Dataset Information

Anaplastic Lymphoma Kinase Rearrangement in Digestive Tract Cancer: Implication for Targeted Therapy in Chinese Population.

ABSTRACT:

Background

Anaplastic lymphoma kinase (ALK) rearrangements define a subgroup of lung cancer which is eligible to targeted kinase inhibition. The aim of this study is to observe the incidence rate of ALK fusion in a large cohort of Chinese digestive tract cancer patients.

Patients and methods

Tissue microarray (TMA) was constructed from 808 digestive tract cancer cases, including 169 esophageal squamous cell carcinoma, 182 gastric cancer and 457 colorectal cancer (CRC) cases. We tested all cases for ALK expression via a fully automated immunohistochemistry (IHC) assay. The IHC-positive cases were subjected to fluorescence in situ hybridization (FISH), real-time polymerase chain reaction (qRT-PCR), target gene enrichment and sequencing for confirmation of ALK gene rearrangement and discovery of novel fusion partner.

Results

Among the tested cases, 2 (0.44%) CRC cases showed positive both by IHC and FISH. By qRT-PCR, EML4-ALK fusion was found in one IHC-positive CRC case. In another IHC-positive CRC case, target gene enrichment and sequencing revealed ALK was fused to a novel partner, spectrin beta non-erythrocytic 1 (SPTBN1). One gastric cancer case showed partially positive IHC result, but no fusion was found by FISH and gene sequencing.

Conclusions

The incidence rate of ALK gene fusion in Chinese CRC patients was 0.44%,but not detectable in gastric and esophageal cancers. The novel SPTBN1 -ALK fusion, together with other ALK fusion genes, may become a potential target for anti-ALK therapy.

SUBMITTER: Ying J

PROVIDER: S-EPMC4683076 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Anaplastic Lymphoma Kinase Rearrangement in Digestive Tract Cancer: Implication for Targeted Therapy in Chinese Population.

Ying Jianming J Lin Chen C Wu Jian J Guo Lei L Qiu Tian T Ling Yun Y Shan Ling L Zhou Haitao H Zhao Dongbing D Wang Jian J Liang Jianwei J Zhao Jianjun J Jiao Yuchen Y Lu Ning N Zhao Hong H

PloS one 20151217 12

<h4>Background</h4>Anaplastic lymphoma kinase (ALK) rearrangements define a subgroup of lung cancer which is eligible to targeted kinase inhibition. The aim of this study is to observe the incidence rate of ALK fusion in a large cohort of Chinese digestive tract cancer patients.<h4>Patients and methods</h4>Tissue microarray (TMA) was constructed from 808 digestive tract cancer cases, including 169 esophageal squamous cell carcinoma, 182 gastric cancer and 457 colorectal cancer (CRC) cases. We te ...[more]

PMID: 26678488

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

Anaplastic Lymphoma Kinase Rearrangement in Digestive Tract Cancer: Implication for Targeted Therapy in Chinese Population.

Background

Patients and methods

Results

Conclusions

Publications

Anaplastic Lymphoma Kinase Rearrangement in Digestive Tract Cancer: Implication for Targeted Therapy in Chinese Population.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets