RESUMEN
Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer.
Asunto(s)
Neoplasias del Sistema Nervioso Central/genética , Neurofibromina 1/antagonistas & inhibidores , Leucemia-Linfoma Linfoblástico de Células Precursoras/genética , Proteína p53 Supresora de Tumor/antagonistas & inhibidores , Ensayos Antitumor por Modelo de Xenoinjerto , Animales , Línea Celular Tumoral , Neoplasias del Sistema Nervioso Central/metabolismo , Niño , Ensayos Clínicos como Asunto , Modelos Animales de Enfermedad , Genómica , Humanos , Ratones , Mutación , Neuroblastoma/genética , Neuroblastoma/metabolismo , Neurofibromina 1/genética , Neurofibromina 1/metabolismo , Osteosarcoma/genética , Osteosarcoma/metabolismo , Leucemia-Linfoma Linfoblástico de Células Precursoras/metabolismo , Recurrencia , Rabdomiosarcoma/genética , Rabdomiosarcoma/metabolismo , Sarcoma de Ewing/genética , Sarcoma de Ewing/metabolismo , Proteína p53 Supresora de Tumor/genética , Proteína p53 Supresora de Tumor/metabolismo , Secuenciación del Exoma , Tumor de Wilms/genética , Tumor de Wilms/metabolismoRESUMEN
Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8(+) T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.
