Germline mutations in TP53 cause a rare high penetrance cancer syndrome, Li–Fraumeni syndrome (LFS). Here, we identified a rare TP53 tetramerization domain missense mutation, c.1000G>C;p.G334R, in a family with multiple late-onset LFS-spectrum cancers. Twenty additional c.1000G>C probands and one c.1000G>A proband were identified, and available tumors showed biallelic somatic inactivation of TP53. The majority of families were of Ashkenazi Jewish descent, and the TP53 c.1000G>C allele was found on a commonly inherited chromosome 17p13.1 haplotype. Transient transfection of the p.G334R allele conferred a mild defect in colony suppression assays. Lymphoblastoid cell lines from the index family in comparison with TP53 normal lines showed that although classical p53 target gene activation was maintained, a subset of p53 target genes (including PCLO, PLTP, PLXNB3, and LCN15) showed defective transactivation when treated with Nutlin-3a. Structural analysis demonstrated thermal instability of the G334R-mutant tetramer, and the G334R-mutant protein showed increased preponderance of mutant conformation. Clinical case review in comparison with classic LFS cohorts demonstrated similar rates of pediatric adrenocortical tumors and other LFS component cancers, but the latter at significantly later ages of onset. Our data show that TP53 c.1000G>C;p.G334R is found predominantly in Ashkenazi Jewish individuals, causes a mild defect in p53 function, and leads to low penetrance LFS.

Significance:

TP53 c.1000C>G;p.G334R is a pathogenic, Ashkenazi Jewish–predominant mutation associated with a familial multiple cancer syndrome in which carriers should undergo screening and preventive measures to reduce cancer risk.

Li–Fraumeni syndrome (LFS; ref. 1) is caused by germline mutations in the TP53 gene that disrupt the tumor-suppressive function of p53 (chromosome 17p13.1; OMIM 191170). p53 is a transcription factor responsible for genomic integrity and transactivation of downstream genes critical for cell-cycle arrest, DNA repair, and/or apoptosis (2). Classic LFS is characterized by a diverse spectrum of malignancies that may begin in childhood and continues through life, including soft tissue and bone sarcomas, breast cancers, brain and adrenocortical tumors (ACT), and leukemia (1). Families with incomplete features of LFS are referred to as Li–Fraumeni-Like (LFL) for which several clinical definitions have been proposed (3). It is recommended that individuals with germline TP53 pathogenic mutations undergo high-risk surveillance, with the option of tissue-specific prophylactic surgery (4). Recently, surveillance in TP53 carriers using whole-body MRI has shown clinical utility (4).

The majority of cancer-associated TP53 mutations are missenses in the DNA-binding domain (DBD; ref. 5). However, pathogenic mutations can be found in the tetramerization domain (TD), resulting in alteration of p53 oligomerization, integrity of the tetramer, and DNA affinity (6, 7). The most well-documented germline TD missense mutation is the c.1010G>A, p.R337H (rs121912664) founder mutation clustered in the southern Brazilian population (8). A significant portion of families with c.1010G>A;p.R337H have a predominance of childhood ACT (9). Functionally, TD missense mutations have variable properties; some but not all variants reduce transactivation, inhibit oligomerization, or cause temperature or pH-dependent tetramer instability (10, 11).

Despite the robust functional characterization of the majority of possible TP53 variants (12), significant challenges in clinically validated variant classification remain. Research beyond the DBD is limited, and in vitro p53 studies may not be accurate at predicting clinical phenotype. Rare variants may not be seen at sufficient frequency within a single commercial laboratory to allow effective use of internal cross-family data in variant classification. In addition, phenotypic and cosegregation data are not routinely pooled across laboratories nor made openly accessible to the clinical genetics community. These issues are particularly relevant with respect to rare hypomorphic mutations with potentially attenuated clinical phenotypes. Consequently, collaborations between academic centers and commercial laboratories are necessary to assemble enough cases for analysis of rare suspected pathogenic mutations. Presently, there are large-scale initiatives to enhance data sharing and communications among gene-specific experts, as represented by National Institute of Healthy Initiative Clinical Genome Resource (ClinGen; ref. 13); however, these initiatives are still in their infancy. We present aggregate data between multiple clinical sites and two laboratories supplemented with functional and structural studies for a rare TP53 TD mutation, c.1000G>C;p.G334R, which has discordant classifications across clinical laboratories and has been reviewed by ClinGen's “TP53 variant curation expert panel” and determined to be a variant of uncertain significance based upon preexisting published data.

Case ascertainment

The index proband carrying TP53 c.1000G>C;p.G334R was identified in a research sequencing (RS) study (14), and all family members were studied under an Institutional Review Board (IRB)–approved protocol. Additional probands/families with TP53 c.1000G>C;p.G334R were ascertained from clinical practice sites, genetic testing laboratories, and online resources. Clinical practice ascertainment was conducted via the National Society of Genetic Counselors ListServ and direct inquiry with academic medical centers. Genetic testing laboratory cases were ascertained from Ambry Genetics, Inc. and Memorial Sloan Kettering Cancer Center's “Integrated Mutation Profiling of Actionable Cancer Targets” (MSKCC IMPACT) dataset. Online resources included review of the R20 release of the International Agency for Research on Cancer (IARC) TP53 database (5) and a literature search. From the total of 22 p.G334R cases, 21 have c.1000G>C and 1 proband from the IARC database has c.1000G>A. The study team and genetic testing laboratory reviewed all ascertained cases to minimize duplicative counting. Available data on proband and family clinical history and genetic testing were collected centrally. TP53 c.1000G>C;p.G334R classifications were ascertained via ClinVar (15), clinical genetic testing (CGT) reports, and direct inquiry with Laboratory Directors. Published population datasets and functional studies were retrieved from the Catalogue for Somatic Mutations in Cancer (COSMIC; cancer.sanger.ac.uk), the IARC database (5), and gnomAD (16). Analysis of cancer spectrum in pathogenic and likely pathogenic TP53 mutation carriers in the Penn LFS cohort and IARC was as described (17), updated for the IARC R20 version. IARC probands with likely pathogenic or pathogenic mutations in TP53 were identified and divided into p.R337H carriers and non-R337H carriers, excluding p.G334R carriers. For Penn LFS, both p.R337H and p.G334R carriers were excluded as there were insufficient numbers of p.R337H carriers for independent analysis. Breast cancer–free survival curves were created using the earliest age of breast cancer diagnosis, or youngest age in IARC if unaffected with breast cancer, for all female probands. Cancer-free survival curves were created using the earliest age of cancer diagnosis, or youngest age in IARC if unaffected with cancer, for all probands. For analysis of cancer rates in probands, probands with multiple primary cancers were included in each applicable cancer group. For analysis of cancer rates in families, families were included if they carried a likely pathogenic or pathogenic mutation and had three or more generations in IARC (average number of individuals per family was six for non-R337H and five for R337H families). SEER data were downloaded from https://seer.cancer.gov/csr/1975_2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017 (18).

TP53 genotyping and cancer susceptibility gene analysis

TP53 genotypes were obtained by direct review of RS results for the probands of families G334R-1 (14) and G334R-21 (19); by publication review for the probands of families G334R-8 (20) and G334R-20 (21); by review of CGT reports for the probands of families G334R-2 through 7; and by report from the laboratory director of the two genetic testing company cohorts (probands of families G334R-9 through 19). Germline whole-exome sequencing (WES) was also performed for the G334R-2 proband, and the raw WES BAM file for G334R-21 germline was downloaded under approved dbGAP project #21931 and analyzed as described (22). Genotyping of additional G334R-1 family members was performed by Sanger sequencing of the genomic region containing the variant. Primers were developed using NCBI Primer Design software (Supplementary Table S1A), and PCR products were generated with GoTaq Hot Start Polymerase (Promega). Where available, the RS or CGT was reviewed for any mutations in other cancer susceptibility genes (Supplementary Table S2). TP53 genotype for the proband of family G334R-22 (IARC 996) was obtained from the IARC database (5).

Ancestry and haplotype analysis

Ancestry was ascertained by self-report in the clinical and laboratory cohorts. For haplotype analysis, a panel of 15 microsatellite markers and SNP inside and outside the TP53 locus were used to assess chromosome 17p13 haplotype shared among available DNA samples from TP53 c.1000G>C carriers (Supplementary Table S1B). The presence of TP53 c.1000G>C allele was evaluated in Ashkenazi Jewish control population sequencing data (23), in the gnomAD database (v2.1.1 cancer and noncancer cohorts; ref. 16), in a germline genetic testing laboratory dataset (Ambry Genetics, Inc.) comprising 309,222 samples, and in a tumor genetic testing laboratory dataset (MSKCC IMPACT) comprising 21,729 samples.

In silico analysis

In silico tools used to predict pathogenicity were SIFT, PolyPhen2_HDIV, PolyPhen2_HVAR, LRT, MutationTaster, MutationAssessor, BayesDel, AlignGVGD, FATHMM, GERP++, PhyloP, and SiPhy. The interpretation of mutation effect and the molecular modeling of TP53 p.G334R was predicted using Pymol molecular graphics program (http://pymol.sourceforge.net) and SWISS-MODEL (https://swissmodel.expasy.org/). For the SWISS-MODEL analysis, the p53-G334R substitution was modeled onto the wild-type tetramer structure (p53-WT, PDB:1OLH) and the resultant structure prediction analyzed with MolProbity (24).

