Comprehensive molecular comparison of BRCA1 hypermethylated and BRCA1 mutated triple negative breast cancers

Dominik Glodzik, Ana Bosch, Johan Hartman, Mattias Aine, Johan Vallon-Christersson, Christel Reuterswärd, Anna Karlsson, Shamik Mitra, Emma Niméus, Karolina Holm, Jari Häkkinen, Cecilia Hegardt, Lao H Saal, Christer Larsson, Martin Malmberg, Lisa Rydén, Anna Ehinger, Niklas Loman, Anders Kvist, Hans Ehrencrona, Serena Nik-Zainal, Åke Borg, Johan Staaf, Dominik Glodzik, Ana Bosch, Johan Hartman, Mattias Aine, Johan Vallon-Christersson, Christel Reuterswärd, Anna Karlsson, Shamik Mitra, Emma Niméus, Karolina Holm, Jari Häkkinen, Cecilia Hegardt, Lao H Saal, Christer Larsson, Martin Malmberg, Lisa Rydén, Anna Ehinger, Niklas Loman, Anders Kvist, Hans Ehrencrona, Serena Nik-Zainal, Åke Borg, Johan Staaf

Abstract

Homologous recombination deficiency (HRD) is a defining characteristic in BRCA-deficient breast tumors caused by genetic or epigenetic alterations in key pathway genes. We investigated the frequency of BRCA1 promoter hypermethylation in 237 triple-negative breast cancers (TNBCs) from a population-based study using reported whole genome and RNA sequencing data, complemented with analyses of genetic, epigenetic, transcriptomic and immune infiltration phenotypes. We demonstrate that BRCA1 promoter hypermethylation is twice as frequent as BRCA1 pathogenic variants in early-stage TNBC and that hypermethylated and mutated cases have similarly improved prognosis after adjuvant chemotherapy. BRCA1 hypermethylation confers an HRD, immune cell type, genome-wide DNA methylation, and transcriptional phenotype similar to TNBC tumors with BRCA1-inactivating variants, and it can be observed in matched peripheral blood of patients with tumor hypermethylation. Hypermethylation may be an early event in tumor development that progress along a common pathway with BRCA1-mutated disease, representing a promising DNA-based biomarker for early-stage TNBC.

Conflict of interest statement

J.H. has received speakers honoraria and travel support from Roche, advisory board fees from MSD, Novartis and Roche, and institutional research grants from Cepheid and Novartis. Anna Ehinger has received speakers honoraria from Novartis, Amgen, Roche, and advisory board fees from Roche. Ana Bosch has participated in advisory boards for Novartis and Pfizer, and has received travel support from Roche. The remaining authors declare no competing interests.

