Analysis of BRCA1, BRCA2 and PALB2 related Fanconi anemia identifies scope to expand disease phenotypic features and predict breast cancer risk in heterozygotes

Abstract

Recessive Fanconi anemia (FA) phenotype is used in classification of BRCA1/FANCS, BRCA2/FANCD1 and PALB2/FANCN variants with respect to dominant hereditary breast-ovarian cancer syndrome. We assessed its utility by examining the spectrum of phenotypes observed in individuals biallelic for BRCA1, BRCA2 or PALB2 pathogenic variants, and exploring the relationship between cancer presentation and allele severity score based on variant molecular features. A data collection instrument comprising 158 Human Phenotype Ontology (HPO) terms was used to document clinical features for individuals with FA from published and/or prospectively collected (total n=172, 43 previously unpublished) phenotypic data. Unique FA-related variants (15 BRCA1, 123 BRCA2, 22 PALB2) were annotated for predicted molecular impact, location, observed splicing or functional impact, and potential in-frame splice rescue. Annotations were used to assign different permutations of allele severity scores, which were assessed for correlation with FA presentation features. The association of BRCA1 and BRCA2 allele severity score with magnitude of breast cancer risk in heterozygotes was evaluated using case-control analysis. Patient-detected features extended beyond the FA ORPHA:84 HPO list, including 84 terms related by hierarchy, and 94 novel terms. Genotype severity score was significantly associated with age at cancer diagnosis in BRCA2 FA individuals (p=1.8×10^-8). A similar permutation approach revealed significant differences in magnitude of breast cancer risk according to BRCA1 and BRCA2 allele severity score in heterozygotes. Findings indicate potential to redefine the existing list of FA-related HPO terms, and to use an allele severity scoring approach to predict cancer risk in both FA patients and heterozygotes.

Figure 1:. Schematic of overall study design and workflow.

See Methods and Supplementary Methods for further details on data collection, annotation, and analysis.

Figure 2:. Overlap of 158 Human Phenotype Ontology terms from three major sources.

Human Phenotype Ontology (HPO) terms included in the prospective data collection instrument were derived from Orphanet for FA (ORPHA:84, n=106) and VACTERL-H (ORPHA:3412, n=33), and ‘Other sources’ based on a review of published studies and collaborators informing development of the data collection instrument (n=42). Overlap of terms between sources was as follows: 11 VACTERL-H terms overlapped with FA; 12 ‘Other sources’ terms overlapped with FA; 3 terms were common to all three sources. The breakdown of HPO terms according to their sources is shown in Table S2.

Figure 3:. Distribution of ‘Other’ Human Phenotype Ontology terms obtained in prospective data collection.

The 178 ‘Other’ terms were distributed across phenotypic categories as follows: Neoplasms (n=2, specified by histology); Growth (n=3); Limbs and Musculoskeletal (n=36); Hair, Skin and Nails (n=14); Blood and Blood-forming tissues (n=6); Head or Neck (n=19); Nervous System (n=27); Eye (n=15); Ear (n=10); Digestive (n=11); Genitourinary (n=17); Cardiovascular (n=10); Respiratory (n=1); Prenatal/Birth (n=3); Endocrine (n=5).

Figure 4.. Overview of Phenotypic Data in Human Phenotype Ontology Terms.

This network visualization illustrates the distribution and relationships of phenotypic terms as defined by the Human Phenotype Ontology (HPO). Each circle (node) represents an HPO term, and edges (grey lines) indicate hierarchical or ontological connections among those terms, following the hierarchy of the HPO. Two key categories of terms are displayed: (1) New terms (orange nodes): these are phenotypic features previously not reported as related to Fanconi anemia, and not directly linked to HPO terms in the data collection instrument; (2) Existing terms (blue nodes): well-established HPO terms and those related by hierarchy. Node size reflects the relative frequency of a particular phenotype in the dataset—larger circles denote phenotypes observed more frequently among the subjects studied. The legend lists the specific phenotypic features representing >20% of observations, with numbers for each of these features shown on the network

Figure 5:. Comparison between frequency of reported phenotypes in published versus prospective reports for FA individuals according to phenotypic category.

Y axis shows % of individuals reported to have one characteristic in a given phenotypic category. Figure 5A compares the frequency of phenotypes among individuals diagnosed with FA under age 5 years (including prenatal FA individuals), between published (n=93), and prospective (n=64) FA individuals. Figure 5B compares the frequency of phenotypes among individuals diagnosed with FA over age 5 between published (n=35) and prospective (n=9) FA individuals. See Table 2 for details of statistical comparisons between groups.

Figure 6:. Kaplan-Meier probabilities and Cox regression analysis assessing association of genotype severity score with age at diagnosis of cancer for BRCA2 FA individuals, for best-fitting genotype severity score model.

For this model, the allele severity scores were based on: PTC (noNMD) score 2; PTC exon 10, 11, 12 score 1 (exon rescue); PTC exon 4–7 score 1 (potential multi-exon rescue); PTC exon 14 score 1 (potential escape from NMD due to large exon size); other PTC score 0; Splice site dinucleotide with regulatory impact score 1; splicing complete impact and observed transcript expected to under NMD score 0; splicing complete impact and transcript encodes larger in-frame deleted expected to escape NMD score 1; splicing partial impact score 1; missense outside domain (all with splice impact) score 0; missense in domain predicted unstable score 1; missense in domain predicted stable score 2. See Table S7 for further description of the annotations informing allele severity scores. Survival plots and Cox proportional hazard Ratios are estimated, using genotype severity score 0 as reference, for severity scores 1, 2, 3 and 4.

Figure 7.. Kaplan-Meier probabilities and Cox regression analysis assessing association of genotype severity score with age at diagnosis of cancer for BRCA2 FA individuals, considering allele score components of genotype severity.

For this model, the allele severity scores were applied as for best-fitting genotype severity model shown in Figure 6, but genotype severity was grouped to consider the component allele severity score, as follows: genotype score 0, all based on allele severity 0+0; genotype score 1, all based on allele severity 0+1; genotype score 2 based on allele severity 1+1; genotype score 2 or greater (score 2, 3, or 4) where at least one allele was coded with severity score 2, and the remaining allele was score 0 or 1 or 2. Survival plots and Cox proportional hazard Ratios are estimated, using genotype severity score 0 as reference.