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Population-based estimates of age-specific cumulative risk of breast cancer for pathogenic variants in ATM

Abstract

Background

Multigene panel tests for breast cancer predisposition routinely include ATM as it is now a well-established breast cancer predisposition gene.

Methods

We included ATM in a multigene panel test applied to the Australian Breast Cancer Family Registry (ABCFR), a population-based case–control–family study of breast cancer, with the purpose of estimating the prevalence and penetrance of heterozygous ATM pathogenic variants from the family data, using segregation analysis.

Results

The estimated breast cancer hazard ratio for carriers of pathogenic ATM variants in the ABCFR was 1.32 (95% confidence interval 0.45–3.87; P = 0.6). The estimated cumulative risk of breast cancer to age 80 years for heterozygous ATM pathogenic variant carriers was estimated to be 13% (95% CI 4.6–30).

Conclusions

Although ATM has been definitively identified as a breast cancer predisposition gene, further evidence, such as variant-specific penetrance estimates, are needed to inform risk management strategies for carriers of pathogenic variants to increase the clinical utility of population testing of this gene.

Background

Ataxia-Telangiectasia Mutated (ATM) encodes a protein kinase involved in DNA damage repair. Bi-allelic pathogenic variants in ATM cause Ataxia–Telangiectasia (A–T), a complex phenotype with poor prognosis. Heterozygous carriers do not display the clinical features of A–T except for an increased predisposition to various cancers, including breast cancer. Women who meet genetic testing criteria due to a personal or family history of breast cancer and are heterozygous carriers of a pathogenic variant in ATM have been estimated to be at a two–fourfold increase in breast cancer risk compared to non-carriers [1, 2]. Several studies have reported breast cancer risk associated with carrying one missense pathogenic variant in ATM (c.7271T>G) to be high, setting it apart from other pathogenic variants in ATM in terms of the magnitude of associated breast cancer risk (e.g., OR 11.0 (1.42–85.7) p = 0.0019 [3]). Previously, using a population-based family study, we estimated the penetrance of ATM c.7271T>G to be 52% (95% CI 28–80%; HR = 8.6; 95% CI 3.9–18.9; P < 0.0001) [4]. Goldgar et al. estimated penetrance of likely pathogenic variants in ATM using data from 27 families (15 of which carried c.7271T > G) to be 60% to age 80 years [5].

Two recent large-scale, landmark studies have provided more insight into the prevalence of ATM pathogenic variants in population settings [6, 7]. In these studies, 0.6–0.7% of affected women who did not carry a pathogenic variant in BRCA1 or BRCA2 were found to carry a pathogenic variant in ATM. Dorling et al. estimated an odds ratio (OR) of breast cancer risk of 2.1 (95% confidence interval (CI) 1.35–3.23, p < 0.001) for ATM pathogenic variant carriers compared to non-carriers. Hu et al. estimated an OR of 1.8 (95% CI 1.46–2.27, p < 0.001) and, by combining this OR with the SEER breast cancer incidence rates for the population, derived an estimate of lifetime absolute risk of breast cancer greater than 20% for ATM pathogenic variant carriers. These studies have clearly established the relative risk of breast cancer associated with ATM pathogenic variants for women in the general population. However, for the purposes of genetic counselling, estimates of age-specific cumulative risks (penetrance) are more clinically useful yet are limited for ATM pathogenic variants.

We conducted a genetic screen of ATM in the Australian Breast Cancer Family Registry (ABCFR), an Australian population-based case–control–family study of breast cancer, with the purpose of estimating the prevalence and penetrance of ATM pathogenic variants in this cohort.

Methods

Study participants and genomic data generation

The ABCFR is a population based, case–control–family study of breast cancer, carried out in Australia (Melbourne and Sydney) as part of the international Breast Cancer Family Registry (BCFR). Case-probands were over-sampled for those with early-onset breast cancer, but were sampled irrespective of family history. Blood-derived germline DNA from 1480 case probands and 864 control probands were screened by targeted-sequencing of the coding regions and proximal intron–exon junctions of BRCA1 (NM_007294.4), BRCA2 (NM_000059.4) and ATM (NM_000051.4). Details of study participant characteristics and selection, sequencing and data processing and variant filtering and annotation methods have been published previously [8] and are summarized in Additional file 1: Fig. S1.

