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05/2007: pages 257-275;
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Roman Rouzier,
Charles M Perou,
W Fraser Symmans,
Nuhad Ibrahim,
Massimo Cristofanilli,
Keith Anderson,
Kenneth R Hess,
James Stec, Mark Ayers,
Peter Wagner,
Paolo Morandi,
Chang Fan,
Islam Rabiul,
Jeffrey S Ross,
Gabriel N Hortobagyi,
Lajos Pusztai
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ABSTRACT: Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy.
Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported "breast intrinsic" gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class.
The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61 genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor-negative subtypes.
The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel- and doxorubicin-containing preoperative chemotherapy than the luminal and normal-like cancers.
Clinical Cancer Research 09/2005; 11(16):5678-85. · 7.74 Impact Factor
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James Stec,
Jing Wang,
Kevin Coombes, Mark Ayers,
Sebastian Hoersch,
David L Gold,
Jeffrey S Ross,
Kenneth R Hess,
Stephen Tirrell,
Gerald Linette,
Gabriel N Hortobagyi,
W Fraser Symmans,
Lajos Pusztai
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ABSTRACT: We examined how well differentially expressed genes and multigene outcome classifiers retain their class-discriminating values when tested on data generated by different transcriptional profiling platforms. RNA from 33 stage I-III breast cancers was hybridized to both Affymetrix GeneChip and Millennium Pharmaceuticals cDNA arrays. Only 30% of all corresponding gene expression measurements on the two platforms had Pearson correlation coefficient r >or= 0.7 when UniGene was used to match probes. There was substantial variation in correlation between different Affymetrix probe sets matched to the same cDNA probe. When cDNA and Affymetrix probes were matched by basic local alignment tool (BLAST) sequence identity, the correlation increased substantially. We identified 182 genes in the Affymetrix and 45 in the cDNA data (including 17 common genes) that accurately separated 91% of cases in supervised hierarchical clustering in each data set. Cross-platform testing of these informative genes resulted in lower clustering accuracy of 45 and 79%, respectively. Several sets of accurate five-gene classifiers were developed on each platform using linear discriminant analysis. The best 100 classifiers showed average misclassification error rate of 2% on the original data that rose to 19.5% when tested on data from the other platform. Random five-gene classifiers showed misclassification error rate of 33%. We conclude that multigene predictors optimized for one platform lose accuracy when applied to data from another platform due to missing genes and sequence differences in probes that result in differing measurements for the same gene.
Journal of Molecular Diagnostics 09/2005; 7(3):357-67. · 3.58 Impact Factor
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Roman Rouzier,
Radhika Rajan,
Peter Wagner,
Kenneth R Hess,
David L Gold,
James Stec, Mark Ayers,
Jeffrey S Ross,
Peter Zhang,
Thomas A Buchholz,
Henry Kuerer,
Marjorie Green,
Banu Arun,
Gabriel N Hortobagyi,
W Fraser Symmans,
Lajos Pusztai
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ABSTRACT: Breast cancers show variable sensitivity to paclitaxel. There is no diagnostic test to identify tumors that are sensitive to this drug. We used U133A chips to identify genes that are associated with pathologic complete response (pCR) to preoperative paclitaxel-containing chemotherapy in stage I-III breast cancer (n = 82). Tau was the most differentially expressed gene. Tumors with pCR had significantly lower (P < 0.3 x 10(-5)) mRNA expression. Tissue arrays from 122 independent but similarly treated patients were used for validation by immunohistochemistry. Seventy-four percent of pCR cases were tau protein negative; the odds ratio for pCR was 3.7 (95% confidence interval, 1.6-8.6; P = 0.0013). In multivariate analysis, nuclear grade (P < 0.01), age <50 (P = 0.03), and tau-negative status (P = 0.04) were independent predictors of pCR. Small interfering RNA experiments were performed to examine whether down-regulation of tau increases sensitivity to chemotherapy in vitro. Down-regulation of tau increased sensitivity of breast cancer cells to paclitaxel but not to epirubicin. Tubulin polymerization assay was used to assess whether tau modulates binding of paclitaxel to tubulin. Preincubation of tubulin with tau resulted in decreased paclitaxel binding and reduced paclitaxel-induced microtubule polymerization. These data suggest that low tau expression renders microtubules more vulnerable to paclitaxel and makes breast cancer cells hypersensitive to this drug. Low tau expression may be used as a marker to select patients for paclitaxel therapy. Inhibition of tau function might be exploited as a therapeutic strategy to increase sensitivity to paclitaxel.
