Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Karn, Thomas; Metzler, Dirk; Ruckhäberle, Eugen; Hanker, Lars; Gätje, Regine; Solbach, Christine; Ahr, A.; Schmidt, Marcus; Holtrich, Uwe; Kaufmann, M.; Rody, Achim

doi:10.1007/s10549-009-0416-z

Cited by 51 publications

(56 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Raw GE values for each data set (n = 21) were normalized independently using RMA procedure. The Affymetrix probes 205225_at, 208305_at and 216836_s_at were chosen to represent ER, PR, and HER2 expression, respectively (64). For each data set, empirical expression distributions of ER, PR, and HER2 were analyzed using a 2-component Gaussian mixture distribution model and parameters were estimated by maximum likelihood optimization, using optim function (R statistical software, http:// www.r-project.org).…”

Section: Methodsmentioning

confidence: 99%

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Lehmann¹,

Bauer²,

Chen³

et al. 2011

View full text Add to dashboard Cite

Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted "driver" signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Lehmann¹,

Bauer²,

Chen³

et al. 2011

View full text Add to dashboard Cite

show abstract

“…A database of n = 3030 Affymetrix HG-U133A microarrays from treatment-naïve primary breast cancer samples was established, as we have recently described [27]. We included 238 of our own samples (datasets Frankfurt, Frankfurt-2, and Frankfurt-3) which have been described previously [28][29][30][31][32] as well as 2,792 samples from 22 different publicly available datasets (Supplementary Table S1) .…”

Section: Microarray Datamentioning

confidence: 99%

“…The clinical characteristics of the patients in the different datasets are also summarized in Supplementary Table S1. The ER status, PgR status, and HER2 status was determined by microarray as described previously [27]. The subset of 2,158 ER positive tumors was selected for this study from the complete database of 3,030 samples.…”

Section: Microarray Datamentioning

confidence: 99%

“…Thus, any local recurrence events are missing from these five datasets. However, as we have previously demonstrated no significant difference in relative survival was found when comparing the samples where only the DMFS endpoint was available to those using the RFS endpoint in this dataset [27]. Hence, we used in the context of this study either the RFS endpoint as disease-free survival (DFS) or the DMFS endpoint if RFS was not available.…”

Section: Statistical Analysesmentioning

confidence: 99%

See 1 more Smart Citation

Clinical relevance of the putative stem cell marker p63 in breast cancer

Hanker

Karn

Ruckhaeberle

et al. 2009

Breast Cancer Res Treat

View full text Add to dashboard Cite

P63 is a member of the p53 family. This protein is crucial for the maintenance of a stem cell population in the human epithelium and necessary for the normal development of all epithelial tissues including mammary glands. In normal breast tissue, the p63 seems to be a specific myoepithelial cell marker. P63 expression has been described in highly aggressive ER negative basal-like breast tumors. The value of p63 expression in ER positive disease is less clear. The expression levels of p63 mRNA by Affymetrix microarray analysis in a combined cohort of 2,158 ER positive breast cancers and its prognostic and predictive impact were analyzed. Tumor samples containing large amounts of benign breast tissue, which will interfere with p63 measurement, were excluded prior to the analysis. Survival analysis revealed a better prognosis of ER positive breast cancer expressing p63 (n = 410; P \ 0.036). No correlation of p63 with standard parameters was observed. In a subgroup analysis, endocrinetreated patients with high p63 expression showed a better prognosis than low p63 expression (P = 0.06; n = 186). In untreated patients, this effect was less clear (n = 148; P = 0.5). P63 is a positive prognostic factor in endocrinetreated ER positive breast cancer and might influence responsiveness to endocrine treatment. Thus, p63 could be helpful as a predictive factor for endocrine therapy.

show abstract

“…Validation of the LC-MS results was done by immunohistochemistry and Western blot analyses using an anti-glutathione S-transferase P1 (GSTP1) antibody (HPA019869, Sigma). Isolation of RNA and expression profiling using Affymetrix Human Genome U133A microarrays was performed as described previously [12][13][14]. All reported p values are two sided, and p values of less than 0.05 were considered to indicate a significant result.…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Proteomics – Differences in Protein Expression between Estrogen Receptor-Positive and -Negative Tumors Identified by Tandem Mass Tag Technology

Ruckhäberle

Karn²,

Hanker³

et al. 2010

Breast Care

Self Cite

View full text Add to dashboard Cite

Background: Proteomic analysis has become an effective tool in breast cancer research. In this study, we applied the new gel-free tandem mass tag (TMT) reference method for the first time in breast cancer. Materials and Methods: Proteomic analysis was used to compare 10 estrogen receptor (ER)-positive and 10 ER-negative samples. The results of the proteomic approach were validated by Western blot, immunohistochemistry and gene expression analysis. Results: 17 proteins with significant differences in expression were identified. 13 proteins were overexpressed in ER-negative tumors and 4 were overexpressed in ER-positive samples. All these proteins were characterized by relatively high cellular abundance. Conclusions: Our results demonstrate that the gel-free TMT approach allows the quantification of differences in protein expression levels. Further improvement of the sensitivity by subfractionation of the tissue should allow also the identification of low-abundance proteins and might lead to the use of this method in breast cancer research.

show abstract

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Cited by 51 publications

References 62 publications

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Clinical relevance of the putative stem cell marker p63 in breast cancer

Breast Cancer Proteomics – Differences in Protein Expression between Estrogen Receptor-Positive and -Negative Tumors Identified by Tandem Mass Tag Technology

Contact Info

Product

Resources

About