivary metabolites were significantly different between OSCC/OED and PSOML. The ability to discriminate OSCC/OED from PSOML is important for dentists who are not oral surgery specialists. These salivary metabolites showed potential for non-invasive screening to discriminate OSCC/OED from PSOML. CLINICAL RELEVANCE: Salivary metabolites in this study showed potential for non-invasive screening to discriminate OSCC/OED from PSOML.

Discovery and Validation of Salivary Extracellular RNA Biomarkers for Noninvasive Detection of Gastric Cancer.

BACKGROUND: Biomarkers are needed for noninvasive early detection of gastric cancer (GC). We investigated salivary extracellular RNA (exRNA) biomarkers as potential clinical evaluation tools for GC. METHODS: Unstimulated whole saliva samples were prospectively collected from 294 individuals (163 GC and 131 non-GC patients) who underwent endoscopic evaluation at the Samsung Medical Center in Korea. Salivary transcriptomes of 63 GC and 31 non-GC patients were profiled, and mRNA biomarker candidates were verified with reverse transcription quantitative real-time PCR (RT-qPCR). In parallel, microRNA (miRNA) biomarkers were profiled and verified with saliva samples from 10 GC and 10 non-GC patients. Candidate biomarkers were validated with RT-qPCR in an independent cohort of 100/100 saliva samples from GC and non-GC patients. Validated individual markers were configured into a best performance panel. RESULTS: We identified 30 mRNA and 15 miRNA candidates whose expression pattern associated with the presence of GC. Among them, 12 mRNA and 6 miRNA candidates were verified with the discovery cohort by RT-qPCR and further validated with the independent cohort (n = 200). The configured biomarker panel consisted of 3 mRNAs (SPINK7, PPL, and SEMA4B) and 2 miRNAs (MIR140-5p and MIR301a), which were all significantly down-regulated in the GC group, and yielded an area under the ROC curve (AUC) of 0.81 (95% CI, 0.72-0.89). When combined with demographic factors, the AUC of the biomarker panel reached 0.87 (95% CI, 0.80-0.93). CONCLUSIONS: We have discovered and validated a panel of salivary exRNA biomarkers with credible clinical performance for the detection of GC. Our study demonstrates the potential utility of salivary exRNA biomarkers in screening and risk assessment for GC.

Salivary exRNA biomarkers to detect gingivitis and monitor disease regression.

AIM: This study tests the hypothesis that salivary extracellular RNA (exRNA) biomarkers can be developed for gingivitis detection and monitoring disease regression. MATERIALS AND METHODS: Salivary exRNA biomarker candidates were developed from a total of 100 gingivitis and non-gingivitis individuals using Affymetrix's expression microarrays. The top 10 differentially expressed exRNAs were tested in a clinical cohort to determine whether the discovered salivary exRNA markers for gingivitis were associated with clinical gingivitis and disease regression. For this purpose, unstimulated saliva was collected from 30 randomly selected gingivitis subjects, the gingival and plaque indexes scores were taken at baseline, 3 and 6 weeks and salivary exRNAs were assayed by means of reverse transcription quantitative polymerase chain reaction. RESULTS: Eight salivary exRNA biomarkers developed for gingivitis were statistically significantly changed over time, consistent with disease regression. A panel of four salivary exRNAs [SPRR1A, lnc-TET3-2:1, FAM25A, CRCT1] can detect gingivitis with a clinical performance of 0.91 area under the curve, with 71% sensitivity and 100% specificity. CONCLUSIONS: The clinical values of the developed salivary exRNA biomarkers are associated with gingivitis regression. They offer strong potential to be advanced for definitive validation and clinical laboratory development test.

Evaluation of a novel saliva-based epidermal growth factor receptor mutation detection for lung cancer: A pilot study.

BACKGROUND: This article describes a pilot study evaluating a novel liquid biopsy system for non-small cell lung cancer (NSCLC) patients. The electric field-induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids. METHODS: Saliva and plasma of 17 patients were collected from three cancer centers prior to and after surgical resection. The EFIRM method was then applied to the collected samples to assay for exon 19 deletion and p.L858 mutations. EFIRM results were compared with cobas results of exon 19 deletion and p.L858 mutation detection in cancer tissues. RESULTS: The EFIRM method was found to detect exon 19 deletion with an area under the curve (AUC) of 1.0 in both saliva and plasma samples in lung cancer patients. For L858R mutation detection, the AUC of saliva was 1.0, while the AUC of plasma was 0.98. Strong correlations were also found between presurgery and post-surgery samples for both saliva (0.86 for exon 19 and 0.98 for L858R) and plasma (0.73 for exon 19 and 0.94 for L858R). CONCLUSION: Our study demonstrates the feasibility of utilizing EFIRM to rapidly, non-invasively, and conveniently detect epidermal growth factor receptor mutations in the saliva of patients with NSCLC, with results corresponding perfectly with the results of cobas tissue genotyping.

Cheek cell-derived α-synuclein and DJ-1 do not differentiate Parkinson's disease from control.

