RESUMEN
Tumor-specific antigens (TSA) are central elements in the immune control of cancers. To systematically explore the TSA genome, we developed a computational technology called heterogeneous expression profile analysis (HEPA), which can identify genes relatively uniquely expressed in cancer cells in contrast to normal somatic tissues. Rating human genes by their HEPA score enriched for clinically useful TSA genes, nominating candidate targets whose tumor-specific expression was verified by reverse transcription PCR (RT-PCR). Coupled with HEPA, we designed a novel assay termed protein A/G-based reverse serological evaluation (PARSE) for quick detection of serum autoantibodies against an array of putative TSA genes. Remarkably, highly tumor-specific autoantibody responses against seven candidate targets were detected in 4% to 11% of patients, resulting in distinctive autoantibody signatures in lung and stomach cancers. Interrogation of a larger cohort of 149 patients and 123 healthy individuals validated the predictive value of the autoantibody signature for lung cancer. Together, our results establish an integrated technology to uncover a cancer-specific antigen genome offering a reservoir of novel immunologic and clinical targets.
Asunto(s)
Antígenos de Neoplasias/aislamiento & purificación , Estudios de Asociación Genética/métodos , Genómica/métodos , Inmunoterapia , Terapia Molecular Dirigida , Neoplasias/terapia , Antígenos de Neoplasias/genética , Antígenos de Neoplasias/inmunología , Estudios de Casos y Controles , Femenino , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Estudio de Asociación del Genoma Completo , Humanos , Inmunoterapia/métodos , Masculino , Análisis por Micromatrices , Terapia Molecular Dirigida/métodos , Neoplasias/genética , Neoplasias/inmunología , Especificidad de Órganos/genética , Integración de SistemasRESUMEN
Cancer genomes contain many aberrant gene fusions-a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery.