Abstract
Male breast cancer (MBC) is extremely rare and accounts for less than 1% of all breast malignancies. Therefore, clinical management of MBC is currently guided by research on the disease in females. In this study, DNA obtained from 45 formalin-fixed paraffin-embedded (FFPE) MBCs with and 90 MBCs (52 FFPE and 38 fresh-frozen) without matched normal tissues was subjected to massively parallel sequencing targeting all exons of 1943 cancer-related genes. The landscape of mutations and copy number alterations was compared to that of publicly available estrogen receptor (ER)-positive female breast cancers (smFBCs) and correlated to prognosis. From the 135 MBCs, 90% showed ductal histology, 96% were ER-positive, 66% were progesterone receptor (PR)-positive, and 2% HER2-positive, resulting in 50, 46 and 4% luminal A-like, luminal B-like and basal-like cases, respectively. Five patients had Klinefelter syndrome (4%) and 11% of patients harbored pathogenic BRCA2 germline mutations. The genomic landscape of MBC to some extent recapitulated that of smFBC, with recurrent PIK3CA (36%) and GATA3 (15%) somatic mutations, and with 40% of the most frequently amplified genes overlapping between both sexes. TP53 (3%) somatic mutations were significantly less frequent in MBC compared to smFBC, whereas somatic mutations in genes regulating chromatin function and homologous recombination deficiency-related signatures were more prevalent. MDM2 amplifications were frequent (13%), correlated with protein overexpression (P = 0.001) and predicted poor outcome (P = 0.007). In conclusion, despite similarities in the genomic landscape between MBC and smFBC, MBC is a molecularly unique and heterogeneous disease requiring its own clinical trials and treatment guidelines.
Introduction
Male breast cancer (MBC) is a rare disease accounting for about 1% of all breast cancers (Jemal et al. 2010, Giordano 2018). Five-year overall survival is lower compared to female patients, related to the older age at diagnosis and more advanced stage at presentation (Giordano et al. 2004, Stang & Thomssen 2008). Major genetic factors associated with an increased risk of MBC include BRCA2 and BRCA1 germline mutations, Klinefelter syndrome (KS) and family history. Additional suspected genetic factors include CHEK2 and PALB2 mutations, with less supporting evidence for AR and FANCM gene mutations, CYP17 polymorphism and Cowden syndrome (Weiss et al. 2005, Silvestri et al. 2017, 2018). Epidemiologic risk factors include hormonal imbalance and radiation exposure (Thomas et al. 1994, Abdelwahab Yousef 2017). The majority of MBCs are invasive ductal carcinomas with high estrogen (ER) and progesterone (PR) receptor levels without overexpression of HER2 (Kornegoor et al. 2012c ).
Because of its low incidence, MBC has not been studied as extensively as female breast cancer (FBC). Few retrospective studies have included more than 100 cases. Therefore, despite possible differences in pathogenesis, biology and genetics between both sexes, treatment strategies for MBC have largely been extrapolated from FBC. This, in combination with the older age at diagnosis, hampers improvement in outcome as has been seen in FBC over the last decades.
Recent large-scale cross-platform projects have provided a detailed characterization of the genomic landscape of FBC (Cancer Genome Atlas Network 2012, Curtis et al. 2012, Nik-Zainal et al. 2016, Pereira et al. 2016, Bailey et al. 2018, Berger et al. 2018). The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Network 2012) reported the enrichment of specific mutations in PIK3CA (47%), GATA3 (14%), MAP3K1 (13%), TP53 (12%), CDH1 (10%) and MAP2K4 (7%) with the luminal A subtype and in TP53 (31%), PIK3CA (32%) and MAP3K1 (5%) within the luminal B subtype (Gao et al. 2015). Integrated genomic/transcriptomic analysis of breast cancers from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) identified 40 putative mutation driver genes (Curtis et al. 2012, Pereira et al. 2016). Subsequent studies identified overlapping as well as new probable driver mutations (Nik-Zainal et al. 2016, Bailey et al. 2018, Berger et al. 2018).
Genomic data on MBC are scarce. On a molecular level, like FBC, MBC is a heterogeneous disease with differences in transcriptional (Callari et al. 2011, Johansson et al. 2012), copy number (Johansson et al. 2011, Tommasi et al. 2011, Biesma et al. 2015, Piscuoglio et al. 2016), microRNA (Fassan et al. 2009, Lehmann et al. 2010) and methylation (Kornegoor et al. 2012a , Johansson et al. 2015, Rizzolo et al. 2018) patterns. Genomic gains appear to be more common in MBC than in FBC and often involve whole chromosome arms, while genomic losses and high-level amplifications are less frequent (Johansson et al. 2011, Tommasi et al. 2011, Kornegoor et al. 2012b , Lacle et al. 2013, 2015, Biesma et al. 2015). Luminal A-like and luminal B-like MBC subtypes seem to exhibit remarkably similar copy number aberration profiles, in contrast to their distinct landscape of somatic mutations (Piscuoglio et al. 2016). Piscuoglio et al. recently studied mutations of 59 MBCs by massively parallel sequencing the exons of 241 genes and demonstrated that MBCs less frequently harbored PIK3CA (20%) and TP53 mutations (8.5%) than subtype-matched (sm)FBC (Piscuoglio et al. 2016). The most frequently mutated genes in luminal A-like MBC included PIK3CA and MAP3K1 (both at 12%), whereas the most frequently mutated genes in luminal B-like MBC were PIK3CA (24%) and GATA3 (21%).
Thus, MBCs share many features with FBCs, but there is evidence for distinct differences with potential implications for clinical management. Here we (i) used targeted capture massively parallel sequencing of 1943 cancer-related genes to study the mutational and copy number landscape in a cohort of 135 MBCs, including 5 patients with KS and (ii) compared these profiles to those of smFBCs.
Materials and methods
Patients and samples
MBC samples were collected from ten pathology laboratories throughout The Netherlands. Patients were diagnosed between 1986 and 2011. Pathology reports were used to extract age, tumor size and lymph node status. Tumor slides were reviewed by three expert breast pathologists (PJvD, RK, MML) to confirm diagnosis and demarcate normal and tumor areas. Histological type (WHO) and grade were assessed as described previously (Elston & Ellis 1991, Van Diest et al. 1992). Patient and tumor characteristics are summarized in Table 1. All samples and databases were anonymized before use. The Dutch national ethical guidelines (www.federa.org) state that no ethical approval is required for the use of anonymous leftover tissue, and this is also part of the standard treatment agreement (Van Diest 2002).
