American Association for Cancer Research
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Supplementary Table S2 from Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes

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posted on 2023-04-21, 14:00 authored by Navonil De Sarkar, Robert D. Patton, Anna-Lisa Doebley, Brian Hanratty, Mohamed Adil, Adam J. Kreitzman, Jay F. Sarthy, Minjeong Ko, Sandipan Brahma, Michael P. Meers, Derek H. Janssens, Lisa S. Ang, Ilsa M. Coleman, Arnab Bose, Ruth F. Dumpit, Jared M. Lucas, Talina A. Nunez, Holly M. Nguyen, Heather M. McClure, Colin C. Pritchard, Michael T. Schweizer, Colm Morrissey, Atish D. Choudhury, Sylvan C. Baca, Jacob E. Berchuck, Matthew L. Freedman, Kami Ahmad, Michael C. Haffner, R. Bruce Montgomery, Eva Corey, Steven Henikoff, Peter S. Nelson, Gavin Ha

PTM peak data and phenotype 47 fragment variability Sheet 1: PDX sample representation in 3 histone PTM CUT&RUN nucleosome profiling assays (H3K4me1, H2K27ac and H3K27me3). Sheet 2: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for coefficient of variation in the 47 phenotype defining gene bodies (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted). Sheet 3: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for coefficient of variation in the 47 phenotype defining gene promoters (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted).

Funding

National Cancer Institute (NCI)

United States Department of Health and Human Services

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U.S. Department of Defense (DOD)

NIH Office of the Director (OD)

National Institute of General Medical Sciences (NIGMS)

United States Department of Health and Human Services

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Prostate Cancer Foundation (PCF)

V Foundation for Cancer Research (VFCR)

Brotman Baty Institute for Precision Medicine

Fund for Innovation in Cancer Informatics (ICI)

Doris Duke Charitable Foundation (DDCF)

Wong Family Award in Translational Oncology and Dana-Farber Cancer Institute Medical Oncology grant

H.L. Snyder Medical Research Foundation

Cutler Family Fund for Prevention and Early Detection

Claudia Adams Barr Program for Innovative Cancer Research

American Society of Clinical Oncology (ASCO)

Kure It Cancer Research Foundation

Pharmaceutical Research and Manufacturers of America Foundation (PhRMA Foundation)

Office of Research Infrastructure Programs, National Institutes of Health (ORIP)

History

ARTICLE ABSTRACT

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 GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology. This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology.This article is highlighted in the In This Issue feature, p. 517

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