Recently, α-synuclein (α-syn) and DJ-1, 2 proteins critically involved in Parkinson's disease (PD), have been shown to be present in saliva, suggesting their potential utility as biomarkers of PD. However, the origin and influence of demographic characteristics (e.g., age or sex) on these proteins are unknown. We identified cheek epithelium, which forms the majority of the cellular component of saliva and is readily accessible clinically, as 1 of several potential sources of salivary α-syn and DJ-1. However, no PD-related trend in the cellular component was present. In the supernatant collected from 198 healthy subjects, no correlation was seen between salivary DJ-1 or α-syn with age. When male and female subjects were analyzed separately, a weak age-dependent increase in DJ-1 level was present in male subjects, along with slightly increased α-syn in female subjects. These results, albeit largely negative, provide critical information for understanding the salivary gland pathology and saliva as a PD biomarker source, and must be considered in future investigations of salivary changes in PD.

Development of transcriptomic biomarker signature in human saliva to detect lung cancer.

Lung cancer is the leading cause of cancer death for both men and women worldwide. Since most of the symptoms found for lung cancer are nonspecific, diagnosis is mostly done at late and progressed stage with the consecutive poor therapy outcome. Effective early detection techniques are sorely needed. The emerging field of salivary diagnostics could provide scientifically credible, easy-to-use, non-invasive and cost-effective detection methods. Recent advances have allowed us to develop discriminatory salivary biomarkers for a variety of diseases from oral to systematic diseases. In this study, salivary transcriptomes of lung cancer patients were profiled and led to the discovery and pre-validation of seven highly discriminatory transcriptomic salivary biomarkers (BRAF, CCNI, EGRF, FGF19, FRS2, GREB1, and LZTS1). The logistic regression model combining five of the mRNA biomarkers (CCNI, EGFR, FGF19, FRS2, and GREB1) could differentiate lung cancer patients from normal control subjects, yielding AUC value of 0.925 with 93.75 % sensitivity and 82.81 % specificity in the pre-validation sample set. These salivary mRNA biomarkers possess the discriminatory power for the detection of lung cancer. This report provides the proof of concept of salivary biomarkers for the non-invasive detection of the systematic disease. These results poised the salivary biomarkers for the initiation of a multi-center validation in a definitive clinical context.

Prevalidation of salivary biomarkers for oral cancer detection.

BACKGROUND: Oral cancer is the sixth most common cancer with a 5-year survival rate of approximately 60%. Presently, there are no scientifically credible early detection techniques beyond conventional clinical oral examination. The goal of this study is to validate whether the seven mRNAs and three proteins previously reported as biomarkers are capable of discriminating patients with oral squamous cell carcinomas (OSCC) from healthy subjects in independent cohorts and by a National Cancer Institute (NCI)-Early Detection Research Network (EDRN)-Biomarker Reference Laboratory (BRL). METHODS: Three hundred and ninety-five subjects from five independent cohorts based on case controlled design were investigated by two independent laboratories, University of California, Los Angeles (Los Angeles, CA) discovery laboratory and NCI-EDRN-BRL. RESULTS: Expression of all seven mRNA and three protein markers was increased in OSCC versus controls in all five cohorts. With respect to individual marker performance across the five cohorts, the increase in interleukin (IL)-8 and subcutaneous adipose tissue (SAT) was statistically significant and they remained top performers across different cohorts in terms of sensitivity and specificity. A previously identified multiple marker model showed an area under the receiver operating characteristic (ROC) curve for prediction of OSCC status ranging from 0.74 to 0.86 across the cohorts. CONCLUSIONS: The validation of these biomarkers showed their feasibility in the discrimination of OSCCs from healthy controls. Established assay technologies are robust enough to perform independently. Individual cutoff values for each of these markers and for the combined predictive model need to be further defined in large clinical studies. IMPACT: Salivary proteomic and transcriptomic biomarkers can discriminate oral cancer from control subjects.

Variations of oral microbiota are associated with pancreatic diseases including pancreatic cancer.

OBJECTIVE: The associations between oral diseases and increased risk of pancreatic cancer have been reported in several prospective cohort studies. In this study, we measured variations of salivary microbiota and evaluated their potential associations with pancreatic cancer and chronic pancreatitis. METHODS: This study was divided into three phases: (1) microbial profiling using the Human Oral Microbe Identification Microarray to investigate salivary microbiota variation between 10 resectable patients with pancreatic cancer and 10 matched healthy controls, (2) identification and verification of bacterial candidates by real-time quantitative PCR (qPCR) and (3) validation of bacterial candidates by qPCR on an independent cohort of 28 resectable pancreatic cancer, 28 matched healthy control and 27 chronic pancreatitis samples. RESULTS: Comprehensive comparison of the salivary microbiota between patients with pancreatic cancer and healthy control subjects revealed a significant variation of salivary microflora. Thirty-one bacterial species/clusters were increased in the saliva of patients with pancreatic cancer (n=10) in comparison to those of the healthy controls (n=10), whereas 25 bacterial species/clusters were decreased. Two out of six bacterial candidates (Neisseria elongata and Streptococcus mitis) were validated using the independent samples, showing significant variation (p<0.05, qPCR) between patients with pancreatic cancer and controls (n=56). Additionally, two bacteria (Granulicatella adiacens and S mitis) showed significant variation (p<0.05, qPCR) between chronic pancreatitis samples and controls (n=55). The combination of two bacterial biomarkers (N elongata and S mitis) yielded a receiver operating characteristic plot area under the curve value of 0.90 (95% CI 0.78 to 0.96, p<0.0001) with a 96.4% sensitivity and 82.1% specificity in distinguishing patients with pancreatic cancer from healthy subjects. CONCLUSIONS: The authors observed associations between variations of patients' salivary microbiota with pancreatic cancer and chronic pancreatitis. This report also provides proof of salivary microbiota as an informative source for discovering non-invasive biomarkers of systemic diseases.