Clinico-pathologic features and immunohistochemical subtypes of the 135 male breast cancers included in this study.
| Clinico-pathologic features | n = 135 | % | Actual % |
|---|---|---|---|
| Median age at diagnosis | 67 (range 32–89) | ||
| Median mitotic activity index | 10 (range 0–56) | ||
| Median tumor size (cm) | 2.3 (range 0.8–11) | ||
| Type | |||
| Ductal | 121 | 89.6 | 90.3 |
| Lobular | 2 | 1.5 | 1.5 |
| Other | 11 | 8.1 | 8.2 |
| Missing | 1 | 0.7 | |
| Histologic gradea | |||
| 1 | 27 | 20.0 | 22.7 |
| 2 | 49 | 36.3 | 41.2 |
| 3 | 43 | 31.9 | 36.1 |
| Missing | 16 | 11.9 | |
| Mitotic activity index/2 mm2 | |||
| <8 mitoses | 50 | 37.0 | 40.7 |
| 8–14 mitoses | 35 | 25.9 | 28.5 |
| >14 mitoses | 38 | 28.1 | 30.9 |
| Missing | 12 | 8.9 | |
| ER status | |||
| Negative | 6 | 4.4 | 4.5 |
| Positive | 128 | 94.8 | 95.5 |
| Missing | 1 | 0.7 | |
| PR status | |||
| Negative | 45 | 33.3 | 33.8 |
| Positive | 88 | 65.2 | 66.2 |
| Missing | 2 | 1.5 | |
| HER2 status | |||
| Negative | 129 | 95.6 | 97.7 |
| Positive | 3 | 2.2 | 2.3 |
| Missing | 3 | 2.2 | |
| LN status | |||
| Negative | 48 | 35.6 | 41.4 |
| Positive | 68 | 50.4 | 58.6 |
| Missing | 19 | 14.1 | |
| Ki-67 labeling index | |||
| Low (<14%) | 94 | 69.6 | 71.8 |
| High (≥14%) | 37 | 27.4 | 28.2 |
| Missing | 4 | 3.0 | |
| Surrogate intrinsic subtypeb | |||
| Luminal A-like | 66 | 48.9 | 50.4 |
| Luminal B-like | 60 | 44.4 | 45.8 |
| HER2-driven | 0 | 0.0 | 0.0 |
| Basal-like | 5 | 3.7 | 3.8 |
| Missing | 4 | 3.0 | |
aAccording to the Nottingham grading system (Elston & Ellis 1991); bdefined by immunohistochemistry according to St. Gallen criteria (Goldhirsch et al. 2013).
ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; LN, lymph node; PR, progesterone receptor.
Immunohistochemistry was repeated to classify MBCs into the four different intrinsic subtypes according to the St. Gallen International criteria for FBC (Goldhirsch et al. 2013). Luminal A-like tumors were defined as ER-positive (1%) and PR-positive (≥20% positive cells), HER2-negative and Ki67 low (<14% positive cells), whereas the remaining ER-positive tumors were classified as luminal B-like. HER2-driven tumors were defined as ER/PR negative and HER2 overexpressed or amplified (ASCO/CAP guidelines; Wolff et al. 2013), and basal-like tumors as ER/PR/HER2 negative. The DNA extraction procedure is detailed in Supplementary methods (see section on supplementary data given at the end of this article). All tumor samples consisted of 60% or more neoplastic cells, as assessed by pathology review.
Targeted capture massively parallel sequencing
Sequencing libraries were prepared from 600 ng of sheared DNA as previously described with a few modifications (Harakalova et al. 2011). After end repair samples were A-tailed, followed by T-tailed adaptor ligation. Samples were pooled and subjected to hybrid capture enrichment using a custom SureSelect assay (Agilent) containing baits targeting all exons of 1943 genes including known oncogenes, tumor suppressor genes, all kinases and genes involved in important pathways related to tumorigenesis and anti-cancer treatment (e.g., angiogenesis; apoptosis; and EGFR, PIK3CA, TGFβ, mTOR and VEGF pathways were included) (Supplementary Table 1). Pre-barcoded individual libraries were pooled, enriched and subsequently sequenced on the SOLiD 5500 system (Thermo Fisher). Supplementary Table 2 summarizes sequencing statistics. All sequencing data have been deposited in EGA under accession number EGAS00001002683.
SOLiD data were processed with our in-house developed pipeline v1.2.1 (https://github.com/UMCUGenetics/IAP) by Genome Analysis Toolkit (GATK) v3.2.2 calling for all samples according to best practices guidelines, including somatic mutation analysis for matched pairs (Mckenna et al. 2010, Van der Auwera et al. 2013). Filtering was applied to select high quality variants (coverage ≥10, allele fraction ≥0.15) and to exclude germline variants (population frequency <5%), full details available in Supplementary methods.
Copy number alteration (CNA) analysis was performed using Codecz v1.0.1 with the appropriate design file kinome_design_SS_V2_110811. For FFPE samples all (normal) controls were merged into a control pool against which the tumors were analyzed. For FF samples, each sample was compared to the complete FF tumor data set as a means of normalization. Z-scores were calculated as described by Hoogstraat et al. (2015).
Mutational signature analysis was performed on MBCs with available ER and HER2 status (n = 129) and matched 1:2 with FBCs from TCGA with the same receptor status (n = 258). Mutational signatures were defined by deconstructSigs using all SNVs for samples with ≥20 somatic SNVs, as previously described (Rosenthal et al. 2016, Mueller et al. 2018).
In addition, targeted next-generation sequencing (NGS) of the complete coding sequences of BRCA1 and BRCA2 was performed on normal tissue (germline) using the Oncomine BRCA FFPE gene panel (ThermoFisher) according to the manufacturer’s instructions. In short, 20 ng FFPE DNA was used for library preparation with 24 PCR cycles. Emulsion PCR and enrichment were performed using the Ion PGM Template OT2 200 Template Kit and the Ion One Touch 2 instrument (ThermoFisher). Sequencing was performed using the Ion PGM Sequencing 200 kit v2 using the Ion 318 chip (ThermoFisher). Samples were run on the Ion Torrent PGM System (ThermoFisher). Sequencing data were analyzed using SeqNext (JSI medical systems).
Sanger sequencing
A selection of NGS variants was validated by Sanger sequencing. After Exonuclease I – Shrimp Alkaline Phosphatase enzymatic cleanup of amplified PCR products, BigDye Terminator v1.1 Cycle Sequencing (ThermoFisher) of 1 µL PCR product and Sephadex G-50 based sequencing reaction cleanup, sequencing products were analyzed on a 3730 DNA Analyzer (ThermoFisher). Primers are listed in Supplementary Table 3. Sanger sequencing validation is described in the Supplementary results section.
MDM2 immunohistochemistry
Tissue microarray (triplicate cores) or full 4 µm slides were deparaffinized, pretreated for 24 min in CC1 (EDTA pH8.0) and stained on a Ventana Benchmark XT autostainer using a mouse monoclonal anti-MDM2 antibody (ThermoFisher Scientific, clone IF2, dilution 1/25). All samples with sufficient tumor material (n = 106) were scored as positive (nuclear staining in at least 5% positive tumor cells) or negative by consensus of two observers.
