Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets.
Fanying TangDuo XuShangqian WangChen Khuan WongAlexander Martinez-FundichelyCindy J LeeSandra CohenJane ParkCorinne E HillKenneth Wha EngRohan BarejaTeng HanEric Minwei LiuAnn PalladinoWei DiWangxin GuoWassim AbidaShaham BegLoredana PucaMaximiliano MenesesElisa De StanchinaMichael F BergerAnuradha GopalanLukas E DowJuan-Miguel MosqueraHimisha BeltranCora N SternbergPing ChiHoward I ScherAndrea SbonerJuliet ChenEkta KhuranaPublished in: Science (New York, N.Y.) (2022)
In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR) dependence leads to clinically aggressive tumors with few therapeutic options. We used ATAC-seq (assay for transposase-accessible chromatin sequencing), RNA-seq, and DNA sequencing to investigate 22 organoids, six patient-derived xenografts, and 12 cell lines. We identified the well-characterized AR-dependent and neuroendocrine subtypes, as well as two AR-negative/low groups: a Wnt-dependent subtype, and a stem cell-like (SCL) subtype driven by activator protein-1 (AP-1) transcription factors. We used transcriptomic signatures to classify 366 patients, which showed that SCL is the second most common subtype of CRPC after AR-dependent. Our data suggest that AP-1 interacts with the YAP/TAZ and TEAD proteins to maintain subtype-specific chromatin accessibility and transcriptomic landscapes in this group. Together, this molecular classification reveals drug targets and can potentially guide therapeutic decisions.
Keyphrases
- single cell
- rna seq
- transcription factor
- stem cells
- high throughput
- genome wide
- dna damage
- end stage renal disease
- gene expression
- prostate cancer
- chronic kidney disease
- dna binding
- deep learning
- ejection fraction
- newly diagnosed
- machine learning
- single molecule
- binding protein
- dna methylation
- peritoneal dialysis
- genome wide identification
- prognostic factors
- immune response
- mesenchymal stem cells
- emergency department
- protein protein
- big data
- childhood cancer
- benign prostatic hyperplasia
- data analysis
- inflammatory response
- bone marrow
- amino acid