Discovery of salivary transcriptomic biomarkers for ovarian cancer
ABSTRACT: Ovarian cancer is the leading cause of death in gynecological diseases, and has been considered as one of the most fatal cancers due to lack of reliable detection strategy in the early stage. Therefore the capability to detect the morbidity initiation with an sensitive and effective approach is one of the most desirable goals for curing ovarian cancer. In this study, we used microarray technology for salivary mRNA biomarkers discovery, and evaluated the performance and translational utilities of discovered markers from a clinical study using an independent sample cohort . We used microarrays to profile and compare the gene expressions between ovairan cancer patient and matched controls, and identified seven down-regulated genes after the validation study. To find salivary transcriptomic biomarkers for ovarian cancer, salivary transcriptomes in 11 ovarian cancer patients and 11 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 array, and followed by t-test and fold-change analyses. The biomarker candidates selected from the microarray results were subjected to clinical validation using an independent sample cohort by RT-qPCR. The prediction power of biomarkers was analyzed by logistic regression approach
Project description:Ovarian cancer is the leading cause of death in gynecological diseases, and has been considered as one of the most fatal cancers due to lack of reliable detection strategy in the early stage. Therefore the capability to detect the morbidity initiation with an sensitive and effective approach is one of the most desirable goals for curing ovarian cancer. In this study, we used microarray technology for salivary mRNA biomarkers discovery, and evaluated the performance and translational utilities of discovered markers from a clinical study using an independent sample cohort . We used microarrays to profile and compare the gene expressions between ovairan cancer patient and matched controls, and identified seven down-regulated genes after the validation study. Overall design: To find salivary transcriptomic biomarkers for ovarian cancer, salivary transcriptomes in 11 ovarian cancer patients and 11 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 array, and followed by t-test and fold-change analyses. The biomarker candidates selected from the microarray results were subjected to clinical validation using an independent sample cohort by RT-qPCR. The prediction power of biomarkers was analyzed by logistic regression approach
Project description:This is a pilot study. We are trying to detect potential salivary biomarkers in mice with a pancreatic tumor. Global gene expression profiling has shown great promise in high-throughput biomarker discovery for early disease detection in body fluids such as saliva, which is accessible, cost-effective, and non-invasive. However, this goal has not been fully realized because saliva, like many clinical samples, contains partially fragmented and degraded RNAs that are difficult to amplify and detect with prevailing technologies. Here, using nanogram scale salivary RNA as a proof-of-principle example, we describe our progress with a novel poly-A tail independent mRNA amplification strategy combined with the Affymetrix GeneChip Exon arrays. We defined a Salivary Exon Core Transcriptome (SECT) with highly similar expression profiles in healthy individuals verified by quantitative PCR. Informatics analysis of SECT provided important mechanistic insight to their potential origin and function. Finally we demonstrated the diagnostic potential of true exon level expression profiling approach with salivary exon biomarkers that accurately discriminated gender in healthy individuals. Recent studies have demonstrated that discriminatory salivary biomarkers can be readily detected upon the development of systemic diseases such as pancreatic cancer, breast cancer, lung cancer and ovarian cancer. However, the utility of salivary biomarkers for the detection of systemic diseases has been undermined due to the absence of biological and mechanistic rationale why distal diseases from the oral cavity would lead to the development of discriminatory biomarkers in saliva. Here, we examine the hypothesis that pancreatic tumor-derived exosomes are mechanistically involved in the development of pancreatic cancer-discriminatory salivary transcriptomic biomarkers. We first developed a pancreatic cancer mouse model that yielded discriminatory salivary biomarkers by implanting the mouse pancreatic cancer cell line Pan02 into the pancreas of the syngeneic host C57BL/6. The role of pancreatic cancer-derived exosomes in the development of discriminatory salivary biomarkers was then tested by engineered a Pan02 cell line that is suppressed for exosome biogenesis, implanted into the C56BL/6 mouse and examine if the discriminatory salivary biomarker profile was ablated or disrupted. Suppression of exosome biogenesis results in the ablation of discriminatory salivary biomarker development. This study supports that tumor-derived exosomes provide a mechanism in the development of discriminatory biomarkers in saliva and distal systemic diseases. We analyzed saliva from 6 healthy mice and 6 mice with a pancreatic tumor using the Affymetrix Mouse Exon Genome 430 2.0 platform. Array data was processed by dChip. No techinical replicates were performed.
