scholarly journals Validation of a Pancreatic Cancer Detection Test in New-Onset Diabetes Using Cell-Free DNA 5-Hydroxymethylation Signatures

2021 ◽  
Author(s):  
David Haan ◽  
Anna Bergamaschi ◽  
Gulfem Guler ◽  
Verena Friedl ◽  
Yuhong Ning ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Validation Dataset ◽  
Cell Free Dna ◽  
Independent Validation ◽  
Free Dna ◽  
Onset Diabetes ◽  
Increased Risk ◽  
New Onset

BACKGROUND Pancreatic cancer (PaC) has poor (10%) 5–year overall survival, largely due to predominant late-stage diagnosis. Patients with new-onset diabetes (NOD) are at a six– to eightfold increased risk for PaC. We developed a pancreatic cancer detection test for the use in a clinical setting that employs a logistic regression model based on 5–hydroxymethylcytosine (5hmC) profiling of cell-free DNA (cfDNA). METHODS: cfDNA was isolated from plasma from 89 subjects with PaC and 596 case–control non–cancer subjects, and 5hmC libraries were generated and sequenced. These data coupled with machine–learning, were used to generate a predictive model for PaC detection, which was independently validated on 79 subjects with PaC, 163 non–cancer subjects, and 506 patients with non–PaC cancers. RESULTS: The area under the receiver operating characteristic curve for PaC classification was 0.93 across the training data. Training sensitivity was 58.4% (95% confidence interval [CI]: 47.5–68.6) after setting a classification probability threshold that resulted in 98% (95% CI: 96.5–99) specificity. The independent validation dataset sensitivity and specificity were 51.9% (95% CI: 40.4–63.3) and 100.0% (95% CI: 97.8–100.0), respectively. Early–stage (stage I and II) PaC detection was 47.6% (95% CI: 23%–58%) and 39.4% (95% CI: 32%–64%) in the training and independent validation datasets, respectively. Sensitivity and specificity in NOD patients were 55.2% [95% CI: 35.7–73.6] and 98.4% [95% CI: 91.3–100.0], respectively. The PaC signal was identified in intraductal papillary mucinous neoplasm (64%), pancreatitis (56%), and non-PaC cancers (17%). CONCLUSIONS: The pancreatic cancer detection assay showed robust performance in the tested cohorts and carries the promise of becoming an essential clinical tool to enable early detection in high–risk NOD patients.

Pancreatic cancer detection using EpiDetect signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16265-e16265
Author(s):  
Gulfem Guler ◽  
Anna Bergamaschi ◽  
David Haan ◽  
Michael Kesling ◽  
Yuhong Ning ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Model Performance ◽  
Training Data ◽  
Diabetes Diagnosis ◽  
Whole Genome ◽  
Data Set ◽  
Independent Validation ◽  
Onset Diabetes ◽  
New Onset

e16265 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite its low incidence rate, owing to a 5-year survival rate of 10%. It is often asymptomatic in early stage, resulting in the majority of diagnoses occurring when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. Diabetes can be an early symptom of PaCa. Indeed, 25% of PaCa patients had a preceding diabetes diagnosis. Among all people with new onset diabetes (NOD), 0.85% will be diagnosed with PaCa within 3 years, which represents 6-8 fold increased risk for PaCa compared to the general population. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage by finding it sooner. Methods: Whole blood was obtained from a cohort of 117 PaCa patients as well as 800 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC and low pass whole genome libraries were generated and sequenced. The EpiDetect assay combines 5hmC and whole genome sequencing data and were generated using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA epigenomic and genomic signal from PaCa patients and non-cancer controls. We first trained stacked ensemble models on PaCa and non-cancer samples utilizing 5hmC, fragmentation and CNV-based biomarkers from cfDNA. These models performed stably with a median of 72.8% sensitivity and 90.1% specificity measured across 25 outer fold iterations using the training data set, which was composed of 50% early stage (Stages I & II) disease. The final binomial ensemble model was trained using all of the training data, yielding an area under the receiver operating characteristic curve (auROC) of 0.9, with 75% sensitivity and 89% specificity. This model was then tested on an independent validation data set from 33 PaCa patients (24 with diabetes, 15 of which was NOD) and 202 non-cancer control patients (76 with diabetes, 51 of which was NOD) and yielded a classification performance auROC of 0.9 with 67% sensitivity at 92% specificity. Lastly, model performance in the subset of patient cohort with NOD only had an auROC of 0.87 with 60% sensitivity at 88% specificity. Conclusions: Our results indicate that 5hmC profiles along with CNV and fragmentation patterns from cfDNA can be used to detect PaCa in plasma-derived cfDNA. Overall, model performance was stable and consistent between the training and independent validation datasets. A larger clinical study is under development to investigate the utility of the model described in this pilot study in identifying occult PaCa within the NOD population, with the aim of shifting diagnosis to early stage and potentially improving patient outcomes.

