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ABSTRACT: RNA expression profiles are increasingly used to diagnose and classify disease, based on expression patterns of as many as several thousand RNAs. To ensure quality of expression profiling services in clinical settings, a standard operating procedure incorporates multiple quality indicators and controls, beginning with preanalytic specimen preparation and proceeding thorough analysis, interpretation, and reporting. Before testing, histopathological examination of each cellular specimen, along with optional cell enrichment procedures, ensures adequacy of the input tissue. Other tactics include endogenous controls to evaluate adequacy of RNA and exogenous or spiked controls to evaluate run- and patient-specific performance of the test system, respectively. Unique aspects of quality assurance for array-based tests include controls for the pertinent outcome signatures that often supersede controls for each individual analyte, built-in redundancy for critical analytes or biochemical pathways, and software-supported scrutiny of abundant data by a laboratory physician who interprets the findings in a manner facilitating appropriate medical intervention. Access to high-quality reagents, instruments, and software from commercial sources promotes standardization and adoption in clinical settings, once an assay is vetted in validation studies as being analytically sound and clinically useful. Careful attention to the well-honed principles of laboratory medicine, along with guidance from government and professional groups on strategies to preserve RNA and manage large data sets, promotes clinical-grade assay performance.
The Journal of molecular diagnostics: JMD 01/2012; 14(1):1-11. · 3.48 Impact Factor
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ABSTRACT: Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification.
To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors.
Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability.
Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.
BMC Medical Genomics 06/2011; 4:54. · 3.69 Impact Factor
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ABSTRACT: RNA profiling is increasingly used to predict drug response, dose, or toxicity based on analysis of drug pharmacokinetic or pharmacodynamic pathways. Before implementing multiplexed RNA arrays in clinical practice, validation studies are carried out to demonstrate sufficient evidence of analytic and clinical performance, and to establish an assay protocol with quality assurance measures. Pathologists assure quality by selecting input tissue and by interpreting results in the context of the input tissue as well as the technologies that were used and the clinical setting in which the test was ordered. A strength of RNA profiling is the array-based measurement of tens to thousands of RNAs at once, including redundant tests for critical analytes or pathways to promote confidence in test results. Instrument and reagent manufacturers are crucial for supplying reliable components of the test system. Strategies for quality assurance include careful attention to RNA preservation and quality checks at pertinent steps in the assay protocol, beginning with specimen collection and proceeding through the various phases of transport, processing, storage, analysis, interpretation, and reporting. Specimen quality is checked by probing housekeeping transcripts, while spiked and exogenous controls serve as a check on analytic performance of the test system. Software is required to manipulate abundant array data and present it for interpretation by a laboratory physician who reports results in a manner facilitating therapeutic decision-making. Maintenance of the assay requires periodic documentation of personnel competency and laboratory proficiency. These strategies are shepherding genomic arrays into clinical settings to provide added value to patients and to the larger health care system.
Pharmacogenomics and Personalized Medicine 01/2011; 4:95-107.
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Zhiyuan Hu,
Cheng Fan,
Chad Livasy,
Xiaping He,
Daniel S Oh,
Matthew G Ewend,
Lisa A Carey,
Subbaya Subramanian,
Robert West,
Francis Ikpatt,
Olufunmilayo I Olopade,
Matt van de Rijn,
Charles M Perou
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ABSTRACT: Tumor metastases pose the greatest threat to a patient's survival, and thus, understanding the biology of disseminated cancer cells is critical for developing effective therapies.
Microarrays and immunohistochemistry were used to analyze primary breast tumors, regional (lymph node) metastases, and distant metastases in order to identify biological features associated with distant metastases.
When compared with each other, primary tumors and regional metastases showed statistically indistinguishable gene expression patterns. Supervised analyses comparing patients with distant metastases versus primary tumors or regional metastases showed that the distant metastases were distinct and distinguished by the lack of expression of fibroblast/mesenchymal genes, and by the high expression of a 13-gene profile (that is, the 'vascular endothelial growth factor (VEGF) profile') that included VEGF, ANGPTL4, ADM and the monocarboxylic acid transporter SLC16A3. At least 8 out of 13 of these genes contained HIF1alpha binding sites, many are known to be HIF1alpha-regulated, and expression of the VEGF profile correlated with HIF1alpha IHC positivity. The VEGF profile also showed prognostic significance on tests of sets of patients with breast and lung cancer and glioblastomas, and was an independent predictor of outcomes in primary breast cancers when tested in models that contained other prognostic gene expression profiles and clinical variables.
