Literature DB >> 29522266

Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for Hereditary Breast and Ovarian Cancer.

Jan Hauke1, Judit Horvath2, Eva Groß3, Andrea Gehrig4, Ellen Honisch5, Karl Hackmann6, Gunnar Schmidt7, Norbert Arnold8, Ulrike Faust9, Christian Sutter10, Julia Hentschel11, Shan Wang-Gohrke12, Mateja Smogavec13, Bernhard H F Weber14, Nana Weber-Lassalle1, Konstantin Weber-Lassalle1, Julika Borde1, Corinna Ernst1, Janine Altmüller15,16,17, Alexander E Volk18, Holger Thiele15,16,17, Verena Hübbel1, Peter Nürnberg15,16,17, Katharina Keupp1, Beatrix Versmold1, Esther Pohl1, Christian Kubisch18, Sabine Grill3, Victoria Paul2, Natalie Herold1, Nadine Lichey2, Kerstin Rhiem1, Nina Ditsch19, Christian Ruckert2, Barbara Wappenschmidt1, Bernd Auber7, Andreas Rump6, Dieter Niederacher5, Thomas Haaf4, Juliane Ramser3, Bernd Dworniczak2, Christoph Engel20,21, Alfons Meindl3, Rita K Schmutzler1, Eric Hahnen1.   

Abstract

The prevalence of germ line mutations in non-BRCA1/2 genes associated with hereditary breast cancer (BC) is low, and the role of some of these genes in BC predisposition and pathogenesis is conflicting. In this study, 5589 consecutive BC index patients negative for pathogenic BRCA1/2 mutations and 2189 female controls were screened for germ line mutations in eight cancer predisposition genes (ATM, CDH1, CHEK2, NBN, PALB2, RAD51C, RAD51D, and TP53). All patients met the inclusion criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for germ line testing. The highest mutation prevalence was observed in the CHEK2 gene (2.5%), followed by ATM (1.5%) and PALB2 (1.2%). The mutation prevalence in each of the remaining genes was 0.3% or lower. Using Exome Aggregation Consortium control data, we confirm significant associations of heterozygous germ line mutations with BC for ATM (OR: 3.63, 95%CI: 2.67-4.94), CDH1 (OR: 17.04, 95%CI: 3.54-82), CHEK2 (OR: 2.93, 95%CI: 2.29-3.75), PALB2 (OR: 9.53, 95%CI: 6.25-14.51), and TP53 (OR: 7.30, 95%CI: 1.22-43.68). NBN germ line mutations were not significantly associated with BC risk (OR:1.39, 95%CI: 0.73-2.64). Due to their low mutation prevalence, the RAD51C and RAD51D genes require further investigation. Compared with control datasets, predicted damaging rare missense variants were significantly more prevalent in CHEK2 and TP53 in BC index patients. Compared with the overall sample, only TP53 mutation carriers show a significantly younger age at first BC diagnosis. We demonstrate a significant association of deleterious variants in the CHEK2, PALB2, and TP53 genes with bilateral BC. Both, ATM and CHEK2, were negatively associated with triple-negative breast cancer (TNBC) and estrogen receptor (ER)-negative tumor phenotypes. A particularly high CHEK2 mutation prevalence (5.2%) was observed in patients with human epidermal growth factor receptor 2 (HER2)-positive tumors.
© 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Entities:  

Keywords:  Breast cancer predisposition; hereditary breast cancer

Mesh:

Substances:

Year:  2018        PMID: 29522266      PMCID: PMC5911592          DOI: 10.1002/cam4.1376

Source DB:  PubMed          Journal:  Cancer Med        ISSN: 2045-7634            Impact factor:   4.452


Introduction

The prevalence of heterozygous BRCA1 and BRCA2 germ line mutations and their associated risks for breast cancer (BC) and ovarian cancer (OC) have been extensively studied 1. Pathogenic BRCA1 and BRCA2 germ line mutations were found in approximately 17% of the index patients with BC who met the inclusion criteria of the German Consortium for Hereditary Breast and Ovarian Cancer (GC‐HBOC) for germ line testing 2. With the advent of next‐generation sequencing (NGS), germ line testing for hereditary BC/OC could be extended beyond the analysis of the BRCA1 and BRCA2 genes 3. However, gene panel sequencing and the testing of a number of potential risk genes are challenging, as international guidelines for the clinical management of patients carrying mutations in non‐BRCA1/2 genes do not exist. Established non‐BRCA1/2, BC/OC risk genes are rarely mutated 4, 5, 6, 7, and data regarding their contribution to BC/OC risk are often controversial. Hence, verification is needed before the non‐BRCA1/2 genes generally included in gene panel testing can be treated as confirmed BC/OC risk genes 8, 9. Moreover, criteria that predict mutation probabilities in non‐BRCA1/2 genes are largely unknown and may differ from those that predict mutation probabilities in BRCA1 and BRCA2. The GC‐HBOC established multi‐gene panel testing in 2015. In addition to BRCA1 and BRCA2, eight genes were defined as “core genes” based on data available at the time of the gene panel design, that suggested their association with BC (ATM, CDH1, CHEK2, NBN, PALB2, and TP53) or OC (RAD51C and RAD51D) 10, 11, 12, 13. With a relative risk of 5.3 (90% CI: 3.0–9.4), deleterious mutations in the PALB2 gene appeared to confer high BC risk 14, 15, 16, 17. Lower relative risks were reported for mutations in the CHEK2 (3.0, 90% CI: 2.6–3.5) 18, 19 and ATM genes (2.8, 90% CI: 2.2–3.7) 3, 20, 21, 22, 23. The NBN gene was considered as a BC predisposition gene mainly based on the genotyping results of a common founder mutation, c.657_661del, p.(Lys219Asnfs*16). For this variant, a meta‐analysis of 10 studies revealed a pooled OR of 2.66 (95% CI: 1.82–3.90; P < .001) 24. Mutations in TP53 and CDH1 are associated with multiple cancer types, one of which is BC. For CDH1 mutations, a relative BC risk of 6.6 (90% CI: 2.2–19.9; P = .004) was reported 25, whereas reliable risk estimates for TP53 mutations are missing. Mutations in the RAD51C and RAD51D genes have shown clear evidence of an association with OC, whereas evidence of an association with BC is limited 10, 26, 27. The aim of this study was to investigate the associations of germ line mutations in selected non‐BRCA1/2 genes with BC risk and BC phenotype in a sample of 5589 BRCA1/2 negative BC index patients who were recruited and counseled at university hospital‐based centers of the GC‐HBOC.

