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Integrative analysis of ultra-deep RNA-seq reveals alternative promoter usage as a mechanism of activating oncogenic programmes during prostate cancer progression

Abstract

Transcription factor (TF) proteins regulate gene activity by binding to regulatory regions, most importantly at gene promoters. Many genes have alternative promoters (APs) bound by distinct TFs. The role of differential TF activity at APs during tumour development is poorly understood. Here we show, using deep RNA sequencing in 274 biopsies of benign prostate tissue, localized prostate tumours and metastatic castration-resistant prostate cancer, that AP usage increases as tumours progress and APs are responsible for a disproportionate amount of tumour transcriptional activity. Expression of the androgen receptor (AR), the key driver of prostate tumour activity, is correlated with elevated AP usage. We identified AR, FOXA1 and MYC as potential drivers of AP activation. DNA methylation is a likely mechanism for AP activation during tumour progression and lineage plasticity. Our data suggest that prostate tumours activate APs to magnify the transcriptional impact of tumour drivers, including AR and MYC.

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Fig. 1: Activation and upregulation of APs are associated with increased expression of disease-related genes during PCa progression.
Fig. 2: FOXA1 binding and androgen signalling are associated with AP usage in PCa.
Fig. 3: MYC is a potential driver of AP activation in mCRPC.
Fig. 4: AP usage reflects lineage plasticity in response to therapy.
Fig. 5: Activation of APs is associated with DNA hypomethylation.

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Data availability

RNA-seq and whole-genome bisulfite sequencing data that support the findings of this study have been deposited in the European Genome–Phenome Archive (EGA) under accession code EGAS00001006275, and the SRA repository with Bioproject number PRJNA479544. Previously published RNA-seq data that were re-analysed here are available under accession code GSE115414, EGAD00001004424 and GSE119757. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The R scripts and source data used to reproduce all figures and tables in this manuscript are available via GitHub at https://github.com/DavidQuigley/WCDT_alternative_promoter. The source data to reproduce all the figures and tables are available via Zenodo at https://zenodo.org/records/10966958.

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Acknowledgements

We thank the patients who selflessly contributed samples to this study and without whom this research would not have been possible. This research was supported by a Stand Up To Cancer—Prostate Cancer Foundation Prostate Cancer Dream Team Award (SU2C-AACR-DT0812 to E.J.S.) and by the Movember Foundation. Stand Up To Cancer is a division of the Entertainment Industry Foundation. This research grant was administered by the American Association for Cancer Research, the scientific partner of SU2C. R.A. and M.S. were funded by a Prostate Cancer Foundation Young Investigator Award. D.A.Q. was funded by Young Investigator and Challenge awards from the PCF, by the UCSF Benioff Initiative for Prostate Cancer Research and by the United States Department of Defense (HT9425-23-PCRP-DSA). F.Y.F. was funded by Prostate Cancer Foundation Challenge Awards. Additional funding was provided by a UCSF Benioff Initiative for Prostate Cancer Research award. F.Y.F. was supported by National Institutes of Health (NIH)/National Cancer Institute (NCI) 1R01CA230516-01. F.Y.F. was supported by NIH/NCI 1R01CA227025 and Prostate Cancer Foundation (PCF) 17CHAL06. F.Y.F. was supported by NIH P50CA186786.

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The studies were conceptualized and designed by M.Z., F.Y.F. and D.A.Q. Data analysis was carried out by M.Z., M.S., R.S., A.L. and H.X.D. Validation experiments were performed by X.C. and H.L. Biopsy samples were processed by A.F. and K.F. Resources were contributed by P.G.F., R.A., J.J.A., E.J.S. and the SU2C/PCF West Coast Prostate Cancer Dream Team. Supervision was provided by C.A.M., F.Y.F. and D.A.Q. The first draft of the manuscript was written by M.Z., M.S. and D.A.Q. All authors revised and approved the manuscript.

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Correspondence to David A. Quigley.

