Computational analysis of somatic evolution across primary and metastatic castration-resistant prostate cancer (mCRPC) using large-scale, multi-cohort genomic datasets.
This repository contains the R analysis and visualization workflows supporting our study:
Rajaei M., Yang A., Cross C.N., Glasmacher K., Fisk J.N., Perry E.B., Mandell J.D., Gaffney S.G., Yamaguchi T.N., Livingstone J., Costa J., Humphrey P., Cannataro V.L., Boutros P.C., Townsend J.P. Somatic evolution of prostate cancer: mutation, selection, and epistasis across disease stages. bioRxiv (2025).
We analyzed 2,704 primary and mCRPC tumors from multiple cohorts and sequencing platforms to characterize how mutation, selection, and genetic interactions shape prostate cancer evolution.
The study quantifies mutation rates and somatic selection across disease stages and identifies synergistic and antagonistic epistatic interactions among key cancer drivers, revealing a dynamic evolutionary landscape from tumor initiation to mCRPC.
The study combines tumors profiled using:
- Whole-genome sequencing (WGS)
- Whole-exome sequencing (WES)
- Targeted sequencing
The analyses address several major questions:
- How do somatic mutation profiles change across stages of prostate cancer?
- Which driver genes experience the strongest selection during primary tumor development and metastatic progression?
- How do mutation rates and cancer effect sizes differ across disease stages?
- Which driver mutations are selected early versus late during tumor evolution?
- How does the presence of one driver mutation alter selection on subsequent mutations?
- Which genetic interactions may contribute to progression toward metastatic and treatment-resistant disease?
The integrated dataset contains 2,704 prostate tumors. Samples were excluded if they contained only nucleotide base substitutions at known germline variant sites, within repetitive regions, or no nucleotide base substitutions, yielding a final set of 2,618 samples for downstream analysis
| Disease group | Number of tumors |
|---|---|
| Primary prostate cancer | 1,593 |
| Metastatic castration-resistant prostate cancer (mCRPC) | 1,025 |
| Total | 2,618 |
Primary tumors were additionally stratified by Gleason Grade Group to investigate evolutionary differences across disease severity.
The combined dataset incorporates multiple independent prostate cancer cohorts, including data from:
- TCGA
- MSK cohorts
- SU2C/PCF
- Armenia cohort
- Boutros cohort
- Additional published prostate cancer sequencing studies
The combined dataset includes:
| Sequencing strategy | Approximate number of tumors |
|---|---|
| Whole-genome sequencing | 293 |
| Whole-exome sequencing | 1,122 |
| Targeted sequencing | 1,289 |
The overall workflow can be summarized as:
Multiple prostate cancer cohorts
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Mutation data harmonization
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Disease-stage classification
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Mutation prevalence analysis
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Trinucleotide mutation profiles
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Gene-specific mutation rates
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Cancer effect size analysis
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┌─────┴─────┐
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Stage-specific Driver-specific
selection selection
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└─────┬─────┘
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Pairwise epistasis
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Evolutionary interpretation
Figure_1_prevalence.R
Prevalence of variants in 16 selected driver genes, in low-grade primary tumors, high-grade primary tumors, and mCRPC.
Figure_2_trinucleotide_mutation_profiles.R
Percent of single-nucleotide somatic variants within each trinucleotide context in low-grade primary tumors, high-grade primary tumors, and mCRPC.
Figure_3_gene_mutrate.R
The gene-level mutation rates spanning from organogenesis to low-grade primary tumors, organogenesis to high-grade primary tumors, organogenesis to mCRPC, and in organogenesis to mCRPC versus organogenesis to low-grade primary tumorigenesis, organogenesis to mCRPC versus organogenesis to high-grade primary tumorigenesis, organogenesis to mCRPC versus primary tumorigenesis.
Figure_4_CES.R
Gene-level estimates and 95% confidence intervals for scaled selection coefficients on somatic variants in oncogenic sites of 16 genes that are known to act as drivers in prostate cancer tumorigenesis and metastasis.
Figure_5_SPOP.R
Scaled selection coefficients for recurrent single-nucleotide variant amino-acid substitutions in SPOP during the evolutionary trajectory from prostate organogenesis to primary and mCRPC tumors.
