Androgen receptor-negative prostate cancer is vulnerable to SWI/SNF-targeting degrader molecules

Description

Abstract

Abstract

The switch/sucrose non-fermentable (SWI/SNF) chromatin remodeling complex is frequently deregulated during progression to castration-resistant prostate cancer (CRPC). Proteolysis targeting chimera (PROTAC) therapies degrading SWI/SNF ATPases offer a novel approach to interfere with androgen receptor (AR) signaling in AR-dependent CRPC (CRPC-AR). To explore the utility of SWI/SNF therapy beyond AR-sensitive CRPC, we investigated SWI/SNF ATPase targeting agents in AR-negative CRPC. SWI/SNF targeting PROTAC treatment of cell lines and organoid models reduced the viability of not only CRPC-AR but also WNT-signaling dependent AR-negative CRPC (CRPC-WNT), which accounts for about 10% of all clinical CRPC cases. In CRPC-WNT models, we discovered that SWI/SNF ATPase SMARCA4 depletion interfered with WNT signaling via the master transcriptional regulator TCF7L2 (TCF4). Functionally, TCF7L2 maintains proliferation via the MAPK signaling axis in this subtype of CRPC by forming a complex with β-Catenin and AP-1 transcription factor c-JUN. These data suggest a mechanistic rationale for MAPK inhibition or interventions that disrupt the formation of the pro-proliferative TCF7L2-β-Catenin-JUN complex in the CRPC-WNT subclass of advanced prostate cancer.

Methods

Cell lines and compounds

PCa cell lines (LNCaP, 22Rv1, VCaP, PC3, DU145, NCI-H660, C4-2), other cell lines (HEK293T, DLD1) and benign prostate line (RWPE-1) were purchased from ATCC and maintained according to ATCC protocols. Patient-derived CRPC organoids (WCM and MSK) were established and maintained as organoids in Matrigel drops according to the previously described protocol⁷⁰. LNCaP-AR cells were a kind gift from Dr. Sawyers and Dr. Mu (Memorial Sloan Kettering Cancer Center) and were cultured as previously described⁵. All used cell lines and their phenotype are listed in Supplementary Table 1. Cell cultures were regularly tested for Mycoplasma contamination and confirmed to be negative. Genentech Inc. synthesized A947, its epimer (A858), FHD-286 and AU-15330. Cobimetinib, Trametinib, VL285 and CHIR99021 were purchased from SelleckChem. BRM014 was purchased from MedChemExpress. All drugs used in this study are listed in Supplementary Table 2.

Single-cell RNA-sequencing by SORT-seq library generation and analysis

SORT-seq was performed using Single Cell Discoveries (SCD) service. Organoids were treated for 72h with a control epimer (A858) or active compound (A947) at 1 µM, and 1x10e6 cells were harvested in PBS. Harvested cells were stained with 100ng/ml DAPI to stain dead cells. Using a cell sorter (conducted by Flow Cytometry Core, DBMR, Bern) and the recommended settings (Single Cell Discoveries B.V.), DAPI-negative cells were sorted as single cells in 376 wells of four 384-well plates containing immersion oil per condition. Resulting in a theoretical cell number of 1504 cells per condition. All post-harvesting steps were performed at 4°C. Plates were snap-frozen on dry ice for 15 minutes and sent out for sequencing at Single Cell Discoveries B.V.

Data were analyzed using the Seurat package v.4.3.0⁸⁰. Cell QC filtering was done using the following thresholds: nCount > 4000, nFeature > 1000, percent.mito < 25, log10GenesPerUMI > 0.85. Differential gene expression analysis between clusters was done with Seurat::FindAllMarkers. Module scores were generated with Seurat::AddModuleScore. Gene set enrichment analysis was done with the package fgsea v.1.24.0⁸¹ and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Gene regulatory networks analysis was done with pySCENIC v.0.12.1⁸². Overall analysis was done in R v.4.2.2.

