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.

Project description:Endometrial cancer is the most common gynaecological cancer in western countries, being the endometrioid tumours the most common subtype. Most patients are diagnosed at an early stage and present an excellent prognosis. However, a consistent number of those continue to suffer recurrence, without means to be identified by risk classification systems. Thus, finding a reliable marker to predict recurrence becomes an important unmet clinical issue. ALCAM is a cell–cell adhesion molecule, member of the Immunoglobulin superfamily, which has been associated to the carcinogenesis of many cancers. In here, we first determined the value of ALCAM as marker of recurrence in endometrioid endometrial cancer by conducting a retrospective multicenter study in 174 primary tumours. In early stage patients (N=134), recurrence-free survival was poorer in patients with ALCAM-positive compared to ALCAM-negative tumours (HR 4.237; 95%CI 1.01-17.76). This difference was more significant in patients with early stage moderately-poorly differentiated tumours (HR 9,259; 95%CI 2.12-53.47). In multivariate analysis, ALCAM-positivity was an independent prognostic factor in early stage disease (HR 6.027, 95% CI 1.41-25.74). Then, we demonstrated in vitro a role for ALCAM in cell migration and invasion by using a loss-of-function model in two endometrial cancer cell lines. Moreover, ALCAM depletion resulted in a reduced primary tumour size and reduced metastatic local spread in an orthotopic murine model. Gene expression analysis of ALCAM-depleted cell lines supported that motility, invasiveness, cellular assembly and organization were the most deregulated functions associated to ALCAM. Finally, we evidenced some of the downstream effector genes that are involved in ALCAM mediated cell migration; specifically FLNB, TXNRD1 and LAMC2 were validated. In conclusion, our results highlight the potential of ALCAM as a recurrent biomarker in early stage endometrioid endometrial cancer and point to ALCAM as an important molecule in endometrial cancer dissemination by regulating cell migration, invasion and metastasis.

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

Project description:Global gene expression patterns associated with early stage endometrial cancer have been reported, but changes in molecular expression associated with tumor grade, depth of myometrial invasion, and non-endometrioid histology have not been previously elucidated. Our group hypothesized there are unique genetic events underlying early endometrial carcinogenesis. Ninety-two samples of pathologically reviewed stage I endometrial cancers (80 endometrioid and 12 serous) with a heterogeneous distribution of grade and depth of myometrial invasion (i.e. 9 IAG1, 14 IAG2, 7 IAG3, 14 IBG1, 12 IBG2, 13 IBG3, 7 ICG1, 10 ICG2, and 6 ICG3) were examined in relation to 12 samples of atrophic endometrium from postmenopausal women. Specimens were analyzed using oligonucleotide microarray analysis and a subset of the differentially expressed transcripts was validated using quantitative PCR. Comparison of early stage cancers with normal endometrium samples by the univariate t-test with 10,000 permutations identified 900 genes that were differentially regulated by at least 4-fold at a p value of <0.001. Unsupervised analysis revealed that when compared to normal endometrium, serous and endometrioid stage I cancers appeared to have similar expression patterns. However, when compared in the absence of normal controls, they were distinct. Differential expression analysis revealed a number of transcripts that were common as well as unique to both histologic types. This data uncovers previously unrecognized, novel pathways involved in early stage endometrial cancers and identifys targets for prevention strategies that are inclusive of both endometrioid and serous histologic subtypes. Ninety-one samples of pathologically reviewed stage I endometrial cancers (79 endometrioid and 12 serous) with a heterogeneous distribution of grade and depth of myometrial invasion (i.e. 9 IAG1, 14 IAG2, 7 IAG3, 14 IBG1, 12 IBG2, 13 IBG3, 7 ICG1, 10 ICG2, and 6 ICG3) were examined in relation to 12 samples of atrophic endometrium from postmenopausal women. Specimens were analyzed using oligonucleotide array analysis.

			Action	DRS
	SRR16311600.fastq.gz	Fastqsanger.gz
	SRR16311601.fastq.gz	Fastqsanger.gz
	SRR16311602.fastq.gz	Fastqsanger.gz
	SRR16311603.fastq.gz	Fastqsanger.gz
	SRR16311604.fastq.gz	Fastqsanger.gz

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