Related visualization:
SPOP_model_recurrent_resized.png
Figure_6_AR.R
Scaled selection coefficients of recurrent single-nucleotide variant amino-acid substitutions in AR along the step from primary tumors to mCRPC.
Related visualization:
AR_labeled_2.png
Figure_7_Pairwise_Epistatic_Effects.R
Pairwise epistatic effect trends of 15 genes, focusing on SPOP, PIK3CA, TP53, and AR.
- Mutation load and mutation rates increase during prostate cancer progression, while trinucleotide mutational patterns remain relatively stable.
- SPOP mutations in the BRD3-binding domain experience strong early positive selection and increase subsequent selection for RHOA while decreasing selection for TP53.
- CUL3 shows antagonistic selective epistasis with both SPOP and PIK3CA.
- KMT2C mutations increase selection for subsequent TP53 mutations.
- PTEN mutations increase selection for both PIK3CA and AR, revealing strong synergistic epistatic interactions.
Together, these results reveal a dynamic landscape of mutation, selection, and epistasis across prostate cancer progression.
| File | Analysis |
|---|---|
Figure_1_prevalence.R |
Somatic mutation prevalence across disease groups |
Figure_2_trinucleotide_mutation_profiles.R |
Trinucleotide mutation-spectrum analysis |
Figure_3_gene_mutrate.R |
Gene-specific mutation-rate analysis |
Figure_4_CES.R |
Cancer effect size analysis |
Figure_5_SPOP.R |
SPOP-specific evolutionary analysis |
Figure_6_AR.R |
AR-specific evolutionary analysis |
Figure_7_Pairwise_Epistatic_Effects.R |
Pairwise epistasis among prostate cancer drivers |
Figure_S1.R |
Supplementary analysis |
Figure_S2.R |
Supplementary analysis |
Figure_S3.R |
Supplementary analysis |
new_sequential_lik.R |
Sequential likelihood/statistical modeling functions |
The repository contains harmonized mutation data from multiple prostate cancer cohorts, including:
MSK_341_final.maf.txt
MSK_410_final.maf.txt
MSK_468_final.maf.txt
SU2C_final.maf.txt
tcga_final.maf.txt
tcga_wgs_final.maf.txt
armenia_final.maf.txt
boutros_final.maf.txt
prad_armenia_final.maf.txt
prad_boutros_wgs_final.maf.txt
Mutation data are primarily represented using Mutation Annotation Format (MAF) files.
SureSelect_All_Exon_covered_regions.bed
msk_341_exons.bed
msk_410_exons.bed
msk_468_exons.bed
gleason.txt
gleason_age_comparison.txt
The analyses were primarily developed using:
- R
- Linux
- High-performance computing environments
- Git/GitHub for version control
The main figure scripts correspond directly to analyses presented in the associated manuscript.
A typical analysis workflow is:
1. Obtain and harmonize cohort-specific mutation data
2. Define genomic coverage for each sequencing platform
3. Assign samples to disease-stage groups
4. Analyze mutation prevalence
5. Calculate trinucleotide mutation profiles
6. Estimate gene-specific mutation rates
7. Quantify cancer effect sizes
8. Analyze individual prostate cancer drivers
9. Estimate pairwise epistatic effects
10. Generate manuscript figures
Rajaei M, Yang A, Cross CN, Glasmacher K, Fisk JN, Perry EB, Mandell JD, Gaffney SG, Yamaguchi TN, Livingstone J, Costa J, Humphrey P, Cannataro VL, Boutros PC, Townsend JP. Somatic evolution of prostate cancer: mutation, selection, and epistasis across disease stages. bioRxiv. 2025.
Moein Rajaei, Ph.D. Computational Biologist | Genomics & Bioinformatics Scientist
- GitHub: https://github.com/moeinrajaei
- ORCID: https://orcid.org/my-orcid?orcid=0009-0006-7079-2033
- Google Scholar: https://scholar.google.com/citations?user=LbgtrokAAAAJ&hl=en&oi=ao
- LinkedIn: https://www.linkedin.com/in/moein-rajaei-ph-d-3a5a136b/
If you use code or analytical methods from this repository, please cite:
Rajaei M, et al. Somatic evolution of prostate cancer: mutation, selection, and epistasis across disease stages. bioRxiv. 2025.