Gene Expression Profiling Of Breast Cancer To Identify Subtypes And To Predict Local Recurrence After Breast Conserving Therapy
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Gene Expression Profiling Of Breast Cancer To Identify Subtypes And To Predict Local Recurrence After Breast Conserving Therapy
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Author : Bas Kreike
language : en
Publisher:
Release Date : 2011
Gene Expression Profiling Of Breast Cancer To Identify Subtypes And To Predict Local Recurrence After Breast Conserving Therapy written by Bas Kreike and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Gene Expression Profiling Of The Breast Tumour Microenvironment
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Author : Grzegorz Finak
language : en
Publisher:
Release Date : 2008
Gene Expression Profiling Of The Breast Tumour Microenvironment written by Grzegorz Finak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
"Breast cancer is a very heterogeneous disease. This heterogeneity can be observed at many levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Recent studies have demonstrated that the tumour microenvironment also plays a critical role in cancer initiation and progression. Genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer, and have identified gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer-related pathways and processes. However, little work has been done in this context to examine the role of the tumour microenvironment in determining breast cancer outcome, or in defining breast cancer heterogeneity. Additionally, little is known about gene expression in histologically normal tissue adjacent to breast tumour, if this is influenced by the tumour, and how this compares with non-tumour-bearing breast tissue. By applying laser--capture microdissection and gene expression profiling to clinical breast cancer specimens the research presented in this thesis addresses these questions. We have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty. We determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumour-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumour tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favourable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis. Our data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression data set for comparative studies of tumour expression profiles. We compared gene expression profiles of tumour stroma from primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumour--derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node--negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumour progression. We show that gene expression in the breast tumour microenvironment is highly heterogeneous, identifying at least six different classes of tumour stroma with distinct expression patterns and distinct biological processes. Two of these classes recapitulate the processes identified in the stroma-derived prognostic predictor, while the others are new classes of stroma associated with distinct clinical outcomes. One of these is associated with matrix remodelling and is strongly associated with the basal molecular subtype of breast cancer. The remainder are independent of the previously published molecular subtypes of breast cancer. Additionally, based on independent data from over 800 tumors, the combinations of stroma classes and breast cancer subtypes identify new subgroups of breast tumors that show better discrimination between good and poor outcome individuals than the molecular breast cancer subtypes or the stroma classes alone, suggesting a novel classification scheme for breast cancer. This research demonstrates an important role for the tumour microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome. Novel therapies could be targeted at the processes that define the stroma classes suggesting new avenues for individualized treatment."--
Molecular Subtypes Of Estrogen Receptor Positive Breast Cancers Predict Clinical Behavior
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Author : Daniel Alan Kerr
language : en
Publisher:
Release Date : 2010
Molecular Subtypes Of Estrogen Receptor Positive Breast Cancers Predict Clinical Behavior written by Daniel Alan Kerr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Breast categories.
Current markers for breast cancer, ER, PR, EGFR and HER-2/neu imperfectly predict prognosis or therapy response. Studies by my mentor revealed distinct molecular subtypes with different clinical characteristics using gene expression profiles from LCM-procured carcinoma cells. Subtypes A and B, although exhibiting ER-positive cancers, had vastly different clinical outcomes. The goal was to derive a clinically relevant subset of genes that predicts breast cancer behavior. Using ERTargetDB and literature review, a ten gene subset of estrogen receptor-associated genes among 100 candidates was validated by qPCR and correlated with biochemical and clinical parameters in primary breast cancer biopsies. These results were combined with expression of genes for conventional biomarkers and assessed for prediction of prognosis in a population of ER-positive, early stage primary breast cancers. All genes except PTGDS exhibited a positive relationship with ER and PR levels. Expression of subtype A genes (BCL2, CAXII, ERBB4, LIV1 and RERG) was significantly decreased in cancers exhibiting increased EGFR protein, although this relationship was not observed for HER-2/neu. Subtype B genes were not altered as a function of EGFR or HER-2/neu levels. Expression of subtype A genes was significantly decreased in cancers with positive lymph nodes, higher grade and larger tumors compared to cancers with subtype B gene expression. Surprisingly, PGR gene expression independently predicted prognosis. Survival analyses of ER+/PR+ cancers revealed multi-gene models classifying risk of recurrence and mortality. LIV1, CD34, EDG1 and NQO1 expression distinguished prognosis differently in node-negative breast cancers compared to the node-positive population. Compared to standard clinical indicators of prognosis using the Adjuvant! Online algorithm, multi-gene models provided superior assessment of risk of recurrence and mortality. Survival analyses of patients with ER+/PR+ breast cancers treated with adjuvant Tamoxifen revealed three multi-gene models significantly predicting recurrence and mortality even after adjusting for age, nodal status, chemotherapy and radiation therapy. Bayesian modeling applied in silico using microarray data identified regulatory networks involving interactions among the ten candidate genes suggesting relationships with cancer differentiation and growth. In summary, a novel subset of 14 ER-associated genes was derived that both predicts risk of recurrence of breast carcinoma and response to Tamoxifen treatment.