Genetic variant selection

Our statistical analyses focused on rare pathogenic or predicted deleterious variants, hereafter refer to as “pathogenic”. Rare variants were defined as those identified in the non-Finnish European population of gnomAD [9] and in the ABCFR with a minor allele frequency (MAF) ≤ 0.001. To define pathogenic variants, ClinVar annotations of “Pathogenic” or “Likely Pathogenic” were used (accessed July 2021). Predicted deleterious variants included truncating variants that were not present in ClinVar and a subset of missense substitutions as described below.

For ATM, the specific domains in which missense substitutions have been more commonly associated with A-T are the FAT, kinase and FATC domains. Therefore, missense substitutions were scored using the web version of Align-GVGD [10], and our statistical analysis included missense substitutions that i) fell into the PFAM FAT (residues 2096–2849), PPI3_PI4_kinase (residues 2713–2962) and FATC (residues 3025–3056) domain definitions and ii) received an Align-GVGD grade of C55 or C65, indicating that they were evolutionary unlikely (deleterious).

Statistical analyses

Hazard ratios (HRs) and age-specific cumulative risks (penetrance) were estimated as described in detail in [8]. Briefly, HRs for carriers of pathogenic ATM variants were estimated by segregation analysis as implemented in the statistical package MENDEL version 3.2, then the estimated cumulative risk to a given age was derived from the estimated HR. All estimates were appropriately adjusted for the population-based ascertainment of the families, and an unmeasured polygene was used to model any residual familial aggregation of breast cancer. Non-carrier incidences were chosen so that the average incidence for carriers and non-carriers (weighted by the carrier frequency) was the age-specific population incidence rates for Australia in the period 1998–2002, as obtained from Cancer Incidence in Five Continents [11]. The population cumulative risk to age 80 was taken to be 10.9%. The allele frequency of all pathogenic ATM variants combined was taken to be 0.001. All p values were 2-sided, and a p value threshold of 0.05 was used to define statistical significance.

Results

Targeted-sequencing was successfully performed on the germline DNA of 1476/1480 (99.7%) case-probands and 861/864 (99.7%) control-probands. A pathogenic ATM variant was identified in 25/1476 (1.7%) of case-probands and 9/864 (1.0%) of control-probands (Table 1, Additional file 2: Table S1 provides ClinVar and Align-GVGD/domain information, Additional file 3: Table S2 provides baseline characteristics by carrier status). None of the probands were found to also carry a pathogenic variant in BRCA1 or BRCA2.

Table 1 ATM variants identified by targeted-sequencing in the case-and control-probands participating in the Australian Breast Cancer Family Registry

The risk estimates were based on 1029 relatives of the 34 probands who carried a pathogenic ATM variant. Of these relatives, 33 had germline DNA for testing, and 19 were female breast cancer cases. In addition, a number of relatives had cancers of other anatomical sites (though only breast cancer contributed to our analyses): 20 lung, 20 prostate, 12 colorectum, 8 stomach and 56 at other anatomical sites (none reported more than five times). The relatives included 17 known carriers and 16 known non-carriers of the pathogenic ATM variant identified in the proband, though ungenotyped people also contributed to our estimates via their phenotypes and their relationships to genotyped people.

The estimated breast cancer HR for carriers of pathogenic ATM variants in the ABCFR was 1.32 (95% CI 0.45–3.87; P = 0.6). Excluding the rare missense variants that are predicted to be deleterious but do not yet have a ClinVar classification (Additional Table 1) did not change the HR for carriers (1.36 (95% CI 0.44–4.16; P = 0.6). Based on the above HR estimate for all pathogenic ATM variants combined, cumulative risks for these carriers to various ages were calculated (Fig. 1, Additional file 4: Table S3). Carriers had a 13% (95% CI 4.6–30) probability of developing breast cancer by the age of 80 years.

Fig. 1
figure 1

Average age-specific cumulative risk (penetrance) of breast cancer, for Australian women (dotted line) and for female carriers of pathogenic ATM variants combined (solid line) and pathogenic CHEK2 variants combined (dashed line), with confidence intervals for carriers (grey region)

Discussion

Variant classification remains a critical challenge to fully realize the clinical utility of genetic testing for ATM. This important issue and others have been identified as areas of priority by the International Consortium on ATM and Cancer, initiated in 2019, which brings together researchers and clinicians who aim to use a collaborative, multidisciplinary approach to addressing key questions about the cancer risks for carriers of a pathogenic ATM variant [12].

