The diagnostic value of pancreatic juice protein biomarkers for pancreatic cancer detection

Due to late diagnosis, high incidence of metastasis, and poor survival rate, pancreatic cancer is one of the most leading cause of cancer-related death. Although manifold recent efforts have been done to achieve an early diagnosis of pancreatic cancer, CA-19.9 is currently the unique biomarker that is adopted for the detection, despite its limits in terms of sensitivity and specificity. To identify potential protein biomarkers for pancreatic ductal adenocarcinoma (PDAC), we used three model liposomes as nanoplatforms that accumulate proteins from human plasma and studied the composition of this biomolecular layer, which is known as protein corona. Indeed, plasma proteins adsorb on nanoparticle surface according to their abundance and affinity to the employed nanomaterial, thus even small differences between healthy and PDAC protein expression levels can be, in principle, detected. By mass spectrometry experiments, we quantified such differences and identified possible biomarkers for PDAC. Some of them are already known to exhibit different expressions in PDAC proteomes, whereas the role of other relevant proteins is still not clear. Therefore, we predict that the employment of nanomaterials and their protein corona may represent a useful tool to amplify the detection sensitivity of cancer biomarkers, which may be used for the early diagnosis of PDAC, with clinical implication for the subsequent therapy in the context of personalized medicine.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btab044 ◽

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

Download Full-text

Progress and potential of exosome analysis for early pancreatic cancer detection

Expert Review of Molecular Diagnostics ◽

10.1080/14737159.2016.1187563 ◽

2016 ◽

Vol 16 (7) ◽

pp. 757-767 ◽

Cited By ~ 5

Author(s):

Ulrike Erb ◽

Margot Zöller

Keyword(s):

Pancreatic Cancer ◽

Cancer Detection

Download Full-text

Development of extracellular vesicles-based classifier for detection of early-stage bladder, ovarian, and pancreatic cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.3048 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 3048-3048

Author(s):

Juan Pablo Hinestrosa ◽

Razelle Kurzrock ◽

Jean Lewis ◽

Nick Schork ◽

Ashish M. Kamat ◽

...

Keyword(s):

Pancreatic Cancer ◽

Confidence Interval ◽

Extracellular Vesicles ◽

Metastatic Disease ◽

Cancer Detection ◽

Early Stage ◽

Expression Patterns ◽

Stepwise Logistic Regression ◽

Liquid Biopsies ◽

Cancer Early Detection

3048 Background: Many cancers are lethal because they present with metastatic disease. Because localized/resectable tumors produce vague symptoms, diagnosis is delayed. In pancreatic cancer, only ̃10% of patients survive five years, and it will soon become the second leading cause of cancer-related deaths in the USA. For patients with metastatic disease, the 2- and 5-year survival is < 10% and ̃3%, respectively. For the few patients with local disease, 5-year survival is ̃40%. Many other cancers have comparable differences between early- and late-stage disease. It is apparent a diagnostic assay for early-stage cancers would transform the field by minimizing the need for aggressive surgeries and other harsh interventions, and by its potential to increase survival. Identifying cancer-specific aberrations in blood-based “liquid” biopsies offers a prospect for a non-invasive cancer detection tool. In the bloodstream, there are extracellular vesicles (EVs) with cargoes including membrane and cytosolic proteins, as well as RNA and lipids derived from their parent cells. Methods: We used an alternating current electrokinetics (ACE) microarray to isolate EVs from the plasma of stage I and II bladder (N = 48), ovarian (N = 42), and pancreatic cancer patients (N = 44), and healthy volunteers (N = 110). EVs were analyzed using multiplex protein immunoassays for 54 cancer-related proteins. EV protein expression patterns were analyzed using stepwise logistic regression followed by a split between training and test sets (67%/33% respectively). This process enabled biomarker selection and generation of a classifier to discriminate between cancer and healthy donors. Results: The EV protein-based classifier had an overall area under curve (AUC) of 0.95 with a sensitivity of 71.2% (69.4% – 73.0%, at 95% confidence interval) at > 99% specificity. The classifier’s performance for the pancreatic cancer cohort was very strong, with overall sensitivity of 95.7% (94.6% – 96.9%, at 95% confidence interval) at > 99% specificity. Conclusions: EV-associated proteins may enable early cancer detection where surgical resection is most likely to improve outcomes. The classifier’s performance for the initial three cancers studied showed encouraging results. Future efforts will include examining additional cancer types and evaluating the classifier performance using samples from donors with related benign conditions with the aim of a pan-cancer early detection assay.

Download Full-text