Gene expression profiling for prediction of response to chemotherapy

Numerous genes whose expression is controlled by complex regulatory networks are involved in the development and progression of each cancer,and those genes will be the key factors for determining each characteristic of the tumor. The recent development of DNA microarray and related technologies provides an opportunity to perform more detailed characterization( profiling) of individual tumor cells. Indeed, the gene expression profile of a tumor provides a unique molecular portrait or signature that can be correlated with clinical behavior and drug responsiveness. The development of personalized( or customized) medicine and molecularly targeted drugs is enthusiastically awaited based on the results of research examining genomic diversity such as SNPs( single nucleotide polymorphisms), gene expression profiling with DNA microarrays, and analysis of protein expression and interactions.     

Gene expression profiling identifies molecular subtypes of inflammatory breast cancer

Breast cancer is a heterogeneous disease. Comprehensive gene expression profiles obtained using DNA microarrays have revealed previously indistinguishable subtypes of noninflammatory breast cancer (NIBC) related to different features of mammary epithelial biology and significantly associated with survival. Inflammatory breast cancer (IBC) is a rare, particular, and aggressive form of disease. Here we have investigated whether the five molecular subtypes described for NIBC (luminal A and B, basal, ERBB2 overexpressing, and normal breast-like) were also present in IBC. We monitored the RNA expression of approximately 8,000 genes in 83 breast tissue samples including 37 IBC, 44 NIBC, and 2 normal breast samples. Hierarchical clustering identified the five subtypes of breast cancer in both NIBC and IBC samples. These subtypes were highly similar to those defined in previous studies and associated with similar histoclinical features. The robustness of this classification was confirmed by the use of both alternative gene set and analysis method, and the results were corroborated at the protein level. Furthermore, we show that the differences in gene expression between NIBC and IBC and between IBC with and without pathologic complete response that we have recently reported persist in each subtype. Our results show that the expression signatures defining molecular subtypes of NIBC are also present in IBC. Obtained using different patient series and different microarray platforms, they reinforce confidence in the expression-based molecular taxonomy but also give evidence for its universality in breast cancer, independently of a specific clinical form.     

Gene expression profiling of breast cancer

Numerous genes are controlled by complex regulatory networks and involved in the development and progression of breast cancer, and these genes are the key factors determining each characteristic of the tumor. Gene expression profiling, a large scale analysis of gene expression, has created new possibilities for the molecular characterization of cancer. Systematic analysis of expression patterns of thousands of genes in tumor cells using DNA microarrays and correlation of these patterns to specific features of phenotypic variation may provide the basis for an improved taxonomy of cancer. These profiles have the potential to explain the genetic heterogeneity of breast cancer and allow treatment strategies to be planned in accordance with their probability of success in individual patients.     

Gene expression profiling in breast cancer

PURPOSE OF REVIEW: Gene expression profiling has highlighted the biologic heterogeneity of breast cancer and has begun to influence the ability of the medical community to individualize patient therapy. The review is intended to highlight the most important advances in the field over recent years with an emphasis on those most relevant to the practicing oncologist. RECENT FINDINGS: Two prognostic profiling assays, the Mammaprint and Oncotype Dx, are in phase III clinical trials designed to evaluate their contribution to therapeutic decision making. Predictive profiles for both chemotherapy and targeted therapy are also in development. In addition, application of genetic profiling techniques to a variety of tumor types is starting to identify those processes, like proliferation, that are integral to carcinogenesis as a whole. SUMMARY: The biologic heterogeneity of breast cancer has become clearer through genome-wide profiling technologies. Validation of the clinical utility of prognostic profiles may enable oncologists to better identify those patients whose prognosis justifies more intensive therapy, while predictive profiles may soon be able to determine which type of chemotherapy a patient should receive. In addition, profiling is starting to identify new therapeutic targets which will point the field of breast cancer oncology in new directions.     

Gene expression profiling of primary breast cancer

Gene expression profiling is a method to measure the expression of a large number of genes in tissue specimens simultaneously. This analytical technique is actively explored as an emerging diagnostic tool for breast cancer. An important assumption behind this research is that the constellation of multiple genes will be more predictive of clinical outcome than any single gene alone. Gene expression signatures were shown to predict prognosis of breast cancer as well as response to particular chemotherapy regimens. The first multigene predictor of prognosis after tamoxifen therapy is already commercially available in the United States. This article reviews recent advances in the clinical application of this technique to breast cancer.     