Prognostic Factors In Cancer
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Author : Paul Hermanek
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Prognostic Factors In Cancer written by Paul Hermanek and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Medical categories.
M. K. Gospodarowicz, P. Hermanek, and D. E. Henson Attention to innovations in cancer treatment has tended to eclipse the importance of prognostic assessment. However, the recognition that prognostic factors often have a greater impact on outcome than available therapies and the proliferation of biochemical, molecular, and genetic markers have resulted in renewed interest in this field. The outcome in patients with cancer is determined by a combination of numerous factors. Presently, the most widely recognized are the extent of disease, histologic type of tumor, and treatment. It has been known for some time that additional factors also influence outcome. These include histologic grade, lymphatic or vascular invasion, mitotic index, performance status, symptoms, and most recently genetic and biochemical markers. It is the aim of this volume to compile those prognostic factors that have emerged as important determinants of outcome for tumors at various sites. This compilation represents the first phase of a more extensive process to integrate all prognostic factors in cancer to further enhance the prediction of outcome following treatment. Certain issues surround ing the assessment and reporting of prognostic factors are also considered. Importance of Prognostic Factors Prognostic factors in cancer often have an immense influence on outcome, while treatment often has a much weaker effect. For example, the influence of the presence of lymph node involvement on survival of patients with metastatic breast cancer is much greater than the effect of adjuvant treatment with tamoxifen in the same group of patients [5].
Computational Driven Understanding Of The Regulatory Mechanisms Of The Breast Cancer Transcriptome And Its Implications For Drug Treatment
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Author : Shujun Huang
language : en
Publisher:
Release Date : 2021
Computational Driven Understanding Of The Regulatory Mechanisms Of The Breast Cancer Transcriptome And Its Implications For Drug Treatment written by Shujun Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Breast cancer (BC) is the most commonly diagnosed cancer and the major cause of cancer mortality among women worldwide. As a heterogenous disease, BC can be divided into four major molecular subtypes: luminal A, luminal B, HER2-enriched, and triple negative breast cancer (TNBC). The TNBC subtype shows the shortest survival time among the four groups and lacks effective targeted therapeutic strategies. Different BC subtypes display different transcriptome profiles. However, the dysregulated pathways and transcriptional regulators underlying the gene expression profiles of different BC subtypes have yet to be fully elucidated. Current research is investigating the dysregulated genes in BC with the aim to identify potential gene targets for BC while overlooking the fact these genes are often part of a pathway. Moreover, among the multi-omics data, gene expression profiles have been shown to be the most informative data for developing anti-cancer drug response prediction models in silico. But these models typically were developed with individual genes. Therefore, this thesis aimed to explore the breast cancer transcriptome with a focus on the TNBC subtype to address three major questions: 1) exploring the regulatory mechanisms driving the unique expression pattern of different BC subtypes; 2) identifying compounds that could affect the expression pattern of the top dysregulated pathways in BC; and 3) developing a drug response prediction model by using BC pathway activity profiles inferred from the transcriptome profiles. To address the first question, we collected the multi-omics data of BC samples from The Cancer Genome Atlas (TCGA) dataset, including gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) profiles, the transcription factor (TF)-binding data from TRRUST v2.0, and the miRNA-binding data from starBase v3.0. Using these data, the Lasso regression-based integrative analysis identified 25, 20, 15 and 24 key regulators