Patients and Methods

Patient sample

All patients met the inclusion criteria of the GC‐HBOC for germ line testing (Table S1). Of note, the GC‐HBOC inclusion criteria are not restricted to familial cases and also consider patients with early‐onset BC (age at first diagnosis [AAD] before 36 years), bilateral BC (AAD before 51 years), and patients affected by BC and OC even in the absence of a family history of BC and OC. In the absence of a patient with OC in a family, the available BC patient with the youngest AAD was defined as the index patient. In families with OC, a BC patient was defined as the index patient only when DNA derived from the OC patient was not available for genetic testing. Overall, 5589 female index patients with BC and without a personal history of OC were included in this study. All patients were counseled at a participating GC‐HBOC center. Physicians qualified in genetic counseling recorded personal and family BC/OC history, information regarding age at first BC diagnosis and tumor receptor status. Written informed consent was obtained from all patients, and ethical approval was granted by the ethics committee of the University of Cologne (07‐048). All patients were tested negative for pathogenic germ line variants in the BRCA1 and BRCA2 genes, including large genomic rearrangements (LGRs).

Control sample

Two publicly accessible control datasets (ExAC and FLOSSIES) and sequencing data from 2189 geographically matched female controls were used in this study (Table 1). From the Exome Aggregation Consortium (ExAC) 28, we requested a dataset of individuals of European, non‐Finnish ancestry, excluding samples from The Cancer Genome Atlas (TCGA). This dataset comprises a total of 27,173 samples, which were analyzed by whole exome sequencing. The FLOSSIES project provides a dataset of 7325 women of European American ancestry (https://whi.color.com). All participating women have remained cancer‐free until at least 70 years of age. Germ line DNA samples of all participants were screened for variants in 27 established or suggested BC predisposition genes, including the eight selected non‐BRCA1/2 genes. In addition, we sequenced germ line DNA samples of 2189 female control individuals of German descent (geographically matched controls; GMCs) by NGS and analyzed these samples for variants in the eight selected non‐BRCA1/2 genes. Healthy controls were recruited by a study on genetic factors of a noncancer, age‐related phenotype, and a study on civilization diseases. The studies were approved by the local ethic committees, and all participants gave their written informed consent. At the time of blood draw, all GMCs were at least 40 years old (mean age 63, range 40–92) and cancer‐free.
Table 1

Clinical and BC tumor characteristics observed in the study sample (n = 5589) and information on control reference groups (Geographically matched controls, GMCs; FLOSSIES, Female controls of European American ancestry, older than age 70 years and cancer‐free; ExAC, Non‐Finnish Europeans from the Exome Aggregation Consortium, excluding TCGA data) used in this study

Number%
BC index patients, overall5589100.0
Unilateral BC496088.7
Bilateral BC62911.3
Without OC family history465583.3
With OC family history93416.7
BC index patients, age at first BC diagnosisa 5589100.0
<40144025.8
40–49214038.3
50–59126922.7
≥6069112.4
Data not available490.9
BC index patients, ER/PR/HER2 status available3104100.0
ER‐positive235575.9
ER‐negative74924.1
PR‐positive218870.5
PR‐negative91629.5
HER2‐positive65721.2
HER2‐negative244778.8
TNBC48215.5
Non‐TNBC262284.5
GMCs, age at blood draw2189100
40–491476.7
50–5932414.8
≥60171978.5
FLOSSIES7325100.0
ExAC27,173100.0

Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple‐negative breast cancer.

The age at first diagnosis (AAD) was available for 5540 of 5589 BC index patients (mean AAD 46.7 years, range 17–92 years).

Clinical and BC tumor characteristics observed in the study sample (n = 5589) and information on control reference groups (Geographically matched controls, GMCs; FLOSSIES, Female controls of European American ancestry, older than age 70 years and cancer‐free; ExAC, Non‐Finnish Europeans from the Exome Aggregation Consortium, excluding TCGA data) used in this study Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple‐negative breast cancer. The age at first diagnosis (AAD) was available for 5540 of 5589 BC index patients (mean AAD 46.7 years, range 17–92 years).

Gene panel analysis

Genomic DNA was isolated from venous blood samples. NGS and data analysis were carried out at each participating center using Illumina sequencing platforms, employing either the customized TruRisk® (Agilent or Illumina), a customized HaloPlex (Agilent, Santa Clara, California, USA), or the TruSight™ Cancer Sequencing Panel (Illumina, San Diego, California, USA) for target enrichment. All gene panels covered the eight selected non‐BRCA1/2 core genes. The diagnostic pipelines of the labs involved have been successfully tested in European Molecular Genetics Quality Network (EMQN) schemes. Since LGRs cannot be detected reliably on the basis of NGS‐data 29, this mutation type was not included in this study. All deleterious variants affecting canonical non‐BRCA1/2 core gene transcripts (ATM, NM_000051.3; CDH1, NM_004360.3; CHEK2, NM_007194; NBN, NM_002485.4; PALB2, NM_024675.3; RAD51C, NM_058216.2; RAD51D, NM_002878.3; TP53, NM_000546.5) were routinely verified by Sanger sequencing.

Variant classification

Variant classification was performed in accordance with the regulations of the international ENIGMA consortium 30 (Evidence‐based Network for the Interpretation of Germ line Mutant Alleles; https://enigmaconsortium.org; version 1.1: 26 March 2015). All genetic variants were classified using a five‐tier variant classification system as proposed by the Unclassified Genetic Variants Working Group of the International Agency for Research on Cancer (IARC) (deleterious = class 5, likely deleterious = class 4, variant of uncertain significance (VUS) = class 3, likely benign = class 2, and benign = class 1) 31. According to the ENIGMA regulations 30, variants reported with a minor allele frequency (MAF) ≥1% in control reference groups (e.g., ExAC excluding TCGA, FLOSSIES) were generally considered benign (class 1). For simplification, class 4/5 variants were defined as “deleterious variants.” To investigate the associations of germ line mutations in selected non‐BRCA1/2 genes with BC risk, only protein truncating variants (PTVs) were considered in patients and controls. PTVs were defined as nonsense, frameshift, or essential splice‐site variants affecting the invariant splice sites or the last nucleotide of an exon. As suggested by Lilyquist et al., protein truncating variants in the last exon or within the last 55 bp of the penultimate exon were classified as VUS, unless a known functional domain was disrupted 32. For the identification of potentially damaging, rare missense variants we employed two in silico prediction tools (SIFT and MutationTaster). Missense variants were defined as potentially damaging when predicted deleterious by both tools (Alamut version 2.10; Interactive Biosoftware, Rouen, France ) as of 30 November 2017.

