LFSpro-package: TP53 mutation carrier estimation and cancer risk predictions

In wwylab/LFSPRO: TP53 germline mutation carrier estimation and cancer risk predictions

TP53 mutation carrier estimation and cancer risk predictions

Description

TP53 germline mutations are the main cause of Li-Fraumeni syndrome. This package is designed to estimate probabilities that: 1) the counselee is a TP53 germline mutation carrier, 2) the counselee develops any cancer in future, 3) the counselee develops breast cancer, sarcoma or any other cancers in future, 4) the counselee develops a first or second primary cancer in future, on the basis of his/her family cancer history. The package also provides functions for using the LFS classic and Chompret criteria.

Details

Package:	LFSPRO
Type:	Package
Version:	2.0.3
Date:	2021-09-20
License:	GPL-3

Note

New features in 2.0.3: bugs fixed in risk.mpc and risk.cs functions. Simplify the parameter interface of risk.mpc.

New features in 2.0.1: lfspro.mode is replaced by lfspro, for simplicity, which estimates future cancer risks based on primary cancer history.

New features in 2.0.0: lfspro.mode is added to replace lfspro to account for additional utilities in cancer-specific and multiple primary cancer risk predictions. Simply specify parameter "mode" to call desired functions.

New features in 1.0.5: (1) add function lfsChompret2015, (2) update the the default MAF and de novo mutation rate.

Author(s)

Gang Peng, SeungJun Shin, Jingxiao Chen, Nam Nguyen, Wenyi Wang

Maintainer: Wenyi Wang <[e-mail]>, Nam Nguyen <[e-mail]>

References

Peng, G., Bojadzieva, J., Ballinger, M. L., Li, J., Blackford, A. L., Mai, P. L., Savage, S. A., Thomas, D. M., Strong, L. C., … Wang, W. (2017). Estimating TP53 Mutation Carrier Probability in Families with Li-Fraumeni Syndrome Using LFSPRO. Cancer Epidemiology, Biomarker and Prevention, 26(6), 837-844. DOI: 10.1158/1055-9965.EPI-16-0695

Shin, S. J., Ning, J., Bojadzieva, J., Strong, L. C., and Wang, W. (2018). Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome. Biostatistics, 00, 1–16. https://doi.org/10.1093/biostatistics/kxy066

Shin, S. J., Yuan, Y., Strong, L. C., Bojadzieva, J., and Wang, W. (2018). Bayesian Semiparametric Estimation of Cancer-Specific Age-at-Onset Penetrance With Application to Li-Fraumeni Syndrome. Journal of the American Statistical Association, 1–12. https://doi.org/10.1080/01621459.2018.1482749

Chen, S., Wang, W., Broman, K. and Parmigiani, G. (2004) BayesMendel: An R Environment for Mendelian Risk Prediction. Statistical Application in Genetics and Molecular Biology, 3(1): Article 21. DOI: 10.2202/1544-6115.1063

See Also

lfspro, lfsClassic, lfsChompret2009

Examples

fam.id <- c("fam1","fam2","fam2","fam2","fam2")
id <- c(9,1,2,100,200)
counselee.id <- data.frame(fam.id, id)

# LFS classic criteria
lfsClassic(fam.data, cancer.data, counselee.id)
# Chompret criteria
lfsChompret2015(fam.data, cancer.data, counselee.id)

lfspro(fam.data, cancer.data, counselee.id)

wwylab/LFSPRO documentation built on Feb. 1, 2023, 1:05 a.m.