. 2023 Aug 15;83(16):2763-2774.

doi: 10.1158/0008-5472.CAN-23-0593.

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer

Arian Lundberg^{1

2}, Meng Zhang^{1

2}, Rahul Aggarwal^{1

3}, Haolong Li^{1

2}, Li Zhang^{1

4}, Adam Foye^{1

2}, Martin Sjöström^{1

2}, Jonathan Chou^{1

3}, Kevin Chang^{1

3}, Thaidy Moreno-Rodriguez^{1

5}, Raunak Shrestha^{1

2}, Avi Baskin^{1

2}, Xiaolin Zhu^{1

3}, Alana S Weinstein^{1

2}, Noah Younger^{1

3}, Joshi J Alumkal⁶, Tomasz M Beer⁷, Kim N Chi⁸, Christopher P Evans^{9

10}, Martin Gleave⁸, Primo N Lara^{9

11}, Rob E Reiter^{12

13}, Matthew B Rettig^{12

13

14}, Owen N Witte¹⁵, Alexander W Wyatt^{8

16}, Felix Y Feng^#^{1

2

3

5}, Eric J Small^#^{1

3}, David A Quigley^#^{1

5

4}

Affiliations

¹ Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California.
² Department of Radiation Oncology, University of California San Francisco, San Francisco, California.
³ Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, California.
⁴ Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.
⁵ Department of Urology, University of California San Francisco, San Francisco, California.
⁶ Division of Hematology and Oncology, University of Michigan Rogel Cancer Center, Ann Arbor, Michigan.
⁷ Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.
⁸ Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
⁹ Comprehensive Cancer Center, University of California Davis, Sacramento, California.
¹⁰ Department of Urologic Surgery, University of California Davis, Sacramento, California.
¹¹ Division of Hematology Oncology, Department of Internal Medicine, University of California Davis, Sacramento, California.
¹² Departments of Medicine, Hematology/Oncology and Urology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
¹³ Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California.
¹⁴ VA Greater Los Angeles Healthcare System, Los Angeles, California.
¹⁵ Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
¹⁶ Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada.

^# Contributed equally.

PMID: 37289025
PMCID: PMC10425725
DOI: 10.1158/0008-5472.CAN-23-0593

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer

Arian Lundberg et al. Cancer Res. 2023.

. 2023 Aug 15;83(16):2763-2774.

doi: 10.1158/0008-5472.CAN-23-0593.

Authors

Affiliations

¹ Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California.
² Department of Radiation Oncology, University of California San Francisco, San Francisco, California.
³ Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, California.
⁴ Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.
⁵ Department of Urology, University of California San Francisco, San Francisco, California.
⁶ Division of Hematology and Oncology, University of Michigan Rogel Cancer Center, Ann Arbor, Michigan.
⁷ Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.
⁸ Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
⁹ Comprehensive Cancer Center, University of California Davis, Sacramento, California.
¹⁰ Department of Urologic Surgery, University of California Davis, Sacramento, California.
¹¹ Division of Hematology Oncology, Department of Internal Medicine, University of California Davis, Sacramento, California.
¹² Departments of Medicine, Hematology/Oncology and Urology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
¹³ Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California.
¹⁴ VA Greater Los Angeles Healthcare System, Los Angeles, California.
¹⁵ Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
¹⁶ Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada.

^# Contributed equally.

PMID: 37289025
PMCID: PMC10425725
DOI: 10.1158/0008-5472.CAN-23-0593

Abstract

Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterized treatment-emergent mCRPC by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing from 210 tumors. AR-/NE- tumors were clinically and molecularly distinct from other mCRPC subtypes, with the shortest survival, amplification of the chromatin remodeler CHD7, and PTEN loss. Methylation changes in CHD7 candidate enhancers were linked to elevated CHD7 expression in AR-/NE+ tumors. Genome-wide methylation analysis nominated Krüppel-like factor 5 (KLF5) as a driver of the AR-/NE- phenotype, and KLF5 activity was linked to RB1 loss. These observations reveal the aggressiveness of AR-/NE- mCRPC and could facilitate the identification of therapeutic targets in this highly aggressive disease.

Significance: Comprehensive characterization of the five subtypes of metastatic castration-resistant prostate cancer identified transcription factors that drive each subtype and showed that the double-negative subtype has the worst prognosis.

