A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features

AimsTo identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing.Methods and ResultsWe identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas.ConclusionsFemale, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases.研究目的：明确ROS1重排型非小细胞肺癌（non-small-cell lung carcinomas, NSCLCs）的临床与组织病理学特征，并构建预测模型以预先筛选适合接受分子检测的患者。 研究方法与结果：本研究在1165例经实时荧光定量PCR（real-time PCR）与荧光原位杂交（fluorescence in situ hybridization, FISH）确认的非小细胞肺癌患者中，筛选出27例ROS1重排型肺腺癌（lung adenocarcinomas）病例；随后通过单变量与多变量分析筛选与ROS1重排相关的预测因子，并最终构建了预测模型。采用ROS1免疫组化（immunochemistry）检测，1165例患者中有59例存在不同程度的ROS1表达。其中，免疫组化染色评分为3+的28例病例中，19例（占比68%，19/28）、评分为2+的17例病例中8例（占比47%，8/17）经实时荧光定量PCR与荧光原位杂交验证存在ROS1重排。在手术切除组中，腺泡状生长模式最为常见（占比57%，8/14）；而在活检组中，实体生长模式最为多见（占比78%，7/13）。经多因素logistic回归（logistic regression）分析，本研究确定女性性别、筛状结构及砂粒体的存在是与ROS1重排相关性最强的三项指标，并据此构建了肺腺癌ROS1重排状态的预测模型。 研究结论：女性性别、筛状结构及砂粒体的存在是判断ROS1重排状态的三项最强预测指标，所构建的预测公式有助于筛选ROS1重排型非小细胞肺癌，尤其适用于ROS1免疫组化结果存疑的病例。