Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.
Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.
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DOI：
10.1186/s12920-020-00782-1
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发表时间：
2020-10-22
影响因子：
2.7
通讯作者：
Huang J
中科院分区：
文献类型：
--
作者：
Choi Y;Qu J;Wu S;Hao Y;Zhang J;Ning J;Yang X;Lofaro L;Pankratz DG;Babiarz J;Walsh PS;Billatos E;Lenburg ME;Kennedy GC;McAuliffe J;Huang J
支气管镜检查对可疑肺癌的诊断敏感性低，导致许多不确定的结果。支气管基因组分类器（BGC）的开发旨在通过在支气管镜检查不确定时识别肺癌低风险患者来帮助患者管理。BGC在肺癌诊断中的气道上皮基因表达（AEGIS）试验中对患者进行了培训和验证。一个现代患者队列，BGC登记研究，显示了与AEGIS队列的关键临床因素的差异，吸烟史较少，结节较小，年龄较大。此外，我们发现了影响基因表达和潜在伪装基因组癌症信号的干扰因素（吸入药物和样本采集时间）。在这项研究中，我们利用多个队列和下一代测序技术开发了一个强大的基因组测序分类器（GSC）。为了解决人口组成变化和干扰因素，我们协同三种算法策略：1）临床显性和基因组显性模型的集成; 2）分层回归模型的开发，其中在模型中拟合基因组影响之前回归临床变量的主要影响;和3）基因组和临床相互作用项的靶向放置以稳定干扰因子的作用。最终的GSC模型使用1232个基因和四个临床协变量-年龄，包年，吸入药物使用和标本采集时间。在验证集（N = 412）中，GSC将低和中等试验前风险受试者向下分类为极低和低试验后风险，特异性为45%（95%CI 37-53%），灵敏度为91%（95%CI 81-97%），阴性预测值为95%（95%CI 89-98%）。12%的中度试验前风险受试者被上调为高试验后风险，阳性预测值为65%（95%CI 44-82%），27%的高试验前风险受试者被上调为极高试验后风险，阳性预测值为91%（95%CI 78-97%）。GSC克服了干扰因素的影响，并在多个队列中实现了一致的性能。它在癌症风险的向下和向上分类中证明了诊断准确性，为许多支气管镜检查结果不确定的患者提供了医生可操作的信息。
Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
4.3
作者：
Tukey, Melissa H.;Wiener, Renda Soylemez
通讯作者：
Wiener, Renda Soylemez
影响因子：
46.9
作者：
Risso, Davide;Ngai, John;Speed, Terence P.;Dudoit, Sandrine
通讯作者：
Dudoit, Sandrine
DOI：
10.1016/s0378-3758(02)00388-9
发表时间：
2003-12-01
影响因子：
0.9
作者：
van der Laan, MJ;Pollard, KS
通讯作者：
Pollard, KS