Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer

Potential of C-X-C-Chemokine-Receptor-Type-4-Directed PET/CT Using [¹⁸F]AlF-NOTA-QHY-04 in Identifying Molecular Subtypes of Small Cell Lung Cancer

OBJECTIVE

Molecular subtyping of small-cell lung cancer (SCLC) has major implications for prognostic relevance and treatment guidance. This study aimed to explore the feasibility of a novel tracer targeting C-X-C-chemokine-receptor-type-4 (CXCR4) for distinguishing different SCLC subtypes.

MATERIALS AND METHODS

Thirty-five patients with pathologically confirmed SCLC were enrolled in this prospective study. Immunohistochemical staining was performed to classify the molecular subtypes into SCLC-A, SCLC-N, SCLC-P, and SCLC-I. [¹⁸F]AlF-NOTA-QHY-04 PET/CT parameters were obtained, including the maximum, mean, and peak standard uptake values (SUVmax, SUVmean, and SUVpeak, respectively) and the ratios of tumors (T) and normal tissues (NT) based on the SUVmax (T/NT). These parameters were compared among the molecular subtypes. A receiver operating characteristic (ROC) curve was used to analyze the performance of the parameters for distinguishing SCLC-N from other subtypes and neuroendocrine (NE) subtypes (SCLC-A and SCLC-N) from non-NE subtypes (SCLC-P and SCLC-I).

RESULTS

The molecular subtypes were SCLC-A (n = 17), SCLC-N (n = 6), SCLC-P (n = 7), and SCLC-I (n = 5). The SCLC-N subtype exhibited significantly higher uptake in both primary tumors and lymph node metastases than the other three subtypes (P < 0.05). When SCLC-N was compared with the other three subtypes combined (referred to as "other SCLCs"), all parameters were significantly higher in the SCLC-N group (P < 0.05). ROC analysis showed that these parameters had high accuracy in distinguishing SCLC-N from other SCLCs (area under ROC curve: 0.868-0.948 for primary tumors and 0.783-0.888 for lymph node metastases). Compared with the non-NE group, the SUVmax, SUVmean, and T/NTlung were significantly higher in the NE group for primary tumors. ROC analysis showed moderate accuracy in distinguishing between the NE and non-NE groups (ROC area: 0.692-0.786 for primary tumors and 0.692-0.815 for lymph node metastases).

CONCLUSION

Our preliminary findings indicate that CXCR4-directed PET/CT imaging using [¹⁸F]AlF-NOTA-QHY-04 may differentiate between SCLC-N and other molecular subtypes and between NE and non-NE subtypes of SCLC.

Transcriptomic analysis of transformed small-cell lung cancer from EGFR-mutated lung adenocarcinoma reveals distinct subgroups and precision therapy opportunities

BACKGROUND

Small-cell lung cancer (SCLC) transformation is one of the major mechanisms of resistance to Epidermal Growth Factor Receptor tyrosine kinase inhibitors (EGFR-TKIs). Chemotherapy is typically the recommended treatment for transformed SCLC (T-SCLC), similar to primary SCLC. However, the benefits of chemotherapy alone are minimal. Prior research highlights differences between the biological traits of T-SCLC and primary SCLC or EGFR-mutated lung adenocarcinoma (LUAD). This study aims to elucidate the molecular characteristics of T-SCLC and identify potential treatment modalities.

METHODS

We retrospectively collected tissue samples from LUAD, T-SCLC post-EGFR-TKI resistance, and primary SCLC. Genomics, transcriptomics, and proteomics were performed to clarify the differences between T-SCLC, LUAD, and primary SCLC. Hierarchical clustering analysis was then used to categorize the molecular subtype of T-SCLC, followed by a survival analysis based on these subtypes.

RESULTS

A study involving 61 patients investigated differences between LUAD, SCLC, and primary SCLC. RNA sequencing revealed distinct gene expression variations, particularly up-regulation in PPM1E, INSM1, and KCNC1 genes in T-SCLC. Pathway analysis linked T-SCLC to the cell cycle and neural differentiation. By conducting Hierarchical clustering analysis on RNA-seq data, the entire population can be categorized into two distinct groups. While certain T-SCLC showed similarities and differences compared to SCLC, with subtypes: LUAD-like and Non-LUAD-like. Notably, the LUAD-like subtype had significantly higher NKX2-1 expression (mean 371.8 vs. 41.8, P < 0.0001). T-SCLC treatment approaches were categorized into matched and unmatched groups, delineated by the alignment of specific therapies with corresponding pathologies. The matched group (13 cases) exhibited a significantly prolonged median progression-free survival compared to the unmatched group (10 cases) (5.4 months vs. 3.6 months, P = 0.02).

CONCLUSIONS

T-SCLC exhibits marked molecular distinctiveness, setting it apart not only from LUAD but also from classical SCLC. This distinction extends to its classification into two discernible molecular subtypes: LUAD-like and Non-LUAD-like. Customizing therapeutic protocols to align with these specific subtypes have the potential to identify the most appropriate treatment for T-SCLC.

A four gene risk score model for prognosis and immune microenvironment insights in small cell lung cancer based on CAF functional-related genes

Small cell lung cancer (SCLC) is still one of the most formidable challenges in oncology. In this study, we introduce an innovative risk scoring model rooted in cancer-associated fibroblast (CAF)-related functional genes, designed to predict patient prognosis and illuminate the microenvironment of SCLC. Through Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves, our model could effectively classify patients into high- and low-risk groups, with distinct survival outcomes and remarkable predictive accuracy, which has been evidenced by the AUC values. The low-risk patients showed a more active immune environment, characterized by more infiltration of dendritic cells, natural killer cells, and higher expression of immune co-stimulation molecules. On the contrary, high-risk patients displayed an enrichment of DNA repair and glycolysis pathways associated with tumor aggressiveness and treatment resistance. These results suggest that the risk model offers a nuanced view of response to immunotherapy that may guide the identification of patients who may benefit from immunotherapy. Moreover, we also verified the function of the key gene UBE2E2 by SCLC cell line experiments. Silencing UBE2E2 results in decreased cell proliferation and migration as well as increased apoptosis, which enhances its important role in SCLC biology. In summary, our study highlights the prognostic potential of the CAF-related functional gene risk model and its implications for predicting immune microenvironment status and guiding SCLC treatment strategies.

Cell-TIMP: Cellular Trajectory Inference Based on Morphological Parameters

Traditional approaches to studying cellular morphology rely on geometric metrics from stained images. However, staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Applying this framework to leukemia and breast cancer metastasis, we identified key shape changes linked to disease progression, highlighting the method's potential to enhance understanding of complex biological dynamics.

Molecular heterogeneity of small cell lung cancer and new therapeutic possibilities: a narrative review of the literature

Background and Objective

Small cell lung cancer (SCLC) is an aggressive disease commonly occurring in individuals with a history of heavy smoking. Despite recent approvals of chemotherapy and immunotherapy in the first-line treatment of extensive-stage SCLC, it maintains a poor prognosis. Moreover, only a small percentage of patients benefits from the addition of immunotherapy to platinum-based chemotherapy. The lack of significant progress in therapeutic options unrevealed the urgent need for a deeper understanding of tumor biology and easy-to-use predictive biomarkers, aiming to better tailor the treatment strategy. The aim of this review is to summarize recent evidence about the biology, molecular heterogeneity, as well as tumor microenvironment (TME) of SCLC and their forefront therapeutic implications.

