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| Figure: Moore, et al., Nat Commun (2025) |
A team at OHSU reports a plasma cell-free RNA (cfRNA) signature (29 genes) that distinguishes pancreatic ductal adenocarcinoma (PDAC) from benign and look-alike pancreatic conditions before patients undergo EUS-FNA. In an external validation cohort, the model reached AUC ≈ 0.90, and it outperformed CA19-9. The clever bit is a cfRNA-specific normalization that corrects for pre-analytical noise (like platelet activation) — long a headache in liquid-biopsy RNA.
Why this matters
PDAC is often found late, when treatment options are limited. EUS-FNA is sensitive but invasive, expensive, and not universally accessible; CA19-9 is widely used but poor at early diagnosis and has low positive predictive value, so it’s not recommended for screening asymptomatic people. A robust, non-invasive triage test could help decide who actually needs EUS-FNA and reduce delays.
What’s new in this paper
• Pre-diagnostic sampling. Blood was drawn before EUS in high-risk or symptomatic patients — the real-world scenario where a triage test would be used. Two cohorts: CEDAR (n=153) for discovery and BCC (n=95) for external validation.
• cfRNA-specific normalization. The authors model each gene’s signal as a mix of intrinsic (true circulating) and extrinsic (handling-induced) components, learned with non-negative matrix factorization, then normalize them separately and recombine (“cf-normalization”). This tackles the notorious effect of platelet activation on plasma RNA profiles.
• 29-gene PDAC panel. Differential expression in discovery led to a 29-gene set. A random forest classifier (with SMOTE to handle class imbalance) produced a PDAC score. Cross-validation AUCs were ~0.95 inside CEDAR and ~0.90 on BCC (e.g., 0.896 when comparing PDAC vs all others).
• Beating CA19-9. The cfRNA classifier outperformed CA19-9 alone; adding CA19-9 to the model didn’t meaningfully improve accuracy.
Biology highlight: Why cfRNA is different (and powerful)
cfDNA reports mutations and methylation; cfRNA reports tissue programs — gene expression states that can reflect tumor activity, immune context, and distant organ responses. Here, several up-regulated markers traced to liver-related genes (e.g., SERPINA1, CRP), and deconvolution suggested a larger liver contribution to the cfRNA pool in PDAC — even in early-stage patients without liver metastasis. This hints at an early pancreas-liver axis (pre-metastatic signaling or systemic response) surfacing directly in cfRNA.
Big picture: cfRNA complements cfDNA by capturing tissue-of-origin and pathway signals, which multiple studies show can aid cancer detection and typing.
The pre-analytics problem — and a credible fix
In plasma RNA work, how you collect, spin, freeze, and thaw blood can swamp biological signal. Residual platelets release RNAs and EVs ex vivo, distorting profiles — a major reason cfRNA signatures have struggled to travel. The study’s intrinsic/extrinsic modeling and cf-normalization target exactly this pain point and likely explain the strong external performance.
How could this be used clinically?
Think of it as an EUS triage step for people at elevated risk or with concerning symptoms:
1. Blood draw → cfRNA PDAC score
2. High score: prioritize EUS-FNA and surgical/oncology consults
3. Borderline score: pair with imaging, repeat test, or add orthogonal markers (cfDNA methylation, EV proteins)
4. Low score: consider deferring invasive work-up, tighten surveillance
It’s not a population-wide screening test (and the paper doesn’t claim that); it targets the indicated group where decision support is most actionable.
Key numbers at a glance
• Cohorts: 153 discovery / 95 validation (pre-EUS blood draws).
• Signature: 29 cfRNA genes; RF classifier with SMOTE.
• External validation: AUC ≈ 0.90 (e.g., 0.896 vs all non-PDAC diagnoses).
• Against CA19-9: cfRNA > CA19-9; combining both didn’t add much.
Caveats (the honest bits)
• Small PDAC n (20 and 21 cases per cohort) and single-center recruitment could inflate performance; needs multi-site prospective validation.
• Intended use is pre-diagnostic triage among high-risk/symptomatic patients — not general screening.
• Assay practicality: Whole-transcriptome sequencing is slow/pricey; a targeted panel will be needed for clinical deployment.
• CA19-9 context: Long-standing limitations remain; it’s not recommended for asymptomatic screening and can be elevated in benign disease.
My take for RNA folks
• The normalization strategy is the star: it squarely addresses ex vivo confounders, a key translational barrier in cfRNA. If reused across diseases, it could standardize multi-site cfRNA studies.
• The liver signal is intriguing — cfRNA may capture organ cross-talk (pre-metastatic niche biology) years before imaging does. Follow-up mechanistic work would be fascinating.
• Expect multi-analyte panels next (cfRNA + cfDNA methylation + EV proteins) to push sensitivity/specificity further.
Glossary (30-second refresher)
• cfRNA: RNA fragments circulating in plasma inside EVs, protein complexes, or lipoproteins; reflects active gene expression from multiple tissues.
• EUS-FNA: Endoscopic ultrasound–guided fine-needle aspiration; sensitive for PDAC but invasive.
• CA19-9: A serum glycan antigen used in PDAC management, not reliable for early detection or screening.
Original Study:
Moore, T.W., Spiliotopoulos, E., Callahan, R.L. et al. Cell free RNA detection of pancreatic cancer in pre diagnostic high risk and symptomatic patients. Nat Commun 16, 7345 (2025). https://doi.org/10.1038/s41467-025-62685-y
Keywords: cell-free RNA, cfRNA, liquid biopsy, pancreatic cancer, pancreatic ductal adenocarcinoma, PDAC, early detection, RNA sequencing
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Keywords: cell-free RNA, cfRNA, liquid biopsy, pancreatic cancer, pancreatic ductal adenocarcinoma, PDAC, early detection, RNA sequencing, transcriptome, biomarker panel, 29-gene signature, non-invasive blood test, EUS-FNA triage, CA19-9 comparison, AUC 0.90, random forest classifier, SMOTE oversampling, pre-analytical variation, platelet activation, intrinsic–extrinsic normalization, liver-derived cfRNA, tissue deconvolution, Human Protein Atlas, validation cohort, CEDAR cohort, BCC cohort, Knight Cancer Institute, Nature Communications 2025, molecular diagnostics, precision oncology, pancreatitis vs PDAC, IPMN, high-risk symptomatic patients; moleculardiagnostics.blogspot.com
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