EGFR Thr790Leu as a Potential Resistance Mechanism to First-Generation EGFR Tyrosine Kinase Inhibitor May Respond to Osimertinib in Patients With Lung Adenocarcinoma.

INTRODUCTION: It has been well established that EGFR Thr790Met is one of the major resistance mechanisms to first- and second-generation EGFR tyrosine kinase inhibitors (TKIs). Nevertheless, whether EGFR Thr790Leu (T790L), which shares the mutation site of Thr790 with EGFR Thr790Met, mediates resistance to EGFR TKIs remains elusive. The treatment options for patients harboring this rare mutation have not been reported. METHODS: Capture-based targeted ultradeep sequencing was performed on tumor and plasma samples collected at various treatment milestones from three patients with advanced lung adenocarcinoma undergoing targeted therapy. RESULTS: Needle biopsy of lymph node metastasis from patient 1 revealed EGFR T790L at disease progression on first-line treatment of gefitinib. Patient 2 had EGFR T790L identified from needle biopsy of lung tissue at disease progression on icotinib treatment. This patient was subsequently treated with osimertinib and achieved stable disease with a progression-free survival of 9 months. For patient 3, at disease recurrence after surgery, resected lung tumor tissue was retrieved for molecular profiling and revealed EGFR exon 19 deletion and EGFR T790L. The patient subsequently received osimertinib treatment and continued to benefit for 16 months and counting. She has maintained stable disease at the time of submission of this manuscript. CONCLUSIONS: We revealed for the first time that EGFR T790L may serve as a potential resistance mechanism to first-generation EGFR TKIs. We also report the first clinical evidence of efficacy generated by osimertinib in patients with lung adenocarcinoma harboring primary or acquired EGFR T790L, shedding light on treatment options for this subset of patients.

SUVmax of 18FDG PET/CT Predicts Histological Grade of Lung Adenocarcinoma.

OBJECTIVES: The relationship between the 18FDG PET-CT maximum standard uptake value (SUVmax) and the type of lung adenocarcinoma is still not established. The aim of this study was to investigate the relationship between SUVmax value and histological grade and pathological subtype of lung adenocarcinoma, and to determine the optimum SUVmax cutoffs for distinguishing different histological grades. MATERIALS AND METHODS: The data of 618 lung adenocarcinoma patients were retrospectively analyzed. The relationship between SUVmax measured on preoperative 18FDG-PET-CT and the histological grade and pathological subtype was examined. The Kruskal-Wallis test was used to compare differences among groups, and the Bonferroni-Dunn test for pairwise comparison among groups. ROC analysis was applied to determine the optimal cut-off values for distinguishing different groups. In addition, the cut-off value was verified in an independent cohort of 85 consecutive lung adenocarcinoma cases. RESULTS: The SUVmax was significantly different between the low, intermediate, and high-grade groups(p < .001). SUVmax value increased with increase in the degree of malignancy. The optimal cut-off value for identifying low-grade tumors was 2.01 (sensitivity 90.4%, specificity 86.9%, area under the curve [AUC] = 0.928, 95% confidence interval: 0.91-0.95; p < .001). The optimal cutoff SUVmax value for identifying high-grade tumors was 7.41 (sensitivity 79.8%, specificity 73.5%, AUC = 0.830, 95% confidence interval: 0.79-0.87; p < .001). The validation experiment showed that the coincidence rate was 88.89% in the low-level group, 64.15% in the middle-level group, and 78.57% in the high-level group. CONCLUSION: SUVmax can be used to predict pathological subtype and histological grade of lung adenocarcinoma. Thus, 18FDG PET-CT can serve as a noninvasive tool for precise diagnosis and help in the preoperative formulation of patient-specific treatment strategies.

Radiomics Signature Facilitates Organ-Saving Strategy in Patients With Esophageal Squamous Cell Cancer Receiving Neoadjuvant Chemoradiotherapy.

After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans. METHOD: All eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively. RESULTS: Among the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases. CONCLUSION: The model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.