Figures

Fig. 1. Study scheme, performed analyses, and…
Fig. 1. Study scheme, performed analyses, and cohorts used.
Gray boxes indicate a cohort of samples.
Fig. 2. BRCA1 hypermethylation, gene expression, and…
Fig. 2. BRCA1 hypermethylation, gene expression, and HRD association.
a Hierarchical clustering (ward.D2 linkage, Euclidean distance) of DNA methylation data (beta-values shown as a heatmap) for 30 CpGs associated with the BRCA1 gene (transcription start site (TSS): −1500b to +500 bp) in Illumina MethylationEPIC data for 235 SCAN-B TNBCs, including 57 tumors classified as hypermethylated by pyrosequencing (black column sample annotation bars). Gray CpG annotation bars (rows) indicate a promoter associated CpGs according to Illumina EPIC annotations. bBRCA1 mRNA expression (FPKM) across the 237 SCAN-B cases stratified by gene abrogation status. p Values calculated using t-test. Top axis shows number of cases per group. c Left: proportions of rearrangement signature 3 (RS3) versus patient stratifications based on BRCA1/2 mutation status, BRCA1 methylation status, and BRCA1 LOH in the total SCAN-B cohort, excluding the small HRDetect-intermediate group. For non-BRCA1/2 cases, tumors are stratified by HRDetect-high or low classification. Right: proportion of deletions with microhomology across the same patient subgroups. Top axes show number of cases per group. All cases do not have assigned RS3 rearrangements. dBRCA1 CpG allele methylation versus estimated tumor % by the WGS specific Battenberg algorithm (https://github.com/cancerit/cgpBattenberg) for all 237 SCAN-B cases. Black dotted line corresponds to a linear regression fit for the 57 hypermethylated cases specifically. e Circos plot and depiction of mutational substitution (S3, S8, and S13) and rearrangement signatures (RS2 and RS3) as defined in ref. of PD35990a. This case harbors both a BRCA2 variant and BRCA1 hypermethylation but has a genetic phenotype of BRCA1-deficient cancer. Circos plot depicting from outermost rings heading inwards: karyotypic ideogram outermost. Base substitutions next, plotted as rainfall plots (log10 intermutation distance on radial axis, Ring with short green lines, insertions; ring with short red lines, deletions. Major copy number allele ring (green, gain), minor copy number allele ring (red, loss), Central lines represent rearrangements (green, tandem duplications; red, deletions; blue, inversions; gray, interchromosomal events). FPKM fragments per kilobase of transcript per Million mapped reads. All p values reported from statistical tests are two-sided. Source data are provided as a Source Data file.
Fig. 3. BRCA1 methylation in peripheral blood,…
Fig. 3. BRCA1 methylation in peripheral blood, gene expression subtypes, and prognosis after therapy.
a Distribution of age at diagnosis for 43 germline screened SCAN-B patients harboring germline BRCA1 loss of function variants (n = 9) or no germline variants (n = 34) further stratified by tumor BRCA1 hypermethylation status. bBRCA1 CpG allele methylation frequency in matched peripheral blood DNA from non-germline SCAN-B patients stratified by their tumor methylation status (methylated = 1) from panel (a). cBRCA1 CpG allele methylation frequency in peripheral blood DNA from a combined analysis of 105 SCAN-B cases analyzed using the same instrument settings and reagent lots, including 55 of 57 hypermethylated cases, and stratified by patient age and tumor methylation status. Hard brackets ([]) imply ≥ or ≤, respectively. d Age at diagnosis for 237 SCAN-B patients stratified by BRCA1 and BRCA2 status. The number of methylated cases is less than 57 as two cases have concurrent BRCA2 mutations (one germline, one somatic nonpathogenic). e Molecular subtype proportions in BRCA1 hypermethylated SCAN-B cases for PAM50, CIT, IntClust 10, and TNBCtype. CIT subtypes; mApo, molecular apocrine. IntClust 10 subtypes; cluster 10 corresponding to basal-like tumors by other subtyping schemes. TNBCtype subtypes; BL1, basal-like 1: BL 2, basal-like 2: IM, immunomodulatory: M, mesenchymal: MSL, mesenchymal stem-like: LAR, luminal androgen receptor: UNC, uncertain. f Univariate Cox regression using invasive disease-free survival (IDFS) as clinical endpoint for different variables in 149 SCAN-B patients eligible for outcome analysis after standard of care adjuvant chemotherapy. HR: hazard ratio. NHG: grade, G2 equals grade 2, G3 equals grade 3. N0: node-negative. A Zph p value < 0.05 corresponds to that the proportional hazard assumption is not fulfilled. An (n =) indication in the right axis indicates that not all 149 cases were used due to missing values. g Kaplan–Meier analysis using IDFS as clinical endpoint for SCAN-B patients eligible for outcome analysis after adjuvant chemotherapy. Top panel shows the 149 patients stratified by BRCA1 hypermethylation status alone, center panel shows stratification including also BRCA1-null cases, and bottom panel a comparison between only hypermethylated and BRCA1-null patients. All p values reported from statistical tests are two-sided. Source data are provided as a Source Data file.
Fig. 4. Genetic phenotypes of BRCA1 hypermethylated…
Fig. 4. Genetic phenotypes of BRCA1 hypermethylated and BRCA1-null TNBC.
