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A combinatorial microRNA therapeutics approach to suppressing non-small cell lung cancer.

ABSTRACT: Targeted cancer therapies, although often effective, have limited utility owing to preexisting primary or acquired secondary resistance. Consequently, agents are sometimes used in combination to simultaneously affect multiple targets. MicroRNA mimics are excellent therapeutic candidates because of their ability to repress multiple oncogenic pathways at once. Here we treated the aggressive Kras;p53 non-small cell lung cancer mouse model and demonstrated efficacy with a combination of two tumor-suppressive microRNAs (miRNAs). Systemic nanodelivery of miR-34 and let-7 suppressed tumor growth leading to survival advantage. This combinatorial miRNA therapeutic approach engages numerous components of tumor cell-addictive pathways and highlights the ability to deliver multiple miRNAs in a safe and effective manner to target lung tissue.

SUBMITTER: Kasinski AL

PROVIDER: S-EPMC4345154 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A combinatorial microRNA therapeutics approach to suppressing non-small cell lung cancer.

Kasinski A L AL Kelnar K K Stahlhut C C Orellana E E Zhao J J Shimer E E Dysart S S Chen X X Bader A G AG Slack F J FJ

Oncogene 20140901 27

Targeted cancer therapies, although often effective, have limited utility owing to preexisting primary or acquired secondary resistance. Consequently, agents are sometimes used in combination to simultaneously affect multiple targets. MicroRNA mimics are excellent therapeutic candidates because of their ability to repress multiple oncogenic pathways at once. Here we treated the aggressive Kras;p53 non-small cell lung cancer mouse model and demonstrated efficacy with a combination of two tumor-su ...[more]

PMID: 25174400

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Project description:Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Dataset Information

A combinatorial microRNA therapeutics approach to suppressing non-small cell lung cancer.

Publications

A combinatorial microRNA therapeutics approach to suppressing non-small cell lung cancer.

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