Gene expression profiling: Decoding breast cancer

Gene expression assays that are used in daily clinical practice for treating early breast cancer patients have been introduced in the clinic only recently. This review discusses the development of these arrays, summarizes the validation of those that are commercially available and indicates how the information provided by these assays can help in the care of patients. The review also provides an extensive overview of commercially available assays focusing on MammaPrint, the first and only assay for breast cancer management that has been cleared by the FDA.     

预测乳腺癌新辅助化疗疗效的多基因表达谱研究进展

新辅助治疗是在除外转移的情况下,在局部治疗前(手术或放疗)进行的全身药物治疗.乳腺癌的异质性决定了乳腺癌对药物治疗的敏感性存在较大的差异,用多基因表达谱模型来预测新辅助治疗疗效,为乳腺癌个体化治疗方案的选择提供了新的途径.文章简述预测性多基因表达谱在乳腺癌治疗中的应用.     

高通量基因检测系统MammaPrint与乳腺癌预后预测

化疗是乳腺癌患者术后最重要的辅助治疗手段.由于传统的临床病理指标不能准确预测患者复发风险,因此不能准确筛选出哪些患者需要化疗、哪些患者不需要化疗.高通量基因检测系统MammaPrint已被证实可较准确预测相当一部分乳腺癌患者复发风险,因而可用于指导个体化治疗.本文就MammaPrint的临床应用及研究现状作一综述.     

PDL1 expression in inflammatory breast cancer is frequent and predicts for the pathological response to chemotherapy

We retrospectively analyzed PDL1 mRNA expression in 306 breast cancer samples, including 112 samples of an aggressive form, inflammatory breast cancer (IBC). PDL1 expression was heterogeneous, but was higher in IBC than in non-IBC. Compared to normal breast samples, PDL1 was overexpressed in 38% of IBC. In IBC, PDL1 overexpression was associated with estrogen receptor-negative status, basal and ERBB2-enriched aggressive subtypes, and clinico-biological signs of anti-tumor T-cell cytotoxic response. PDL1 overexpression was associated with better pathological response to chemotherapy, independently of histo-clinical variables and predictive gene expression signatures. No correlation was found with metastasis-free and overall specific survivals. In conclusion, PDL1 overexpression in IBC correlated with better response to chemotherapy. This seemingly counterintuitive correlation between expression of an immunosuppressive molecule and improved therapeutic response may be resolved if PDL1 expression is viewed as a surrogate marker of a strong antitumor immune response among patients treated with immunogenic chemotherapy. In such patients, PDL1 inhibition could protect activated T-cells or reactivate inhibited T-cells and improve the therapeutic response, notably when associated with immunogenic chemotherapy.     

Gene expression profiling of breast tumor cell lines to predict for therapeutic response to microtubule-stabilizing agents

Microtubule-targeting agents, including taxanes (Tax) and ixabepilone (Ixa), are important components of modern breast cancer chemotherapy regimens, but no molecular parameter is currently available that can predict for their efficiency. We sought to develop pharmacogenomic predictors of Tax- and Ixa-response from a large panel of human breast tumor cell lines (BTCL), then to evaluate their performance in clinical samples. Thirty-two BTCL, representative of the molecular diversity of breast cancers (BC), were treated in vitro with Tax (paclitaxel (Pac), docetaxel (Doc)), and ixabepilone (Ixa), then classified as drug-sensitive or resistant according to their 50% inhibitory concentrations (IC50s). Baseline gene expression data were obtained using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays. Gene expression set (GES) predictors of response to taxanes were derived, then tested for validation internally and in publicly available gene expression datasets. In vitro IC50s of Pac and Doc were almost identical, whereas some Tax-resistant BTCL retained sensitivity to Ixa. GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined. They displayed a limited number of overlapping genes. Both were validated by leave-n-out cross-validation (n = 4; for overall accuracy (OA), P = 0.028 for Tax, and P = 0.0005 for Ixa). The GES predictor of Tax-sensitivity was tested on publicly available external datasets and significantly predicted Pac-sensitivity in 16 BTCL (P = 0.04 for OA), and pathological complete response to Pac-based neoadjuvant chemotherapy in BC patients (P = 0.0045 for OA). Applying Tax and Ixa-GES to a dataset of clinically annotated early BC patients identified subsets of tumors with potentially distinct phenotypes of drug sensitivity: predicted Ixa-sensitive/Tax-resistant BC were significantly (P < 0.05, Fischer’s exact test) more frequently ER/PR-positive, Ki67-negative, and luminal subtype than predicted Ixa-resistant/Tax-sensitive BC. Genomic predictors for Tax- and Ixa-sensitivity can be derived from BTCL and may be helpful for better selecting cytotoxic treatment in BC patients.     