Tumor profiling

DNA was extracted from tumors in family G334R-2 (breast cancer), G334R-3 (adrenocortical carcinoma), G334R-4 (ACT), G334R-6 (ACT), G334R-17 (pancreatic cancer), and G334R-18 (pancreatic cancer). G334R-2 breast tumor and normal blood leukocyte DNA was prepared as described (22). Tumor and normal DNA libraries underwent WES with downstream analysis for somatic mutational profiles using Mutect2, homologous recombination deficiency scores, and mutational signature analysis using deconstructSigs as described (22). DNA from G334R-3 and G334R-4 tumors underwent the Comprehensive Cancer Panel targeted massively parallel sequencing assay at the Division of Genomic Diagnostics at the Children's Hospital of Philadelphia (25). DNA from the G334R-6 tumor and normal blood leukocyte DNA underwent amplification by PCR of the microsatellite marker VNTR(p53) for analysis of loss of heterozygosity (LOH). DNA from G334R-17 and G334R-18 tumors underwent clinical IMPACT sequencing at Memorial Sloan Kettering Cancer Center (26). The raw WES BAM file for G334R-21 tumor was downloaded under approved dbGAP project #21931 and analyzed as described (22).

Cell lines and culture conditions

WT and G334R/+ lymphoblastoid cells (LCL) were created from patients under an IRB-approved protocol via immortalization using Epstein–Barr virus. G334R/+ LCLs were genotyped to confirm TP53 c.1000G>C;p.G334R mutation positivity using Sanger sequencing as above (Supplementary Table S1A), and all four cell lines underwent WES to confirm the absence of other TP53 mutations. LCLs were grown in RPMI 1640 (10-040-CV; Corning Cellgro) supplemented with 15% FBS (HyClone, GE Healthcare Life Sciences), 4 mmol/L l-Glutamine (Corning Cellgro), and 1% Penicillin/Streptomycin (Corning Cellgro). H1299 (lung adenocarcinoma, p53-null) cells were obtained from the ATCC, multiple batches were frozen upon receipt, and new aliquots were used for this study every 6 months. H1299 cells were grown in DMEM (10-013-CM; Corning Cellgro) with 10% FBS and 1% Pen/Strep. Saos-2 cells were a gift to GPZ from Dr. Varda Rotter, and identity confirmed by short tandem repeat profiling (Supplementary Table S1C). Saos-2 cells were grown in DMEM with l-Glucose (Thermo Fisher Scientific) supplemented with 10% FBS, MEM Non-Essential Amino Acids solution (Thermo Fisher Scientific), 2.5 mmol/L glutamine, and penicillin/streptomycin. Cells were grown in a 5% CO2 incubator at 37°C. Cells were tested for mycoplasma every 6 months, as well as immediately prior to RNA sequencing (RNA-seq), by the University of Pennsylvania Cell Center Services (Philadelphia, PA), or prior to functional analysis by St. Jude Children's Research Hospital.

Antibodies and reagents

Antibodies used were purchased from: GAPDH (2118S), MDM2 (86934S), p21 (2947T), PUMA (12450T), and cleaved lamin A (2035S; Cell Signaling Technology); p53 (DO-1) (OP-43), p53 (pAb240, mutant p53 conformation) (OP-29), and PGC1-α (ST1202; Millipore Sigma); LCN15 (PA5-31997; Thermo Fisher Scientific); p53 (FL-393; Bioss Antibodies); and Plexin B3 (AF4958; R&D Systems). Nutlin-3a (SML-0580-5MG) was purchased from Millipore Sigma. Constructs containing the c.1000G>A and c.1000G>C alleles were created by site direct mutagenesis using QuikChange II kit (Stratagene) in the pCMV-Neo-Bam expression vector containing WT TP53 cDNA as template.

Immunoblotting and immunofluorescence analysis

For Western blot analysis, 50 to 100 μg of whole-cell lysate was resolved over SDS-PAGE gels using precast 10% NuPAGE Bis-Tris gels (Thermo Fisher Scientific) and transferred onto PVDF membranes (IPVH00010, pore size: 0.45 μm; Millipore Sigma) prior to analysis. Secondary antibodies conjugated to horseradish peroxidase were used at a dilution of 1:10,000 (Jackson Immunochemicals). ECL (Amersham; RPN2232) was applied to blots for 2 to 5 minutes, and protein levels were detected using autoradiography. Densitometry quantification of protein signals was performed using ImageJ software (National Institutes of Health). For indirect immunofluorescence, cells were fixed 48 hours after transfection in 4% paraformaldehyde for 10 minutes at room temperature, followed by permeabilization in 0.25% Triton X-100 for 5 minutes at room temperature. Cells were blocked in 1% BSA/4% goat serum for 30 minutes at room temperature. Primary antibodies were used to label mutant or total p53 (Pab240 or p53FL-393, respectively; 1:200), incubated at 4°C overnight, followed by incubation in Alexa-488– and Alexa-594–conjugated secondary (1:400 or 1:200, respectively) for 45 minutes at room temperature. Slides were mounted with mounting media with DAPI and imaged on a Nikon TE2000 inverted microscope.

Transfections and p53 promoter transactivation assay

Constructs containing the c.1000G>A and c.1000G>C alleles were created by site direct mutagenesis using QuikChange II kit (Stratagene) in the pCMV-Neo-Bam expression vector containing WT TP53 cDNA as template. Constructs encoding p53 wild-type (TP53-WT), p53-R175H (TP53-R175H), and p53-G334R (c.1000G>A and c.1000G>C) were transiently transfected into p53-deficient Saos-2 cells. Luciferase transactivation assay was performed as described (27).

Transfections and colony formation assays

Transfections were performed on H1299 (p53-null) cells using the Lipofectamine LTX with Plus Reagent (Thermo Fisher Scientific) according to the manufacturer's instructions. Cells were transfected with the respective p53 constructs. Forty-eight hours following transfection, 50,000 cells were plated in triplicate in 6-well plates in the presence of 400 μg/mL G418 (Cell Center Services). Crystal violet staining was performed as described (28).

RNA-seq and quantitative PCR

Note that 3′ mRNA-seq libraries were generated from DNase I–treated total RNA using the QuantSeq FWD library preparation kit (Lexogen) according to the manufacturer's directions. Overall library size was determined using the Agilent Tapestation and the DNA 5000 Screentape (Agilent). Libraries were quantitated using real- time PCR (Kapa Biosystems). Libraries were pooled, and high-output single-read 75-base-pair next-generation sequencing was done on a NextSeq500 (Illumina). RNA-seq data were aligned using Bowtie2 (29) against hg19 build of the human genome, and RSEM version 1.2.12 software (30) was used to estimate raw read counts for each gene using Ensemble v84 transcriptome information. DESeq2 (31) was used to estimate the significance of the differential expression between sample groups. Overall gene expression changes were considered significant if they passed an FDR threshold of <20%. Genes that significantly (FDR < 5%) responded at 24 hours in WT p53, but had significantly (nominal P < 0.05) less response in p.G334R p53 were considered and ranked by the fold difference in response. The RNA-seq data were deposited to the Gene Expression Omnibus database (http://ncbi.nlm.nih.gov/geo) with accession number GSE143741. Quantitative RT-PCR was performed as described (32).

Structural analysis

The G334R mutation was introduced into a previously described p53 TD construct (33), modified to include a tobacco etch virus cleavage site using Quikchange mutagenesis (Agilent). Proteins were expressed in E. coli BL21(DE3). Cultures were grown at 37°C until the OD600 reached approximately 0.8. The cultures were then moved to 16°C, and protein expression was induced via the addition of 0.5 mmol/L IPTG. Cultures were harvested after overnight expression via centrifugation. Cell pellets were resuspended in a buffer containing 50 mmol/L Tris, 500 mmol/L NaCl, and 0.5 mmol/L TCEP at pH 7.5. Cells were lysed via sonication. Lysate was cleared via centrifugation at 30,000 × g for 30 minutes and then purified via Ni2+ chromatography. Proteins were eluted from the resin with an elution buffer containing 25 mmol/L Tris, 100 mmol/L NaCl, 300 mmol/L imidazole, and 0.5 mmol/L TCEP at pH 7.5. The 6x-his tag was then removed by overnight incubation at 4°C with tobacco etch virus while dialyzing against a buffer containing 20 mmol/L sodium phosphate and 250 mmol/L NaCl at pH 7.2. The tag was removed by orthogonal Ni2+ chromatography. Thermal denaturation curves were collected by measuring the molar ellipticity at 222 nm on a Jasco 101 spectropolarimeter as a function of temperature. Circular dichroism (CD) melting curves were then fit to a two-state model to determine the melting temperature and stability of the proteins according to the method of Greenfield using Prism (34). All CD experiments were performed with a Jasco J-1500 Spectrometer at a protein concentration of 10 μmol/L.

