Purpose: The resistance to the EGFR tyrosine kinase inhibitors (TKI) is a major concern in non-small cell lung cancer (NSCLC) treatment. T790M mutation in EGFR accounts for nearly 50% of the acquired resistance to EGFR-TKIs. Earlier studies suggested that T790M mutation was also detected in TKI-na€ ve NSCLCs in a small cohort. Here, we use an ultra-sensitive droplet digital PCR (ddPCR) technique to address the incidence and clinical significance of pretreatment T790M in a larger cohort.Experimental Design: ddPCR was established as follows: wildtype or T790M mutation-containing DNA fragments were cloned into plasmids. Candidate threshold was identified using wild-type plasmid, normal human genomic DNA, and human A549 cell line DNA, which expresses wild type. Surgically resected tumor tissues from 373 NSCLC patients with EGFR-activating mutations were then examined for the presence of T790M using ddPCR.Results: Our data revealed a linear performance for this ddPCR method (R 2 ¼ 0.998) with an analytical sensitivity of approximately 0.001%. The overall incidence of the pretreatment T790M mutation was 79.9% (298/373), and the frequency ranged from 0.009% to 26.9%. The T790M mutation was detected more frequently in patients with a larger tumor size (P ¼ 0.019) and those with common EGFRactivating mutations (P ¼ 0.022), as compared with the others.Conclusions: The ultra-sensitive ddPCR assay revealed that pretreatment T790M was found in the majority of NSCLC patients with EGFR-activating mutations. ddPCR should be utilized for detailed assessment of the impact of the low frequency pretreatment T790M mutation on treatment with EGFR-TKIs. Clin Cancer Res; 21(15); 3552-60. Ó2015 AACR.
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.
The mutational spectrum is associated with smoking, body mass index, and other environmental factors, as well as with ERβ expression. Little association was observed between HPV and NSCLC.
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