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1.
Cell ; 155(3): 552-66, 2013 Oct 24.
Artículo en Inglés | MEDLINE | ID: mdl-24243015

RESUMEN

Context-specific molecular vulnerabilities that arise during tumor evolution represent an attractive intervention target class. However, the frequency and diversity of somatic lesions detected among lung tumors can confound efforts to identify these targets. To confront this challenge, we have applied parallel screening of chemical and genetic perturbations within a panel of molecularly annotated NSCLC lines to identify intervention opportunities tightly linked to molecular response indicators predictive of target sensitivity. Anchoring this analysis on a matched tumor/normal cell model from a lung adenocarcinoma patient identified three distinct target/response-indicator pairings that are represented with significant frequencies (6%-16%) in the patient population. These include NLRP3 mutation/inflammasome activation-dependent FLIP addiction, co-occurring KRAS and LKB1 mutation-driven COPI addiction, and selective sensitivity to a synthetic indolotriazine that is specified by a seven-gene expression signature. Target efficacies were validated in vivo, and mechanism-of-action studies informed generalizable principles underpinning cancer cell biology.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/metabolismo , Ensayos de Selección de Medicamentos Antitumorales , Indoles/farmacología , Neoplasias Pulmonares/metabolismo , Triazinas/farmacología , Animales , Proteína Reguladora de Apoptosis Similar a CASP8 y FADD/metabolismo , Carcinoma de Pulmón de Células no Pequeñas/patología , Proteínas Portadoras , Línea Celular Tumoral , Proteína Coatómero/metabolismo , Femenino , Genes ras , Xenoinjertos , Humanos , Neoplasias Pulmonares/patología , Lisosomas/metabolismo , Ratones , Terapia Molecular Dirigida , Proteína con Dominio Pirina 3 de la Familia NLR , Trasplante de Neoplasias , Fosforilación Oxidativa
2.
Genomics Inform ; 11(1): 55-7, 2013 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-23613684

RESUMEN

Growing numbers of studies employ cell line-based systematic short interfering RNA (siRNA) screens to study gene functions and to identify drug targets. As multiple sources of variations that are unique to siRNA screens exist, there is a growing demand for a computational tool that generates normalized values and standardized scores. However, only a few tools have been available so far with limited usability. Here, we present siMacro, a fast and easy-to-use Microsoft Office Excel-based tool with a graphic user interface, designed to process single-condition or two-condition synthetic screen datasets. siMacro normalizes position and batch effects, censors outlier samples, and calculates Z-scores and robust Z-scores, with a spreadsheet output of >120,000 samples in under 1 minute.

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