Statistics
Associations between gene copy number or mutation status and clinicopathological features were calculated using the Pearson chi-square test (or Fisher’s exact test when appropriate) for categorical variables (IBM SPSS Statistics, version 21). Associations were evaluated for all genes and only significant associations were reported. Mitotic activity index (MAI) was dichotomized as ≤14 or >14 mitoses/2 mm2, tumor size as ≤2 or >2 cm, age as ≤60 or >60 and histological grade as grade 1/2 versus grade 3 (Elston & Ellis 1991). Mutation load and CNA load were dichotomized at the median (7 and 62, respectively). Associations of mutational load with clinicopathological characteristics are described in the Supplementary results section. Cluster analysis for CNAs was performed in R using hclust (default) and heatmap.2. Five-year overall survival (OS) was analyzed with Kaplan–Meier plots/log-rank test. Backward LR Cox proportional hazards models were used to estimate risk of death adjusted for parameters significant or with trend in univariate analyses. Hazard ratios (HRs) were calculated with 95% confidence intervals (CIs). For multivariable analysis, P values >0.1 were removed from the model. MedCalc statistical software was used for comparison of SNV and CNA proportions to publicly available METABRIC and MBC data. Corrections for multiple comparisons were applied according to Holm–Bonferroni. For pathway analysis, PANTHER, version 11.1 was used (released 2016-10-24) (Mi et al. 2017).
Results
Characteristics of the cohort
From the 135 MBCs, 90% showed ductal histology, 96% were ER positive, 66% were PR positive and 2% HER2 positive, resulting in 50, 46 and 4% luminal A-like, luminal B-like and basal-like cases, respectively. None of the cases was HER2 driven (Table 1). Five patients had KS (4%) and 11% (5/44 with paired normal tissue) of patients harbored pathogenic BRCA2 germline mutations. The median age at diagnosis was 67 years. Follow-up was available for 111/135 patients. Median follow-up was 52 months (range 1–243), and 59 patients were alive at the time of last follow-up. Tumor size and lymph node status were significant predictors of poor 5-year OS (P = 0.037 and P = 0.019, respectively). Luminal B-like (P = 0.122), PR negative (P = 0.128) and poorly differentiated (P = 0.094) tumors tended to be associated with worse 5-year OS compared to luminal A-like, PR-positive and well-differentiated tumors, respectively. Luminal B-like MBCs were associated with high grade (P = 0.00004), high MAI (P = 0.018) and PR negativity (P < 0.00001) when compared to luminal A-like MBCs.
The majority of BRCA2 germline mutation carriers presented with poorly differentiated tumors (4/5 were high grade; P = 0.149) at a similar age as non-BRCA2-mutation carriers (median 71 (range 44–78) vs 69 (range 32–87) years, respectively). KS patients tended to be younger (3 of 5 were aged ≤60; P = 0.120), with generally well-differentiated (5/5; P = 0.158), PR-positive tumors (5/5; P = 0.166).
Somatic mutations
Somatic mutations are listed in Supplementary Table 4. Overlapping FBC mutation-driver genes from four studies (Curtis et al. 2012, Nik-Zainal et al. 2016, Pereira et al. 2016, Bailey et al. 2018, Berger et al. 2018) were specifically compared to MBC. Of the 89 overlapping genes, 21 were not targeted and 1 (HRAS) was insufficiently covered by our panel, resulting in 67 interrogated genes. The remaining genes were mutated in 0–36% of MBC. Table 2 compares somatic mutation frequencies with FBC and MBC (Piscuoglio et al. 2016). Somatic mutations in TP53 were rare in MBC (3%). In fact we detected only four (two in matched normal, four in the entire cohort) somatic mutations in TP53, two of them known from FBC and being likely oncogenic (OncoKB; Chakravarty et al. 2017), the other two unknown. None of the hotspots from FBC (R175, R248 and R273) were affected.
Comparison of somatic mutation frequency with female breast cancer and male breast cancer.
| Gene | Studies where driver | n | MBC (n = 135), % | METABRIC ER+ FBC (n = 1825)a, % | P value | TCGA ER+ FBC (n = 600)b, % | P value | Berger BRCA (n = 982)b, % | P value | Bailey BRCA (n = 779) , % | P value | Piscuoglio et al. (n = 59), % | P value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PIK3CA | 1,2,3,4 | 108 | 36.1 | 44.5 | 0.0876 | 39.8 | 0.4688 | 34.7 | 0.7720 | 34.4 | 0.7280 | 20.3 | 0.0346 |
| KMT2C | 1,3,4 | 122 | 20.5 | 12.3 | 0.0087 | 8.8 | 0.0001 | NA | NA | 9.5 | 0.0003 | 5.1 | 0.0074 |
