Samsung Genome Institute, Samsung Medical Center, Seoul, Korea.
Department of Digital Health, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Korea.
J Immunother Cancer. 2020 Oct;8(2). doi: 10.1136/jitc-2020-001199.
Tumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored.
We comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker's predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156).
Low tumor purity was common (range 30%-45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016).
Our data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.
肿瘤突变负荷（TMB）的测量受到样本肿瘤纯度低的限制，这可能会影响免疫治疗反应的预测，尤其是在使用基于全外显子组测序的 TMB（wTMB）时。通过深度覆盖更高的靶向面板测序 TMB（pTMB）可以克服这个问题，但这一点尚未得到探索。
我们全面重新分析了四个接受免疫检查点抑制剂（ICI）治疗队列的公共数据集（采用 pTMB 或 wTMB），以测试每种生物标志物对低纯度样本（截止值：30%）的预测能力。为了验证，对来自乳腺癌患者的相同肿瘤标本进行了配对基因组分析（配对-BRCA，n=165）和接受 ICI 治疗的晚期非小细胞肺癌患者（配对-NSCLC，n=156），以直接比较 wTMB 和 pTMB。
真实世界的 ICI 治疗患者样本中，肿瘤纯度低的情况很常见（范围为 30%-45%）。在公共队列的生存分析中，当肿瘤纯度低时，wTMB 不能预测免疫治疗的临床获益（对数秩 p=0.874），而 pTMB 可以有效地对生存结果进行分层（对数秩 p=0.020）。在配对-BRCA 和配对-NSCLC 队列中，pTMB 受肿瘤纯度的影响较小，在低等位基因频率下检测到的体细胞变异明显更多（p<0.001）。我们发现，在大部分克隆变异未被全外显子组测序检测到的低纯度样本中，wTMB 显著低估。有趣的是，pTMB 比 wTMB 更能准确预测免疫治疗后的无进展生存期（PFS），因为其在低肿瘤纯度亚组中的性能更优（p=0.054 与 p=0.358）。多变量分析显示，pTMB（p=0.016），而不是 wTMB（p=0.32），即使在低纯度样本中，也是 PFS 的独立预测因子。在低纯度亚组中，使用 pTMB 的净重新分类指数为 21.7%（p=0.016）。
我们的数据表明，由于其对低等位基因分数的难以检测变异具有更高的敏感性，靶向深度测序的 TMB 特征可能具有预测 ICI 反应的潜在优势。因此，pTMB 可以作为一种宝贵的生物标志物，在临床试验和临床试验之外的实践中都有应用，因为它可以可靠地减轻纯度相关的偏差。