Methods

A literature search was conducted using PubMed, focusing on articles published in English from 1981 to October 2024. Studies on SCLC biology and subclassification were selected for further analysis and integrated in the current narrative review.

Key Content and Findings

SCLC entity implies four distinct molecular subtypes based on transcription factors expression, specifically achaete-scute homolog 1 (ASCL1), neurogenic differentiation 1 (NEUROD1), POU class 2 homeobox 3 (POU2F3), and yes-associated protein 1 (YAP1), reflecting the tumor heterogeneity in terms of gene expression, transcriptional profiles, immune infiltration, and treatment sensitivity. Recently, a new subgroup, "SCLC-I", has been proposed to replace the YAP1 subtype, showing higher responsiveness to immunotherapy. The TME, implying immune cell infiltration and their interactions with cancer cells, plays a crucial role in determining SCLC's sensitivity to immunotherapy.

Conclusions

Advances in SCLC molecular characterization and the development of targeted therapies against specific molecular pathways might improve patients' clinical outcome, supporting a more personalized approach to this complex disease.

Single-cell RNA sequencing reveals cellular and molecular heterogeneity in extensive-stage small cell lung cancer with different chemotherapy responses

Despite its rapid growth and early metastasis, small cell lung cancer (SCLC) is more chemosensitive than other lung cancers. However, some patients with extensive-stage SCLC (ES-SCLC) do not respond to first-line chemotherapy, resulting in poorer prognoses due to inter- and intratumoral heterogeneity. In this study, we conducted single-cell RNA sequencing of 9 treatment-naive ES-SCLC samples. Based on comprehensive imaging evidence collected before and after two cycles of first-line chemotherapy and sample types, the 9 samples were categorized into three groups: progressive disease with the pleural effusion sample (PD_PE group, n = 1), progressive disease with the primary tumor samples (PD_TU group, n = 2), and partial response with the primary tumor samples (PR_TU group, n = 6). Based on transcriptomic landscape and cell type composition, the PD samples represent a multicellular ecosystem distinct from PR samples. The immune response, along with the elevated expression of immune-related genes such as LTF, SLPI, SPARC and IGLV1-51, might correlate with a poor first-line chemotherapy response in ES-SCLC. We also observed that T cells, particularly effector T cells, were more abundant in PD_TU group, with TNFA signaling via NFκB being significantly enriched. The PD_TU group was strongly enriched with macrophages and tumor-associated macrophages (TAMs), and angiogenesis in TAMs was highly enriched. Immunomodulatory fibroblasts were highly abundant in PD_TU group, and the pathways of epithelial-mesenchymal transition and angiogenesis were upregulated. This study offers the first comprehensive insights into the cellular and molecular heterogeneity in treatment-naive patients with ES-SCLC with different chemotherapy responses.

Dynamic evolution and antitumor mechanisms of CXCR6+CD8+ T cells in small cell lung cancer treated with low-dose radiotherapy and immunotherapy

BACKGROUND

Patients with small-cell lung cancer (SCLC) have the poor prognosis. Current research suggested that low-dose radiotherapy (LDRT) combined with immunotherapy can enhance the immunogenicity of tumor cells, thereby improving antigen presentation and promoting the intratumoral infiltration of CD8+ T cells, which significantly extends the survival of patients. However, the change trajectory of T cells, and the mechanisms underlying the promotion of intratumoral infiltration of CD8+ T cells, and the enhancement of their cytotoxic functions remain to be elucidated.

METHODS

To delineate the dynamic changes of T cells, we collected tumors from Kaede tumor-bearing mice that had undergone radioimmunotherapy. Using flow cytometry, we sorted intratumoral-infiltrating immune cells, which were required for single-cell RNA sequencing, at various time points (Kaede Red: derived from tumor-draining lymph node [TDLN]). The results obtained from the sequencing analysis were further validated through experiments, such as flow cytometry, immunofluorescence, and analysis of clinical cohort data.

RESULTS

Here, we observed stem-like T cells migrating from the TDLN to the tumor site and differentiating into effector phenotypes within the tumor. Dendritic cells (DCs) are the key cluster that induces the differentiation of stem-like T cell into effector phenotypes. Moreover, SCLC patients with a high infiltration of tumor-specific CXCR6+CD8+ T cells exhibited a supportive TME and longer survival time (P < 0.001).

CONCLUSIONS

This study delineates the change trajectory of CD8+ T cells, identifies the crucial role of DCs in T cell differentiation, and highlights the significance of tumor-specific CXCR6+CD8+ T cells in anti-tumor immunity. Future therapeutic strategies for SCLC could focus on enhancing the infiltration of activated DCs and CXCR6+CD8+ T cells within the tumor microenvironment to improve treatment efficacy.

Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage, thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrates high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA, which allows analysis to be combined with existing short-read RNA-seq data sets. In our investigation of BTK and SF3B1 genes in samples from patients with chronic lymphocytic leukemia (CLL), we detect SF3B1 isoforms and mutations with high sensitivity. Integration with short-read single-cell RNA sequencing (scRNA-seq) data reveals significant gene expression differences in SF3B1-mutated CLL cells, although it does not impact the sensitivity of the anticancer drug venetoclax. scRaCH-seq's capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

Current and future therapies for small cell lung carcinoma

Small cell lung cancer (SCLC) is an aggressive malignancy characterized by rapid proliferation and high metastatic potential. It is characterized by universal inactivation of and RB1, overexpression of the MYC family and dysregulation of multiple oncogenic signaling pathways. Among different patients, SCLCs are similar at the genetic level but exhibit significant heterogeneity at the molecular level. The classification of SCLC has evolved from a simple neuroendocrine (NE)/non-neuroendocrine (non-NE) classification system to a transcription factor-based molecular subtype system; lineage plasticity adds further complexity and poses challenges for therapeutic development. While SCLC is initially sensitive to platinum-based chemotherapy, resistance develops rapidly, leading to a dismal prognosis. Various antibodies, including PD-1/PD-L1 inhibitors and antibody‒drug conjugates, have been introduced into clinical practice or are being evaluated in clinical trials. However, their therapeutic benefits for SCLC patients remain limited. This review summarizes SCLC carcinogenic mechanisms, tumor heterogeneity, and the immune microenvironment of SCLC, with a focus on recent advances in metastasis and resistance mechanisms. Additionally, the corresponding clinical progress in tackling these challenges is discussed.

