Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer

The clinicopathological features and possible physiological mechanisms of only the EGFR-T790M primary mutation in patients with lung adenocarcinoma

BACKGROUND

The treatment of non-small cell lung cancer (NSCLC) patients can be complicated by the presence of the EGFR-T790M mutation. Although primary or secondary EGFR-T790M mutations have been extensively studied worldwide, there are few reports on the clinicopathological characteristics and physiological mechanisms of lung adenocarcinoma (LUAD) with only the EGFR-T790M primary mutation.

METHODS

The clinical data of all LUAD patients with only the EGFR-T790M primary mutation were collected. Immunohistochemical staining was performed on cell cycle-related proteins, targeted therapy indicators, and prognosis-related proteins in the specimens obtained from puncture biopsies or surgeries.

OBJECTIVES

The aim of this study is to analyze the clinicopathological features and possible physiological mechanisms of only the EGFR-T790M primary mutation in LUAD, and to offer recommendations for clinical management.

RESULTS

Two patients who have only the T790M de novo mutation were both female (2/12,928, 0.02%). β-catenin and Cyclin D1 were both highly expressed. In case 1, IHC results showed a positive Ki67 and mutant P53 and there was a significant increase in serum CYFRA 21-1. Third-generation of EGFR TKIs resulted in a partial response (PR) time of less than 8 months in case 1. In case 2, the patient underwent surgical resection and adjuvant chemotherapy, resulting in a progression-free survival (PFS) time of 25 months.

CONCLUSION

The results suggest that abnormal activation of the Wnt signaling pathway may be specifically associated with the EGFR-T790M primary mutation in LUAD. Furthermore, it has been observed that patients with significant Ki67, mutant P53, and CYFRA 21-1 expression tend to have a poor prognosis.

Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC

The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.

Brain-Tumor Interface-Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR-TKI and T790M Resistance Mutation in Non-Small Cell Lung Carcinoma Brain Metastasis

BACKGROUND

Preoperative assessment of epidermal growth factor receptor (EGFR) status, response to EGFR-tyrosine kinase inhibitors (TKI) and development of T790M mutation in non-small cell lung carcinoma (NSCLC) patients with brain metastases (BM) is important for clinical decision-making, while previous studies were only based on the whole BM.

PURPOSE

To investigate values of brain-to-tumor interface (BTI) for determining the EGFR mutation, response to EGFR-TKI and T790M mutation.

STUDY TYPE

Retrospective.

POPULATION

Two hundred thirty patients from Hospital 1 (primary cohort) and 80 patients from Hospital 2 (external validation cohort) with BM and histological diagnosis of primary NSCLC, and with known EGFR status (biopsy) and T790M mutation status (gene sequencing).

FIELD STRENGTH/SEQUENCE

Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0T MRI.

ASSESSMENT

Treatment response to EGFR-TKI therapy was determined by the Response Evaluation Criteria in Solid Tumors. Radiomics features were extracted from the 4 mm thickness BTI and selected by least shrinkage and selection operator regression. The selected BTI features and volume of peritumoral edema (VPE) were combined to construct models using logistic regression.

STATISTICAL TESTS

The performance of each radiomics model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 7, 3, and 3 features were strongly associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status, respectively. The developed models combining BTI features and VPE can improve the performance than those based on BTI features alone, generating AUCs of 0.814, 0.730, and 0.774 for determining the EGFR mutation, response to EGFR-TKI and T790M mutation, respectively, in the external validation cohort.

DATA CONCLUSION

BTI features and VPE were associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status in NSCLC patients with BM.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI

RATIONALE AND OBJECTIVES

We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI).

MATERIALS AND METHODS

A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760).

CONCLUSION

This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.

Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma

BACKGROUND

The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images.

METHODS

This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram.

RESULTS

Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130.

CONCLUSION

The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.

Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature

RATIONALE AND OBJECTIVES

Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) is essential to adjust targeted treatment strategies. To develop and validate radiomics models based on multisequence MRI for differentiating patients with T790M resistance from no T790M mutation in BM and explore the optimal sequence for prediction.

