Dataset Information

Detection of novel and potentially actionable anaplastic lymphoma kinase (ALK) rearrangement in colorectal adenocarcinoma by immunohistochemistry screening.

ABSTRACT: Anaplastic lymphoma kinase (ALK) rearrangement has been detected in colorectal carcinoma (CRC) using advanced molecular diagnostics tests including exon scanning, fluorescence in situ hybridization (FISH), and next generation sequencing (NGS). We investigated if immunohistochemistry (IHC) can be used to detect ALK rearrangement in gastrointestinal malignancies.Tissue microarrays (TMAs) from consecutive gastric carcinoma (GC) and CRC patients who underwent surgical resection at Samsung Medical Center, Seoul, Korea were screened by IHC using ALK monoclonal antibody 5A4. IHC positive cases were confirmed by FISH, nCounter assays, and NGS-based comprehensive genomic profiling (CGP). ALK IHC was further applied to CRC patients enrolled in a pathway-directed therapeutic trial.Four hundred thirty-two GC and 172 CRC cases were screened by IHC. No GC sample was ALK IHC positive. One CRC (0.6%) was ALK IHC positive (3+) that was confirmed by ALK FISH and a novel CAD-ALK (C35; A20) fusion variant that resulted from a paracentric inversion event inv(2)(p22-21p23) was identified by CGP. One out of 50 CRC patients enrolled in a pathway-directed therapeutic trial was ALK IHC positive (3+) confirmed by ALK FISH and found to harbor the EML4-ALK (E21, A20) fusion variant by CGP. Growth of a tumor cell line derived from this EML4-ALK CRC patient was inhibited by ALK inhibitors crizotinib and entrectinib.ALK IHC is a viable screening strategy for identifying ALK rearrangement in CRC. ALK rearrangement is a potential actionable driver mutation in CRC based on survival inhibition of patient tumor-derived cell line by potent ALK inhibitors.

SUBMITTER: Lee J

PROVIDER: S-EPMC4695188 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of novel and potentially actionable anaplastic lymphoma kinase (ALK) rearrangement in colorectal adenocarcinoma by immunohistochemistry screening.

Lee Jeeyun J Kim Hee Cheol HC Hong Jung Yong JY Wang Kai K Kim Sun Young SY Jang Jiryeon J Kim Seung Tae ST Park Joon Oh JO Lim Ho Yeong HY Kang Won Ki WK Park Young Suk YS Lee Jiyun J Lee Woo Yong WY Park Yoon Ah YA Huh Jung Wook JW Yun Seong Hyeon SH Do In-Gu IG Kim Seok Hyung SH Balasubramanian Sohail S Stephens Philip J PJ Ross Jeffrey S JS Li Gang Gary GG Hornby Zachary Z Ali Siraj M SM Miller Vincent A VA Kim Kyoung-Mee KM Ou Sai-Hong Ignatius SH

Oncotarget 20150901 27

<h4>Purpose</h4>Anaplastic lymphoma kinase (ALK) rearrangement has been detected in colorectal carcinoma (CRC) using advanced molecular diagnostics tests including exon scanning, fluorescence in situ hybridization (FISH), and next generation sequencing (NGS). We investigated if immunohistochemistry (IHC) can be used to detect ALK rearrangement in gastrointestinal malignancies.<h4>Experimental designs</h4>Tissue microarrays (TMAs) from consecutive gastric carcinoma (GC) and CRC patients who under ...[more]

PMID: 26172300

Similar Datasets

Project description:Purpose:To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods:This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results:In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538-0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630-0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion:Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

Dataset Information

Detection of novel and potentially actionable anaplastic lymphoma kinase (ALK) rearrangement in colorectal adenocarcinoma by immunohistochemistry screening.

Publications

Detection of novel and potentially actionable anaplastic lymphoma kinase (ALK) rearrangement in colorectal adenocarcinoma by immunohistochemistry screening.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets