Early detection of sporadic pancreatic cancer: summative review.
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ABSTRACT:
SUBMITTER: Chari ST
PROVIDER: S-EPMC4467589 | biostudies-literature | 2015 Jul
REPOSITORIES: biostudies-literature
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Project description:AbstractDespite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
| S-EPMC8041569 | biostudies-literature
Project description:Pancreatic cancer (PC) is a highly lethal malignancy with near 100% mortality. This is in part due to the fact that most patients present with metastatic or locally advanced disease at the time of diagnosis. Significantly, in nearly 95% of PC patients there is neither an associated family history of PC nor of diseases known to be associated with an increased risk of PC. These groups of patients who comprise the bulk of PC cases are termed as "sporadic PC" in contrast to the familial PC cases that comprise only about 5% of all PCs. Given the insidious onset of the malignancy and its extreme resistance to chemo and radiotherapy, an abundance of research in recent years has focused on identifying biomarkers for the early detection of PC, specifically aiming at the sporadic PC cohort. However, while several studies have established that asymptomatic individuals with a positive family history of PC and those with certain heritable syndromes are candidates for PC screening, the role of screening in identifying sporadic PC is still an unsettled question. The present review attempts to assess this critical question by investigating the recent advances made in molecular markers with potential use in the early diagnosis of sporadic PC - the largest cohort of PC cases worldwide. It also outlines a novel yet simple risk factor based stratification system that could be potentially employed by clinicians to identify those individuals who are at an elevated risk for the development of sporadic PC and therefore candidates for screening.
| S-EPMC3014374 | biostudies-literature
Project description:Because early detection of pancreatic cancer is the best way to cure this disease, investigators continue to try to identify accurate markers of early pancreatic cancer. Because early-stage pancreatic cancer is generally asymptomatic, the only reliable way to detect it is by targeting individuals at increased risk for pancreatic screening.
| S-EPMC3079322 | biostudies-literature
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 subjects’ 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.
| S-ECPF-GEOD-14245 | biostudies-other
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.
2008-12-30 | E-GEOD-14245 | biostudies-arrayexpress
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
2008-12-31 | GSE14245 | GEO
Project description:The prognosis of pancreatic cancer is extremely poor compared to other cancers. One of the reasons for this is the difficulty of early diagnosis. Surveillance using cancer biomarkers and image diagnosis can enable early detection and has improved the prognosis of hepatocellular carcinoma in Japan. However, it is very difficult to detect pancreatic cancer at an early stage using cancer biomarkers and image diagnosis alone. Fucosylation is one of the most important types of glycosylation involved in cancer and inflammation. We have developed a novel glycocancer biomarker, fucosylated haptoglobin (Fuc-Hpt), and have investigated its usefulness for the diagnosis of pancreatic cancer over approximately 10 years. Recently, we also found that most pancreatic tissues surrounding pancreatic cancer exhibit chronic pancreatitis with fibrosis and/or fatty degeneration. Certain forms of chronic pancreatitis might indicate high risk for the development of pancreatic cancer. In this review, we provide a historical summary of our research on Fuc-Hpt as a cancer biomarker, and discuss a potential early detection system for pancreatic cancer.
