Bas Kreike

Refinement of breast cancer classification by molecular characterization of histological special types.

Most invasive breast cancers are classified as invasive ductal carcinoma not otherwise specified (IDC NOS), whereas about 25% are defined as histological ‘special types’. These special‐type breast cancers are categorized into at least 17 discrete pathological entities; however, whether these also constitute discrete molecular entities remains to be determined. Current therapy decision‐making is increasingly governed by the molecular classification of breast cancer (luminal, basal‐like, HER2+). The molecular classification is derived from mainly IDC NOS and it is unknown whether this classification applies to all histological subtypes. We aimed to refine the breast cancer classification systems by analysing a series of 11 histological special types [invasive lobular carcinoma (ILC), tubular, mucinous A, mucinous B, neuroendocrine, apocrine, IDC with osteoclastic giant cells, micropapillary, adenoid cystic, metaplastic, and medullary carcinoma] using immunohistochemistry and genome‐wide gene expression profiling. Hierarchical clustering analysis confirmed that some histological special types constitute discrete entities, such as micropapillary carcinoma, but also revealed that others, including tubular and lobular carcinoma, are very similar at the transcriptome level. When classified by expression profiling, IDC NOS and ILC contain all molecular breast cancer types (ie luminal, basal‐like, HER2+), whereas histological special‐type cancers, apart from apocrine carcinoma, are homogeneous and only belong to one molecular subtype. Our analysis also revealed that some special types associated with a good prognosis, such as medullary and adenoid cystic carcinomas, display a poor prognosis basal‐like transcriptome, providing strong circumstantial evidence that basal‐like cancers constitute a heterogeneous group. Taken together, our results imply that the correct classification of breast cancers of special histological type will allow a more accurate prognostication of breast cancer patients and facilitate the identification of optimal therapeutic strategies. Copyright

Lung metastasis genes couple breast tumor size and metastatic spread

The association between large tumor size and metastatic risk in a majority of clinical cancers has led to questions as to whether these observations are causally related or whether one is simply a marker for the other. This is partly due to an uncertainty about how metastasis-promoting gene expression changes can arise in primary tumors. We investigated this question through the analysis of a previously defined “lung metastasis gene-expression signature” (LMS) that mediates experimental breast cancer metastasis selectively to the lung and is expressed by primary human breast cancer with a high risk for developing lung metastasis. Experimentally, we demonstrate that the LMS promotes primary tumor growth that enriches for LMS+ cells, and it allows for intravasation after reaching a critical tumor size. Clinically, this corresponds to LMS+ tumors being larger at diagnosis compared with LMS− tumors and to a marked rise in the incidence of metastasis after LMS+ tumors reach 2 cm. Patients with LMS-expressing primary tumors selectively fail in the lung compared with the bone or other visceral sites and have a worse overall survival. The mechanistic linkage between metastasis gene expression, accelerated tumor growth, and likelihood of metastatic recurrence provided by the LMS may help to explain observations of prognostic gene signatures in primary cancer and how tumor growth can both lead to metastasis and be a marker for cells destined to metastasize.

MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

MicroRNAs (miRNA) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 noninvasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed sequence-dependent miRNA target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most noninvasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including miR-98/let-7), with most miRNA changes apparent already in the noninvasive carcinomas. In addition, patients that went on to develop metastasis showed increased expression of mir-423, and triple-negative breast carcinomas were most distinct from other tumor subtypes due to upregulation of the mir~17-92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested.

An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer

Individualization of cancer management requires prognostic markers and therapy-predictive markers. Prognostic markers assess risk of disease progression independent of therapy, whereas therapy-predictive markers identify patients whose disease is sensitive or resistant to treatment. We show that an experimentally derived IFN-related DNA damage resistance signature (IRDS) is associated with resistance to chemotherapy and/or radiation across different cancer cell lines. The IRDS genes STAT1, ISG15, and IFIT1 all mediate experimental resistance. Clinical analyses reveal that IRDS(+) and IRDS(−) states exist among common human cancers. In breast cancer, a seven–gene-pair classifier predicts for efficacy of adjuvant chemotherapy and for local-regional control after radiation. By providing information on treatment sensitivity or resistance, the IRDS improves outcome prediction when combined with standard markers, risk groups, or other genomic classifiers.

Predicting a local recurrence after breast-conserving therapy by gene expression profiling

IntroductionTo tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients.MethodsBy using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy.ResultsValidation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence.ConclusionOur findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy.

The molecular underpinning of lobular histological growth pattern: a genome‐wide transcriptomic analysis of invasive lobular carcinomas and grade‐ and molecular subtype‐matched invasive ductal carcinomas of no special type