| PBRM1 | 1,2,3 | 105 | 20.0 | 1.5 | <0.0001 | 0.7 | <0.0001 | NA | NA | NA | NA | NA | NA |
| GATA3 | 1,2,3,4 | 132 | 15.2 | 14.4 | 0.8008 | 14.3 | 0.7903 | 12.8 | 0.4429 | 12.6 | 0.4113 | 15.3 | 0.9859 |
| NSD1 | 3,4 | 126 | 15.1 | NA | NA | 1.0 | <0.0001 | NA | NA | NA | NA | NA | NA |
| NOTCH1 | 2,3 | 27 | 14.8 | 4.4 | 0.0101 | 0.3 | <0.0001 | NA | NA | NA | NA | NA | NA |
| SETD2 | 2,3 | 125 | 13.6 | 2.4 | <0.0001 | 1.7 | <0.0001 | NA | NA | NA | NA | NA | NA |
| NF1 | 1,2,3,4 | 99 | 13.1 | 3.5 | <0.0001 | 2.3 | <0.0001 | 4.6 | 0.0004 | 3.9 | 0.0001 | 0.0 | 0.0038 |
| ARID1A | 1,2,3,4 | 123 | 13.0 | 5.1 | 0.0002 | 4.7 | 0.0005 | 4.3 | <0.0001 | 3.3 | <0.0001 | 1.7 | 0.0145 |
| ATRX | 2,3 | 72 | 12.5 | NA | NA | 1.8 | <0.0001 | NA | NA | NA | NA | 0.0 | 0.0051 |
| APC | 2,3 | 108 | 12.0 | 1.9 | <0.0001 | 1.2 | <0.0001 | NA | NA | NA | NA | 1.7 | 0.0219 |
| NOTCH2 | 2,3 | 116 | 11.2 | NA | NA | 2.3 | <0.0001 | NA | NA | NA | NA | NA | NA |
| NCOR1 | 1,2,3 | 120 | 10.8 | 4.0 | 0.0004 | 5.0 | 0.0144 | NA | NA | 4.7 | 0.0065 | 0.0 | 0.0090 |
| CREBBP | 2,3 | 103 | 10.7 | NA | NA | 1.2 | <0.0001 | NA | NA | NA | NA | NA | NA |
| KDM6A | 1,2,3 | 51 | 9.8 | 1.7 | <0.0001 | 1.7 | 0.0002 | NA | NA | NA | NA | NA | NA |
| ATM | 2,3,4 | 85 | 9.4 | NA | NA | 2.3 | 0.0005 | 3.3 | 0.0047 | NA | NA | 0.0 | 0.0157 |
| USP9X | 1,2,3 | 77 | 9.1 | 2.6 | 0.0008 | 1.0 | <0.0001 | NA | NA | NA | NA | NA | NA |
| ATR | 2,3 | 99 | 8.1 | 3.9 | 0.0405 | 1.0 | <0.0001 | NA | NA | NA | NA | 1.7 | 0.0946 |
| AKT1 | 1,2,3,4 | 101 | 7.9 | 4.7 | 0.1505 | 3.2 | 0.0237 | 3.0 | 0.0103 | 3.2 | 0.0192 | 0.0 | 0.0272 |
| KMT2D | 2,3 | 115 | 7.8 | 6.8 | 0.6808 | 1.8 | 0.0003 | NA | NA | NA | NA | 3.4 | 0.2598 |
| BRAF | 2,3 | 65 | 7.7 | 0.6 | <0.0001 | 0.5 | <0.0001 | NA | NA | NA | NA | 3.4 | 0.3024 |
| MED12 | 3,4 | 92 | 7.6 | NA | NA | 2.2 | 0.0042 | NA | NA | NA | NA | 3.4 | 0.2890 |
| EGFR | 2,3 | 108 | 6.5 | 1.4 | 0.0001 | 0.7 | <0.0001 | NA | NA | NA | NA | 1.7 | 0.1669 |
| STAG2 | 2,3 | 55 | 5.5 | NA | NA | 0.8 | 0.0022 | NA | NA | NA | NA | NA | NA |
| TAF1 | 3,4 | 75 | 5.3 | 1.5 | 0.0114 | 1.7 | 0.0405 | 2.8 | 0.2197 | NA | NA | NA | NA |
| RASA1 | 3,4 | 77 | 5.2 | NA | NA | 0.3 | <0.0001 | 0.8 | 0.0004 | NA | NA | NA | NA |
| BRCA2 | 1,2,3 | 78 | 5.1 | 1.5 | 0.0145 | 2.2 | 0.1255 | NA | NA | NA | NA | 0.0 | 0.0794 |
| MAP3K1 | 1,2,3,4 | 123 | 4.9 | 11.5 | 0.0241 | 10.7 | 0.0480 | 9.1 | 0.1181 | 8.3 | 0.1925 | 8.5 | 0.3423 |
| SMAD4 | 1,2,3 | 105 | 4.8 | 1.2 | 0.0022 | 1.0 | 0.0038 | NA | NA | NA | NA | NA | NA |
| CIC | 2,3 | 89 | 4.5 | NA | NA | 0.8 | 0.0038 | NA | NA | NA | NA | NA | NA |
| MSH2 | 2,3 | 92 | 4.3 | NA | NA | 1.3 | 0.0383 | NA | NA | NA | NA | 0.0 | 0.1077 |
| BRCA1 | 1,2,3,4 | 117 | 4.3 | 1.2 | 0.0055 | 1.8 | 0.0925 | 3.0 | 0.4460 | 3.1 | 0.4956 | 1.7 | 0.3722 |
| ERBB3 | 2,3,4 | 117 | 4.3 | 2.6 | 0.2718 | 2.2 | 0.1871 | NA | NA | NA | NA | 1.7 | 0.3722 |
| SMARCA4 | 2,3 | 74 | 4.1 | NA | NA | 0.8 | 0.0125 | NA | NA | NA | NA | NA | NA |
| CASP8 | 2,3,4 | 112 | 3.6 | 1.1 | 0.0206 | 1.3 | 0.0807 | 2.1 | 0.3111 | 2.1 | 0.3212 | NA | NA |
| CDH1 | 1,2,3,4 | 118 | 3.4 | 11.4 | 0.0069 | 16.8 | 0.0002 | 13.3 | 0.0019 | 13.1 | 0.0024 | 1.7 | 0.5217 |
| TP53 | 1,2,3,4 | 125 | 3.2 | 20.6 | <0.0001 | 20.8 | <0.0001 | 34.7 | <0.0001 | 33.8 | <0.0001 | 8.5 | 0.1211 |
| MEN1 | 1,2,3 | 102 | 2.9 | 1.1 | 0.1036 | 0.8 | 0.0609 | NA | NA | NA | NA | NA | NA |
| PMS2 | 2,3 | 120 | 2.5 | NA | NA | 0.7 | 0.0706 | NA | NA | NA | NA | 0.0 | 0.2219 |
| RB1 | 1,2,3,4 | 44 | 2.3 | 1.7 | 0.7620 | 1.5 | 0.6791 | 2.8 | 0.8436 | 2.7 | 0.8731 | 0.0 | 0.2440 |
| AXIN1 | 2,3 | 91 | 2.2 | NA | NA | 0.5 | 0.0749 | NA | NA | NA | NA | NA | NA |
| EP300 | 3,4 | 92 | 2.2 | 2.6 | 0.8134 | 1.0 | 0.3171 | 2.2 | 1.0000 | NA | NA | NA | NA |
| GNAS | 2,3 | 96 | 2.1 | NA | NA | 1.2 | 0.4741 | NA | NA | NA | NA | NA | NA |
| PTEN | 1,2,3,4 | 96 | 2.1 | 4.0 | 0.3490 | 5.3 | 0.1761 | 6.1 | 0.1081 | 5.8 | 0.1302 | 1.7 | 0.8615 |
| CHEK2 | 1,3 | 53 | 1.9 | 0.8 | 0.3888 | 0.8 | 0.4139 | NA | NA | NA | NA | 0.0 | 0.2897 |
| TBL1XR1 | 1,3 | 115 | 1.7 | 0.7 | 0.2310 | 1.7 | 1.0000 | NA | NA | NA | NA | NA | NA |
| MLH1 | 2,3 | 117 | 1.7 | NA | NA | 1.0 | 0.5096 | NA | NA | NA | NA | 1.7 | 1.0000 |
| FBXW7 | 2,3,4 | 118 | 1.7 | 0.9 | 0.3920 | 0.8 | 0.3567 | 1.9 | 0.8799 | 1.8 | 0.9391 | NA | NA |
| KRAS | 1,2,3,4 | 118 | 1.7 | 0.5 | 0.0942 | 0.8 | 0.3567 | 0.8 | 0.3273 | 0.9 | 0.4172 | 0.0 | 0.3152 |