Statistical analysis of data

Unless otherwise stated, all experiments were carried out in triplicate (n = 3) on all cell lines. For in vitro studies, the two-tailed unpaired Student t test was performed. All in vitro data are reported as the mean ± SD unless stated otherwise. Clinical data were compared using Fisher exact tests. Statistical analysis was performed using GraphPad Prism. P values are as indicated: *, P < 0.05; **, P < 0.01; and ***, P < 0.001 (Fig. 2), and *, P < 0.01; **, P < 0.003 (Bonferroni corrected, Fig. 4).

WES identified TP53 c.1000G>C;p.G334R in a pair of third-degree relatives (14) from a family (G334R-1) with multiple LFS-component cancers mostly occurring in the fourth to ninth decades, as well as five family members with multiple primary malignancies (Fig. 1A and Table 1; Supplementary Fig. S1). In silico modeling programs based on amino acid substitution characteristics and phylogeny predicted c.1000G>C;p.G334R was damaging (Supplementary Table S3). No additional predicted pathogenic mutations were shared by the cousin pair by WES analysis (Supplementary Table S4). Seven other independent families with c.1000G>C were ascertained from clinical genetics practices (Table 1; Supplementary Fig. S1). One family showed segregation of LFS-component cancers to third-degree relatives (G334R-6, Fig. 1B; Supplementary Fig. S1). Although most affected individuals had adult-onset cancers (Table 1), six individuals from four families had pediatric ACTs, including one family with pediatric ACTs in a sibling pair (G334R-4, Fig. 1C; Supplementary Fig. S1). In total, eight individuals from four families had multiple primary tumors (Supplementary Fig. S1). All probands were negative for other cancer predisposition gene mutations when tested (Table 1; Supplementary Table S2), except one family also segregated the AJ founder mutation BRCA2 c.5974delT;p.Ser1982fs (G334R-4; Supplementary Fig. S1 and Supplementary Table S2).

Figure 1.

Families with TP53 c.1000G>C;p.G334R. A, Abbreviated pedigree of the index family, G334R-1 showing four mutation positive individuals (denoted with “+”) and two obligate carriers (mother and maternal uncle of proband, denoted with “oc”). Both proband and mother have multiple primary LFS-component cancers (breast, sarcoma, colon, and brain cancers). Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. B, Abbreviated pedigree of family G334R-6 showing tracking of the mutation (denoted with “+”) from a proband with multiple primary LFS-component cancers (breast, sarcoma) to her great-nephew with an ACT. Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. C, Abbreviated pedigree of family G334R-4 showing a mutation positive sib ship (denoted with “+”) both with ACTs. Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. D, Identification of a chromosome 17 c.1000G>C haplotype from position 6,653,587 to 8,868,384 (GRCh37/hg19) in four independent families. Schematic diagram of chromosome 17 showing the identification and location (based on GRCh37) of genotyped SNPs and microsatellites analyzed in haplotype determination. The shared common haplotype is represented by a green bar in the pedigrees. Families used for identification of the haplotype are G334R-1, -2, -3, and -6.

Figure 1.

Families with TP53 c.1000G>C;p.G334R. A, Abbreviated pedigree of the index family, G334R-1 showing four mutation positive individuals (denoted with “+”) and two obligate carriers (mother and maternal uncle of proband, denoted with “oc”). Both proband and mother have multiple primary LFS-component cancers (breast, sarcoma, colon, and brain cancers). Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. B, Abbreviated pedigree of family G334R-6 showing tracking of the mutation (denoted with “+”) from a proband with multiple primary LFS-component cancers (breast, sarcoma) to her great-nephew with an ACT. Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. C, Abbreviated pedigree of family G334R-4 showing a mutation positive sib ship (denoted with “+”) both with ACTs. Mutation-positive individuals (“+”) underwent either targeted panel testing clinically or site-specific TP53 testing. D, Identification of a chromosome 17 c.1000G>C haplotype from position 6,653,587 to 8,868,384 (GRCh37/hg19) in four independent families. Schematic diagram of chromosome 17 showing the identification and location (based on GRCh37) of genotyped SNPs and microsatellites analyzed in haplotype determination. The shared common haplotype is represented by a green bar in the pedigrees. Families used for identification of the haplotype are G334R-1, -2, -3, and -6.

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Table 1.

Characteristics of families and probands found to carry TP53 c.1000C>G;p.G334R substitution.