PubMed Disclaimer

Figures

Figure 1. mCRPC tumors cluster into five groups using the expression of androgen, neuroendocrine, and squamous gene panels. A and B, Heat map representing RNA-seq gene expression level of AR, NE, and squamous (SQUAM) gene panels of mCRPC tumors from the WCDT cohort (A; refs. 5, 11) and the Beltran and colleagues cohort (B; ref. 7). Results are expressed as log2 TPM (z-score) and colored from low (blue) to high (yellow) expression level. AR gene panel includes AR and AR-regulated genes, NE gene panels (NE1 and NE2) include NE related genes, and SQUAM panel includes genes associated with squamous cell differentiation. The expression levels of genes included in neuroendocrine prostate cancer (NEPC) panel from Beltran and colleagues cohort (7) were used to assign a binary classification (Binary class) of the samples based on their gene expression. White, adenocarcinoma tumors; black, small cell NEPC. AR and NEPC signature scores were calculated based on the AR and NEPC-related gene expression values as reported previously (7). The tumor subtypes can be read as follows: AR+/NE−, dark turquoise; ARL/NE−, dark orange; AR−/NE−, light purple; AR−/NE+, pink; AR+/NE+, light green. — **Figure 1.**
mCRPC tumors cluster into five groups using the expression of androgen, neuroendocrine, and squamous gene panels. A and B, Heat map representing RNA-seq gene expression level of AR, NE, and squamous (SQUAM) gene panels of mCRPC tumors from the WCDT cohort (A; refs. 5, 11) and the Beltran and colleagues cohort (B; ref. 7). Results are expressed as log₂ TPM (z-score) and colored from low (blue) to high (yellow) expression level. AR gene panel includes AR and AR-regulated genes, NE gene panels (NE1 and NE2) include NE related genes, and SQUAM panel includes genes associated with squamous cell differentiation. The expression levels of genes included in neuroendocrine prostate cancer (NEPC) panel from Beltran and colleagues cohort (7) were used to assign a binary classification (Binary class) of the samples based on their gene expression. White, adenocarcinoma tumors; black, small cell NEPC. AR and NEPC signature scores were calculated based on the AR and NEPC-related gene expression values as reported previously (7). The tumor subtypes can be read as follows: AR+/NE−, dark turquoise; ARL/NE−, dark orange; AR−/NE−, light purple; AR−/NE+, pink; AR+/NE+, light green.

Figure 2. Distinct clinical outcomes associated with the five subtypes of mCRPC. A, Heat map representing results of ssGSEAs and colored according to the legends. B, Kaplan–Meier curves representing clinical outcome of patients in the WCDT cohort, using survival from date of biopsy acquisition as the clinical outcome. Pairwise test conducted between AR−/NE− and other subtypes. The tumor subtypes can be read as follows: AR+/NE−, dark turquoise; ARL/NE−, dark orange; AR−/NE−, light purple; AR−/NE+, pink; AR+/NE+, light green. — **Figure 2.**
Distinct clinical outcomes associated with the five subtypes of mCRPC. A, Heat map representing results of ssGSEAs and colored according to the legends. B, Kaplan–Meier curves representing clinical outcome of patients in the WCDT cohort, using survival from date of biopsy acquisition as the clinical outcome. Pairwise test conducted between AR−/NE− and other subtypes. The tumor subtypes can be read as follows: AR+/NE−, dark turquoise; ARL/NE−, dark orange; AR−/NE−, light purple; AR−/NE+, pink; AR+/NE+, light green.

Figure 3. Somatic and structural alterations associated with subtypes of mCRPC. A, Top rows show mCRPC subtypes, ETS family fusions, TMPRSS2-ERG fusions, tumor purity, and tumor ploidy in the WCDT cohort. Bottom rows show occurrence of AR, AR enhancer, PTEN, RB1, TP53, MYC, BRCA2, and CHD7 alterations in each sample. Tumors are sorted by their subtypes. Alteration frequency is shown to the right. B, Bar plots representing alteration frequency (%) of AR, PTEN, RB1, TP53, MYC, BRCA2, and CHD7 genes within each subtype. In both panels, types of alterations are colored (and/or marked with symbols) according to the legends. — **Figure 3.**
Somatic and structural alterations associated with subtypes of mCRPC. A, Top rows show mCRPC subtypes, ETS family fusions, TMPRSS2-ERG fusions, tumor purity, and tumor ploidy in the WCDT cohort. Bottom rows show occurrence of AR, AR enhancer, *PTEN*, *RB1*, *TP53, MYC, BRCA2*, and *CHD7* alterations in each sample. Tumors are sorted by their subtypes. Alteration frequency is shown to the right. B, Bar plots representing alteration frequency (%) of AR, *PTEN*, *RB1*, *TP53, MYC, BRCA2*, and *CHD7* genes within each subtype. In both panels, types of alterations are colored (and/or marked with symbols) according to the legends.