Uncovering Astrocyte Morphological Dynamics Using Optical Diffraction Tomography and Shape-Based Trajectory Inference

Astrocytes, integral components of the central nervous system, are increasingly recognized for their multifaceted roles beyond support cells. Despite their acknowledged importance, understanding the intricacies of astrocyte morphological dynamics remains limited. Our study marks the first exploration of astrocytes using optical diffraction tomography (ODT), establishing a label-free, quantitative method to observe morphological changes in astrocytes over a 7-day in-vitro period. ODT offers quantitative insights into cell volume, dry mass, and area through label-free, real-time measurements-capabilities that are challenging to achieve with conventional imaging techniques. Through comprehensive analysis of 3D refractive index maps and shape characterization techniques, we capture the developmental trajectory and dynamic morphological transformations of astrocytes. Specifically, our observations reveal increased area and a transition to larger, flattened shapes, with alterations in cell volume and density, indicating shifts in cellular composition. By employing unsupervised clustering and pseudotime trajectory analysis, we introduce a novel morphological trajectory inference for neural cells, tracking the morphological evolution of astrocytes from elongated to evenly spread shapes. This analysis marks the first use of trajectory inference based solely on morphology for neural cell types, laying a foundation for future studies employing ODT to examine astrocyte dynamics and neural cell interactions across diverse substrates.

Metabolic reprogramming of neutrophils in the tumor microenvironment: Emerging therapeutic targets

Neutrophils are pivotal in the immune system and have been recognized as significant contributors to cancer development and progression. These cells undergo metabolic reprogramming in response to various stimulus, including infections, diseases, and the tumor microenvironment (TME). Under normal conditions, neutrophils primarily rely on aerobic glucose metabolism for energy production. However, within the TME featured by hypoxic and nutrient-deprived conditions, they shift to altered anaerobic glycolysis, lipid metabolism, mitochondrial metabolism and amino acid metabolism to perform their immunosuppressive functions and facilitate tumor progression. Targeting neutrophils within the TME is a promising therapeutic approach. Yet, focusing on their metabolic pathways presents a novel strategy to enhance cancer immunotherapy. This review synthesizes the current understanding of neutrophil metabolic reprogramming in the TME, with an emphasis on the underlying molecular mechanisms and signaling pathways. Studying neutrophil metabolism in the TME poses challenges, such as their short lifespan and the metabolic complexity of the environment, necessitating the development of advanced research methodologies. This review also discusses emerging solutions to these challenges. In conclusion, given their integral role in the TME, targeting the metabolic pathways of neutrophils could offer a promising avenue for cancer therapy.

Differential expression of the MYC-Notch axis drives divergent responses to the front-line therapy in central and peripheral extensive-stage small-cell lung cancer

Central and peripheral extensive-stage small-cell lung cancer (ES-SCLC) are reported to be two distinct tumor entities, but their responses to the front-line therapies and underlying biological mechanisms remain elusive. In this study, we first compared the outcomes of central and peripheral ES-SCLC receiving front-line chemotherapy or chemo-immunotherapy with a cohort of 265 patients. Then we performed single-cell RNA sequencing (scRNA-seq) on nine treatment-naïve ES-SCLC samples to investigate potential mechanisms underlying the response differences. Under chemotherapy, the peripheral type had a lower objective response rate (44.8% vs. 71.2%, p = 0.008) and shorter progression-free survival (median 3.4 vs. 5.1 months, p = 0.001) than the central type. When comparing chemo-immunotherapy with chemotherapy, the peripheral type showed a greater potential to reduce progression (HR, 0.18 and 0.52, respectively) and death (HR, 0.44 and 0.91 respectively) risks than the central type. Concerning the scRNA-seq data, the peripheral type was associated with chemo-resistant and immune-responsive tumoral and microenvironmental features, including a higher expression level of MYC-Notch-non-neuroendocrine (MYC-Notch-non-NE) axis and a more potent antigen presentation and immune activation status. Our results revealed that central and peripheral ES-SCLC had distinct responses to front-line treatments, potentially due to differential activation statuses of the MYC-Notch-non-NE axis.

Whole-section digital analysis of immune profiles in surgically resected small cell lung carcinoma and their associations with molecular subtypes

Background

The molecular subtype-specific features of the tumor immune microenvironment (TIME) in small cell lung carcinoma (SCLC) remain poorly understood. We aimed to analyze the immune profiles in surgically resected SCLC and their associations with molecular subtypes.

Methods

Tumor samples from 83 treatment-naive SCLC patients who underwent surgical resection were analyzed. The protein expression of subtype-defining markers (ASCL1, NEUROD1, POU2F3, and YAP1) and nine immune-related markers were assessed using whole-section immunohistochemistry. Digital image analysis was employed for precise quantification of immune cell infiltrates and distributions. The findings were subsequently correlated with clinicopathological parameters and patient prognoses.

Results

Unsupervised hierarchical clustering categorized the tumors into four molecular subtypes: achaete-scute homologue 1-dominant (ASCL1; SCLC-A, 71.1%, n=59), neuronal differentiation factor 1-dominant (NEUROD1; SCLC-N, 12.1%, n=10), POU class 2 homeobox 3-dominant (POU2F3; SCLC-P, 10.8%, n=9), and quadruple-negative (SCLC-QN, 6.0%, n=5). Expression of major histocompatibility complex class I (MHC I) and class II (MHC II; P=0.02, P=0.02), tumor programmed death-ligand 1 (PD-L1; P=0.006), and an inflamed phenotype characterized by CD8+/CD3+ T cells (P=0.001, P=0.003) were more prominent in SCLC-P tumors compared to other subtypes. Additionally, SCLC-P tumors demonstrated the highest levels of MHC II (P=0.04) and PD-L1 expression on both tumor and stromal cells (P=0.003, P=0.01). The tumor proportion score of PD-L1 positively correlated with tumor expression levels of POU2F3 (rho=0.297, P=0.006) and MHC I (rho=0.239, P=0.03), as well as the combined positive score of PD-L1 (rho=0.222, P=0.04; rho=0.433, P<0.001). Intra-tumoral tertiary lymphoid structures (intra-TLS) and peri-tumoral TLS (peri-TLS) were observed in 60.2% (n=50) and 96.4% (n=80) of patients, respectively. High intra-TLS density was more frequently associated with SCLC-P tumors (P=0.02). Notably, low peri-TLS density and stromal PD-L1 expression were linked to improved overall survival (OS) and progression-free survival (PFS), respectively.

Conclusions

This study highlights the heterogeneity of the TIME across molecular subtypes of SCLC. The SCLC-P subtype and MHC I expression may serve as predictive biomarkers for immunotherapy response, while peri-TLS density and stromal PD-L1 expression might serve as prognostic indicators in resected SCLC.

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Single-Cell RNA Sequencing Reveals Extensive Heterogeneity and Unique Gene Trajectories in Non-Transformed and Transformed Human Lung Epithelial Cells: Insights into the Role of LncRNAs in Tumor Heterogeneity

Lung cancer exhibits substantial inter- and intra-tumor heterogeneity, with features that present significant challenges in advancing biomarker discovery and the development of targeted therapeutics. To fill this gap, we employed single-cell RNA sequencing (scRNA-seq) and advanced bioinformatics tools to evaluate the transcriptomic heterogeneity of immortalized, non-transformed (BEAS2B) and transformed (H460) lung epithelial cell lines and their responses to carcinogen challenge. Gene expression profiles resolved four primary clusters further discretized into unique subclusters based on genetic signatures and phenotypic profiles. Profiles of long non-coding RNAs (lncRNAs) identified microRNA host genes, antisense RNA genes, divergent transcript, and long intergenic non-coding RNAs as contributors to cellular heterogeneity. These findings indicate that distinct patterns of gene expression, remarkably in lncRNAs, define cellular heterogeneity in non-transformed versus transformed cells. These features can be exploited for the development of therapies directed at specific cell subpopulations in precancerous lesions and within lung tumors.

