Campesato Luís Felipe, Barroso-Sousa Romualdo, Jimenez Leandro, Correa Bruna R, Sabbaga Jorge, Hoff Paulo M, Reis Luiz F L, Galante Pedro Alexandre F, Camargo Anamaria A
Instituto Ludwig de Pesquisa Sobre o Câncer, São Paulo, Brazil.
Hospital Sírio-Libanês, São Paulo, Brazil.
Oncotarget. 2015 Oct 27;6(33):34221-7. doi: 10.18632/oncotarget.5950.
Cancer gene panels (CGPs) are already used in clinical practice to match tumor's genetic profile with available targeted therapies. We aimed to determine if CGPs could also be applied to estimate tumor mutational load and predict clinical benefit to PD-1 and CTLA-4 checkpoint blockade therapy. Whole-exome sequencing (WES) mutation data obtained from melanoma and non-small cell lung cancer (NSCLC) patients published by Snyder et al. 2014 and Rizvi et al. 2015, respectively, were used to select nonsynonymous somatic mutations occurring in genes included in the Foundation Medicine Panel (FM-CGP) and in our own Institutional Panel (HSL-CGP). CGP-mutational load was calculated for each patient using both panels and was associated with clinical outcomes as defined and reported in the original articles. Higher CGP-mutational load was observed in NSCLC patients presenting durable clinical benefit (DCB) to PD-1 blockade (FM-CGP P=0.03, HSL-CGP P=0.01). We also observed that 69% of patients with high CGP-mutational load experienced DCB to PD-1 blockade, as compared to 20% of patients with low CGP-mutational load (FM-CGP and HSL-CGP P=0.01). Noteworthy, predictive accuracy of CGP-mutational load for DCB was not statistically different from that estimated by WES sequencing (P=0.73). Moreover, a high CGP-mutational load was significantly associated with progression-free survival (PFS) in patients treated with PD-1 blockade (FM-CGP P=0.005, HR 0.27, 95% IC 0.105 to 0.669; HSL-CGP P=0.008, HR 0.29, 95% IC 0.116 to 0.719). Similar associations between CGP-mutational load and clinical benefit to CTLA-4 blockade were not observed. In summary, our data reveals that CGPs can be used to estimate mutational load and to predict clinical benefit to PD-1 blockade, with similar accuracy to that reported using WES.
癌症基因检测板（CGP）已在临床实践中用于将肿瘤的基因图谱与可用的靶向治疗进行匹配。我们旨在确定CGP是否也可用于估计肿瘤突变负荷，并预测对PD-1和CTLA-4免疫检查点阻断疗法的临床获益。分别使用Snyder等人2014年和Rizvi等人2015年发表的黑色素瘤和非小细胞肺癌（NSCLC）患者的全外显子组测序（WES）突变数据，来选择发生在Foundation Medicine检测板（FM-CGP）和我们自己的机构检测板（HSL-CGP）所包含基因中的非同义体细胞突变。使用这两种检测板计算每位患者的CGP突变负荷，并将其与原始文章中定义和报告的临床结果相关联。在对PD-1阻断呈现持久临床获益（DCB）的NSCLC患者中观察到更高的CGP突变负荷（FM-CGP P=0.03，HSL-CGP P=0.01）。我们还观察到，69%的高CGP突变负荷患者对PD-1阻断有DCB，而低CGP突变负荷患者中这一比例为20%（FM-CGP和HSL-CGP P=0.01）。值得注意的是，CGP突变负荷对DCB的预测准确性与WES测序估计的准确性在统计学上无差异（P=0.73）。此外，在接受PD-1阻断治疗的患者中，高CGP突变负荷与无进展生存期（PFS）显著相关（FM-CGP P=0.005，HR 0.27，95% IC 0.105至0.669；HSL-CGP P=0.008，HR 0.29，95% IC 0.116至0.719）。未观察到CGP突变负荷与CTLA-4阻断的临床获益之间有类似关联。总之，我们的数据表明，CGP可用于估计突变负荷并预测对PD-1阻断的临床获益，其准确性与使用WES报告的相似。