Satisfies LFS criteriag
Fam IDaSitebRace/AJcSexAlleleTP53 sequencing; BRCA statusdClinical testing/variant classeProband cancer history (age at diagnosis)fCancer family history (age of diagnosis)fClassicChompretLFL-BirchLFL-EelesTumor studiesh
Clinical Cohort 
G334R-1 (ref. 14) Penn EA/AJ G>C RS/CGT Negative Ambry/VUS MP (BC 37 yr, BC 65 yr, GBM 84 yr) MP (BC 45 yr, BC 66 yr, Obl); MP (BC 49 yr, CRC 49 yr, Sarc 52 yr (Mut+); MP (BC 59 yr, BC 71 yr, Mut+); CRC 56 yr; MP (Leukemia 74 yr, Kidney 74 yr, Lymphoma 79 yr); BC 60 yr; MP (Mel 18 yr, Mel 39)  NA 
G334R-2 Penn EA/AJ G>C CGT Negative Myriad/VUS BC (Her2+) 30 yr BC 50 yr; BC 55 yr; Leukemia 55 yr Somatic TP53 p.N247I; HRD score = 75, MutSigs 1,2,10 
G334R-3 COH/SJ EA/AJ G>C CGT Not tested Ambry/VUS ACC 18 mo BC 42 yr; BC 40 yr; Thyroid 32 yr TP53 LOH 
G334R-4 CHOP EA/AJ G>C TT; CGT BRCA2CHOP/LP ACT 17 mo ACT 4y (Mut+) TP53 LOH & BRCA2 c.5946delT heterozygous 
G334R-5 Cooper EA/AJ G>C CGT Negative GeneDx/VUS BC 53 yr, meningioma MP (BC 61 yr, Lung 84); BC 42 yr; BC unk, CRC unk, BC unk, BC 42 yr, BC unk NA 
G334R-6 DFCI/Stanford/SJ EA/AJ G>C CGT Not tested COH/LP MP (BC 49 yr, Sarc 59 yr) ACC 5 yr (Mut+); PA Unk (Mut+); ACC 2 yr; Leukemia 2 yr; MP (Kidney 75 yr, Panc 82 yr); BC 57 yr; Lung 50 yr; Unk site 42 yr NA; ACT of TDR - TP53 LOH 
G334R-7 (ref. 10) CHOP/COH/SK EA/AJ G>C CGT Not tested Sick Kids/LP ACC 21 mo Sarc 41 yr; Lung 40 yr; Lung 58 yr NA 
G334R-8 (ref. 20) DFCI EA/AJ G>C RS/CGT Negative Penn/LP BC (Her2+) 44 yr BC Unk; Panc 49 yr; Brain 12 yr; BC 50 yr; CRC 60 yr; MP (BBC >50 yr) NA 
Genetic Testing Diagnostic Laboratory Cohort (Ambry Genetics Laboratory, Inc.) 
G334R-9 Ambry EA/AJ G>C CGT Negative Ambry/VUS MP (Kidney 41 yr, Panc 69 yr) BC 68 yr; BC 60s; BC 50s; Heme 60s NA 
G334R-10 Ambry Mixed Race/AJ G>C CGT Negative Ambry/VUS Unaffected 31 yr BC 41 yr; Panc 70s NA 
G334R-11 Ambry EA/AJ G>C CGT Negative Ambry/VUS MP (BC 50 yr, BC 64 yr) BC 35 yr; BC 68 yr; MP (BC 41 yr, BC 45 yr); BC 40s; BC <50 yr; Prostate 67 yr; BC 60 yr NA 
G334R-12 Ambry EA/AJ G>C CGT Negative Ambry/VUS BC 43 yr Heme <10 yr; Lung 62 yr; Prostate Unk; BC Unk; BC Unk; BC Unk NA 
G334R-13 Ambry EA/AJ G>C CGT Negative Ambry/VUS Unaffected 53 yr BC 53 yr; BC Unk; CRC Unk; BC 55 yr; Prostate 55 yr; BC 60s NA 
G334R-14 Ambry EA/Unk G>C CGT Negative Ambry/VUS Skin 62 yr Eso 70 yr; Panc 84 yr; Lung Unk; Mel 62 yr; Heme 62 yr; BC Unk; Heme 62 yr; BC unk; BC Unk; Brain 62; Gastric 48 yr; BC 42 yr NA 
G334R-15 Ambry EA/Unk G>C CGT Negative Ambry/VUS BC 50 yr Pros 60 yr; BC 70 yr; MP (BC 50 yr, BC 68 yr, BC 77 yr); CRC 80 yr NA 
G334R-16 Ambry EA/Unk G>C CGT Negative Ambry/VUS BC 65 yr MP (BC 45 yr, BC 47 yr); BC 65 yr; BC 78 yr; CRC 80 yr NA 
MSKCC IMPACT Cohort 
G334R-17 MSKCC EA/AJ G>C CGT Negative MSKDMP/VUS Panc, 67 yr NA ? (All criteria) Somatic TP53 p.K319X (met) 
G334R-18 MSKCC EA/AJ G>C CGT Negative MSKDMP/VUS Panc, 72 yr NA ? (All criteria) Somatic TP53 E171H* & H168_T170dup 
G334R-19 MSKCC AA/non-AJ G>C CGT Negative MSKDMP/VUS Pituitary adenoma, 36 yr NA ? (All criteria) NA 
IARC/Literature Review 
G334R-20 (ref. 21) Mayo Unk/Unk G>C RS Negative NA BC 38 yr NA ? (All criteria) NA 
G334R-21 (ref. 19) TCGA EA/Unk G>C RS Negative Unk/LP OC 76 yr NA ? (All criteria) TP53 LOH (83% VAF) 
G334R-22 (ref. 11) IARC Unk/Unk Unk G>A IARC; Unk Unk/Unk ACC 2 yr NA NA 
Satisfies LFS criteriag
Fam IDaSitebRace/AJcSexAlleleTP53 sequencing; BRCA statusdClinical testing/variant classeProband cancer history (age at diagnosis)fCancer family history (age of diagnosis)fClassicChompretLFL-BirchLFL-EelesTumor studiesh
Clinical Cohort 
G334R-1 (ref. 14) Penn EA/AJ G>C RS/CGT Negative Ambry/VUS MP (BC 37 yr, BC 65 yr, GBM 84 yr) MP (BC 45 yr, BC 66 yr, Obl); MP (BC 49 yr, CRC 49 yr, Sarc 52 yr (Mut+); MP (BC 59 yr, BC 71 yr, Mut+); CRC 56 yr; MP (Leukemia 74 yr, Kidney 74 yr, Lymphoma 79 yr); BC 60 yr; MP (Mel 18 yr, Mel 39)  NA 
G334R-2 Penn EA/AJ G>C CGT Negative Myriad/VUS BC (Her2+) 30 yr BC 50 yr; BC 55 yr; Leukemia 55 yr Somatic TP53 p.N247I; HRD score = 75, MutSigs 1,2,10 
G334R-3 COH/SJ EA/AJ G>C CGT Not tested Ambry/VUS ACC 18 mo BC 42 yr; BC 40 yr; Thyroid 32 yr TP53 LOH 
G334R-4 CHOP EA/AJ G>C TT; CGT BRCA2CHOP/LP ACT 17 mo ACT 4y (Mut+) TP53 LOH & BRCA2 c.5946delT heterozygous 
G334R-5 Cooper EA/AJ G>C CGT Negative GeneDx/VUS BC 53 yr, meningioma MP (BC 61 yr, Lung 84); BC 42 yr; BC unk, CRC unk, BC unk, BC 42 yr, BC unk NA 
G334R-6 DFCI/Stanford/SJ EA/AJ G>C CGT Not tested COH/LP MP (BC 49 yr, Sarc 59 yr) ACC 5 yr (Mut+); PA Unk (Mut+); ACC 2 yr; Leukemia 2 yr; MP (Kidney 75 yr, Panc 82 yr); BC 57 yr; Lung 50 yr; Unk site 42 yr NA; ACT of TDR - TP53 LOH 
G334R-7 (ref. 10) CHOP/COH/SK EA/AJ G>C CGT Not tested Sick Kids/LP ACC 21 mo Sarc 41 yr; Lung 40 yr; Lung 58 yr NA 
G334R-8 (ref. 20) DFCI EA/AJ G>C RS/CGT Negative Penn/LP BC (Her2+) 44 yr BC Unk; Panc 49 yr; Brain 12 yr; BC 50 yr; CRC 60 yr; MP (BBC >50 yr) NA 
Genetic Testing Diagnostic Laboratory Cohort (Ambry Genetics Laboratory, Inc.) 
G334R-9 Ambry EA/AJ G>C CGT Negative Ambry/VUS MP (Kidney 41 yr, Panc 69 yr) BC 68 yr; BC 60s; BC 50s; Heme 60s NA 
G334R-10 Ambry Mixed Race/AJ G>C CGT Negative Ambry/VUS Unaffected 31 yr BC 41 yr; Panc 70s NA 
G334R-11 Ambry EA/AJ G>C CGT Negative Ambry/VUS MP (BC 50 yr, BC 64 yr) BC 35 yr; BC 68 yr; MP (BC 41 yr, BC 45 yr); BC 40s; BC <50 yr; Prostate 67 yr; BC 60 yr NA 
G334R-12 Ambry EA/AJ G>C CGT Negative Ambry/VUS BC 43 yr Heme <10 yr; Lung 62 yr; Prostate Unk; BC Unk; BC Unk; BC Unk NA 
G334R-13 Ambry EA/AJ G>C CGT Negative Ambry/VUS Unaffected 53 yr BC 53 yr; BC Unk; CRC Unk; BC 55 yr; Prostate 55 yr; BC 60s NA 
G334R-14 Ambry EA/Unk G>C CGT Negative Ambry/VUS Skin 62 yr Eso 70 yr; Panc 84 yr; Lung Unk; Mel 62 yr; Heme 62 yr; BC Unk; Heme 62 yr; BC unk; BC Unk; Brain 62; Gastric 48 yr; BC 42 yr NA 
G334R-15 Ambry EA/Unk G>C CGT Negative Ambry/VUS BC 50 yr Pros 60 yr; BC 70 yr; MP (BC 50 yr, BC 68 yr, BC 77 yr); CRC 80 yr NA 
G334R-16 Ambry EA/Unk G>C CGT Negative Ambry/VUS BC 65 yr MP (BC 45 yr, BC 47 yr); BC 65 yr; BC 78 yr; CRC 80 yr NA 
MSKCC IMPACT Cohort 
G334R-17 MSKCC EA/AJ G>C CGT Negative MSKDMP/VUS Panc, 67 yr NA ? (All criteria) Somatic TP53 p.K319X (met) 
G334R-18 MSKCC EA/AJ G>C CGT Negative MSKDMP/VUS Panc, 72 yr NA ? (All criteria) Somatic TP53 E171H* & H168_T170dup 
G334R-19 MSKCC AA/non-AJ G>C CGT Negative MSKDMP/VUS Pituitary adenoma, 36 yr NA ? (All criteria) NA 
IARC/Literature Review 
G334R-20 (ref. 21) Mayo Unk/Unk G>C RS Negative NA BC 38 yr NA ? (All criteria) NA 
G334R-21 (ref. 19) TCGA EA/Unk G>C RS Negative Unk/LP OC 76 yr NA ? (All criteria) TP53 LOH (83% VAF) 
G334R-22 (ref. 11) IARC Unk/Unk Unk G>A IARC; Unk Unk/Unk ACC 2 yr NA NA 

aFamilies that have been previously reported: Proband of family 1(14); Proband of family 7(10); Proband (IARC ID: RAT13–3-I-1) of family 8/IARC-854(20); Proband of family 20(21); Proband (IARC ID: KAN14-1) of family 21/IARC-946(19), Proband (IARC ID: WAS15-12-1) of family 22/IARC-996(11).

bPenn, Penn Medicine; CHOP, Children's Hospital of Philadelphia; COH, City of Hope; DFCI, Dana Farber Cancer Institute; MSKCC, Memorial Sloan Kettering Cancer Center; MSKDMP, Memorial Sloan Kettering Diagnostic Molecular Pathology; SJ, St. Jude; SK, Sick Kids. Family G334R-9 was also reported in IARC. Probands or family members of families 1, 3, and 7 also had testing at Ambry Genetics but are not included in the Clinical Diagnostic Laboratory Cohort.

cEA, European American; AJ, Ashkenazi Jewish; Unk, Unknown; Cau, Caucasian.

dCGT, clinical genetic testing; TT, tumor testing; IARC, International Agency for Research on Cancer.

eVUS, variant of uncertain significance; LP, likely pathogenic; P, pathogenic.

fIndividuals reported for lineage suspected to carry the mutation, relationships to proband and other cancer family history found in Supplementary Fig. S2 and Supplementary Table 2; yr, years; mo, months; MP, multiple primary cancers; BC, breast cancer; ACC, adrenocortical carcinoma; Sarc, sarcoma; GBM, glioblastoma; CRC, colorectal; Eso, esophageal; Heme, hematological malignancy NOS; Mel, melanoma; OC, ovarian cancer; Panc, pancreatic; PA, pituitary adenoma; Obl, obligate carrier of mutation; Mut+, positive for TP53 c.1000G>C.

gLFS Criteria(3); “X,” Criteria satisfied; “-,” Deduction can be made that diagnostic criteria not met based upon absent reported family history; “?,” Unknown, cannot assess diagnostic criteria (met/not met) due to lack of established lineage or available family history data.

hcnLOH, Copy neutral LOH; VAF, variant allele frequency; NA, data not available.

