Biology subjects

Bojadzieva, J.

Publications and source records attributed to Bojadzieva, J..

3 recordsLinked to original sources

bioRxiv2020

A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome

De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation (FM)) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: 1) cancer and mutation spectra along with parental ages were similarly distributed; 2) ascertainment criteria like early-onset breast cancer (age 20 to 35 years) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs versus 25 FMs); 3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome, and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.

genetics↗

bioRxiv2019

Risk of multiple primary cancer diagnosis over age in families of Li-Fraumeni syndrome: a single institution perspective

Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with TP53 germline mutations and an increased lifetime risk of multiple primary cancers (MPC). Penetrance estimation of time to first and second primary cancer within LFS remains challenging due to limited data availability and the difficulty of accounting for the characteristic effects of a primary cancer on the penetrance of a second primary cancer. Using a recurrent events survival modeling approach, we estimated penetrance for both first and second primary cancer diagnosis from a pediatric sarcoma cohort (number families=189; Single primary cancer, or SPC=771; MPC=87). We then validated the risk prediction performance using an independent clinical cohort of TP53 tested individuals from MD Anderson (SPC=102; MPC=58). Our penetrance estimates are dependent on TP53 genotype, sex, and, importantly, age of diagnosis (AoD) for the first PC. We observed the later the AoD is, the shorter the gap time is for this person to be diagnosed with a second PC. We achieved an Area under the ROC curve (AUC) of 0.77 for predicting individual outcomes of MPC vs. SPC. In summary, we have provided the first set of penetrance estimates for SPC and MPC for TP53 mutation carriers, and demonstrated its accuracy for cancer risk assessment. Our open-source R package, LFSPRO, provides future risk estimates for SPC and MPC among TP53 germline mutation carriers.\n\nSignificanceOur tool can be used to support clinical counseling of LFS cancer survivors for better health management.

genetics↗

bioRxiv2019

Risk of differential cancer types over age in families with Li-Fraumeni syndrome: a validation study using multi-center cohorts

Li-Fraumeni syndrome (LFS) is a rare hereditary cancer syndrome associated with an autosomal dominant mutation inheritance in the TP53 tumor suppressor gene and a wide spectrum of cancer diagnoses. An accurate estimation of the penetrance of different cancer types in LFS is crucial to improve the clinical characterization and management of high-risk individuals of LFS. Our competing risk-based statistical model incorporates the pedigree structure efficiently into the penetrance estimation and corrects for the ascertainment bias. A set of TP53 penetrance for three cancer types (breast, BR/sarcoma, SA/others, OT) involved in LFS is then estimated from 186 pediatric-sarcoma families collected at MD Anderson Cancer Center. The penetrance was then validated on a mixed cohort of clinically ascertained families with cancer (total number of families=668). The age-dependent onset probability distributions of specific cancer types are different. For breast cancer, the TP53 penetrance goes up at an earlier age than the reported BRCA1/2 penetrance. We validated the prediction performance of the penetrance estimates via two independent cohorts combined (BR=85, SA=540, OT=158). We obtained areas under the ROC curves (AUCs) of 0.92 (BR), 0.75 (SA), and 0.81 (OT). Our R package, LFSPRO, provides risk estimates for the diagnosis of breast cancer, sarcoma, other cancers or death before cancer diagnosis in future.\n\nSignificanceCancer-specific penetrance can facilitate clinical characterization of LFS and will contribute to the management of families at high risk of LFS.

genetics↗