Project description:A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. In this study, we have conducted a prospective sample collection and retrospective blinded validation (PRoBE design) to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. The Affymetrix HG U133 Plus 2.0 Array and 2D-DIGE were used to profile transcriptomes and proteomes in saliva supernatants respectively. Significant variations of salivary transcriptomic and proteomic profiles were observed between breast cancer patients and healthy controls. Eleven transcriptomic biomarker candidates and two proteomic biomarker candidates were selected for a preclinical validation using an independent sample set. Transcriptomic biomarkers were validated by RT-qPCR and proteomic biomarkers were validated by quantitative protein immunoblot. Eight mRNA biomarkers and one protein biomarker have been validated for breast cancer detection, yielding ROC-plot AUC values between 0.665 and 0.959. This report provides proof of concept of salivary biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers’ discriminatory power paves the way for a PRoBE-design definitive validation study. Keywords: Salivary biomarker, Breast cancer, Early detection, Salivary transcriptome, Salivary proteome This study, which was approved by the UCLA and Cedars-Sinai Medical Center Institutional Review Boards (#06-07-043 and #3870, respectively), started sample collection in February 2007. The sample collection followed the PRoBE principle (prospective specimen collection). The saliva bank for breast cancer project at the UCLA Dental Research Institute in collaboration with Cedars Sinai Medical Center has collected 200 saliva samples since 2007 with all subjects recruited from the Saul and Joyce Brandman Breast Cancer Center. Of these, 113 samples, including 41 breast cancer patients and 72 healthy control individuals, were selected for the discovery and validation phase of this study. Inclusion criteria of cancer patients consisted of a confirmed diagnosis of breast cancer. Exclusion criteria of cancer patients included therapy/surgery and/or a diagnosis of other malignancies within 5 years prior to saliva collection. Exclusion criteria of control patients included a diagnosis of any malignancies within 5 years prior to saliva collection. Written informed consents and questionnaire data sheets were obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were consistently collected, stabilized, and preserved as previously described. The sample supernatants were reserved at -80 C prior to assay. This study consisted of a discovery phase, followed by an independent preclinical validation phase. Of the 113 samples, 10 breast cancer samples and 10 healthy control samples were chosen for the discovery phase. All breast cancer cases are invasive ductal carcinoma (IDC), the most common type of breast cancer. Biomarkers identified from the discovery studies were first verified using the discovery sample set. An independent sample set, including 31 breast cancer patients and 62 healthy control subjects, was used for the biomarker validation phase.
Project description:Early surgery is vital in the treatment of highly fatal pancreatic cancer (PC). But there is no valuable and non-invasive biomarker to screen PC currently. Studies showed many salivary molecules can detect several systemic diseases. We aimed to investigate whether salivary microRNAs (miRNAs) can act as a biomarker to detect resectable PC. By Agilent microarray salivary miRNAs were profiled from saliva samples from 8 patients with resectable PC and 8 healthy controls. Candidate biomarkers discovered from the profile were subjected to validation by qPCR.
Project description: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.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.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.
Project description:<h4>Objective</h4>We systematically investigated and assessed the alterations of salivary glycopatterns and possibility as biomarkers for diagnosis of early-stage breast cancer.<h4>Design</h4>Alterations of salivary glycopatterns were probed using lectin microarrays and blotting analysis from 337 patients with breast benign cyst or tumor (BB) or breast cancer (I/II stage) and 110 healthy humans. Their diagnostic models were constructed by a logistic stepwise regression in the retrospective cohort. Then, the performance of the diagnostic models were assessed by ROC analysis in the validation cohort. Finally, a double-blind cohort was tested to confirm the application potential of the diagnostic models.<h4>Results</h4>The diagnostic models were constructed based on 9 candidate lectins (e.g., PHA-E+L, BS-I, and NPA) that exhibited significant alterations of salivary glycopatterns, which achieved better diagnostic powers with an AUC value >0.750 (p<0.001) for the diagnosis of BB (AUC: 0.752, sensitivity: 0.600, and specificity: 0.835) and I stage breast cancer (AUC: 0.755, sensitivity: 0.733, and specificity: 0.742) in the validation cohort. The diagnostic model of I stage breast cancer exhibited a high accuracy of 0.902 in double-blind cohort.<h4>Conclusions</h4>This study could contribute to the screening for patients with early-stage breast cancer based on precise alterations of salivary glycopatterns.
Project description:We have performed gene expression microarray analysis to profile transcriptomic signatures between cancer and noncancerous patients Gastric cancer is currently the second leading cause of cancer deaths. Due to the difficulty of diagnosing patients in the early stages of gastric cancer, it is critical to develop a method that can diagnose the disease at the early stage to allow for better treatment options. In this study, we discovered salivary transcriptomic and miRNA biomarkers for the detection of gastric cancer and identified there are mRNA-miRNA correlations in saliva. RNA was extracted from saliva supernatant and mRNA candidates were identified that can distinguish gastric cancer from non-gastric cancer patients
Project description: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.
Project description:Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Salivary transcriptomic biomarker discovery was performed on 10 lung cancer patients and 10 matched controls. Seven messenger RNA biomarkers were discovered and pre-validated This study consisted of two phases, including a discovery phase, followed by a pre-validation phase. 10 lung cancer samples and 10 matched control samples were chosen for the biomarker discovery phase. The transscriptomic approach profiled the saliva supernatant samples from 10 lung cancer patients and 10 healthy control subjects using the Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA). Biomarkers identified from the microarray study were first verified using the discovery sample set (10 lung cancer and 10 healthy control).
Project description: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.