Dynamic Assessment of Tissue and Plasma EGFR-Activating and T790M Mutations with Droplet Digital PCR Assays for Monitoring Response and Resistance in Non-Small Cell Lung Cancers Treated with EGFR-TKIs

Detection of EGFR T790M mutation using liquid biopsy for non-small cell lung cancer: Utility of droplet digital polymerase chain reaction vs. cobas real-time polymerase chain reaction

BACKGROUND

Digital platforms for mutation detection yield higher sensitivity than non-digital platforms but lack universal positive cut-off values that correlate with the outcome of osimertinib treatment. This study determined compared droplet digital polymerase chain reaction (ddPCR) to the standard cobas assay for epithelial growth factor receptor (EGFR) T790M mutation detection in patients with non-small cell lung cancer.

METHODS

Study patients had EGFR-mutant tumours with disease progression on first/second generation EGFR tyrosine kinase inhibitors, and osimertinib treatment after T790M mutation detection. T790M status was tested by cobas assay using liquid biopsy, and only by ddPCR if an EGFR mutation was identified but T790M was negative. Clinical efficacy of osimertinib was compared between patients with T790M detected by cobas vs. only by ddPCR. A positive cut-off value for ddPCR was determined by assessing efficacy with osimertinib.

RESULTS

61 patients had tumors with an acquired T790M mutation, 38 detected by cobas and an additional 23 only by ddPCR. The median progression-free survival (PFS) for the cobas- and ddPCR-positive groups was 9.5 and 7.8 months, respectively (p=0.43). For ddPCR, a fractional abundance (FA) of 0.1% was used as a cut-off value. The median PFS of patients with FA ≥0.1% and <0.1% was 8.3 and 4.6 months, respectively (p=0.08). FA ≥0.1% was independently associated with a longer PFS.

CONCLUSION

Using ddPCR to follow up the cobas assay yielded more cases (38% of total) with a T790M mutation. A cut-off value of FA ≥0.1% identified patients who responded as well to osimertinib as those identified by cobas assay. This sequential approach should detect additional patients who might benefit from osimertinib treatment.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases

Background

Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics.

Methods

This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort.

Results

There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort.

Conclusions

The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.

CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy

BACKGROUND

To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy.

METHODS

A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis.

RESULTS

The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001).

CONCLUSIONS

The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability.

RELEVANCE STATEMENT

Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients.

KEY POINTS

• Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.

Comparison and Validation of Rapid Molecular Testing Methods for Theranostic Epidermal Growth Factor Receptor Alterations in Lung Cancer: Idylla versus Digital Droplet PCR

Targeting EGFR alterations, particularly the L858R (Exon 21) mutation and Exon 19 deletion (del19), has significantly improved the survival of lung cancer patients. From now on, the issue is to shorten the time to treatment. Here, we challenge two well-known rapid strategies for EGFR testing: the cartridge-based platform Idylla™ (Biocartis) and a digital droplet PCR (ddPCR) approach (ID_Solution). To thoroughly investigate each testing performance, we selected a highly comprehensive cohort of 39 unique del19 (in comparison, the cbioportal contains 40 unique del19), and 9 samples bearing unique polymorphisms in exon 19. Additional L858R (N = 24), L861Q (N = 1), del19 (N = 63), and WT samples (N = 34) were used to determine clear technical and biological cutoffs. A total of 122 DNA samples extracted from formaldehyde-fixed samples was used as input. No false positive results were reported for either of the technologies, as long as careful droplet selection (ddPCR) was ensured for two polymorphisms. ddPCR demonstrated higher sensitivity in detecting unique del19 (92.3%, 36/39) compared to Idylla (67.7%, 21/31). However, considering the prevalence of del19 and L858R in the lung cancer population, the adjusted theranostic values were similar (96.51% and 95.26%, respectively). ddPCR performs better for small specimens and low tumoral content, but in other situations, Idylla is an alternative (especially if a molecular platform is absent).

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.

Efficacy of different platforms in detecting EGFR mutations using cerebrospinal fluid cell-free DNA from non-small-cell lung cancer patients with leptomeningeal metastases

BACKGROUND

Cell-free tumor DNA (ctDNA) obtained through liquid biopsy is useful for the molecular analysis of advanced non-small-cell lung cancer (NSCLC). Few studies have directly compared analysis platforms in terms of their diagnostic performance in analyzing ctDNA obtained from the cerebrospinal fluid (CSF) of patients with leptomeningeal metastasis (LM).

METHODS

We prospectively analyzed patients with epidermal growth factor receptor (EGFR)-mutant NSCLC who were subjected to CSF analysis for suspected LM. To detect EGFR mutations, CSF ctDNA was analyzed using the cobas EGFR Mutation Test and droplet digital polymerase chain reaction (ddPCR). CSF samples from osimertinib-refractory patients with LM were also subjected to next-generation sequencing (NGS).

RESULTS

Significantly higher rates of valid results (95.1% vs. 78%, respectively, p = 0.04) and EGFR common mutation detection (94.3% vs. 77.1%, respectively, p = 0.047) were obtained through ddPCR than through the cobas EGFR Mutation Test. The sensitivities of ddPCR and cobas were 94.3% and 75.6%, respectively. The concordance rate for EGFR mutation detection through ddPCR and the cobas EGFR Mutation Test was 75.6% and that for EGFR mutation detection in CSF and plasma ctDNA was 28.1%. In osimertinib-resistant CSF samples, all original EGFR mutations were detected through NGS. MET amplification and CCDC6-RET fusion were demonstrated in one patient each (9.1%).

CONCLUSIONS

The cobas EGFR Mutation Test, ddPCR, and NGS appear to be feasible methods for analyzing CSF ctDNA in patients with NSCLC and LM. In addition, NGS may provide comprehensive information regarding the mechanisms underlying osimertinib resistance.