Article
Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.
Nature medicine - 1 Jun 2024
Widman Adam J, Shah Minita, Frydendahl Amanda, Halmos Daniel, Khamnei Cole C, Øgaard Nadia, Rajagopalan Srinivas, Arora Anushri, Deshpande Aditya, Hooper William F, Quentin Jean, Bass Jake, Zhang Mingxuan, Langanay Theophile, Andersen Laura, Steinsnyder Zoe, Liao Will, Rasmussen Mads Heilskov, Henriksen Tenna Vesterman, Jensen Sarah Østrup, Nors Jesper, Therkildsen Christina, Sotelo Jesus, Brand Ryan, Schiffman Joshua S, Shah Ronak H, Cheng Alexandre Pellan, Maher Colleen, Spain Lavinia, Krause Kate, Frederick Dennie T, den Brok Wendie, Lohrisch Caroline, Shenkier Tamara, Simmons Christine, Villa Diego, Mungall Andrew J, Moore Richard, Zaikova Elena, Cerda Viviana, Kong Esther, Lai Daniel, Malbari Murtaza S, Marton Melissa, Manaa Dina, Winterkorn Lara, Gelmon Karen, Callahan Margaret K, Boland Genevieve, Potenski Catherine, Wolchok Jedd D, Saxena Ashish, Turajlic Samra, Imielinski Marcin, Berger Michael F, Aparicio Sam, Altorki Nasser K, Postow Michael A, Robine Nicolas, Andersen Claus Lindbjerg, Landau Dan A
Abstract excerpt
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...
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