Unveiling the Journey Behind a Breakthrough in Early Detection of Pancreatic Cancer

New paper demonstrates effectiveness of exosome isolation technology in detecting Stage I and II pancreatic ductal adenocarcinoma (PDAC)
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Pancreatic cancer is a formidable adversary in oncology, ranked as the third leading cause of cancer-related deaths with a mere 5-year survival rate of 12.5%.1 Over 90% of pancreatic cancer cases are attributed to pancreatic ductal adenocarcinoma (PDAC), one of the most aggressive and lethal malignancies.2,3 Pancreatic cancer’s symptoms often do not appear until the disease has reached late stages, hindering traditional treatment options such as surgery, chemotherapy, and radiation.

Using science-backed research and data, we have the potential to impact patient health on a sizable scale through effective, regular surveillance that leads to early disease detection. However, a current lack of effective screening techniques contributes to the rise in late-stage diagnoses. Today’s imaging technology presents challenges such as a lack of patient adherence, inaccessibility to top-tier imaging facilities, and the ever-present risk of malignancies emerging between consecutive imaging sessions.4-7 Additionally, available liquid biopsy modalities for early PDAC diagnosis remain suboptimal regarding sensitivity and patient accessibility.

The answer to these challenges lies within our biology. Imagine the inner workings of the body as a superhighway. Traveling on this highway are tiny particles known as exosomes, which can be considered cargo trucks in the body, carrying vital health information between organs. Exosomes provide many advantages as a biomarker because they are ubiquitous in the human body, can be identified in different biological fluids, and can be measured at any time. Contrast this to the more commonly known biomarker circulating tumor DNA (ctDNA), which is released from tumor cells via apoptosis, necrosis, or active release into the blood. Tumor growth rate and cell turnover strongly affect the amount of ctDNA found in the blood, which may limit usefulness. Exosomes, however, are not impacted by these biological processes. Having the ability to intercept these trucks and easily inspect their cargo opens the door for researchers to detect challenging diseases, such as pancreatic cancer, at their earliest stages.

There have been many roadblocks on the path to isolating exosomes, limiting the advancement of exosome-based research. Historically, detecting and recovering exosomes to analyze the biomarkers they carry is a time-consuming, arduous process that is challenged further by their small sizes and low buoyant density. Biological Dynamics has created proprietary technology that is proving to be a precise, automated, and cost-effective method for capturing these particles and analyzing the biomarkers they carry to provide useful information about tumors and other conditions.

In this study, our team utilized Biological Dynamics’ ExoVita™ Pancreas assay, powered by the ExoVerita™ platform, to efficiently isolate extracellular vesicles, including exosomes, from undiluted blood plasma samples using alternating current electrokinetics (ACE) technology. By analyzing the information carried by these exosomes through machine learning, our platform has shown effectiveness in detecting early-stage diseases across multiple areas, especially cancer.8-13

Combining our technology platform with our ExoVita Pancreas assay, we successfully identified early-stage PDAC from liquid biopsy samples in a cohort of 650 patients. Machine learning algorithms facilitated the identification of protein markers instrumental in developing a diagnostic classifier for early-stage PDAC detection. Among the 650 patients, 105 had early-stage pancreatic cancer (PDAC stages I and II), and 545 were healthy (controls). The classifier's proficiency was evident with an AUC of 0.971, demonstrating 93.3% sensitivity and 91.0% specificity.

A subsequent independent evaluation on an ethnically diverse cohort of 113 patients (30 with early-stage pancreatic cancer and 83 healthy) reaffirmed the classifier's precision, with a sensitivity of 90.0% and specificity of 92.8%. Notably, our analytical scope encompassed individuals with benign pancreatic conditions, such as pancreatitis and new-onset diabetes, ensuring a more comprehensive assessment.

The classifier's resilience was further ascertained through a perturbation analysis against variations in EV protein readings, yielding a high average AUC of 0.967, an average sensitivity of 92.7%, and an average specificity of 89.4%. These results indicate that the classifier is resilient to potential variations in EV protein measurements.