Thirteen additional TP53 c.1000G>C;p.G334R carriers were identified from two genetic testing laboratory cohorts and literature review (Table 1). Eight probands were identified among 309,922 (0.003%) individuals undergoing germline testing at Ambry Genetics Laboratories, and three probands were identified among 21,729 (0.014%) individuals undergoing tumor genomic profiling at MSKCC. Three probands were unaffected but from multicancer families. Eleven probands had a variety of tumor types in the fifth to eighth decades, including two with multiple primary tumors. No other pathogenic mutations or variants of uncertain significance were identified in other cancer predisposition genes, including BRCA1/2 in these probands (Table 1; Supplementary Table S2).

Ancestry was available for 16 probands with TP53 c.1000G>C, p.G334R. All eight families in the clinical cohort, and seven of eight from the two genetic testing cohorts, reported AJ ancestry (Table 1). The mutation was found at an approximately 10-fold enrichment in AJ versus non-AJ individuals in both genetic testing cohorts (0.023% AJ vs. 0.001% of Caucasian non-AJ and 0.07% AJ vs. 0.005% of non-AJ, Supplementary Table S5). The TP53 c.1000G>C mutation was not reported in the noncancer cohort of gnomAD (v2.1.1), including 10,036 AJ alleles (ref. 16; Supplementary Table S3), nor 1,154 chromosomes from an AJ cancer-free cohort (23). Microsatellite and SNP analysis demonstrated that nine individuals from four families shared a common chromosome 17p13.1 haplotype (Fig. 1D).

In three available ACTs and the ovarian cancer, TP53 LOH was identified (Table 1). Somatic biallelic inactivation of BRCA2 was not found in the ACT also carrying a BRCA2 mutation (Table 1). Two pancreatic cancers and one breast cancer had a second somatic TP53 mutation (Table 1), although mutational phase was unavailable. The breast cancer sample from G334R-2 underwent WES, demonstrating typical breast tumor mutational signatures, high levels of genomic instability, and no other breast cancer driver mutations, characteristics consistent with p53-mutant breast tumors (ref. 35; Table 1).

Previous functional studies of another p.G334R allele, c.1000G>A, have shown normal to impaired transactivation activity (42%–145% of WT activity) dependent on cell type (6, 7, 11, 12). In the interest of rigor, we created plasmids encoding both c.1000G>A and c.1000G>C alleles. In transient transfection assays, these showed comparable transactivation activity toward a canonical p53 promoter, similar to WT p53 (Supplementary Fig. S2). However, in colony suppression assays, these two p.G334R alleles showed mildly impaired suppression ability compared with WT TP53 (Fig. 2A,C) and functioned at a level similar to another TD mutant p.R337H. We then studied immortalized LCLs created from the two cousins of the index family, compared with two TP53 WT LCLs from unrelated individuals of AJ descent. WES of these four cell lines did not identify any additional TP53 or other cancer-related mutations (Supplementary Table S4). p53 steady-state levels were noted to be higher in G334R-mutant lines compared with WT LCLs when treated with both the MDM2 inhibitor Nutlin-3a and cisplatin (Supplementary Fig. S3). The G334R-mutant cell lines retained the ability to induce MDM2 and p21 in response to 8 and 24 hours of treatment with Nutlin-3a, as expected (refs. 11, 12; Fig. 2D, F, and G). RNA-seq data from the four lines confirmed that transactivation of canonical p53 target genes in response to Nutlin at 24 hours was comparable in G334R mutant compared with WT LCLs (Supplementary Fig. S4). However, a subset of p53 target genes induced by Nutlin treatment in TP53 WT LCLs showed significantly impaired induction in G334R LCLs (Supplementary Table S6); these genes include PCLO, PLTP, PLXNB3, and LCN15 (Fig. 2E,G).

Figure 2.

Analysis of p53 function in TP53 G334R-mutant cell lines. A, Colony formation assays in H1299 cells transfected with wild-type TP53 (WT), CMV vector alone, and expression constructs containing the c.1000G>C;p.G334R allele (G334R G>C), the c.1000G>A;p.G334R allele (G334R G>A), the p.R337H allele, as well as mock-transfected cells. Colonies stained with crystal violet and counted as per inset. Shown are representative images of triplicate wells from the same experiment. All experiments were performed in triplicate, and the average numbers of colonies from two biological and three technical replicates are depicted in B, including SD. C, Confirmation that transfected H1299 cells expressed equivalent levels of each p53 protein; the slight shift in mobility of p53 is due to Pro72Arg, which does not alter colony suppression. D, Two TP53 WT LCL and LCLs made from the cousin pair of Family G334R-1 were treated for the indicated times with 10 μmol/L Nutlin-3a (Nutlin). Western blot of PGC1α, MDM2, p53, cleaved lamin A (CLA), PUMA, and GAPDH. E, Scatter plot highlighting the top differentially regulated genes between WT and G334R/+ cells treated with Nutlin. The data depict average changes of two replicates from two WT and p.G334R/+ LCLs treated with 10 μmol/L Nutlin-3a (Nutlin) for 24 hours versus untreated condition. Known direct p53 targets with significantly less response of at least two-fold in p.G334R/+ cells are highlighted. F, Quantitative PCR analysis of MDM2 and LCN15 expression levels in two WT and two p.G334R LCLs after 24 hours of 10 μmol/L Nutlin. G, Western blots of LCN15 and PLXNB3. Data shown are representative of three technical replicates and two biological replicates. *, P < 0.05; **, P < 0.01; ***, P < 0.001; n.s., nonsignificant.

Figure 2.

Analysis of p53 function in TP53 G334R-mutant cell lines. A, Colony formation assays in H1299 cells transfected with wild-type TP53 (WT), CMV vector alone, and expression constructs containing the c.1000G>C;p.G334R allele (G334R G>C), the c.1000G>A;p.G334R allele (G334R G>A), the p.R337H allele, as well as mock-transfected cells. Colonies stained with crystal violet and counted as per inset. Shown are representative images of triplicate wells from the same experiment. All experiments were performed in triplicate, and the average numbers of colonies from two biological and three technical replicates are depicted in B, including SD. C, Confirmation that transfected H1299 cells expressed equivalent levels of each p53 protein; the slight shift in mobility of p53 is due to Pro72Arg, which does not alter colony suppression. D, Two TP53 WT LCL and LCLs made from the cousin pair of Family G334R-1 were treated for the indicated times with 10 μmol/L Nutlin-3a (Nutlin). Western blot of PGC1α, MDM2, p53, cleaved lamin A (CLA), PUMA, and GAPDH. E, Scatter plot highlighting the top differentially regulated genes between WT and G334R/+ cells treated with Nutlin. The data depict average changes of two replicates from two WT and p.G334R/+ LCLs treated with 10 μmol/L Nutlin-3a (Nutlin) for 24 hours versus untreated condition. Known direct p53 targets with significantly less response of at least two-fold in p.G334R/+ cells are highlighted. F, Quantitative PCR analysis of MDM2 and LCN15 expression levels in two WT and two p.G334R LCLs after 24 hours of 10 μmol/L Nutlin. G, Western blots of LCN15 and PLXNB3. Data shown are representative of three technical replicates and two biological replicates. *, P < 0.05; **, P < 0.01; ***, P < 0.001; n.s., nonsignificant.

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To investigate the biochemical impact of the p.G334R substitution, three-dimensional modeling of the p53-G334R mutant TD was performed, confirming that Gly-334 is the hinge residue located in the loop connecting the α-helix and β-sheet in each protomer (refs. 36, 37; Fig. 3A). MolProbity analysis predicted a potentially incompatible clash of Arg-333 of the A-protomer with Glu-349 of the C-protomer (Clash score 1.75). We therefore assessed thermal stability of the G334R tetramer and found that G334R exhibits mildly decreased thermal stability compared with WT (Fig. 3B). We next tested the possibility that this amino acid substitution altered the folding of p53. Using the pAb240 conformation-specific antibody, we found that a significant percentage of cells transfected with either p.G334R allele showed evidence for p53 protein in the mutant conformation (Fig. 3C and D). This suggests an explanation for the increased steady-state level of G334R in untreated LCLs (Fig. 2D). These combined data suggest that the G334R-mutant protein may be defective in transcription of a subset of genes due to increased propensity to adopt a mutant conformation.

Figure 3.

Structural analysis of p53-G334R tetramer. A, Structural modeling analysis of p53-G334R–mutant tetramer. Pymol analysis (PDB, Protein Data Bank code 1PES) showing the TD comprising residues 310–360. G334 is in a surface-exposed loop. Carbon, nitrogen, and oxygen atoms are depicted in gray, blue, and red, respectively. The glycine to arginine mutation at position 334 is shown in magenta sticks. The side-chains of R337 and D352 are shown as sticks to highlight the close proximity of this salt bridge to residue 334. The salt bridge is denoted with a dashed yellow line. The surface depicts the space occupied by the nonmutated protein. The cartoon representation of each monomer subunit is colored differently to highlight the symmetry of the tetramer. B, Thermal stability analysis of WT versus p53-G334R tetramer demonstrating fraction of the tetramer that is denatured with increasing temperature. Inset demonstrates the deltaG of WT versus p53-G334–mutant tetramer. C, H1299 cells were transfected with TP53 vector (WT) and vectors containing the c.1000G>C;p.G334R allele (G334R G>C), the c.1000G>A;p.G334R allele (G334R G>A), and the p.R337H allele. Cells were subjected to immunofluorescence analysis of total (FL-393) and mutant conformation (pAb240) p53 staining. All experiments were done in triplicate. D, Quantification of the fraction of cells containing any staining for pAb240 and mutant conformation (pAb240) p53 staining. **, P < 0.01.