Role of CD4+ T cells in cancer immunity: a single-cell sequencing exploration of tumor microenvironment

Recent oncological research has intensely focused on the tumor immune microenvironment (TME), particularly the functions of CD4 + T lymphocytes. CD4+ T lymphocytes have been implicated in antigen presentation, cytokine release, and cytotoxicity, suggesting their contribution to the dynamics of the TME. Furthermore, the application of single-cell sequencing has yielded profound insights into the phenotypic diversity and functional specificity of CD4+ T cells in the TME. In this review, we discuss the current findings from single-cell analyses, emphasizing the heterogeneity of CD4+ T cell subsets and their implications in tumor immunology. In addition, we review the critical signaling pathways and molecular networks underpinning CD4+ T cell activities, thereby offering novel perspectives on therapeutic targets and strategies for cancer treatment and prognosis.

Transcriptional regulatory network analysis identifies GRN as a key regulator bridging chemotherapy and immunotherapy response in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive and heterogeneous subtype, representing 15% of lung cancer cases. Although SCLC initially responds to etoposide and platinum (EP) chemotherapy, nearly all patients relapse with resistant tumors. While recent advances in immunotherapy have shown promise, only 10-20% of patients benefit, and effective stratification methods are lacking. The mechanisms of resistance to both therapeutics remain obscure. In this study, we aimed to gain insights into those leveraging a recent surge in the field of SCLC genomics. We constructed a regulatory network for SCLC and identified granulin precursor (GRN) as a hub of EP response associated genes. GRN-low patients showed improved survival with chemotherapy, while GRN-high patients exhibited resistance. GRN overexpression in SCLC cells conferred resistance to EP treatment and suppressed neuroendocrine features. GRN and its associated genes were linked to cancer cell intrinsic immunogenicity, and single-cell RNA-seq data revealed that GRN expression is particularly high in subsets of tumor-associated macrophages. In concordance with these findings, GRN-low tumors showed significantly better survival with chemo-immunotherapy, while GRN-high tumors did not benefit from additional immunotherapy. GRN-high tumors, associated with non-neuroendocrine (non-NE) subtypes, had a higher level of macrophage infiltration, potentially contributing to immunotherapy resistance. These results highlight GRN as a critical regulator of chemo-resistance and a potential biomarker for immunotherapy resistance in SCLC. Targeted therapeutic strategies for GRN-low patients could improve outcomes, while new approaches are needed for GRN-high patients. Overall, our findings implicate GRN as a bridge between chemotherapy and immunotherapy resistance through GRN-mediated mechanisms.

Single-cell RNA sequencing reveals tumor heterogeneity in small cell neuroendocrine cervical carcinoma

Small cell neuroendocrine cervical carcinoma (SCNECC) is an aggressive gynecological malignancy with poor prognosis. The precision therapeutic strategies for SCNECC are severely limited by the complex tumor microenvironment. Here, we mapped the single-cell landscape of a total of six samples from matched SCNECC cancerous foci and normal adjacent cervical tissues. Through analysis of 68,455 high-quality cells, malignant epithelial cells were identified with increased neuroendocrine differentiation and reduced keratinization. Within four epithelial cell clusters, the key transcription factors ASCL1, NEUROD1, POU2F3, and YAP1 defined molecular subtypes. Transitional trajectory among subtypes characterized two distinct carcinogenesis pathways in SCNECC. The P-type SCNECC showed potentially enhanced immune infiltration over other subtypes. Intercellular communication analysis identified several immune checkpoints and differentially expressed signaling pathways among subtypes. Through western blotting, the TC-YIK cell line was identified as an N-type SCNECC cell with high expression of SLFN11 and mTOR. Based on immunohistochemical staining of malignant subtyping markers, a cohort of 66 SCNECC patients from our hospital were divided into five subtypes. We further combined YAP1 expression with other clinicopathological factors (Cox p < 0.05) to establish a prognostic nomogram. Overall, these findings provide clues for tumorigenesis, precision treatments and prognostic prediction in SCNECC.

The analysis of molecular classification of pulmonary neuroendocrine tumors and relationship between YAP1 and efficacy

A novel molecular classification for small cell lung cancer (SCLC) has been established utilizing the transcription factors achaete-scute homologue 1 (ASCL1), neurogenic differentiation factor 1 (NeuroD1), POU class 2 homeobox 3 (POU2F3), and yes-associated protein 1 (YAP1). This classification was predicated on the transcription factors. Conversely, there is a paucity of information regarding the distribution of these markers in other subtypes of pulmonary neuroendocrine tumors (PNET). Clinical and survival data for PNET patients were gathered from January 2008 to December 2020. Immunohistochemical analysis was employed to evaluate the expression. The relationship between YAP1 expression and outcomes in patients with pulmonary large cell neuroendocrine carcinoma (LCNEC) was examined. Data from low-grade PNET patients who had previously undergone immunotherapy were retrospectively gathered and analyzed. The ASCL1 positive rate was markedly elevated in SCLC (7.1% vs. 60%; P < 0.001) and LCNEC patients (7.1% vs. 38.5%; P = 0.034) compared to PC patients. The YAP1-positive rate was elevated in LCNEC compared to SCLC (43.6% vs. 20%, P = 0.028) and pulmonary carcinoid (PC) patients (43.6% vs. 21.4%; P = 0.021). The DLL3-positive rate in SCLC patients was greater than in SCLC and PC patients (37.1% vs. 23.1% vs. 0%; P = 0.028, P = 0.021). A significant level of tumor heterogeneity was noted, with SCLC and LCNEC patients exhibiting markedly higher heterogeneity than PC patients (65.7% vs. 56.3% vs. 21.4%; P = 0.005, P = 0.025). In patients with LCNEC, YAP1 positivity exhibited no correlation with PD-L1 expression (17.1% vs. 45.7%, P = 0.518). Tumor heterogeneity was also noted in transformed SCLC, with no significant differences in the expression levels of transcription factors between transformed and traditional SCLC. In 13 LCNEC patients with a history of ICI application, YAP1 exhibited no significant effect on PFS (P = 0.331) or OS (P = 0.17) in the subgroup analysis of LCNEC patients. Among the 14 patients with low-grade PNET who underwent immunotherapy, the disease control rate was 85.7%. Patients with high-grade PNET have high levels of expression of ASCL1 and DLL3, whereas patients with LCNEC have high levels of expression of YAP1. With regard to the transcription factor level, it was found that patients with SCLC and LCNEC had a much higher degree of tumor heterogeneity than those with PC. In patients with LCNEC who were receiving monotherapy of ICIs or chemotherapy in combination with ICIs, the expression of YAP1 did not appear to have any clear impact on the prognosis. This is due to the limited sample size of the study, which requires additional investigation. When compared to the expression of TFs in regular SCLC, the expression of TFs in converted SCLC is comparable.

ATAD2 is a potential immunotherapy target for patients with small cell lung cancer harboring HLA-A∗0201

BACKGROUND

Small cell lung cancer (SCLC) represents a highly aggressive neuroendocrine tumour with a dismal prognosis. Currently, the identification of a specific tumour antigen that can facilitate immune-based therapies for SCLC remains elusive.

