Department of Medicine, Division of Respirology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; Centre for Outcomes Research and Evaluation - Research Institute of the McGill University Health Center, Montreal, 1001 Decarie Blvd., QC, Canada.
Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Research Molecular Pathology Center, Lady Davis Institute, 3755 Côte Ste-Catherine Road, Montreal, QC, Canada.
Cancer Treat Res Commun. 2021;29:100484. doi: 10.1016/j.ctarc.2021.100484. Epub 2021 Oct 29.
Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data.
Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy.
Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM.
We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%).
Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.
评估了将下一代测序 (NGS) 信息整合用于区分多原发肺癌和肺内转移的方法。我们使用概率模型、全面的组织学评估和 NGS 对患者进行分类。整合 NGS 数据证实了初始诊断（n=41），修订了诊断（n=12），同时导致非信息性数据（n=8）。整合 NGS 数据可显著提高诊断准确性。
区分多原发肺癌 (MPLC) 和肺内转移 (IPM) 具有挑战性。本研究的目的是评估 NGS 信息如何整合到诊断策略中。
对多个肺腺癌患者使用全面的组织学评估和 NGS 进行分类。我们通过机会计算每个对具有独立突变的联合概率（因此被归类为 MPLC）。这些概率是通过每个突变的边际突变率和已知的驱动基因与不同基因座之间的负相关性来计算的。根据这些 NGS 驱动的数据，将病例重新分类为 MPLC 或 IPM。
我们分析了 61 名患者共 131 个肿瘤。最常见的突变为 KRAS（57.3%），其在肺癌中的发生率高于预期（p<0.001）。在 131 个肿瘤中，有 25 个未检测到突变（19.1%）。在 46 名患者中发现了肿瘤部位之间不一致的分子发现（75.4%）；11 名患者（18.0%）具有一致的分子发现，4 名患者（6.6%）在 3 个部位中的 2 个部位具有一致的分子发现。整合 NGS 数据后，41 名患者（67.2%）的初始诊断得到证实，12 名患者（19.7%）的诊断被修订，8 名患者（13.1%）的诊断被认为无信息。
整合 NGS 数据的信息可能显著提高诊断和分期的准确性。
