早期发现可以显着降低肺癌死亡率。这里介绍的是一种开发基于克隆造血突变的血液筛查组的方法。动物模型研究表明，肿瘤浸润免疫细胞中的克隆造血突变可以调节癌症进展，代表潜在的预测生物标志物。这项研究的目的是确定血液样本中这些突变的克隆扩增是否可以预测肺癌的发生。使用肺癌样本的测序数据鉴定出肿瘤浸润免疫细胞中的一组 98 种潜在致病性克隆造血突变。这些突变被用作开发逻辑回归机器学习模型的预测因子。该模型使用来自 18 个不同队列的 578 个肺癌样本和 545 个非癌症样本的测序数据进行了测试。逻辑回归模型正确分类了肺癌和非癌症血液样本，敏感性为 94.12%（95% 置信区间：92.20-96.04%），特异性为 85.96%（95% 置信区间：82.98-88.95%）。我们的结果表明，使用这种方法开发出准确的基于血液的肺癌筛查组合是可能的。与目前正在开发的大多数其他“液体活检”不同，这里提出的方法基于标准测序方案，并使用相对少量的合理选择的突变作为预测因子。版权所有：© 2024 Anandakrishnan 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Early detection can significantly reduce mortality due to lung cancer. Presented here is an approach for developing a blood-based screening panel based on clonal hematopoietic mutations. Animal model studies suggest that clonal hematopoietic mutations in tumor infiltrating immune cells can modulate cancer progression, representing potential predictive biomarkers. The goal of this study was to determine if the clonal expansion of these mutations in blood samples could predict the occurrence of lung cancer. A set of 98 potentially pathogenic clonal hematopoietic mutations in tumor infiltrating immune cells were identified using sequencing data from lung cancer samples. These mutations were used as predictors to develop a logistic regression machine learning model. The model was tested on sequencing data from a separate set of 578 lung cancer and 545 non-cancer samples from 18 different cohorts. The logistic regression model correctly classified lung cancer and non-cancer blood samples with 94.12% sensitivity (95% Confidence Interval: 92.20-96.04%) and 85.96% specificity (95% Confidence Interval: 82.98-88.95%). Our results suggest that it may be possible to develop an accurate blood-based lung cancer screening panel using this approach. Unlike most other "liquid biopsies" currently under development, the approach presented here is based on standard sequencing protocols and uses a relatively small number of rationally selected mutations as predictors.Copyright: © 2024 Anandakrishnan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.