Proceedings of the National Academy of Sciences 07/2005; 102(23):8315-20. · 9.68 Impact Factor
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ABSTRACT: In this second part of the two-part review of breast cancer biomarkers and molecular medicine, the first section will consider additional breast cancer prognostic factors, including oncogenes, tumor suppressor genes, cell adhesion molecules, invasion-associated proteins and proteases, hormone receptor proteins, drug resistance proteins, apoptosis regulators, transcription factors, telomerase, DNA repair and methylation and transcriptional profiling using high-density genomic microarrays. The second section will consider the prediction of therapy response using the techniques of pharmacogenetics and pharmacogenomics.
Expert Review of Molecular Diagnostics 04/2004; 4(2):169-88. · 4.86 Impact Factor
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ABSTRACT: The testing of newly diagnosed breast cancer specimens for HER-2/neu status has achieved "standard of practice" status for the management of breast cancer in the United States. The discussion as to the best method to determine HER-2/neu status in these samples continues, with the fluorescence in situ hybridization method gaining popularity owing to the recent evidence that it, in comparison with immunohistochemical analysis, might more accurately predict clinical responses to trastuzumab-based therapies. With trastuzumab achieving excellent results in the treatment of HER-2/neu-positive advanced disease and under extensive evaluation in major clinical trials for its potential efficacy when used at earlier clinical stages, the potential role(s) for HER-2/neu testing as a predictor of response to other therapies being resolved by large prospective clinical outcome studies, and the more convenient gene-based chromogenic in situ hybridization technique "waiting in the wings," the saga of HER-2/neu testing in breast cancer will continue to unfold over the next several years.
American Journal of Clinical Pathology 01/2004; 120 Suppl:S53-71. · 2.60 Impact Factor
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ABSTRACT: The first part of this two-part review of established and emerging breast cancer biomarkers and molecular diagnostics considers breast cancer predisposition, screening tests for diagnosis, diagnosis using small specimens and metastatic lesions, micrometastatic disease and breast cancer prognosis assessment. Prognostic factors covered in this review include: cytogenetic markers, DNA ploidy and S phase determination, cell proliferation markers, cell cycle regulators and growth factor measurements including epithelial growth factor receptor, HER-2/neu and a variety of other relevant molecules controlling proliferation, differentiation and angiogenesis. The first section of part two will continue the consideration of breast cancer prognostic factors including oncogenes, tumor suppressor genes, cell adhesion molecules, invasion-associated proteins and proteases, hormone receptor proteins, drug resistance proteins, apoptosis regulators, transcription factors, telomerase, DNA repair and methylation and transcriptional profiling using high-density genomic microarrays. The second section of part two will consider the prediction of therapy response using the techniques of pharmacogenetics and pharmacogenomics.
Expert Review of Molecular Diagnostics 10/2003; 3(5):573-85. · 4.86 Impact Factor
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Lajos Pusztai, Mark Ayers,
James Stec,
Edward Clark,
Kenneth Hess,
David Stivers,
Andrew Damokosh,
Nour Sneige,
Thomas A Buchholz,
Francisco J Esteva,
Banu Arun,
Massimo Cristofanilli,
Daniel Booser,
Marguerite Rosales,
Vicente Valero,
Constantine Adams,
Gabriel N Hortobagyi,
W Fraser Symmans
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ABSTRACT: The purpose of this study was to determine whether comprehensive transcriptional profiles (TPs) can be obtained from single-passage fine-needle aspirations (FNAs) of breast cancer and to explore whether profiles capture routine clinicopathological parameters. Experimental Design: Expression profiles were available on 38 patients with stage I-III breast cancer who underwent FNA at the time of diagnosis. [(33)P]dCTP-labeled cDNA probes were generated and hybridized to cDNA membrane microarrays that contained 30,000 human sequence clones, including 10,890 expressed sequence tags.
The median total RNA yield from the biopsies was 2 micro g (range, 1-25 micro g). The cellular composition of each biopsy was examined and, on average, 79% of the cells were cancer cells. TP was successfully performed on all 38 of the biopsies. Unsupervised complete-linkage hierarchical clustering with all of the biopsies revealed an association between estrogen receptor (ER) status and gene expression profiles. There was a strong correlation between ER status determined by TP and measured by routine immunohistochemistry (P = 0.001). A similar strong correlation was seen with HER-2 status determined by fluorescent in situ hybridization (P = 0.0002). Using the first 18 cases as the discovery set, we established a cutoff of 2.0 and 18.0 for ER and HER-2 mRNA levels, respectively, to distinguish clinically-negative from -positive cases. We also identified 105 genes (excluding the ER gene) the expression of which correlated highly with clinical ER status. Twenty tumors were used for prospective validation. HER-2 status was correctly identified in all 20 of the cases, based on HER-2 mRNA content detected by TP. ER status was correctly identified in 19 of 20 cases. When the marker set of 105 genes was used to prospectively predict ER status, TP-based classification correctly identified 9 of 10 of the ER-positive and 7 of 10 of the ER-negative tumors. We also explored supervised cluster analysis using various functional categories of genes, and we observed a clear separation between ER-negative and ER-positive tumors when genes involved in signal transduction were used for clustering.
These results demonstrate that comprehensive TP can be performed on FNA biopsies. TPs reliably measure conventional single-gene prognostic markers such as ER and HER-2. A complex pattern of genes (not including ER) can also be used to predict clinical ER status. These results demonstrate that needle biopsy-based diagnostic microarray tests may be developed that could capture conventional prognostic information but may also contain additional clinical information that cannot currently be measured with other methods.
Clinical Cancer Research 08/2003; 9(7):2406-15. · 7.74 Impact Factor
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W Fraser Symmans, Mark Ayers,
Edwin A Clark,
James Stec,
Kenneth R Hess,
Nour Sneige,
Thomas A Buchholz,
Savitri Krishnamurthy,
Nuhad K Ibrahim,
Aman U Buzdar,
Richard L Theriault,
Marguerite F M Rosales,
Eva S Thomas,
Karin M Gwyn,
Marjorie C Green,
Abdul R Syed,
Gabriel N Hortobagyi,
Lajos Pusztai
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ABSTRACT: Gene expression profiling should be applicable to needle biopsy samples if microarray technology is to become practically useful for clinical research or management of breast carcinoma. This study compared gene expression profiles derived from fine-needle aspiration biopsy (FNAB) and from core needle biopsy (CBX).
Total RNA was extracted from single FNAB and CBX samples. Corresponding pairs of FNAB and CBX were analyzed for similarity of gene expression profiles using cDNA microarrays that contain 30721 human sequences. A subset of genes that distinguished CBX samples from FNAB samples was evaluated in a larger group of needle biopsy samples and in a published genomic database derived from 78 sporadic breast carcinomas with known clinical outcome.
Sixty-eight patients with newly diagnosed breast carcinoma were included in the current study. Sixty-five patients underwent FNAB (17 had both FNAB and CBX) and 3 underwent CBX only. Extracted RNA was of suitable quality for hybridization in 46 (71%) FNABs and 15 (75%) CBXs. Total RNA yield in those samples was similar for single-pass FNAB (mean = 3.6 microg and median = 2.2 microg; n = 46) and CBX (mean = 2.8 microg and median = 2.0 microg; n = 15), with 1 microg or more of total RNA in all cases. Transcriptional profiling was performed successfully in all cases when it was attempted, in a total of 50 samples (38 FNABs and 12 CBXs), including matched FNAB and CBX samples from 10 patients. There were differences in gene expression profiles in 10 matched FNAB and CBX sample pairs. Genes that were expressed differently in CBX samples, compared with FNAB samples, were recognized as being predominantly from the endothelium, fibroblasts, myofibroblasts or smooth muscle, and histiocytes. Corresponding microscopic cell counts from FNABs demonstrated means of 80% tumor cells, 15% lymphocytes, and 5% stromal cells, whereas CBXs contained 50% tumor cells, 20% lymphocytes, and 30% stromal cells. Considering that CBXs are approximately six-fold richer in nonlymphoid stromal cells than FNABs and that CBXs differentially express a set of recognized stromal genes, the authors used these biopsies to define a transcriptional profile of breast carcinoma stroma. A set of 120 genes differentially expressed in CBXs was assessed independently in a published breast carcinoma genomic database to classify breast carcinomas based on stromal gene expression. Subgroups of tumors with low or high stromal signal were identified, but there was no correlation with the development of systemic metastases within 5 years.
Both FNAB and CBX yield a similar quality and quantity of total RNA and are suitable for cDNA microarray analyses in approximately 70-75% of single-pass samples. Transcriptional profiles from FNAB and CBX of the same tumor generally are similar and are driven by the tumor cell population. The authors concluded that each technique has relative advantages. The FNABs provide transcriptional profiles that are a purer representation of the tumor cell population, whereas transcriptional profiles from CBXs include more representation from nonlymphoid stromal elements. Selection of the preferred needle biopsy sampling technique for genomic studies of breast carcinomas should depend on whether variable stromal gene expression is desirable in the samples.