The 57 hypermethylated and 25 BRCA1-null SCAN-B cases were combined with 27 BRCA1-null cases from Nik-Zainal et al.. a Frequency of copy number alterations across the genome for BRCA1 hypermethylated and BRCA1-null cases. b Frequency of copy number amplification for driver genes defined in Nik-Zainal et al.. c Frequency of mutations (insertions, deletions, substitutions) for driver genes defined in Nik-Zainal et al.. Only genes with >1% alteration in the BRCA1-null cohort are shown. Displayed mutations in BRCA1 are somatic. d Total number of substitutions, indels, and rearrangements per sample for BRCA1 hypermethylated versus BRCA1-null groups. Only cases sequenced to at least 30-fold depth included. p Values calculated using Wilcoxon’s test. Top axes show number of cases per group. e Left panel shows distribution of mutational signature (defined in Nik-Zainal et al.) proportions per sample between hypermethylated versus BRCA1-null cases. Proportions are calculated as the number of substitutions for a signature divided by the total number of substitutions from all signatures. Right panel shows proportion of the APOBEC Substitution Signature 2 in non-basal-like tumors from Nik-Zainal et al.. Top axis indicates number of samples per group. All outliers are not shown due to y-axis scale. f Distribution of rearrangement signature proportions per sample between hypermethylated and BRCA1-null cases. g Hierarchical clustering of combined substitution and rearrangement signature proportions using Pearson correlation and Ward.D linkage in the 109 combined cases. h Principal component analysis of proportions of substitution and rearrangement signatures in the 109 combined cases, illustrated by the first two principal components representing most variation. i Principal component analysis of the proportions of the contributions of HRDetect components (as defined in ref. ) per sample (obtained from), illustrated by the first two principal components representing most variation. The analysis only included the 25 BRCA1-null and 57 hypermethylated SCAN-B cases. All p values reported from statistical tests are two-sided. Source data are provided as a Source Data file.
Fig. 5. DNA methylation and transcriptional characteristics…
Fig. 5. DNA methylation and transcriptional characteristics in BRCA1 hypermethylated and BRCA1-null TNBC.
a Hierarchical clustering (ward.D2 linkage, Euclidean distance) of DNA methylation data (beta-values, heatmap) from 235 SCAN-B cases for 32 CpGs associated with the BRCA1 gene. The 32 CpGs were differentially methylated between 25 BRCA1-null and 57 BRCA1 hypermethylated SCAN-B cases through supervised analysis of Illumina EPIC data. Black sample annotations correspond to hypermethylated cases by pyrosequencing, gray to unmethylated. Gray CpG annotation bars (rows) indicate belonging to the canonical BRCA1 promoter (+500 to −1500 base pairs, chr17:43124984–43126983). BRCA1 mRNA expression (FPKM) is shown for each case as bar plot expression above the CpG heatmap. b Unsupervised hierarchical gene expression-based clustering of 52 BRCA1-null and 57 BRCA1 hypermethylated cases combined from the SCAN-B and Jönsson et al. cohorts using Pearson correlation as distance and Ward.D linkage based on 7224 genes with standard deviation >0.6 in mean-centered expression across all samples. Colored boxes indicate the seven top subclusters defined from the hierarchical tree. TNBCtype subtypes; BL1, basal-like 1: BL 2, basal-like 2: IM, immunomodulatory: M, mesenchymal: MSL, mesenchymal stem-like: UNC, uncertain. PAM50 and TNBCtype subtypes were not available for the Jönsson et al. cases. c Summarized results from consensus clustering, using a range between 3 and 7 cluster solutions (k, columns) and the same gene set and samples as in (b). For each cluster solution, the percentage of BRCA1 hypermethylated (left matrix) and BRCA1-null (right matrix) cases in the different defined clusters are shown (rows). d Principal component analysis based on the expression of the 7224 genes to relate variance in gene expression with BRCA1 status. A more intense red color for a principal component (columns) with a variable (rows) implies a stronger association with variance in expression for that component. Components represent variation in decreasing strength (first component largest variation with contributed percentage variation listed). A variable strongly associated with variation in expression is thus represented by an intense red principal component capturing a high percentage of the variation. All p values reported from statistical tests are two-sided. Source data are provided as a Source Data file.
Fig. 6. Immune cell infiltration phenotypes in…
Fig. 6. Immune cell infiltration phenotypes in BRCA1-null and hypermethylated cases.
a PD-L1 scoring of 53 BRCA1 hypermethylated and 25 BRCA1-null SCAN-B cases using the Roche SP-142 antibody that is evaluated in immune cells. To the left, proportion of positive cases (≥1%), to the right distribution of actual PD-L1 scores for the two groups. b Scoring of tumor infiltrating lymphocytes (TILs) in 52 BRCA1 hypermethylated and 24 BRCA1-null SCAN-B cases based on available whole section H&E slides. c Total number of expressed neoantigens per sample as calculated from substitutions by NeoPredPipe for BRCA1-null and BRCA1 hypermethylated SCAN-B cases. d Neoantigens as shown in (c), but stratified for sample groups also by PD-L1 IHC status. Four hypermethylated cases did not have available PD-L1 immunohistochemistry data. All p values reported from statistical tests are two-sided. Source data are provided as a Source Data file.

