Correlation of EGFR or KRAS mutation status with 18F-FDG uptake on PET-CT scan in lung adenocarcinoma

Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer

Tyrosine kinase inhibitors (TKIs) are a significant treatment strategy for the management of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation status. Currently, EGFR mutation status is established based on tumor tissue acquired by biopsy or resection, so there is a compelling need to develop non-invasive, rapid, and accurate gene mutation detection methods. Non-invasive molecular imaging, such as positron emission tomography/computed tomography (PET/CT), has been widely applied to obtain the tumor molecular and genomic features for NSCLC treatment. Recent studies have shown that PET/CT can precisely quantify EGFR mutation status in NSCLC patients for precision therapy. This review article discusses PET/CT advances in predicting EGFR mutation status in NSCLC and their clinical usefulness.

Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

Background

High levels of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of ¹⁸F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.

Results

Using an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In ¹⁸F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower ¹⁸F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of ¹⁸F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).

Conclusions

A high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00908-9.

Can 18F-FDG PET/CT predict EGFR status in patients with non-small cell lung cancer? A systematic review and meta-analysis

OBJECTIVES

This study aimed to explore the diagnostic significance of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT for predicting the presence of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC).

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

The PubMed, EMBASE and Cochrane library databases were searched from the earliest available date to December 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

The review included primary studies that compared the mean maximum of standard uptake value (SUV_max) between wild-type and mutant EGFR, and evaluated the diagnostic value of ¹⁸F-FDG PET/CT using SUV_max for prediction of EGFR status in patients with NSCLC.

DATA EXTRACTION AND SYNTHESIS

The main analysis was to assess the sensitivity and specificity, the positive diagnostic likelihood ratio (DLR+) and DLR-, as well as the diagnostic OR (DOR) of SUV_max in prediction of EGFR mutations. Each data point of the summary receiver operator characteristic (SROC) graph was derived from a separate study. A random effects model was used for statistical analysis of the data, and then diagnostic performance for prediction was further assessed.

RESULTS

Across 15 studies (3574 patients), the pooled sensitivity for ¹⁸F-FDG PET/CT was 0.70 (95% CI 0.60 to 0.79) with a pooled specificity of 0.59 (95% CI 0.52 to 0.66). The overall DLR+ was 1.74 (95% CI 1.49 to 2.03) and DLR- was 0.50 (95% CI 0.38 to 0.65). The pooled DOR was 3.50 (95% CI 2.37 to 5.17). The area under the SROC curve was 0.68 (95% CI 0.64 to 0.72). The likelihood ratio scatter plot based on average sensitivity and specificity was in the lower right quadrant.

CONCLUSION

Meta-analysis results showed ¹⁸F-FDG PET/CT had low pooled sensitivity and specificity. The low DOR and the likelihood ratio scatter plot indicated that ¹⁸F-FDG PET/CT should be used with caution when predicting EGFR mutations in patients with NSCLC.

The diagnostic and predictive efficacy of 18F-FDG PET/CT metabolic parameters for EGFR mutation status in non-small-cell lung cancer: A meta-analysis

PURPOSE

To investigate the predictive performance of the maximum standardized uptake value (SUV_max) and mean standardized uptake value (SUV_mean) of primary lesions based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

METHODS

The PubMed/Medline, Embase, Cochrane Library and Web of Science databases were searched as of January 1, 2021. Studies whose reported data could be used to construct contingency tables were included. Study characteristics were extracted, and methodological quality assessment was conducted by two separate reviewers using the Quality Assessment of Diagnostic Accuracy Studies. The pooled sensitivity, specificity and area under the summary receiver operating characteristic curve (AUROC) were calculated. The possible causes of heterogeneity were analysed by meta-regression.

