RESUMEN
Squamous cell carcinomas (SCCs) are one of the most frequent forms of human malignancy, but, other than TP53 mutations, few causative somatic aberrations have been identified. We identified NOTCH1 or NOTCH2 mutations in ~75% of cutaneous SCCs and in a lesser fraction of lung SCCs, defining a spectrum for the most prevalent tumor suppressor specific to these epithelial malignancies. Notch receptors normally transduce signals in response to ligands on neighboring cells, regulating metazoan lineage selection and developmental patterning. Our findings therefore illustrate a central role for disruption of microenvironmental communication in cancer progression. NOTCH aberrations include frameshift and nonsense mutations leading to receptor truncations as well as point substitutions in key functional domains that abrogate signaling in cell-based assays. Oncogenic gain-of-function mutations in NOTCH1 commonly occur in human T-cell lymphoblastic leukemia/lymphoma and B-cell chronic lymphocytic leukemia. The bifunctional role of Notch in human cancer thus emphasizes the context dependency of signaling outcomes and suggests that targeted inhibition of the Notch pathway may induce squamous epithelial malignancies.
Asunto(s)
Carcinoma de Células Escamosas/genética , Comunicación Celular/genética , Neoplasias Pulmonares/genética , Receptor Notch1/genética , Receptor Notch2/genética , Transducción de Señal/genética , Neoplasias Cutáneas/genética , Secuencia de Bases , Codón sin Sentido/genética , Ensayo de Cambio de Movilidad Electroforética , Humanos , Escala de Lod , Datos de Secuencia Molecular , Análisis de Secuencia de ADNRESUMEN
Glioma diagnosis is based on histomorphology and grading; however, such classification does not have predictive clinical outcome after glioblastomas have developed. To date, no bona fide biomarkers that significantly translate into a survival benefit to glioblastoma patients have been identified. We previously reported that the IDH mutant G-CIMP-high subtype would be a predecessor to the G-CIMP-low subtype. Here, we performed a comprehensive DNA methylation longitudinal analysis of diffuse gliomas from 77 patients (200 tumors) to enlighten the epigenome-based malignant transformation of initially lower-grade gliomas. Intra-subtype heterogeneity among G-CIMP-high primary tumors allowed us to identify predictive biomarkers for assessing the risk of malignant recurrence at early stages of disease. G-CIMP-low recurrence appeared in 9.5% of all gliomas, and these resembled IDH-wild-type primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, mesenchymal stem cell-like epigenomic phenotype, and genomic instability. Molecular abnormalities of longitudinal G-CIMP offer possibilities to defy glioblastoma progression.
Asunto(s)
Neoplasias Encefálicas/patología , Metilación de ADN , Glioma/patología , Recurrencia Local de Neoplasia/genética , Adulto , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/mortalidad , Neoplasias Encefálicas/terapia , Islas de CpG , Femenino , Inestabilidad Genómica , Glioma/genética , Glioma/mortalidad , Glioma/terapia , Humanos , Isocitrato Deshidrogenasa/genética , Estimación de Kaplan-Meier , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Mutación , Clasificación del Tumor , Células Madre Neoplásicas/citología , Células Madre Neoplásicas/metabolismo , Fenotipo , PronósticoRESUMEN
During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.
RESUMEN
BACKGROUND: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. RESULTS: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. CONCLUSIONS: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.