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Charles M. Perou

Bio: Charles M. Perou is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 156, co-authored 573 publications receiving 202951 citations. Previous affiliations of Charles M. Perou include North Carolina Central University & University of Chicago.


Papers
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01 Jan 2002
TL;DR: From the Departments of Pathology,* Health Research and Policy, Statistics, Genetics, Biochemistry and Howard Hughes Medical Institute, Stanford University, Stanford, California and Applied Genomics, Inc., Huntsville, Alabama and Sunnyvale, California
Abstract: From the Departments of Pathology,* Health Research and Policy, Statistics, Genetics, Biochemistry and Howard Hughes Medical Institute,*** Stanford University, Stanford, California; the Departments of Genetics and Pathology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina; Institute of Pathology, University of Basel, Basel, Switzerland; Cancer Genetics Branch, National Institutes of Health, Bethesda, Maryland; Diomeda Life Sciences Inc., Rockville, Maryland; the Department of Surgery,** University of Basel, Basel, Switzerland; UniversitatsFrauenklinik, University of Basel, Basel, Switzerland; Kreiskrankenhaus, Lorrach, Germany; Frauenklinik, Rheinfelden, Germany; Applied Genomics, Inc., Huntsville, Alabama and Sunnyvale, California

1 citations

Proceedings ArticleDOI
TL;DR: This work aimed to identify lncRNAs bound to the estrogen receptor alpha 1 protein (ESR1) that promote late-stage relapse breast cancer and discovered 1192 altered lnc RNAs when comparing the metastatic to the primary samples.
Abstract: Breast cancer (BC) is the second most common newly diagnosed cancer and the second leading cause of cancer death among women in the United States. Despite the proven benefits of adjuvant endocrine therapy in women with hormone receptor positive BC, relapses still occur even after initial treatment with endocrine therapy for 5 years, referred to as late-stage relapse. Long non-coding RNAs (lncRNAs) have been shown to be dysregulated in breast cancer. Recent studies have also shown lncRNAs to function by interfacing with corresponding RNA binding proteins to play critical regulatory roles of diverse cellular processes. Therefore, we hypothesize that lncRNAs may interact with ER to regulate genes promoting late-stage relapse. To address this, we aimed to identify lncRNAs bound to the estrogen receptor alpha 1 protein (ESR1) that promote late-stage relapse breast cancer. We first used transcriptome sequencing to identify altered expression levels of lncRNAs between 72 primary tumors and 24 late-stage relapse breast cancer patients. We detected 1192 altered lncRNAs when comparing the metastatic to the primary samples (FDR Citation Format: Jessica Monique Silva-Fisher, Abdallah M. Eteleeb, Torsten Nielsen, Charles M. Perou, Jorge S. Reis-Filho, Mathew J. Ellis, Elaine R. Mardis, Christopher A. Maher. Discovery and characterization of late-stage breast cancer estrogen receptor alpha 1 bound long non-coding RNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2547. doi:10.1158/1538-7445.AM2017-2547

1 citations

Proceedings ArticleDOI
TL;DR: It is found that the mesenchymal M-Wnt cell line is highly responsive to changes in dietary energy balance status relative to the E-WNT differentiated epithelial cell line, which demonstrates that clinical subtypes of breast cancer are differentially regulated by energy balance modulation.
Abstract: Background: Epidemiological evidence suggests a potential role of obesity in regulating clinical subtype, differentiation status, and prognosis of breast cancer. Specifically, an elevated waist-hip ratio is associated with increased risk and progression of basal-like breast cancer, an aggressive form characterized by heterogeneous tumors typically enriched in a putative tumor initiating cell (TIC) population. However, the exact mechanism of obesity-driven tumor progression remains unknown. Therefore, we hypothesized that obesity regulates a plastic population of multipotent malignant cells. Materials, Methods, and Results: To test this hypothesis, we generated and characterized two distinct murine mammary tumor cell lines derived from MMTV-Wnt-1 transgenic mice, designated M-Wnt and E-Wnt. M-Wnt cells displayed a mesenchymal morphology while E-Wnt cells had an epithelial morphology. M-Wnt cells harbored a large CD44+/CD24- putative TIC population (62% +/−7.8), had significant mammosphere forming capacity (>30% of cells form mammospheres, p Control, p=0.011; CR Discussion: In conclusion, we found that the mesenchymal M-Wnt cell line is highly responsive to changes in dietary energy balance status relative to the E-Wnt differentiated epithelial cell line, which demonstrates that clinical subtypes of breast cancer are differentially regulated by energy balance modulation. Additionally, our data demonstrates, for the first time, that energy balance modulation (ie, CR and obesity) directly regulates EMT, in response to local upregulation of TGF-β signaling. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr S6-5.

1 citations

Posted ContentDOI
22 Feb 2021-bioRxiv
TL;DR: In this article, the authors employed preclinical syngeneic p53 null mouse models of TNBC to develop a treatment regimen that harnessed the immunostimulatory effects of low-dose chemotherapy coupled with the pharmacologic inhibition of TAMs.
Abstract: Immunosuppressive elements within the tumor microenvironment such as Tumor Associated Macrophages (TAMs) can present a barrier to successful anti-tumor responses by cytolytic T cells. We employed preclinical syngeneic p53 null mouse models of TNBC to develop a treatment regimen that harnessed the immunostimulatory effects of low-dose chemotherapy coupled with the pharmacologic inhibition of TAMs. Combination therapy was used to successfully treat several highly aggressive, claudin-low murine mammary tumors and lung metastasis. Long-term responders developed tertiary lymphoid structures co-infiltrated by T and B cells at the treatment site. Mechanistically, CD86+ antigen-experienced T cells exhibited polyclonal expansion and resulted in exceptional responses upon tumor rechallenge. Combination treatment also eliminated lung metastases. High dimensional transcriptomic data for CD45+ immune cells lead to the identification of an aberrant developmental trajectory for TAMs that were resistant to treatment. Signatures derived from these TAM populations were predictive of patient response to our therapy. This study illustrates the complexity of tumor infiltrating myeloid cells and highlights the importance of personalized immuno-genomics to inform therapeutic regimens. Statement of significance Triple negative breast cancer is aggressive and hard to treat as it has no targeted therapies. Targeting immunosuppressive macrophages in murine models of TNBC alongside an immunostimulatory chemotherapy achieved long-term primary tumor regression in multiple murine mouse models. The transcriptomic heterogeneity between TAMs in phenotypically similar models can be used to uncover future therapeutic targets. Additionally, signatures derived from these murine models can be applied to TNBC patient data sets to predict cohorts of patients that will respond to the treatment strategy.

1 citations


Cited by
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Journal ArticleDOI
04 Mar 2011-Cell
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.

51,099 citations

Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

34,830 citations

Journal ArticleDOI
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

30,684 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which opens up the possibility of studying the biological relevance of small expression differences.
Abstract: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.

18,261 citations