Biomedical subjects

Charles M Perou

Publications and source records attributed to Charles M Perou.

At least 19 recordsLinked to original sources

PubMed2026

Molecular evaluation of residual disease following neoadjuvant chemotherapy in triple-negative breast cancer CALGB 40603 (Alliance).

BACKGROUNDDespite therapeutic advances in early-stage triple-negative breast cancer (TNBC), residual disease (RD) following neoadjuvant therapy remains a key predictor of a worse prognosis and obstacle to improving patient outcomes.METHODSTo better characterize RD and identify survival-associated features, we performed comprehensive transcriptomic profiling of 340 pretreatment stage II/III TNBCs and 70 matched posttreatment RD samples from the randomized CALGB 40603 (Alliance) phase II clinical trial. To explore preclinical treatment strategies for RD, patient-derived xenograft (PDX) mouse models mimicking RD were treated with antibody-drug conjugates (ADCs).RESULTSOur study shows prognostic genomic features measured pretreatment may differ from prognostic features measured posttreatment from RD specimens. Patients with a genomic PAM50 subtype of basal-like in RD specimens had a poor survival outcome, and their matching pretreatment tumors were characterized by elevated chromosomal amplifications of oncogenic drivers and significantly reduced B and T cell expression features. Paired analyses of basal-like RD and matched pretreatment tumors revealed further lymphocyte depletion in RD, along with lower expression of MHC class I and interferon signaling, indicating an immune-cold RD microenvironment. Treatment of a basal-like and conventional chemotherapy-resistant PDX model, resembling basal-like RD, with sacituzumab govitecan or trastuzumab deruxtecan produced a marked antitumor response.CONCLUSIONRD biology differs from pretreatment tumors, with basal-like subtype RD following neoadjuvant chemotherapy being immune cold and associated with poor survival. Preclinical modeling suggests this high-risk group may benefit from adjuvant ADC therapy.TRIAL REGISTRATIONClinicalTrials.gov NCT00861705.FUNDINGNIH NCI U10CA180821 (Alliance for Clinical Trials in Oncology), NCI U24CA176171 (Alliance for Clinical Trials in Oncology), NCI UG1CA233373 (Alliance for Clinical Trials in Oncology), NCI Breast SPORE program P50-CA058223; Susan G. Komen SAC-160074; Breast Cancer Research Foundation BCRF-23-127; NIH NCI R01-CA229409; UNC LCCC Triple Negative Breast Cancer Center.

Humans↗

PubMed2025

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago. Among HER2-negative patients, a transformer-based model trained using a signature composed of estrogen-regulated genes, proliferation, apoptosis, and interferon response genes predicted pCR with an AUC of 0.794, outperforming models based on clinical features alone (AUC 0.704, p = 0.001), pathologist TIL assessment, and a model trained directly to predict response from I-SPY2 cases. Tertiles of this signature stratify patients into clinically relevant groups with increasing likelihood of complete response, with pCR rates ≥50% in the top tertile regardless of treatment or hormone receptor status. Additional transformer-based signature models predicted response to specific therapies (but not chemotherapy alone), including a HER2 signaling signature in IO-treated patients, and a claudin-low signature in bevacizumab treated patients. In HER2- cohorts with available gene expression data and histology, models trained on expression data performed similarly to digital histology predictions, but the combination of gene expression and histology outperformed histology alone. These findings suggest that histology-based GES provides additive information to RNA sequencing data and can inform precision treatment selection across breast cancer subtypes.

Journal Article↗

PubMed2007

Somatic sequence alterations in twenty-one genes selected by expression profile analysis of breast carcinomas.

INTRODUCTION: Genomic alterations have been observed in breast carcinomas that affect the capacity of cells to regulate proliferation, signaling, and metastasis. Re-sequence studies have investigated candidate genes based on prior genetic observations (changes in copy number or regions of genetic instability) or other laboratory observations and have defined critical somatic mutations in genes such as TP53 and PIK3CA. METHODS: We have extended the paradigm and analyzed 21 genes primarily identified by expression profiling studies, which are useful for breast cancer subtyping and prognosis. This study conducted a bidirectional re-sequence analysis of all exons and 5', 3', and evolutionarily conserved regions (spanning more than 16 megabases) in 91 breast tumor samples. RESULTS: Eighty-seven unique somatic alterations were identified in 16 genes. Seventy-eight were single base pair alterations, of which 23 were missense mutations; 55 were distributed across conserved intronic regions or the 5' and 3' regions. There were nine insertion/deletions. Because there is no a priori way to predict whether any one of the identified synonymous and noncoding somatic alterations disrupt function, analysis unique to each gene will be required to establish whether it is a tumor suppressor gene or whether there is no effect. In five genes, no somatic alterations were observed. CONCLUSION: The study confirms the value of re-sequence analysis in cancer gene discovery and underscores the importance of characterizing somatic alterations across genes that are related not only by function, or functional pathways, but also based upon expression patterns.

