Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

Adam J. Widman*, Minita Shah, Amanda Frydendahl, Daniel Halmos, Cole C. Khamnei, Nadia Øgaard, Srinivas Rajagopalan, Anushri Arora, Aditya Deshpande, William F. Hooper, Jean Quentin, Jake Bass, Mingxuan Zhang, Theophile Langanay, Laura Andersen, Zoe Steinsnyder, Will Liao, Mads Heilskov Rasmussen, Tenna Vesterman Henriksen, Sarah Østrup JensenJesper Nors, Christina Therkildsen, Jesus Sotelo, Ryan Brand, Joshua S. Schiffman, Ronak H. Shah, Alexandre Pellan Cheng, Colleen Maher, Lavinia Spain, Kate Krause, Dennie T. Frederick, Wendie den Brok, Caroline Lohrisch, Tamara Shenkier, Christine Simmons, Diego Villa, Andrew J. Mungall, Richard Moore, Elena Zaikova, Viviana Cerda, Esther Kong, Daniel Lai, Murtaza S. Malbari, Melissa Marton, Dina Manaa, Lara Winterkorn, Karen Gelmon, Margaret K. Callahan, Genevieve Boland, Catherine Potenski, Jedd D. Wolchok, Ashish Saxena, Samra Turajlic, Marcin Imielinski, Michael F. Berger, Sam Aparicio, Nasser K. Altorki, Michael A. Postow, Nicolas Robine, Claus Lindbjerg Andersen, Dan A. Landau*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

11 Citationer (Scopus)

Abstract

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.

OriginalsprogEngelsk
TidsskriftNature Medicine
Vol/bind30
Nummer6
Sider (fra-til)1655-1666
Antal sider12
ISSN1078-8956
DOI
StatusUdgivet - jun. 2024

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