Cancer 07/2003; 97(12):2960-71. · 4.77 Impact Factor
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ABSTRACT: The HER-2/neu oncogene encodes a transmembrane tyrosine kinase receptor with extensive homology to the epidermal growth factor receptor. In this review, the association of HER-2/neu gene and protein abnormalities with prognosis and response to therapy with trastuzumab and to other therapies in breast cancer is presented. By considering a series of 80 published studies encompassing more than 25,000 patients, the relative advantages and disadvantages of Southern blotting, polymerase chain reaction amplification, and fluorescence in situ hybridization assays designed to detect HER-2/neu gene amplification are compared with HER-2/neu protein overexpression assays performed by immunohistochemical techniques applied to frozen and paraffin-embedded tissues and enzyme immunoassays performed on tumor cytosols. The significance of HER-2/neu overexpression in ductal carcinoma in situ and the HER-2/neu status in uncommon female breast conditions and male breast cancer are also considered. The role of HER-2/neu testing for the prediction of response to trastuzumab therapy in breast cancer is presented as well as its potential impact on responses to standard and newer hormonal therapies, cytotoxic chemotherapy, and radiation. The review also evaluates the status of serum-based testing for circulating HER-2/neu receptor protein and its ability to predict disease outcome and therapy response.
The Oncologist 02/2003; 8(4):307-25. · 3.91 Impact Factor
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ABSTRACT: DNA microarrays represent an important new tool to analyze human tissues. The technology enables investigators to measure the expression of several thousand mRNA species simultaneously in a biological specimen. This process, called transcriptional profiling, represents a technological breakthrough in the analysis of biological specimens. It may be used to screen for individual genes that are differentially expressed between normal and diseased tissues in the hope of finding novel targets for drug development or finding new single-gene markers of clinical outcome. Microarrays are also applied to learn about the complex biology of cancer by simultaneously monitoring interactions between hundreds of genes during experimental conditions in vitro or during therapy in vivo. Analysis of gene expression patterns may also be used as a classification tool to sort cancer into various clinically relevant subgroups that is not currently possible with other methods. The first clinically important applications of this technology will likely be its use as a tool to refine diagnosis and improve the accuracy of predictions of prognosis and response to therapy. DNA microarrays in several "proof-of-principle" experiments have demonstrated that they can predict important clinical outcomes, including outcomes that cannot currently be predicted with other methods, but the true clinical utility and the limits of this exciting new technology are yet to be established. This paper reviews the current methodology and applications of this technique as they relate to clinical oncology.
The Oncologist 02/2003; 8(3):252-8. · 3.91 Impact Factor
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Thomas A Buchholz,
David N Stivers,
James Stec, Mark Ayers,
Edward Clark,
Andrew Bolt,
Aysegul A Sahin,
W Fraser Symmans,
Kenneth R Hess,
Henry M Kuerer,
Vicente Valero,
Gabriel N Hortobagyi,
Lajos Pusztai
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ABSTRACT: The purpose of this study was to analyze global gene expression changes in serial tumor core biopsy specimens taken during neoadjuvant chemotherapy for primary breast cancer.
Core biopsy specimens from tumors were obtained before treatment and 24 and/or 48 hours after treatment from 21 women who were beginning chemotherapy for breast cancer. RNA was extracted, and radiolabeled complementary DNA was synthesized. The complementary DNA probes were hybridized to high-density microarray membranes that contained more than 25,000 human sequence clones. Hierarchical cluster analysis was used to compare the degree of similarity between expression profiles.
Twenty-five (45%) of the 56 available core specimens yielded sufficient quantity and quality RNA for microarray analysis. Microarray profiles were performed only on samples from patients with pretreatment and posttreatment specimens, resulting in serial data sets for five patients (14 specimens). The serial samples from individual patients clustered more closely than the samples taken from different patients. Analyses of the variance of individual gene expression showed that there were significantly fewer genes with fivefold differences in expression in an individual tumor at different times (average, 359 genes) versus pretreatment samples of different tumors (average, 732 genes). Patients with a good pathological response to treatment had gene patterns that clustered distinctly from those of poor responders. Significant transcriptional response occurred in all patients during therapy. Surprisingly, all patients had different genes change after chemotherapy, with no single gene having a significant expression change in all five patients.
This is the first report to show global gene expression changes during chemotherapy in a human solid tumor. Comprehensive gene expression profiles of more than 25,000 genes can be obtained from core biopsy specimens. A remarkable diversity in transcriptional response was observed for individual cases. Further data are needed to determine whether gene profiling can predict response to chemotherapy.
The Cancer Journal 8(6):461-8. · 3.26 Impact Factor