For ATM, as is the case for most breast cancer predisposition genes, truncating variants are, with a few exceptions, predicted to lead to loss of protein function and are classified as pathogenic. However, focusing on the FAT, kinase and FATC domains in ATM, Tavtigian et al. reported that the risk associated with carrying missense variants identified in these three domains (in aggregate) could be higher than that of protein truncating variants (in aggregate) [13]. Only a handful of ATM missense variants have been reported to be pathogenic in ClinVar. Missense variants represent a large proportion of the rare variants identified in our study: 70/129 (54%) of all rare variants in our study were missense substitutions but only 3/70 are classified as pathogenic in ClinVar.

We previously calculated cumulative risk estimates for CHEK2 in the ABCFR and observed that the penetrance estimates for pathogenic variants in CHEK2 and ATM are not statistically different (Fig. 1) [8]. There is an urgent and currently unmet need to provide robust information that can inform risk management strategies for carriers of pathogenic variants in intermediate risk genes such as ATM and CHEK2, as these genes are now routinely included on gene panels for cancer predisposition.

While these two genes are considered bona fide breast cancer predisposition genes, national best practice recommendations are only emerging to guide the management of women found to carry pathogenic variants in these genes. This situation results in a feeling of uncertainty and anxiety in these women [14].

Conclusion

Further international collaboration is required, potentially via the newly formed International Consortium on ATM and Cancer [12], to refine the penetrance estimates, identify relevant modifying factors (including the polygenic risk score), and the risk of other cancers for carriers of ATM pathogenic variant carriers.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article. used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

ABCFR:

Australian Breast Cancer Family Registry

Align-GVGD:

Align Grantham Variation Grantham Deviation

A-T:

Ataxia-Telangiectasia

CI:

Confidence interval

HR:

Hazard ratio

MAF:

Minor allele frequency

References

  1. Couch FJ, Shimelis H, Hu C, Hart SN, Polley EC, Na J, Hallberg E, Moore R, Thomas A, Lilyquist J, et al. Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol. 2017;3(9):1190–6.

    Article  Google Scholar 

  2. LaDuca H, Polley EC, Yussuf A, Hoang L, Gutierrez S, Hart SN, Yadav S, Hu C, Na J, Goldgar DE, et al. A clinical guide to hereditary cancer panel testing: evaluation of gene-specific cancer associations and sensitivity of genetic testing criteria in a cohort of 165,000 high-risk patients. Genet Med. 2020;22(2):407–15.

    CAS  Article  Google Scholar 

  3. Southey MC, Goldgar DE, Winqvist R, Pylkas K, Couch F, Tischkowitz M, Foulkes WD, Dennis J, Michailidou K, van Rensburg EJ et al: PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016.

  4. Bernstein JL, Teraoka S, Southey MC, Jenkins MA, Andrulis IL, Knight JA, John EM, Lapinski R, Wolitzer AL, Whittemore AS, et al. Population-based estimates of breast cancer risks associated with ATM gene variants c.7271T>G and c.1066–6T>G (IVS10-6T>G) from the Breast Cancer Family Registry. Hum Mutat. 2006;27(11):1122–8.

    CAS  Article  Google Scholar 

  5. Goldgar DE, Healey S, Dowty JG, Da Silva L, Chen X, Spurdle AB, Terry MB, Daly MJ, Buys SM, Southey MC, et al. Rare variants in the ATM gene and risk of breast cancer. Breast Cancer Res. 2011;13(4):R73.

    CAS  Article  Google Scholar 

  6. Dorling L, Carvalho S, Allen J, Gonzalez-Neira A, Luccarini C, Wahlstrom C, Pooley KA, Parsons MT, Fortuno C, Wang Q, et al. Breast cancer risk genes - association analysis in more than 113,000 women. N Engl J Med. 2021;384(5):428–39.

    CAS  Article  Google Scholar 

  7. Hu C, Hart SN, Gnanaolivu R, Huang H, Lee KY, Na J, Gao C, Lilyquist J, Yadav S, Boddicker NJ et al (2021) A population-based study of genes previously implicated in breast cancer. New Engl J Med

  8. Nguyen-Dumont T, Dowty JG, Steen JA, Renault A-L, Hammet F, Mahmoodi M, Theys D, Rewse A, Tsimiklis H, Winship IM, et al. Population-based estimates of the age-specific cumulative risk of breast cancer for pathogenic variants in CHEK2: findings from the Australian Breast Cancer Family Registry. Cancers. 2021;13(6):1378.

    CAS  Article  Google Scholar 

  9. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43.

    CAS  Article  Google Scholar 

  10. Tavtigian SV, Byrnes GB, Goldgar DE, Thomas A. Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications. Hum Mutat. 2008;29(11):1342–54.