RNA-seq library generation and processing

For bulk RNA-seq, organoids were treated with A858 or A947 (1µM) for 24h and 48h (3 biological replicates per condition). RNA was extracted using the RNeasy Kit (Qiagen); library generation and subsequent sequencing was performed by the clinical genomics lab (CGL) at the University of Bern.

Sequencing reads were aligned against the human genome hg38 with STAR v.2.7.3a⁸³. Gene counts were generated with RSEM v.1.3.2⁸⁴, whose index was generated using the GENCODE v33 primary assembly annotation. Differential gene expression analysis was done with DESeq2 v.1.34.0⁸⁵. Gene set enrichment analysis was done with the package fgsea v.1.20.0⁸¹ and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Analysis was done in R v.4.1.2.

TCF7L2 ChIP-seq library generation and processing

For the ChIP-Seq assay, chromatin was prepared from 2 biological replicates of WCM1078 treated with A858 or A947 (1µM) for 4h, and ChIP-Seq assays were then performed by Active Motif Inc. using an antibody against TCF7L2 (Cell Signalling, cat#2569). ChIP-seq sequence data was processed using an ENCODE-DC/chip-seq-pipeline2 -based workflow (https://github.com/ENCODE-DCC/chip-seq-pipeline2). Briefly, fastq files were aligned on the hg38 human genome reference using Bowtie2  (v2.2.6) followed by alignment sorting (samtools v1.7) of resulting bam files with filtering out of unmapped  reads and keeping reads with mapping quality higher than 30. Duplicates were removed with Picard’s MarkDuplicates  (v1.126) function, followed by indexation of resulting bam files with samtools. For each bam file, genome coverage was computed with bedtools (v2.26.0), followed by the generation of bigwig  (wigToBigWig v377) files. Peaks were called with macs2 (v2.2.4) for each treatment sample using a pooled input alignment (.bam file) as control. Downstream analyses were performed with DiffBind v3.11.1 with default parameters, except for summits=250 in dba.count(). dba.contrast() and dba.analyzed() were used to  compute significant differential peaks with DESeq2. 

ATAC-seq library generation and processing

ATAC-seq was performed from 50’000 cryo-preserved cells per condition (1µM A858 and 1µM A947, n = 3 biological replicates) treated for 4h and analyzed as described in previous study⁸⁶. Briefly, 50,000 cryo-preserved cells per condition were lysed for 5 minutes on ice and tagmented for 30 minutes at 37°C, followed by DNA isolation. DNA was barcoded and amplified before sequencing.

PRO-cap library generation and processing

For PRO-cap, approximately 30 million cells were processed per sample as previously described^87,88. Library preparations for two biological replicates were performed separately. Cells were permeabilized, and run-on reactions were performed. After RNA isolation, two adaptor ligations and reverse transcription were performed with custom adaptors. Between adaptor ligations, cap state selection reactions were carried out using a series of enzymatic steps. RNA washes, phenol:chloroform extractions and ethanol precipitations were conducted between reactions. All steps were performed under RNase-free conditions. Libraries were sequenced on Illumina’s NovaSeq lane following PCR amplification and library clean-up. Raw sequencing data was processed as previously described⁸⁹. Briefly, sequencing data was trimmed with fastp version 0.22.0 and then aligned to the human genome (hg38) concatenated with EBV and human rDNA sequences (GenBank U13369.1) using STAR V2.7.10b. Raw alignments were filtered with samtools version 1.18 and deduplicated using umi_tools version 1.1.2. Alignments were converted to bigwig files using bedtools version 2.30.0 and kentUtils bedGraphToBigWig V2.8. Peaks were called using PINTS version 1.1.6⁵⁸. Divergent peaks not overlapping with TSS +/-500 bp (GENCODE V37) were regarded as candidate enhancer RNAs.

Peaks were annotated with HOMER v.4.11 (http://homer.ucsd.edu/). Distal peaks were defined as those peaks in known introns and intergenic regions, and over 2 kb upstream or downstream from known transcription start sites. GIGGLE scores were generated at http://dbtoolkit.cistrome.org. Analysis was done in R v.4.2.2. Heatmaps were generated with deepTools v.3.5.0⁹⁰.

Notes