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FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes

An Author Correction to this article was published on 08 September 2020

Abstract

Mutations in the transcription factor FOXA1 define a unique subset of prostate cancers but the functional consequences of these mutations and whether they confer gain or loss of function is unknown1,2,3,4,5,6,7,8,9. Here, by annotating the landscape of FOXA1 mutations from 3,086 human prostate cancers, we define two hotspots in the forkhead domain: Wing2 (around 50% of all mutations) and the highly conserved DNA-contact residue R219 (around 5% of all mutations). Wing2 mutations are detected in adenocarcinomas at all stages, whereas R219 mutations are enriched in metastatic tumours with neuroendocrine histology. Interrogation of the biological properties of wild-type FOXA1 and fourteen FOXA1 mutants reveals gain of function in mouse prostate organoid proliferation assays. Twelve of these mutants, as well as wild-type FOXA1, promoted an exaggerated pro-luminal differentiation program, whereas two different R219 mutants blocked luminal differentiation and activated a mesenchymal and neuroendocrine transcriptional program. Assay for transposase-accessible chromatin using sequencing (ATAC-seq) of wild-type FOXA1 and representative Wing2 and R219 mutants revealed marked, mutant-specific changes in open chromatin at thousands of genomic loci and exposed sites of FOXA1 binding and associated increases in gene expression. Of note, ATAC-seq peaks in cells expressing R219 mutants lacked the canonical core FOXA1-binding motifs (GTAAAC/T) but were enriched for a related, non-canonical motif (GTAAAG/A), which was preferentially activated by R219-mutant FOXA1 in reporter assays. Thus, FOXA1 mutations alter its pioneering function and perturb normal luminal epithelial differentiation programs, providing further support for the role of lineage plasticity in cancer progression.

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Fig. 1: Recurrent FOXA1 mutations in prostate cancer cluster in the FKHD DNA-binding domain.
Fig. 2: Expression of FOXA1 mutants promotes growth and reveals distinct morphologies for different classes of alterations.
Fig. 3: FOXA1 expression constricts the AR cistrome and promotes AR-independent growth programs.
Fig. 4: FOXA1 mutations cause marked shifts in the chromatin landscape.

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Data availability

The described RNA-seq, ATAC-seq and ChIP–seq data have been deposited in the Gene Expression Omnibus under the following accession numbers: GSE128667 (all data), GSE128421 (ATAC-seq sub-series), GSE128666 (RNA-seq subseries) and GSE128867 (ChIP–seq subseries). Patient predicted FOXA1 mutant status and outcome data from Decipher GRID are available from the authors upon reasonable request.

References

  1. Pomerantz, M. M. et al. The androgen receptor cistrome is extensively reprogrammed in human prostate tumorigenesis. Nat. Genet. 47, 1346–1351 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Grasso, C. S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gerhardt, J. et al. FOXA1 promotes tumor progression in prostate cancer and represents a novel hallmark of castration-resistant prostate cancer. Am. J. Pathol. 180, 848–861 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Jin, H. J., Zhao, J. C., Ogden, I., Bergan, R. C. & Yu, J. Androgen receptor-independent function of FoxA1 in prostate cancer metastasis. Cancer Res. 73, 3725–3736 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Barbieri, C. E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

    Article  CAS  Google Scholar 

  7. Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Annala, M. et al. Frequent mutation of the FOXA1 untranslated region in prostate cancer. Commun. Biology 1, 122 (2018).

    Article  CAS  Google Scholar 

  9. Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Abida, W. et al. Prospective genomic profiling of prostate cancer across disease states reveals germline and somatic alterations that may affect clinical decision making. JCO Precis. Oncol. 2017, https://doi.org/10.1200/PO.17.00029 (2017).

  12. Beltran, H. et al. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat. Med. 22, 298–305 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Liu, D. et al. Impact of the SPOP mutant subtype on the interpretation of clinical parameters in prostate cancer. JCO Precis. Oncol. 2018, 1–13 (2018).

