QUANTITATIVE DETECTION OF CLINICALLY ACTIONABLE MUTATIONS IN LUNG CANCER

描述(申请人提供)：现有的化疗仅使大多数非小细胞肺癌(NSCLC)患者的存活率提高了4%-15%。鉴于目前治疗的成功有限，极有必要改善这些患者的结果。到目前为止，全面的基因组研究表明，不同患者的肺癌差异显著。另一方面，最近针对肺癌特定分子的药物的使用，以及意识到肺癌的基因构成可以预测患者对这些药物的反应，被认为是过去30年来肺癌治疗中最戏剧性的进步之一。这项建议的假设是，使用“下一代”测序技术，对基因突变进行集中和敏感的分析，这些基因已经是目前可用的治疗药物的靶标，将确定哪些非小细胞肺癌患者将是新的临床试验的理想候选者。为了验证这一假设，我们将使用基于捕获的下一代测序分析来对大约48个基因的外显子和周围序列进行深入的测序，这些基因是目前可用的药物的已知靶标。该分析将对常规固定的外科病理标本的DNA进行，以证明实际的临床实用价值。基于精心挑选的400名早期NSCLC患者的高度注释的队列，这项研究产生的肿瘤DNA序列数据将使我们能够解决几个实际问题，这些问题对于将这种方法转化为临床诊断工具很重要。首先，基于极大增加的敏感性水平(1500倍的测序覆盖范围)，我们将在早期确定基因组变异(单核苷酸替换、小插入/缺失和扩增)的总体频率。 非小细胞肺癌分期。其次，我们将定量评估患者之间突变等位基因频率(突变负担)的变异性，并寻找与其他临床或病理参数的相关性，如临床复发或肿瘤组织学特征。最后，我们将检查特定基因组变异与用于分类的传统临床和病理参数之间的潜在相关性。这项工作将使用一种新的带注释的生物标本资源和一种创新的技术方法，测试对非小细胞肺癌患者进行前瞻性、定量突变分析以进行“个性化”治疗选择的临床相关性。

DESCRIPTION (provided by applicant): Existing chemotherapy improves survival by only 4%-15% in most patients with non-small cell lung cancer (NSCLC). There is a tremendous need to improve the outcomes in these patients given the limited success of current therapies. To date, comprehensive genomic studies have shown that lung tumors are remarkably diverse between patients. On the other hand, the recent use of agents that target specific molecules in lung cancer and the realization that the genetic makeup of a lung tumor can predict how patients will respond to each of these drugs has been considered one of the most dramatic advances in lung cancer treatment over the past three decades. The hypothesis of this proposal is that, using 'next-generation' sequencing technology, a focused and sensitive analysis of mutations in genes which are already known targets of currently available therapeutic agents will identify NSCLC patients who would be ideal candidates for novel clinical trials. To test this hypothesis, we will use a capture-based, 'next-generation' sequencing assay to perform 'deep' sequencing of the exons and surrounding sequence of approximately 48 genes that are the known targets of currently available agents. The assay will be performed on DNA from routinely fixed surgical pathology specimens to demonstrate practical clinical utility. Based on a carefully selected, highly annotated cohort of 400, early stage NSCLC patients, tumor DNA sequence data generated from this study will allow us to address several practical questions important for translating this approach into a clinical diagnostic tool. First, based on a vastly increased levelof sensitivity (1,500-fold sequencing coverage), we will determine the overall frequency of genomic variants (single nucleotide substitutions, small insertions/deletions, and amplifications) in early stage NSCLC. Second, we will quantitatively assess the variability of mutated allele frequency ('mutation burden') between patients and seek correlations with other clinical or pathological parameters such as clinical relapse or tumor histological features. Finally, we will examine potential correlations between specific genomic variants and traditional clinical and pathological parameters used for classification. Using both a novel annotated bio specimen resource and an innovative technical approach, this work will test the clinical relevance of performing prospective, quantitative mutation profiling on NSCLC patients for 'personalized' treatment selection.