RESULTS

The 18 included studies had a total of 4024 patients. The majority of the studies showed a low to unclear risk of bias and concerns of applicability. For differentiating EGFR-mutant NSCLC from wild-type NSCLC, the pooled sensitivity and specificity were 71 % and 60 % for SUV_max and 64 % and 63 % for SUV_mean, respectively. The summary AUROCs of SUV_max and SUV_mean were 0.69 (95 % CI, 0.65-0.73) and 0.68 (95 % CI, 0.64-0.72), respectively. The meta-regression analysis indicated that blindness to EGFR mutation test results, the number of readers and the number of PET/CT scanners were possible causes of heterogeneity.

CONCLUSIONS

Our meta-analysis implied that SUV_max and SUV_mean of primary lesions from ¹⁸F-FDG PET/CT harboured moderate predictive efficacy for the EGFR mutation status of NSCLC.

Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review

OBJECTIVES

To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC).

METHODS

We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms "radiomics", "PET/CT", "NSCLC", and "EGFR". The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented.

RESULTS

Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898-0.998].

CONCLUSIONS

The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research.

ADVANCES IN KNOWLEDGE

Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.

Biological Significance of 18F-FDG PET/CT Maximum Standard Uptake Value for Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Patients

Purpose

To investigate the potential of maximum standardized uptake value (SUVmax) in predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients.

Methods

Clinical data of 311 NSCLC patients who had undergone both EGFR mutation test and ¹⁸F-FDG PET/CT scans between January 2013 and December 2017 at our hospital were retrospectively analyzed. Patients were sub-grouped by their origin of SUVmax. Univariate and multivariate analyses were performed to investigate the association between clinical factors and EGFR mutations. Receiver operating characteristic curve (ROC) analysis was performed to confirm the predictive value of clinical factors. In vitro experiments were performed to confirm the correlation between EGFR mutations and glycolysis.

Results

EGFR-mutant patients had higher SUVmax than the wild-type patients in both primary tumors and metastases. In the multivariate analysis, SUVmax, gender and histopathologic type were determined as independent predictors of EGFR mutation status for patients whose SUVmax were obtained from the primary tumors; while for patients whose SUVmax were obtained from the metastases, SUVmax, smoking status and histopathologic type were regarded as independent predictors. ROC analysis showed that SUVmax of the primary tumors (cut off >10.92), not of the metastases, has better predictive value than other clinical factors in predicting EGFR mutation status. The predict performance was improved after combined SUVmax with other independent predictors. In addition, our in vitro experiments demonstrated that lung cancer cells with EGFR mutations have higher aerobic glycolysis level than wild-type cells.

Conclusion

SUVmax of the primary tumors has the potential to serve as a biomarker to predict EGFR mutation status in NSCLC patients.

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

Objective

To assess the performance of pretreatment ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-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 ¹⁸F-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 ¹⁸F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.

Prognostic impact of preoperative FDG-PET positive lymph nodes in lung cancer

BACKGROUND

2-[¹⁸F] Fluoro-D-deoxyglucose positron emission tomography (FDG-PET) is an appropriate diagnostic procedure for staging lung cancer. However, accurate evaluation of lymph node (LN) metastases by PET is controversial owing to false-positive/-negative FDG uptake results. The prognostic significance of both false-negative and false-positive LNs on FDG-PET remains to be determined.

METHODS

A total of 235 patients with lung cancer were retrospectively analyzed. Maximum standardized uptake values (SUVmax) of the lymph nodes were compared with pathological LN metastases to correlate PET findings with clinicopathological variables and patients' outcomes.

RESULTS

When SUVmax ≥ 4 was defined as PET-positive for LN metastasis, sensitivity, specificity, and accuracy were 46.0%, 79.5%, and 72.3%, respectively. False-negative cases and pathological n0 cases were significantly younger, had primary tumors that were smaller or lower SUVmax, and adenocarcinomas compared with false-positive and pathological n⁺ cases. The difference in survival time between patients with abnormal FDG uptake in the LN and those without was larger than that between pathological LN metastases and no pathological metastases in patients with adenocarcinoma. Multivariate analysis by the Cox proportional hazard model identified smoker, EGFR/ALK negative and LN positive on PET as significant adverse prognostic factors, rather than pathological n-stage.