Breast Neoplasms↗

PubMed2006

Gene expression patterns associated with p53 status in breast cancer.

BACKGROUND: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). METHODS: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. RESULTS: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. CONCLUSION: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.

Base Sequence↗

PubMed2006

Concordance among gene-expression-based predictors for breast cancer.

BACKGROUND: Gene-expression-profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity. METHODS: To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression-based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen). RESULTS: We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor-negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification. CONCLUSIONS: Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes.

Analysis of Variance↗

PubMed2006

Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study.

CONTEXT: Gene expression analysis has identified several breast cancer subtypes, including basal-like, human epidermal growth factor receptor-2 positive/estrogen receptor negative (HER2+/ER-), luminal A, and luminal B. OBJECTIVES: To determine population-based distributions and clinical associations for breast cancer subtypes. DESIGN, SETTING, AND PARTICIPANTS: Immunohistochemical surrogates for each subtype were applied to 496 incident cases of invasive breast cancer from the Carolina Breast Cancer Study (ascertained between May 1993 and December 1996), a population-based, case-control study that oversampled premenopausal and African American women. Subtype definitions were as follows: luminal A (ER+ and/or progesterone receptor positive [PR+], HER2-), luminal B (ER+ and/or PR+, HER2+), basal-like (ER-, PR-, HER2-, cytokeratin 5/6 positive, and/or HER1+), HER2+/ER- (ER-, PR-, and HER2+), and unclassified (negative for all 5 markers). MAIN OUTCOME MEASURES: We examined the prevalence of breast cancer subtypes within racial and menopausal subsets and determined their associations with tumor size, axillary nodal status, mitotic index, nuclear pleomorphism, combined grade, p53 mutation status, and breast cancer-specific survival. RESULTS: The basal-like breast cancer subtype was more prevalent among premenopausal African American women (39%) compared with postmenopausal African American women (14%) and non-African American women (16%) of any age (P<.001), whereas the luminal A subtype was less prevalent (36% vs 59% and 54%, respectively). The HER2+/ER- subtype did not vary with race or menopausal status (6%-9%). Compared with luminal A, basal-like tumors had more TP53 mutations (44% vs 15%, P<.001), higher mitotic index (odds ratio [OR], 11.0; 95% confidence interval [CI], 5.6-21.7), more marked nuclear pleomorphism (OR, 9.7; 95% CI, 5.3-18.0), and higher combined grade (OR, 8.3; 95% CI, 4.4-15.6). Breast cancer-specific survival differed by subtype (P<.001), with shortest survival among HER2+/ER- and basal-like subtypes. CONCLUSIONS: Basal-like breast tumors occurred at a higher prevalence among premenopausal African American patients compared with postmenopausal African American and non-African American patients in this population-based study. A higher prevalence of basal-like breast tumors and a lower prevalence of luminal A tumors could contribute to the poor prognosis of young African American women with breast cancer.

Adult↗

PubMed2006

The molecular portraits of breast tumors are conserved across microarray platforms.

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.

Breast Neoplasms↗

PubMed2006

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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 Neoplasms↗

PubMed2006

Evaluating the comparability of gene expression in blood and brain.

