Study / S84QP511E2024-09-13
Integrative identification of non-coding regulatory regions driving metastatic prostate cancer
Brian J. Woo, Ruhollah Moussavi-Baygi, Heather Karner, Mehran Karimzadeh, Hassan Yousefi et al.
About this study
Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.
Full author list & citation
Brian J. Woo, Ruhollah Moussavi-Baygi, Heather Karner, Mehran Karimzadeh, Hassan Yousefi, Sean Lee, Kristle Garcia, Tanvi Joshi, Keyi Yin, Albertas Navickas, Luke A. Gilbert, Bo Wang, Hosseinali Asgharian, Felix Y. Feng, Hani Goodarzi. Integrative identification of non-coding regulatory regions driving metastatic prostate cancer. 2024-09-13. https://doi.org/10.1016/j.celrep.2024.114764
Experiments 1
E09VEMDCB
A lentiMPRA library of 3,665 candidate regulatory sequence elements, scrambled controls, reference sequences, and patient-observed mutant variants was assayed in human C4-2B metastatic prostate-cancer cells. Three biological replicates were profiled through gDNA input and RNA output barcode counts, and logistic regression produced reference-versus-scrambled and mutant-versus-reference enrichment statistics.