New-Onset Diabetes Confers Increased Risk for Pancreatic Cancer in a Large Cohort of US Veterans

2005 ◽  
Vol 100 ◽  
pp. S273
Author(s):  
Samir Gupta ◽  
Dan Bertenthal ◽  
Hui Shen ◽  
Eric Vittinghoff ◽  
Kenneth McQuaid
Keyword(s):  
Pancreatic Cancer ◽  
Large Cohort ◽  
Onset Diabetes ◽  
Increased Risk ◽  
Us Veterans ◽  
New Onset

scholarly journals Enhanced Detection of Genitourinary Cancers Using Fragmentation and Copy Number Profiles Obtained from Urinary Cell-Free DNA

2020 ◽  
Author(s):  
Yang Han ◽  
Xinxin Li ◽  
Mingxin Zhang ◽  
Yang Yang ◽  
Guangzhe Ge ◽  
...  
Keyword(s):  
Copy Number ◽  
Ensemble Classifier ◽  
Validation Dataset ◽  
Molecular Diagnostic ◽  
Cell Free Dna ◽  
Clinical Sensitivity ◽  
Independent Validation ◽  
Free Dna ◽  
The Mean

Abstract Background Recent studies have reported that examining the fragmentation profiles (FP) of plasma cell-free DNA (cfDNA) further improves the clinical sensitivity of tumor detection. We hypothesized that considering the differences of the FP of urinary cfDNA would increase the clinical sensitivity of genitourinary (GU) cancer detection. Methods 177 patients with GU cancer and 94 individuals without tumors were enrolled in the discovery cohort. An independent validation dataset comprising 30 patients without tumors and 66 patients with GU cancer was also collected. We constructed an ensemble classifier, GUIDER, to detect and localize GU cancers using fragmentation and copy number profiles obtained from shallow whole-genome sequencing of urinary cfDNA. Results Urinary cfDNA of patients with GU cancer had a higher proportion of long fragments (209–280 bp) and a lower proportion of short fragments (140–208 bp) compared to controls. The overall mean classification accuracy of the FP was 74.62%–85.39% for different algorithms, and integration of the FP and copy number alteration (CNA) features further enhanced the classification of samples from patients with GU cancer. The mean diagnostic accuracy was further improved by the ensemble classifier GUIDER, which integrated the FP and CNA profiles and resulted in a higher mean accuracy (87.52%) compared to the analysis performed without FP features (74.62%). GUIDER performed well in an independent validation dataset. Conclusions The lengthening and shortening of urinary cfDNA within specific size ranges were identified in patients with GU cancer. Integration of the FP should further enhance the ability to use urinary cfDNA as a molecular diagnostic tool.

scholarly journals Blood levels of adiponectin and IL-1Ra distinguish type 3c from type 2 diabetes: Implications for earlier pancreatic cancer detection in new-onset diabetes

EBioMedicine ◽  
2022 ◽  
Vol 75 ◽  
pp. 103802
Author(s):  
Lucy Oldfield ◽  
Anthony Evans ◽  
Rohith Gopala Rao ◽  
Claire Jenkinson ◽  
Tejpal Purewal ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Cancer ◽  
Cancer Detection ◽  
Blood Levels ◽  
Onset Diabetes ◽  
New Onset

scholarly journals Update from the laboratory: mechanistic studies of pathways of cancer-associated venous thrombosis using mouse models

Hematology ◽  
2019 ◽  
Vol 2019 (1) ◽  
pp. 182-186 ◽  
Author(s):  
Yohei Hisada ◽  
Nigel Mackman
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Venous Thrombosis ◽  
Cancer Patients ◽  
Mouse Models ◽  
Cancer Type ◽  
Cell Free Dna ◽  
Free Dna ◽  
Increased Risk ◽  
Cancer Associated Thrombosis