The availability of an accessible tissue whose gene expression profile is similar to more inaccessible CNS tissues has the potential to advance research in neuropsychiatric disorders. We conducted secondary data analysis of transcriptional profiling of 79 human tissues for 33,698 genes using the Affymetrix U133A microarray augmented with a custom microarray (Affymetrix GNF1H), which were produced by the Genomics Institute of the Novartis Research Foundation (http://symatlas.gnf.org). Our analyses suggested that: (a) on a transcriptome level, whole blood shares significant gene expression similarities with multiple CNS tissues; (b) the median non-parametric correlation between transcripts present in both whole blood and CNS was around 0.5; (c) this correlation of 0.5 was intermediate relative to all tissues in the Novartis data set--less than for the maximum achievable value of 0.85, less than a set of immune tissues (0.64), comparable to a heterogeneous set of somatic tissues (0.57) but greater than muscle (0.48) and peripheral nervous system tissues (0.36); (d) about half of a set of candidate genes relevant to schizophrenia were expressed in both whole blood and prefrontal cortex; and (e) the expression levels of many classes of biologically relevant processes were not significantly different between whole blood and prefrontal cortex. These analyses suggest that gene expression in whole blood is neither perfectly correlated and useful nor perfectly uncorrelated and useless with gene expression in multiple brain tissues. This suggests that the cautious and thoughtful use of peripheral gene expression may be a useful surrogate for gene expression in the CNS when it has been determined that the relevant gene is expressed in both.

Brain↗

PubMed2006

Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers.

PURPOSE: 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. MATERIALS AND METHODS: 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. CONCLUSION: 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.

Breast Neoplasms↗

PubMed2006

Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma.

Microarray profiling of invasive breast carcinomas has identified five distinct subtypes of tumors (luminal A, luminal B, normal breast-like, HER2 overexpressing, and basal-like) that are associated with different clinical outcomes. The basal-like subtype is associated with poor clinical outcomes and is the subtype observed in BRCA1-related breast cancers. The aim of this study was to characterize the histologic and immunophenotypic properties of breast basal-like carcinomas that were first positively identified using DNA microarray analysis. Detailed histologic review was performed on 56 tumors with known microarray profiles (23 basal-like, 23 luminal, and 12 HER2+). Immunohistochemistry for estrogen receptor (ER), HER2, EGFR, smooth muscle actin (SMA), p63, CD10, cytokeratin 5/6, cytokeratin 8/18, and vimentin was performed on 18 basal-like, 16 luminal, and 12 HER2+ tumors. The basal-like tumors were grade 3 ductal/NOS (21/23) or metaplastic (2/23) carcinomas that frequently showed geographic necrosis (17/23), a pushing border of invasion (14/23), and a stromal lymphocytic response (13/23). Most basal-like tumors showed immunoreactivity for vimentin (17/18), luminal cytokeratin 8/18 (15/18), EGFR (13/18), and cytokeratin 5/6 (11/18), while positivity for the myoepithelial markers SMA (4/18), p63 (4/18) and CD10 (2/18) was infrequent. All basal-like tumors tested were ER- and HER2-. Morphologic features significantly associated with the basal-like subtype included markedly elevated mitotic count (P<0.0001), geographic tumor necrosis (P=0.0003), pushing margin of invasion (P=0.0001), and stromal lymphocytic response (P=0.01). The most consistent immunophenotype seen in the basal-like tumors was negativity for ER and HER2, and positivity for vimentin, EGFR, cytokeratin 8/18, and cytokeratin 5/6. The infrequent expression of myoepithelial markers in basal-like carcinomas does not support a direct myoepithelial cell derivation of these tumors. These findings should further assist in the identification of basal-like carcinomas in clinical specimens, facilitating treatment and epidemiologic studies of this tumor subtype.

Actins↗

PubMed2006

Common markers of proliferation.

When normal tissue and tumour samples are compared by microarray analysis, the biggest differences most often occur in the expression levels of genes that control cell proliferation. However, this difference is detected whenever mRNA samples that are taken from two cell populations with different proliferation rates are compared. Although the exact genes that comprise this 'proliferation signature' often differ, they are almost always genes that are involved in the fundamental process of cell proliferation. Can the proliferation signature be used to improve our understanding of the cell cycle and cancer pathogenesis, as well as being used as a biomarker for cancer diagnosis and prognosis?

Biomarkers, Tumor↗

PubMed2006

Gene expression profiles do not consistently predict the clinical treatment response in locally advanced breast cancer.

