# Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic Adenocarcinoma

> **NIH NIH R33** · UNIVERSITY OF PENNSYLVANIA · 2024 · $369,607

## Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death in
the United States with an overall 5-year survival of 9%. Diagnosis and staging continue to rely
on endoscopic biopsy and imaging, and as such most patients are diagnosed at an advanced
stage. Sufficiently sensitive and specific screening tests for early disease remain elusive.
Moreover, while curative-intent surgery is an option for patients whose disease is confined to
the pancreas, distinguishing patients with metastases who are unlikely to benefit from surgery,
remains challenging due to occult metastases not detectable by imaging. To address these
challenges, several blood-based liquid biopsy biomarkers have been developed but show low
sensitivity for detection of early-stage disease. We have recently shown that circulating tumor
derived extracellular vesicles(EVs) can be isolated from blood and their RNA cargo used to
diagnose early pancreatic cancer and stage disease. These findings suggest an opportunity to
improve patient outcomes through development of a non-invasive diagnostic for pancreatic
cancer. However, as has been well documented, EVs are highly heterogeneous in their
expression of protein surface markers and their nucleic acid and protein cargo, and originate
from multiple cell types in the tumor micro environment (TME) (e.g. tumor cells, tumor
associated macrophages). The ultimate goal of this proposal is to address a fundamental
technological unmet need in EV diagnostics, by further developing our new approach to EV
subpopulation isolation using magnetic nanopores, which combines the benefits of nano-scale
sorting with sufficiently fast flow rates (106x faster than typical nanofluidic approaches) to be
practical for clinical diagnostics. In this R33, we develop this approach into a multiplexed EV
assay that will allow multiple unique EV sub-populations - based on surface marker expression-
to be isolated and their RNA cargo profiled. Building on our prior work that demonstrated the
value of analyzing single EV-subpopulations, and improved sensitivity of a multi-analyte vs
single analyte test, we will develop a multi-analyte EV-based assay that algorithmically
combines tumor associated EV RNA from multiple circulating EV isolates from the TME, as well
as Circulating cell-free DNA (ccfDNA) concentration, circulating tumor DNA-based KRAS
mutation detection, and CA19-9 using machine learning.

## Key facts

- **NIH application ID:** 10847347
- **Project number:** 5R33CA278551-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Erica Carpenter
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $369,607
- **Award type:** 5
- **Project period:** 2023-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10847347

## Citation

> US National Institutes of Health, RePORTER application 10847347, Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic Adenocarcinoma (5R33CA278551-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10847347. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*