Statistical analysis

We performed case–control analyses for the investigation of the association of variants with the BC phenotype and case–case analyses for comparison of molecular subgroups. Univariate logistic regression was performed to estimate odds ratios (OR) and 95% confidence intervals (95%CI) using SPSS Statistics, Version 25 (IBM, Armonk, New York, USA). Fisher's exact test and the Student's t‐test (for age‐related analysis) were used to calculate levels of significance, with P‐values <0.05 considered significant.

Results

Associations of protein truncating variants in the selected core genes with BC

Detailed information on the patient sample is given in Table 1. Only protein truncating variants (PTVs) were considered to investigate the associations of germ line mutations in selected non‐BRCA1/2 genes with BC risk in patients and controls. A list of all PTVs identified in the study sample is provided in Table S2. Among 5589 index patients with BC, 274 patients (4.9%) carried PTVs in the selected non‐BRCA1/2 genes. The overall occurrence of PTVs in the selected core genes was markedly lower in all three control datasets compared with BC index patients. In the ExAC control dataset, 389 out of 27,173 individuals (1.4%) carried PTVs, which was comparable to the hypernormal control datasets FLOSSIES (67 of 7325 women, 0.9%) and GMCs (33 of 2189 women, 1.5%). When comparing mutation prevalence in BC index patients with ExAC data on a gene‐specific level, significant associations were observed for ATM (OR: 3.63, 95% CI 2.67–4.94; P < .0001), CHEK2 (OR: 2.93, 95% CI 2.29–3.75; P < .0001), PALB2 (OR: 9.53, 95% CI 6.25–14.51; P < .0001), CDH1 (OR: 17.04, 95% CI 3.54–82; P < .0001), and TP53 (OR: 7.30, 95% CI 1.22–43.68; P = .038) (Table 2). In contrast, we did not observe significant associations between PTVs in the NBN gene and BC (OR: 1.39, 95% CI 0.73–2.64; P = .363). When comparing the PTV prevalence in BC index patients with hypernormal controls (FLOSSIES, GMCs), higher ORs were observed for ATM, CHEK2, and PALB2 than were observed in comparison with ExAC data (Table 2). Again, no association between PTVs in the NBN gene and BC was observed. In contrast to the aforementioned genes, the analysis of RAD51C and RAD51D revealed ambiguous results. For RAD51C, a significant association with BC was observed when comparing PTV prevalence with FLOSSIES but not when compared with ExAC data or GMCs (Table 2). For RAD51D, ORs of 3.04 (vs. ExAC data) and 3.28 (vs. FLOSSIES) were observed, though both comparisons did not reach levels of significance (Table 2).
Table 2

Prevalence of protein truncating variants (PTVs) in eight non‐BRCA1/2 cancer predisposition genes in 5589 BC index patients compared with control datasets (ExAC, FLOSSIES, and GMCs). Percentages of individuals carrying a mutation in the respective datasets are shown in parentheses

GeneBC n = 5589 (%)ExAC n = 27,173 (%)FLOSSIES n = 7325 (%)GMCs n = 2189 (%)BC vs. ExAC OR (95% CI, P a)BC vs. FLOSSIES OR (95% CI, P a)BC vs. GMCs OR (95% CI, P a)
ATM 71 (1.27)96 (0.35)14 (0.19)9 (0.41)3.63 (2.67–4.94, <0.0001)6.72 (3.78–11.93, <0.0001)3.12 (1.56–6.25, 0.0004)
CDH1 7 (0.13)2 (0.01)0 (0.0)0 (0.0)17.04 (3.54–82, <0.0001)n.a. (n.a., 0.0028)n.a. (n.a., 0.2020)
CHEK2 103 (1.84)172 (0.63)28 (0.38)11 (0.50)2.93 (2.29–3.75, <0.0001)4.87 (3.20–7.40, <0.0001)3.72 (1.99–6–94, <0.0001)
CHEK2, c.1100del79 (1.41)127 (0.47)22 (0.30)8 (0.37)3.02 (2.28–4.01, <0.0001)4.71 (2.93–7.56, <0.0001)3.91 (1.87–8.05, <0.0001)
NBN 12 (0.21)42 (0.15)14 (0.19)9 (0.41)1.39 (0.73–2.64, 0.3630)1.12 (0.53–2.43, 0.8438)0.52 (0.22–1.24, 0.1466)
PALB2 64 (1.15)33 (0.12)7 (0.10)2 (0.09)9.53 (6.25–14.51, <0.0001)12.11 (5.55–26.45 < 0.0001)12.67 (3.10–51.79, <0.0001)
RAD51C 9 (0.16)34 (0.13)2 (0.03)2 (0.09)1.29 (0.62–2.69, 0.5409)5.91 (1.28–27.34, 0.0129)1.76 (0.38–8.17, 0.7384)
RAD51D 5 (0.09)8 (0.03)2 (0.03)0 (0.0)3.04 (0.99–9.30, 0.0558)3.28 (0.64–16.91, 0.2512)n.a. (n.a., 0.3308)
TP53 3 (0.05)2 (0.01)0 (0.0)0 (0.0)7.30 (1.22–43.68, 0.0378)n.a. (n.a., 0.0810)n.a. (n.a., 0.5640)
PTVs2743896733
Carriersb 272 (4.87)389 (1.43)67 (0.91)33 (1.51)

OR, odds ratio; CI, confidence interval.

Fisher's exact test.

Two patients carried two PTVs (ATM and CHEK2; NBN and RAD51C).

Prevalence of protein truncating variants (PTVs) in eight non‐BRCA1/2 cancer predisposition genes in 5589 BC index patients compared with control datasets (ExAC, FLOSSIES, and GMCs). Percentages of individuals carrying a mutation in the respective datasets are shown in parentheses OR, odds ratio; CI, confidence interval. Fisher's exact test. Two patients carried two PTVs (ATM and CHEK2; NBN and RAD51C).