    Google Scholar 

  14. Armenia, J. et al. The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 50, 645–651 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Karthaus, W. R. et al. Identification of multipotent luminal progenitor cells in human prostate organoid cultures. Cell 159, 163–175 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gao, N. et al. Forkhead box A1 regulates prostate ductal morphogenesis and promotes epithelial cell maturation. Development 132, 3431–3443 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Bose, R. et al. ERF mutations reveal a balance of ETS factors controlling prostate oncogenesis. Nature 546, 671–675 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. King, J. C. et al. Cooperativity of TMPRSS2–ERG with PI3-kinase pathway activation in prostate oncogenesis. Nat. Genet. 41, 524–526 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Blattner, M. et al. SPOP mutation drives prostate tumorigenesis in vivo through coordinate regulation of PI3K/mTOR and AR signaling. Cancer Cell 31, 436–451 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hieronymus, H. et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell 10, 321–330 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, X. et al. DNA-mediated dimerization on a compact sequence signature controls enhancer engagement and regulation by FOXA1. Nucleic Acids Res. 46, 5470–5486 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  24. Watson, P. A. et al. Constitutively active androgen receptor splice variants expressed in castration-resistant prostate cancer require full-length androgen receptor. Proc. Natl Acad. Sci. USA 107, 16759–16765 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Motallebipour, M. et al. Differential binding and co-binding pattern of FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2 cells revealed by ChIP–seq. Genome Biol. 10, R129 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Peng, W., Bao, Y. & Sawicki, J. A. Epithelial cell-targeted transgene expression enables isolation of cyan fluorescent protein (CFP)-expressing prostate stem/progenitor cells. Transgenic Res. 20, 1073–1086 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vaezi, A., Bauer, C., Vasioukhin, V. & Fuchs, E. Actin cable dynamics and Rho/Rock orchestrate a polarized cytoskeletal architecture in the early steps of assembling a stratified epithelium. Dev. Cell 3, 367–381 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Koo, B.-K. et al. Controlled gene expression in primary Lgr5 organoid cultures. Nat. Methods 9, 81–83 (2011).

    Article  PubMed  CAS  Google Scholar 

  30. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  31. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Love, M. I., Huber, W., & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Smith, B. A. et al. A basal stem cell signature identifies aggressive prostate cancer phenotypes. Proc. Natl Acad. Sci. USA 112, E6544–E6552 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Klein, E. A. et al. A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy. Eur. Urol. 67, 778–786 (2015).

    Article  PubMed  Google Scholar 

  36. Boormans, J. L. et al. Identification of TDRD1 as a direct target gene of ERG in primary prostate cancer. Int. J. Cancer 133, 335–345 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Ross, A. E. et al. Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate- and high-risk men. Eur. Urol. 69, 157–165 (2016).

    Article  PubMed  Google Scholar 

  38. Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Erho, N. et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8, e66855 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Karnes, R. J. et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J. Urol. 190, 2047–2053 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Den, R. B. et al. Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 89, 1038–1046 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Feng, J., Liu, T., Qin, B., Zhang, Y. & Liu, X. S. Identifying ChIP–seq enrichment using MACS. Nat. Protoc. 7, 1728–1740 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5, 1752–1779 (2011).

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We thank P. Iaquinta, B. Carver, Z. Cao, I. Ostrovnaya, H. Hieronymous, W. Abida, E. Wasmuth, K. Lawrence, T. Nadkarni, S. P. Gao and all members of the Sawyers laboratory for comments; Memorial Sloan Kettering Cancer Center core facilities; N. Fan and D. Yarilin from the MSKCC Molecular Cytology Core Facility; the MSKCC Integrated Genomics Operation; the New York Genome Center for RNA sequencing; and R. J. Karnes (Department of Urology, Mayo Clinic), R. B. Den (Department of Radiation Oncology, Thomas Jefferson University), E. A. Klein (Glickman Urological and Kidney Institute, Cleveland Clinic) and Bruce Trock (Department of Urology, Johns Hopkins University) for providing access to patient outcome data. Some of the results shown here are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). E.J.A. was supported by an American Association for Cancer Research Basic Cancer Research Fellowship, the MSKCC Translational Research Oncology Training Program and the MSKCC Functional Genomics Initiative. R.D. was supported by NIH training grant 1T32GM083937. Z.Z. is supported by the NCI Predoctoral to Postdoctoral Fellow Transition Award (F99/K00 award ID: F99CA223063). R.B. was supported by grants from the Department of Defense (W81XWH1510277), NCI (1K08CA226348-01) and the Prostate Cancer Foundation. D.L., A.S. and C.E.B. were supported by the NCI (K08CA187417-01, R01CA215040-01 and P50CA211024-01 to C.E.B.), a Urology Care Foundation Rising Star in Urology Research Award (C.E.B.), a Damon Runyon Cancer Research Foundation MetLife Foundation Family Clinical Investigator Award (C.E.B.) and the Prostate Cancer Foundation (C.E.B). C.L.S. is an investigator of the Howard Hughes Medical Institute and this project was supported by NIH grants CA155169, CA193837, CA224079, CA092629, CA160001 and CA008748, the Starr Cancer Consortium grant I10-0062 and the Functional Genomics Initiative at MSKCC.

