Detection of epidermal growth factor receptor mutations in exhaled breath condensate using droplet digital polymerase chain reaction

The detection of certain oncogenic driver mutations, including those of epidermal growth factor receptor (EGFR), is essential for determining treatment strategies for advanced non‑small cell lung cancer (NSCLC). The current study assessed the feasibility of testing exhaled breath condensate (EBC) for EGFR mutations by droplet digital PCR (ddPCR). Samples were collected from 12 patients with NSCLC harboring EGFR mutations that were admitted to Okayama University Hospital between June 1, 2014 and December 31, 2017. A total of 21 EBC samples were collected using the RTube™ method and EGFR mutations (L858R, exon 19 deletions or T790M) were assessed through ddPCR analysis (EBC‑ddPCR). A total of 3 healthy volunteer samples were also tested to determine a threshold value for each mutation. Various patient characteristics were determined, including sex (3 males and 9 females), age (range 54‑81 years; median, 66 years), smoking history (10 had never smoked; 2 were former smokers), histology (12 patients exhibited adenocarcinoma), clinical stage (9 patients were stage IV; 3 exhibited post‑operative recurrence) and EGFR mutation type (4 had L858R; 8 had exon 19 deletions; 8 had T790M). EBC‑ddPCR demonstrated positive droplets in 8 of the 12 patients. The sensitivity and specificity of each mutation was as follows: 27.3 and 80.0% for EGFR L858R, 30.0 and 90.9% for EGFR Ex19del, and 22.2 and 100% for EGFR T790M. EBC‑ddPCR analysis of EGFR mutations exhibited modest sensitivity and acceptable specificity. EBC‑ddPCR is a minimally invasive and replicable procedure and may be a complementary method for EGFR testing in patients where blood or tissue sampling proves difficult

Identification of targetable kinases in idiopathic pulmonary fibrosis

Background Tyrosine kinase activation plays an important role in the progression of pulmonary fibrosis. In this study, we analyzed the expression of 612 kinase-coding and cancer-related genes using next-generation sequencing to identify potential therapeutic targets for idiopathic pulmonary fibrosis (IPF). Methods Thirteen samples from five patients with IPF (Cases 1-5) and eight samples from four patients without IPF (control) were included in this study. Six of the thirteen samples were obtained from different lung segments of a single patient who underwent bilateral pneumonectomy. Gene expression analysis of IPF lung tissue samples (n = 13) and control samples (n = 8) was performed using SureSelect RNA Human Kinome Kit. The expression of the selected genes was further confirmed at the protein level by immunohistochemistry (IHC). Results Gene expression analysis revealed a correlation between the gene expression signatures and the degree of fibrosis, as assessed by Ashcroft score. In addition, the expression analysis indicated a stronger heterogeneity among the IPF lung samples than among the control lung samples. In the integrated analysis of the 21 samples, DCLK1 and STK33 were found to be upregulated in IPF lung samples compared to control lung samples. However, the top most upregulated genes were distinct in individual cases. DCLK1, PDK4, and ERBB4 were upregulated in IPF case 1, whereas STK33, PIM2, and SYK were upregulated in IPF case 2. IHC revealed that these proteins were expressed in the epithelial layer of the fibrotic lesions. Conclusions We performed a comprehensive kinase expression analysis to explore the potential therapeutic targets for IPF. We found that DCLK1 and STK33 may serve as potential candidate targets for molecular targeted therapy of IPF. In addition, PDK4, ERBB4, PIM2, and SYK might also serve as personalized therapeutic targets of IPF. Additional large-scale studies are warranted to develop personalized therapies for patients with IPF

Japanese Lung Cancer Society Guidelines for Stage IV NSCLC With EGFR Mutations

Patients with NSCLC in East Asia, including Japan, frequently contain EGFR mutations. In 2018, we published the latest full clinical practice guidelines on the basis of those provided by the Japanese Lung Cancer Society Guidelines Committee. The purpose of this study was to update those recommendations, especially for the treatment of metastatic or recurrent EGFR-mutated NSCLC. We conducted a literature search of systematic reviews of randomized controlled and nonrandomized trials published between 2018 and 2019 that multiple physicians had reviewed independently. On the basis of those studies and the advice from the Japanese Society of Lung Cancer Expert Panel, we developed updated guidelines according to the Grading of Recommendations, Assessment, Development, and Evaluation system. We also evaluated the benefits of overall and progression-free survival, end points, toxicities, and patients’ reported outcomes. For patients with NSCLC harboring EGFR-activating mutations, the use of EGFR tyrosine kinase inhibitors (EGFR TKIs), especially osimertinib, had the best recommendation as to first-line treatment. We also recommended the combination of EGFR TKI with other agents (platinum-based chemotherapy or antiangiogenic agents); however, it can lead to toxicity. In the presence of EGFR uncommon mutations, except for an exon 20 insertion, we also recommended the EGFR TKI treatment. However, we could not provide recommendations for the treatment of EGFR mutations with immune checkpoint inhibitors, including monotherapy, and its combination with cytotoxic chemotherapy, because of the limited evidence present in the literature. The 2020 Japanese Lung Cancer Society Guidelines can help community-based physicians to determine the most appropriate treatments and adequately provide medical care to their patients

Investigation of DNN-Based Audio-Visual Speech Recognition (Special Section on Recent Advances in Machine Learning for Spoken Language Processing)

Audio-Visual Speech Recognition (AVSR) is one of techniques to enhance robustness of speech recognizer in noisy or real environments. On the other hand, Deep Neural Networks (DNNs) have recently attracted a lot of attentions of researchers in the speech recognition field, because we can drastically improve recognition performance by using DNNs. There are two ways to employ DNN techniques for speech recognition: a hybrid approach and a tandem approach; in the hybrid approach an emission probability on each Hidden Markov Model (HMM) state is computed using a DNN, while in the tandem approach a DNN is composed into a feature extraction scheme. In this paper, we investigate and compare several DNN-based AVSR methods to mainly clarify how we should incorporate audio and visual modalities using DNNs. We carried out recognition experiments using a corpus CENSREC-1-AV, and we discuss the results to find out the best DNN-based AVSR modeling. Then it turns out that a tandem-based method using audio Deep Bottle-Neck Features (DBNFs) and visual ones with multi-stream HMMs is the most suitable, followed by a hybrid approach and another tandem scheme using audio-visual DBNFs