Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer

Genome-Wide Analysis Identifies Nuclear Factor 1C as a Novel Transcription Factor and Potential Therapeutic Target in SCLC

INTRODUCTION

Recent insights regarding mechanisms mediating stemness, heterogeneity, and metastatic potential of lung cancers have yet to be fully translated to effective regimens for the treatment of these malignancies. This study sought to identify novel targets for lung cancer therapy.

METHODS

Transcriptomes and DNA methylomes of 14 SCLC and 10 NSCLC lines were compared with normal human small airway epithelial cells (SAECs) and induced pluripotent stem cell (iPSC) clones derived from SAEC. SCLC lines, lung iPSC (Lu-iPSC), and SAEC were further evaluated by DNase I hypersensitive site sequencing (DHS-seq). Changes in chromatin accessibility and depths of transcription factor (TF) footprints were quantified using Bivariate analysis of Genomic Footprint. Standard techniques were used to evaluate growth, tumorigenicity, and changes in transcriptomes and glucose metabolism of SCLC cells after NFIC knockdown and to evaluate NFIC expression in SCLC cells after exposure to BET inhibitors.

RESULTS

Considerable commonality of transcriptomes and DNA methylomes was observed between Lu-iPSC and SCLC; however, this analysis was uninformative regarding pathways unique to lung cancer. Linking results of DHS-seq to RNA sequencing enabled identification of networks not previously associated with SCLC. When combined with footprint depth, NFIC, a transcription factor not previously associated with SCLC, had the highest score of occupancy at open chromatin sites. Knockdown of NFIC impaired glucose metabolism, decreased stemness, and inhibited growth of SCLC cells in vitro and in vivo. ChIP-seq analysis identified numerous sites occupied by BRD4 in the NFIC promoter region. Knockdown of BRD4 or treatment with Bromodomain and extra-terminal domain (BET) inhibitors (BETis) markedly reduced NFIC expression in SCLC cells and SCLC PDX models. Approximately 8% of genes down-regulated by BETi treatment were repressed by NFIC knockdown in SCLC, whereas 34% of genes repressed after NFIC knockdown were also down-regulated in SCLC cells after BETi treatment.

CONCLUSIONS

NFIC is a key TF and possible mediator of transcriptional regulation by BET family proteins in SCLC. Our findings highlight the potential of genome-wide chromatin accessibility analysis for elucidating mechanisms of pulmonary carcinogenesis and identifying novel targets for lung cancer therapy.

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery

The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care enhancing image reconstruction, detecting cancer or predicting individual risk to support treatment decisions and patient management. The entry into clinical care is determined by technological feasibility, integration into effective workflows, and immediacy of benefits. At the same time, research is advancing the integration of imaging data and other modalities such as genomics, and the linking of observations made at large scale with the understanding of underlying biological processes. AI will have impact in imaging and precision medicine not only because of the successful application of techniques established in other domains, but primarily because of the effective joint development of new technology and corresponding advance of diagnosis and care.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures

BACKGROUND

This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT).

METHODS

Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking.

RESULTS

A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC.

CONCLUSIONS

Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.