QUANTITATIVE DETECTION OF CLINICALLY ACTIONABLE MUTATIONS IN LUNG CANCER

描述（由申请人提供）：在大多数非小细胞肺癌（NSCLC）患者中，现有的化疗仅能提高4%-15%的生存率。鉴于目前治疗方法的有限成功，改善这些患者的预后是非常必要的。迄今为止，全面的基因组研究表明，患者之间的肺肿瘤存在显著差异。另一方面，最近在肺癌中使用靶向特定分子的药物，以及认识到肺肿瘤的基因组成可以预测患者对每种药物的反应，这被认为是过去三十年来肺癌治疗中最具戏剧性的进步之一。该提案的假设是，使用“下一代”测序技术，对目前已知的治疗药物靶点的基因突变进行集中和敏感的分析，将确定NSCLC患者，这些患者将成为新型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”测序试验对大约48个基因的外显子和周围序列进行“深度”测序，这些基因是目前可用药物的已知靶标。该分析将在常规固定手术病理标本的DNA上进行，以证明实际的临床效用。基于一个精心挑选的、高度注释的400名早期非小细胞肺癌患者队列，本研究产生的肿瘤DNA序列数据将使我们能够解决几个实际问题，这些问题对于将该方法转化为临床诊断工具很重要。首先，基于大大提高的灵敏度水平（1500倍测序覆盖率），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

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.