Author information

Authors and Affiliations

Authors

Contributions

E.J.A. and C.L.S. conceived and oversaw the project, performed data interpretation, and co-wrote the manuscript. E.J.A. and E.H. performed immunoblots, in vitro cell growth assays, lumen formation assays, lumen area quantification, processed organoids for immunohistochemistry and prepared experiments for RNA-seq and ATAC-seq. E.J.A., E.H. and W.R.K. made 3D organoid lines. E.J.A., W.R.K., E.H. and P.A.W. cloned plasmid reagents. E.J.A., E.H., W.R.K. and Z.Z. carried out in vivo experiments. E.J.A., R.B. and D.L. performed RNA-seq analysis and GSEA. E.J.A., R.B., D.L., A.S., Y.L., E.D. and C.E.B. performed analysis of human prostate cancer cohorts. A.G. optimized and carried out ATAC and ChIP protocols. R.D., S.C., H.C. and C.S.L. carried out ATAC-seq and ChIP–seq data analysis. All authors made intellectual contributions and reviewed the manuscript.

Corresponding author

Correspondence to Charles L. Sawyers.

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Competing interests

C.L.S. serves on the board of directors of Novartis, is a co-founder of ORIC Pharm and co-inventor of enzalutamide and apalutamide. He is a science advisor to Agios, Beigene, Blueprint, Column Group, Foghorn, Housey Pharma, Nextech, KSQ, Petra and PMV. He was a co-founder of Seragon, purchased by Genentech/Roche in 2014. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Patients with predicted FOXA1 mutant status have worse outcomes.

a, Co-crystal structure of the FKHD domain of FOXA3 in complex with DNA resembling the FKHD consensus sequence (PDB 1VTN), with residues and folds of interest indicated, including α-helix 3 (orange), which sits in the major groove of DNA, and Wing2 (cyan), which undergoes frequent mutation in prostate cancer. b, Kaplan–Meier plot showing significantly different clinical outcomes of time to biochemical recurrence (BCR, top) or progression to metastatic disease (MET, bottom) for predicted FOXA1 mutant cases vs wild type in the GRID cohort. The difference of MET and BCR survival curves was tested with the R survdiff function, using the G-rho family of tests, without adjustments for multiple comparisons. RP, radical prostatectomy. c, Associations between predicted FOXA1 mutation status and clinical variables using univariate analysis of the GRID cohort, with FOXA1 wild type as reference. The GRID cohort included 1,626 radical prostatectomy tumour samples. The centre values represent the median odds ratio via univariate analysis. The error bars represent first and third quartiles of odds ratio. The lines represent minimum and maximum odds ratio. Univariate logistic regression analyses were performed on the GRID cohort to test the statistical association between FOXA1 mutant status and clinical variables via generalized linear test, without adjustments for multiple comparisons. The test was two-sided with the significance level of P < 0.05 as the cut-off. ADT, androgen deprivation therapy; PSA, prostate-specific antigen; RT, radiotherapy.

Extended Data Fig. 2 Details of FOXA1 luciferase reporter assay.

a, Schematic of FOXA1 luciferase reporter, depicting the modified response elements (at wobble positions within the canonical FOXA1 motif) cloned in tandem upstream of a minimal promoter driving luciferase expression. b, Dose–response curve of FOXA1 luciferase reporter activity in response to increased amounts of Foxa1WT cDNA introduced into the system, expressed as a relative response ratio with 100% Foxa1WT cDNA set to 1 and 0% Foxa1WT cDNA (100% ‘stuffer’ DNA) set to 0. Data are from three biological replicates, central line and error bars represent mean ±  standard deviation. c, Western blot of allelic series of FOXA1 mutants in HEK293T cells 24 h after transfection with equal amounts of cDNA as used in FOXA1 luciferase reporter assay. The ΔF254/E255 and ΔM253–N256 mutants are shown as F254_E255del and M253_N256del, respectively, in the Extended Data. CYCLO B, loading control cyclophilin B. Representative blot, experiment repeated three independent times with similar results. For source gel data, see Supplementary Fig. 1