METHODS

We employed liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyse cancer/testis antigens (CTAs) in SCLC cell lines and human tumour specimens. Immunohistochemistry of clinical specimens was performed to compare protein expression in SCLC, non-small cell lung cancer (NSCLC), and matched normal-adjacent tissues. Additionally, publicly available RNA sequencing databases were interrogated to identify gene expression patterns in different SCLC subtypes and in different disease stages.

FINDINGS

Distinct numbers and types of CTAs were identified across SCLC subtypes, with significantly higher expression levels of ATPase family AAA domain-containing protein 2 (ATAD2) observed in SCLC compared to normal adjacent tissues and NSCLC tissues. A dynamic expression pattern of ATAD2 was found throughout the clinical course of SCLC and exhibited a positive correlation with achaete-scute family bHLH transcription factor 1 (ASCL1) expression in SCLC. Immunopeptidomics analysis identified the YSDDDVPSV sequence derived from the HLA-A∗02:01 restriction epitope of ATAD2 as a highly promising tumour antigen candidate for potential immunotherapy applications. YSDDDVPSV immunopeptides were confirmed to be present in SCLC-A and SCLC-N with HLA-A∗02:01 restriction. Notably, HLA-A∗02:01 T cells exhibited a robust response upon stimulation with YSDDDVPSV immunopeptide pulsed by T2 cells.

INTERPRETATION

Our findings highlight the potential of targeting the ATAD2 YSDDDVPSV immunopeptide for SCLC immunotherapy, thereby offering a promising avenue for the development of adoptive T cell therapies to effectively treat ASCL1-positive or NEUROD1-positive SCLC carrying HLA-A∗02:01.

FUNDING

This study was supported by the National key R&D program of China (2022YFC2505000); National Natural Science Foundation of China (NSFC) general program (82272796) NSFC special program (82241229); CAMS Innovation Fund for Medical Sciences (CIFMS 2022-I2M-1-009); CAMS Key Laboratory of Translational Research on Lung Cancer (2018PT31035); Aiyou foundation (KY201701). National key R&D program of China (2022YFC2505004). NSFC general program (81972905). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022012).

Integration of Single-Cell and Bulk Transcriptomes to Identify a Poor Prognostic Tumor Subgroup to Predict the Prognosis of Patients with Early-stage Lung Adenocarcinoma

Background: Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal technology for investigating novel therapeutic targets in cancer. Despite its significance, there remains a scarcity of studies utilizing this technology to address treatment strategies specifically tailored for early-stage lung adenocarcinoma (LUAD). Consequently, this study aimed to investigate the tumor microenvironment (TME) characteristics and develop a prognostic model for early-stage LUAD. Methods: The markers identifying cell types were obtained from the CellMarker database and published research. The SCEVAN package was employed for identifying malignant lung epithelial cells. Single-cell downstream analyses were conducted using the SCP package, encompassing gene set enrichment analysis, enrichment analysis, pseudotime trajectory analysis, and differential expression analysis. Calibration curves, receiver operating characteristic curves, and decision curve analysis were employed to assess the performance of the prognostic model for LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blot, cell transfection, cell proliferation, and cell invasion assays were performed to validate the expression and biological function. Results: Seven cell types were distinguished in the scRNA-seq dataset through the utilization of cell markers documented in published literature. Four subpopulations of early-stage LUAD tumor cells exhibited a high degree of heterogeneity. The prognostic model constructed by PERP and KRT8 showed a great prediction for distinguishing the early-stage LUAD and normal tissues. The validation of PERP and KRT8 expression levels was carried out through both RT-qPCR and western blot analyses. Eventually, in vitro experiments, including CCK8, colony formation, EdU, and transwell assays, confirmed that KRT8 and PERP could promote LUAD cell proliferation and migration. Conclusions: Our study provided a comprehensive characterization of the TME in LUAD through integrative single-cell and bulk transcriptomic analyses. We identified dynamic transitions from normal epithelial cells to tumor cells, revealing the heterogeneity and evolution of malignant LUAD cells. The novel prognostic model based on KRT8 and PERP demonstrated robust predictive performance, offering a promising tool for early-stage LUAD risk stratification. Functional experiments further confirmed that KRT8 and PERP promote tumor proliferation and migration, providing new insights into their roles as therapeutic targets.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Lung cancer organoid-based drug evaluation models and new drug development application trends

Lung cancer is a malignant tumor with high incidence and mortality rates in both men and women worldwide. Although anticancer drugs are prescribed to treat lung cancer patients, individual responses to these drugs vary, making it crucial to identify the most suitable treatment for each patient. Therefore, it is necessary to develop an anticancer drug efficacy prediction model that can analyze drug efficacy before patient treatment and establish personalized treatment strategies. Unlike two-dimensional (2D) cultured lung cancer cells, lung cancer organoid (LCO) models have a three-dimensional (3D) structure that effectively mimics the characteristics and heterogeneity of lung cancer cells. Lung cancer patient-derived organoids (PDOs) also have the advantage of recapitulating histological and genetic characteristics similar to those of patient tissues under in vitro conditions. Due to these advantages, LCO models are utilized in various fields, including cancer research, and precision medicine, and are especially employed in various new drug development processes, such as targeted therapies and immunotherapy. LCO models demonstrate potential applications in precision medicine and new drug development research. This review discusses the various methods for implementing LCO models, LCO-based anticancer drug efficacy analysis models, and new trends in lung cancer-targeted drug development.

Fuel for thought: targeting metabolism in lung cancer

For over a century, we have appreciated that the biochemical processes through which micro- and macronutrients are anabolized and catabolized-collectively referred to as "cellular metabolism"-are reprogrammed in malignancies. Cancer cells in lung tumors rewire pathways of nutrient acquisition and metabolism to meet the bioenergetic demands for unchecked proliferation. Advances in precision medicine have ushered in routine genotyping of patient lung tumors, enabling a deeper understanding of the contribution of altered metabolism to tumor biology and patient outcomes. This paradigm shift in thoracic oncology has spawned a new enthusiasm for dissecting oncogenotype-specific metabolic phenotypes and creates opportunity for selective targeting of essential tumor metabolic pathways. In this review, we discuss metabolic states across histologic and molecular subtypes of lung cancers and the additional changes in tumor metabolic pathways that occur during acquired therapeutic resistance. We summarize the clinical investigation of metabolism-specific therapies, addressing successes and limitations to guide the evaluation of these novel strategies in the clinic. Beyond changes in tumor metabolism, we also highlight how non-cellular autonomous processes merit particular consideration when manipulating metabolic processes systemically, such as efforts to disentangle how lung tumor cells influence immunometabolism. As the future of metabolic therapeutics hinges on use of models that faithfully recapitulate metabolic rewiring in lung cancer, we also discuss best practices for harmonizing workflows to capture patient specimens for translational metabolic analyses.

