AI-Driven Liquid Biopsy Platforms for Early Detection and Longitudinal Monitoring of Solid Tumors

Liquid biopsy has transformed oncology by enabling minimally invasive access to tumor derived material through circulating tumor DNA, cell free DNA, and related analytes. For rare or difficult to diagnose cancers such as high grade serous ovarian cancer and mesothelioma, conventional diagnostic pathways often detect disease at an advanced stage. Recent advances in high-throughput sequencing, computational biology, and artificial intelligence now allow researchers to extract multiple layers of biological signal from a single blood draw or archival cytology sample, including somatic mutations, copy number alterations, and cell free DNA fragmentation patterns. Fragmentomics in particular has emerged as a powerful, complementary readout of tumor biology that can be captured from the same sequencing data used for mutational and copy number analysis.This Research Topic aims to bring together computational and translational research that pushes liquid biopsy from proof of concept toward clinical utility across the full disease course, from pre symptomatic screening to real time monitoring of treatment response and resistance. We are particularly interested in work that integrates multiple liquid biopsy signal types, mutational, copy number, and fragmentomic, into unified machine learning or bioinformatics pipelines, since these layers are increasingly generated from the same underlying sequencing run and combining them can improve sensitivity and specificity beyond any single modality. We also welcome studies addressing the translational and validation challenges that stand between promising computational classifiers and adoption in clinical decision making, including reproducibility across cohorts, standardization of sample handling, and integration with existing diagnostic workflows.We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:- AI and machine learning classifiers for early, pre symptomatic cancer detection from liquid biopsy or archival cytology samples- Cell free DNA fragmentomics, including fragment size profiling, end motif analysis, and nucleosome positioning, for tumor detection and tissue of origin inference- Multi modal liquid biopsy platforms combining ctDNA, fragmentomic, and copy number signals for minimal residual disease detection and therapy monitoring- Bioinformatics pipelines for shallow whole genome or targeted sequencing analysis of circulating tumor DNA to track tumor heterogeneity and drug resistancePlease note: manuscripts consisting solely of bioinformatics or computational analysis of sole public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic. Topic editors: Laura Mannarino, Zhifu Sun Source: https://www.frontiersin.org/research-topics/85582/ai-driven-liquid-biopsy-platforms-for-early-detection-and-longitudinal-monitoring-of-solid-tumors