Figure 4. Hypomethylation in the putative enhancer regions of CHD7 is correlated with elevated gene expression in AR−/NE+. Integration of gene expression and DNA-methylation data for the CHD7 gene. A, Box plots representing CHD7 gene expression in the five mCRPC subtypes, colored according to the key below the plot. B, Top, chromosomal location of the CHD7 gene along with H3K27ac ChIP-seq marker, DHS, and DMRs in AR−/NE+ tumors compared with AR+/NE−. Bottom, ChIP-seq data for ASCL1 in different cell lines as indicated in the panels. The vertical dashed green and red lines show the transcription start site and transcription end site of the CHD7 gene, respectively. The yellow bar indicates the canonical promoter region of CHD7. C–F, Box plots showing mean methylation level per sample in DMR1 (C), DMR2 (D), DMR3 (E), and DMR4 (F) for AR−/NE−, AR−/NE+, and AR+/NE− subtypes. Pearson correlations were calculated between CHD7 gene expression and mean methylation of each sample at DMRs1–4. Box plots should be interpreted as follows: horizontal lines, median values; boxes extend from the 25th to the 75th percentile of each group's distribution of values; vertical extending lines, adjacent values (the most extreme values within 1.5 interquartile range of the 25th and 75th percentile of each group). Differences between groups were assessed by the Kruskal–Wallis test. Significance is indicated as follows: ns, not significant; *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001. G, Venn diagram representing the overlap between the top 10 TF motifs enriched at each DMR location. Neuroendocrine-lineage motifs found in DMRs are labeled in the panel. — **Figure 4.**
Hypomethylation in the putative enhancer regions of *CHD7* is correlated with elevated gene expression in AR−/NE+. Integration of gene expression and DNA-methylation data for the *CHD7* gene. A, Box plots representing *CHD7* gene expression in the five mCRPC subtypes, colored according to the key below the plot. B, Top, chromosomal location of the *CHD7* gene along with H3K27ac ChIP-seq marker, DHS, and DMRs in AR−/NE+ tumors compared with AR+/NE−. Bottom, ChIP-seq data for *ASCL1* in different cell lines as indicated in the panels. The vertical dashed green and red lines show the transcription start site and transcription end site of the *CHD7* gene, respectively. The yellow bar indicates the canonical promoter region of *CHD7*. **C–F,** Box plots showing mean methylation level per sample in DMR1 (C), DMR2 (D), DMR3 (E), and DMR4 (F) for AR−/NE−, AR−/NE+, and AR+/NE− subtypes. Pearson correlations were calculated between *CHD7* gene expression and mean methylation of each sample at DMRs1–4. Box plots should be interpreted as follows: horizontal lines, median values; boxes extend from the 25th to the 75th percentile of each group's distribution of values; vertical extending lines, adjacent values (the most extreme values within 1.5 interquartile range of the 25th and 75th percentile of each group). Differences between groups were assessed by the Kruskal–Wallis test. Significance is indicated as follows: ns, not significant; *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001. G, Venn diagram representing the overlap between the top 10 TF motifs enriched at each DMR location. Neuroendocrine-lineage motifs found in DMRs are labeled in the panel.

Figure 5. Gene expression and DNA methylation analysis converges on KLF5 TF in AR−/NE− tumors. A, Heat map representing differentially expressed TFs in five subtypes of mCRPC. B, Heat map representing top 20 enriched TFs in hypomethylated regions of the five mCRPC subtypes. TFs are ranked by log (P value). The color intensity indicates the rank of the TFs from most enriched (dark red) to least enriched (white). — **Figure 5.**
Gene expression and DNA methylation analysis converges on *KLF5* TF in AR−/NE− tumors. A, Heat map representing differentially expressed TFs in five subtypes of mCRPC. B, Heat map representing top 20 enriched TFs in hypomethylated regions of the five mCRPC subtypes. TFs are ranked by log (P value). The color intensity indicates the rank of the TFs from most enriched (dark red) to least enriched (white).

Figure 6. Association between KLF5 TF enrichment and RB1 gene loss in AR−/NE− tumors. A, Rank order plots show the enrichment rank of KLF5 in AR−/NE− and AR−/NE+ subtypes on the left to right. Dashed red color indicates rank 20. B, Bar plots showing the gene set enrichment analyses for genes mapped to the KLF5 motif. Dashed line, FDR = 0.05. C, Scatter plots representing Spearman correlation between KLF5 gene expression and KRT5, KRT8, RB1, and CCNB2 genes. D, Scatter plots showing the relation between RB1 gene expression and RB1 copy numbers (top row), KLF5 gene expression and KLF5 copy numbers (middle row), and KLF5 gene expression and RB1 copy number (bottom row). — **Figure 6.**
Association between *KLF5* TF enrichment and *RB1* gene loss in AR−/NE− tumors. A, Rank order plots show the enrichment rank of *KLF5* in AR−/NE− and AR−/NE+ subtypes on the left to right. Dashed red color indicates rank 20. B, Bar plots showing the gene set enrichment analyses for genes mapped to the KLF5 motif. Dashed line, FDR = 0.05. C, Scatter plots representing Spearman correlation between *KLF5* gene expression and *KRT5*, *KRT8*, *RB1*, and *CCNB2* genes. D, Scatter plots showing the relation between *RB1* gene expression and *RB1* copy numbers (top row), *KLF5* gene expression and *KLF5* copy numbers (middle row), and *KLF5* gene expression and *RB1* copy number (bottom row).

