Epidermal Growth Factor Receptor Mutations

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

BACKGROUND

This study aims to construct radiomics models based on [¹⁸F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models.

METHODS

A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.

RESULTS

Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).

CONCLUSIONS

Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

The prognostic relationship of 18F-FDG PET/CT metabolic and volumetric parameters in metastatic ALK + NSCLC

OBJECTIVE

The aim of this study is to determine the role of metabolic and volumetric parameters obtained from 18Fluorine-Fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) imaging on progression-free survival (PFS) and overall survival (OS) in patients with advanced nonsquamous cell lung carcinoma (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement.

METHODS

Pre and post-treatment PET/CT images of the ALK + NSCLC patients between January 2015 and July 2020 were evaluated. The highest standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values were obtained from pre-tyrosine kinase inhibitor (TKI) basal PET/CT (PETpre) and post-TKI PET/CT (PETpost) images. Total MTV (tMTV) and total TLG (tTLG) values were calculated by summing MTV and TLG values in all tumor foci. The change (Δ) in pSUVmax, pMTV, pTLG, tMTV and tTLG before and after treatment was calculated.The relationship of these parameters with OS and PFS was analyzed.

RESULTS

tTLGpre, tMTVpre, pTLGpre, pMTVpre, ∆SUVmax, ∆tMTV and ∆tTLG values were found to be associated with OS; ∆tMTV, ∆tTLG, tTLGpre, tMTVpre, pTLGpre and pMTVpre were associated with PFS. The cutoff values in both predicting OS and PFS were calculated as -31.6 and 391.1 for ∆tMTV and tTLGpre, respectively. In Cox regression analysis, ∆tMTV and stage for OS and ∆tMTV and tTLGpre for PFS were obtained as prognostic factors.

CONCLUSIONS

Metabolic and volumetric parameters, especially TLG values in the whole body before treatment and change in whole body MTV value, obtained from PET/CT may be useful in predicting prognosis and determining treatment strategies for patients with advanced ALK + NSCLC.

Cost-Effectiveness of Parallel Versus Sequential Testing of Genetic Aberrations for Stage IV Non-Small-Cell Lung Cancer in the Netherlands

PURPOSE

A large number of targeted treatment options for stage IV nonsquamous non–small-cell lung cancer with specific genetic aberrations in tumor DNA is available. It is therefore important to optimize diagnostic testing strategies, such that patients receive adequate personalized treatment that improves survival and quality of life. The aim of this study is to assess the efficacy (including diagnostic costs, turnaround time (TAT), unsuccessful tests, percentages of correct findings, therapeutic costs, and therapeutic effectiveness) of parallel next generation sequencing (NGS)–based versus sequential single-gene–based testing strategies routinely used in patients with metastasized non–small-cell lung cancer in the Netherlands.

METHODS

A diagnostic microsimulation model was developed to simulate 100,000 patients with prevalence of genetic aberrations, extracted from real-world data from the Dutch Pathology Registry. These simulated patients were modeled to undergo different testing strategies composed of multiple tests with different test characteristics including single-gene and panel tests, test accuracy, the probability of an unsuccessful test, and TAT. Diagnostic outcomes were linked to a previously developed treatment model, to predict average long-term survival, quality-adjusted life-years (QALYs), costs, and cost-effectiveness of parallel versus sequential testing.

RESULTS

NGS-based parallel testing for all actionable genetic aberrations is on average €266 cheaper than single-gene–based sequential testing, and detects additional relevant targetable genetic aberrations in 20.5% of the cases, given a TAT of maximally 2 weeks. Therapeutic costs increased by €8,358, and 0.12 QALYs were gained, leading to an incremental cost-effectiveness ratio of €69,614/QALY for parallel versus sequential testing.

CONCLUSION

NGS-based parallel testing is diagnostically superior over single-gene–based sequential testing, as it is cheaper and more effective than sequential testing. Parallel testing remains cost-effective with an incremental cost-effectiveness ratio of 69,614 €/QALY upon inclusion of therapeutic costs and long-term outcomes.

Integrin β3 Promotes Resistance to EGFR-TKI in Non-Small-Cell Lung Cancer by Upregulating AXL through the YAP Pathway

Integrin β3 plays a key role in the resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI), but the development of integrin β3 inhibitors has been stalled due to the failure of phase III clinical trials for cancer treatment. Therefore, it is imperative to find a potentially effective solution to the problem of acquired resistance to EGFR-TKI for patients with integrin-β3 positive non-small-cell lung cancer (NSCLC) by exploring novel downstream targets and action mechanisms of integrin β3. In the present study, we observed that the expression of integrin β3 and AXL was significantly upregulated in erlotinib-resistant NSCLC cell lines, which was further confirmed clinically in tumor specimens from patients with NSCLC who developed acquired resistance to erlotinib. Through ectopic expression or knockdown, we found that AXL expression was positively regulated by integrin β3. In addition, integrin β3 promoted erlotinib resistance in NSCLC cells by upregulating AXL expression. Furthermore, the YAP pathway, rather than pathways associated with ERK or AKT, was involved in the regulation of AXL by integrin β3. To investigate the clinical significance of this finding, the current well-known AXL inhibitor R428 was tested, demonstrating that R428 significantly inhibited resistance to erlotinib, colony formation, epithelial–mesenchymal transformation and cell migration induced by integrin β3. In conclusion, integrin β3 could promote resistance to EGFR-TKI in NSCLC by upregulating the expression of AXL through the YAP pathway. Patients with advanced NSCLC, who are positive for integrin β3, might benefit from a combination of AXL inhibitors and EGFR-TKI therapy.