for luminal A, luminal B, HER2-enriched and TNBC subtypes, respectively. A further look at the TNBC regulators found that many of them are regulating the FOXM1 (i.e., PID_FOXM1_PATHWAY) and PPARA (i.e., BIOCARTA_PPARA_PATHWAY) pathways. To address the second question, we focused on the FOXM1 and PPARA pathways. Using the Connectivity Map (CMAP) database, which provides drug-induced gene expression changes in MCF7 cell lines, we investigated how different compounds change the activity and expression pattern of the two pathways. Nineteen drugs (such as 5109870, MG-132, MG-262, celastrol, resveratrol, and cephaeline) were identified to decrease the FOXM1 pathway activity scores and reverse the FOXM1 pathway expression pattern while 13 drugs (such as cephaeline, pararosaniline, cycloheximide, monensin, wortmannin, and raloxifene) were identified to increase the PPARA pathway activity scores and reverse the PPARA pathway expression pattern. It may be of interest to validate these compounds experimentally. To address the third question, we collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values of these cell lines to 220 drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on three types of drug information. ML-CDN2 demonstrated good prediction performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all cell line-drug pairs of 0.873. Moreover, the ML-CDN2 model could be used to predict the drug response for new BC cell line samples or new BC patient-derived samples. This thesis demonstrated the transcriptional regulators underlying the transcriptome profiles in different BC subtypes. Moreover, this thesis demonstrated the implications of the BC transcriptome in drug treatment by identifying the drugs to modulate the two dysregulated pathways in BC and developing the anti-cancer drug response prediction model for BC by incorporating the transcriptome profiles.
Can Gene Expression Pattern Analysis Predict Recurrence In Node Negative Breast Cancer
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Author :
language : en
Publisher:
Release Date : 2002
Can Gene Expression Pattern Analysis Predict Recurrence In Node Negative Breast Cancer written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Some breast cancers spread (metastasize) to distant sites, putting the patient at high risk of death from this disorder. Clinicians now use tumor size, tumor appearance, and especially the presence of metastasis (cancer spread to local lymph nodes, or "node-positive breast cancer") to estimate the risk of early breast cancer death. These measures are imperfect, since 30% of the patients who should have a good outcome (no cancer spread to local lymph nodes, or "node-negative breast cancer"), eventually recur and die of breast cancer. Because breast cancer metastasis is so hard to predict, and so deadly, moat low-risk node-negative breast cancer patients receive the same drug therapies routinely given to high-risk node-positive patients. This means that the majority of the low- risk node-negative breast cancer patients receive aggressive treatment they do not need. Our objective is to identify biomarkers that better define the metastatic potential of a node-negative breast cancer. We hypothesize that patterns of gene expression exist that distinguish primary breast cancers at low versus high risk of metastatic spread, and that these patterns can be ascertained using cDNA expression array technology, comparing frozen primary breast cancers of known good versus bad outcome. Multivariate analyses between these genes and with existing prognostic factors will determine the value of this approach in selecting optimal treatment strategies for women with node-negative breast cancer. With this information, clinicians could identify node-negative patients who require additional drug therapy for their disease, and could avoid over-treating those patients with vary low risk of metastatic disease.
Biopsy Interpretation Of The Breast
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Author : Stuart J. Schnitt
language : en
Publisher: Lippincott Williams & Wilkins
Release Date : 2012-10-08
Biopsy Interpretation Of The Breast written by Stuart J. Schnitt and has been published by Lippincott Williams & Wilkins this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-08 with Medical categories.