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Competing interests

J.J.A. has consulted for or held advisory roles at Astellas Pharma, Bayer and Janssen Biotech Inc. He has received research funding from Aragon Pharmaceuticals Inc., Astellas Pharma, Novartis, Zenith Epigenetics Ltd. and Gilead Sciences Inc. F.Y.F. has consulted for Astellas, Bayer, Blue Earth Diagnostics, BMS, EMD Serono, Exact Sciences, Foundation Medicine, Janssen Oncology, Myovant, Roivant and Varian, and serves on the Scientific Advisory Board for BlueStar Genomics and SerImmune. F.Y.F. has patent applications with Decipher Biosciences on molecular signatures in PCa unrelated to this work. F.Y.F. has a patent application licensed to PFS Genomics/Exact Sciences. F.Y.F. has patent applications with Celgene unrelated to this work. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Optimization for promoter activity estimation.

a) Overall schematic of the analysis. PAIR: from the Henri Mondor institution, CPCG: Canadian Prostate Cancer Genome Network, WCDT: West Coast Dream Team, t-SCNC: treatment-emergent small cell neuroendocrine carcinoma. b) An illustration of the promoter activity estimation methods. Solid boxes represent exons while the lines represent introns. The promoters (P1, P2, P3) are defined as the first 5’ TSSs (transcription start sites) of overlapping first exons. The splice junction reads (SJ) from the overlapping first exons were summed and log2-normalized to represent the promoter activities. The activity of the internal promoter P2 can be corrected by the split read ratios or split read subtractions method to exclude transcriptional activity from isoform B (TXB) (see Methods for details). TX: transcript, SJ: splice junction. c) Correlations between the CAGE (cap analysis of gene expression) tag reads and the promoter activities calculated using RNA-seq data of non-internal and internal promoters without and with corrections. (Spearman’s correlation test, two-sided) The matching CAGE and RNA-seq data from the same samples were from FANTOM5. Upper row: representative correlation plots showing one human adult testis sample. Lower row: a box plot showing Spearman’s correlation coefficients for all 67 samples with matching CAGE and RNA-seq data. Box plots show data from the 25th to the 75th percentile, with the median as a line inside the box. Whiskers extend to 1.5 times the interquartile range (IQR) from the lower and upper quartiles. d) Number of high confidence promoters (see Methods for details) in the non-internal and internal category. e) A representative sample down sampled to 31.25 million (M), 62.5 M, 125 M, 250 M, 500 M, and 750 M reads from 1000 M. The bars show the number of active promoters detected at each read depth (left y axis). Points show the number of new promoters detected per million reads (right y axis).

Extended Data Fig. 2 Activation of additional promoters is associated with gene expression upregulation.

a) The number of active promoters normalized to the number of expressed genes for each individual sample grouped by disease stages (N = 8, 147, 104 for benign, localized and mCRPC). Genes with nonzero counts were considered as expressed. Box plots show data from the 25th to the 75th percentile, with the median as a line inside the box. Whiskers extend to 1.5 times the interquartile range (IQR) from the lower and upper quartiles. b) Upregulated and downregulated genes were identified by differential gene expression analysis. Bar plot shows the percentage of genes in each category that switch between single-promoter active and multiple-promoter active in benign prostate and localized PCa (left) or mCRPC (right). Activated: switch from SP (single-promoter active) in benign to MP (multiple-promoter active) in tumors. Deactivated: switch from MP in benign to SP in tumors. Inactive: SP in both benign and tumors. Constitutively active: MP in both benign and tumors. c) The RNA-seq coverage across gene body from 5’ to 3’ for ten random samples from each of the dataset (PAIR, CPCG, and WCDT) in our data collection. d) The EDASeq bias plot of the positional biases in unnormalized promoter counts of all samples from the RNA-seq datasets (PAIR, CPCG, and WCDT) in our data collection. e) The analysis of number of genes switching from single promoter active in benign prostate to multiple promoters active in localized (left) or mCRPC (right) using the RNA-seq dataset all down-sampled to 80 M reads/sample. SP: single -promoter active, MP: multiple-promoter active. (Fisher’s exact tests, two-sided). f. Principal component analysis of all samples of different disease stages from three cohorts using the down-sampled RNA-seq dataset.