Deleterious variants in non‐BRCA1/2 genes in the overall patient sample

Overall, heterozygous deleterious variants in at least one of the eight non‐BRCA1/2 core genes were present in 339 of the 5589 BC index patients (6.1%, Table 3). The highest prevalence of deleterious variants was observed in the CHEK2 gene (138 carriers, 2.5%), followed by ATM (81 carriers, 1.4%) and PALB2 (68 carriers, 1.2%). The prevalence of deleterious variants in the CDH1, NBN, RAD51C, RAD51D, and TP53 genes in the overall patient sample was 0.3% or lower for each gene (Table 3). Overall, 147 distinct deleterious variants were identified in 339 patients, of which 45 were recurrent (Table). With a carrier frequency of 1.4%, the c.1100del founder variant in the CHEK2 gene was the most prevalent deleterious variant observed (Table 3).
Table 3

Deleterious variants in eight non‐BRCA1/2 core genes identified in 5589 BC index patients. Total numbers of patients carrying deleterious variants in each gene are given. Percentages of patients carrying a deleterious variant in the respective subgroup are shown in parentheses

GeneBC index patients
All, n = 5589 (%)Bilateral BC, n = 629 (%)Unilateral BC, n = 4960 (%)Bilateral vs. unilateral BC OR (95% CI, P a)
ATM 81 (1.45)10 (1.59)71 (1.43)1.11 (0.57–2.17, 0.7229)
CDH1 8 (0.14)1 (0.16)7 (0.14)1.13 (0.14–9.17, 1.0000)
CHEK2 a 138 (2.47)20 (3.18)118 (2.38)1.35 (0.83–2.18, 0.2196)
CHEK2, c.1100del79 (1.41) 16 (2.54) 63 (1.27) 2.03 (1.17–3.53, 0.0180)
NBN 12 (0.21)0 (0.00)12 (0.24)n.a. (n.a., 0.3830)
PALB2 68 (1.22) 14 (2.23) 54 (1.09) 2.07 (1.14–3.75, 0.0201)
RAD51C 11 (0.20)1 (0.16)10 (0.20)0.79 (0.10–6.17, 1.0000)
RAD51D 6 (0.11)1 (0.16)5 (0.10)1.58 (0.18–13.53, 0.5116)
TP53 17 (0.30) 5 (0.79) 12 (0.24) 3.30 (1.16–9.41, 0.0348)
Deleterious variants341 52 289 1.46 (1.07–1.98, 0.0211)
Carriersb 339 (6.07) 52 (8.27) 287 (5.79) 1.50 (1.10–2.04, 0.0124)

The disease‐associated CHEK2 variant c.470C>T, p.I157T was classified as a low‐risk variant for BC based on a recent meta‐analysis including 15,985 BC cases and 18,609 controls from eight studies (OR: 1.58, 95% CI 1.42–1.75; P < .00001)37. In our study, the c.470C>T, p.I157T was heterozygously present in 86 out of 5589 BC index patients (1.54%). In contrast, 44 heterozygous mutation carriers were identified among the 2189 hypernormal female controls of German descent analyzed in this study (2.0%). Due to the lack of association with BC in our sample, this variant was not considered to be a deleterious mutation in our study.

Two patients carried two deleterious variants (ATM and CHEK2; NBN and RAD51C). Mutation prevalence showing significant differences according to the subgroups (bilateral BC vs. unilateral BC) are shown in bold. n.a. = not applicable.

Deleterious variants in eight non‐BRCA1/2 core genes identified in 5589 BC index patients. Total numbers of patients carrying deleterious variants in each gene are given. Percentages of patients carrying a deleterious variant in the respective subgroup are shown in parentheses The disease‐associated CHEK2 variant c.470C>T, p.I157T was classified as a low‐risk variant for BC based on a recent meta‐analysis including 15,985 BC cases and 18,609 controls from eight studies (OR: 1.58, 95% CI 1.42–1.75; P < .00001)37. In our study, the c.470C>T, p.I157T was heterozygously present in 86 out of 5589 BC index patients (1.54%). In contrast, 44 heterozygous mutation carriers were identified among the 2189 hypernormal female controls of German descent analyzed in this study (2.0%). Due to the lack of association with BC in our sample, this variant was not considered to be a deleterious mutation in our study. Two patients carried two deleterious variants (ATM and CHEK2; NBN and RAD51C). Mutation prevalence showing significant differences according to the subgroups (bilateral BC vs. unilateral BC) are shown in bold. n.a. = not applicable.

Deleterious variants in non‐BRCA1/2 genes according to personal and family history of cancer

Among the 5589 BC index patients, 629 were affected by bilateral BC. In this subgroup, 8.3% of patients (52 of 629) carried a deleterious variant in at least one of the eight selected genes. This was significantly higher than the prevalence of deleterious variants in the 4960 BC index patients affected by unilateral BC (5.8%, 289 in 4960; P = .021). On a gene‐specific level, the CHEK2 c.1000del founder variant (2.5% vs. 1.3%; P = .018), deleterious variants in the PALB2 gene (2.2% vs. 1.1%; P = .020), and deleterious variants in the TP53 gene (0.79% vs. 0.24%; P = .035) were significantly associated with bilateral BC (Table 3). Of the 5589 BC index patients, 934 reported a family history of OC. The prevalence of deleterious variants was not significantly different in BC index patients with a family history of OC versus the 4655 BC index patients who had no family history of OC (data not shown).

Deleterious variants in non‐BRCA1/2 genes according to age at first diagnosis of BC

In a gene‐specific analysis, only patients carrying deleterious TP53 variants showed a younger mean age at first BC diagnosis (TP53: 39.7 years, range 23–71 years) compared with the overall sample (46.7 years, range 17–92 years), with differences reaching levels of significance (P = .004, Student's t‐test, Table 4). For example, 52.9% (9 of 17) of the TP53 mutation carriers showed an age at first BC diagnosis below the age of 40 years compared with 26.0% (1440 of 5540) of the BC index patients overall (P = .021, Table 4). Overall, no significant difference in age at first BC diagnosis was observed between patients carrying deleterious variants and the overall sample (Table 4).
Table 4

Age at first diagnosis of BC in the overall sample and according to germ line mutation status. Included are patients carrying deleterious variants in the respective risk gene. Compared with the overall sample, only TP53 mutation carriers showed a significantly younger age at first diagnosis

GeneAge at first diagnosis
TotalMean (years)Median (years)Range (years)<40 years (% of carriers)<50 years (% of carriers)<60 years (% of carriers)
Overalla 554046.74617–921440 (26.0)3580 (64.6)4849 (87.5)
ATM 8145.04527–8028 (34.6)58 (71.6)71 (87.7)
CDH1 845.14333–593 (37.5)6 (75.0)8 (100.0)
CHEK2 13847.14629–7535 (25.4)92 (66.7)120 (87.0)
NBN 1246.35033–743 (25.0)6 (50.0)11 (91.7)
PALB2 6845.84628–7820 (29.4)44 (64.7)62 (91.2)
RAD51C 1144.94833–534 (36.4)6 (54.5)11 (100.0)
RAD51D 646.84933–601 (16.7)3 (50.0)5 (83.3)
TP53 1739.73523–719 (52.9)14 (82.4)16 (94.1)
All carriers33945.94523–8094 (27.7)228 (67.3)301 (88.8)

The age at first diagnosis of BC was available for 5540 of 5589 BC index patients.

