Abstract B072: Plasma whole-genome sequencing to monitor pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDA) is a highly aggressive disease with a 5-year survival rate of 12%. Patients are typically diagnosed at the metastatic stage due to lack of symptoms and current treatment options do not effectively control the disease. There is an urgent need to develop sensitive strategies to detect PDA at an early stage before it spreads. Characterizing circulating tumor DNA (ctDNA) in plasma is an effective approach to detect and monitor cancer. Typically, cancer genomes contain thousands of mutations, but targeted sequencing approaches only focus on pre-determined driver genes, which limits their performance in detecting ctDNA. Whole-genome sequencing (WGS) however, tracks all the tumor mutations simultaneously, and has a higher sensitivity than targeted approaches, especially in low tumor burden samples. In this study, we aim to use plasma WGS to build a liquid biopsy platform for PDA detection and characterization. To date, we have established the largest plasma WGS (20-60x) PDAC cohort which consists of over 250 baseline and longitudinal plasma samples from 205 PDA patients, with an additional 45 plasma samples from age-matched healthy individuals. Paired tumor tissue WGS and RNAseq are also available. We developed and validated a bioinformatics pipeline to detect cancer mutations in plasma and estimate plasma tumor fraction (TFx) for tumor burden. In the PDA cohort, plasma TFx increased with clinical stages (medians of 0.5% in resectable, 0.8% in locally advanced and 5% in metastatic), as did the ctDNA detection rate which was measured by whether the plasma TFx was significantly higher than controls (87%, 91% and 95%). Plasma TFx positively correlated with liver metastasis size (R=0.68, P<0.001), but not with the primary pancreatic tumor size. These results suggest that in PDA, metastases are the major site to shed ctDNA, but not primary tumors. We also found that liver metastasis size had a stronger correlation with plasma TFx than with serum CA19-9 (R=0.32, P=0.002). Baseline plasma TFx was associated with shorter overall survival (OS) in both resectable (P=0.013) and metastatic (P=0.022) cases. By analyzing the paired tumor WGS and RNAseq, we found that higher plasma TFx was associated with higher cell cycle, lower immunity and polyploid genomes in the tumors. No difference was found in plasma TFx between basal-like and classical PDA subtypes. In summary, the current study provides insights into the interplay between ctDNA dynamics, tumor biology and clinical features in PDA. Alongside with these results, we will provide a freely accessible computational platform for ctDNA genomic profiling, PDA early detection and progression monitoring using plasma. Citation Format: Yuanchang Fang, Michelle Chan-Seng-Yue, Karen Ng, Amy Zhang, Tuan Hoang, Gun Ho Jang, Sabiq Chaudhary, Eugenia Flores-Figueroa, Daniela Bevacqua, Stephanie Ramotar, Ayelet Borgida, Shawn Hutchinson, Anna Dodd, Julie Wilson, Barbara Grünwald, Robert Grant, Erica Tsang, George Zogopoulos, Jennifer Knox, Masoom Haider, Steven Gallinger, Faiyaz Notta. Plasma whole-genome sequencing to monitor pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B072.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".