Figure 3.

Structural analysis of p53-G334R tetramer. A, Structural modeling analysis of p53-G334R–mutant tetramer. Pymol analysis (PDB, Protein Data Bank code 1PES) showing the TD comprising residues 310–360. G334 is in a surface-exposed loop. Carbon, nitrogen, and oxygen atoms are depicted in gray, blue, and red, respectively. The glycine to arginine mutation at position 334 is shown in magenta sticks. The side-chains of R337 and D352 are shown as sticks to highlight the close proximity of this salt bridge to residue 334. The salt bridge is denoted with a dashed yellow line. The surface depicts the space occupied by the nonmutated protein. The cartoon representation of each monomer subunit is colored differently to highlight the symmetry of the tetramer. B, Thermal stability analysis of WT versus p53-G334R tetramer demonstrating fraction of the tetramer that is denatured with increasing temperature. Inset demonstrates the deltaG of WT versus p53-G334–mutant tetramer. C, H1299 cells were transfected with TP53 vector (WT) and vectors containing the c.1000G>C;p.G334R allele (G334R G>C), the c.1000G>A;p.G334R allele (G334R G>A), and the p.R337H allele. Cells were subjected to immunofluorescence analysis of total (FL-393) and mutant conformation (pAb240) p53 staining. All experiments were done in triplicate. D, Quantification of the fraction of cells containing any staining for pAb240 and mutant conformation (pAb240) p53 staining. **, P < 0.01.

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Our experimental evidence indicates that TP53 c.1000G>C;p.G334R has a partial defect in p53 function, similar to another hypomorphic TD mutation, p.R337H. Concordant with this mild defect, the median age of onset of all cancers, including breast cancers, in p.G334R probands was significantly later than probands in the Penn LFS and IARC non-p.R337H cohorts (Fig. 4A and B), though earlier compared with general population data from SEER (Fig 4C and D). Although the median age of all cancers was significantly later in p.G334R compared with p.R337H probands, the median age of breast cancer was similar (Fig. 4A and B). Overall, the lifetime cumulative incidence of cancer is similar across all four cohorts. The rate of ACTs was similar in the p.G334R probands compared with Penn LFS and IARC non-p.R337H probands, although significantly lower than the rate in p.R337H probands (Fig. 4E and F). Conversely, although breast cancer rates were similar in p.G334R, Penn LFS, and IARC non-p.R337H probands, they were significantly higher than in p.R337H probands. Sarcomas were statistically less likely in p.G334R probands compared with the IARC non-p.R337H probands, although observed at a similar rate to p.R337H probands (Fig. 4E). Analysis of family data confirms these observations seen in probands (Fig. 4F). In addition, the family data show a statistically significant lower incidence of brain cancer compared with IARC non-p.R337H families and a statistically significant higher rate of hematologic malignancies in p.G334R families compared with Penn LFS and p.R337H families (Fig. 4E and F).

Figure 4.

Age of cancer onset and cancer spectrum in probands and families with TP53 c.1000G>C;p.G334R. A, Kaplan–Meier curves showing fraction of probands without a breast cancer diagnosis from families with TP53 c.1000G>C;p.G334R compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. B, Kaplan–Meier curves showing fraction of probands without any cancer diagnosis from families with TP53 c.1000C>G;p.G334R compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. C, Proportion of probands with age of onset of breast cancer in the indicated deciles from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort, the IARC dataset split into carriers and noncarriers of p.R337H, and national SEER data. D, Proportion of probands with age of onset of any cancer in the indicated deciles from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort, the IARC dataset split into carriers and noncarriers of p.R337H, and national SEER data. E, Fraction of probands with the indicated LFS-component cancers from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. F, Fraction of families with at least one family member with the indicated LFS-component cancers from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. *, P < 0.01; **, P < 0.003 (Bonferroni corrected).

Figure 4.

Age of cancer onset and cancer spectrum in probands and families with TP53 c.1000G>C;p.G334R. A, Kaplan–Meier curves showing fraction of probands without a breast cancer diagnosis from families with TP53 c.1000G>C;p.G334R compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. B, Kaplan–Meier curves showing fraction of probands without any cancer diagnosis from families with TP53 c.1000C>G;p.G334R compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. C, Proportion of probands with age of onset of breast cancer in the indicated deciles from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort, the IARC dataset split into carriers and noncarriers of p.R337H, and national SEER data. D, Proportion of probands with age of onset of any cancer in the indicated deciles from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort, the IARC dataset split into carriers and noncarriers of p.R337H, and national SEER data. E, Fraction of probands with the indicated LFS-component cancers from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. F, Fraction of families with at least one family member with the indicated LFS-component cancers from TP53 c.1000G>C;p.G334R families compared with an institutional LFS cohort and the IARC dataset, split into carriers and noncarriers of p.R337H. *, P < 0.01; **, P < 0.003 (Bonferroni corrected).

Close modal

Herein, we report that TP53 c.1000G>C;p.G334R is an AJ-predominant mutation causing a mild defect in p53 function likely due to decreased thermo-stability, which leads to a lower penetrance LFS phenotype. The TP53 p.G334R mutation demonstrates variable penetrance, with enrichment of the canonical LFS cancer, pediatric ACTs, in some families; whereas other families predominantly show later onset classic LFS cancers, such as breast cancer and hematologic malignancies, or multiple unaffected carriers.

Pediatric ACTs are extremely rare, diagnosed at 0.3 cases per million per year (38), and are highly predictive of an underlying germline TP53 mutation (11). Therefore, the high rate of pediatric ACT in our p.G334R cohort, particularly the sibling pair both with pediatric ACTs, is strong support for this mutation being causative of the observed phenotype. Overall, the later onset presentation of adult-onset cancers coupled with pediatric ACT enrichment seen with p.G334R probands and families is similar to that observed with another TD mutation p.R337H (39), although the ACT enrichment is not as striking in the p.G334R cohort compared with p.R337H. The low incidence of sarcoma in our p.G334R cohort has also been observed in p.R337H lineages as compared with classic LFS families (39).

Further studies of p.G334R and other TD mutations under different physiologic conditions are necessary to understand the etiology of the variable penetrance of mutations in this domain. It is possible that lineage-specific factors, cell type–specific changes, or other physiologic conditions, such as aging, lead to decreased thermo-stability of the p53-G334R–mutant tetramer only in specific target tissues. Pathogenesis in p53-G334R–mutant cells may relate to the defective transactivation of the subset of p53 target genes that are not responsive to Nutlin in our analyses and suggest that phenotyping of TP53 TD mutations should differ from that used for DBD mutations (39). Finally, the cancer predisposition associated with the TP53 c.1000G>C;p.G334R allele may be due to genetic modifiers that affect cancer risk either in concert with or independently from the p53-G334R mutation. Modifiers both within TP53 (PIN3 and PEX4 polymorphisms) and outside of TP53 (MDM2 SNP309-G) are known to influence tumor risk and age of onset in TP53 germline carriers (40–42). Given the apparent progressively younger of age of onset of cancer in many of our p.G334R families, this or other mechanisms of genetic anticipation may also play an important role in p.G334R families (43). Similarly, modifier effects have been demonstrated with the TD Brazilian founder, p.R337H (44, 45). Whether a variant(s) in linkage disequilibrium with c.1000G>C is acting cooperatively to alter p53 function remains unanswered.

Our data also show that the TP53 c.1000G>C mutation is present on a commonly inherited haplotype in nine individuals from four independent families and seen predominantly in individuals of Ashkenazi Jewish descent, suggesting that it is a founder mutation. Although a number of founder mutations have been described in the Ashkenazi Jewish population (46), this is the first TP53 founder mutation to be described in this population. Future studies as more carriers are identified are necessary to investigate the age of this founder haplotype.

At present, discordant classification of TP53 c.1000G>C and other presumed hypomorphic alleles in TP53 and other cancer susceptibility genes are profoundly challenging in clinical genetics. Implementation of cancer screening modalities has demonstrated morbidity and mortality benefits for many patients with high penetrance inherited cancer susceptibility gene mutations (47–49). However, the best medical management strategies for patients with hypomorphic alleles are currently unclear. Reduced intensity or a bimodal screening approach based on age is likely preferable to either full screening or none at all. It is critical that translational approaches, as in this study, are applied more broadly to hypomorphic alleles in cancer susceptibility genes with the goal of precision implementation of appropriate life-saving cancer screening and treatment modalities.