Age at first diagnosis of BC in the overall sample and according to germ line mutation status. Included are patients carrying deleterious variants in the respective risk gene. Compared with the overall sample, only TP53 mutation carriers showed a significantly younger age at first diagnosis The age at first diagnosis of BC was available for 5540 of 5589 BC index patients.

Deleterious variants in BC index patients according to ER/PR/HER2 status

Information regarding hormone receptor (ER/PR) and HER2 status was available for a subgroup of 3104 of the 5589 BC index cases. Within this subgroup, 482 showed a triple‐negative breast cancer (TNBC) phenotype (15.5%, Table 1). The mutation prevalence in patients with ER‐positive BC was significantly increased in patients with ER‐negative tumors (7.3% vs. 4.7%, P = .014; Table 5). In a gene‐specific analysis, this difference was highest in the ATM (1.83% vs. 0.53%, P = .009; Table 5) and CHEK2 genes (3.2% vs. 1.9%, P = .060; Table 5). The overall prevalence of deleterious variants was not significantly different when stratified by PR or HER2 status (Table 5). On a gene‐specific level, however, a particularly high CHEK2 mutation prevalence was observed in patients with HER2‐positive tumors compared with patients with HER2‐negative tumors (5.2% vs. 2.3%; P < .001; Table 5). TNBC predicts a significantly lower mutation probability in the selected genes. While deleterious mutations were present in 186 of the 2622 patients with non‐TNBC (7.1%), only 20 of the 482 patients with TNBC (4.1%) carried a deleterious mutation (P = .017; Table 5). Both the ATM and CHEK2 genes showed significantly higher mutation rates in patients with non‐TNBC versus patients with a TNBC tumor phenotype (ATM: 1.7% vs. 0.4%; P = .026; CHEK2: 3.3% vs. 0.8%, P = .002; Table 5).
Table 5

Deleterious variants in the eight selected non‐BRCA1/2 core genes identified in 5589 BC index patients. Total numbers of patients carrying deleterious variants in each gene are given. Percentages of patients carrying a mutation in the respective subgroup are shown in parentheses. Mutation prevalence showing significant differences according to the subgroups are shown in bold (ER‐positive vs. ER‐negative; PR‐positive vs. PR‐negative; HER2‐positive vs. HER2‐negative; non‐TNBC vs. TNBC)

GeneBC index patients
ER/PR/HER2 status available n = 3104 (%)ER‐positive n = 2355 (%)ER‐negative n = 749 (%)PR‐positive n = 2188 (%)PR‐negative n = 916 (%)HER2‐positive n = 657 (%)HER2‐negative n = 2447 (%)Non‐TNBC n = 2622 (%)TNBC n = 482 (%)
ATM 47 (1.51) 43 (1.83) 4 (0.53) 39 (1.78)8 (0.87)7 (1.07)40 (1.63) 45 (1.72) 2 (0.41)
CDH1 7 (0.23)6 (0.25)1 (0.13)4 (0.18)3 (0.33)1 (0.15)6 (0.25)6 (0.23)1 (0.21)
CHEK2 90 (2.90)76 (3.23)14 (1.87)70 (3.20)20 (2.18) 34 (5.18) 56 (2.29) 86 (3.28) 4 (0.83)
CHEK2, c.1100del52 (1.68)43 (1.83)9 (1.20)42 (1.92)10 (1.09) 20 (3.04) 32 (1.31) 49 (1.87)3 (0.62)
NBN 5 (0.16)4 (0.17)1 (0.13)3 (0.14)2 (0.22)0 (0.00)5 (0.20)4 (0.15)1 (0.21)
PALB2 40 (1.29)31 (1.32)9 (1.20)29 (1.33)11 (1.20)4 (0.61)36 (1.47)32 (1.22)8 (1.66)
RAD51C 4 (0.13)3 (0.13)1 (0.13)2 (0.09)2 (0.22)1 (0.15)3 (0.12)3 (0.11)1 (0.21)
RAD51D 3 (0.10)1 (0.04)2 (0.27)1 (0.05)2 (0.22)0 (0.00)3 (0.12)1 (0.04)2 (0.41)
TP53 10 (0.32)7 (0.30)3 (0.40)8 (0.37)2 (0.22)5 (0.76)5 (0.20)9 (0.34)1 (0.21)
Carriers 206 (6.64) 171 (7.26) 35 (4.67) 156 (7.13) 50 (5.46) 52 (7.91) 154 (6.29) 186 (7.09) 20 (4.15)
Deleterious variants in the eight selected non‐BRCA1/2 core genes identified in 5589 BC index patients. Total numbers of patients carrying deleterious variants in each gene are given. Percentages of patients carrying a mutation in the respective subgroup are shown in parentheses. Mutation prevalence showing significant differences according to the subgroups are shown in bold (ER‐positive vs. ER‐negative; PR‐positive vs. PR‐negative; HER2‐positive vs. HER2‐negative; non‐TNBC vs. TNBC)

Variants of uncertain significance (VUS) and rare missense variants in the overall patient sample

In 827 of the 5589 index patients (14.8%; Table S2), we identified 421 distinct variants of uncertain significance (VUS). Most VUS were missense variants (396 of 421; 94.1%). Most VUS were present in the ATM gene (affecting 322 of 5589 patients; 5.8%), followed by the CHEK2 (196 of 5589 patients; 3.5%) and PALB2 genes (76 of 5589 patients; 1.4%). The vast majority of patients with an uncertain genetic report carry a single VUS in the selected genes (761 of 829; 91.8%). To examine the potential association of missense variants in the 8 investigated genes with BC risk, we next focused on rare missense variants (MAF < 0.1%), which were predicted to be damaging by both, SIFT and MutationTaster (Table S3). Compared with ExAC controls, rare CHEK2 and TP53 missense variants predicted damaging by both tools were significantly more prevalent in BC patients (1.43% vs. 0.71%; P < .0001 and 0.41% vs. 0.18%; P = .002). Prevalence of these rare missense variants in ATM, PALB2, RAD51C, and RAD51D was only marginally increased in comparison with ExAC controls, which differences not reaching levels of statistical significance.

