Predicting Response Prognosis in Pediatric Cancers

预测儿童癌症的反应预后

• 批准号: 10703008
• 负责人: Javed Khan
• 金额: $ 17.09万
• 依托单位: DIVISION OF CLINICAL SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703008
• 关键词: 1p36; Age; Algorithms; Amino Acids; Biological; Cell Culture Techniques; Cessation of life; Chemicals; Childhood Soft Tissue Sarcoma; Clinical; Collaborations; Companions; Custom; DNA Sequence Alteration; Data; Databases; Diagnosis; Disease-Free Survival; Enrollment; FOXO1A gene; Fingerprint; Gene Amplification; Gene Deletion; Gene Expression Profiling; Genes; Genetic; Genomics; Goals; Histology; Incidence; International; Isotopes; Isotopically-Coded Affinity Tagging; Label; Light; MYCN gene; Malignant - descriptor; Malignant Childhood Neoplasm; Malignant Neoplasms; Measurement; Mesenchymal Cell Neoplasm; Methods; Molecular; Mutation; Neural Crest; Neuroblastoma; Outcome; Pathway interactions; Patients; Pattern; Pattern Recognition; Pediatric Oncology Group; Phosphorylation; Ploidies; Prognosis; Prognostic Marker; Property; Proteins; Proteomics; Reagent; Recurrent disease; Research; Research Design; Rhabdomyosarcoma; Sampling; Series; Somatic Mutation; Stable Isotope Labeling; Survival Analysis; Survival Rate; Techniques; Therapeutic; Tissues; Tumor Biology; United Kingdom; aggressive therapy; artificial neural network; base; biomarker development; cDNA Arrays; cancer genomics; cell type; conventional therapy; diagnostic biomarker; differential expression; driver mutation; effective therapy; exome sequencing; genetic variant; genome sequencing; genomic biomarker; genomic profiles; high risk; insertion/deletion mutation; next generation; next generation sequencing; outcome prediction; phosphoproteomics; predicting response; prognostic; protein expression; risk stratification; targeted treatment; therapeutic target; transcriptome sequencing; treatment group; tumor; whole genome

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.

神经母细胞瘤是起源于神经嵴的癌症，其预后取决于发病时的年龄、分期、组织学、MYCN扩增的存在、染色体倍性和1p36缺失状态。对于这种和其他恶性肿瘤预后好坏的分子机制知之甚少。我们已经证明，癌症可以在基因表达谱的基础上诊断，使用cDNA微阵列和复杂的模式识别算法，如人工神经网络。肿瘤基因组学组与治疗应用研究产生有效治疗（TARGET）小组合作扩展了这项研究，使用下一代全基因组、外显子组和转录组测序对一系列临床注释的神经母细胞瘤样本进行更广泛的基因组分析。通过这些方法，我们可以识别体细胞突变、肿瘤特异性表达模式或指纹，从而唯一地识别预后不良的群体，以及与特定遗传畸变（包括MYCN扩增）相关的群体。横纹肌肉瘤（Rhabdomyosarcoma， RMS）是儿童最常见的软组织肉瘤。尽管进行了积极的治疗，但转移性或复发性疾病患者的5年生存率仍然很低，而且PAX-FOXO1融合状态之外，没有基因组标记物可用于风险分层。我们提出了一项国际联合研究，旨在确定驱动突变的发生率及其与临床结果的关系。患者和方法：从儿童肿瘤组（COG）试验的患者和恶性间充质肿瘤（MMT）和RMS2005试验的英国患者中收集的肿瘤样本进行定制捕获测序。鉴定突变、缺失、基因缺失和扩增，并进行生存分析。我们将建立一个可搜索的配套数据库（https://clinomics.ccr.cancer.gov/clinomics/public/），包含所有基因组变异，以及包括生存数据在内的临床注释。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，从而确定赋予这些生物学特性的基因。同位素编码亲和标签（ICAT）、细胞培养氨基酸稳定同位素标记（SILAC）和磷酸化蛋白质组学分析可以定量测量不同细胞类型和组织中的蛋白质表达水平和磷酸化状态。在这些方法中，可以通过分别用试剂的轻同位素和重同位素形式对两个样品进行化学标记来比较来自两个样品的蛋白质。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个差异表达蛋白，这些蛋白存在于预后较差（死亡）和良好（无事件生存3年）的肿瘤之间。这些蛋白及其磷酸化状态提示了高风险患者的潜在治疗靶点、诊断和预后标记，并为这些对常规治疗无效的肿瘤的生物学研究提供了重要线索。