Gino In, Jeremy Mason, Sonia Lin, Paul K Newton, Peter Kuhn, Jorge Nieva
{"title":"Development of metastatic brain disease involves progression through lung metastases in <i>EGFR</i> mutated non-small cell lung cancer.","authors":"Gino In, Jeremy Mason, Sonia Lin, Paul K Newton, Peter Kuhn, Jorge Nieva","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Lung cancer is often classified by the presence of oncogenic drivers, such as epidermal growth factor receptor (<i>EGFR</i>), rather than patterns of anatomical distribution. While metastatic spread may seem a random and unpredictable process, we explored the possibility of using its quantifiable nature as a measure of describing and comparing different subsets of disease. We constructed a database of 664 non-small cell lung cancer (NSCLC) patients treated at the University of Southern California Norris Comprehensive Cancer Center and the Los Angeles County Medical Center. Markov mathematical modeling was employed to assess metastatic sites in a spatiotemporal manner through every time point in progression of disease. Our findings identified a preferential pattern of primary lung disease progressing through lung metastases to the brain amongst <i>EGFR</i> mutated (<i>EGFR</i> <sup>m</sup>) NSCLC patients, with exon 19 deletions or exon 21 L858R mutations, as compared to <i>EGFR</i> wild type (<i>EGFR</i> <sup>wt</sup>). The brain was classified as an anatomic \"sponge\", with a higher ratio of incoming to outgoing spread, for <i>EGFR</i> <sup>m</sup> NSCLC. Bone metastases were more commonly identified in <i>EGFR</i> <sup>wt</sup> patients. Our study supports a link between the anatomical and molecular characterization of lung metastatic cancer. Improved understanding of the differential biology that drives discordant patterns of anatomic spread, based on genotype specific profiling, has the potential to improve personalized oncologic care.</p>","PeriodicalId":91466,"journal":{"name":"Convergent science physical oncology","volume":"3 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166474/pdf/nihms916878.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Convergent science physical oncology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/7/13 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Lung cancer is often classified by the presence of oncogenic drivers, such as epidermal growth factor receptor (EGFR), rather than patterns of anatomical distribution. While metastatic spread may seem a random and unpredictable process, we explored the possibility of using its quantifiable nature as a measure of describing and comparing different subsets of disease. We constructed a database of 664 non-small cell lung cancer (NSCLC) patients treated at the University of Southern California Norris Comprehensive Cancer Center and the Los Angeles County Medical Center. Markov mathematical modeling was employed to assess metastatic sites in a spatiotemporal manner through every time point in progression of disease. Our findings identified a preferential pattern of primary lung disease progressing through lung metastases to the brain amongst EGFR mutated (EGFRm) NSCLC patients, with exon 19 deletions or exon 21 L858R mutations, as compared to EGFR wild type (EGFRwt). The brain was classified as an anatomic "sponge", with a higher ratio of incoming to outgoing spread, for EGFRm NSCLC. Bone metastases were more commonly identified in EGFRwt patients. Our study supports a link between the anatomical and molecular characterization of lung metastatic cancer. Improved understanding of the differential biology that drives discordant patterns of anatomic spread, based on genotype specific profiling, has the potential to improve personalized oncologic care.
癌症通常根据致癌驱动因素的存在进行分类,如表皮生长因子受体(EGFR),而不是解剖分布模式。虽然转移性传播可能看起来是一个随机和不可预测的过程,但我们探索了使用其可量化性质来描述和比较不同疾病子集的可能性。我们建立了南加州大学诺里斯综合癌症中心和洛杉矶县医疗中心664名非小细胞肺癌(NSCLC)患者的数据库。Markov数学模型用于在疾病进展的每个时间点以时空方式评估转移部位。我们的研究结果确定,与EGFR野生型(EGFR-wt)相比,在EGFR突变(EGFR-m)的NSCLC患者中,外显子19缺失或外显子21 L858R突变的原发性肺病通过肺转移到大脑的优先模式。对于EGFR m NSCLC,大脑被归类为解剖上的“海绵”,具有较高的传入和传出扩散比率。骨转移更常见于EGFR-wt患者。我们的研究支持了肺转移性癌症的解剖学和分子特征之间的联系。基于基因型特异性分析,更好地理解导致解剖扩散不一致模式的差异生物学,有可能改善个性化肿瘤学护理。