Targeting transcription-replication conflicts is an effective therapeutic principle for MYCN-driven neuroblastoma [ChIPseq_IMR5_RNAseqH1]
Targeting the function of MYC proteins for therapy is a major challenge in tumor biology. MYC proteins are transcription factors that can globally release RNA polymerase II (RNAPII) from core promoters. In neuroblastoma, the MYC paralogue, MYCN, and the Aurora-A kinase form a complex during S phase that stabilizes MYCN and enhances Aurora-A kinase activity. Here we show that Aurora-A phosphorylates histone H3 at serine 10 in S phase and suppresses transcription-dependent R-loop formation. Inhibition of Aurora-A causes global stalling of RNAPII at pause sites and exon/intron boundaries and induces transcription/replication conflicts, activating the Ataxia telangiectasia and Rad3 related (ATR) kinase. Activation of ATR is required to prevent the accumulation of double-strand breaks. In genetically-engineered mice with MYCN-driven neuroblastoma, combined inhibition of Aurora-A and ATR induces rampant tumor-specific apoptosis and tumor regression, leading to permanent eradication of tumors in a subset of mice. The therapeutic efficacy is not only due to tumor cell-intrinsic mechanisms, but also engages the host immune system for tumor eradication. Since stabilization of MYCN promotes promoter-proximal transcription termination, we propose that MYCN/Aurora-A complex formation enables tumor cells to prevent transcription-replication conflicts and that targeting this mechanism is an effective therapy for MYCN-driven neuroblastoma. Overall design: S phase synchronized IMR-5 cells with inducible RNaseH1 treated for 24 h with 1 µg/ml Doxycycline; ChIP-Sequencing of total RNA Polymerase II and Serine 2 phosphorylated RNA Polymerase II A combined 'Input.fastq' file was generated by merging the files IR166_INPUT_IMR5_RnaseH_Dox_DMSO.fastq, IR166_INPUT_IMR5_RnaseH_Dox_MK.fastq, IR166_INPUT_IMR5_RnaseH_Dox_MLN.fastq, IR166_INPUT_IMR5_RnaseH_EtOH_DMSO.fastq, IR166_INPUT_IMR5_RnaseH_EtOH_MK.fastq, IR166_INPUT_IMR5_RnaseH_EtOH_MLN.fastq, reseq_IR166_INPUT_IMR5_RnaseH_Dox_DMSO.fastq, reseq_IR166_INPUT_IMR5_RnaseH_Dox_MK.fastq, reseq_IR166_INPUT_IMR5_RnaseH_Dox_MLN.fastq, reseq_IR166_INPUT_IMR5_RnaseH_EtOH_DMSO.fastq, reseq_IR166_INPUT_IMR5_RnaseH_EtOH_MK.fastq & reseq_IR166_INPUT_IMR5_RnaseH_EtOH_MLN.fastq. The file Input.fastq was then randomly subsampled to 6068151 and 7845813 reads, resulting in the files Input_sorted6068151.bedgraph and Input_sorted7845813.bedgraph, which were used as input controls for the A10- and the S2P-ChIPseq samples, respectively. A combined Input_H3.3.fastq file was generated by merging the files IR181_Input_Dox_DMSO.fastq, IR181_Input_Dox_MLN.fastq, IR181_Input_EtOH_DMSO.fastq & IR181_Input_EtOH_MLN.fastq. The file 'Input_H3.3.fastq' was then randomly subsampled to 11604734 reads, resulting in the file Input_sorted11604734.bedgraph, which was used as input control for the H3.3-ChIPseq samples.