Discussion

In our sample of 5589 BC index cases, we confirmed that PTVs in the ATM, CDH1, CHEK2, PALB2, and TP53 genes increase BC risk. The ORs for the most frequently mutated genes (ATM (OR: 3.63, 95% CI 2.67–4.94), CHEK2 (OR: 2.93, 95% CI 2.29–3.75), and PALB2 (OR: 9.53, 95% CI 6.25–14.51) are compatible with published data, according to a meta‐analysis focusing on PTVs 33. In this meta‐analysis, an aggregated OR of 3.20 (95% CI 2.04–5.04; analysis of 4266 cases and 5566 controls) was calculated for ATM, an aggregated OR of 3.25 (95% CI 2.55–4.13, analysis of 7263 cases and 13,785 controls) was calculated for CHEK2, and an aggregated OR of 21.40 (95% CI 10.10–45.32, analysis of 5862 cases and 17,453 controls) was calculated for PALB2. Based on a recent analysis of a large series of BC patients of European ancestry, Couch et al. reported ORs of 3.13 (95% CI 2.33–4.23) for ATM, 2.23 (95% CI 1.85–2.69) for CHEK2, and 7.67 (95% CI 5.19–11.50) for PALB2 when focusing on PTVs 4. Similar ORs were also observed for these genes in the Australian population 9. For the syndrome‐associated genes CDH1 and TP53, we confirm significant associations with BC, though with wide confidence intervals. In contrast to previous studies focusing on the c.657_661del, p.(Lys219Asnfs*16) founder mutation 24, we could not confirm NBN as a BC predisposition gene (OR: 1.39, 95% CI 0.73–2.64; P = .363; Table 2). This is consistent with the panel gene analyses by Couch et al. (OR: 1.27, 95% CI 0.81–2.01; P = .32) and Thompson et al. (OR: 0.67, 95% CI 0.11–4.0; P = 1.00) 4, 9. For RAD51D mutations, we demonstrated some indication of an association with BC (OR: 3.04, 95% CI 0.99–9.30; P = .0558), which is compatible with the results obtained by Couch et al. using the same control dataset (OR: 2.90, 95% CI 1.12–7.21; P = .02) 4. However, these associations did not reach levels of significance. Thus, we suggest that larger collaborative studies are necessary to assess the role of RAD51D in BC pathogenesis. The same holds true for RAD51C mutations for which we showed an elevated prevalence in BC index patients versus hypernormal controls but not versus ExAC data (Table 2). We demonstrated significantly higher mutation prevalence in bilateral versus unilateral BC cases, with highest differences in CHEK2, PALB2, and TP53 (Table 3). In agreement with the data presented here, Couch et al. showed that pathogenic variants in CHEK2, PALB2, and TP53 were associated with bilateral BC 4. A young age at BC disease onset, a personal or family history of OC, and the occurrence of the TNBC tumor phenotype predict high mutation probabilities in the BRCA1 gene and to a lesser extent in the BRCA2 gene 2. However, these criteria do not effectively enrich for patients with mutations in non‐BRCA1/2 genes. In this investigation, age at first BC diagnosis did not significantly predict mutation probabilities overall, with the exception of TP53 mutations, which is well in line with published data 34, 35. The mutation prevalence in non‐BRCA1/2 genes stratified by age likewise did not differ markedly in the studies of Thompson et al. and Buys et al. 5, 9. Of note, the subgroup of patients with TNBC, a tumor phenotype closely associated with a high BRCA1 mutation prevalence 35, 36, showed lower mutation probabilities in non‐BRCA1/2 genes, especially for ATM and CHEK2 mutations, we identified a negative association with this subtype (Table 5). Similar results were observed by Buys et al., demonstrating significantly lower mutation probabilities for both genes in patients with TNBC versus other subtypes 5. Deleterious ATM and CHEK2 mutations were particularly frequent in ER‐positive tumors, while CHEK2 mutations were also frequently found in HER2‐positive tumors. Our study confirmed the benefit of multi‐gene testing for risk assessment in BC/OC families. Here, we identified deleterious variants in validated BC predisposition genes (ATM, CDH1, CHEK2, PALB2, and TP53) in 312 of 5589 BC index cases, enabling the offer of predictive testing and adjusted surveillance programs in these families. Of note, we identified a high prevalence of VUS which is still a major drawback of multi‐gene testing in a diagnostic setting.

Conflict of Interest

The authors have nothing to disclose. Table S1. Inclusion criteria for germline testing. Table S2. Class 3, 4 and 5 mutations identified in the study sample. Table S3. Prevalence of rare missense variants. Click here for additional data file.
  36 in total

1.  An evaluation of copy number variation detection tools from whole-exome sequencing data.

Authors:  Renjie Tan; Yadong Wang; Sarah E Kleinstein; Yongzhuang Liu; Xiaolin Zhu; Hongzhe Guo; Qinghua Jiang; Andrew S Allen; Mingfu Zhu
Journal:  Hum Mutat       Date:  2014-05-01       Impact factor: 4.878

2.  Inherited Mutations in Women With Ovarian Carcinoma.

Authors:  Barbara M Norquist; Maria I Harrell; Mark F Brady; Tom Walsh; Ming K Lee; Suleyman Gulsuner; Sarah S Bernards; Silvia Casadei; Qian Yi; Robert A Burger; John K Chan; Susan A Davidson; Robert S Mannel; Paul A DiSilvestro; Heather A Lankes; Nilsa C Ramirez; Mary Claire King; Elizabeth M Swisher; Michael J Birrer
Journal:  JAMA Oncol       Date:  2016-04       Impact factor: 31.777

3.  Germline mutations in breast and ovarian cancer pedigrees establish RAD51C as a human cancer susceptibility gene.

Authors:  Alfons Meindl; Heide Hellebrand; Constanze Wiek; Verena Erven; Barbara Wappenschmidt; Dieter Niederacher; Marcel Freund; Peter Lichtner; Linda Hartmann; Heiner Schaal; Juliane Ramser; Ellen Honisch; Christian Kubisch; Hans E Wichmann; Karin Kast; Helmut Deissler; Christoph Engel; Bertram Müller-Myhsok; Kornelia Neveling; Marion Kiechle; Christopher G Mathew; Detlev Schindler; Rita K Schmutzler; Helmut Hanenberg
Journal:  Nat Genet       Date:  2010-04-18       Impact factor: 38.330