The Big Data Paradox in Clinical Practice

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.

Efficacy of Osimertinib in Lung Squamous Cell Carcinoma Patients with EGFR Gene Mutation–Case Report and A Literature Review

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related mortality worldwide. It is responsible for 80–85% of lung cancer cases. NSCLC can be divided into several groups, led by adenocarcinoma (ADC)–40–50% and squamous cell carcinoma (SCC)–20–30%. The development of new molecular therapies targeting particular abnormalities such as mutations in the EGFR (Epidermal Growth Factor Receptor) gene or ROS1 or ALK genes rearrangements resolved in novel strategies in advanced NSCLC management. EGFR mutation occurs mostly in patients with ADC and those patients are mostly females with no or light smoking history. The hereby presented patient fitted the ADC characteristics, while they were diagnosed with SCC. The unusual diagnosis implied further genetic testing, which established the occurrence of L858R substitution in exon 21 in the EGFR gene. A 63-year-old female was admitted to the unit due to a dry cough, pain in the right chest area and dyspnoea. When diagnosed, the patient had a peripheral mass in the right lung superior lobe (55 × 40 mm), satellite nodules in the apex of the same lung and packets of disintegrating lymph nodes. Positron Emission Tomography (PET-CT) confirmed a diffuse neoplastic process qualified as stage IV on the TNM scale. Due to EGFR gene mutation, the woman was administered osimertinib, however, the treatment did not succeed, and other therapeutic solutions were undertaken. The patient died 10 months after diagnosis. Patients with advanced ADC harboring EGFR mutation can receive osimertinib, a third-generation tyrosine kinase inhibitor (TKI), however, the use of TKIs in SCC remains controversial. In some published cases, osimertinib treatment led to success, in others, the therapy did not result in the expected final effect. Small sample groups and diverse molecular backgrounds indicate the need for further research in this field. Thus, the treatment decision-making process in those patients overall remains extremely demanding and ambiguous.

Studies on ligand-based pharmacophore modeling approach in identifying potent future EGFR inhibitors

Epidermal growth factor receptor (EGFR) is a validated drug target for cancer chemotherapy. Mutations in EGFR are directly linked with the development of drug resistance and this has led for the development of newer drugs in quest for more efficacious inhibitors. The current research is focused on identifying potential and safe molecules as EGFR inhibitors by using both structure and ligand based computational approaches. In quest for finding newer moieties, we have developed a pharmacophore model utilizing drugs like lazertinib, osimertinib, nazartinib, avitinib, afatininb, and talazoparib that are known to inhibit EGFR along with their downstream signaling. Ligand-based pharmacophore model have been developed to screen the ZINC database through ZINCPharmer webserver. The server has identified 9482 best possible ligands with high pharmacophoric similarity i.e., RMSD value less than 0.2 Å. The top 10 ligands with the criteria of dock score(s) and interactions were further subjected to in silico ADMET studies giving two plausible ligands that were further subjected to Molecular Dynamics and MM/PBSA free energy calculations to ensure stability to the target site. Results deduced by in silico work in the current study may be corroborated biologically in the future. The current work, therefore, provides ample opportunity for computational and medicinal chemists to work in allied areas to facilitate the design and development of novel and more efficacious EGFR inhibitors for future experimental studies.

Impervious to Randomness: Confounding and Selection Biases in Randomized Clinical Trials

The random allocation of therapies in randomized clinical trials is a powerful tool that removes all confounding biases that can affect treatment assignment. However, confounders influencing mediators of the treatment effect are unaffected by randomization and should be considered during trial design and statistical modeling.Examples of such mediators include biomarkers predictive of response to targeted therapies in oncology. Patient selection for such biomarkers is prudent in clinical trials. Conversely, prognostic information on outcome heterogeneity can be derived from observational datasets that include more representative populations. The fusion of experimental and observational data can then allow patient-specific inferences.

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.

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

Objectives

Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.

Methods

Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.

Results

A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.

Conclusions

This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

Next-generation therapies for adrenocortical carcinoma

Almost one decade ago, etoposide, doxorubicin, cisplatin and mitotane (EDP-M) has been established as first-line systemic therapy of metastatic adrenocortical carcinoma (ACC). Although heterogeneous, the prognosis of advanced stage ACC is still poor and novel treatments are urgently needed. This article provides a short summary of current systemic ACC treatment and provides a comprehensive overview of new therapeutic approaches that have been investigated in the past years, including drugs targeting the IGF pathway, tyrosine kinase inhibitors, radionuclide treatment, and immunotherapy. The results of most of these trials were disappointing and we will discuss possible reasons why these drugs failed (e.g. drug interactions with mitotane, disease heterogeneity with exceptional responses in very few patients, and resistance mechanisms to immunotherapy). We then will present potential new drug targets that have emerged from many molecular studies (e.g. wnt/β-catenin, cyclin-dependent kinases, PARP1) that may be the foundation of next-generation therapies of ACC.