Dataset Information

Sweyjawbu expression is a predictor of ALK rearrangement status in lymphoma.

ABSTRACT: In recent years molecular subtyping has become an important tool for accurate diagnosis of many cancers; for example, the detection of ALK rearrangements in lymphoma and lung cancer helps clinicians provide more precise diagnosis and treatment. Fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) are two routine approaches used to detect ALK rearrangements. However, difficulties with acquisition of biopsy samples, high costs, and long waiting time for results negatively impact the application of these methods. A rapid and inexpensive alternative would be a useful complement to current ALK rearrangement detection. We identified a novel gene, sweyjawbu, from Affymetrix microarray studies. Its expression correlated strongly with ALK in an analysis of 1037 cancer cell lines (correlation coefficient = 0.92). By comparing sweyjawbu transcript levels, it was possible to discriminate 12 ALK rearrangement-positive lymphoma samples from 64 ALK rearrangement-negative lymphomas. Moreover, combining measurements of sweyjawbu expression and the ratio of the 5' and 3' portions of the ALK transcript provided even more accurate identification of ALK rearrangement-positive lymphomas. This novel approach is an excellent complement or alternative to existing FISH and IHC methodologies.

SUBMITTER: Xue K

PROVIDER: S-EPMC5352370 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sweyjawbu expression is a predictor of ALK rearrangement status in lymphoma.

Xue Kai K Ye Xun X Liu Fang F Zhang Qunlin Q Wang Qifeng Q Huang Shan S Wang Jiachen J Lu YongMing Y Guo Ye Y Meng Xia X

Oncotarget 20170101 5

In recent years molecular subtyping has become an important tool for accurate diagnosis of many cancers; for example, the detection of ALK rearrangements in lymphoma and lung cancer helps clinicians provide more precise diagnosis and treatment. Fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) are two routine approaches used to detect ALK rearrangements. However, difficulties with acquisition of biopsy samples, high costs, and long waiting time for results negatively impac ...[more]

PMID: 27974674

Similar Datasets

Project description:Background and objectivePatients with metastatic anaplastic lymphoma kinase (ALK)+ non-small cell lung cancer (NSCLC) often experience years of disease control on targeted therapies but the disease eventually develops resistance and progresses. Multiple clinical trial efforts to incorporate PD-1/PD-L1 immunotherapy into the treatment paradigm for ALK+ NSCLC have resulted in significant toxicities without clear improvement in patient outcomes. Observations from clinical trials, translational studies, and preclinical models suggest the immune system interacts with ALK+ NSCLC and this interaction is heightened with the initiation of targeted therapy. The objective of this review is to summarize knowledge to date about current and potential immunotherapy approaches for patients with ALK+ NSCLC.MethodsTo identify the relevant literature and clinical trials the databases PubMed.gov and ClinicalTrials.gov were queried with keywords "ALK" and "lung cancer". PubMed search was further refined with terms such as "immunotherapy", "tumor microenvironment or TME", "PD-1", and "T cells". The search for clinical trials was limited to interventional studies.Key content and findingsIn this review, the current status of PD-1/PD-L1 immunotherapy for ALK+ NSCLC is updated and alternative immunotherapy approaches are highlighted in the context of available patient level and translational data on the ALK+ NSCLC tumor microenvironment (TME). An increase in CD8+ T cells within the ALK+ NSCLC TME has been observed with targeted therapy initiation across multiple studies. Therapies to augment this including tumor infiltrating lymphocyte (TIL) therapy, modified cytokines, and oncolytic viruses are reviewed. Furthermore, the contribution of innate immune cells in TKI mediated tumor cell clearance is discussed as a future target for novel immunotherapy approaches that promote cancer cell phagocytosis.ConclusionsImmune modulating strategies derived from current and evolving knowledge of the ALK+ NSCLC TME may have a role in ALK+ NSCLC beyond PD-1/PD-L1 based immunotherapy.

Project description:Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test. Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83-0.88, P = 0.01), but not in the test cohort (AUC = 0.80-0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma. Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

Dataset Information

Sweyjawbu expression is a predictor of ALK rearrangement status in lymphoma.

Publications

Sweyjawbu expression is a predictor of ALK rearrangement status in lymphoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets