bims-fragic 2025-05-25 papers

Issue of 2025–05–25
three papers selected by
Laura Mannarino, Humanitas Research

Sci Adv. 2025 May 23. 11(21): eads5002

Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer.

Carolyn Hruban, Daniel C Bruhm, Inna M Chen, Shashikant Koul, Akshaya V Annapragada, Nicholas A Vulpescu, Sarah Short, Susann Theile, Kavya Boyapati, Bahar Alipanahi, Zachary L Skidmore, Alessandro Leal, Stephen Cristiano, Vilmos Adleff, Julia S Johannsen, Robert B Scharpf, Zachariah H Foda, Jillian Phallen, Victor E Velculescu.

Determining response to therapy for patients with pancreatic cancer can be challenging. We evaluated methods for assessing therapeutic response using cell-free DNA (cfDNA) in plasma from patients with metastatic pancreatic cancer in the CheckPAC trial (NCT02866383). Patients were evaluated before and after initiation of therapy using tumor-informed plasma whole-genome sequencing (WGMAF) and tumor-independent genome-wide cfDNA fragmentation profiles and repeat landscapes (ARTEMIS-DELFI). Using WGMAF, molecular responders had a median overall survival (OS) of 319 days compared to 126 days for nonresponders [hazard ratio (HR) = 0.29, 95% confidence interval (CI) = 0.11-0.79, P = 0.011]. For ARTEMIS-DELFI, patients with low scores after therapy initiation had longer median OS than patients with high scores (233 versus 172 days, HR = 0.12, 95% CI = 0.046-0.31, P < 0.0001). We validated ARTEMIS-DELFI in patients with pancreatic cancer in the PACTO trial (NCT02767557). These analyses suggest that noninvasive mutation and fragmentation-based cfDNA approaches can identify therapeutic response of individuals with pancreatic cancer.

DOI: https://doi.org/10.1126/sciadv.ads5002

Genome Biol. 2025 May 23. 26(1): 141

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.

Haichao Wang, Paulius D Mennea, Yu Kiu Elkie Chan, Zhao Cheng, Maria C Neofytou, Arif Anwer Surani, Aadhitthya Vijayaraghavan, Emma-Jane Ditter, Richard Bowers, Matthew D Eldridge, Dmitry S Shcherbo, Christopher G Smith, Florian Markowetz, Wendy N Cooper, Tommy Kaplan, Nitzan Rosenfeld, Hui Zhao.

Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes and validated in 1182 plasma samples from published studies. Our results clarify the variations from library preparation and feature quantification methods. We design the Trim Align Pipeline and cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualization to standardize multi-modal feature engineering and integration for machine learning.

Keywords: Cancer genomics; CfDNA; Feature extraction; Fragmentomics

DOI: https://doi.org/10.1186/s13059-025-03607-5

NPJ Precis Oncol. 2025 May 20. 9(1): 147

An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples.

Gustavo Arango-Argoty, Marzieh Haghighi, Gerald J Sun, Elizabeth Y Choe, Aleksandra Markovets, J Carl Barrett, Zhongwu Lai, Etai Jacob.

Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.

DOI: https://doi.org/10.1038/s41698-025-00921-w