Crosstalk between cancer-associated fibroblasts and non-neuroendocrine tumor cells in small cell lung cancer involves in glycolysis and antigen-presenting features

BACKGROUND

Small cell lung cancer (SCLC) is a highly fatal malignancy, the complex tumor microenvironment (TME) is a critical factor affecting SCLC progression. Cancer-associated fibroblasts (CAFs) are crucial components of TME, yet their role in SCLC and the underlying mechanisms during their interaction with SCLC cells remain to be determined.

METHODS

Microenvironmental cell components were estimated using transcriptome data from SCLC tissue available in public databases, analyzed with bioinformatic algorithms. A co-culture system comprising MRC5 fibroblasts and SCLC cell lines was constructed. RNA sequencing (RNA-seq) was performed on co-cultured and separately cultured MRC5 and H196 cells to identify differentially expressed genes (DEGs) and enriched signaling pathways. Glycolysis and STING signaling in SCLC cells were assessed using glucose uptake assays, qRT-PCR, and Western blot analysis. Immunohistochemical staining of SCLC tissue arrays quantified α-SMA, HLA-DRA and CD8 expression.

RESULTS

Non-neuroendocrine (non-NE) SCLC-derived CAFs exhibited more abundance and DEGs than NE SCLC-derived CAFs did, which interact with non-NE SCLC cells can induce the enrichment of glycolysis-related genes, increasement of glucose uptake, upregulation of glycolytic signaling proteins in non-NE SCLC cells and accumulation of lactate in the extracellular environment, confirming CAF-mediated glycolysis promotion. Additionally, glycolysis-induced ATP production activated STING signaling in non-NE SCLC cells, which upregulated T cell chemo-attractants. However, CAF abundance did not correlate with CD8 + T cell numbers in SCLC tissues. Additionally, non-NE SCLC cell-educated CAFs exhibited features of antigen-presenting CAFs (apCAFs), as indicated by the expression of major histocompatibility complex (MHC) molecules. Co-localization of HLA-DRA and α-SMA signals in SCLC tissues confirmed apCAF presence. The apCAFs and CD8 + T cells were co-located in the SCLC stroma, and there was a positive correlation between CAFs and regulatory T cell (Treg) abundance.

CONCLUSION

Our findings suggest that crosstalk between CAFs and non-NE SCLC cells promotes glycolysis in non-NE SCLC cells, thereby increase T cell chemo-attractant expression via activating STING signaling. On the other hand, it promotes the presence of apCAFs, which probably contributes to CD8 + T cell trapping and Treg differentiation. This study emphasizes the pro-tumor function of CAFs in SCLC by promoting glycolysis and impairing T cell function, providing direction for the development of novel therapeutic approaches targeting CAF in SCLC.

Advances in biomarkers for immunotherapy in small-cell lung cancer

Small-cell lung cancer (SCLC) is a refractory cancer with rapid growth and high aggressiveness. Extensive-stage SCLC is initially sensitive to chemotherapy; however, drug resistance and recurrence occur rapidly, resulting in a poor survival outcome due to lack of subsequently efficient therapy. The emergence of immune checkpoint inhibitors (ICIs) generated a new landscape of SCLC treatment and significantly prolonged the survival of patients. However, the unselected immunotherapy restrains both beneficiary population and responsive period in SCLC compared to the other tumors. The complex tumor origin, high heterogeneity, and immunosuppressive microenvironment may disturb the value of conventional biomarkers in SCLC including programmed cell death 1 ligand 1 and tumor mutation burden. Transcriptional regulator-based subtypes of SCLC are current research hotspot, revealing that Y (I) subtype can benefit from ICIs. Additionally, molecules related to immune microenvironment, immunogenicity, epigenetics, and SCLC itself also indicated the therapeutic benefits of ICIs, becoming potential predictive biomarkers. In this review, we discussed the advances of biomarkers for prediction and prognosis of immunotherapy, promising directions in the future, and provide reference and options for precision immunotherapy and survival improvement in patients with SCLC.

Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma

RATIONALE AND OBJECTIVES

This study explored the intratumor heterogeneity (ITH) of esophageal squamous cell carcinoma (ESCC) using computed tomography (CT) and investigated the value of CT-based ITH in predicting the response to immune checkpoint inhibitor (ICI) plus chemotherapy in patients with ESCC.

MATERIALS AND METHODS

This retrospective study included 416 patients with ESCC who received ICI plus chemotherapy at two independent hospitals between January 2019 and July 2022. Multiparametric CT features were extracted from ESCC lesions and screened using hierarchical clustering and dimensionality reduction algorithms. Logistic regression and machine learning models based on selected features were developed to predict treatment response and validated in separate datasets. ITH was quantified using the score calculated by the best-performing model and visualized through feature clustering and feature contribution heatmaps. A gene set enrichment analysis (GSEA) was performed to identify the biological pathways underlying the CT-based ITH.

RESULTS

The extreme gradient boosting model based on CT-derived ITH had higher discriminative power, with areas under the receiver operating characteristic curve of 0.864 (95% confidence interval [CI]: 0.774-0.954) and 0.796 (95% CI: 0.698-0.893) in the internal and external validation sets. The CT-based ITH pattern differed significantly between responding and non-responding patients. The GSEA indicated that CT-based ITH was associated with immunity-, keratinization-, and epidermal cell differentiation-related pathways.

CONCLUSION

CT-based ITH is an effective biomarker for identifying patients with ESCC who could benefit from ICI plus chemotherapy. Immunity-, keratinization-, and epidermal cell differentiation-related pathways may influence the patient's response to ICI plus chemotherapy.

The molecular subtypes of small cell lung cancer defined by key transcription factors and their clinical significance

BACKGROUND

Lung cancer, a prevalent and deadly malignancy, is classified into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). SCLC is further subdivided into four molecular subtypes-SCLC-A, SCLC-N, SCLC-P, and SCLC-I-based on key transcription factor expression.

METHODS

Immunohistochemistry (IHC) was used to assess ASCL1, NEUROD1, and POU2F3 expression in tumor tissues. The H-Score quantified these results. Clinical characteristics, overall survival (OS), progression-free survival (PFS), and treatment responses were analyzed by subtype, and sensitivity to different treatments was assessed. Risk factors were identified through univariate and multivariate analyses.

RESULTS

IHC and H-Score analysis showed that POU2F3 expression was mutually exclusive with ASCL1 or NEUROD1. Subtype distribution was as follows: SCLC-A (40 %), SCLC-N (33 %), SCLC-P (7 %), and SCLC-I (20 %). There were no significant differences in baseline characteristics, OS (p = 0.829), or PFS (p = 0.924) among subtypes. However, the SCLC-I subtype showed a trend toward improved outcomes with platinum-based doublet chemotherapy plus immune checkpoint inhibitors. Multivariate COX regression identified M stage (HR: 1.72, 95 % CI: 1.13-2.63, p = 0.012) and bone metastasis at diagnosis (HR: 1.58, 95 % CI: 1.02-2.43, p = 0.040) as independent risk factors.

CONCLUSION

This study confirmed the SCLC subtyping based on key transcription factors. While no significant differences in OS and PFS among subtypes were found, the SCLC-I subtype showed potential benefit from platinum-based chemotherapy combined with immune checkpoint inhibitors. M stage and bone metastasis at diagnosis were identified as independent risk factors for SCLC.

