scholarly journals Deciphering the Immune–Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

2022 ◽  
Vol 11 ◽  
Author(s):  
Wei-Wei Chen ◽  
Wei Liu ◽  
Yingze Li ◽  
Jun Wang ◽  
Yijiu Ren ◽  
...  

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the “escape” phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lixia Guo ◽  
Ting Zhang ◽  
Ying Xiong ◽  
Yanan Yang

Lung cancer is one of the most common types of human malignancies and the leading cause of cancer-related death. Patients with surgically resectable early stage lung cancer are more likely curable, but currently only a small population of patients can be diagnosed at such a stage, partly due to our incomplete understanding of the biology of lung cancer and the lack of diagnostic and prognostic biomarkers. Recent studies have shown that NOTCH1 is a critical regulator of human carcinogenesis and has been implicated in multiple steps of cancer development and progression. Herein, we review recent findings about the role of NOTCH1 in lung cancer and discuss its potential usefulness as both a therapeutic target and a biomarker for lung cancer.


2021 ◽  
Vol 16 (3) ◽  
pp. S264-S265
Author(s):  
F. Xu ◽  
L. Yang ◽  
C. Liu ◽  
J. Ying ◽  
Y. Wang

Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”


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