Intratumor Heterogeneity and Precision of Microarray-Based Predictors of Breast Cancer Biology and Clinical Outcome
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE23593 | GEO | 2010/08/12
SECONDARY ACCESSION(S): PRJNA131731
REPOSITORIES: GEO
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Project description:Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome Plus 2.0 Arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Precision of predictors of breast cancer biology and clinical outcome were evaluated by interclass correlation.
2010-08-12 | E-GEOD-23593 | biostudies-arrayexpress
Project description:Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome Plus 2.0 Arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Precision of predictors of breast cancer biology and clinical outcome were evaluated by interclass correlation.
| S-ECPF-GEOD-23593 | biostudies-other
Project description:PurposeIdentifying sources of variation in expression microarray data and the effect of variance in gene expression measurements on complex predictive and diagnostic models is essential when translating microarray-based experimental approaches into clinical assays. The technical reproducibility of microarray platforms is well established. Here, we investigate the additional impact of intratumor heterogeneity, a largely unstudied component of variance, on the performance of several microarray-based assays in breast cancer.Patients and methodsGenome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen receptor (ER) and progesterone receptor (PR) status were compared with immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics was evaluated by interclass correlation.ResultsGlobal patterns of gene expression demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single-gene predictors of ER (intraclass correlation coefficient [ICC] = 0.94) and PR expression (ICC = 0.90), and in a multigene predictor of ER pathway activation (ICC = 0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multigene signatures of cancer recurrence (ICC = 0.71) and chemotherapeutic sensitivity (ICC = 0.72 and 0.64).ConclusionIntratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise microarray-based predictions of tumor behavior or clinical outcome in breast cancer patients.
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Project description:Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.
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Project description:Many studies failed to demonstrate benefit from the addition of targeted agents to current standard adjuvant FOLFOX chemotherapy in stage III colorectal cancer (CRC) patients. Intratumor heterogeneity may foster the resistant subclones and leads to cancer recurrence. Here, we built a cancer evolution model and applied machine learning analysis to identify potential therapeutic targets. Among 78 CRC cases, whole-genome (WGS) and deep targeted sequencing data generated from paired blood and primary tumor were used for phylogenetic tree reconstruction. Genetic alterations in the PI3K/AKT, and RTK oncogenic signaling pathways were commonly detected in founding clones. The dominant subclones frequently exhibited dysregulations in the TP53, FBXW7/NOTCH1 tumor suppression, and DNA repair pathways. Fourteen genetic mutations were simultaneously selected by random forest and LASSO methods. The logistic regression model had better accuracy (79%), precision (70%), and recall (65%) and area under the curve (AUC) (82%) for cancer recurrence prediction. Three genes, including MYO18A in the founding clone, FBXW7, and ATM in the dominant subclone, affected the prognosis were selected simultaneously by different feature sets. The in vitro studies, HCT-116 cells transfected with MYO18A siRNA demonstrated a significant reduction in cell migration activity by 20-40%. These results indicate that MYO18A plays a crucial role in the migration of human CRC cells. The cancer evolution model revealed the critical mutations in the founding and dominant subclones. They can be used to predict clinical outcomes and the development of novel therapeutic targets for stage III CRC.
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Project description:Melanoma is a cancer that exhibits one of the most aggressive and heterogeneous features. The incidence rate escalates. A high number of clones harboring various mutations contribute to an exceptional level of intratumor heterogeneity of melanoma. It also refers to metastases which may originate from different subclones of primary lesion. Such component of the neoplasm biology is termed intertumor and intratumor heterogeneity. These levels of tumor heterogeneity hinder accurate diagnosis and effective treatment. The increasing number of research on the topic reflects the need for understanding limitation or failure of contemporary therapies. Majority of analyses concentrate on mutations in cancer-related genes. Novel high-throughput techniques reveal even higher degree of variations within a lesion. Consolidation of theories and researches indicates new routes for treatment options such as targets for immunotherapy. The demand for personalized approach in melanoma treatment requires extensive knowledge on intratumor and intertumor heterogeneity on the level of genome, transcriptome/proteome, and epigenome. Thus, achievements in exploration of melanoma variety are described in details. Particularly, the issue of tumor heterogeneity or homogeneity given BRAF mutations is discussed.
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Project description:Lung cancer is one of the deadliest diseases in the world and is the leading cause of cancer-related deaths. Among the histological types, adenocarcinoma is the most common, and it is characterized by a high degree of heterogeneity at many levels including clinical, behavioral, cellular and molecular. While most lung cancers are known for their aggressive behavior, up to 18.5% of lung cancers detected by CT screening are indolent and put patients at risk for overdiagnosis and overtreatment. The cellular and molecular underpinnings of tumor behavior remain largely unknown. In the recent years, the study of intratumor heterogeneity has become an attractive strategy to understand tumor progression. This review will summarize some of the current known determinants of lung adenocarcinoma behavior and discuss recent efforts to dissect its intratumor heterogeneity.
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Project description:Although 1 in 9 American men will receive a diagnosis of prostate cancer (PC), most men with this diagnosis will not die from it, as most PCs are indolent. However, there is a subset of patients in which the once-indolent PC becomes metastatic and eventually, fatal. In this study, we analyzed microbial compositions of intratumor bacteria in PC to determine the influence of the microbiome on metastatic growth. Using large-scale RNA-sequencing data and corresponding clinical data, we correlated the abundance of microbes to immune pathways and PC risk factors, identifying specific microbes that either significantly deter or contribute to cancer aggressiveness. Interestingly, most of the microbes we found appeared to play anti-tumor roles in PC. Since these anti-tumor microbes were overrepresented in tumor samples, we believe that microbes thrive in the tumor microenvironment, outcompete cancer cells, and directly mitigate tumor growth by recruiting immune cells. These include Listeria monocytogenes, Methylobacterium radiotolerans JCM 2831, Xanthomonas albilineans GPE PC73, and Bradyrhizobium japonicum, which are negatively correlated with Gleason score, Tumor-Node-Metastasis (TNM) stage, prostate-specific antigen (PSA) level, and Androgen Receptor (AR) expression, respectively. We also identified microbes that contribute to tumor growth and are positively correlated with genomic alterations, dysregulated immune-associated (IA) genes, and prostate cancer stem cells (PCSC) genes.
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