Pulmonary Epithelium Cell Fate Determination: Chronic Obstructive Pulmonary Disease, Lung Cancer, or Both

The concurrence of chronic obstructive pulmonary disease (COPD) and lung cancer has been widely reported and extensively addressed by pulmonologists and oncologists. However, most studies have focused on shared risk factors, DNA damage pathways, immune microenvironments, inflammation, and imbalanced proteases/antiproteases. In the present review, we explore the association between COPD and lung cancer in terms of airway pluripotent cell fate determination and discuss the various cell types and signaling pathways involved in the maintenance of lung epithelium homeostasis and their involvement in the pathogenesis of co-occurring COPD and lung cancer.

Novel Immunohistochemical Profiling of Small-Cell Lung Cancer: Correlations Between Tumor Subtypes and Immune Microenvironment

BACKGROUND/OBJECTIVES

Small-cell lung cancer (SCLC) is a highly aggressive malignancy with an emerging molecular classification based on the expression of the transcription factors ASCL1, NEUROD1, and POU2F3. This study aimed to explore the relationship between these novel subtypes and the tumor immune microenvironment (TIME), particularly CD8+ and CD4+ tumor-infiltrating lymphocytes (TILs).

METHODS

In 51 cases of patients with SCLC, immunohistochemical (IHC) stains for ASCL1, NEUROD1, POU2F3, CD56, Ki67, CD8, and CD4 were performed. H-scores for the novel transcription factors were calculated to determine tumor subtype. CD8+ and CD4+ TIL counts were averaged across 10 high-power fields. The Kruskal-Wallis test and subsequent post hoc Dunn tests were used to determine the differences in transcription factor expression and TILs across subtypes.

RESULTS

In our cohort, 68.62% of our cases were SCLC-A, 9.80% were SCLC-N, 7.84% were SCLC-P, and 13.72% were SCLC-I. Significant differences were observed in the expression of ASCL1, NEUROD1, and POU2F3 across subtypes. CD8+ TILs were more abundant in SCLC-P and SCLC-I. CD8+ TILs were negatively correlated with ASCL1 expression (p < 0.05) and positively correlated with POU2F3 expression (p < 0.005).

CONCLUSIONS

This study highlights the need to integrate the novel SCLC classification with data regarding the TIME to better inform patient prognosis and treatment.

Comprehensive genomic and spatial immune infiltration analysis of survival outliers in extensive-stage small cell lung cancer receiving first-line chemoimmunotherapy

BACKGROUND

A minority of patients with extensive-stage small cell lung cancer (ES-SCLC) exhibit prolonged survival following first-line chemoimmunotherapy, which warrants the use of reliable biomarkers. Here, we investigated the disparities in genomics and immune cell spatial distribution between long- and short-term survival of patients with ES-SCLC.

METHODS

We retrospectively recruited 11 long-term (>2 years) and 13 short-term (<9 months) ES-SCLC survivors receiving first-line chemoimmunotherapy. The samples were processed using targeted next-generation sequencing (tNGS), programmed death ligand-1 staining, multiplex immunohistochemical staining for immune cells (mIHC), tumor mutation burden (TMB), and chromosomal instability score measurements. The expression of putative genes in SCLC at the bulk and single-cell RNA-sequencing levels, as well as the role of putative genes in pan-cancer immunotherapy cohorts, were analyzed.

RESULTS

At the genomic level, a greater proportion of the smoking signature and higher TMB (>3.1) were associated with favorable survival. At the single-gene and pathway levels, tNGS revealed that MCL1 and STMN1 amplification and alterations in the apoptosis pathway were more common in short-term survivors, whereas alterations in the DLL3, KMT2B, HGF, EPHA3, ADGRB3, lysine deprivation, and HGF-cMET pathways were observed more frequently in long-term survivors. mIHC analysis of immune cells with different spatial distributions revealed that long-term survivors presented increased numbers of M1-like macrophages in all locations and decreased numbers of CD8+ T cells in the tumor stroma. Bulk transcriptomic analysis demonstrated that high levels of STMN1 and DLL3 represented an immune-suppressive tumor immune microenvironment (TIME), whereas HGF indicated an immune-responsive TIME. The expression levels of our putative genes were comparative in both TP53/RB1 mutant-type and TP53/RB1 wild-type. At the single-cell level, STMN1, MCL1, and DLL3 were highly expressed among all molecular subtypes (SCLC-A, SCLC-N, and SCLC-P), with STMN1 being enriched in cell division and G2M checkpoint pathways.

CONCLUSIONS

For ES-SCLC patients receiving first-line chemoimmunotherapy, alterations in DLL3, KMT2B, HGF, EPHA3, and ADGRB3 and a greater proportion of M1-like macrophages infiltration in all locations were predictors of favorable survival, while MCL1 and STMN1 amplification, as well as a greater proportion of CD8+ T cells infiltrating the tumor stroma, predicted worse survival.

Transcriptomic Heterogeneity of EGFR-Mutant Non-Small Cell Lung Cancer Evolution Toward Small-Cell Lung Cancer

PURPOSE

Histologic transformation from EGFR-mutant non-small cell lung cancer (NSCLC) to small-cell lung cancer (SCLC) is a key mechanism of resistance to EGFR tyrosine kinase inhibitors (TKI). However, transcriptomic changes between NSCLC and transformed SCLC (t-SCLC) remain unexplored.

EXPERIMENTAL DESIGN

We conducted whole-transcriptome analysis of 59 regions of interest through the spatial profiling of formalin-fixed, paraffin-embedded tissues obtained from 10 patients (lung adenocarcinoma, 22; combined SCLC/NSCLC, 7; and t-SCLC, 30 regions of interests). Transcriptomic profiles and differentially expressed genes were compared between pre- and post-transformed tumors.

RESULTS

Following EGFR-TKI treatment, 93.7% (15/16) of t-SCLC components evolved into neuroendocrine-high subtypes (SCLC-A or SCLC-N). The transition to t-SCLC occurred regardless of EGFR-TKI treatment and EGFR mutational status, with a notable decrease in EGFR expression (P < 0.001) at both mRNA and protein levels. Pathway analysis revealed that gene overexpression was related to epigenetic alterations in t-SCLC. Interestingly, histone deacetylase inhibitors restored EGFR expression in SNU-2962A cells and their organoid model. The synergistic effects of third-generation EGFR-TKI osimertinib and the histone deacetylase inhibitor fimepinostat were validated in both in vitro and in vivo models.

CONCLUSIONS

Our study demonstrated that most t-SCLC cases showed neuronal subtypes with low EGFR expression. Differentially expressed gene analysis and t-SCLC preclinical models identified an epigenetic modifier as a promising treatment strategy for t-SCLC.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Activating Invasion and Metastasis in Small Cell Lung Cancer: Role of the Tumour Immune Microenvironment and Mechanisms of Vasculogenesis, Epithelial-Mesenchymal Transition, Cell Migration, and Organ Tropism

BACKGROUND

Small cell lung cancer (SCLC) harbours the most aggressive phenotype of all lung cancers to correlate with its bleak prognosis. The aggression of SCLC is partially attributable to its strong metastatic tendencies. The biological processes facilitating the metastasis in SCLC are still poorly understood and garnering a deeper understanding of these processes may enable the exploration of additional targets against this cancer hallmark in the treatment of SCLC.

