Genome study charts genetic landscape of lung cancer
Notice bibliographique
Résumé
AbstractComprehensive analysis of DNA from human lung tumors uncovers morethan 50 common genetic abnormalities, less than half involve knowncancer genes; Work highlights role of key lung cancer gene.An international team of scientists today announced the results of asystematic effort to map the genetic changes underlying lung cancer,the world's leading cause of cancer deaths. Appearing in the November4 advance online issue of the journal Nature, the research provides acomprehensive view of the abnormal genetic landscape in lung cancercells, revealing more than 50 genomic regions that are frequentlygained or lost in human lung tumors. While one-third of these regionscontain genes already known to play important roles in lung cancer,the majority harbor new genes yet to be discovered. Flowing from thiswork, the scientists uncovered a critical gene alteration - notpreviously linked to any form of cancer - that is implicated in asignificant fraction of lung cancer cases, shedding light on thebiological basis of the disease and a potential new target fortherapy."This view of the lung cancer genome is unprecedented, both in itsbreadth and depth," said senior author Matthew Meyerson, a seniorassociate member of the Broad Institute of MIT and Harvard and anassociate professor at Dana-Farber Cancer Institute and HarvardMedical School. "It lays an essential foundation, and has alreadypinpointed an important gene that controls the growth of lung cells.This information offers crucial inroads to the biology of lung cancerand will help shape new strategies for cancer diagnosis and therapy.""The genomic landscape of lung cancer gives us a systematic pictureof this terrible disease, confirming things we know, but alsopointing us to many missing pieces of the puzzle," said Eric Lander,one of the study's co-authors and the founding director of the BroadInstitute of MIT and Harvard. "More broadly, the study represents ageneral approach that can and should be used to analyze all types ofcancer. Indeed, the study was designed as a pilot project for an evenmore comprehensive effort to unearth the genetic causes of cancer."Lung cancer is the leading cause of cancer deaths worldwide - eachyear more than 1 million people die of the disease, including morethan 150,000 in the United States. New approaches to treatment relyon a deeper understanding of what goes wrong in cells to spur cancergrowth. Through decades of research, it has become clear that lungcancer - like most human cancers - stems mainly from DNA changes thataccrue in cells throughout a person's life. But the nature of thesechanges and their biological consequences remain largely unknown.To assemble a genome-wide catalog of genetic differences in lungcancer cells, a large-scale project was recently launched in lungadenocarcinoma. The effort, known as the Tumor Sequencing Project(TSP), unites scientists and clinicians throughout the cancerresearch community.The TSP researchers studied more than 500 tumor specimens from lungcancer patients. Access to this large collection of high-qualitysamples made it possible to determine the genetic changes sharedamong different patients - such recurring changes can highlightimportant genes involved in cancer growth. "This project was madepossible through the foresight of a dedicated group of oncologists,pathologists, and surgeons, who carefully and diligently preservedtissues from lung cancer patients over many years," said Meyerson.To analyze DNA from each lung tumor, the scientists relied on recentgenomic technologies to scan the human genome for hundreds ofthousands of genetic markers, called single nucleotide polymorphismsor SNPs. This high-resolution view helped pinpoint which parts of thetumor genome were present in excess copies or missing altogether. Theregions of genomic aberration were then identified with newanalytical tools, including a computational method called GISTIC andmethods for visualizing SNP data developed by co-first authors GaddyGetz and Barbara Weir and co-authors Rameen Beroukhim and JimRobinson.From this work, the researchers uncovered a total of 57 genomicchanges that occur frequently in lung cancer patients. Of these, onlyabout 15 are linked to genes previously known to be involved in lungadenocarcinoma. The rest, though, remain to be discovered.Strikingly, the most common abnormality identified in the Naturestudy involves a region on chromosome 14 that encompasses two knowngenes, neither of which had been previously associated with cancer.Through additional studies in cancer cells, co-first author Sue-AnnWoo and other researchers at Dana-Farber Cancer Institute revealedthat one of the genes, NKX2.1, influences cancer cell growth. TheNKX2.1 gene normally acts as a sort of "master regulator" -controlling the activity of other key genes - in a special group ofcells lining the lungs' tiny air sacs, called alveoli. Thisdiscovery, that a gene functioning in a select group of cells ratherthan all cells can promote cancer growth, may have broad implicationsfor the design of novel, molecularly targeted cancer drugs.The second phase of the TSP, now underway, will examine the same lungtumor samples analyzed in the first phase, but at an even greaterlevel of genetic detail. Using high-throughput DNA sequencingmethods, the scientists will characterize small changes in thegenetic code of several hundred human genes, which are alreadyimplicated in other cancers or more generally in cell growth.Participating institutions in the TSP include three large-scale DNAsequencing centers - Baylor College of Medicine, Broad Institute ofMIT and Harvard, and Washington University - and six medicalinstitutions - Brigham and Women's Hospital, Dana-Farber CancerInstitute, M.D. Anderson Cancer Center, Memorial Sloan-KetteringCancer Center, the University of Michigan, and Washington University.Investigators from Nagoya City University, the Ontario CancerInstitute/Princess Margaret Hospital, and the University ofTexas-Southwestern Medical School also participated in the SNP study.In addition to Matthew Meyerson and Eric Lander, the scientificleaders of the TSP include Harold Varmus of the MemorialSloan-Kettering Cancer Center, Richard Gibbs of the Baylor College ofMedicine, and Richard Wilson of Washington University in Saint Louis.The TSP is helping to lay the foundation for future large-scalecancer genome projects, including The Cancer Genome Atlas (TCGA)pilot project. In December 2005, the National Human Genome ResearchInstitute and the National Cancer Institute launched the TCGA pilotto test the feasibility of a comprehensive, systematic approach toexploring the genomics of a wide range of common human cancers. Inits pilot phase, TCGA is focusing on glioblastoma multiforme, themost common form of brain cancer; ovarian cancer; and squamous celllung cancer.Data access:All data generated by the TSP are being made available to thescientific community in public databases, including:caintegrator-info.nci.nih.gov/csp. Data can also be accessed throughthe Broad Institute website, at: www.broad.mit.edu/tsp.About the Broad Institute of MIT and HarvardThe Broad Institute of MIT and Harvard was founded in 2003 to bringthe power of genomics to biomedicine. It pursues this mission byempowering creative scientists to construct new and robust tools forgenomic medicine, to make them accessible to the global scientificcommunity, and to apply them to the understanding and treatment ofdisease.The Institute is a research collaboration that involves faculty,professional staff and students from throughout the MIT and Harvardacademic and medical communities. It is jointly governed by the twouniversities.Organized around Scientific Programs and Scientific Platforms, theunique structure of the Broad Institute enables scientists tocollaborate on transformative projects across many scientific andmedical disciplines.For further information about the Broad Institute, go to www.broad.mit.edu.About Dana-Farber Cancer Institute:Dana-Farber Cancer Institute (www.dana-farber.org) is a principalteaching affiliate of the Harvard Medical School and is among theleading cancer research and care centers in the United States. It isa founding member of the Dana-Farber/Harvard Cancer Center (DF/HCC),designated a comprehensive cancer center by the National CancerInstitute.Media contact:Nicole Davis, Broad Institute of MIT and Harvard; (617) 258-0952;ndavis@broad.mit.edu
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».