Abstract Cancer patients have an increased risk of venous thromboembolism (VTE). The rate of VTE varies with cancer type, with pancreatic cancer having one of the highest rates, suggesting that there are cancer type–specific mechanisms of VTE. Risk assessment scores, such as the Khorana score, have been developed to identify ambulatory cancer patients at high risk of VTE. However, the Khorana score performed poorly in discriminating pancreatic cancer patients at risk of VTE. Currently, thromboprophylaxis is not recommended for cancer outpatients. Recent clinical trials showed that factor Xa (FXa) inhibitors reduced VTE in high-risk cancer patients but also increased major bleeding. Understanding the mechanisms of cancer-associated thrombosis should lead to the development of safer antithrombotic drugs. Mouse models can be used to study the role of different prothrombotic pathways in cancer-associated thrombosis. Human and mouse studies support the notion that 2 prothrombotic pathways contribute to VTE in pancreatic cancer patients: tumor-derived, tissue factor–positive (TF+) extracellular vesicles (EVs), and neutrophils and neutrophil extracellular traps (NETs). In pancreatic cancer patients, elevated levels of plasma EVTF activity and citrullinated histone H3 (H3Cit), a NET biomarker, are independently associated with VTE. We observed increased levels of circulating tumor-derived TF+ EVs, neutrophils, cell-free DNA, and H3Cit in nude mice bearing human pancreatic tumors. Importantly, inhibition of tumor-derived human TF, depletion of neutrophils, or administration of DNAse I to degrade cell-free DNA (including NETs) reduced venous thrombosis in tumor-bearing mice. These studies demonstrate that tumor-derived TF+ EVs, neutrophils, and cell-free DNA contribute to venous thrombosis in a mouse model of pancreatic cancer.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

2021 ◽  
Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong
Keyword(s):  
Test Data ◽  
Cancer Detection ◽  
Blood Test ◽  
Class Imbalance ◽  
Cancer Type ◽  
Protein Biomarkers ◽  
Class Imbalance Problem ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection

New‐onset Diabetes as a Harbinger of Pancreatic Cancer: is Early Diagnosis Possible?

2021 ◽  
pp. 409-417
Author(s):  
Dana K. Andersen ◽  
Suresh T. Chari ◽  
Eithne Costello ◽  
Tatjana Crnogorac‐Jurcevic ◽  
Phil A. Hart ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Diagnosis ◽  
Onset Diabetes ◽  
New Onset

A dual target sequencing solution to assess genomic and epigenomic alterations in cell-free DNA with no sample splitting.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3043-3043
Author(s):  
Grace Q. Zhao ◽  
Yun Bao ◽  
Heng Wang ◽  
Wanping Hu ◽  
John Coller ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Dna Analysis ◽  
Residual Disease ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Assay Sensitivity ◽  
Cell Free Dna ◽  
Target Sequencing ◽  
Free Dna ◽  
Dual Analysis

3043 Background: Assessing the genomic and epigenomic changes on plasma cell-free DNA (cfDNA) using next-generation sequencing (NGS) has become increasingly important for cancer detection and treatment selection guidance. However, two major hurdles of existing targeted NGS methods make them impractical for the clinical setting. First, there is no comprehensive, end to end, kit solution available for targeted methylation sequencing (TMS), let alone one that analyzes both mutation and methylation information in one assay. Second, the low yield of cfDNA from clinical blood samples presents a major challenge for conducting multi-omic analysis. Thus, an assay that is capable of both genomic and epigenomic analysis would be advantageous for clinical research and future diagnostic assays. Methods: Here, we report the performance of Point-n-SeqTM dual analysis, a kit solution that can provide in-depth DNA analysis with highly flexible and customizable focused panels to enable both genomic and epigenomic analysis without sample splitting. With custom panels of tens to thousands of markers designed with > 99% first-pass success rate, we conducted both performance validation and multi-center, multi-operator, reproducibility studies. Using spike-in titration of cancer cell-line gDNA with known mutation and methylation profiles, Point-n-Seq assay achieved a reliable detection level down to 0.003% of tumor DNA with a linear relationship between the measured and expected fractions. Benchmarked with conventional targeted sequencing and methylation sequencing, Point-n-Seq solution also demonstrated improved performance, speed and shortened hands-on time. Results: In a pilot clinical study, a colorectal cancer (CRC) TMS panel covering 560 methylation markers and a mutation panel with > 350 hotspot mutations in 22 genes were used in the dual assay. Using 1ml of plasma from late-stage CRC patients, cancer-specific methylation signals were detected in all samples tested, and oncogenic mutations. In an early-stage cohort (33 stage I/II CRC patient ), comparison of the analysis between tumor-informed, personalized-mutation panels (̃100 private SNVs) for each patient and the tumor-independent CRC methylation panels were conducted. The initial results showed that tumor-independent TMS assay achieved a comparable detection compared to the personalized tumor-informed approach. Moreover, cfDNA size information (fragmentome) is also integrated into the analysis of the same Point-n-Seq workflow to improve the assay sensitivity. Conclusions: Point-n-Seq dual analysis is poised to advance both research and clinical applications of early cancer detection, minimal residual disease (MRD), and monitoring.

Prognostic significance of blood-based multi-cancer detection in plasma cell-free DNA

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Oscar D. Pons-Belda ◽  
Amaia Fernandez-Uriarte ◽  
Annie Ren ◽  
Eleftherios P. Diamandis
Keyword(s):  
Plasma Cell ◽  
Cancer Detection ◽  
Prognostic Significance ◽  
Cell Free Dna ◽  
Free Dna
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