Suspected clonal hematopoiesis as a natural functional assay of TP53 germline variant pathogenicity

Phenotype analysis of families with TP53 germline variants at the Center for Familial Breast and Ovarian Cancer, Cologne

PURPOSE

Tumor protein p53 (TP53) pathogenic variant (PV) carriers are identified during genetic testing for hereditary causes of cancer. PVs in TP53 are associated with the Li-Fraumeni syndrome (LFS), and thus, surveillance and preventive measures are important for TP53 PV carriers. However, the penetrance of TP53 PVs can be low if the Chompret criteria are not fulfilled. In this study, we compared the phenotypic characteristics of families that did and did not fulfill the LFS criteria according to Chompret.

METHODS

The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) database was used to identify index patients with a likely pathogenic/pathogenic TP53 variant and their family members. The study investigated the type of variant, pedigree, age of onset, number of primary tumors, and histological type of BC.

RESULTS

TP53 PV were present in the index cases of 35 families, 57% (20/35) of which fulfilled the Chompret criteria. The median age of onset at first BC diagnosis was lower in families that fulfilled the Chompret criteria compared to those who did not. Four of all diseased individuals were minors (4%; 4/105) when malignancy was first diagnosed. Sarcomas and brain tumors occurred in 10% (10/105) and in 7% (7/105) of all diseased persons, respectively. BC was the most frequently occurring first tumor (60%; 62/105) and additional malignancy (45%; 20/44) in this cohort. Subsequent malignancies developed in 31% (20/65) of the individuals who fulfilled the Chompret criteria compared with 15% (6/40) of those who did not.

CONCLUSION

The tumor spectrum and age of onset found in this study showed that tumors other than BC had low disease penetrance in TP53 PV carriers identified using the GC-HBOC criteria for genetic testing.

Estimating clinical risk in gene regions from population sequencing cohort data

While pathogenic variants can significantly increase disease risk, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2, large cohort studies find no significant association between breast cancer and rare missense variants collectively. Here, we introduce REGatta, a method to estimate clinical risk from variants in smaller segments of individual genes. We first define these regions by using the density of pathogenic diagnostic reports and then calculate the relative risk in each region by using over 200,000 exome sequences in the UK Biobank. We apply this method in 13 genes with established roles across several monogenic disorders. In genes with no significant difference at the gene level, this approach significantly separates disease risk for individuals with rare missense variants at higher or lower risk (BRCA2 regional model OR = 1.46 [1.12, 1.79], p = 0.0036 vs. BRCA2 gene model OR = 0.96 [0.85, 1.07] p = 0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare our method with existing methods and the use of protein domains (Pfam) as regions and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors and are potentially useful for improving risk assessment for genes associated with monogenic diseases.

Estimating clinical risk in gene regions from population sequencing cohort data

While pathogenic variants significantly increase disease risk in many genes, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2 , large cohort studies find no significant association between breast cancer and rare germline missense variants collectively. Here we introduce REGatta, a method to improve the estimation of clinical risk in gene segments. We define gene regions using the density of pathogenic diagnostic reports, and then calculate the relative risk in each of these regions using 109,581 exome sequences from women in the UK Biobank. We apply this method in seven established breast cancer genes, and identify regions in each gene with statistically significant differences in breast cancer incidence for rare missense carriers. Even in genes with no significant difference at the gene level, this approach significantly separates rare missense variant carriers at higher or lower risk ( BRCA2 regional model OR=1.46 [1.12, 1.79], p=0.0036 vs. BRCA2 gene model OR=0.96 [0.85,1.07] p=0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare with existing methods and the use of protein domains (Pfam) as regions, and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors which can potentially be used to improve risk assessment and clinical management.