Source data

Extended Data Fig. 3 Inducible overexpression of FOXA1 variants influences organoid lumen size and morphology.

a, Schematic of doxycycline-inducible pCW-FOXA1 constructs used in the study. b, Western blot analysis of lysates from pCW-FOXA1(WT) organoids following acute doxycycline treatment. Representative blot, experiment repeated two independent times with similar results. For source gel data, see Supplementary Fig. 1. c, Western blot analysis of lysates from organoids following long-term doxycycline treatment. Sizes of endogenous and Flag-tagged FOXA1 are noted, as well as the smaller truncated form from G275X at the expected size ~38 kDa. Representative blot, experiment repeated three independent times with similar results. For source gel data, see Supplementary Fig. 1. d, Quantification of lumen areas measured at ten days after seeding. Solid black bar represents geometric mean. Values for sample size (indicated as dots) and P values are as follows: EV (292), +WT (284, P < 0.0001 over EV), +R219S (60, <0.0001), +F254_E255del (119, <0.0001), +D226N (120, <0.0001), +R261C (114, <0.0001), +R219C (333, 0.2915), +G275X (75, <0.0001), +M253_N256del (150, 0.2006), +M253K (63, 0.2343), +Y259S (32, 0.2045), +Y259C (45, 0.0082), +F266L (107, 0.1219), +H247Q (63, 0.8343), +H247R (180, <0.0001), +H247Y (71, 0.9104). All P values are relative to WT unless noted, calculated using unpaired, two-tailed Student’s t-test. Colours represent location of mutation within FOXA1. e, Histology and immunohistochemistry of organoid lines overexpressing additional alleles of FOXA1 via the doxycycline-inducible pCW vector 10 days after seeding. Images from a single biological experiment. 

Source data

Extended Data Fig. 4 Analysis of FOXA1 alterations in FOXA1-deleted or PTEN-deleted contexts.

a, CRISPR–Cas9-mediated knockdown of Foxa1 results in a markedly altered morphology. Organoids lacking Foxa1 (sgFoxa1) have a reduced capacity to form lumens while maintaining expression of AR and the basal marker p63. sgNT (guide RNA targeting human gene AAVS1) serves as a negative control. b, Western blot analysis of lysates from organoids carrying control guide RNA (sgNT) or guide RNA targeting Foxa1. Representative blot, experiment repeated three times with similar results. For source gel data, see Supplementary Fig. 1. c, Quantification of organoids containing lumens, seven days after trypsinization in normal organoid media. Data are from three biological replicates, bars represent mean ±  standard deviation, P value calculated using unpaired, two-tailed Student’s t-test. d, Sequence indicating the location of three silent point mutations introduced upstream of the PAM sequence for Foxa1-targeting RNA sgFoxa1_1. e, Western blot analysis of lysates from organoids carrying either CRISPR-Zeo-sgGFP (CZsgGFP) or CRISPR-Zeo-sgFoxa1_1 (CZsgFoxa1) in addition to the pCW construct indicated, either EV or with a Foxa1 allele present, plus or minus doxycycline treatment for ten days. Representative blot, experiment repeated twice with similar results. For source gel data, see Supplementary Fig. 1. f, Images of organoid lines carrying various combinations of guide RNA and cDNAs, ten days after doxycycline treatment. g, Quantification of lumen-containing organoids in lines with endogenous Foxa1 deleted via CRISPR–Cas9 (sgFoxa1, sgGFP as control guide) and overexpression of CRIPSR-resistant Foxa1WT or mutant cDNA ten days after seeding. Data are from two biological replicates, bars represent mean. h, Western blot analysis of lysates from PTEN-deficient organoids grafted into mice, with doxycycline-induced overexpression of appropriate FOXA1 variants. Representative blot, experiment repeated twice with similar results. For source gel data, see Supplementary Fig. 1. i, Overexpression of FOXA1(WT) or FOXA1(G275X) in sgPTEN organoids promotes tumour growth in mice at six weeks after engraftment into the flank of NOD scid gamma mice. Data are from the following number of tumours: EV = 8, +WT = 8, +R219S = 10, +F254_E255del = 10, +G275X = 9, +ERG = 10. Data are mean ± s.d., P values calculated using unpaired, two-tailed Student’s t-test vs EV. Colours represent location of mutation within FOXA1. j, Representative histology and immunohistochemistry (IHC) of a single tumour for given PTEN-deficient, FOXA1-expressing lines. Histology and immunohistochemistry were performed on 5–9 tumours per line, from a single in vivo experiment, with similar results. 