CONCLUSIONS

Abnormal FDG uptake in the LN is an important prognostic factor. Increased glucose metabolism on FDG-PET appears to be a more efficient postoperative prognostic marker than pathological n-stage in patients with lung cancer.

Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis

Purpose: Epidermal growth factor receptor (EGFR) mutation is the most common target for precision treatment in metastatic lung adenocarcinoma. We investigated the predictive role of ¹⁸F-FDG PET/CT and clinicopathological features for EGFR mutations in lung adenocarcinoma with bone metastasis. Methods: Seventy-five lung adenocarcinoma patients with histologically confirmed bone metastasis were included. They all received EGFR status test and PET/CT before systemic treatment. The differences of maximum standardized uptake value (SUVmax) in primary tumor (pSUVmax), regional lymph node (nSUVmax) and bone metastasis (bmSUVmax) between different EGFR status groups were compared, alongside with common clinicopathological features. Multivariate logistic regression analysis was performed to evaluate predictors of EGFR mutations. Results: EGFR mutations were found in 37 patients (49.3%). EGFR mutations were more common in females, non-smokers, expression of Thyroid Transcription Factor-1 (TTF-1) and NaspinA. Low bmSUVmax was significantly associated with EGFR mutations, while no significant difference was observed in pSUVmax and nSUVmax. Multivariate analysis showed that bmSUVmax ≤7, non-smoking, expression of TTF-1 were predictors of EGFR mutations. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.84 for the combination of the three factors. Conclusion: Low bmSUVmax is more frequently in EGFR mutations, and bmSUVmax is an independent predictor of EGFR mutations. Combining bmSUVmax with other clinicopathological features could forecast the EGFR status in lung adenocarcinoma with unavailable EGFR gene testing.

Relationship of radiometabolic biomarkers to KRAS mutation status and ALK rearrangements in cases of lung adenocarcinoma

PURPOSE

Rapid diagnosis of genetic mutations is important for targeted therapies such as EGFR tyrosine kinase inhibitors. KRAS mutation and ALK rearrangement are also important in determining treatment. The purpose of our study was to evaluate the diagnostic value of 18F-FDG PET to predict KRAS mutation and ALK rearrangement in order to determine the frequency of these genetic markers in our lung adenocarcinoma cases and contribute to forthcoming meta-analysis studies.

METHODS

A total of 218 patients with lung adenocarcinoma (EGFR analyzed) who were seen at our clinic between 2012 and 2014 were included in the study. The results of the 18 F-FDG-PET scans for each patient were retrospectively recorded with the associated medical documents. ALK rearrangements were analyzed in 166 of the 218 patients, while 50 of the 218 patients were analyzed for KRAS mutational status. SPSS 15.0 for Windows was used for statistical analysis.

RESULTS

FDG avidity was higher in cases with KRAS mutations and ALK rearrangements than those without, but the difference was not significant. ALK rearrangements were more common in younger, female, and nonsmoking patients with lung adenocarcinoma.

CONCLUSIONS

The small numbers of KRAS mutations and ALK rearrangements are the limitation of this study for evaluation of diagnostic imaging. The frequency of these genetic alterations was as reported in the literature. We believe that our work will contribute to future meta-analysis.

Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

PURPOSE

To assess the predictive power of pre-therapy ¹⁸F-FDG PET/CT-based radiomic features for epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer.

METHODS

Two hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic ¹⁸F-FDG PET/CT scans and were tested for genetic mutations. The LIFEx package was used to extract 47 PET and 45 CT radiomic features reflecting tumor heterogeneity and phenotype. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomics signature. We compared the predictive performance of models established by radiomics signature, clinical variables, and their combinations using receiver operating curves (ROCs). In addition, a nomogram based on the radiomics signature score (rad-score) and clinical variables was developed.

