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1.
Cell ; 164(1-2): 293-309, 2016 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-26771497

RESUMO

Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations and point mutations. Still, identifying causal variants and emergent vulnerabilities that arise as a consequence of genetic alterations remain major challenges. We performed whole-genome small hairpin RNA (shRNA) "dropout screens" on 77 breast cancer cell lines. Using a hierarchical linear regression algorithm to score our screen results and integrate them with accompanying detailed genetic and proteomic information, we identify vulnerabilities in breast cancer, including candidate "drivers," and reveal general functional genomic properties of cancer cells. Comparisons of gene essentiality with drug sensitivity data suggest potential resistance mechanisms, effects of existing anti-cancer drugs, and opportunities for combination therapy. Finally, we demonstrate the utility of this large dataset by identifying BRD4 as a potential target in luminal breast cancer and PIK3CA mutations as a resistance determinant for BET-inhibitors.


Assuntos
Algoritmos , Neoplasias da Mama/genética , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Proteínas de Ciclo Celular , Linhagem Celular Tumoral , Classe I de Fosfatidilinositol 3-Quinases , Análise por Conglomerados , Resistencia a Medicamentos Antineoplásicos , Dosagem de Genes , Perfilação da Expressão Gênica , Estudo de Associação Genômica Ampla , Humanos , Modelos Lineares , Proteínas Nucleares/genética , Fosfatidilinositol 3-Quinases , Fatores de Transcrição/genética
2.
Cell ; 159(6): 1461-75, 2014 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-25433701

RESUMO

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.


Assuntos
Algoritmos , Neoplasias da Mama/genética , Animais , Teorema de Bayes , Neoplasias da Mama/patologia , Linhagem Celular Tumoral , Variações do Número de Cópias de DNA , Feminino , Estudo de Associação Genômica Ampla , Humanos , Camundongos Endogâmicos NOD , Camundongos SCID , Interferência de RNA
3.
Cell ; 143(6): 1005-17, 2010 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-21129771

RESUMO

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.


Assuntos
Teorema de Bayes , Proteínas Ativadoras de GTPase/metabolismo , Melanoma/genética , Proteínas rab de Ligação ao GTP/metabolismo , Proteínas Ativadoras de GTPase/genética , Perfilação da Expressão Gênica , Humanos , Fator de Transcrição Associado à Microftalmia/genética , Fator de Transcrição Associado à Microftalmia/metabolismo , Transporte Proteico , Proteínas rab de Ligação ao GTP/genética , Proteínas rab27 de Ligação ao GTP
4.
Genes Dev ; 31(6): 553-566, 2017 03 15.
Artigo em Inglês | MEDLINE | ID: mdl-28404630

RESUMO

The female mammary gland is a very dynamic organ that undergoes continuous tissue remodeling during adulthood. Although it is well established that the number of menstrual cycles and pregnancy (in this case transiently) increase the risk of breast cancer, the reasons are unclear. Growing clinical and experimental evidence indicates that improper involution plays a role in the development of this malignancy. Recently, we described the miR-424(322)/503 cluster as an important regulator of mammary epithelial involution after pregnancy. Here, through the analysis of ∼3000 primary tumors, we show that miR-424(322)/503 is commonly lost in a subset of aggressive breast cancers and describe the genetic aberrations that inactivate its expression. Furthermore, through the use of a knockout mouse model, we demonstrate for the first time that loss of miR-424(322)/503 promotes breast tumorigenesis in vivo. Remarkably, we found that loss of miR-424(322)/503 promotes chemoresistance due to the up-regulation of two of its targets: BCL-2 and insulin-like growth factor-1 receptor (IGF1R). Importantly, targeted therapies blocking the aberrant activity of these targets restore sensitivity to chemotherapy. Overall, our studies reveal miR-424(322)/503 as a tumor suppressor in breast cancer and provide a link between mammary epithelial involution, tumorigenesis, and the phenomenon of chemoresistance.


Assuntos
Neoplasias da Mama/genética , MicroRNAs/genética , Animais , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/mortalidade , Linhagem Celular Tumoral , Resistencia a Medicamentos Antineoplásicos , Feminino , Deleção de Genes , Genes Supressores de Tumor , Humanos , Neoplasias Mamárias Experimentais/genética , Camundongos , Gravidez , Complicações Neoplásicas na Gravidez/genética , Proteínas Proto-Oncogênicas c-bcl-2/genética , Receptor IGF Tipo 1 , Receptores de Somatomedina/genética , Fosfatases cdc25/genética
5.
Genes Dev ; 28(7): 765-82, 2014 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-24636986

RESUMO

The mammary gland is a very dynamic organ that undergoes continuous remodeling. The critical regulators of this process are not fully understood. Here we identify the microRNA cluster miR-424(322)/503 as an important regulator of epithelial involution after pregnancy. Through the generation of a knockout mouse model, we found that regression of the secretory acini of the mammary gland was compromised in the absence of miR-424(322)/503. Mechanistically, we show that miR-424(322)/503 orchestrates cell life and death decisions by targeting BCL-2 and IGF1R (insulin growth factor-1 receptor). Furthermore, we demonstrate that the expression of this microRNA cluster is regulated by TGF-ß, a well-characterized regulator of mammary involution. Overall, our data suggest a model in which activation of the TGF-ß pathway after weaning induces the transcription of miR-424(322)/503, which in turn down-regulates the expression of key genes. Here, we unveil a previously unknown, multilayered regulation of epithelial tissue remodeling coordinated by the microRNA cluster miR-424(322)/503.


Assuntos
Epitélio/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Glândulas Mamárias Animais/metabolismo , MicroRNAs/genética , MicroRNAs/metabolismo , Animais , Morte Celular/genética , Linhagem Celular , Feminino , Técnicas de Inativação de Genes , Humanos , Glândulas Mamárias Animais/citologia , Camundongos Knockout , Regiões Promotoras Genéticas/genética , Proteínas Proto-Oncogênicas c-bcl-2/metabolismo , Receptor IGF Tipo 1/metabolismo , Transdução de Sinais , Fator de Crescimento Transformador beta1/metabolismo , Desmame
6.
BMC Bioinformatics ; 11: 189, 2010 Apr 14.
Artigo em Inglês | MEDLINE | ID: mdl-20398270

RESUMO

BACKGROUND: Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect. RESULTS: We present JISTIC, a tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer. JISTIC is an improvement over the widely used GISTIC algorithm. We compared the performance of JISTIC versus GISTIC on a dataset of glioblastoma copy number variation, JISTIC finds 173 significant regions, whereas GISTIC only finds 103 significant regions. Importantly, the additional regions detected by JISTIC are enriched for oncogenes and genes involved in cell-cycle and proliferation. CONCLUSIONS: JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions. The software and documentation are freely available and can be found at: http://www.c2b2.columbia.edu/danapeerlab/html/software.html.


Assuntos
Aberrações Cromossômicas , Genômica/métodos , Neoplasias/genética , Software , Bases de Dados Genéticas , Variação Genética , Genoma Humano , Humanos , Análise de Sequência com Séries de Oligonucleotídeos
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