References

    1. Gluz O, et al. Triple-negative breast cancer–current status and future directions. Ann. Oncol. 2009;20:1913–1927.
    1. Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N. Engl. J. Med. 2010;363:1938–1948.
    1. Sharma P. Biology and Management of Patients With Triple-Negative Breast Cancer. Oncologist. 2016;21:1050–1062.
    1. Winter C, et al. Targeted sequencing of BRCA1 and BRCA2 across a large unselected breast cancer cohort suggests that one-third of mutations are somatic. Ann. Oncol. 2016;27:1532–1538.
    1. Lord CJ, Ashworth A. The DNA damage response and cancer therapy. Nature. 2012;481:287–294.
    1. Farmer H, et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 2005;434:917–921.
    1. Brok WDd, et al. Homologous recombination deficiency in breast cancer: a clinical review. JCO Precis. Oncol. 2017;1:1–13.
    1. Ray-Coquard I, et al. Olaparib plus bevacizumab as first-line maintenance in ovarian cancer. N. Engl. J. Med. 2019;381:2416–2428.
    1. van Verschuer VM, et al. Tumor-associated inflammation as a potential prognostic tool in BRCA1/2-associated breast cancer. Hum. Pathol. 2015;46:182–190.
    1. Jiang T, et al. Predictors of chemosensitivity in triple negative breast cancer: an integrated genomic analysis. PLoS Med. 2016;13:e1002193.
    1. Nolan E, et al. Combined immune checkpoint blockade as a therapeutic strategy for BRCA1-mutated breast cancer. Sci. Transl. Med. 2017;9:eaal4922.
    1. Akashi-Tanaka S, et al. BRCAness predicts resistance to taxane-containing regimens in triple negative breast cancer during neoadjuvant chemotherapy. Clin. Breast Cancer. 2015;15:80–85.
    1. Telli ML, et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin. Cancer Res. 2016;22:3764–3773.
    1. Zhu X, et al. Hypermethylation of BRCA1 gene: implication for prognostic biomarker and therapeutic target in sporadic primary triple-negative breast cancer. Breast Cancer Res. Treat. 2015;150:479–486.
    1. Yamashita N, et al. Epigenetic inactivation of BRCA1 through promoter hypermethylation and its clinical importance in triple-negative breast cancer. Clin. Breast Cancer. 2015;15:498–504.
    1. Sharma P, et al. The prognostic value of BRCA1 promoter methylation in early stage triple negative breast cancer. J. Cancer Ther. Res. 2014;3:1–11.
    1. Xu Y, et al. Promoter methylation of BRCA1 in triple-negative breast cancer predicts sensitivity to adjuvant chemotherapy. Ann. Oncol. 2013;24:1498–1505.
    1. Sharma P, et al. Impact of homologous recombination deficiency biomarkers on outcomes in patients with triple-negative breast cancer treated with doxorubicin-based adjuvant chemotherapy (SWOG S9313) Ann. Oncol. 2017;29:654–660.
    1. Jacot W, et al. BRCA1 promoter hypermethylation is associated with good prognosis and chemosensitivity in triple-negative breast cancer. Cancers. 2020;12:828.
    1. Brianese RC, et al. BRCA1 deficiency is a recurrent event in early-onset triple-negative breast cancer: a comprehensive analysis of germline mutations and somatic promoter methylation. Breast Cancer Res. Treat. 2018;167:803–814.
    1. Xie Y, Gou Q, Wang Q, Zhong X, Zheng H. The role of BRCA status on prognosis in patients with triple-negative breast cancer. Oncotarget. 2017;8:87151–87162.
    1. Staaf J, et al. Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study. Nat. Med. 2019;25:1526–1533.
    1. Jonsson G, et al. The retinoblastoma gene undergoes rearrangements in BRCA1-deficient basal-like breast cancer. Cancer Res. 2012;72:4028–4036.
    1. Nik-Zainal S, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature. 2016;534:47–54.
    1. Yang D, et al. Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer. J. Am. Med. Assoc. 2011;306:1557–1565.
    1. Davies H, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 2017;23:517–525.
    1. Nik-Zainal S, Morganella S. Mutational signatures in breast cancer: the problem at the DNA level. Clin. Cancer Res. 2017;23:2617–2629.
    1. Parker JS, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 2009;27:1160–1167.
    1. Guedj M, et al. A refined molecular taxonomy of breast cancer. Oncogene. 2012;31:1196–1206.
    1. Curtis C, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346–352.
    1. Lehmann BD, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 2011;121:2750–2767.
    1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA. 2001;98:5116–5121.
