2021 WHO Classification of Lung Cancer: A Globally Applicable and Molecular Biomarker-Relevant Classification

Molecular and Pathologic Characterization of YAP1-Expressing Small Cell Lung Cancer Cell Lines Leads to Reclassification as SMARCA4-Deficient Malignancies

PURPOSE

The classification of small cell lung cancer (SCLC) into distinct molecular subtypes defined by ASCL1, NEUROD1, POU2F3, or YAP1 (SCLC-A, -N, -P, or -Y) expression, paves the way for a personalized treatment approach. However, the existence of a distinct YAP1-expressing SCLC subtype remains controversial.

EXPERIMENTAL DESIGN

To better understand YAP1-expressing SCLC, the mutational landscape of human SCLC cell lines was interrogated to identify pathogenic alterations unique to SCLC-Y. Xenograft tumors, generated from cell lines representing the four SCLC molecular subtypes, were evaluated by a panel of pathologists who routinely diagnose thoracic malignancies. Diagnoses were complemented by transcriptomic analysis of primary tumors and human cell line datasets. Protein expression profiles were validated in patient tumor tissue.

RESULTS

Unexpectedly, pathogenic mutations in SMARCA4 were identified in six of eight SCLC-Y cell lines and correlated with reduced SMARCA4 mRNA and protein expression. Pathologist evaluations revealed that SMARCA4-deficient SCLC-Y tumors exhibited features consistent with thoracic SMARCA4-deficient undifferentiated tumors (SMARCA4-UT). Similarly, the transcriptional profile SMARCA4-mutant SCLC-Y lines more closely resembled primary SMARCA4-UT, or SMARCA4-deficient non-small cell carcinoma, than SCLC. Furthermore, SMARCA4-UT patient samples were associated with a YAP1 transcriptional signature and exhibited strong YAP1 protein expression. Together, we found little evidence to support a diagnosis of SCLC for any of the YAP1-expressing cell lines originally used to define the SCLC-Y subtype.

CONCLUSIONS

SMARCA4-mutant SCLC-Y cell lines exhibit characteristics consistent with SMARCA4-deficient malignancies rather than SCLC. Our findings suggest that, unlike ASCL1, NEUROD1, and POU2F3, YAP1 is not a subtype defining transcription factor in SCLC. See related commentary by Rekhtman, p. 1708.

Prognostic implications of synaptophysin, CD56, thyroid transcription factor-1, and Ki-67 in pulmonary high-grade neuroendocrine carcinomas

BACKGROUND

The correlation between the expression of immunohistochemical markers and the clinicopathological characteristics of pulmonary high-grade neuroendocrine carcinomas (HGNEC) and its impact on the clinical outcomes of individuals with HGNEC has not yet been explored.

METHODS

This study enrolled patients diagnosed with HGNEC between April 2015 and July 2023. Based on the expression levels of synaptophysin (Syn), the neural cell adhesion molecule (CD56), thyroid transcription factor-1 (TTF-1), and Ki-67, a comprehensive analysis was conducted. This involved a comparison of clinicopathological characteristics, chemosensitivity, overall survival (OS), and progression-free survival (PFS). Furthermore, the study identified prognostic factors associated with patient survival through univariate and multivariate analyses.

RESULTS

Eighty-two patients were analyzed. Significant differences were identified in tumor stage (χ2 = 5.473, P = 0.019), lymphatic invasion (χ2 = 8.839, P = 0.003), and distant metastasis (χ2 = 5.473, P = 0.019), respectively, between the CD56 positive and negative groups. A significant difference in lymphatic invasion was observed (χ2 = 9.949, P = 0.002) between the CD56 positive and negative groups. A significant difference in vascular invasion was observed (χ2 = 5.106, P = 0.024) between the low and high Ki-67 groups. Compared to the Syn negative group, the Syn positive group had significantly shorter PFS (P = 0.006). Compared to the Syn negative group, the Syn positive group had significantly shorter OS (P = 0.004). The CD56 positive group also had significantly shorter OS than the CD56 negative group (P = 0.027). Univariate analysis revealed that tumor stage and Syn expression were associated with OS and PFS. Lymphatic invasion and CD56 expression were associated with OS. Multivariate analysis revealed that tumor stage was the strongest predictor of poor prognosis for OS (hazard ratio [HR] 0.551, 95 % confidence interval [CI] 0.328-0.927, P = 0.025) and PFS (HR 0.409, 95 % CI 0.247-0.676, P < 0.001).

CONCLUSIONS

Positive expression of Syn was associated with reduced PFS and OS, while positive CD56 expression was correlated with a shorter OS in HGNEC. The TNM stage was an independent risk factor that significantly influenced PFS and OS in patients with HGNEC. More studies are needed to make further progress in future treatment.

Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.

Methods

The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.

Results

424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.

Conclusion

Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

Combined time-restricted feeding and cisplatin enhance the anti-tumor effects in cisplatin-resistant and -sensitive lung cancer cells

Combination therapy as an important treatment option for lung cancer has been attracting attention due to the primary and acquired resistance of chemotherapeutic drugs in the clinical application. In the present study, as a new therapy strategy, concomitant treatment with time-restricted feeding (TRF) plus cisplatin (DDP) on lung cancer growth was investigated in DDP-resistant and DDP-sensitive lung cancer cells. We first found that TRF significantly enhanced the drug susceptibility of DDP in DDP-resistant A549 (A549/DDP) cell line, illustrated by reversing the inhibitory concentration 50 (IC₅₀) values of A549/DDP cells to normal level of parental A549 cells. We also found that TRF markedly enhanced DDP inhibition on cell proliferation, migration, as well as promoted apoptosis compared to the DDP alone group in A549, H460 and A549/DDP cells lines. We further revealed that the synergistic anti-tumor effect of combined DDP and TRF was greater than that of combined DDP and simulated fasting condition (STS), a known anti-tumor cellular medium. Moreover, mRNA sequence analysis from A549/DDP cell line demonstrated the synergistic anti-tumor effect involved in upregulated pathways in p53 signaling pathway and apoptosis. Notably, compared with the DDP alone group, combination of TRF and DDP robustly upregulated the P53 protein expression without mRNA level change by regulating its stability via promoting protein synthesis and inhibiting degradation, revealed by cycloheximide and MG132 experiments. Collectively, our results suggested that TRF in combination with cisplatin might be an additional novel therapeutic strategy for patients with lung cancer.

Correlation between PD-L1 Expression of Non-Small Cell Lung Cancer and Data from IVIM-DWI Acquired during Magnetic Resonance of the Thorax: Preliminary Results

This study aims to investigate the correlation between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in magnetic resonance imaging (MRI) and programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC). Twenty-one patients diagnosed with stage III NSCLC from April 2021 to April 2022 were included. The tumors were distinguished into two groups: no PD-L1 expression (<1%), and positive PD-L1 expression (≥1%). Conventional MRI and IVIM-DWI sequences were acquired with a 1.5-T system. Both fixed-size ROIs and freehand segmentations of the tumors were evaluated, and the data were analyzed through a software using four different algorithms. The diffusion (D), pseudodiffusion (D*), and perfusion fraction (pf) were obtained. The correlation between IVIM parameters and PD-L1 expression was studied with Pearson correlation coefficient. The Wilcoxon−Mann−Whitney test was used to study IVIM parameter distributions in the two groups. Twelve patients (57%) had PD-L1 ≥1%, and 9 (43%) <1%. There was a statistically significant correlation between D* values and PD-L1 expression in images analyzed with algorithm 0, for fixed-size ROIs (189.2 ± 65.709 µm²/s × 104 in no PD-L1 expression vs. 122.0 ± 31.306 µm²/s × 104 in positive PD-L1 expression, p = 0.008). The values obtained with algorithms 1, 2, and 3 were not significantly different between the groups. The IVIM-DWI MRI parameter D* can reflect PD-L1 expression in NSCLC.

Reproducible and sensitive micro-tissue RNA sequencing from formalin-fixed paraffin-embedded tissues for spatial gene expression analysis

Spatial transcriptome analysis of formalin-fixed paraffin-embedded (FFPE) tissues using RNA-sequencing (RNA-seq) provides interactive information on morphology and gene expression, which is useful for clinical applications. However, despite the advantages of long-term storage at room temperature, FFPE tissues may be severely damaged by methylene crosslinking and provide less gene information than fresh-frozen tissues. In this study, we proposed a sensitive FFPE micro-tissue RNA-seq method that combines the punching of tissue sections (diameter: 100 μm) and the direct construction of RNA-seq libraries. We evaluated a method using mouse liver tissues at two years after fixation and embedding and detected approximately 7000 genes in micro-punched tissue-spots (thickness: 10 μm), similar to that detected with purified total RNA (2.5 ng) equivalent to the several dozen cells in the spot. We applied this method to clinical FFPE specimens of lung cancer that had been fixed and embedded 6 years prior, and found that it was possible to determine characteristic gene expression in the microenvironment containing tumor and non-tumor cells of different morphologies. This result indicates that spatial gene expression analysis of the tumor microenvironment is feasible using FFPE tissue sections stored for extensive periods in medical facilities.