4.  The effect of CHEK2 variant I157T on cancer susceptibility: evidence from a meta-analysis.

Authors:  Fei-fei Han; Chang-long Guo; Li-hong Liu
Journal:  DNA Cell Biol       Date:  2013-05-13       Impact factor: 3.311

5.  A recurrent mutation in PALB2 in Finnish cancer families.

Authors:  Hannele Erkko; Bing Xia; Jenni Nikkilä; Johanna Schleutker; Kirsi Syrjäkoski; Arto Mannermaa; Anne Kallioniemi; Katri Pylkäs; Sanna-Maria Karppinen; Katrin Rapakko; Alexander Miron; Qing Sheng; Guilan Li; Henna Mattila; Daphne W Bell; Daniel A Haber; Mervi Grip; Mervi Reiman; Arja Jukkola-Vuorinen; Aki Mustonen; Juha Kere; Lauri A Aaltonen; Veli-Matti Kosma; Vesa Kataja; Ylermi Soini; Ronny I Drapkin; David M Livingston; Robert Winqvist
Journal:  Nature       Date:  2007-02-07       Impact factor: 49.962

6.  Associations Between Cancer Predisposition Testing Panel Genes and Breast Cancer.

Authors:  Fergus J Couch; Hermela Shimelis; Chunling Hu; Steven N Hart; Eric C Polley; Jie Na; Emily Hallberg; Raymond Moore; Abigail Thomas; Jenna Lilyquist; Bingjian Feng; Rachel McFarland; Tina Pesaran; Robert Huether; Holly LaDuca; Elizabeth C Chao; David E Goldgar; Jill S Dolinsky
Journal:  JAMA Oncol       Date:  2017-09-01       Impact factor: 31.777

7.  Germline Mutation Status, Pathological Complete Response, and Disease-Free Survival in Triple-Negative Breast Cancer: Secondary Analysis of the GeparSixto Randomized Clinical Trial.

Authors:  Eric Hahnen; Bianca Lederer; Jan Hauke; Sibylle Loibl; Sandra Kröber; Andreas Schneeweiss; Carsten Denkert; Peter A Fasching; Jens U Blohmer; Christian Jackisch; Stefan Paepke; Bernd Gerber; Sherko Kümmel; Christian Schem; Guido Neidhardt; Jens Huober; Kerstin Rhiem; Serban Costa; Janine Altmüller; Claus Hanusch; Holger Thiele; Volkmar Müller; Peter Nürnberg; Thomas Karn; Valentina Nekljudova; Michael Untch; Gunter von Minckwitz; Rita K Schmutzler
Journal:  JAMA Oncol       Date:  2017-10-01       Impact factor: 31.777

8.  Gene-panel sequencing and the prediction of breast-cancer risk.

Authors:  Douglas F Easton; Paul D P Pharoah; Antonis C Antoniou; Marc Tischkowitz; Sean V Tavtigian; Katherine L Nathanson; Peter Devilee; Alfons Meindl; Fergus J Couch; Melissa Southey; David E Goldgar; D Gareth R Evans; Georgia Chenevix-Trench; Nazneen Rahman; Mark Robson; Susan M Domchek; William D Foulkes
Journal:  N Engl J Med       Date:  2015-05-27       Impact factor: 91.245

9.  Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results.

Authors:  Sharon E Plon; Diana M Eccles; Douglas Easton; William D Foulkes; Maurizio Genuardi; Marc S Greenblatt; Frans B L Hogervorst; Nicoline Hoogerbrugge; Amanda B Spurdle; Sean V Tavtigian
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

10.  Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers.

Authors:  Karoline B Kuchenbaecker; John L Hopper; Daniel R Barnes; Kelly-Anne Phillips; Thea M Mooij; Marie-José Roos-Blom; Sarah Jervis; Flora E van Leeuwen; Roger L Milne; Nadine Andrieu; David E Goldgar; Mary Beth Terry; Matti A Rookus; Douglas F Easton; Antonis C Antoniou; Lesley McGuffog; D Gareth Evans; Daniel Barrowdale; Debra Frost; Julian Adlard; Kai-Ren Ong; Louise Izatt; Marc Tischkowitz; Ros Eeles; Rosemarie Davidson; Shirley Hodgson; Steve Ellis; Catherine Nogues; Christine Lasset; Dominique Stoppa-Lyonnet; Jean-Pierre Fricker; Laurence Faivre; Pascaline Berthet; Maartje J Hooning; Lizet E van der Kolk; Carolien M Kets; Muriel A Adank; Esther M John; Wendy K Chung; Irene L Andrulis; Melissa Southey; Mary B Daly; Saundra S Buys; Ana Osorio; Christoph Engel; Karin Kast; Rita K Schmutzler; Trinidad Caldes; Anna Jakubowska; Jacques Simard; Michael L Friedlander; Sue-Anne McLachlan; Eva Machackova; Lenka Foretova; Yen Y Tan; Christian F Singer; Edith Olah; Anne-Marie Gerdes; Brita Arver; Håkan Olsson
Journal:  JAMA       Date:  2017-06-20       Impact factor: 56.272

View more
  40 in total

1.  Prevalence of disease-causing genes in Japanese patients with BRCA1/2-wildtype hereditary breast and ovarian cancer syndrome.

