Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.
Creators: name: Widman, Adam J
name: Shah, Minita
name: Frydendahl, Amanda
name: Halmos, Daniel
name: Khamnei, Cole C
name: Øgaard, Nadia
name: Rajagopalan, Srinivas
name: Arora, Anushri
name: Deshpande, Aditya
name: Hooper, William F
name: Quentin, Jean
name: Bass, Jake
name: Zhang, Mingxuan
name: Langanay, Theophile
name: Andersen, Laura
name: Steinsnyder, Zoe
name: Liao, Will
name: Rasmussen, Mads Heilskov
name: Henriksen, Tenna Vesterman
name: Jensen, Sarah Østrup
name: Nors, Jesper
name: Therkildsen, Christina
name: Sotelo, Jesus
name: Brand, Ryan
name: Schiffman, Joshua S
name: Shah, Ronak H
name: Cheng, Alexandre Pellan
name: Maher, Colleen
name: Spain, Lavinia
name: Krause, Kate
name: Frederick, Dennie T
name: Brok, Wendie den
name: Lohrisch, Caroline
name: Shenkier, Tamara
name: Simmons, Christine
name: Villa, Diego
name: Mungall, Andrew J
name: Moore, Richard
name: Zaikova, Elena
name: Cerda, Viviana
name: Kong, Esther
name: Lai, Daniel
name: Malbari, Murtaza S
name: Marton, Melissa
name: Manaa, Dina
name: Winterkorn, Lara
name: Gelmon, Karen
name: Callahan, Margaret K
name: Boland, Genevieve
name: Potenski, Catherine
name: Wolchok, Jedd D
name: Saxena, Ashish
name: Turajlic, Samra
name: Imielinski, Marcin
name: Berger, Michael F
name: Aparicio, Sam
name: Altorki, Nasser K
name: Postow, Michael A
name: Robine, Nicolas
name: Andersen, Claus Lindbjerg
name: Landau, Dan A
Publisher: name: The Francis Crick Institute
Year: 2024
Subjects: classificationCode: Tumour Biology
subject: Tumour Biology
classificationCode: Signalling & Oncogenes
subject: Signalling &amp; Oncogenes
classificationCode: Genetics & Genomics
subject: Genetics &amp; Genomics
classificationCode: Computational & Systems Biology
subject: Computational &amp; Systems Biology
classificationCode: Genome Integrity & Repair
subject: Genome Integrity &amp; Repair

description: In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.