| ASXL1 | 2,3 | 121 | 1.7 | 2.2 | 0.7147 | 0.2 | 0.0249 | NA | NA | NA | NA | NA | NA |
| TBX3 | 1,2,3,4 | 70 | 1.4 | 5.8 | 0.1174 | 4.0 | 0.2776 | 3.6 | 0.3303 | 3.1 | 0.4217 | 1.7 | 0.8907 |
| BAP1 | 1,3 | 76 | 1.3 | 1.0 | 0.7994 | 0.7 | 0.5726 | NA | NA | NA | NA | NA | NA |
| ERBB2 | 1,2,3 | 91 | 1.1 | 2.6 | 0.3739 | 2.2 | 0.4910 | NA | NA | 2.6 | 0.2815 | 1.7 | 0.7553 |
| CTNNB1 | 3,4 | 104 | 1.0 | NA | NA | 0.3 | 0.2988 | 0.7 | 0.7324 | NA | NA | 0.0 | 0.4424 |
| RUNX1 | 1,2,3,4 | 112 | 0.9 | 35 | 0.1378 | 4.7 | 0.0629 | 4.8 | 0.0567 | 5.0 | 0.0501 | 0.0 | 0.4662 |
| FGFR2 | 2,3 | 114 | 0.9 | NA | NA | 1.3 | 0.7233 | NA | NA | NA | NA | 3.4 | 0.2362 |
| PIK3R1 | 1,2,3,4 | 117 | 0.9 | 2.3 | 0.3187 | 1.2 | 0.7809 | 3.5 | 0.1324 | 2.7 | 0.2419 | 0.0 | 0.4661 |
| CDKN1B | 1,2,3,4 | 117 | 0.9 | 1.3 | 0.7103 | 1.7 | 0.5245 | 1.4 | 0.6575 | 1.2 | 0.7777 | 1.7 | 0.6420 |
| NRAS | 2,3 | 127 | 0.8 | 0.1 | 0.0563 | 0.2 | 0.2655 | NA | NA | NA | NA | 0.0 | 0.4920 |
| CCND1 | 3,4 | 116 | 0.0 | NA | NA | 0.2 | 0.6300 | 0.3 | 0.5549 | NA | NA | NA | NA |
| CDKN2A | 1,2,3 | 119 | 0.0 | 0.6 | 0.3970 | 0.3 | 0.5500 | NA | NA | NA | NA | 1.7 | 0.1549 |
| ESR1 | 2,3 | 85 | 0.0 | NA | NA | 0.8 | 0.4083 | NA | NA | NA | NA | 0.0 | 1.0000 |
| MAP2K4 | 1,2,3,4 | 91 | 0.0 | 3.9 | 4.7 | 0.0348 | 4.4 | 0.0412 | 4.0 | 0.0522 | 1.7 | 0.1549 | |
| NF2 | 2,3 | 102 | 0.0 | 0.7 | 0.3967 | 0.3 | 0.5799 | NA | NA | NA | NA | 0.0 | 1.0000 |
| PPP2R1A | 3,4 | 115 | 0.0 | NA | NA | 0.2 | 0.1353 | 0.8 | 0.3360 | NA | NA | NA | NA |
| RHOA | 2,3 | 118 | 0.0 | NA | NA | 0.8 | 0.3300 | NA | NA | NA | NA | NA | NA |
| STK11 | 2,3 | 55 | 0.0 | 0.8 | 0.5056 | 0.0 | 1.0000 | NA | NA | NA | NA | NA | NA |
| HRAS | 2,3 | Insufficient coverage | 0.2 | – | 0.0 | – | NA | NA | NA | NA | 0.0 | – | |
| ARID5B | 3,4 | Not targeted | 1.3 | – | 0.3 | – | 1.0 | – | NA | NA | NA | NA | |
| B2M | 3,4 | Not targeted | NA | NA | 0.3 | – | 0.3 | – | NA | NA | NA | NA | |
| BCOR | 2,3 | Not targeted | NA | NA | 0.8 | – | NA | NA | NA | NA | NA | NA | |
| CBFB | 1,2,3 | Not targeted | 5.7 | – | 3.3 | – | NA | NA | 2.3 | – | 0.0 | – | |
| CHD4 | 3,4 | Not targeted | NA | NA | 3.0 | – | 3.3 | – | 2.6 | – | 1.7 | – | |
| CTCF | 1,2,3,4 | Not targeted | 2.7 | – | 2.7 | – | 2.6 | – | 2.6 | – | 1.7 | – | |
| DNMT3A | 2,3 | Not targeted | NA | NA | 0.8 | – | NA | NA | NA | NA | NA | NA | |
| FOXA1 | 2,3 | Not targeted | NA | NA | 4.0 | – | NA | NA | 3.5 | – | 1.7 | – | |
| FOXP1 | 1,2 | Not targeted | 0.9 | – | 1.0 | – | NA | NA | NA | NA | NA | NA | |
| GPS2 | 1,3 | Not targeted | 1.3 | – | 1.3 | – | NA | NA | 1.0 | – | NA | NA | |
| LATS1 | 3,4 | Not targeted | NA | NA | 0.7 | – | 1.1 | – | NA | NA | NA | NA | |
| MLLT4 | 1,2 | Not targeted | 2.6 | – | 1.8 | – | NA | NA | NA | NA | NA | NA | |
| NIPBL | 3,4 | Not targeted | NA | NA | 1.7 | – | 3.0 | – | NA | NA | NA | NA | |
| PHF6 | 2,3 | Not targeted | NA | NA | 0.5 | – | NA | NA | NA | NA | NA | NA | |
| RNF43 | 3,4 | Not targeted | NA | NA | 0.8 | – | 1.0 | – | NA | NA | NA | NA | |
| RPL22 | 3,4 | Not targeted | NA | NA | 0.5 | – | 0.4 | – | NA | NA | NA | NA | |
| SF3B1 | 1,2,3 | Not targeted | 3.5 | – | 2.3 | – | NA | NA | 2.1 | – | NA | NA | |
| SPOP | 3,4 | Not targeted | NA | NA | 0.5 | – | 0.9 | – | NA | NA | NA | NA | |
| TET2 | 2,3 | Not targeted | NA | NA | 0.8 | – | NA | NA | NA | NA | NA | NA | |
| ZFHX3 | 3,4 | Not targeted | NA | NA | 2.3 | – | NA | NA | NA | NA | 5,1% | – | |
| ZFP36L1 | 1,2,3 | Not targeted | 1.0 | – | 1.2 | – | NA | NA | NA | NA | NA | NA | |
| PIK3CA | 1,2,3,4 | 108 | 36.1 | 44.5 | 0.0876 | 39.8 | 0.4688 | 34.7 | 0.7720 | 34.4 | 0.7280 | 20.3 | 0.0346 |
Studies: 1 = METABRIC; 2 = Nik-Zainal et al. 2016; 3 = Bailey et al. 2018; 4 = Berger et al. 2018 PAN/GYN.
aMETABRIC: Nature 2012 + Nature 2016; data accessed through cBioPortal dd March 15, 2019; bTCGA: Cell 2015; data accessed through cBioPortal dd March 15, 2019.
NA, not available.
PIK3CA (36%), KMT2C (21%), PBRM1 (20%) and GATA3 (15%) were identified as most frequently mutated genes in this study (Fig. 1). The pattern of somatic mutations found in PIK3CA resembled that of FBC: all mutations found in MBC were missense mutations and 75–80% (12/15 in matched normal; 30/40 in the entire cohort) affected the FBC hotspots (H1047, E542 and E545; Supplementary Fig. 1). Besides PIK3CA, other recurrent mutations in MBC included a K358 frameshift GATA3 mutation (4/25 GATA3 mutations), the E17K AKT1 mutation (3/8 AKT1 mutations) and the Q26K NCOR1 mutation (4/21 NCOR1 mutations).
