Shallow whole genome sequencing of ctDNA samples from DETECT study

A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival from the paper "A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival", by Beddowes et al. Mol.Onc.

Type: Whole Genome Sequencing
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD50000001333	Shallow whole genome sequencing of samples from the paper Beddowes et al. A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival. Mol. One 2025. This includes 1048 longitudinal plasma samples from 149 patients and 35 normals. Samples were sequenced with shallow whole genome sequencing for copy number estimation. All the files are single-end fastq files.	Illumina HiSeq 2500	1048