Source data

Extended Data Fig. 5 Analysis of the interplay between AR and FOXA1 in mouse organoids expressing FOXA1 variants.

a, Box plot representations of normalized counts from AR (left) and FOXA1 (right) ChIP–seq shown in Fig. 3a to quantify the reduction in AR binding following FOXA1 wild-type or mutant overexpression, and the increase in FOXA1 wild-type binding at those sites where AR is lost. Box: 25th to 75th percentile; band: median; top whisker: 75th percentile plus 1.5 times interquartile range; bottom whisker: 25th percentile minus 1.5 times interquartile range. Sample size = 2,914 peaks. P values calculated using an unpaired, one-sided Wilcoxon test. b, Western blot analysis of lysates from AR-deficient organoids generated using CRISPR–Cas9 carrying representative Foxa1 alleles. Levels are significantly reduced but AR is not completely absent (as seen on the long exposure);  this is a bulk population rather than single cell clones and thus a small number of cells escaped CRISPR–Cas9-mediated Ar deletion. Cells were treated with doxycycline for at least ten days. Representative blot, experiment repeated twice with similar results. For source gel data, see Supplementary Fig. 1. c, Expression of mouse orthologues of AR-target genes found in the AR signature used in TCGA cohort analysis based on mouse organoid RNA-seq. Genes depicted are those that have a mouse orthologue of the human gene found in the signature, and a significant expression change (DESeq2 adjusted P < 0.05) compared to EV control at 11 days of doxycycline treatment, as well as Psca, an AR-target gene expressed in mouse organoids. Data are from RNA-seq of three biological replicates. FE, F254_E255del. d, FOXA1(F254_E255del) signature can predict mutant tumours in TCGA. Hierarchical clustering and heat map of significantly differentially expressed genes between mouse FOXA1(F254_E255del) organoids and EV control (FDR ≤ 1 × 10−10). Human homologues of differentially expressed genes (DEGs) from this analysis were used to cluster FOXA1 mutant tumours (n = 14) and can detect nearly all FOXA1 mutant human tumours (P = 2.1 × 10−8) out of the 333 TCGA samples, 199 of which are ETS+. Two-sided Fisher’s exact test was used to test the enrichment of FOXA1 mutant samples within each sub-cluster, without adjustments for multiple comparisons.

Source data

Extended Data Fig. 6 Integrated analysis of ChIP–seq, ATAC-seq and RNA-seq data in FOXA1 mutant organoid lines.

