Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities

Yang, Lin; Zhang, Zicheng; Dong, Jiyan; Zhang, Yong; Yang, Zongguo; Guo, Yongli; Sun, Xiaowei; Li, Junling; Xing, Puyuan; Jin, Ying; Zhou, Meng

doi:10.1016/j.phrs.2023.106844

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Wu et al employed a combination of spatial transcriptomics and multi-regional scRNA-seq to comprehensively characterize the transcriptional landscape of both SCLC tumor cells and the adjacent TME [ 87 ]. Meanwhile, Yang et al extensively characterized the immune microenvironment in SCLC by integrating transcriptomics and protein profiling, identifying two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype) of SCLC [ 88 ]. By examining the spatial microenvironment of the tumor using spatial transcriptomics techniques, these studies challenged the conventional belief that SCLC is a homogeneous disease.…”

Section: Applications Of Spatial Transcriptomics Sequencing Technolog...mentioning

confidence: 99%

Advances in spatial transcriptomics and its applications in cancer research

Jin,

Zuo,

et al. 2024

Mol Cancer

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Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

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Section: Applications Of Spatial Transcriptomics Sequencing Technolog...mentioning

confidence: 99%

Advances in spatial transcriptomics and its applications in cancer research

Jin,

Zuo,

et al. 2024

Mol Cancer

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“…4,5 The immune landscape within the tumour microenvironment (TME) has been widely recognized as a crucial determinant of prognosis and the antitumour immune response. 6 Increasing evidence suggests that smoking cigarettes significantly affects the immune regulation. The remarkable anticancer impact of immune checkpoint blockade therapy has expanded the treatment repertoire for nonsmall cell lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Common immunological and prognostic features of lung and bladder cancer via smoking‐related genes: PRR11 gene as potential immunotherapeutic target

Wang,

Zhu,

Zhang

et al. 2024

J Cellular Molecular Medi

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Smoking is a well‐known risk factor for non‐small‐cell lung cancer (NSCLC) and bladder urothelial carcinoma (BLCA). Despite this, there has been no investigation into a prognostic marker based on smoking‐related genes that could universally predict prognosis in these cancers and correlate with immune checkpoint therapy. This study aimed to identify smoking‐related differential genes in NSCLC and BLCA, analyse their roles in patient prognosis and immune checkpoint therapy through subgroup analyses, and shed light on PRR11 as a crucial prognostic gene in both cancers. By examining PRR11 co‐expressed genes, a prognostic model was constructed and its impact on immunotherapy for NSCLC and BLCA was evaluated. Molecular docking and tissue microarray analyses were conducted to explore the correlation between PRR11 and its reciprocal gene SPDL1. Additionally, miRNAs associated with PRR11 were analysed. The study confirmed a strong link between smoking‐related genes, prognosis, and immune checkpoint therapy in NSCLC and BLCA. PRR11 was identified as a key smoking‐associated gene that influences the efficacy of immune checkpoint therapy by modulating the stemness of these cancers. A prognostic model based on PRR11 co‐expressed genes in BLCA was established and its prognostic value was validated in NSCLC. Furthermore, it was found that PRR11 regulates PDL1 via SPDL1, impacting immunotherapeutic efficacy in both cancers. The involvement of hsa‐miR‐200b‐3p in the regulation of SPDL1 expression by PRR11 was also highlighted. Overall, the study elucidates that PRR11 modulates patient immunotherapy by influencing PDL1 expression through its interaction with SPDL1, with potential upstream regulation by hsa‐miR‐200b‐3p.

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“…The current clinical and pathological features used for prognostic assessment and treatment decision-making in SCLC have certain limitations, especially in predicting individual patient responses and survival outcomes. Several efforts have been made to uncover the complex heterogeneity of the disease, including investigations into neuroendocrine differentiation, transcriptionally defined subtypes and tumor microenvironment features [4][5][6][7] . Although this knowledge has greatly improved our understanding of the molecular mechanisms underlying SCLC heterogeneity and provided prognostic and theragnostic implications to some extent, their heterogeneity application in clinical trials and routine patient care is limited by several challenges, including the quantity and quality of the samples, trans-platform reproducibility, expensive and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Zhang,

Yang,

Chen

et al. 2024

npj Digit. Med.

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Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

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Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities

Cited by 8 publications

References 46 publications

Advances in spatial transcriptomics and its applications in cancer research

Advances in spatial transcriptomics and its applications in cancer research

Common immunological and prognostic features of lung and bladder cancer via smoking‐related genes: PRR11 gene as potential immunotherapeutic target

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

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