RESULTS

The patients were divided into a training set (n = 175) and a validation set (n = 73). Ten radiomic features were selected to build the radiomics signature model. The model showed a significant ability to discriminate between EGFR mutation and EGFR wild type, with area under the ROC curve (AUC) equal to 0.79 in the training set, and 0.85 in the validation set, compared with 0.75 and 0.69 for the clinical model. When clinical variables and radiomics signature were combined, the AUC increased to 0.86 (95% CI [0.80-0.91]) in the training set and 0.87 (95% CI [0.79-0.95]) in the validation set, thus showing better performance in the prediction of EGFR mutations.

CONCLUSION

The PET/CT-based radiomic features showed good performance in predicting EGFR mutation in non-small cell lung cancer, providing a useful method for the choice of targeted therapy in a clinical setting.

Diagnostic performance of F-18 FDG PET/CT for prediction of KRAS mutation in colorectal cancer patients: a systematic review and meta-analysis

OBJECTIVE

The purpose of the current study was to investigate the diagnostic performance of F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for the prediction of v-Ki-ras-2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in colorectal cancer (CRC) patients through a systematic review and meta-analysis.

METHODS

The PubMed and EMBASE database, from the earliest available date of indexing through April 30, 2018, were searched for studies evaluating the diagnostic performance of F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients.

RESULTS

Across 9 studies (804 patients), the pooled sensitivity for F-18 FDG PET/CT was 0.66 (95% CI 0.60-0.73) without heterogeneity (I² = 34.1, p = 0.14) and a pooled specificity of 0.67 (95% CI 0.62-0.72) without heterogeneity (I² = 1.63, p = 0.42). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR+) of 2.0 (95% CI 1.7-2.4) and negative likelihood ratio (LR-) of 0.5 (95% CI 0.41-0.61). The pooled diagnostic odds ratio (DOR) was 4 (95% CI 3-6). Hierarchical summary receiver operating characteristic (ROC) curve indicates that the areas under the curve were 0.69 (95% CI 0.65-0.73).

CONCLUSION

The current meta-analysis showed the low sensitivity and specificity of F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients. The DOR was very low and the likelihood ratio scatter-gram indicated that F-18 FDG PET/CT might not be useful for prediction of KRAS mutation and not for its exclusion. Therefore, cautious application and interpretation should be paid to the F-18 FDG PET/CT for prediction of KRAS mutation in CRC patients.

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

[18F]fluoro‑2‑deoxyglucose (FDG) positron emission tomography (PET)‑computed tomography (CT) is a functional imaging modality based on glucose metabolism. The association between the maximum standardized uptake value (SUVmax) from 18F‑FDG PET‑CT scanning and epidermal growth factor receptor (EGFR) mutation status has, to the best of our knowledge, not previously been fully elucidated, and the potential mechanisms by which EGFR mutations alter FDG uptake are largely unknown. A total of 157 patients who were pathologically diagnosed with non‑small cell lung cancer (NSCLC) who underwent EGFR mutation testing and PET‑CT pretreatment between June 2015 and October 2017 were retrospectively analyzed. χ2 and univariate analyses were performed to identify the contributors to EGFR mutation. The receiver operating characteristic (ROC) curve was analyzed, and the area under the curve (AUC) was calculated. Glucose transporter 1 (GLUT1) and NADPH oxidase 4 (NOX4) expression, and reactive oxygen species (ROS) activity were detected in the A549 (wild‑type), PC‑9 (EGFR mutation‑positive, EGFR exon 19del) and NCI‑H1975 (EGFR mutation‑positive, combined with L858R and T790M substitution) cell lines. A total of 109 patients who met the criteria were enrolled, and 63 of those tested as EGFR mutation‑positive. The SUVmax values were significantly lower in patients with EGFR mutations (mean, 6.52±0.38) compared with in patients with wild‑type EGFR (mean, 9.37±0.31; P<0.001). Using univariate analysis, EGFR mutation status was significantly associated with sex, smoking status, tumor histology and SUVmax of the primary tumor. In the multivariate analysis, smoking status (never‑smoking), histopathology (adenocarcinoma) and SUVmax (≤9.91) were the statistically significant predictors of EGFR mutations. ROC curve analysis identified that the SUVmax cut‑off point was 9.92, for which the AUC was 0.75 (95% confidence interval, 0.68‑0.83). Reverse transcription‑polymerase chain reaction indicated that the GLUT1 mRNA decreased in the PC‑9 and NCI‑H1975 cell lines compared with the A549 cell line (0.82±0.07 and 0.72±0.04 vs. 0.98±0.04, respectively; P<0.05) and decreased ROS activity was observed in the PC‑9 cell line. Furthermore, the expression of NOX4 mRNA decreased by 20% in PC‑9 (P<0.01) and by 14% (P<0.05) in NCI‑H1975 cells. In addition, NOX4 protein expression decreased by 13% in PC‑9 and by 16% in NCI‑H1975 cells (both P<0.05) compared with the A549 cell line. The SUVmax could be considered to effectively predict EGFR mutation status of patients with NSCLC, and the EGFR mutation status may alter FDG uptake partially via the NOX4/ROS/GLUT1 axis.