See this image and copyright information in PMC

Cited by

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition.
Wilkinson S, Ku AT, Lis RT, King IM, Low D, Trostel SY, Bright JR, Terrigino NT, Baj A, Fenimore JM, Li C, Vo B, Jansen CS, Ye H, Whitlock NC, Harmon SA, Carrabba NV, Atway R, Lake R, Kissick HT, Pinto PA, Choyke PL, Turkbey B, Dahut WL, Karzai F, Sowalsky AG. Wilkinson S, et al. medRxiv [Preprint]. 2024 Feb 11:2024.02.09.24302395. doi: 10.1101/2024.02.09.24302395. medRxiv. 2024. PMID: 38370835 Free PMC article. Preprint.
Androgen deprivation induces double-null prostate cancer via aberrant nuclear export and ribosomal biogenesis through HGF and Wnt activation.
Kim WK, Buckley AJ, Lee DH, Hiroto A, Nenninger CH, Olson AW, Wang J, Li Z, Vikram R, Adzavon YM, Yau TY, Bao Y, Kahn M, Geradts J, Xiao GQ, Sun Z. Kim WK, et al. Nat Commun. 2024 Feb 9;15(1):1231. doi: 10.1038/s41467-024-45489-4. Nat Commun. 2024. PMID: 38336745 Free PMC article.
Unravelling the Role of Iroquois Homeobox 4 and its Interplay with Androgen Receptor in Prostate Cancer.
Janaththani P, Tevz G, Fernando A, Malik A, Rockstroh A, Kryza T, Walpole C, Moya L, Lehman M, Nelson C; Australian Prostate Cancer BioResource; Practical Consortium; Srinivasan S, Clements J, Batra J. Janaththani P, et al. Res Sq [Preprint]. 2023 Oct 24:rs.3.rs-3295914. doi: 10.21203/rs.3.rs-3295914/v1. Res Sq. 2023. PMID: 38076926 Free PMC article. Preprint.
The Multifaceted Role of Osteopontin in Prostate Pathologies.
Silver SV, Popovics P. Silver SV, et al. Biomedicines. 2023 Oct 26;11(11):2895. doi: 10.3390/biomedicines11112895. Biomedicines. 2023. PMID: 38001899 Free PMC article. Review.
ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer.
Qian C, Yang Q, Rotinen M, Huang R, Kim H, Gallent B, Yan Y, Cadaneanu RM, Zhang B, Kaochar S, Freedland SJ, Posadas EM, Ellis L, Vizio DD, Morrissey C, Nelson PS, Brady L, Murali R, Campbell MJ, Yang W, Knudsen BS, Mostaghel EA, Ye H, Garraway IP, You S, Freeman MR. Qian C, et al. bioRxiv [Preprint]. 2023 Oct 12:2023.09.28.560025. doi: 10.1101/2023.09.28.560025. bioRxiv. 2023. PMID: 37905039 Free PMC article. Preprint.

See all "Cited by" articles

References

1. Cancer facts & figures 2022 [Internet]. American Cancer Society, Atlanta, Ga; 2022. Available from:https://www.cancer.org/cancer/prostate-cancer/detection-diagnosis-stagin....
1. Watson PA, Arora VK, Sawyers CL. Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer. Nat Rev Cancer 2015;15:701–11. - PMC - PubMed
1. Taplin M-E, Bubley GJ, Shuster TD, Frantz ME, Spooner AE, Ogata GK, et al. . Mutation of the androgen-receptor gene in metastatic androgen-independent prostate cancer. N Engl J Med 1995;332:1393–8. - PubMed
1. Robinson D, Van Allen EM, Wu Y-M, Schultz N, Lonigro RJ, Mosquera J-M, et al. . Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–28. - PMC - PubMed
1. Quigley DA, Dang HX, Zhao SG, Lloyd P, Aggarwal R, Alumkal JJ, et al. . Genomic hallmarks and structural variation in metastatic prostate cancer. Cell 2018;174:758–69. - PMC - PubMed

Publication types

Actions
Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Substances

Actions
Actions
Actions

Grants and funding

P50 CA186786/CA/NCI NIH HHS/United States

LinkOut - more resources

Full Text Sources
Medical
- Genetic Alliance
Research Materials
- NCI CPTC Antibody Characterization Program
Miscellaneous
- NCI CPTAC Assay Portal

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer

Affiliations

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer

Authors

Affiliations

Abstract

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Miscellaneous

Abstract

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Miscellaneous