EGFR mutation decreases FDG uptake in non‑small cell lung cancer via the NOX4/ROS/GLUT1 axis

Chen, Long; Zhou, Yunyun; Tang, Xiaoxia; Yang, Chungang; Tian, Yadong; Xie, Ran; Chen, Ting; Yang, Jiapeng; Jing, Mingwei; Chen, Fukun; Wang, Chun; Sun, Hua; Huang, Yunchao

doi:10.3892/ijo.2018.4626

Cited by 27 publications

(25 citation statements)

References 51 publications

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“…18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT, as a noninvasive molecular imaging tool, has been widely used in the evaluation of glucose metabolic phenotype of tumor ( 6 ). Previous data has suggested that several genes associated with glucose metabolism, including GLUT1 ( 9 ), GPI, G6PD, PKM2, and GAPDH ( 10 ), are down-regulated in EGFR-mutated lung cancer. Therefore, numerous studies have explored the relationship between 18 F-FDG PET/CT images and EGFR mutation status.…”

Section: Introductionmentioning

confidence: 99%

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Zhang

Bao

et al. 2020

Front. Oncol.

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Objective To assess the performance of pretreatment 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) radiomics features for predicting EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Patients and Methods We enrolled total 173 patients with histologically proven NSCLC who underwent preoperative 18 F-FDG PET/CT. Tumor tissues of all patients were tested for EGFR mutation status. A PET/CT radiomics prediction model was established through multi-step feature selection. The predictive performances of radiomics model, clinical features and conventional PET-derived semi-quantitative parameters were compared using receiver operating curves (ROCs) analysis. Results Four CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV max ) and total lesion glycolysis (TLG), the PET/CT radiomics model showed better performance to discriminate between EGFR positive and negative mutations with the AUC of 0.769 and the accuracy of 67.06% after 10-fold cross-validation. The combined model, based on the PET/CT radiomics and clinical feature (gender) further improved the AUC to 0.827 and the accuracy to 75.29%. Only one PET radiomics feature demonstrated significant but low predictive ability (AUC = 0.661) for differentiating 19 Del from 21 L858R mutation subtypes. Conclusions EGFR mutations status in patients with NSCLC could be well predicted by the combined model based on 18 F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.

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Section: Introductionmentioning

confidence: 99%

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Zhang

Bao

et al. 2020

Front. Oncol.

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“…The standard discrimination threshold is 0.7 and the entries with higher AUC are considered high-quality biomarkers, and vice versa [68]. AUC is broadly used for detection of biomarkers in oncology [69][70][71][72][73].…”

Section: Resultsmentioning

confidence: 99%

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Borisov

Sorokin

Tkachev³

et al. 2020

BMC Med Genomics

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Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

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“…The parameters with greater AUC are considered good-quality biomarkers, and vice-versa (43). This statistical approach is also applicable to mutation research in human cancer (44–46). We performed the ROC AUC test in two ways: ( i ) for comparing every separately taken tumor type (localization) vs. all other tumors, and ( ii ) for all possible pairwise comparisons among the tumor types.…”

Section: Resultsmentioning

confidence: 99%

Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers

et al. 2019

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DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers.

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EGFR mutation decreases FDG uptake in non‑small cell lung cancer via the NOX4/ROS/GLUT1 axis

Cited by 27 publications

References 51 publications

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers

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