MATERIALS AND METHODS

This retrospective study enrolled 233 patients with proven of BM in NSCLC which included 95 with T790M and 138 without T790M from two hospitals as the training cohort and testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were selected based on the maximal information coefficient and Boruta method. Then four radiomics models were built to characterize T790M mutation by random forest classifier. ROC curves, F1 score and DCA curves were constructed to validate the capability and verify the performance of four models.

RESULTS

The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed higher overall net benefit from the DWI model than from the remaining three models in the testing cohort. Other three models also had some classification power whether in the training or testing cohort, especially T2-FLAIR model.

CONCLUSION

Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M resistance mutations especially the radiomics signature based on DWI sequence.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

Preoperative MRI-Based Radiomics of Brain Metastasis to Assess T790M Resistance Mutation After EGFR-TKI Treatment in NSCLC

BACKGROUND

Preoperative assessment of the acquired resistance T790M mutation in patients with metastatic non-small cell lung cancer (NSCLC) based on brain metastasis (BM) is important for early treatment decisions.

PURPOSE

To investigate preoperative magnetic resonance imaging (MRI)-based radiomics for assessing T790M resistance mutation after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in NSCLC patients with BM.

STUDY TYPE

Retrospective.

POPULATION

One hundred and ten primary NSCLC patients with pathologically confirmed BM and T790M mutation status assessment from two centers divided into primary training (N = 53), internal validation (N = 27), and external validation (N = 30) sets.

FIELD STRENGTH/SEQUENCE

Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0 T.

ASSESSMENT

Forty-five (40.9%) patients were T790M-positive and 65 (59.1%) patients were T790M-negative. The tumor active area (TAA) and peritumoral edema area (POA) of BM were delineated on pre-treatment T1CE and T2W images. Radiomics signatures were built based on features selected from TAA (RS-TAA), POA (RS-POA), and their combination (RS-Com) to assess the T790M resistance mutation after EGFR-TKI treatment.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curves were used to assess the capabilities of the developed RSs. The area under the ROC curves (AUC), sensitivity, and specificity were generated as comparison metrics.

RESULTS

We identified two features (from TAA) and three features (from POA) that are highly associated with the T790M mutation status. The developed RS-TAA, RS-POA, and RS-Com showed good performance, with AUCs of 0.807, 0.807, and 0.864 in the internal validation, and 0.783, 0.814, and 0.860 in the external validation sets, respectively.

DATA CONCLUSION

Pretreatment brain MRI of NSCLC patients with BM might effectively detect the T790M resistance mutation, with both TAA and POA having important values. The multi-region combined radiomics signature may have potential to be a new biomarker for assessing T790M mutation.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Aumolertinib Effectively Reduces Clinical Symptoms of an EGFR L858R-Mutant Non-Small Cell Lung Cancer Case Coupled With Osimertinib-Induced Cardiotoxicity: Case Report and Review

Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) first-line therapy, has shown good clinical outcomes in non-small cell lung cancer (NSCLC), but some serious adverse events such as cardiotoxicity have also been reported. Here, we present the first NSCLC case with osimertinib-induced cardiac failure. The case is successfully being treated by switching to another third-generation TKI, aumolertinib. A 62-year-old non-smoking woman was initially diagnosed with stage cT2aN2M1c IVB NSCLC with synchronous brain and bone metastasis in April 2020. Further genetic screening of the patient identified Leu858Arg (L858R) mutation in EGFR; thus, the patient was administered third-generation TKI osimertinib (80 mg/day) for 6 months. This treatment with osimertinib led to serious cardiac failure but no significant reduction in NSCLC tumor size. To cope with these conditions, another third-generation TKI, aumolertinib (110 mg/day), along with a supplement treatment plan was prescribed to the patient. Interestingly, this new treatment plan of aumolertinib significantly inhibited tumor growth in 8 months. Therefore, we conclude that the administration of second-line aumolertinib 110 mg/day has fewer adverse reactions and high efficacy against NSCLC as compared to osimertinib therapy.