PT - JOURNAL ARTICLE AU - Navonil De Sarkar AU - Robert D. Patton AU - Anna-Lisa Doebley AU - Brian Hanratty AU - Adam J. Kreitzman AU - Jay F. Sarthy AU - Minjeong Ko AU - Mohamed Adil AU - Sandipan Brahma AU - Michael P. Meers AU - Derek H. Janssens AU - Lisa A. Ang AU - Ilsa Coleman AU - Arnab Bose AU - Ruth F. Dumpit AU - Jared M. Lucas AU - Talina A. Nunez AU - Holly M. Nguyen AU - Heather M. McClure AU - Colin C. Pritchard AU - Michael T. Schweizer AU - Colm Morrissey AU - Atish D. Choudhury AU - Sylvan C. Baca AU - Jacob E. Berchuck AU - Matthew L. Freedman AU - Kami Ahmad AU - Michael C. Haffner AU - Bruce Montgomery AU - Eva Corey AU - Steven Henikoff AU - Peter S. Nelson AU - Gavin Ha TI - Nucleosome patterns in circulating tumor DNA reveal transcriptional regulation of advanced prostate cancer phenotypes AID - 10.1101/2022.06.21.496879 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.06.21.496879 4099 - http://biorxiv.org/content/early/2022/06/25/2022.06.21.496879.short 4100 - http://biorxiv.org/content/early/2022/06/25/2022.06.21.496879.full 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.