Extended Data Fig. 3 Alternative promoter usage occurs in cancer related genes.

a) Density plot of the Spearman’s correlation rho values between absolute promoter activity and corresponding gene expression for upregulated APs, downregulated APs and non-differential promoters in genes with differential APs in localized PCa vs benign prostate. b) Pathway enrichment analysis of genes with upregulated APs in mCRPC vs benign. Highlighted in red are pathways enriched for the genes with upregulated APs but not in upregulated genes. P values were calculated using hypergeometric tests. X axis shows the p values without multi-test adjustments, but the coloring was based on Benjamini Hochberg (BH)-adjusted p values. Dashed line shows unadjusted p value 0.05. c) Pathway enrichment analysis result of genes upregulated in mCRPC vs benign prostate. P values were calculated using hypergeometric tests. X axis shows the p values without multi-test adjustments, but the coloring was based on BH-adjusted p values. Dashed line shows unadjusted p value 0.05.

Extended Data Fig. 4 Alternative promoter usage is associated with AR levels.

a) Correlation between the number of upregulated APs in individual mCRPC samples with AR expression levels. 95% confidence interval for the predictions from a linear model is displayed. (Spearman’s correlation test, two-sided). b) The percentage of AR and FOXA1 co-binding in the FOXA1 bound upregulated APs in localized PCa and mCRPC (Fisher’s exact test, two-sided).

Extended Data Fig. 5 Alternative promoter usage is associated with driver transcription factors.

a, b) Unibind results showing significance of overlap between transcription factor (TF) ChIP-seq peaks and upregulated APs in localized PCa (A) or mCRPC (B). Each dot represents one ChIP-seq dataset. TFs were ranked by the most significant ChIP-seq dataset. P values were calculated using Fisher’s exact tests. Y axis shows the p values without multi-test adjustments, but the horizontal dashed line shows the corresponding BH-adjusted p value 0.05. c) Pathway enrichment analysis of genes with APs upregulated in mCRPC vs benign prostate and overlapping with MYC ChIP-seq peaks in LNCaP cells. P values were calculated using hypergeometric tests. X axis shows the p values without multi-test adjustments, but the coloring was based on BH-adjusted p values. Dashed line shows unadjusted p value 0.05.

Extended Data Fig. 6 Enriched pathways in genes whose promoters are bound by MYC, EZH2 or both.

Pathway enrichment analyses of genes with promoters overlapping with EZH2 LNCaP ChIP-seq peaks only (A), with MYC LNCaP ChIP-seq peaks only (B), and with both MYC and EZH2 LNCaP ChIP-seq peaks (C). P values were calculated using hypergeometric tests. X axis shows the p values without multi-test adjustments, but the coloring was based on BH-adjusted p values. Dashed line shows unadjusted p value 0.05.

Extended Data Fig. 7 Alternative promoter usage reflects lineage plasticity in mCRPC.

a) Unibind results showing significance of overlap between TF ChIP-seq peaks and downregulated APs in t-SCNC vs adenocarcinoma mCRPC. Each dot represents one ChIP-seq dataset. TFs were ranked by the most significant ChIP-seq dataset. P values were calculated using Fisher’s exact tests. Y axis shows the p values without multi-test adjustments, but the horizontal dashed line shows the corresponding BH-adjusted p value 0.05. b) Histogram showing the distribution of gastrointestinal (GI) scores across mCRPC samples. Dashed line splits the fourth quartile vs others.

Extended Data Fig. 8 DNA methylation at alternative promoters is anticorrelated with their activity.

a) Correlation between the promoter activity fold change and methylation differences at differentially active APs between mCRPC t-SCNC and adenocarcinoma mCRPC. 95% confidence interval for the predictions from a linear model is displayed. (Spearman’s correlation test, two-sided). b) Unibind results showing significance of overlap between TF ChIP-seq peaks and upregulated APs in mCRPC t-SCNC vs adenocarcinoma that overlapped with differentially hypomethylated regions in t-SCNC. Each dot represents one ChIP-seq dataset. TFs were ranked by the most significant ChIP-seq dataset. P values were calculated using Fisher’s exact tests. Y axis shows the p values without multi-test adjustments, but the horizontal dashed line shows the corresponding BH-adjusted p value 0.05.

Supplementary information

Supplementary Information

This file contains a list of Stand Up 2 Cancer (SU2C)/Prostate Cancer Foundation (PCF) West Coast Prostate Cancer Dream Team members and affiliations.

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Supplementary Table 1

Supplementary Tables 1–8.

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Zhang, M., Sjöström, M., Cui, X. et al. Integrative analysis of ultra-deep RNA-seq reveals alternative promoter usage as a mechanism of activating oncogenic programmes during prostate cancer progression. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01438-3

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