a, Cluster 0 peaks have higher FOXA1 ChIP–seq signal in F254_E255del mutant organoid than empty vector control. Box plots show normalized day five AR ChIP–seq signal and FOXA1 ChIP–seq signal across different organoid lines at peaks from cluster 0, where normalization is based on background ChIP signal. FOXA1 ChIP signal is significantly higher in F254_E255del (FE) and in WT compared to EV control (P values are listed in Supplementary Table 11). Sample size = 5,260 peaks. b, Cluster 1 peaks have higher FOXA1 ChIP–seq signal and lower AR ChIP–seq signal in FOXA1(WT)-overexpressing organoids than in EV control. Box plots show normalized day five AR ChIP–seq signal and FOXA1 ChIP–seq signal across different organoid lines at peaks from cluster 1, where normalization is based on background ChIP signal. FOXA1 ChIP signal is significantly higher, and AR ChIP signal significantly lower, in WT compared to EV control. Sample size = 1,493 peaks. c, Cluster 3 peaks have higher FOXA1 ChIP–seq signal in R219S organoid than EV control. Box plots show normalized day five AR ChIP–seq signal and FOXA1 ChIP–seq signal across different organoid lines at peaks from cluster 3, where normalization is based on background ChIP signal. FOXA1 ChIP signal is significantly higher in R219S compared to EV control. Sample size = 6,641 peaks. d, Cluster 5 peaks have higher FOXA1 ChIP–seq signal and lower AR ChIP–seq signal in R219S organoid than EV control. Box plots show normalized day five AR ChIP–seq signal and FOXA1 ChIP–seq signal across different organoid lines at peaks from cluster 5, where normalization is based on background ChIP signal. FOXA1 ChIP signal is significantly higher, and AR ChIP signal significantly lower, in R219S compared to EV control. Sample size = 1,983 peaks. In ad, box: 25th to 75th percentile; band: median; top whisker: 75th percentile plus 1.5 times interquartile range; bottom whisker: 25th percentile minus 1.5 times interquartile range. P values calculated using an unpaired, one-sided Wilcoxon test. e, Genes associated with cluster 0 are significantly induced in F254_E255del mutant organoids. Top, plots show empirical cumulative distribution of log2 expression changes at 24 h vs day 0 in WT (left), F254_E255del mutant (middle) and R219S mutant (right) organoids for all expressed genes (black), genes associated with at least one ATAC-seq peak in cluster 0 (cluster 0-associated genes, red), and the top quartile of these genes based on number of assigned cluster 0 peaks (strong cluster 0-associated genes, yellow). Cluster 0-associated genes show strong expression induction compared to all genes in F254_E255del as well as in WT (red vs black) but not in R219. Bottom, As a control, similar cumulative log2 expression changes for cluster 1-associated genes (red) or strong cluster 1-associated genes (yellow) do not show significant induction in F254_E255del. All P values are listed in Supplementary Table 12 and are one-sided Wilcoxon rank-sum tests. f, Genes associated with cluster 0 are significantly induced in F254–E255del mutant organoids. Top, plots show empirical cumulative distribution of log2 expression changes at 11 days vs day 0 in WT (left), F254_E255del mutant (middle) and R219S mutant (right) organoids for all expressed genes (black), genes associated with at least one ATAC-seq peak in cluster 0 (cluster 0-associated genes, red), and the top quartile of these genes based on number of assigned cluster 0 peaks (strong cluster 0-associated genes, yellow). Cluster 0-associated genes show strong expression induction compared to all genes in F254_E255del as well as in WT but not in R219. Bottom, As a control, similar cumulative log2 expression changes for cluster 1-associated genes (red) or strong cluster 1-associated genes (yellow) do not show significant induction in F254_E255del. All P values are listed in Supplementary Table 12 and are one-sided Wilcoxon rank-sum tests. g, Genes associated with clusters 3 and 5 are significantly induced in R219S mutant organoid. Top, plots show empirical cumulative distribution of log2 expression changes at 24 h vs day 0 in WT (left), F254_E255del mutant (middle) and R219S mutant (right) organoids for all expressed genes (black), genes associated with at least one ATAC-seq peak in cluster 3 (cluster 3-associated genes, red), and the top quartile of these genes based on number of assigned cluster 0 peaks (strong cluster 3-associated genes, yellow). Cluster 3-associated genes show strong expression induction compared to all genes in R219S but not in WT or F254_E255del. Bottom, similar analysis for cumulative log2 expression changes for cluster 5-associated genes (red) and strong cluster 5-associated genes (yellow). These genes are significantly induced in R219S and repressed in F254_E255del in WT for this time point. All P values are listed in Supplementary Table 12 and are one-sided Wilcoxon rank-sum tests. h, Genes associated with clusters 3 and 5 are significantly induced in R219S mutant organoid. Top, Plots show empirical cumulative distribution of log2 expression changes at day 11 vs day 0 in WT (left), F254_E255del mutant (middle) and R219S mutant (right) organoids for all expressed genes (black), genes associated with at least one ATAC-seq peak in cluster 3 (cluster 3-associated genes, red), and the top quartile of these genes based on number of assigned cluster 0 peaks (strong cluster 3-associated genes, yellow). Cluster 3-associated genes show strong expression induction compared to all genes in R219S but not in WT or F254_E255del. Bottom, similar analysis for cumulative log2 expression changes for cluster 5-associated genes (red) and strong cluster 5-associated genes (yellow). These genes are significantly induced in R219S and repressed in F254_E255del. All P values are listed in Supplementary Table 12 and are one-sided Wilcoxon rank-sum tests. Source data