Neoadjuvant treatment offers an opportunity to correlate molecular variables to treatment response and to explore mechanisms of drug resistance in vivo. Here, we present a statistical analysis of large-scale gene expression patterns and their relationship to response following neoadjuvant chemotherapy in locally advanced breast cancers. We analyzed cDNA expression data from 81 tumors from two patient series, one treated with doxorubicin alone (51) and the other treated with 5-fluorouracil and mitomycin (30), and both were previously studied for correlations between TP53 status and response to therapy. We observed a low frequency of progressive disease within the luminal A subtype from both series (2 of 36 versus 13 of 45 patients; P = 0.0089) and a high frequency of progressive disease among patients with luminal B type tumors treated with doxorubicin (5 of 8 patients; P = 0.0078); however, aside from these two observations, no other consistent associations between response to chemotherapy and tumor subtype were observed. These specific associations could possibly be explained by covariance with TP53 mutation status, which also correlated with tumor subtype. Using supervised analysis, we could not uncover a gene profile that could reliably (>70% accuracy and specificity) predict response to either treatment regimen.

Adult↗

PubMed2006

AlphaB-crystallin is a novel oncoprotein that predicts poor clinical outcome in breast cancer.

Recent gene profiling studies have identified a new breast cancer subtype, the basal-like group, which expresses genes characteristic of basal epithelial cells and is associated with poor clinical outcomes. However, the genes responsible for the aggressive behavior observed in this group are largely unknown. Here we report that the small heat shock protein alpha-basic-crystallin (alphaB-crystallin) was commonly expressed in basal-like tumors and predicted poor survival in breast cancer patients independently of other prognostic markers. We also demonstrate that overexpression of alphaB-crystallin transformed immortalized human mammary epithelial cells (MECs). In 3D basement membrane culture, alphaB-crystallin overexpression induced luminal filling and other neoplastic-like changes in mammary acini, while silencing alphaB-crystallin by RNA interference inhibited these abnormalities. alphaB-Crystallin overexpression also induced EGF- and anchorage-independent growth, increased cell migration and invasion, and constitutively activated the MAPK kinase/ERK (MEK/ERK) pathway. Moreover, the transformed phenotype conferred by alphaB-crystallin was suppressed by MEK inhibitors. In addition, immortalized human MECs overexpressing alphaB-crystallin formed invasive mammary carcinomas in nude mice that recapitulated aspects of human basal-like breast tumors. Collectively, our results indicate that alphaB-crystallin is a novel oncoprotein expressed in basal-like breast carcinomas that independently predicts shorter survival. Our data also implicate the MEK/ERK pathway as a potential therapeutic target for these tumors.

Breast Neoplasms↗

PubMed2006

A genome-wide screen for promoter methylation in lung cancer identifies novel methylation markers for multiple malignancies.

BACKGROUND: Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The "rules" governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype would identify pathways important as therapeutic targets. METHODS AND FINDINGS: In an effort to identify new cancer-specific methylation markers, we employed a high-throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5' CpG islands, are induced from undetectable levels by 5-aza-2'-deoxycytidine in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (n = 20) and adjacent nonmalignant tissue (n = 20) showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or peripheral blood cells. We studied the eight most frequently and specifically methylated genes from our lung cancer dataset in breast cancer (n = 37), colon cancer (n = 24), and prostate cancer (n = 24) along with counterpart nonmalignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors. CONCLUSIONS: By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent targets for early detection screening or therapeutic intervention.

Adenocarcinoma↗

PubMed2006

siRNA: a potential tool for future breast cancer therapy?

The siRNA technology is a promising research tool for use in functional genomics, and it also shows potential for use in future therapy. Thus far, siRNAs have been used to specifically silence disease-associated alleles in animal models. The technology is still quite new, and the biological mechanisms underlying RNAi and siRNA-mediated knockdown of gene expression are not yet fully understood. The main issues when siRNAs are designed are efficiency and specificity, and it is of great importance to consider possible off-target effects in the siRNA design. One major challenge in siRNA-based therapy is the development of systems for efficient delivery to the target cells. A large number of tools have, over the last few years, been designed for the delivery of DNA and RNA for gene therapy, and extensive efforts are now placed into developing clinical applications of siRNAs in a range of human diseases, including breast cancer.

Breast Neoplasms↗

PubMed2005

Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer.

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.

Breast Neoplasms↗

PubMed2005

Breast cancer molecular subtypes respond differently to preoperative chemotherapy.

PURPOSE: 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. EXPERIMENTAL DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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.

Adult↗