Authors:  Tomoko Kaneyasu; Seiichi Mori; Hideko Yamauchi; Shozo Ohsumi; Shinji Ohno; Daisuke Aoki; Shinichi Baba; Junko Kawano; Yoshio Miki; Naomichi Matsumoto; Masao Nagasaki; Reiko Yoshida; Sadako Akashi-Tanaka; Takuji Iwase; Dai Kitagawa; Kenta Masuda; Akira Hirasawa; Masami Arai; Junko Takei; Yoshimi Ide; Osamu Gotoh; Noriko Yaguchi; Mitsuyo Nishi; Keika Kaneko; Yumi Matsuyama; Megumi Okawa; Misato Suzuki; Aya Nezu; Shiro Yokoyama; Sayuri Amino; Mayuko Inuzuka; Tetsuo Noda; Seigo Nakamura
Journal:  NPJ Breast Cancer       Date:  2020-06-12

2.  PALB2 c.2257C>T truncating variant is a Greek founder and is associated with high breast cancer risk.

Authors:  Andromachi Vagena; Myrto Papamentzelopoulou; Despoina Kalfakakou; Panagoula Kollia; Christos Papadimitriou; Amanda Psyrri; Paraskevi Apostolou; George Fountzilas; Irene Konstantopoulou; Drakoulis Yannoukakos; Florentia Fostira
Journal:  J Hum Genet       Date:  2019-05-14       Impact factor: 3.172

3.  A Population-Based Study of Genes Previously Implicated in Breast Cancer.

Authors:  Chunling Hu; Steven N Hart; Rohan Gnanaolivu; Hongyan Huang; Kun Y Lee; Jie Na; Chi Gao; Jenna Lilyquist; Siddhartha Yadav; Nicholas J Boddicker; Raed Samara; Josh Klebba; Christine B Ambrosone; Hoda Anton-Culver; Paul Auer; Elisa V Bandera; Leslie Bernstein; Kimberly A Bertrand; Elizabeth S Burnside; Brian D Carter; Heather Eliassen; Susan M Gapstur; Mia Gaudet; Christopher Haiman; James M Hodge; David J Hunter; Eric J Jacobs; Esther M John; Charles Kooperberg; Allison W Kurian; Loic Le Marchand; Sara Lindstroem; Tricia Lindstrom; Huiyan Ma; Susan Neuhausen; Polly A Newcomb; Katie M O'Brien; Janet E Olson; Irene M Ong; Tuya Pal; Julie R Palmer; Alpa V Patel; Sonya Reid; Lynn Rosenberg; Dale P Sandler; Christopher Scott; Rulla Tamimi; Jack A Taylor; Amy Trentham-Dietz; Celine M Vachon; Clarice Weinberg; Song Yao; Argyrios Ziogas; Jeffrey N Weitzel; David E Goldgar; Susan M Domchek; Katherine L Nathanson; Peter Kraft; Eric C Polley; Fergus J Couch
Journal:  N Engl J Med       Date:  2021-01-20       Impact factor: 91.245

4.  PALB2 germline mutations in a multi-gene panel testing cohort of 1905 breast-ovarian cancer patients in Argentina.

Authors:  Ariana Gonzalez; Franco Del Greco; Laura Vargas-Roig; Bianca Brun; Gonzalo Tabares; Alejandra Mampel; Cecilia Montes; Claudia Martin; Marcela Lopez; Norma Rossi; Luisina Bruno; Carolina Ponce; Patricia Quaglio; Alvaro Yanzi; Santiago Acevedo; Lilia Lugo; Paula Lopez Breccia; Silvia Avila; Silvina Sisterna; María Soledad Del Castillo; Martín Vazquez; Lina M Nuñez
Journal:  Breast Cancer Res Treat       Date:  2022-05-24       Impact factor: 4.872

5.  Prevalence of germline variants in consensus moderate-to-high-risk predisposition genes to hereditary breast and ovarian cancer in BRCA1/2-negative Brazilian patients.

Authors:  Renan Gomes; Pricila da Silva Spinola; Ayslan Castro Brant; Bruna Palma Matta; Caroline Macedo Nascimento; Silvia Maria de Aquino Paes; Cibele Rodrigues Bonvicino; Anna Claudia Evangelista Dos Santos; Miguel Angelo Martins Moreira
Journal:  Breast Cancer Res Treat       Date:  2020-10-30       Impact factor: 4.872

Review 6.  The role of genomics in global cancer prevention.

Authors:  Ophira Ginsburg; Paul Brennan; Patricia Ashton-Prolla; Anna Cantor; Daniela Mariosa
Journal:  Nat Rev Clin Oncol       Date:  2020-09-24       Impact factor: 66.675

7.  Mutations in BRCA1/2 and Other Panel Genes in Patients With Metastatic Breast Cancer -Association With Patient and Disease Characteristics and Effect on Prognosis.

Authors:  Peter A Fasching; Siddhartha Yadav; Chunling Hu; Marius Wunderle; Lothar Häberle; Steven N Hart; Matthias Rübner; Eric C Polley; Kun Y Lee; Rohan D Gnanaolivu; Peyman Hadji; Hanna Hübner; Hans Tesch; Johannes Ettl; Friedrich Overkamp; Michael P Lux; Arif B Ekici; Bernhard Volz; Sabrina Uhrig; Diana Lüftner; Markus Wallwiener; Volkmar Müller; Erik Belleville; Michael Untch; Hans-Christian Kolberg; Matthias W Beckmann; André Reis; Arndt Hartmann; Wolfgang Janni; Pauline Wimberger; Florin-Andrei Taran; Tanja N Fehm; Diethelm Wallwiener; Sara Y Brucker; Andreas Schneeweiss; Andreas D Hartkopf; Fergus J Couch
Journal:  J Clin Oncol       Date:  2021-03-29       Impact factor: 44.544

8.  The RAD51D c.82G>A (p.Val28Met) variant disrupts normal splicing and is associated with hereditary ovarian cancer.

Authors:  Ciyu Yang; Angela G Arnold; Amanda Catchings; Vikas Rai; Zsofia K Stadler; Liying Zhang
Journal:  Breast Cancer Res Treat       Date:  2021-01-16       Impact factor: 4.872

Review 9.  Cancer Progress and Priorities: Breast Cancer.

Authors:  Serena C Houghton; Susan E Hankinson
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2021-05       Impact factor: 4.090

Review 10.  First international workshop of the ATM and cancer risk group (4-5 December 2019).

Authors:  Fabienne Lesueur; Douglas F Easton; Anne-Laure Renault; Sean V Tavtigian; Jonine L Bernstein; Zsofia Kote-Jarai; Rosalind A Eeles; Dijana Plaseska-Karanfia; Lidia Feliubadaló; Banu Arun; Natalie Herold; Beatrix Versmold; Rita Katharina Schmutzler; Tú Nguyen-Dumont; Melissa C Southey; Leila Dorling; Alison M Dunning; Paola Ghiorzo; Bruna Samia Dalmasso; Eve Cavaciuti; Dorothée Le Gal; Nicholas J Roberts; Mev Dominguez-Valentin; Matti Rookus; Alexander M R Taylor; Alisa M Goldstein; David E Goldgar; Dominique Stoppa-Lyonnet; Nadine Andrieu
Journal:  Fam Cancer       Date:  2021-06-14       Impact factor: 2.375

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.