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Extended Data Fig. 7 Motif analysis of ATAC-sequencing and modification of FOXA1 reporter assay for evaluation of non-canonical FOXA1 motif.

a, FIMO motif analysis of ATAC-seq clusters. Summary of motif enrichments and depletion results for each cluster relative to the background of all differentially accessible peaks, as reported by binomial Z-score. The top 15 enriched database motifs for expressed transcription factors are shown for each cluster. In addition, enrichment–depletion results for four additional FOXA1-related motifs are shown: convergent and divergent dimer motifs, and altered FOXA1 core binding motifs with either G/A or C/T at position 6. Transcription factors in parentheses represent motifs inferred from other species. Complete lists can be found in Supplementary Tables 310. b, Top motif identified de novo using HOMER on ATAC-seq cluster 3 (R219S-specific) with motif core indicated, and variation from canonical FOXA1 motif depicted. P values derived from one-sided binomial test. c, Schematic of reporter design. The canonical response element reporter is the same reporter used in Fig. 2, with various iterations of the canonical FOXA1 motif in tandem. The non-canonical motif has substitutions at position 6, indicated in pink, to reflect the newly identified motif enriched in cluster 3 of ATAC-seq. Note that the orientation of the upper motif cartoon and the sequence in the reporter schematic are the reverse complement of the motif identified by HOMER (GTAAAR). Modified base is noted in position 6. d, Dose–response curve for activity of both FOXA1 luciferase reporters in response to increased amounts of Foxa1WT cDNA introduced into the system. Data shown are one representative biological replicate of three carried out, all showing same trends, but absolute luciferase/Renilla ratios vary from experiment to experiment. e, Results of reporter assays expressed as a relative response ratio, normalized to level of FOXA1(WT) activity for a given reporter. Data are from three biological replicates, mean ± s.d. Unpaired, two-tailed Student’s t-test.Source data

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Extended Data Fig. 8 Insert size distributions for ATAC-seq experiments, and track figures demonstrating peak reproducibility across ATAC-seq replicates.

a, Representative insert size distributions computed from individual ATAC-seq experiments based on aligned read pairs, showing modes corresponding to nucleosome-free regions, mono-nucleosomal fragments, and di-nucleosomal fragments. b, Signal tracks for individual replicate ATAC-seq experiments at the Runx2, Plekha5 and Mbnl1 loci show reproducibility of accessibility events. DEseq scaling factors estimated from the atlas of IDR-reproducible peaks were used for library size normalization. Source data

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Extended Data Fig. 9 ATAC-seq peak annotation distributions.

Fraction of peaks annotated as promoter, intergenic, intronic and exonic for full atlas of reproducible peaks, differentially accessible peaks, and by ATAC-seq cluster. See Supplementary Table 15 for full annotation counts. Source data

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Extended Data Fig. 10 MA plots for differential accessibility analysis.

a, MA plots for differential accessibility analysis relative to EV controls. Representative MA plots (log (fold change) vs mean read counts) for differential peak accessibility analysis of FOXA1 mutant- and WT-expressing organoid lines vs empty vector controls at day 0, day 1, and day 5. Peaks that are significantly differential at FDR-corrected P < 0.05 are shown in colour. Dotted lines at log (fold change) = 2 and log (fold change) = −2 show cut-offs used for requiring robust accessibility changes in pairwise comparisons. b, MA plots for differential accessibility analysis at different time points relative to day 0. Representative MA plots (log (fold change) vs mean read counts) for differential peak accessibility analysis in each organoid line at day 1 vs day 0 and day 5 vs day 0. In a, b, all sample sizes are n = 183,093 (number of peaks in the atlas). Peaks that are significantly differential at FDR-corrected P < 0.05 are shown in colour, using two-sided Wald test with Benjamini–Hochberg correction. Source data

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Supplementary information

Supplementary Figures

Supplementary Figure 1: Western Blot Source Data with Molecular Weight Markers Indicated.

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Supplementary Tables

This file contains Supplementary Tables 1-16 with a guide.

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Adams, E.J., Karthaus, W.R., Hoover, E. et al. FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes. Nature 571, 408–412 (2019). https://doi.org/10.1038/s41586-019-1318-9

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