Invasive lobular carcinoma (ILC) is the most frequent special type of breast cancer. The majority of these tumours are of low histological grade, express hormone receptors, and lack HER2 expression. The pleomorphic variant of ILCs (PLCs) is characterized by atypical cells with pleomorphic nuclei and is reported to have an aggressive clinical behaviour. Expression profiling studies have demonstrated that classic ILCs preferentially display a luminal phenotype, whereas PLCs may be of luminal, HER2 or molecular apocrine subtypes. The aims of this study were two‐fold: to determine the transcriptomic characteristics of lobular carcinomas and to define the genome‐wide transcriptomic differences between classic ILCs and PLCs. To define the transcriptomic characteristics of ILCs, minimizing the impact of histological grade and molecular subtype on the analysis, we subjected a series of grade‐ and molecular subtype‐matched ILCs and invasive ductal carcinomas (IDCs) to genome‐wide gene expression profiling using oligonucleotide microarrays. Hierarchical clustering analysis demonstrated that ILCs formed a separate cluster and a supervised analysis revealed that 5.8% of the transcriptionally regulated genes were significantly differentially expressed in ILCs compared to grade‐ and molecular subtype‐matched IDCs. ILCs displayed down‐regulation of E‐cadherin and of genes related to actin cytoskeleton remodelling, protein ubiquitin, DNA repair, cell adhesion, TGF‐beta signalling; and up‐regulation of transcription factors/immediate early genes, lipid/prostaglandin biosynthesis genes, and cell migration‐associated genes. Supervised analysis of classic ILCs and PLCs demonstrated that less than 0.1% of genes were significantly differentially expressed between these tumour subtypes. Our results demonstrate that ILCs differ from grade‐ and molecular subtype‐matched IDCs in the expression of genes related to cell adhesion, cell‐to‐cell signalling, and actin cytoskeleton signalling. However, classic ILCs and PLCs are remarkably similar at the molecular level and should be considered as part of a spectrum of lesions. Copyright

Metaplastic breast carcinomas are basal-like breast cancers: a genomic profiling analysis

Background Metaplastic breast carcinomas (MBCs) comprise a group of aggressive and chemotherapy resistant cancers characterised by neoplastic cells displaying differentiation towards squamous epithelium or mesenchymal elements. Previous histopathological and immunohistochemical analysis of MBCs suggested that these cancers would have a basal-like profile. Methods We investigated the molecular subtype of 20 MBCs using microarray-based expression profiling data. These data were compared with those of 79 invasive ductal carcinomas (IDCs) of basal-like phenotype by unsupervised hierarchical clustering, supervised analysis and pathway analysis. Results We demonstrate that 95% of all MBCs are of basal-like molecular subtype. Furthermore, unsupervised hierarchical clustering analysis and pathway analysis of the profiles of MBCs revealed that MBCs are part of the spectrum of basal-like breast cancers. Significance analysis of microarrays (SAM) identified 1,385 transcripts differentially expressed between MBCs and IDCs of basal-like phenotype. Pathway analysis using these genes revealed that DNA repair pathways, including BRCA1 pathway, PTEN, a gene whose loss of function is associated with resistance to chemotherapy, and TOP2A, the molecular target of anthracyclines, are significantly downregulated in MBCs compared to basal-like IDCs. These findings may at least in part explain the reported poor responses to chemotherapy of MBCs. Furthermore, MBCs showed significantly higher expression of genes related to myoepithelial differentiation and epithelial to mesenchymal transition (EMT). Conclusions Our results demonstrate that MBCs are part of the spectrum of basal-like breast carcinomas and display a myoepithelial and EMT-like molecular make-up. The reported poorer response to chemotherapeutic agents in patients with MBCs may stem from downregulated DNA damage response pathways, PTEN and TOP2A.

Genomic amplification and oncogenic properties of the GASC1 histone demethylase gene in breast cancer

Earlier, mapping of the 9p23–24 amplicon in esophageal cancer cell lines led us to the positional cloning of gene amplified in squamous cell carcinoma 1 (GASC1), which encodes a nuclear protein with a Jumonji C domain that catalyzes lysine (K) demethylation of histones. However, the transforming roles of GASC1 in breast cancer remain to be determined. In this study, we identified GASC1 as one of the amplified genes for the 9p23–24 region in breast cancer, particularly in basal-like subtypes. The levels of GASC1 transcript expression were significantly higher in aggressive, basal-like breast cancers compared with nonbasal-like breast cancers. Our in vitro assays demonstrated that GASC1 induces transformed phenotypes, including growth factor-independent proliferation, anchorage-independent growth, altered morphogenesis in Matrigel, and mammosphere forming ability, when overexpressed in immortalized, nontransformed mammary epithelial MCF10A cells. Additionally, GASC1 demethylase activity regulates the expression of genes critical for stem cell self-renewal, including NOTCH1, and may be linked to the stem cell phenotypes in breast cancer. Thus, GASC1 is a driving oncogene in the 9p23–24 amplicon in human breast cancer and targeted inhibition of GASC1 histone demethylase in cancer could provide potential new avenues for therapeutic development.

Classification of ductal carcinoma in situ by gene expression profiling.

IntroductionDuctal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas.MethodsGene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases.ResultsDCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS.ConclusionUsing supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples.

The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age

BACKGROUND The majority of breast cancer patients are postmenopausal women who are increasingly being offered adjuvant chemotherapy. Since the beneficial effect of chemotherapy in postmenopausal patients predominantly occurs in the first 5 years after diagnosis, a prognostic marker for early events can be of use for adjuvant treatment decision making. The aim of this study was to evaluate the prognostic value of the 70-gene prognosis signature for early events in postmenopausal patients. METHODS Frozen tumor samples from 148 patients aged 55-70 years were selected (T1-2, N0) and classified by the 70-gene prognosis signature (MammaPrint) into good or poor prognosis. Eighteen percent received hormonal therapy. RESULTS Breast cancer-specific survival (BCSS) at 5 years was 99% for the good-prognosis signature versus 80% for the poor-prognosis signature group (P = 0.036). The 70-gene prognosis signature was a significant and independent predictor of BCCS during the first 5 years of follow-up with an adjusted hazard ratio of 14.4 (95% confidence interval 1.7-122.2; P = 0.01) at 5 years. CONCLUSION The 70-gene prognosis signature can accurately select postmenopausal patients at low risk of breast cancer-related death within 5 years of diagnosis and can be of clinical use in selecting postmenopausal women for adjuvant chemotherapy.