Our study represents a significant advancement in the fight against pancreatic cancer and emphasizes the value of exosomes for early disease diagnosis. Leveraging EV-based liquid biopsy platforms for PDAC's early detection is critical because the process could lead to better treatment outcomes, improved quality of life for patients, reduced healthcare costs, and the potential for more effective interventions. Further investigations on this topic are underway, including a prospective, multicenter, observational registry study – ExoLuminate™ – to evaluate patients at high risk for PDAC (NCT0562552).

 

 

References:

  1. Cancer Stat Facts: Pancreatic Cancer. National Cancer Institute (SEER). Accessed September 18, 2023. https://seer.cancer.gov/statfacts/html/pancreas.html.
  2. Kleeff J, Korc M, Apte M, et al. Pancreatic cancer. Nature Reviews Disease Primers 2, 16022 (2016).
  3. Grossberg AJ, Chu LC, Deig CR, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 70, 375-403 (2020).
  4. Yurgelun MB. Building on more than 20 years of progress in pancreatic cancer surveillance for high-risk individuals. J Clin Onc. 40, 3230-3234 (2022).
  5. Singhi AD, Koay EJ, Chari ST, Maitra A. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology. 156, 2024-2040 (2019).
  6. Klatte DCF, Boekestijn B, Wasser MNJM, et al. Pancreatic cancer surveillance in carriers of a germline CDKN2A pathogenic variant: yield and outcomes of a 20-year prospective follow-up. J Clin Onc. 40, 3267-3277 (2022).
  7. Dbouk M, Gutierrez OIB, Lennon AM, et al. Guidelines on management of pancreatic cysts detected in high-risk individuals: an evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements. Pancreatology. 21, 613-621 (2021).
  8. Sonnenberg A, Marciniak JY, Skowronski EA, et al. Dielectrophoretic isolation and detection of cancer-related circulating cell-free DNA biomarkers from blood and plasma. Electrophoresis. 35, 1828-1836 (2014).
  9. Manouchehri S, Ibsen S, Wright J, et al. Dielectrophoretic recovery of DNA from plasma for the identification of chronic lymphocytic leukemia point mutations. Int J Hematol Oncol. 5, 27-35 (2016).
  10. Lewis JM, Vyas AD, Qui Y, Messer KS, White R, Heller MJ. Integrated analysis of exosomal protein biomarkers on alternating current electrokinetic chips enables rapid detection of pancreatic cancer in patient blood. ACS Nano. 12, 3311-3320 (2018).
  11. Lewis J, Alattar AA, Akers J, Carter BS, Heller M, Chen CC. A pilot proof-of-principle analysis demonstrating dielectrophoresis (DEP) as a glioblastoma biomarker platform. Scientific Reports. 9, 10279 (2019).
  12. Ibsen SD, Wright J, Lewis JM, et al. Rapid isolation and detection of exosomes and associated biomarkers from plasma. ACS Nano. 11, 6641-6651 (2017).
  13. Ibsen S, Sonnenberg A, Schutt C, et al. Nanoparticles: recovery of drug delivery nanoparticles from human plasma using an electrokinetic platform technology. Small. 11(38), 4990-4990 (2015).

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Cancer Nanotechnology
Life Sciences > Biological Sciences > Cancer Biology > Cancer Nanotechnology
Cancer Screening
Life Sciences > Biological Sciences > Cancer Biology > Cancer Screening
Cancers
Life Sciences > Biological Sciences > Cancer Biology > Cancers
Pancreatic Cancer
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Gastrointestinal Diseases > Pancreatic disease > Pancreatic Cancer
Machine Learning
Mathematics and Computing > Computer Science > Artificial Intelligence > Machine Learning
Artificial Intelligence
Mathematics and Computing > Computer Science > Artificial Intelligence

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