Adenocarcinoma of High-Grade Patterns Associated with Distinct Outcome of First-Line Chemotherapy or EGFR-TKIs in Patients of Relapsed Lung Cancer

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features

PURPOSE

This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.

MATERIALS AND METHODS

As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.

RESULTS

Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).

CONCLUSION

Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Neutrophils Recruited by NKX2-1 Suppression via Activation of CXCLs/CXCR2 Axis Promote Lung Adenocarcinoma Progression

NK2 Homeobox 1 (NKX2-1) is a well-characterized pathological marker that delineates lung adenocarcinoma (LUAD) progression. The advancement of LUAD is influenced by the immune tumor microenvironment through paracrine signaling. However, the involvement of NKX2-1 in modeling the tumor immune microenvironment is still unclear. Here, the downregulation of NKX2-1 is observed in high-grade LUAD. Meanwhile, single-cell RNA sequencing and Visium in situ capturing profiling revealed the recruitment and infiltration of neutrophils in orthotopic syngeneic tumors exhibiting strong cell-cell communication through the activation of CXCLs/CXCR2 signaling. The depletion of NKX2-1 triggered the expression and secretion of CXCL1, CXCL2, CXCL3, and CXCL5 in LUAD cells. Chemokine secretion is analyzed by chemokine array and validated by qRT-PCR. ATAC-seq revealed the restrictive regulation of NKX2-1 on the promoters of CXCL1, CXCL2, and CXCL5 genes. This phenomenon led to increased tumor growth, and conversely, tumor growth decreased when inhibited by the CXCR2 antagonist SB225002. This study unveils how NKX2-1 modulates the infiltration of tumor-promoting neutrophils by inhibiting CXCLs/CXCR2-dependent mechanisms. Hence, targeting CXCR2 in NKX2-1-low tumors is a potential antitumor therapy that may improve LUAD patient outcomes.

Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung cancer

Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) are effective for many patients with lung cancer with EGFR mutations. However, not all patients are responsive to EGFR TKIs, including even those harboring EGFR-sensitizing mutations. In this study, we quantified the cells and cellular interaction features of the tumor microenvironment (TME) using routine H&E-stained biopsy sections. These TME features were used to develop a prediction model for survival benefit from EGFR TKI therapy in patients with lung adenocarcinoma and EGFR-sensitizing mutations in the Lung Cancer Mutation Consortium 1 (LCMC1) and validated in an independent LCMC2 cohort. In the validation data set, EGFR TKI treatment prolonged survival in the predicted-to-benefit group but not in the predicted-not-to-benefit group. Among patients treated with EGFR TKIs, the predicted-to-benefit group had prolonged survival outcomes compared with the predicted not-to-benefit group. The EGFR TKI survival benefit positively correlated with tumor-tumor interaction image features and negatively correlated with tumor-stroma interaction. Moreover, the tumor-stroma interaction was associated with higher activation of the hepatocyte growth factor/MET-mediated PI3K/AKT signaling pathway and epithelial-mesenchymal transition process, supporting the hypothesis of fibroblast-involved resistance to EGFR TKI treatment.

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

CD73 Severed as a Potential Prognostic Marker and Promote Lung Cancer Cells Migration via Enhancing EMT Progression

To investigate the expression levels and prognostic value of CD73 in lung cancer. And moreover, to identify the effect and potential mechanism of CD73 on lung cancer cells proliferation and migration. CD73 expression levels in lung cancer were analyzed base on GEPIA2 and GEO database. GEPIA2 and Kaplan-Meier Plotter (KM Plotter) was used to analyzed the correlation between CD73 expression and prognosis. GEO dataset were analyzed via GEO2R. CD73 overexpression cell model was construction via recombinant lentivirus transfection into A549 and NCI-H520 cells. CCK8 assay were used to investigate cells proliferation. Migration and invasion ability were evaluated by scratch and transwell methods. Base on GEPIA2, GSE32683, GSE116959 and GSE37745 dataset, we found that CD73 expression were significant higher in tumor tissues of lung adenocarcinoma (LUAD) compared with that in non-tumor normal tissues and in lung squamous cell carcinoma (LUSC), while there were no significant difference of CD73 expression between LUSC and normal control tissues. Interestingly, a high CD73 level predict poor overall survival (OS) of LUSC. However, GEPIA2 and KM plotter showed the opposite conclusion of prognostic value of CD73 in LUAD. By using cell experiments, we found that CD73 overexpression promoted proliferation and migration of LUAD A549 cells. However, there was no significant effect of CD73 overexpression on LUSC NCI-H520 cells. Furthermore, CD73 overexpression facilitates epithelial to mesenchymal transition (EMT) progression of A549 cells. In conclusion, our results indicated that CD73 expression were increased in LUAD and might be an poor prognostic marker for LUSC patients. CD73 play an important role in LUAD cells proliferation and migration. These data allowed to support CD73 as a therapeutic target for LUAD.