Carcinoma of prostate sequencing of tumor and clinical endpoints (CAPSTONE): Clinical implications of recurrent genomic alterations in lethal prostate cancer.
Authors: Rebernick Ryan , Hammer Liat , McFarlane Matthew , Westbrook Thomas Christopher , Hazime Munna , Hammoud Tanya , Chiu Pin-en , Owens Xavier , Wu Yi-Mi , Robinson Dan R. , Spratt Daniel , Alva Ajjai Shivaram , Jackson William C. , Reichert Zachery R , Alumkal Joshi J. , Chinnaiyan Arul , Cieslik Marcin , Dess Robert Timothy 
Published: February 2025

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Background:
Prostate cancer (PC) is genetically heterogeneous, and genomic alterations may impact prognosis and therapy response. We created CAPSTONE, a database of lethal PC that integrates comprehensive genomic sequencing with deep clinical phenotyping to explore the clinical implications of castrate resistant prostate cancer (CRPC) evolution.
Methods:
PC patients underwent tissue collection (4/05-7/21) for tumor RNA-sequencing and tumor/normal whole exome sequencing (HUM00046018, HUM00048105, HUM00067928, SU2C). Sequencing was processed using Turnkey Precision Oncology. We analyzed somatic and germline mutations, gene fusions, copy number alterations, and chromosomal instability (CIN) along with transcriptomic signatures and pathways. We collected clinical data (05/21-01/22) including overall survival from time of castrate resistant prostate cancer (OScrpc) and from time of biopsy (OSb). Patients were split into discovery and validation cohorts. We used cox proportional hazard models to evaluate OS.
Results:
Data was available for 454 men (n=192, n=262). Median follow up from CRPC was 32.1 (IQR: 14.7-50.1) and 33.3 (IQR: 20.9-55.6) months respectively. Median age at CRPC was 66 (IQR: 60-73) and 67 (IQR: 62-72) years. Of the 1,581 recurrently altered genes (>2%), 72 had a significant (p<0.01) univariate association with OS in the discovery cohort. From these, 3 (RB1, TP53, CDKN1B) were significantly associated with OS in the validation cohort (T1). Subgroup analysis revealed AR mutations but not amplifications were associated with improved OS (T1). AR amplifications were enriched in samples with TP53 alterations (p<1.0e-3, p<1.0e-3) and high CIN (p<1.0e-3, p=8.7e-3). Finally, using discovery data, we generated a gene signature associated with OS independent of TP53, RB1, and CDKN1B and validated this signature in the validation cohort (T1).
Conclusions:
RB1, TP53, and CDKN1B were recurrently altered genes independently associated with worse OS in CRPC. Further, we identified a gene signature associated with poor OS in CRPC independent of these alterations.

Association with OSb in CRPC.

Discovery
Validation

(n=192)

(n=262)

Univariate HR (95% CI) [p]

Multivariate HR (95% CI) [p]

Univariate HR (95% CI) [p]

Multivariate HR (95% CI) [p]

OSb

RB1

2.74 (1.69-4.43) [4.3e-5]

2.5 (1.47-4.24) [0.00067]

2.37 (1.53-3.68) [0.00012]

2.33 (1.49-3.63) [2e-04]

TP53

2.23 (1.57-3.11) [5.9e-6]

2.55 (1.78-3.66) [3.8e-07]

1.52 (1.14-2.03) [0.0046]

1.48 (1.1-1.98) [0.0094]

CDKN1B

3.39 (1.42-8.13) [0.0061]

3.46 (1.47-8.15) [0.0046]

2.47 (1.32-4.6) [0.0045]

2.86 (1.51-5.42) [0.0013]

Gene signature

2.08 (1.72-2.5) [1.5e-14]

1.88 (1.56-2.28) [5e-11]

1.36 (1.17-1.57) [4.3e-05]

1.32 (1.13-1.53) [0.00035]

AR amplification
1.03 (0.74-1.44) [0.86]
-
1.15 (0.86-1.54) [0.34]
-

AR mutation

0.55 (0.3-1) [0.049]

-
0.64 (0.4-1.02) [0.063]
-