Predicting Response Prognosis in Pediatric Cancers
Predicting Response Prognosis in Pediatric Cancers
批准号:
10703008
负责人:
Javed Khan
金额:
$17.09万
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至
关键词:
1p36AgeAlgorithmsAmino AcidsBiologicalCell Culture TechniquesCessation of lifeChemicalsChildhood Soft Tissue SarcomaClinicalCollaborationsCompanionsCustomDNA Sequence AlterationDataDatabasesDiagnosisDisease-Free SurvivalEnrollmentFOXO1A geneFingerprintGene AmplificationGene DeletionGene Expression ProfilingGenesGeneticGenomicsGoalsHistologyIncidenceInternationalIsotopesIsotopically-Coded Affinity TaggingLabelLightMYCN geneMalignant - descriptorMalignant Childhood NeoplasmMalignant NeoplasmsMeasurementMesenchymal Cell NeoplasmMethodsMolecularMutationNeural CrestNeuroblastomaOutcomePathway interactionsPatientsPatternPattern RecognitionPediatric Oncology GroupPhosphorylationPloidiesPrognosisPrognostic MarkerPropertyProteinsProteomicsReagentRecurrent diseaseResearchResearch DesignRhabdomyosarcomaSamplingSeriesSomatic MutationStable Isotope LabelingSurvival AnalysisSurvival RateTechniquesTherapeuticTissuesTumor BiologyUnited Kingdomaggressive therapyartificial neural networkbasebiomarker developmentcDNA Arrayscancer genomicscell typeconventional therapydiagnostic biomarkerdifferential expressiondriver mutationeffective therapyexome sequencinggenetic variantgenome sequencinggenomic biomarkergenomic profileshigh riskinsertion/deletion mutationnext generationnext generation sequencingoutcome predictionphosphoproteomicspredicting responseprognosticprotein expressionrisk stratificationtargeted treatmenttherapeutic targettranscriptome sequencingtreatment grouptumorwhole genome
中文摘要
神经母细胞瘤是起源于神经嵴的癌症,其具有取决于 就诊年龄、分期、组织学、MYCN扩增的存在、染色体倍性,以及 1 p36的缺失状态。很少有人知道的分子机制,赋予良好的或 这种肿瘤和其他恶性肿瘤预后差。我们已经证明癌症可以 诊断的基础上,基因表达谱使用cDNA微阵列和复杂的 模式识别算法,如人工神经网络。肿瘤基因组学部分 与治疗适用性研究合作扩展了这项研究, 生成有效治疗(靶向)组,使用 下一代全基因组、外显子组和转录组测序, 注释的神经母细胞瘤样本。通过这些方法,我们正在识别体细胞突变, 肿瘤特异性表达模式或指纹,可唯一识别不良预后 组,以及那些与特定的遗传畸变,包括MYCN 放大横纹肌肉瘤是儿童最常见的软组织肉瘤。 尽管进行了积极的治疗,但转移性或复发性癌患者的5年生存率 疾病仍然很差,超出PAX-FOXO 1融合状态,没有基因组标记可用于 危险分层我们提出了一个国际财团的研究,旨在确定 驱动突变的发生率及其与临床结果的相关性。患者和 方法:从儿童肿瘤组(COG)招募的患者中收集肿瘤样本 试验和英国患者入组恶性间质肿瘤(MMT)和RMS 2005 对试验进行定制捕获测序。突变、插入缺失、基因缺失和 鉴定扩增,并进行存活分析。我们将制作一个 可搜索的伴随数据库(https://clinomics.ccr.cancer.gov/clinomics/public/),包含 所有基因组变异和包括生存数据的临床注释。通过这些技术,我们 希望对与预后相关的基因组图谱进行分类,从而确定基因 赋予了这些生物特性。同位素编码亲和标签(ICAT),稳定同位素 通过细胞培养物中的氨基酸标记(SILAC)和磷酸化蛋白质组学分析, 定量测量蛋白表达水平和磷酸化状态, 不同的细胞类型和组织。在这些方法中,可以比较来自两个样品的蛋白质 通过用试剂的轻同位素和重同位素形式化学标记两种样品 分别通过这种和其他蛋白质组学方法,我们计划测序和鉴定多达 3000-4000差异表达蛋白质的肿瘤之间的差(死亡)和良好(事件 无生存3年)结果。这些蛋白质及其磷酸化状态表明 作为治疗、诊断和预后标记物的潜在靶点, 并提供了这些肿瘤的生物学的重要线索,这些肿瘤对 常规治疗。
英文摘要
Neuroblastomas are cancers of neural crest origin with variable prognoses depending on age at presentation, stage, histology, presence of MYCN amplification, chromosomal ploidy, and deletion status of 1p36. Very little is known of the molecular mechanisms that confer good or poor prognosis in this and other malignancies. We have demonstrated that cancers can be diagnosed on the basis of gene expression profiling using cDNA microarrays and sophisticated pattern recognition algorithms such as Artificial Neural Networks. The Oncogenomics Section has expanded this study in collaboration with the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) group to perform more extensive genomic analysis using next generation whole genome, exome and transcriptome sequencing on a series of clinically annotated neuroblastoma samples. With these methods we are identifying somatic mutations, and tumor-specific expression patterns, or fingerprints, that uniquely identify a poor prognostic group, as well as those associated with specific genetic aberrations including MYCN amplification. Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. Despite aggressive therapy, the 5-year survival rate for patients with metastatic or recurrent disease remains poor and beyond PAX-FOXO1 fusion status, no genomic markers are available for risk stratification. We present an international consortium study designed to determine the incidence of driver mutations and their association with clinical outcome. Patients and Method: Tumor samples collected from patients enrolled on Children's Oncology Group (COG) trials and United Kingdom patients enrolled on Malignant Mesenchymal Tumour (MMT) and RMS2005 trials were subjected to custom capture sequencing. Mutations, indels, gene deletions and amplifications were identified, and survival analysis will be performed. We will produce a searchable companion database (https://clinomics.ccr.cancer.gov/clinomics/public/), containing all genomic variants, and clinical annotation including survival data. By these techniques, we hope to classify genomic profiles that correlate with prognosis and hence identify the genes that confer these biological properties. Isotope-coded affinity tags (ICAT), stable isotope labeling by amino acids in cell culture (SILAC) and phospho-proteomic analysis allows the quantitative measurement of protein expression levels and the phosphorylation status in different cell types and tissues. In these methods proteins from two samples can be compared by chemically labeling both samples with the light and heavy isotopic forms of a reagent respectively. With this and other proteomic method we plan to sequence and identify up to 3000-4000 differentially expressed proteins between tumors with poor (death) and good (event free survival 3yrs) outcome. These proteins and their phosphorylation status indicates potential targets for therapy, diagnostic and prognostic markers for high-risk patients as well as provide important clues on the biology of these tumors that fail to respond to conventional therapy.
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