    CAS  Article  Google Scholar 

  11. Ferlay J, Steliarova-Foucher E, Forman D. Cancer incidence in five continents, CI5plus: IARC Cancer Base No. 9; 2014.

  12. Lesueur F, Easton DF, Renault AL, Tavtigian SV, Bernstein JL, Kote-Jarai Z, Eeles RA, Plaseska-Karanfia D, Feliubadalo L, Spanish ATMwg et al. First international workshop of the ATM and cancer risk group (4–5 December 2019). Fam Cancer 2021.

  13. Tavtigian SV, Oefner PJ, Babikyan D, Hartmann A, Healey S, Le Calvez-Kelm F, Lesueur F, Byrnes GB, Chuang S-C, Forey N, et al. Rare, evolutionarily unlikely missense substitutions in ATM confer increased risk of breast cancer. Am J Hum Genet. 2009;85(4):427–46.

    CAS  Article  Google Scholar 

  14. Reyes KG, Clark C, Gerhart M, Newson AJ, Ormond KE. "I wish that there was more info": characterizing the uncertainty experienced by carriers of pathogenic ATM and/or CHEK2 variants. Fam Cancer 2021.

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Acknowledgements

We thank all the participants in this study, the entire team of the Australian Breast Cancer Family registry (BCFR-AU) and past and current investigators.

Funding

This work was funded by the U.S. National Institute of Health (grant number RO1CA159868). The ABCFR was supported in Australia by the National Health and Medical Research Council, the New South Wales Cancer Council, the Victorian Health Promotion Foundation, the Victorian Breast Cancer Research Consortium, Cancer Australia, and the National Breast Cancer Foundation. The six sites of the Breast Cancer Family Registry (BCFR) were supported by grant UM1 CA164920 from the U.S. National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the BCFR, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government or the BCFR. TN-D is a National Breast Cancer Foundation (Australia) Career Development Fellow (ECF-17-001). MCS is a National Health and Medical Research Council (NMHRC, Australia) Senior Research Fellow (APP1155163). SL is a Victorian Cancer Agency Early Career Research Fellow (ECRF19020) This work was supported by an NHMRC Program grant (APP1074383), The National Breast Cancer Foundation (BRA-STRAP; NT-15-016), NHMRC European Union Collaborative Research Grant (APP1101400) and Monash University, Clayton, Australia.

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Contributions

Conceptualization, MCS; methodology, TN-D, A-LR and JGD; software, TN-D, JAS and JGD; validation, TN-D, A-LR, JAS; formal analysis, TN-D, A-LR and JGD; resources, GGG, MCS, JLH; data curation, TN-D, A-LR; writing—original draft preparation, TN-D, A-LR, JGD and MCS; writing—review and editing, TN-D, A-LR, JGD, JAS, SL, IMW, GGG, JLH and MCS; project administration, MCS; funding acquisition, MCS and TN-D; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Melissa C. Southey.

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Ethics approval and consent to participate

The study was approved by the Human Research Ethics Committee of the University of Melbourne (1441420, approved on 18 September 2017). Informed consent was obtained from all participants involved in the study.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

. Figure S1 Overview of study workflow.

Additional file 2

. Table S1 Characteristics of the ATM variants identified by targeted-sequencing in the Australian Breast Cancer Family Registry. Legend: HGVSc and HGVSp: variant nomenclature according to the Human Genome Variation Society (HGVS), HGVS.c for coding DNA and HGVS.p for protein variants. ClinVar: classification accessed July 2021. NA indicates that classification was not available. AGVGD: for missense variants only, grade obtained from the web version of Align-GVGD. Only C55 and C65 scores were considered deleterious in this analysis. ATM domain: PFAM FAT (residues 2096–2849), PPI3_PI4_kinase (residues 2713–2962) and FATC (residues 3025–3056) domain definitions were used (missense variants only).

Additional file 3

. Table S2 Baseline characteristics of ATM pathogenic variant carriers, by carrier status.

Additional file 4

. Table S3 Cumulative risks of breast cancer to various ages for carriers of ATM pathogenic variants.

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Renault, AL., Dowty, J.G., Steen, J.A. et al. Population-based estimates of age-specific cumulative risk of breast cancer for pathogenic variants in ATM. Breast Cancer Res 24, 24 (2022). https://doi.org/10.1186/s13058-022-01518-y

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Keywords

  • ATM
  • Breast cancer predisposition
  • Genetic risk factors
  • Age-specific cumulative risk
  • Penetrance