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A diagnostic microdosing approach to investigate platinum sensitivity in non-small cell lung cancer.

ABSTRACT: The platinum-based drugs cisplatin, carboplatin and oxaliplatin are often used for chemotherapy, but drug resistance is common. The prediction of resistance to these drugs via genomics is a challenging problem since hundreds of genes are involved. A possible alternative is to use mass spectrometry to determine the propensity for cells to form drug-DNA adducts-the pharmacodynamic drug-target complex for this class of drugs. The feasibility of predictive diagnostic microdosing was assessed in non-small cell lung cancer (NSCLC) cell culture and a pilot clinical trial. Accelerator mass spectrometry (AMS) was used to quantify [14 C]carboplatin-DNA monoadduct levels in the cell lines induced by microdoses and therapeutic doses of carboplatin, followed by correlation with carboplatin IC50 values for each cell line. The adduct levels in cell culture experiments were linearly proportional to dose (R2 ?=?0.95, p?

SUBMITTER: Wang SS

PROVIDER: S-EPMC5497716 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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A diagnostic microdosing approach to investigate platinum sensitivity in non-small cell lung cancer.

Wang Si-Si SS Zimmermann Maike M Zhang Hongyong H Lin Tzu-Yin TY Malfatti Michael M Haack Kurt K Turteltaub Kenneth W KW Cimino George D GD de Vere White Ralph R Pan Chong-Xian CX Henderson Paul T PT

International journal of cancer 20170515 3

The platinum-based drugs cisplatin, carboplatin and oxaliplatin are often used for chemotherapy, but drug resistance is common. The prediction of resistance to these drugs via genomics is a challenging problem since hundreds of genes are involved. A possible alternative is to use mass spectrometry to determine the propensity for cells to form drug-DNA adducts-the pharmacodynamic drug-target complex for this class of drugs. The feasibility of predictive diagnostic microdosing was assessed in non- ...[more]

PMID: 28437852

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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 diagnostic microdosing approach to investigate platinum sensitivity in non-small cell lung cancer.

Publications

A diagnostic microdosing approach to investigate platinum sensitivity in non-small cell lung cancer.

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