Somatic non-synonymous mutations and recurrent amplifications in 135 male breast cancers. Individual genes are represented as rows, and individual patients are represented as columns. Patients with pathogenic germline BRCA2 mutations or Klinefelter syndrome are indicated, as well as tumor histology and the distribution of surrogate IHC subtype.
Citation: Endocrine-Related Cancer 26, 10; 10.1530/ERC-19-0278
PIK3CA mutations were significantly more abundant in poorly differentiated (P = 0.013; n = 96) tumors with high mitotic activity (P = 0.014; n = 99) and in lymph node-positive (P = 0.006; n = 94) and younger patients (P = 0.046; n = 108). Somatic mutations in PIK3CA were associated with worse 5-year OS (P = 0.026; n = 90; Supplementary Fig. 2). In multivariable analysis, however, PIK3CA somatic mutations did not independently predict survival alongside tumor grade (P = 0.034; HR 2.627) and size (P = 0.001; HR 1.410).
ATM somatic mutations were associated with high tumor grade (P = 0.040; n = 74) and high mitotic counts (P = 0.014; n = 77). NOTCH2 mutations were more frequent in poorly differentiated tumors (P = 0.017; n = 100) without lymph node metastases (P = 0.020; n = 100). ATRX and CREBBP somatic mutations were more frequently observed in KS patients (P = 0.001 (n = 72) and P = 0.030 (n = 103), respectively). PTPRD mutations were more abundant in Ki67-high tumors (P = 0.005; n = 96) and MED12 mutations in luminal A-like tumors (P = 0.026; n = 86).
Compared to mutation frequencies in ER-positive FBC from METABRIC and TCGA, TP53 (both P < 0.0001), MAP3K1 (P = 0.02 and P = 0.05, respectively) and CDH1 (P = 0.007 and P = 0.0002, respectively) somatic mutations were significantly less frequent in MBC (Table 2), whereas particularly PBRM1 (both P < 0.0001), NSD1 (P < 0.0001 TCGA) and SETD2 (both P < 0.0001) mutations were more frequent. Overall, genes regulating chromatin function appeared more often affected in MBC (Fig. 2).
Comparison of MBC somatic mutation frequencies to METABRIC and TCGA ER-positive FBC. Genes have been sorted per function and 95% CI (http://vassarstats.net/prop1.html) have been indicated. * indicates a significant difference with METABRIC and/or TCGA.
Citation: Endocrine-Related Cancer 26, 10; 10.1530/ERC-19-0278
Mutational signature analysis revealed that 24% of MBCs have a dominant signature 3 associated with defective homologous recombination DNA repair, whereas only 13% of TCGA FBCs demonstrated a dominant signature 3 (Fig. 3). In addition, another 24% of MBCs have a dominant signature 8, as compared to 0.4% of FBCs. In a recent review by Nik-Zainal et al., this signature 8 was reported to be increased in homologous recombination deficiency and late in cancer evolution (Nik-Zainal & Morganella 2017).
Mutational signature analysis in MBCs compared to ER/HER2- matched FBCs (TCGA). HRD, homologous recombination defect; MMR, mismatch repair
Citation: Endocrine-Related Cancer 26, 10; 10.1530/ERC-19-0278
Copy number aberrations
CNA load
On average, each tumor harbored 138 gene CNAs (range 6–1230), including amplifications (Z > 2.80) and homozygous deletions (Z < −2.80). We observed no correlation between CNA load and ER/PR status, tumor histology, lymph node status, Ki67 status or age (data not shown). CNA load was not associated with mutational load (P = 0.286) or 5-year survival (P = 0.549) and was similar between luminal A-like and luminal B-like tumors (P = 0.285). CNA load was however higher in poorly differentiated (P = 0.012) and larger tumors (P = 0.045) with high MAI (P = 0.011) (Supplementary Fig. 4). CNA load was significantly higher in tumors harboring mutations in KDM6A (P = 0.006) and NOTCH2 (P = 0.028).
Figure 4 depicts a heatmap of all CNAs in the MBC cohort after unsupervised hierarchical cluster analysis. MBCs of the same surrogate intrinsic molecular subtype did not cluster on the basis of their CNA profile, whereas KS patients seemed to be subgrouped as a result of their CNA-naïve profile with chromosome X gains. In addition, a cluster of 17 mainly luminal A-like MBCs with high genomic instability was apparent. No correlation was observed with clinicopathological variables or survival when compared to the other MBCs but somatic mutation load tended to be slightly higher (P = 0.075).
Heatmap of copy number alterations in the male breast cancers following unsupervised hierarchical cluster analysis. Two clusters seem to stand out: the top cluster with many events dominated by luminal A-like tumors and the bottom cluster dominated by luminal B-like cancers. Klinefelter syndrome patients (gray) show few copy number alterations.
Citation: Endocrine-Related Cancer 26, 10; 10.1530/ERC-19-0278
Amplifications
Analysis of CNAs revealed that MBCs harbored recurrent amplifications on 8p/8q, 16p, 20q and 1q. Supplementary Table 6 summarizes the associations of a set of 220 genes amplified in at least 10% of MBCs with intrinsic subtype, clinicopathological characteristics, SNV load and PIK3CA mutation status. Some of the most recurrently amplified genes in both cohorts (FF and FFPE) included UBR5 (24%; 8q), TSC2 (22%; 16p), ANK1 (22%; 8p), OBSCN (21%; 1q) and WHSC1L1 (21%; 8p). A comparison between amplification frequencies in MBC and smFBC is presented in the same table. Merely 86/220 (39%) frequently amplified genes in MBC were also amplified in more than 10% of ER-positive FBC. PANTHER reactome pathway analysis of these 220 genes demonstrated an overrepresentation of genes involved in extracellular matrix organization (three-fold enrichment; P = 0.0004), chromatin organization (two-fold enrichment; P = 0.0012) and STAT3 signaling (six-fold enrichment; P = 0.046), whereas cell cycle-related genes were relatively underrepresented (0.4-fold enrichment; P = 0.0012) (Mi et al. 2013). Based on The Drug Gene Interaction Database, 19% of these genes (41/220) are clinically actionable and for 26% (57/220), drug interactions have been described (Supplementary Table 6).
Individually, 7/220 genes were indicative of poor 5-year OS when amplified: MDM2, PAK1, SCYL3, TGFB2, CLTC, SMYD3 and ASH1L (Supplementary Table 6). In multivariable analysis including clinical and pathological characteristics, the amplification status of PAK1, SCYL3, MDM2, ASH1L, E2F7, CLTC or TGFB2 remained in the model. In multivariable analysis including all seven genes, PAK1 (HR 3.65; P = 0.011), TGFB2 (HR 3.14; P = 0.011) and E2F7 (HR 2.98; P = 0.017) remained. When all seven gene amplifications as well as PIK3CA mutations were included, only ASH1L (HR 2.44; P = 0.057) and lymph node status (HR 2.44; P = 0.087) remained in the model as independent predictors of survival (Supplementary Table 7).
Of particular interest was MDM2, amplified in as much as 13% of the cohort, and predictor of worse 5-year OS (P = 0.007; Fig. 5) even after correction for other established prognostic variables (P = 0.024; Supplementary Table 7). MDM2 amplifications were more frequent in PR negative (P = 0.004) and poorly differentiated (P = 0.040) tumors (Supplementary Fig. 5). Co-amplifications with COL1A1 (P = 0.007), HER2 (P = 0.009), DNAH11 (P = 0.027), PIK3C2B (P = 0.027) and PPM1D (P = 0.043) were frequent. Tumors harboring PIK3CA mutations (P = 0.005) or NF1 mutations (P = 0.026) were more likely to harbor MDM2 amplifications (Fig. 1 and Supplementary Fig. 5). MDM2 amplifications were significantly correlated with MDM2 protein overexpression (P = 0.001), but protein overexpression did not predict poor outcome (P = 0.337).
MDM2 amplification in male breast cancer is associated with poor 5-year overall survival, PR negativity and high tumor grade.
Citation: Endocrine-Related Cancer 26, 10; 10.1530/ERC-19-0278
Germline mutations
With on average 1723 reads per normal tissue sample, we detected pathogenic BRCA2 germline mutations in 5/44 (11%) matched normal samples using the IonTorrent Oncomine BRCA FFPE gene panel (library preparation failed for one sample; Supplementary Table 5). There was no indication for loss of heterozygosity of the WT BRCA2 allele based on variant allele frequencies. No pathogenic BRCA1 germline mutations were identified. Tumors from BRCA2 germline mutation carriers harbored a similar amount of somatic mutations and total CNAs compared to other MBCs. BRCA2-related tumors appear to have increased CNA counts on chromosome 8q (P = 0.056) accompanied by decreased counts on 8p (P = 0.004). All five BRCA2-carriers demonstrated UBR5 (8q; P = 0.002), ASAP1 (8q; P = 0.001), CSMD3 (8q; P = 0.001) and CCNE2 (8q; P = 0.0002) amplification, but DNAH11 (7p; P = 0.003), RRM2B (8q; P = 0.007), FZD6 (8q; P = 0.010), RUNX1T1 (8q; P = 0.023) and SGK3 (8q; P = 0.023) amplifications were also more frequently seen. We observed no enrichment for particular somatic mutations. None of the BRCA2 germline mutation carriers harbored a PIK3CA or GATA3 mutation (Fig. 1). Within AR, CHEK2 and PALB2 (FANCM was not targeted), we found no likely pathogenic germline mutations. Germline mutations in these genes predicted to have medium or high impact are described in the Supplementary results section.
Discussion
Compared to FBC, MBC has been relatively poorly characterized on the molecular level. Here, we have characterized a cohort of 135 MBC, 46% of which were deemed luminal B-like tumors. This is consistent with a recent pathology review of the EORTC 10085/TBCRC/BIG/NABCG International Male Breast Cancer Program where 49.3% of 1328 MBC were luminal B-like tumors (Doebar et al. 2017). In our MBC series, 46% of patients received radiotherapy, 16% chemotherapy and 42% endocrine therapy. Compared to the EORTC cohort, fewer men received chemotherapy (EORTC 27.7%; 220/794 patients with available data) and endocrine therapy (EORTC 82.6%; 583/706 patients with available data). These discrepancies are likely mainly due to uncertainties in treatment decision making. Studies investigating MBCs at the molecular level and correlating their findings with survival are therefore of the utmost importance.
TP53, CDH1, MAP3K1 and to a lesser extent PIK3CA, TBX3, MAP2K4, RUNX1 and PTEN somatic mutations appear to be less frequent in MBC compared to smFBC, whereas PBRM1, NSD1 and SETD2 somatic mutations seem to be more frequent.
After correction for multiple comparisons, there were no significant differences between our study and the MBC study of Piscuoglio et al. Similar to their MBC study, somatic mutations in TP53 were very rare (Piscuoglio et al. 2016). Their TP53 mutation frequency was however higher (8.5%) than ours (3%), possibly due to a different cohort composition. In FBC, luminal B-like tumors show a higher TP53 mutation frequency than luminal A-like tumors and the MBC set of Piscuoglio et al. was likely enriched for luminal B-like cancers (71% of their cohort) (Cancer Genome Atlas Network 2012, Piscuoglio et al. 2016). In addition, a study using a highly similar predecessor ‘Cancer mini-genome’ gene panel and approach in triple-negative FBC identified TP53 mutations in 55% of the specimens, thereby excluding technical bias (Lips et al. 2015). In contrast to TP53, the pattern of somatic mutations found in PIK3CA resembled that of FBC. Its somatic mutation frequency in our MBC cohort (36%) was in between that reported by Piscuoglio et al. (MBC; 20%) and ER-positive FBC (n = 1431; 48%). PIK3CA mutations were associated with more aggressive tumor characteristics and reduced survival, in line with findings in ER-positive FBC subgroups (Pereira et al. 2016, Ishida et al. 2018).
Aside from PIK3CA, the serine/threonine protein kinase AKT1 was the only other mutated gene with hotspots corresponding to FBC. The majority of AKT1 missense mutations were E17K (4/9) or L52R (2/9) substitutions. Both mutations are known to be oncogenic and there is promising clinical data in FBC patients with AKT1 E17K mutant ER+ ductal breast cancer treated with the pan-AKT targeted inhibitor AZD5363 (level 3A evidence; oncoKB) (Davies et al. 2012, 2015, Addie et al. 2013, Hyman et al. 2017).
As expected from FBC, most GATA3 mutations were likely oncogenic frameshift mutations including K358fs, H435fs, A332fs, R330fs and M294K/R (Cerami et al. 2012, Ellis et al. 2012, Gao et al. 2013). In contrast to Piscuoglio et al., GATA3 somatic mutations were not exclusive to luminal B-like MBC tumors (Piscuoglio et al. 2016). Overall, genes regulating chromatin function appear more often mutated in MBC, and mutational signatures 3 and 8, both associated with homologous recombination deficiency, are more prevalent than in ER/HER2-matched FBC. Of particular clinical importance, homologous recombination deficiency may point to a selective sensitivity to PARP (poly-ADP ribose polymerase) inhibitors (Nik-Zainal & Morganella 2017).
Like FBC, MBC is CNA driven, and despite many similarities in the CNA landscape between MBC and FBC (Curtis et al. 2012, Piscuoglio et al. 2016), only 40% of frequently amplified genes in MBC were also frequently amplified in FBC, underlining differences in biology and genetics. MDM2 amplifications were observed in 13% of the MBC cohort while in FBC, these amplifications occur in merely 3.7% of cases. Since MDM2 mediates ubiquitination of p53, its increased amplification frequency may be related to the decreased TP53 mutation frequency observed in MBC, the two events being mutually exclusive in the same pathway. Of note, besides MDM2, many of the aberrant genes in MBC seem to be involved in the inactivation of the p53 pathway (e.g. amplification of PAK1 and SMYD2, and inactivating mutations in ARID1A, PBRM1 and KMT2C) (Huang et al. 2006, Wu et al. 2014, Woo et al. 2017). MDM2 amplifications predicted more aggressive tumor behavior and correlated with protein overexpression. The high rate of MDM2 overexpression in prostate and ER-positive FBC, and the ability of MDM2 inhibitors to ubiquitinate steroid hormone receptors, has led to the evaluation of this class of drugs in combination with endocrine therapies (CRUKE/12/032). Many MDM2 small-molecule inhibitors have only recently progressed from preclinical development into early clinical trials (such as RG7112, also known as RO5045337, and AMG-232) (Burgess et al. 2016). Thus, MDM2 is a putative therapeutic target in MBC.
Four genes independently predicted poor OS in MBC when amplified: PAK1 (11q13.5-q14.1), ASH1L (1q22), E2F7 (12q21.2) and TGFB2 (1q41). PAK1 (p21 activated kinase 1) amplifications were present at a similar frequency compared to ER-positive FBC (12 vs 10%) (Ong et al. 2011, Curtis et al. 2012, Ye & Field 2012, Radu et al. 2014) and amplification and overexpression of PAK1 is associated with poor outcome in luminal FBC as well. In vitro testing of a small-molecule inhibitor, FRAX1036, in combination with docetaxel, further supported PAK1 as a potential target in breast cancer (Ong et al. 2015). PAK1 has also been implicated in tamoxifen resistance (Holm et al. 2006, Bostner et al. 2007, 2010), and in upregulating genes involved in the Fanconi anemia (FA)/BRCA pathway, paving the way for combined PAK1 and PARP inhibition in FA/BRCA-proficient cancers (Villamar Cruz et al. 2016). ASH1L, encoding a histone methyltransferase (HMT), showed a similar amplification frequency in MBC compared to ER-positive FBC (18 vs 21%). This HMT is classified as dysregulated by genetic alterations and as candidate therapeutic target in FBC (Liu et al. 2015). ASH1L inhibitors are currently being developed (Rogawski et al. 2015). Atypical E2F transcription factor 7 (E2F7) amplification appears to be more frequent in MBC than ER-positive FBC (13 vs 1.4%). E2F7 participates in various processes such as angiogenesis, polyploidization and DNA damage response. E2F7 overexpression leads to tamoxifen resistance in breast cancer cells (Chu et al. 2015) and anthracycline resistance in squamous cell carcinoma (Hazar-Rethinam et al. 2015). A small-molecule pan-E2F inhibitor (HLM006474) has been developed and was proven to be effective in a melanoma and lung cancer cell culture model (Ma et al. 2008, Kurtyka et al. 2014). TGFB2 encodes a secreted ligand of the transforming growth factor-beta superfamily and its mRNA levels predict tamoxifen response in breast cancer cells (Buck et al. 2008). Neutralizing antibodies to TGFB2 have been shown to reverse tamoxifen resistance of human breast carcinomas in vivo (Arteaga et al. 1999). Several pharmacological strategies have been developed to block TGF-beta signaling (Colak & Ten Dijke 2017). Overall, 19% of the most frequently amplified genes in MBC are clinically actionable and for 26%, drug interactions have been described, suggesting many potential druggable targets.
We detected pathogenic BRCA2 germline mutations in 11% of the paired tumor/normal subcohort (5/44). Other larger studies have described similar frequencies (8–14%) (Couch et al. 1996, Basham et al. 2002, Pritzlaff et al. 2017). In contrast to Silvestri et al. who reported a median age at diagnosis for male BRCA2 mutation carriers of 62 years and an inverse relationship between age at diagnosis and tumor grade in 375 male BRCA2 mutation carriers, our BRCA2 cohort consisted of older patients with poorly differentiated tumors (Silvestri et al. 2016). In our study, tumors from BRCA2 germline mutation carriers harbored a similar amount of somatic mutations and total CNAs compared to other MBCs, but appeared to have increased CNA counts on 8q and decreased counts on 8p. In light of the small BRCA2 mutation carrier cohort reported here, results based on this subgroup should however be interpreted with caution.
KS patients tended to be younger at presentation, with generally well-differentiated tumors with few CNAs. The increased risk of developing breast cancer for these KS patients can be attributed to a direct effect of the supernumerary X chromosome(s) or the combined action of abnormal chromosome dosage and hormonal imbalances (Kawakami et al. 2004, Bonomi et al. 2017).
It should be noted that this study used FFPE as well as fresh-frozen material. It is well recognized that the quality of FFPE samples due to fixation and tissue processing is inferior to that of frozen material. DNA extracted from FFPE tissues is fragmented which can lead to sequence artifacts (Do & Dobrovic 2015). Sequence artifacts can be difficult to distinguish from true mutations, especially in the context of tumor heterogeneity and are an increasing interpretive problem in this era of massively parallel sequencing. We therefore included as much fresh-frozen material as possible. Nevertheless, in light of the rarity of MBC, we have included archived FFPE material as well. This might have led to an overestimation of mutational load (Yost et al. 2012).
In conclusion, this comprehensive molecular analysis of MBC suggests that, while MBCs show remarkable similarities to FBC, clear differences are also found, subscribing the amounting evidence that MBC should be seen as an entity of its own. This should also be reflected in the clinical approach that should refrain from merely imitating FBC management. As we have observed some genetic alterations to be clearly more prevalent in MBC, for example MDM2 amplifications and homologous recombination-deficient mutational signatures, focus should be on associated relevant drugs. As such, honoring MBC for its specific molecular portrait may result in improvements in outcome that parallel those we have observed over the last years in FBC.
Supplementary data
This is linked to the online version of the paper at https://doi.org/10.1530/ERC-19-0278.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
This study was funded by the Dutch Cancer Society (UU 2012-5637), and in part by the National Institutes of Health/National Cancer Institute MSK Cancer Center Support Grant P30CA008748. J S R-F is funded in part by the Breast Cancer Research Foundation. The funding agencies had no role in the design of the study, the collection, analysis and interpretation of data and in writing the manuscript.
Ethics approval and consent to participate
The Dutch national ethical guidelines (www.federa.org) state that no ethical approval is required for the use of anonymous leftover tissue, and this is also part of the standard treatment agreement (Van Diest 2002).
Availability of data and materials
All sequencing data have been deposited in EGA under accession number EGAS00001002683. All data generated or analyzed during this study are included in this published article and its Supplementary information files.
Acknowledgements
The authors are grateful to the Borstkanker Onderzoek Groep (BOOG; study nr 2009-04; NCT01101425) for providing us with clinical and biological data of MBC patients.
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