Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Lung cancer ranks first in the incidence and mortality of cancer in the world, of which more than 80% are non-small cell lung cancer (NSCLC). The majority of NSCLC patients are in stage IIIB~IV when they are admitted to hospital and have no opportunity for surgery. Compared with traditional chemotherapy, specific targeted therapy has a higher selectivity and fewer adverse reactions, providing a new treatment direction for advanced NSCLC patients. Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are the widely used targeted therapy for NSCLC patients. Their efficacy and prognosis are closely related to the mutation status of the EGFR gene. Clinically, detecting EGFR gene mutation is often limited by difficulty obtaining tissue specimens, limited detecting technology, and economic conditions, so it is of great clinical significance to find indicators to predict EGFR gene mutation status. Clinicopathological characteristics, tumor markers, liquid biopsy, and other predictors are less invasive, economical, and easier to obtain. They can be monitored in real-time, which is supposed to predict EGFR mutation status and provide guidance for the accurate, individualized diagnosis and therapy of NSCLC patients. This article reviewed the correlation between the clinical indicators and EGFR gene mutation status in NSCLC patients.

Pulmonary tumor with Notochordal differentiation: a case report and morphologic, Immunohistochemical and molecular study of benign Notochordal cell tumor originating in the lung

Background Extraosseous benign notochordal cell tumor is extremely rare, and there are only five reported cases worldwide. The presented case of pulmonary primary benign notochordal cell tumor is the sixth case, but the first to report the deletion mutation of EGFR gene exon 19. Case presentation The patient was a 50-year-old asymptomatic woman, who had been followed up for 3 years for a nodule in the right lung. After ten months of the wedge resection, the patient is alive without evidence of recurrence or metastasis. The tumor was 7 mm in diameter and was well demarcated. The tumor was consisted of a sheet of large round vacuolated cells with small and bland nuclei. No connective tissue containing blood vessels or inflammatory cell infiltration was detected in the stroma. The tumor was positive for CK AE1/AE3, Vimentin, S100 and Brachyury. EGFR gene mutation and amplification were not detected. Conclusions We firstly reported the positive immunohistochemical staining for EGFR and the negative molecular results of EGFR gene of pulmonary primary benign notochordal cell tumor. Due to the rarity of this tumor, more reports are needed to explore pathological characteristics, especially the molecular characteristics, in order to better understand the nature of tumors.

The Airway Microbiota of Non-small-cell Lung Cancer Patients and its Relationship to Tumor Stage and EGFR Gene Mutation

Background: Accumulating studies have suggested the airway microbiota of lung cancer was significantly different from healthy controls. However, little was known about the relationship between airway microbiota and important clinical parameters of lung cancer. In this study, we aimed to explore the association between sputum microbiota and lung cancer stage, lymph node metastasis, intrathoracic metastasis, and Epidermal growth factor receptor (EGFR) gene mutation. Methods: The microbiota of sputum samples from 85 newly diagnosed NSCLC patients were sequenced via 16S rRNA sequencing with V3-V4 region. Sequencing reads were filtered using QIIME2 and clustered against UPARSE. Results: The α diversity and β diversity was significantly different between patients in stage I to II (early stage, ES) and patients in stage III to IV (advanced stage, AS). Lefse identified that genera Granulicatella and Actinobacillus were significantly enriched in ES, and genus Actinomyces were significantly enriched in AS. PICRUSt2 identified NAD salvage pathway was significantly enriched in AS, which was positively associated with Granulicatella. Patients with intrathoracic metastasis were associated with increased genus Peptostreptococcus and incomplete reductive TCA cycle. Enrichment of TCA cycle was associated with increased Peptostreptococcus. Genera Parvimonas, Pseudomona and L-valine biosynthesis were positively associated with lymph node metastasis. L-valine biosynthesis was related with increased Pseudomona. Finally, genus Parvimonas were significantly upregulated in adenocarcinoma patients with EGFR mutation. Conclusion: In conclusion, the taxonomy structure differed between different lung cancer stage. The tumor stage, intrathoracic 1 metastasis, lymph node metastasis, and EGFR mutation were associated with alteration of specific airway genera and metabolic function of sputum microbiota.

Efficiency integrated chemotherapy with EGFR receptor tyrosine kinase inhibitors in patients with non-small cell lung cancer and an activating EGFR gene mutation.

The EGFR gene mutation occurs in 15% of patients with NSCLC. Tumors with such a molecular genetic profile are characterized by high sensitivity to therapy with EGFR tyrosine kinase inhibitors. However, the majority of EGFRm+ patients develop resistance to 1-2 generation TKI therapy after 9-13 months. In our study, we considered an integrated regimen combined use chemotherapy and targeted therapy as a possible way to overcome acquired tumor resistance to TKIs of 1–2 generations. The study included patients with IIIB / IV stages of NSCLC with activating EGFR mutations. Initially there were two months of treatment by gefitinib 250 mg daily. Then, after a 2-week drug-free period, 3 cycles of paclitaxel 175 mg / m2 and carboplatin AUC5 were administrated at days 71-113. Thereafter, gefitinib was re-started on day 135 and continued until disease progression. The median PFS was 20.- months (16.0-23.9 months, CI 95%). One -year progression-free survival (PFS) in patients who completed the chemotherapy stage was 79,6 %, two-year PFS - 38,9%. Brain metastases among patients with a progression of the disease were observed in 12 people (28.6%). The data obtained confirm the promise of using integrated chemotherapy with TKIs as a way to overcome the development of acquired resistance to TKIs of 1–2 generations.

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.