These data identify a compact in vivo hypoxia signature that tends to be present in distant metastasis samples, and which portends a poor outcome in multiple tumor types.This signature suggests that the response to hypoxia includes the ability to promote new blood and lymphatic vessel formation, and that the dual targeting of multiple cell types and pathways will be needed to prevent metastatic spread.
BMC Medicine 04/2009; 7:9. · 6.03 Impact Factor
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Joel S Parker,
Michael Mullins,
Maggie C U Cheang,
Samuel Leung,
David Voduc,
Tammi Vickery,
Sherri Davies,
Christiane Fauron,
Xiaping He, Zhiyuan Hu,
John F Quackenbush,
Inge J Stijleman,
Juan Palazzo,
J S Marron,
Andrew B Nobel,
Elaine Mardis,
Torsten O Nielsen,
Matthew J Ellis,
Charles M Perou,
Philip S Bernard
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ABSTRACT: PURPOSE To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. RESULTS: The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
Journal of Clinical Oncology 03/2009; 27(8):1160-7. · 18.37 Impact Factor
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Rajendra Bhati,
Cam Patterson,
Chad A Livasy,
Cheng Fan,
David Ketelsen, Zhiyuan Hu,
Evangeline Reynolds,
Catherine Tanner,
Dominic T Moore,
Franco Gabrielli,
Charles M Perou,
Nancy Klauber-DeMore
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ABSTRACT: A detailed understanding of the assortment of genes that are expressed in breast tumor vessels is needed to facilitate the development of novel, molecularly targeted anti-angiogenic agents for breast cancer therapies. Rapid immunohistochemistry using factor VIII-related antibodies was performed on sections of frozen human luminal-A breast tumors (n = 5) and normal breast (n = 5), followed by laser capture microdissection of vascular cells. RNA was extracted and amplified, and fluorescently labeled cDNA was synthesized and hybridized to 44,000-element long-oligonucleotide DNA microarrays. Statistical analysis of microarray was used to compare differences in gene expression between tumor and normal vascular cells, and Expression Analysis Systematic Explorer was used to determine enrichment of gene ontology categories. Protein expression of select genes was confirmed using immunohistochemistry. Of the 1176 genes that were differentially expressed between tumor and normal vascular cells, 55 had a greater than fourfold increase in expression level. The extracellular matrix gene ontology category was increased while the ribosome gene ontology category was decreased. Fibroblast activation protein, secreted frizzled-related protein 2, Janus kinase 3, and neutral sphingomyelinase 2 proteins localized to breast tumor endothelium as assessed by immunohistochemistry, showing significantly greater staining compared with normal tissue. These tumor endothelial marker proteins also exhibited increased expression in breast tumor vessels compared with that in normal tissues. Therefore, these genetic markers may serve as potential targets for the development of angiogenesis inhibitors.
American Journal Of Pathology 06/2008; 172(5):1381-90. · 4.89 Impact Factor
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Jason I Herschkowitz,
Karl Simin,
Victor J Weigman,
Igor Mikaelian,
Jerry Usary, Zhiyuan Hu,
Karen E Rasmussen,
Laundette P Jones,
Shahin Assefnia,
Subhashini Chandrasekharan, [......],
Powel H Brown,
Jeffrey E Green,
Levy Kopelovich,
Priscilla A Furth,
Juan P Palazzo,
Olufunmilayo I Olopade,
Philip S Bernard,
Gary A Churchill,
Terry Van Dyke,
Charles M Perou
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ABSTRACT: Although numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors.
Unsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain. However, in each of four other models (TgWAP-T121, TgMMTV-Wnt1, Brca1Co/Co;TgMMTV-Cre;p53+/- and DMBA-induced), tumors with a variety of histologies and expression profiles developed. In many models, similarities to human breast tumors were recognized, including proliferation and human breast tumor subtype signatures. Significantly, tumors of several models displayed characteristics of human basal-like breast tumors, including two models with induced Brca1 deficiencies. Tumors of other murine models shared features and trended towards significance of gene enrichment with human luminal tumors; however, these murine tumors lacked expression of estrogen receptor (ER) and ER-regulated genes. TgMMTV-Neu tumors did not have a significant gene overlap with the human HER2+/ER- subtype and were more similar to human luminal tumors.
Many of the defining characteristics of human subtypes were conserved among the mouse models. Although no single mouse model recapitulated all the expression features of a given human subtype, these shared expression features provide a common framework for an improved integration of murine mammary tumor models with human breast tumors.
Genome biology 02/2007; 8(5):R76. · 6.63 Impact Factor
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ABSTRACT: The prognosis of a patient with estrogen receptor (ER) and/or progesterone receptor (PR) -positive breast cancer can be highly variable. Therefore, we developed a gene expression-based outcome predictor for ER+ and/or PR+ (ie, luminal) breast cancer patients using biologic differences among these tumors.
The ER+ MCF-7 breast cancer cell line was treated with 17beta-estradiol to identify estrogen-regulated genes. These genes were used to develop an outcome predictor on a training set of 65 luminal epithelial primary breast carcinomas. The outcome predictor was then validated on three independent published data sets. Results The estrogen-induced gene set identified in MCF-7 cells was used to hierarchically cluster a 65 tumor training set into two groups, which showed significant differences in survival (P = .0004). Supervised analyses identified 822 genes that optimally defined these two groups, with the poor-prognosis group IIE showing high expression of cell proliferation and antiapoptosis genes. The good prognosis group IE showed high expression of estrogen- and GATA3-regulated genes. Mean expression profiles (ie, centroids) created for each group were applied to ER+ and/or PR+ tumors from three published data sets. For all data sets, Kaplan-Meier survival analyses showed significant differences in relapse-free and overall survival between group IE and IIE tumors. Multivariate Cox analysis of the largest test data set showed that this predictor added significant prognostic information independent of standard clinical predictors and other gene expression-based predictors.
This study provides new biologic information concerning differences within hormone receptor-positive breast cancers and a means of predicting long-term outcomes in tamoxifen-treated patients.
Journal of Clinical Oncology 05/2006; 24(11):1656-64. · 18.37 Impact Factor
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Zhiyuan Hu,
Cheng Fan,
Daniel S Oh,
J S Marron,
Xiaping He,
Bahjat F Qaqish,
Chad Livasy,
Lisa A Carey,
Evangeline Reynolds,
Lynn Dressler, [......],
Laurent Perreard,
Edward Nelson,
Mary Mone,
Heidi Hansen,
Michael Mullins,
John F Quackenbush,
Matthew J Ellis,
Olufunmilayo I Olopade,
Philip S Bernard,
Charles M Perou
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ABSTRACT: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.
A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.
This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.
BMC Genomics 02/2006; 7:96. · 4.07 Impact Factor
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Laurent Perreard,
Cheng Fan,
John F Quackenbush,
Michael Mullins,
Nicholas P Gauthier,
Edward Nelson,
Mary Mone,
Heidi Hansen,
Saundra S Buys,
Karen Rasmussen, [......],
Donna Dreher,
Rhonda Walters,
Joel Parker, Zhiyuan Hu,
Xiaping He,
Juan P Palazzo,
Olufunmilayo I Olopade,
Aniko Szabo,
Charles M Perou,
Philip S Bernard
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ABSTRACT: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation.
Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype.
We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation.
A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.
Breast cancer research: BCR 02/2006; 8(2):R23. · 5.24 Impact Factor
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Zhiyuan Hu,
Cheng Fan,
Daniel Oh,
JS Marron,
Xiaping He,
Bahjat Qaqish,
Chad Livasy,
Lisa Carey,
Evangeline Reynolds,
Lynn Dressler, [......],
Laurent Perreard,
Edward Nelson,
Mary Mone,
Heidi Hansen,
Michael Mullins,
John Quackenbush,
Matthew Ellis,
Olufunmilayo Olopade,
Philip Bernard,
Charles Perou
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ABSTRACT: Abstract
Background
Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.