J. Powers reports receiving personal fees from Myriad Genetic Laboratories, personal fees from Ambry Genetic Laboratories, personal fees from CareVive Systems, Inc., outside the submitted work. M.M. Li reports receiving personal fees from Roche Sequencing Solutions "On the SAB" outside the submitted work. S.M. Domchek reports receiving personal fees from AstraZeneca, personal fees from BMS, and personal fees from Clovis outside the submitted work. J.N. Weitzel reports grants from NIH/NCI R01 CA242218-01 during the conduct of the study; personal fees from AstraZeneca "speakers bureau" outside the submitted work. J.E. Stopfer reports being a paid consultant for AstraZeneca as a member of its Personalized Medicine and Nursing Education Advisory Board. J.S. Dolinsky reports personal fees from Ambry Genetics during the conduct of the study. S. Gutierrez reports personal fees from Ambry Genetics as a full-time employee outside the submitted work. F.J. Couch reports receiving personal fees from Ambry Genetics and grants from NIH during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

J. Powers: Conceptualization, data curation, formal analysis, writing-original draft, writing-review and editing. E.M. Pinto: Conceptualization, resources, data curation, formal analysis, funding acquisition, methodology, writing-original draft, writing-review and editing. T. Barnoud: Formal analysis, investigation, methodology, writing-review and editing. J.C. Leung: Formal analysis, investigation, methodology, writing-review and editing. T. Martynyuk: Formal analysis, investigation, methodology, writing-review and editing. A.V. Kossenkov: Formal analysis, investigation, methodology, writing-review and editing. A.H. Philips: Formal analysis, investigation, methodology, writing-review and editing. H. Desai: Software, formal analysis, investigation, writing-review and editing. R. Hausler: Software, formal analysis, investigation, writing-review and editing. G. Kelly: Investigation, methodology, writing-review and editing. A.N. Le: Data curation, formal analysis, investigation, project administration, writing-review and editing. M.M. Li: Resources, formal analysis, investigation, writing-review and editing. S.P. MacFarland: Resources, writing-review and editing. L.C. Pyle: Resources, writing-review and editing. K. Zelley: Resources, writing-review and editing. K.L. Nathanson: Resources, funding acquisition, writing-review and editing. S.M. Domchek: Resources, funding acquisition, writing-review and editing. T.P. Slavin: Resources, funding acquisition, writing-review and editing. J.N. Weitzel: Resources, funding acquisition, writing-review and editing. J.E. Stopfer: Resources, writing-review and editing. J.E. Garber: Resources, funding acquisition, writing-review and editing. V. Joseph: Resources, formal analysis, investigation, writing-review and editing. K. Offit: Resources, funding acquisition, writing-review and editing. J.S. Dolinsky: Resources, formal analysis, investigation, writing-review and editing. S. Gutierrez: Resources, formal analysis, investigation, writing-review and editing. K. McGoldrick: Resources, formal analysis, investigation, writing-review and editing. F.J. Couch: Resources, funding acquisition, writing-review and editing. B. Levin: Resources, writing-review and editing. M.C. Edelman: Resources, writing-review and editing. C. Fein Levy: Resources, writing-review and editing. S.L. Spunt: Resources, writing-review and editing. R.W. Kriwacki: Resources, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing-review and editing. G.P. Zambetti: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing-review and editing. R.C. Ribeiro: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing-review and editing. M.E. Murphy: Conceptualization, resources, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft. K.N. Maxwell: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft.