Oral squamous cell carcinoma detection by salivary biomarkers in a Serbian population.

Early detection of oral squamous cell cancer (OSCC) is the key to improve the low 5-year survival rate. Using proteomic and genomic technologies we have previously discovered and validated salivary OSCC markers in American patients. The question arises whether these biomarkers are discriminatory in cohorts of different ethnic background. Six transcriptome (DUSP1, IL8, IL1B, OAZ1, SAT1, and S100P) and three proteome (IL1B, IL8, and M2BP) biomarkers were tested on 18 early and 17 late stage OSCC patients and 51 healthy controls with quantitative PCR and ELISA. Four transcriptome (IL8, IL1B, SAT1, and S100P) and all proteome biomarkers were significantly elevated (p<0.05) in OSCC patients. The combination of markers yielded an AUC of 0.86, 0.85 and 0.88 for OSCC total, T1-T2, and T3-T4, respectively. The sensitivity/specificity for OSCC total was 0.89/0.78, for T1-T2 0.67/0.96, and for T3-T4 0.82/0.84. In conclusion, seven of the nine salivary biomarkers (three proteins and four mRNAs) were validated and performed strongest in late stage cancer. Patient-based salivary diagnostics is a highly promising approach for OSCC detection. This study shows that previously discovered and validated salivary OSCC biomarkers are discriminatory and reproducible in a different ethnic cohort. These findings support the feasibility to implement multi-center, multi-ethnicity clinical trials towards the pivotal validation of salivary biomarkers for OSCC detection.

Assessment of Fitts' law for quantifying combined rotational and translational movements.

OBJECTIVE: To develop a model for human performance in combined translational and rotational movements based on Fitts' law. BACKGROUND: Fitts' law has been successfully applied to translational movements in the past, providing generalization beyond a specific task as well as performance predictions. For movements involving both translations and rotations, no equivalent theory exists, making comparisons of input devices for these movements more ambiguous. METHOD: The study consisted of three experiments. In the first two, participants performed either pure translational or pure rotational movements of 1 degree of freedom. The third experiment involved the same movements combined. RESULTS: On average, the performance times for combined movements were equal to the sum of the times for equivalent separate rotational and translational movements. A simple Fitts' law equivalent for combined movements with a similar slope as the separate components was proposed. In addition, a significant degree of coordination of the combined movements was found. This had a strong bias toward a parallel execution in 12 out of 13 participants. CONCLUSION: Combined movements with rotations and translations of 1 degree of freedom can be approximated using a simple Fitts' law equivalent. The rotational and translational components appear to be coordinated by the central nervous system to generate a parallel execution. APPLICATION: The results may help drive human interface designs and provide insights into the coordination of combined movements. Future extensions may be possible for the movements of higher degrees of freedom used in robot teleoperation and virtual reality applications.

Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer.

BACKGROUND: A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection. METHODOLOGY/PRINCIPAL FINDINGS: Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set. CONCLUSIONS: Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.

Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer.

BACKGROUND & AIMS: Lack of detection technology for early pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. New strategies and biomarkers for early detection are sorely needed. In this study, we have conducted a prospective sample collection and retrospective blinded validation to evaluate the performance and translational utilities of salivary transcriptomic biomarkers for the noninvasive detection of resectable pancreatic cancer. METHODS: The Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Biomarkers discovered from the microarray study were subjected to clinical validation using an independent sample set of 30 pancreatic cancer patients, 30 chronic pancreatitis patients, and 30 healthy controls. RESULTS: Twelve messenger RNA biomarkers were discovered and validated. The logistic regression model with the combination of 4 messenger RNA biomarkers (KRAS, MBD3L2, ACRV1, and DPM1) could differentiate pancreatic cancer patients from noncancer subjects (chronic pancreatitis and healthy control), yielding a receiver operating characteristic plot, area under the curve value of 0.971 with 90.0% sensitivity and 95.0% specificity. CONCLUSIONS: The salivary biomarkers possess discriminatory power for the detection of resectable pancreatic cancer, with high specificity and sensitivity. This report provides the proof of concept of salivary biomarkers for the noninvasive detection of a systemic cancer and paves the way for prediction model validation study followed by pivotal clinical validation.