Results
A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.
Conclusion
This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.
BMC Genomics. 01/2006;
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ABSTRACT: Microarray analysis has been shown to improve risk stratification of breast cancer. Breast tumors analyzed by hierarchical clustering of expression patterns of "intrinsic" genes have been reported to subdivide into at least four molecular subtypes that are associated with distinct patient outcomes. Using a supervised method, a 70-gene expression profile has been identified that predicts the later appearance or absence of clinical metastasis in young breast cancer patients. Here, we show that distant metastases display both the same molecular breast cancer subtype as well as the 70-gene prognosis signature as their primary tumors. Our results suggest that the capacity to metastasize is an inherent feature of most breast cancers. Furthermore, our data imply that poor prognosis breast carcinomas classified either by the intrinsic gene set or the 70 prognosis genes represent distinct disease entities that seem sustained throughout the metastatic process.
Cancer Research 11/2005; 65(20):9155-8. · 7.86 Impact Factor
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ABSTRACT: Recently, long oligonucleotide (60- to 70-mer) microarrays for two-color experiments have been developed and are gaining widespread use. In addition, when there is limited availability of mRNA from tissue sources, RNA amplification can and is being used to produce sufficient quantities of cRNA for microarray hybridization. Taking advantage of the selective degradation of RNA under alkaline conditions, we have developed a method to "strip" glass-based oligonucleotide microarrays that use fluorescent RNA in the hybridization, while leaving the DNA oligonucleotide probes intact and usable for a second experiment. Replicate microarray experiments conducted using stripped arrays showed high reproducibility, however, we found that arrays could only be stripped and reused once without compromising data quality. The intraclass correlation (ICC) between a virgin array and a stripped array hybridized with the same sample showed a range of 0.90-0.98, which is comparable to the ICC of two virgin arrays hybridized with the same sample. Using this method, once-stripped oligonucleotide microarrays are usable, reliable, and help to reduce costs.
BioTechniques 02/2005; 38(1):121-4. · 2.67 Impact Factor
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Torsten O Nielsen,
Forrest D Hsu,
Kristin Jensen,
Maggie Cheang,
Gamze Karaca, Zhiyuan Hu,
Tina Hernandez-Boussard,
Chad Livasy,
Dave Cowan,
Lynn Dressler,
Lars A Akslen,
Joseph Ragaz,
Allen M Gown,
C Blake Gilks,
Matt van de Rijn,
Charles M Perou
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ABSTRACT: Expression profiling studies classified breast carcinomas into estrogen receptor (ER)+/luminal, normal breast-like, HER2 overexpressing, and basal-like groups, with the latter two associated with poor outcomes. Currently, there exist clinical assays that identify ER+/luminal and HER2-overexpressing tumors, and we sought to develop a clinical assay for breast basal-like tumors.
To identify an immunohistochemical profile for breast basal-like tumors, we collected a series of known basal-like tumors and tested them for protein patterns that are characteristic of this subtype. Next, we examined the significance of these protein patterns using tissue microarrays and evaluated the prognostic significance of these findings.
Using a panel of 21 basal-like tumors, which was determined using gene expression profiles, we saw that this subtype was typically immunohistochemically negative for estrogen receptor and HER2 but positive for basal cytokeratins, HER1, and/or c-KIT. Using breast carcinoma tissue microarrays representing 930 patients with 17.4-year mean follow-up, basal cytokeratin expression was associated with low disease-specific survival. HER1 expression was observed in 54% of cases positive for basal cytokeratins (versus 11% of negative cases) and was associated with poor survival independent of nodal status and size. c-KIT expression was more common in basal-like tumors than in other breast cancers but did not influence prognosis.
A panel of four antibodies (ER, HER1, HER2, and cytokeratin 5/6) can accurately identify basal-like tumors using standard available clinical tools and shows high specificity. These studies show that many basal-like tumors express HER1, which suggests candidate drugs for evaluation in these patients.
Clinical Cancer Research 09/2004; 10(16):5367-74. · 7.74 Impact Factor