A practical guide for the diagnostic surgical pathologist, this new edition of Biopsy Interpretation of the Breast presents the diverse spectrum of pathologic alterations that occur in the breast in a manner analogous to that in which they are encountered in daily practice. Lesions are grouped together according to their histologic patterns rather than by the traditional benign-malignant categorization in order to simulate the way pathologists face these lesions as they examine microscopic slides on a daily basis. The role of adjunctive studies in solving diagnostic problems in breast pathology is emphasized where appropriate. In addition, the clinical significance and impact on patient management of the various diagnoses are discussed and key clinical and management points highlighted.
Identification Of Lead Molecules For The Treatment Of Triple Negative Breast Cancer Molecular Subtypes From Natural Sources
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Author : Andrew Joshua Robles
language : en
Publisher:
Release Date : 2016
Identification Of Lead Molecules For The Treatment Of Triple Negative Breast Cancer Molecular Subtypes From Natural Sources written by Andrew Joshua Robles and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Breast categories.
Triple negative breast cancers lack estrogen and progesterone receptor expression and do not overexpress human epidermal growth factor receptor 2. While targeted therapies for estrogen/progesterone receptor-positive and human epidermal growth factor receptor 2-amplified breast cancers have improved survival of patients whose cancer expresses these proteins, there are no targeted therapies for triple negative breast cancers. There is a need to identify new therapies and molecular targets for treating triple negative breast cancers, but efforts have been limited by insufficient understanding of these heterogeneous diseases. Recently, gene expression profiling of triple negative breast cancer patients identified six molecular subtypes and representative cell line models, providing an opportunity to identify subtype-specific leads for these cancers. For this project, high-throughput screening was performed to evaluate novel libraries of extracts from Texas plants and diverse fungal cultures for activity in a panel of cell lines modeling five different triple negative breast cancer molecular subtypes. The aim was to identify extracts with selective activity in a single cell line. It was hypothesized that extracts with selective activity in one of these cell lines would target a protein or cellular process critical to the growth of that subtype. Eleven extracts were identified with selective activity against cell lines representing three different triple negative breast cancer molecular subtypes. Bioassay-guided fractionation identified four different classes of compounds with selective activity against cell lines modeling these three molecular subtypes. The compounds identified include maximiscin, mevastatin, two novel oxazoles named 51SC51N and 51SC51O, and deguelin. The mechanisms of action of each compound were investigated in cell line models. Deguelin and maximiscin were also evaluated in xenograft mouse models representing the sensitive subtype. Maximiscin showed selective activity against the MDA-MB-468 cell line, representative of the basal-like 1 subtype of triple negative breast cancer. Cell cycle studies showed that maximiscin caused an accumulation of cells in the Gap 1 phase of the cell cycle, and protein microarray studies indicated that maximiscin increased levels of phosphorylated p53, which was consistent with the observed Gap 1 accumulation. It was hypothesized that maximiscin induces 2-deoxyribonucleic acid damage, and the effects of maximiscin on activation of 2-deoxyribonucleic acid damage response pathways were investigated. Maximiscin was evaluated in vivo and exhibited efficacy in a MDA-MB-468 xenograft mouse model. Mevastatin showed selective cytotoxic activity against MDA-MB-231 and Hs578T cells, modeling the mesenchymal stem-like subtype of triple negative breast cancer. Experiments were conducted to determine if the selective effects of mevastatin, and statins in general, depend on 3-hydroxy-3- methylglutaryl-coenzyme A reductase inhibition, by evaluating the effects of mevalonate on statin activity. Mevalonate inhibited the cytotoxic effects of mevastatin and atorvastatin, suggesting that impairment of mevalonate biosynthesis is involved in their cytotoxic effects. Mechanistic studies with the oxazoles did not provide definitive evidence regarding their mechanisms of action, but medicinal chemistry efforts identified two lead molecules for future in vivo efficacy and mechanistic studies. Deguelin exhibited selective antiproliferative activity against MDA-MB-453 cells, a model of the luminal androgen subtype of triple negative breast cancer. Deguelin’s effects on mammalian target of rapamycin complex 1 signaling and androgen receptor were evaluated in both sensitive MDA-MB-453 and resistant MDA-MB-231 cells. The results suggest that the ability of deguelin to inhibit mammalian target of rapamycin complex 1 and androgen receptor signaling is involved in its selective activity. While deguelin did not show antitumor efficacy in vivo, these findings led to the investigation of Food and Drug Administration-approved agents against a luminal androgen receptor xenograft model. My results demonstrate that compounds with selective activity against triple negative breast cancer subtypes can be identified from nature and identified potential molecular targets for the treatment of these subtypes.