Molecular prediction of the therapeutic response to neoadjuvant chemotherapy in breast cancer

Breast cancer is considered to be relatively sensitive to chemotherapy, and multiple combinations of cytotoxic agents are used as standard therapy. Chemotherapy is applied empirically despite the observation that not all regimens are equally effective across the population of patients. Up to date clinical tests for predicting cancer chemotherapy response are not available, and individual markers have shown little predictive value. A number of microarray studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in breast cancer. The identification of patient subpopulations most likely to respond to therapy is a central goal of recent personalized medicine. We have designed experiments to identify gene sets that will predict treatment-specific response in breast cancer. Taken together with our recent trial about the construction of a high-throughput functional screening system for chemo-sensitivity related genes, studies for drug sensitivity will provide rational strategies for establishment of the prediction system with high accuracy, and identification of ideal targets for drug intervention. This article is based on a presentation delivered at Presidential Symposium 1, “Breast cancer: individualized diagnosis for tailored treatment,” held on 29 June 2007 at the 15th Annual Meeting of the Japanese Breast Cancer Society in Yokohama.     

生存素和乳腺癌耐药蛋白表达与转移性乳腺癌化疗效应的相关性

背景与目的:化疗是转移性乳腺癌的主要治疗手段,但常因盲目选择化疗方案,导致治疗失败.因此,对具有不同分子生物学特点的转移性乳腺癌进行化疗疗效预测,已成为国内外研究热点.本研究探讨生存素(survivin)、乳腺癌耐药蛋白(BCRP)基因在长春瑞滨联合顺铂化疗方案治疗转移性乳腺癌不同疗效组间的表达差异,初步寻找出与该化疗方案敏感性相关的生物学指标.方法:采用半定量逆转录-聚合酶链反应(RT-PCR)检测43例转移性乳腺癌转移灶中生存素和BCRP基因的表达水平,了解其与化疗疗效的关系.结果:长春瑞滨联合顺铂化疗方案总有效率为72.1%(31/43),7例为完全缓解(CR),占16.3%(7/43);部分缓解(PR)24例,占55.8%(24/43);9例稳定(SD),占20.9%(9/43);3例进展(PD),占7.0%(3/43).survivinmRNA与BcRP mRNA在乳腺癌转移灶中的阳性表达率分别为51.2%(22/43)和32.6%(14/43),两者表达差异无显著性(P>0.05),survivin mRNA阴性表达组其化疗疗效高于阳性表达组(p<0.05),BCRP mPNA表达状况与化疗疗效有相关性(P<0.05),survivin mRNA和BCRP mPNA均为阴性表达组的化疗疗效高于其他组(P<0.05).结论:联合检测survivin mRNA和BCRP mRNA表达可作为长春瑞滨联合顺铂化疗方案治疗转移性乳腺癌的疗效预测敏感性指标之一.     

Gene expression profiling using cDNA arrays and prognosis of breast cancer

Breast cancer is a major health problem. Clinical heterogeneity makes prognosis and sensitivity to treatment highly variable among patients. The recently developed cDNA array technology allows to analyse the expression of thousands of genes simultaneously. Results of the pioneering studies are promising: expression profiling of breast tumours using cDNA arrays seems able to identify new prognostic sub-classes unidentifiable using conventional parameters.     

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

Introduction  

Breast cancer is a heterogeneous group of tumors, and can be subdivided on the basis of histopathological features, genetic alterations and gene-expression profiles. One well-defined subtype of breast cancer is characterized by a lack of HER2 gene amplification and estrogen and progesterone receptor expression ('triple-negative tumors'). We examined the histopathological and gene-expression profile of triple-negative tumors to define subgroups with specific characteristics, including risk of developing distant metastases.