We thank Jinling Wang (St. Jude Children's Research Hospital) for technical assistance and Rhonda Kitlas GIllete (University of Pennsylvania) for generation and formatting of pedigrees. Research support for this study was generously provided by NIH grants K08CA215312 (K.N. Maxwell), P30CA016520 (Abramson Cancer Center), R01CA102184 (M.E. Murphy), P30CA021765 (St. Jude's Cancer Center), K99CA241367 (T. Barnoud), K08CA234394 (T.P. Slavin), KL2TR00187903 (L.C. Pyle), R01CA242218 (J.N. Weitzel), RC4CA153828 (J.N. Weitzel), R01CA225662 (F.J. Couch); Basser Center for BRCA at the University of Pennsylvania (K.N. Maxwell, K.L. Nathanson, and S.M. Domchek); Breast Cancer Research Foundation (F.J. Couch, S.M. Domchek, K.L. Nathanson, J.N. Weitzel, J.E. Garber, K. Offit); Burroughs Wellcome Foundation (K.N. Maxwell); International Pediatric Adrenocortical Tumor Registry (St. Jude IPACTR); and American Lebanese Syrian Associated Charities (Emilia M. Pinto, A.H. Philips, R.C. Ribeiro, R.W. Kriwacki, and G.P. Zambetti).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Li
FP
,
Fraumeni
JF
 Jr
. 
Soft-tissue sarcomas, breast cancer, and other neoplasms. A familial syndrome?
Ann Intern Med
1969
;
71
:
747
52
.
2.
Levine
AJ
,
Oren
M.
The first 30 years of p53: growing ever more complex
.
Nat Rev Cancer
2009
;
9
:
749
58
.
3.
McBride
KA
,
Ballinger
ML
,
Killick
E
,
Kirk
J
,
Tattersall
MH
,
Eeles
RA
, et al
Li-Fraumeni syndrome: cancer risk assessment and clinical management
.
Nat Rev Clin Oncol
2014
;
11
:
260
71
.
4.
Kratz
CP
,
Achatz
MI
,
Brugieres
L
,
Frebourg
T
,
Garber
JE
,
Greer
MC
, et al
Cancer screening recommendations for individuals with Li-Fraumeni syndrome
.
Clin Cancer Res
2017
;
23
:
e38
e45
.
5.
Bouaoun
L
,
Sonkin
D
,
Ardin
M
,
Hollstein
M
,
Byrnes
G
,
Zavadil
J
, et al
TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data
.
Hum Mutat
2016
;
37
:
865
76
.
6.
Kawaguchi
T
,
Kato
S
,
Otsuka
K
,
Watanabe
G
,
Kumabe
T
,
Tominaga
T
, et al
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library
.
Oncogene
2005
;
24
:
6976
81
.
7.
Kato
S
,
Han
SY
,
Liu
W
,
Otsuka
K
,
Shibata
H
,
Kanamaru
R
, et al
Understanding the function-structure and function-mutation relationships of p53 tumor suppressor protein by high-resolution missense mutation analysis
.
Proc Natl Acad Sci U S A
2003
;
100
:
8424
9
.
8.
Pinto
EM
,
Billerbeck
AE
,
Villares
MC
,
Domenice
S
,
Mendonca
BB
,
Latronico
AC
. 
Founder effect for the highly prevalent R337H mutation of tumor suppressor p53 in Brazilian patients with adrenocortical tumors
.
Arq Bras Endocrinol Metabol
2004
;
48
:
647
50
.
9.
Ribeiro
RC
,
Sandrini
F
,
Figueiredo
B
,
Zambetti
GP
,
Michalkiewicz
E
,
Lafferty
AR
, et al
An inherited p53 mutation that contributes in a tissue-specific manner to pediatric adrenal cortical carcinoma
.
Proc Natl Acad Sci U S A
2001
;
98
:
9330
5
.
10.
Fischer
NW
,
Prodeus
A
,
Tran
J
,
Malkin
D
,
Gariepy
J
. 
Association between the oligomeric status of p53 and clinical outcomes in li-fraumeni syndrome
.
J Natl Cancer Inst
2018
;
110
:
1418
21
.
11.
Wasserman
JD
,
Novokmet
A
,
Eichler-Jonsson
C
,
Ribeiro
RC
,
Rodriguez-Galindo
C
,
Zambetti
GP
, et al
Prevalence and functional consequence of TP53 mutations in pediatric adrenocortical carcinoma: a children's oncology group study
.
J Clin Oncol
2015
;
33
:
602
9
.
12.
Giacomelli
AO
,
Yang
X
,
Lintner
RE
,
McFarland
JM
,
Duby
M
,
Kim
J
, et al
Mutational processes shape the landscape of TP53 mutations in human cancer
.
Nat Genet
2018
;
50
:
1381
7
.
13.
Rehm
HL
,
Berg
JS
,
Brooks
LD
,
Bustamante
CD
,
Evans
JP
,
Landrum
MJ
, et al
ClinGen–the Clinical Genome Resource
.
N Engl J Med
2015
;
372
:
2235
42
.
14.
Maxwell
KN
,
Wubbenhorst
B
,
D'Andrea
K
,
Garman
B
,
Long
JM
,
Powers
J
, et al
Prevalence of mutations in a panel of breast cancer susceptibility genes in BRCA1/2-negative patients with early-onset breast cancer
.
Genet Med
2015
;
17
:
630
8
.
15.
Landrum
MJ
,
Lee
JM
,
Benson
M
,
Brown
G
,
Chao
C
,
Chitipiralla
S
, et al
ClinVar: public archive of interpretations of clinically relevant variants
.
Nucleic Acids Res
2016
;
44
:
D862
8
.
16.
Karczewski
KJ
,
Francioli
LC
,
Tiao
G
,
Cummings
BB
,
Alföldi
J
,
Wang
Q
, et al
Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes
.
bioRxiv
2019
:
531210
.
17.
MacFarland
SP
,
Zelley
K
,
Long
JM
,
McKenna
D
,
Mamula
P
,
Domchek
SM
, et al
Earlier colorectal cancer screening may be necessary in patients with li-fraumeni syndrome
.
Gastroenterology
2019
;
156
:
273
4
.
18.
Howlader
N
,
Noone
AM
,
Krapcho
M
,
Miller
D
,
Bishop
K
,
Kosary
CL
(
eds
).
SEER cancer statistics review, 1975–2014
.
Bethesda, MD: National Cancer Institute
; 
2017
.
19.
Kanchi
KL
,
Johnson
KJ
,
Lu
C
,
McLellan
MD
,
Leiserson
MD
,
Wendl
MC
, et al
Integrated analysis of germline and somatic variants in ovarian cancer
.
Nat Commun
2014
;
5
:
3156
.
20.
Rath
MG
,
Masciari
S
,
Gelman
R
,
Miron
A
,
Miron
P
,
Foley
K
, et al
Prevalence of germline TP53 mutations in HER2+ breast cancer patients
.
Breast Cancer Res Treat
2013
;
139
:
193
8
.
21.
Couch
FJ
,
Hart
SN
,
Sharma
P
,
Toland
AE
,
Wang
X
,
Miron
P
, et al
Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer
.
J Clin Oncol
2015
;
33
:
304
11
.
22.
Maxwell
KN
,
Wubbenhorst
B
,
Wenz
BM
,
De Sloover
D
,
Pluta
J
,
Emery
L
, et al
BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers
.
Nat Commun
2017
;
8
:
319
.
23.
Vijai
J
,
Topka
S
,
Villano
D
,
Ravichandran
V
,
Maxwell
KN
,
Maria
A
, et al
A recurrent ERCC3 truncating mutation confers moderate risk for breast cancer
.
Cancer Discov
2016
;
6
:
1267
75
.
24.
Chen
VB
,
Arendall
WB
 3rd
,
Headd
JJ
,
Keedy
DA
,
Immormino
RM
,
Kapral
GJ
, et al
MolProbity: all-atom structure validation for macromolecular crystallography
.
Acta Crystallogr D Biol Crystallogr
2010
;
66
:
12
21
.
25.
Surrey
LF
,
MacFarland
SP
,
Chang
F
,
Cao
K
,
Rathi
KS
,
Akgumus
GT
, et al
Clinical utility of custom-designed NGS panel testing in pediatric tumors
.
Genome Med
2019
;
11
:
32
.
26.
Cheng
DT
,
Mitchell
TN
,
Zehir
A
,
Shah
RH
,
Benayed
R
,
Syed
A
, et al
Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology
.
J Mol Diagn
2015
;
17
:
251
64
.
27.
Quinn
EA
,
Maciaszek
JL
,
Pinto
EM
,
Phillips
AH
,
Berdy
D
,
Khandwala
M
, et al
From uncertainty to pathogenicity: clinical and functional interrogation of a rare TP53 in-frame deletion
.
Cold Spring Harb Mol Case Stud
2019
;
5
:
a003921
.
28.
Barnoud
T
,
Budina-Kolomets
A
,
Basu
S
,
Leu
JI
,
Good
M
,
Kung
CP
, et al
Tailoring chemotherapy for the African-centric S47 variant of TP53
.
Cancer Res
2018
;
78
:
5694
705
.
29.
Langmead
B
,
Salzberg
SL.
Fast gapped-read alignment with Bowtie 2
.
Nat Methods
2012
;
9
:
357
9
.
30.
Li
B
,
Dewey
CN
. 
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
.
BMC Bioinformatics
2011
;
12
:
323
.
31.
Love
MI
,
Huber
W
,
Anders
S
. 
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
.
Genome Biol
2014
;
15
:
550
.
32.
Jennis
M
,
Kung
CP
,
Basu
S
,
Budina-Kolomets
A
,
Leu
JI
,
Khaku
S
, et al
An African-specific polymorphism in the TP53 gene impairs p53 tumor suppressor function in a mouse model
.
Genes Dev
2016
;
30
:
918
30
.
33.
DiGiammarino
EL
,
Lee
AS
,
Cadwell
C
,
Zhang
W
,
Bothner
B
,
Ribeiro
RC
, et al
A novel mechanism of tumorigenesis involving pH-dependent destabilization of a mutant p53 tetramer
.
Nat Struct Biol
2002
;
9
:
12
6
.
34.
Greenfield
NJ.
Using circular dichroism collected as a function of temperature to determine the thermodynamics of protein unfolding and binding interactions
.
Nat Protoc
2006
;
1
:
2527
35
.
35.
Donehower
LA
,
Soussi
T
,
Korkut
A
,
Liu
Y
,
Schultz
A
,
Cardenas
M
, et al
Integrated analysis of TP53 gene and pathway alterations in The Cancer Genome Atlas
.
Cell Rep
2019
;
28
:
1370
84
.
36.
Higashimoto
Y
,
Asanomi
Y
,
Takakusagi
S
,
Lewis
MS
,
Uosaki
K
,
Durell
SR
, et al
Unfolding, aggregation, and amyloid formation by the tetramerization domain from mutant p53 associated with lung cancer
.
Biochemistry
2006
;
45
:
1608
19
.
37.
Kamada
R
,
Nomura
T
,
Anderson
CW
,
Sakaguchi
K
. 
Cancer-associated p53 tetramerization domain mutants: quantitative analysis reveals a low threshold for tumor suppressor inactivation
.
J Biol Chem
2011
;
286
:
252
8
.
38.
Lalli
E
,
Figueiredo
BC
. 
Pediatric adrenocortical tumors: what they can tell us on adrenal development and comparison with adult adrenal tumors
.
Front Endocrinol
2015
;
6
:
23
.
39.
Mastellaro
MJ
,
Seidinger
AL
,
Kang
G
,
Abrahao
R
,
Miranda
ECM
,
Pounds
SB
, et al
Contribution of the TP53 R337H mutation to the cancer burden in southern Brazil: Insights from the study of 55 families of children with adrenocortical tumors
.
Cancer
2017
;
123
:
3150
8
.
40.
Bougeard
G
,
Baert-Desurmont
S
,
Tournier
I
,
Vasseur
S
,
Martin
C
,
Brugieres
L
, et al
Impact of the MDM2 SNP309 and p53 Arg72Pro polymorphism on age of tumour onset in Li-Fraumeni syndrome
.
J Med Genet
2006
;
43
:
531
3
.
41.
Fang
S
,
Krahe
R
,
Lozano
G
,
Han
Y
,
Chen
W
,
Post
SM
, et al
Effects of MDM2, MDM4 and TP53 codon 72 polymorphisms on cancer risk in a cohort study of carriers of TP53 germline mutations
.
PLoS One
2010
;
5
:
e10813
.
42.
Bond
GL
,
Hu
W
,
Bond
EE
,
Robins
H
,
Lutzker
SG
,
Arva
NC
, et al
A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in humans
.
Cell
2004
;
119
:
591
602
.
43.
Tabori
U
,
Nanda
S
,
Druker
H
,
Lees
J
,
Malkin
D
. 
Younger age of cancer initiation is associated with shorter telomere length in Li-Fraumeni syndrome
.
Cancer Res
2007
;
67
:
1415
8
.
44.
Marcel
V
,
Palmero
EI
,
Falagan-Lotsch
P
,
Martel-Planche
G
,
Ashton-Prolla
P
,
Olivier
M
, et al
TP53 PIN3 and MDM2 SNP309 polymorphisms as genetic modifiers in the Li-Fraumeni syndrome: impact on age at first diagnosis
.
J Med Genet
2009
;
46
:
766
72
.
45.
Assumpcao
JG
,
Seidinger
AL
,
Mastellaro
MJ
,
Ribeiro
RC
,
Zambetti
GP
,
Ganti
R
, et al
Association of the germline TP53 R337H mutation with breast cancer in southern Brazil
.
BMC Cancer
2008
;
8
:
357
.
46.
Cox
DM
,
Nelson
KL
,
Clytone
M
,
Collins
DL
. 
Hereditary cancer screening: case reports and review of literature on ten Ashkenazi Jewish founder mutations
.
Mol Genet Genomic Med
2018
;
6
:
1236
42
.
47.
Ballinger
ML
,
Best
A
,
Mai
PL
,
Khincha
PP
,
Loud
JT
,
Peters
JA
, et al
Baseline surveillance in Li-Fraumeni syndrome using whole-body magnetic resonance imaging: a meta-analysis
.
JAMA Oncol
2017
;
3
:
1634
9
.
48.
Villani
A
,
Shore
A
,
Wasserman
JD
,
Stephens
D
,
Kim
RH
,
Druker
H
, et al
Biochemical and imaging surveillance in germline TP53 mutation carriers with Li-Fraumeni syndrome: 11 year follow-up of a prospective observational study
.
Lancet Oncol
2016
;
17
:
1295
305
.
49.
Maxwell
KN
,
Domchek
SM
. 
Cancer treatment according to BRCA1 and BRCA2 mutations
.
Nat Rev Clin Oncol
2012
;
9
:
520
8
.