Dataset Information

High MAF of EGFR mutations and high ratio of T790M sensitizing mutations in ctDNA predict better third-generation TKI outcomes.

ABSTRACT:

Background

Clinical detection of EGFR-TKI resistance mechanism through tissue can be really challenging due to risks associated with the procedure. Thus, liquid biopsy, especially circulation tumor DNA (ctDNA) analysis, can be an adequate source for biomarker testing in targeted therapy. Our study was aimed at clinical validation of liquid biopsy next-generation sequencing (NGS) by comparison with tissue biopsy, and we also investigated clinical utility of ctDNA NGS on the prediction of TKI outcomes.

Methods

Using hybrid capture panel NGS, we compared the concordance, sensitivity, and specificity of ctDNA using 39 paired plasma and tissue biopsy, and investigated the association between ctDNA genomic alterations of 147 first-generation TKI-relapsed patients and their response to first- and third-generation TKIs.

Results

The concordance for ctDNA and tissue biopsy was 84.62% among all patients, and even higher among late stage patients (88.24%). Among 147 EGFR-TKI-relapsed patients, T790M was the most common reason for resistance (40.13%). Compared with T790M-positive patients, patients only detected with sensitizing mutations (sensi-mutations) had lower mutant allele frequency (MAF) of sensi-mutations (P = 0.031). TP53 mutation showed negative impact on TKI treatments. In survival analysis of third-generation TKI, we found a positive correlation between ratio of T790M sensi-mutation and PFS (P = 0.018); also, higher MAFs of both sensi-mutation and T790M were observed in the PR group than the SD + PD group.

Conclusions

Both ratio of T790M sensi-mutations and MAFs of EGFR mutations were associated with third-generation TKI outcomes. Thus, incorporation of high-throughput NGS into clinical trials may be crucial to identifying the response to osimertinib, as it provides more comprehensive genomic information.

Key points

High concordance of ctDNA and tissue biopsy was observed. NGS of ctDNA from 147 TKI-relapsed patients showed that both high ratio of T790M sensitizing mutation (sensi-mutation) and high MAFs of mutations were all associated with better third generation TKI treatment outcomes. The quantification of both MAFs and T790M sensi-mutation ratio should be taken into consideration in some clinical situations, and incorporation of high-throughput NGS into clinical trials may be crucial to identifying the response to osimertinib, as it provides more comprehensive genomic information.

SUBMITTER: Li Y

PROVIDER: S-EPMC7262937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Simple Summary Treatment outcomes for non-small cell lung cancer (NSCLC) patients have significantly improved since the introduction of targeted therapy using tyrosine kinase inhibitors (TKI). Despite the initial promising results, most patients develop resistance due to on- or off-target mechanisms. On-target mutations prevent the effective binding of TKIs. In this study, we determined whether the most common on-target resistance mutation for treatment with EGFR-TKI mutation, i.e., the EGFR T790M mutation, can be detected in pre-treatment tissue samples and can predict treatment outcome. We included 33 patients who progressed with a T790M positive relapse upon treatment with EGFR-TKI between 2013 to 2019. Progression-free survival (PFS) time of the single T790M positive patient was 9 months, while the T790M negative patients had a median PFS of 10 months (range 2–27). Our results show that the presence of EGFR T790M mutations in pre-TKI samples is rare, even in patients who subsequently progressed with an EGFR T790M mutation. Abstract EGFR-mutated non-small cell lung cancer (NSCLC) patients can be effectively treated with tyrosine kinase inhibitors (TKI) but frequently present with an EGFR T790M resistance mutation at relapse. We aimed to screen for T790M in pre-treatment formalin-fixed and paraffin-embedded (FFPE) tissue samples of patients with a confirmed T790M mutation at progression. We analyzed 33 pre-treatment DNA samples of NSCLC patients who progressed upon TKI between 2013 to 2019. To establish storage-time dependent formalin fixation-induced background levels for C>T mutations, we analyzed DNA isolated from archival (stored >1 year, n = 22) and recently generated (stored <1 month, n = 11) FFPE samples and included DNA isolated from white blood cells (WBC) (n = 24) as controls. DNA samples were analyzed by droplet digital (dd)PCR, and positivity was defined by outlier detection according to Grubb’s criterion. The T790M background allele frequency levels were 0.160% in DNA isolated from archival-FFPE, 0.100% in fresh FFPE, and 0.035% in WBC. Progression-free survival (PFS) time of the single T790M positive patient was 9 months, while T790M negative patients had a median PFS of 10 months (range 2–27). Proper storage time matched FFPE control samples are essential for reliable detection of T790M mutation at low VAF. The presence of EGFR T790M mutations in pre-TKI samples is rare, even in patients who progressed with EGFR T790M mutations.

Project description:Background and purposeAs a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy.MethodsContrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis.ResultsDisease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients' prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model.ConclusionsRadiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients' prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients' outcomes.