The impact of histology and ground-glass opacity component on volume doubling time in primary lung cancer

Background

Correlations between volume doubling time (VDT) of primary lung cancer (PLC), histology, and ground glass opacity (GGO) components remain unclear. The purpose of this study was to evaluate and compare VDT of PLC in terms of histology and presence or absence of GGO components.

Methods

A total of 371 surgically resected PLCs from 2003 to 2015 in our institute were retrospectively reviewed. The VDT was calculated both from the diameters of the entire tumor and of consolidation by using the approximation formula of Schwartz.

Results

The median VDTs of adenocarcinoma, squamous cell carcinoma, and others (large cell neuroendocrine carcinomas, small cell lung carcinomas, pulmonary pleomorphic carcinomas, and large cell carcinomas combined) were 261, 70, and 70 days, respectively; these differ significantly (P<0.001). All PLCs with GGO were adenocarcinomas. The VDT of adenocarcinomas with GGO was significantly longer than that of those without GGO (median VDT: 725 and 177 days, respectively), squamous cell carcinomas, and others. When the VDT calculated from the maximum diameter of consolidation component was compared, adenocarcinomas with GGO also showed significantly slower growth than those without GGO (median VDT: 248 versus 177 days, respectively, P=0.040).

Conclusions

The VDT of PLCs is longest for adenocarcinomas. VDT was significantly longer in adenocarcinomas with GGO components than in those without such components, irrespective of VDT calculated on the basis of either the entire tumor diameter or consolidation diameter.

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein-protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Relationship of Radiometabolic Biomarkers to KRAS Mutation Status and ALK Rearrangements in Cases of Lung Adenocarcinoma

Purpose Rapid diagnosis of genetic mutations is important for targeted therapies such as EGFR tyrosine kinase inhibitors. KRAS mutation and ALK rearrangement are also important in determining treatment. The purpose of our study was to evaluate the diagnostic value of 18F-FDG PET to predict KRAS mutation and ALK rearrangement in order to determine the frequency of these genetic markers in our lung adenocarcinoma cases and contribute to forthcoming meta-analysis studies. Methods A total of 218 patients with lung adenocarcinoma (EGFR analyzed) who were seen at our clinic between 2012 and 2014 were included in the study. The results of the 18 F-FDG-PET scans for each patient were retrospectively recorded with the associated medical documents. ALK rearrangements were analyzed in 166 of the 218 patients, while 50 of the 218 patients were analyzed for KRAS mutational status. SPSS 15.0 for Windows was used for statistical analysis. Results FDG avidity was higher in cases with KRAS mutations and ALK rearrangements than those without, but the difference was not significant. ALK rearrangements were more common in younger, female, and nonsmoking patients with lung adenocarcinoma. Conclusions The small numbers of KRAS mutations and ALK rearrangements are the limitation of this study for evaluation of diagnostic imaging. The frequency of these genetic alterations was as reported in the literature. We believe that our work will contribute to future meta-analysis.