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Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation
Open accessAbstract
SUMMARY Background Clear cell renal cell carcinoma (ccRCC) displays a variety of clinical behaviors. However, the molecular genetic events driving these behaviors are unknown. We discovered that BAP1 is mutated in approximately 15% of ccRCC and that BAP1 and PBRM1 mutations are largely mutually exclusive. The aim of this study was to investigate the clinicopathological significance of these molecular subtypes and to determine whether patients with BAP1-mutant and PBRM1-mutant tumors had different overall survival. Methods In this retrospective analysis, we assessed 145 patients with primary clear-cell renal-cell carcinoma and defined PBRM1 and BAP1 mutation status from the University of Texas Southwestern Medical Center (UTSW), TX, USA, between 1998 and 2011. We classified patients into those with BAP1-mutant tumors and those with tumors exclusively mutated for PBRM1 (PBRM1-mutant). We used a second independent cohort (n=327) from The Cancer Genome Atlas (TCGA) for validation. In both cohorts, more than 80% of patients had localized or locoregional disease at presentation. Overall both cohorts were similar, although the TCGA had more patients with metastatic and higher-grade disease, and more TCGA patients presented before molecularly targeted therapies became available. Findings The median overall survival in the UTSW cohort was significantly shorter for patients with BAP1-mutant tumors (4.6 years; 95% CI 2.1-7.2), than for patients with PBRM1-mutant tumors (10.6 years; 9.8-11.5), corresponding to a HR of 2.7 (95% CI 0.99-7.6, p=0.044). Median overall survival in the TCGA cohort was 1.9 years (95% CI 0.6-3.3) for patients with BAP1-mutant tumors and 5.4 years (4.0-6.8) for those with PBRM1-mutant tumors. A HR similar to the UTSW cohort was noted in the TCGA cohort (2.8; 95% CI 1.4-5.9; p=0.004). Patients with mutations in both BAP1 and PBRM1, although a minority (three in UTSW cohort and four in TCGA cohort), had the worst overall survival (median 2.1 years, 95% CI 0.3-3.8, for the UTSW cohort, and 0.2 years, 0.0-1.2, for the TCGA cohort). Interpretation Our findings identify mutation-defined subtypes of ccRCC with distinct clinical outcomes, a high-risk BAP1-mutant group and a favorable PBRM1-mutant group. These data establish the basis for a molecular genetic classification of ccRCC that could influence treatment decisions in the future. The existence of different molecular subtypes with disparate outcomes should be considered in the design and evaluation of clinical studies. Funding Cancer Prevention and Research Institution of Texas and NCI.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Siegel, R., Naishadham, D., & Jemal, A. (2012) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20138 · Cited by 10,679
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes
Dalgliesh, G. L., Furge, K., Greenman, C., Chen, L., Bignell, G., Butler, A., Davies, H., Edkins, S., Hardy, C., Latimer, C., et al. (2010) · Nature · doi:10.1038/nature08672 · Cited by 1,173 · cited 2×
“Other genes have been implicated in ccRCC development including SETD2, KDM5C and genes of the MLL family.(10-12, 32) How mutations in these genes relate to mutations in BAP1 and PBRM1 remains to be explored.”
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Metadata
- Authors
- Kapur, P., Peña-Llopis, S., Christie, A., Zhrebker, L., Pavía-Jiménez, A., Rathmell, W. K., Xie, X.-J., & Brugarolas, J.
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 14
- Issue
- 2
- Pages
- 159-167
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2152874907
- Cited by
- 471
- References
- 4
- FWCI
- 37.51
- Type
- journal article
- Language
- en
- Open access
- green
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- Kapur, P., Peña-Llopis, S., Christie, A., Zhrebker, L., Pavía-Jiménez, A., Rathmell, W. K., Xie, X.-J., & Brugarolas, J. (2013). Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. The Lancet Oncology. https://doi.org/10.1016/s1470-2045(12)70584-3
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