RT Journal Article
SR Electronic
T1 Nucleosome patterns in circulating tumor DNA reveal transcriptional regulation of advanced prostate cancer phenotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.21.496879
DO 10.1101/2022.06.21.496879
A1 Navonil De Sarkar
A1 Robert D. Patton
A1 Anna-Lisa Doebley
A1 Brian Hanratty
A1 Adam J. Kreitzman
A1 Jay F. Sarthy
A1 Minjeong Ko
A1 Mohamed Adil
A1 Sandipan Brahma
A1 Michael P. Meers
A1 Derek H. Janssens
A1 Lisa A. Ang
A1 Ilsa Coleman
A1 Arnab Bose
A1 Ruth F. Dumpit
A1 Jared M. Lucas
A1 Talina A. Nunez
A1 Holly M. Nguyen
A1 Heather M. McClure
A1 Colin C. Pritchard
A1 Michael T. Schweizer
A1 Colm Morrissey
A1 Atish D. Choudhury
A1 Sylvan C. Baca
A1 Jacob E. Berchuck
A1 Matthew L. Freedman
A1 Kami Ahmad
A1 Michael C. Haffner
A1 Bruce Montgomery
A1 Eva Corey
A1 Steven Henikoff
A1 Peter S. Nelson
A1 Gavin Ha
YR 2022
UL http://biorxiv.org/content/early/2022/06/25/2022.06.21.496879.abstract
AB Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and NR3C1. Using these features, we designed a prediction model which distinguished NEPC from ARPC in patient plasma samples across three clinical cohorts with 97-100% sensitivity and 85-100% specificity. While phenotype classification is typically assessed by immunohistochemistry or transcriptome profiling, we demonstrate that ctDNA provides comparable results with numerous diagnostic advantages for precision oncology.STATEMENT OF SIGNIFICANCE This study provides key insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. The new methods established for phenotype classification extend the utility of ctDNA beyond assessments of DNA alterations with important implications for molecular diagnostics and precision oncology.Competing Interest StatementThe authors have filed a pending patent application on methodologies developed in this manuscript (G.H., A-L.D., N.D.S., R.D.P., P.S.N.). P.S.N.: Served as a paid consultant to Janssen, Astellas, Pfizer, and Bristol Myers Squibb in work unrelated to the present study. B.M.: Has institutional funding from Clovis, Janssen, Astellas, BeiGene, and AstraZeneca. E.C.: Received research funding under institutional SRA from Janssen Research and Development, Bayer Pharmaceuticals, KronosBio, Forma Pharmaceutics Foghorn, Gilead, Sanofi, AbbVie, and GSK for work unrelated to the present study. M.L.F.: Serves as a consultant to and has equity in Nuscan Diagnostics. This activity is outside of the scope of this manuscript. M.L.F. has a pending patent for detecting NEPC using DNA methylation. M.T.S.: Paid consultant and/or received Honoria from Sanofi, AstraZeneca, PharmaIn and Resverlogix. He has received research funding to his institution from Zenith Epigenetics, Bristol Myers Squibb, Merck, Immunomedics, Janssen, AstraZeneca, Pfizer, Madison Vaccines, Hoffman-La Roche, Tmunity, SignalOne Bio and Ambrx, Inc. All other authors declare no competing interests.