Are triple-negative tumours and basal-like breast cancer synonymous? Authors' response

We read with interest the issues raised by Rakha and colleagues [1] in their response to our recent research article [2], and we are pleased to address them. An important conclusion from our research article is that triple-negative breast cancer can be equated to basal-like breast cancer. In their letter, Rakha and colleagues [1] state that equating triple-negative phenotype (TNP) tumours with basal-like breast cancer is misleading and is not supported by the data we have presented. It is important to realize that, as we have also pointed out in our article [2], the basal-like breast cancer subtype was initially defined based on the gene expression pattern of the so-called ‘intrinsic gene list ’ in only six breast tumours [3]. Since this initial report, the intrinsic gene list that is used to identify basallike breast tumours has been updated multiple times [3-5]

Knowledge driven decomposition of tumor expression profiles

<p>Abstract</p> <p>Background</p> <p>Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.</p> <p>Results</p> <p>We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.</p> <p>Conclusion</p> <p>The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.</p

Triple-negative breast cancer with brain metastases: a comparison between basal-like and non-basal-like biological subtypes

The aim of this study was to divide the group of triple-negative breast cancer patients with brain metastases into basal-like and non-basal-like biological subtypes in order to compare clinical features and survival rates in those two groups. A comprehensive analysis of 111 consecutive triple-negative breast cancer patients with brain metastases treated in the years 2003–2009 was performed. In 75 patients, immunohistochemistry was used as a surrogate of microarray in order to evaluate the expression of three basal markers: cytokeratin 5/6 (CK 5/6), EGFR/HER1 and c-KIT. The basal-like (ER/PgR/HER2-negative, CK5/6positive and/or HER1-positive) and non-basal-like (ER/PgR/HER2-negative, CK5/6-negative, HER1-negative) subsets were selected. Clinical features and survivals were compared in both groups. In the group of 111 triple-negative breast cancer patients, median DFS, OS and survival from brain metastases were 20, 29 and 4 months, respectively. In 75 patients who were evaluable for basal markers, median DFS, OS and survival from brain metastases were 18, 26 and 3.2 months, respectively. In the basal-like subtype, the survival rates were 15, 26 and 3 months, respectively, and in the non-basal-like subtypes, they were 20, 30 and 2.8 months, respectively. No statistically significant differences in survivals were detected between the basal-like and non-basal-like biological subtypes. Factors influencing survival from brain metastases were: Karnofsky performance status (KPS), the status of extracranial disease and age. Biological markers differentiating triple-negative group into basal-like and non-basal-like subtype (CK 5/6, HER1, c-KIT) had no influence on survival. In patients with triple-negative breast cancer and brain metastases, well-known clinical, but not molecular, features correlated with survival

Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas

INTRODUCTION: Breast cancer is a heterogeneous group of tumors, and can be subdivided on the basis of histopathological features, genetic alterations and gene-expression profiles. One well-defined subtype of breast cancer is characterized by a lack of HER2 gene amplification and estrogen and progesterone receptor expression ('triple-negative tumors'). We examined the histopathological and gene-expression profile of triple-negative tumors to define subgroups with specific characteristics, including risk of developing distant metastases. METHODS: 97 triple-negative tumors were selected from the fresh-frozen tissue bank of the Netherlands Cancer Institute, and gene-expression profiles were generated using 35K oligonucleotide microarrays. In addition, histopathological and immunohistochemical characterization was performed, and the findings were associated to clinical features. RESULTS : All triple-negative tumors were classified as basal-like tumors on the basis of their overall gene-expression profile. Hierarchical cluster analysis revealed five distinct subgroups of triple-negative breast cancers. Multivariable analysis showed that a large amount of lymphocytic infiltrate (HR = 0.30, 95% CI 0.09-0.96) and absence of central fibrosis in the tumors (HR = 0.14, 95% CI 0.03-0.62) were associated with distant metastasis-free survival. CONCLUSION: Triple-negative tumors are synonymous with basal-like tumors, and can be identified by immunohistochemistry. Based on gene-expression profiling, basal-like tumors are still heterogeneous and can be subdivided into at least five distinct subgroups. The development of distant metastasis in basal-like tumors is associated with the presence of central fibrosis and a small amount of lymphocytic infiltrat

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal 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. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS 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. CONCLUSION: Using 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

A high-resolution map of the Grp1 locus on chromosome V of potato harbouring broad-spectrum resistance to the cyst nematode species Globodera pallida and Globodera rostochiensis

The Grp1 locus confers broad-spectrum resistance to the potato cyst nematode species Globodera pallida and Globodera rostochiensis and is located in the GP21-GP179 interval on the short arm of chromosome V of potato. A high-resolution map has been developed using the diploid mapping population RHAM026, comprising 1,536 genotypes. The flanking markers GP21 and GP179 have been used to screen the 1,536 genotypes for recombination events. Interval mapping of the resistances to G. pallida Pa2 and G. rostochiensis Ro5 resulted in two nearly identical LOD graphs with the highest LOD score just north of marker TG432. Detailed analysis of the 44 recombinant genotypes showed that G. pallida and G. rostochiensis resistance could not be separated and map to the same location between marker SPUD838 and TG432. It is suggested that the quantitative resistance to both nematode species at the Grp1 locus is mediated by one or more tightly linked R genes that might belong to the NBS-LRR class

GpaXItarl originating from Solanum tarijense is a major resistance locus to Globodera pallida and is localised on chromosome 11 of potato

Resistance to Globodera pallida Rookmaker (Pa3), originating from wild species Solanum tarijense was identified by QTL analysis and can be largely ascribed to one major QTL. GpaXItarl explained 81.3% of the phenotypic variance in the disease test. GpaXItarl is mapped to the long arm of chromosome 11. Another minor QTL explained 5.3% of the phenotypic variance and mapped to the long arm of chromosome 9. Clones containing both QTL showed no lower cyst counts than clones with only GpaXItarl. After Mendelising the phenotypic data, GpaXItarl could be more precisely mapped near markers GP163 and FEN427, thus anchoring GpaXItarl to a region with a known R-gene cluster containing virus and nematode resistance genes

Gene expression profiling integrated into network modelling reveals heterogeneity in the mechanisms of BRCA1 tumorigenesis

Background: gene expression profiling has distinguished sporadic breast tumour classes with genetic and clinical differences. Less is known about the molecular classification of familial breast tumours, which are generally considered to be less heterogeneous. Here, we describe molecular signatures that define BRCA1 subclasses depending on the expression of the gene encoding for oestrogen receptor, ESR1. Methods: for this purpose, we have used the Oncochip v2, a cancer-related cDNA microarray to analyze 14 BRCA1-associated breast tumours. Results: signatures were found to be molecularly associated with different biological processes and transcriptional regulatory programs. The signature of ESR1-positive tumours was mainly linked to cell proliferation and regulated by ER, whereas the signature of ESR1-negative tumours was mainly linked to the immune response and possibly regulated by transcription factors of the REL/NFκB family. These signatures were then verified in an independent series of familial and sporadic breast tumours, which revealed a possible prognostic value for each subclass. Over-expression of immune response genes seems to be a common feature of ER-negative sporadic and familial breast cancer and may be associated with good prognosis. Interestingly, the ESR1-negative tumours were substratified into two groups presenting slight differences in the magnitude of the expression of immune response transcripts and REL/NFκB transcription factors, which could be dependent on the type of BRCA1 germline mutation. Conclusion: this study reveals the molecular complexity of BRCA1 breast tumours, which are found to display similarities to sporadic tumours, and suggests possible prognostic implications