| S-EPMC5084674 | biostudies-literature
Project description:Pancreatic cancer is the fourth leading cause of cancer deaths and there currently is no reliable modality for the early detection of this disease. Here, we identify cancer-specific promoter DNA methylation of BNC1 and ADAMTS1 as a promising biomarker detection strategy meriting investigation in pancreatic cancer.We used a genome-wide pharmacologic transcriptome approach to identify novel cancer-specific DNA methylation alterations in pancreatic cancer cell lines. Of eight promising genes, we focused our studies on BNC1 and ADAMTS1 for further downstream analysis, including methylation and expression. We used a nanoparticle-enabled methylation on beads (MOB) technology to detect early-stage pancreatic cancers by analyzing DNA methylation in patient serum.We identified two novel genes, BNC1 (92%) and ADAMTS1 (68%), that showed a high frequency of methylation in pancreatic cancers (n = 143), up to 100% in PanIN-3 and 97% in stage I invasive cancers. Using the nanoparticle-enabled MOB technology, these alterations could be detected in serum samples (n = 42) from patients with pancreatic cancer, with a sensitivity for BNC1 of 79% [95% confidence interval (CI), 66%-91%] and for ADAMTS1 of 48% (95% CI, 33%-63%), whereas specificity was 89% for BNC1 (95% CI, 76%-100%) and 92% for ADAMTS1 (95% CI, 82%-100%). Overall sensitivity using both markers is 81% (95% CI, 69%-93%) and specificity is 85% (95% CI, 71%-99%).Promoter DNA methylation of BNC1 and ADAMTS1 is a potential biomarker to detect early-stage pancreatic cancers. Assaying the promoter methylation status of these genes in circulating DNA from serum is a promising strategy for early detection of pancreatic cancer and has the potential to improve mortality from this disease.
| S-EPMC4310572 | biostudies-literature
Project description:Alzheimer disease (AD) is the major epidemic of the 21st century, its prevalence rising along with improved human longevity. Early AD diagnosis is key to successful treatment, as currently available therapeutics only allow small benefits for diagnosed AD patients. By contrast, future therapeutics, including those already in preclinical or clinical trials, are expected to afford neuroprotection prior to widespread brain damage and dementia. Brain imaging technologies are developing as promising tools for early AD diagnostics, yet their high cost limits their utility for screening at-risk populations. Blood or plasma transcriptomics, proteomics, and/or metabolomics may pave the way for cost-effective AD risk screening in middle-aged individuals years ahead of cognitive decline. This notion is exemplified by data mining of blood transcriptomics from a published dataset. Consortia blood sample collection and analysis from large cohorts with mild cognitive impairment followed longitudinally for their cognitive state would allow the development of a reliable and inexpensive early AD screening tool.
| S-EPMC6436957 | biostudies-literature
Project description:BACKGROUND:Pancreatic cancer is a deadly disease for which available biomarkers, such as CA19-9, lack the desired sensitivity and specificity for early detection. Additional biomarkers are needed to improve both its sensitivity and specificity. METHODS:Multiplex immunoassays were developed for selected biomarkers using a Bio-Plex 200 system, and analytical performance was optimized. All proteins were analyzed in sera of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC; n = 188) or benign pancreatic conditions (131) and healthy controls (89). The clinical performance of these markers was evaluated individually or in combination for their ability to complement CA19-9 for the early detection of pancreatic cancer. RESULTS:A 6-plex immunoassay was developed with negligible cross-reactivity, wide dynamic range, recovery of 89% to 104%, and intra-assay and interassay precision of 10.2% to 19.6% and 13.7% to 29.3%, respectively. Individually, the best biomarkers to separate PDAC early stage from chronic pancreatitis or intraductal papillary mucinous neoplasm (IPMN) were CA19-9 and MIA or CA19-9 and MIC-1. Logistic regression modeling selected the two-marker panels that significantly improved the individual biomarker performance in discriminating PDAC early stage from chronic pancreatitis (AUCCA19-9+MIA = 0.86 vs. AUCCA19-9 = 0.81 or AUCMIA = 0.75 only, P < 0.05) or IPMN (AUCCA19-9+MIC-1 = 0.81 vs. AUCCA19-9 = 0.75 or AUCMIC-1 = 0.73 only, P < 0.05). It was observed that osteopontin (OPN) outperformed CA19-9 in separating IPMN from chronic pancreatitis (AUCOPN = 0.80 vs. AUCCA19-9 = 0.70, P < 0.01). CONCLUSIONS:The biomarker panels evaluated by assays with high analytical performance demonstrated potential complementary values to CA19-9, warranting additional clinical validation to determine their role in early detection of pancreatic cancer. IMPACT:The validated biomarker panels could lead to earlier intervention and better outcomes.
| S-EPMC6324978 | biostudies-literature