Evidence Based Pathology And Laboratory Medicine
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Author : Alberto M. Marchevsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-07-01
Evidence Based Pathology And Laboratory Medicine written by Alberto M. Marchevsky and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-01 with Medical categories.
Focusing on practical, patient related issues, this volume provides the basic concepts of Evidence Based Medicine (EBM) as they relate to Pathology and Laboratory Medicine and presents various practical applications. It includes EBM concepts for use in the identification of cost-effective panels of immunostains and other laboratory tests and for improvement of diagnostic accuracy based on the identification of selected diagnostic features for particular differential diagnosis. EBM concepts are also put forth for use in Meta-analysis to integrate the results of conflicting literature reports and use of novel analytical tools such as Bayesian belief networks, neural networks, multivariate statistics and decision tree analysis for the development of new diagnostic and prognostic models for the evaluation of patients. This volume will be of great value to pathologists who will benefit from the concepts being promoted by EBM, such as levels of evidence, use of Bayesian statistics to develop diagnostic and other rules and stronger reliance on "hard data" to support therapeutic and diagnostic modalities.
Stratified Informatics Analysis For Breast Cancer
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Author : Robert Lesurf
language : en
Publisher:
Release Date : 2015
Stratified Informatics Analysis For Breast Cancer written by Robert Lesurf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
"Over the past decade, genomic technologies have promised to revolutionize breast cancer research with better predictions of disease progression and patient prognosis through the identification of novel molecular targets. These technologies have led to the discovery of numerous different genomic subtyping schemes, each purported to offer relevant information beyond traditional clinical features. Yet it remains unclear what these schemes have contributed to our understanding of breast cancer biology and the reasons for disease recurrence. To address this issue, we have built a novel framework termed Breast Signature Analysis Tool (BreSAT), along with a companion collection of breast cancer datasets, highly annotated signatures, statistics, and visualizations, designed for accurate and ease-of-use application in breast cancer. This framework represents a new way of cataloguing tumors according to their biological properties, as opposed to broad gene expression profiles. We have applied BreSAT and its associated catalogue of signatures to thousands of breast tumor samples, and identify that tumor properties associated with recurrence are confounded by association with other clinicopathological variables such as estrogen receptor status and the genomic subtype. In addition to identifying properties associated with recurrence in breast cancer, we have sought to discover the molecular markers that drive different tumor phenotypes. We identified that expression of the oncogene MET in mouse mammary glands leads to the generation of tumors with characteristics of human triple-negative breast cancer. Additionally, synergy between MET and loss of p53 in similar mouse model leads the development of tumors a claudin-low phenotype, that arise with a higher penetrance and lower latency. Moreover, MET activity is required for maintenance of the claudin-low morphological phenotype and metastatic capacity of cell lines, suggesting that MET may represent an avenue for targeted therapeutics in human patients with claudin-low breast tumors. Finally, we have discovered that molecular features of breast cancer progression from a non-invasive to an invasive state are confounded by tumor subtype. To find more accurate markers of disease progression, we identified tumor properties that differentiate non-invasive tumors from invasive ones within each subtype. We observed that there is little overlap, suggesting that distinct properties drive tumor progression in different subtypes. Furthermore, we were able to identify a small number of non-invasive breast tumors with molecular features that make them more likely to progress. Together, these discoveries are leading to a more comprehensive understanding of the molecular features that drive breast cancer biology, disease progression, and patient outcome." --