    1. Newman AM, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 2019;37:773–782.
    1. Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18:220.
    1. Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinform. 2017;18:105.
    1. Schenck RO, Lakatos E, Gatenbee C, Graham TA, Anderson ARA. NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline. BMC Bioinform. 2019;20:264.
    1. Shukla SA, et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 2015;33:1152–1158.
    1. Ryden L, et al. Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative. Br. J. Surg. 2018;105:e158–e168.
    1. Runowicz CD, et al. American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline. J. Clin. Oncol. 2016;34:611–635.
    1. Lönning P, Eikesdal H, Löes IM, Knappskog S. Consitutional Mosaic Epimutations—a hidden cause of cancer? Cell Stress. 2019;3:118–135.
    1. Al-Moghrabi N, et al. Methylation of BRCA1 and MGMT genes in white blood cells are transmitted from mothers to daughters. Clin. Epigenet. 2018;10:99.
    1. Chen J, et al. High-resolution bisulfite-sequencing of peripheral blood DNA methylation in early-onset and familial risk breast cancer patients. Clin. Cancer Res. 2019;25:5301–5314.
    1. Lonning PE, et al. White blood cell BRCA1 promoter methylation status and ovarian cancer risk. Ann. Intern. Med. 2018;168:326–334.
    1. Prajzendanc K, et al. BRCA1 promoter methylation in peripheral blood is associated with the risk of triple-negative breast cancer. Int. J. Cancer. 2020;146:1293–1298.
    1. Azzollini J, et al. Constitutive BRCA1 promoter hypermethylation can be a predisposing event in isolated early-onset breast cancer. Cancers. 2019;11:58.
    1. Tang Q, Cheng J, Cao X, Surowy H, Burwinkel B. Blood-based DNA methylation as biomarker for breast cancer: a systematic review. Clin. Epigenet. 2016;8:115.
    1. Polak P, et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat. Genet. 2017;49:1476–1486.
    1. Tutt A, et al. Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial. Nat. Med. 2018;24:628–637.
    1. Isakoff SJ, et al. TBCRC009: a multicenter phase II clinical trial of platinum monotherapy with biomarker assessment in metastatic triple-negative breast cancer. J. Clin. Oncol. 2015;33:1902–1909.
    1. Zhao EY, et al. Homologous recombination deficiency and platinum-based therapy outcomes in advanced breast cancer. Clin. Cancer Res. 2017;23:7521–7530.
    1. Telli ML, et al. Homologous recombination deficiency (HRD) status predicts response to standard neoadjuvant chemotherapy in patients with triple-negative or BRCA1/2 mutation-associated breast cancer. Breast Cancer Res. Treat. 2018;168:625–630.
    1. Sobral-Leite M, et al. Assessment of PD-L1 expression across breast cancer molecular subtypes, in relation to mutation rate, BRCA1-like status, tumor-infiltrating immune cells and survival. Oncoimmunology. 2018;7:e1509820.
    1. Loi S, et al. Tumor-infiltrating lymphocytes and prognosis: a pooled individual patient analysis of early-stage triple-negative breast cancers. J. Clin. Oncol. 2019;37:559–569.
    1. Solinas C, et al. BRCA gene mutations do not shape the extent and organization of tumor infiltrating lymphocytes in triple negative breast cancer. Cancer Lett. 2019;450:88–97.
    1. Saal LH, et al. The Sweden Cancerome Analysis Network—Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine. Genome Med. 2015;7:20.
    1. SCAN-B. (2020).
    1. Saal LH, et al. Recurrent gross mutations of the PTEN tumor suppressor gene in breast cancers with deficient DSB repair. Nat. Genet. 2008;40:102–107.
    1. Du P, et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11:587.
    1. Gene Expression Omnibus. (2020).
    1. Brueffer C, et al. Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative. JCO Precis. Oncol. 2018;2:1–18.
    1. Paquet ER, Hallett MT. Absolute assignment of breast cancer intrinsic molecular subtype. J. Natl Cancer Inst. 2015;107:357.
    1. Ali HR, et al. Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biol. 2014;15:431.
    1. Chen X, et al. TNBCtype: a subtyping tool for triple-negative breast cancer. Cancer Inf. 2012;11:147–156.
    1. Schmid P, et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N. Engl. J. Med. 2018;379:2108–2121.
    1. Hudis CA, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J. Clin. Oncol. 2007;25:2127–2132.

Source: PubMed

3
Předplatit