RECENT FINDINGS

This narrative review will discuss the proposed molecular mechanisms by which the cancer hallmark of activating invasion and metastasis is featured in SCLC through important steps of the metastatic pathway, and address the various molecular targets that may be considered for therapeutic intervention. The tumour immune microenvironment plays an important role in facilitating immunotherapy resistance, whilst the poor infiltration of natural killer cells in particular fosters a pro-metastatic environment in SCLC. SCLC vasculogenesis is achieved through VEGF expression and vascular mimicry, and epithelial-mesenchymal transition is facilitated by the expression of the transcriptional repressors of E-cadherin, the suppression of the Notch signalling pathway and tumour heterogeneity. Nuclear factor I/B, selectin and B1 integrin hold important roles in SCLC migration, whilst various molecular markers are expressed by SCLC to assist organ-specific homing during metastasis. The review will also discuss a recent article observing miR-1 mRNA upregulation as a potential therapeutic option in targeting the metastatic activity of SCLC.

CONCLUSION

Treatment of SCLC remains a clinical challenge due to its recalcitrant and aggressive nature. Amongst the many hallmarks used by SCLC to enable its aggressive behaviour, that of its ability to invade surrounding tissue and metastasise is particularly notable and understanding the molecular mechanisms in SCLC metastasis can identify therapeutic targets to attenuate SCLC aggression and improve mortality.

Single-cell and spatial proteo-transcriptomic profiling reveals immune infiltration heterogeneity associated with neuroendocrine features in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine malignancy featured by cold tumor immune microenvironment (TIME), limited benefit from immunotherapy, and poor survival. The spatial heterogeneity of TIME significantly associated with anti-tumor immunity has not been systemically studied in SCLC. We performed ultra-high-plex Digital Spatial Profiling on 132 tissue microarray cores from 44 treatment-naive limited-stage SCLC tumors. Incorporating single-cell RNA-sequencing data from a local cohort and published SCLC data, we established a spatial proteo-transcriptomic landscape covering over 18,000 genes and 60 key immuno-oncology proteins that participate in signaling pathways affecting tumorigenesis, immune regulation, and cancer metabolism across 3 pathologically defined spatial compartments (pan-CK-positive tumor nest; CD45/CD3-positive tumor stroma; para-tumor). Our study depicted the spatial transcriptomic and proteomic TIME architecture of SCLC, indicating clear intra-tumor heterogeneity dictated via canonical neuroendocrine subtyping markers; revealed the enrichment of innate immune cells and functionally impaired B cells in tumor nest and suggested potentially important immunoregulatory roles of monocytes/macrophages. We identified RE1 silencing factor (REST) as a potential biomarker for SCLC associated with low neuroendocrine features, more active anti-tumor immunity, and prolonged survival.

The co-location of CD14+APOE+ cells and MMP7+ tumour cells contributed to worse immunotherapy response in non-small cell lung cancer

Intra-tumour immune infiltration is a crucial determinant affecting immunotherapy response in non-small cell lung cancer (NSCLC). However, its phenotype and related spatial structure have remained elusive. To overcome these restrictions, we undertook a comprehensive study comprising spatial transcriptomic (ST) data (28 712 spots from six samples). We identified two distinct intra-tumour infiltration patterns: immune exclusion (characterised by myeloid cells) and immune activation (characterised by plasma cells). The immune exclusion and immune activation signatures showed adverse and favourable roles in NSCLC patients' survival, respectively. Notably, CD14+APOE+ cells were recognised as the main cell type in immune exclusion samples, with increased epithelial‒mesenchymal transition and decreased immune activities. The co-location of CD14+APOE+ cells and MMP7+ tumour cells was observed in both ST and bulk transcriptomics data, validated by multiplex immunofluorescence performed on 20 NSCLC samples. The co-location area exhibited the upregulation of proliferation-related pathways and hypoxia activities. This co-localisation inhibited T-cell infiltration and the formation of tertiary lymphoid structures. Both CD14+APOE+ cells and MMP7+ tumour cells were associated with worse survival. In an immunotherapy cohort from the ORIENT-3 clinical trial, NSCLC patients who responded unfavourably exhibited higher infiltration of CD14+APOE+ cells and MMP7+ tumour cells. Within the co-location area, the MK, SEMA3 and Macrophage migration inhibitory factor (MIF) signalling pathway was most active in cell‒cell communication. This study identified immune exclusion and activation patterns in NSCLC and the co-location of CD14+APOE+ cells and MMP7+ tumour cells as contributors to immune resistance.

Innovative aspects and applications of single cell technology for different diseases

Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.

Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine learning: bioinformatics multi-omics study

BACKGROUND

Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibroblasts-related genes (FRG) in bladder cancer still need to be explored, and there is a shortage of an ideal predictive model or molecular subtype for the progression and immune therapeutic assessment for bladder cancer, especially muscular-invasive bladder cancer based on the FRG.

MATERIALS AND METHODS

CAF-related genes of bladder cancer were identified by analysing single-cell RNA sequence datasets, and bulk transcriptome datasets and gene signatures were used to characterize them. Then, 10 types of machine learning algorithms were utilised to determine the hallmark FRG and construct the FRG index (FRGI) and subtypes. Further molecular subtypes combined with CD8+ T-cells were established to predict the prognosis and immune therapy response.

RESULTS

Fifty-four BLCA-related FRG were screened by large-scale scRNA-sequence datasets. The machine learning algorithm established a 3-genes FRGI. High FRGI represented a worse outcome. Then, FRGI combined clinical variables to construct a nomogram, which shows high predictive performance for the prognosis of bladder cancer. Furthermore, the BLCA datasets were separated into two subtypes - fibroblast hot and cold types. In five independent BLCA cohorts, the fibroblast hot type showed worse outcomes than the cold type. Multiple cancer-related hallmark pathways are distinctively enriched in these two types. In addition, high FRGI or fibroblast hot type shows a worse immune therapeutic response. Then, four subtypes called CD8-FRG subtypes were established under the combination of FRG signature and activity of CD8+ T-cells, which turned out to be effective in predicting the prognosis and immune therapeutic response of bladder cancer in multiple independent datasets. Pathway enrichment analysis, multiple gene signatures, and epigenetic alteration characterize the CD8-FRG subtypes and provide a potential combination strategy method against bladder cancer.

CONCLUSIONS

In summary, the authors established a novel FRGI and CD8-FRG subtype by large-scale datasets and organised analyses, which could accurately predict clinical outcomes and immune therapeutic response of BLCA after surgery.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

Emerging advances in defining the molecular and therapeutic landscape of small-cell lung cancer

Small-cell lung cancer (SCLC) has traditionally been considered a recalcitrant cancer with a dismal prognosis, with only modest advances in therapeutic strategies over the past several decades. Comprehensive genomic assessments of SCLC have revealed that most of these tumours harbour deletions of the tumour-suppressor genes TP53 and RB1 but, in contrast to non-small-cell lung cancer, have failed to identify targetable alterations. The expression status of four transcription factors with key roles in SCLC pathogenesis defines distinct molecular subtypes of the disease, potentially enabling specific therapeutic approaches. Overexpression and amplification of MYC paralogues also affect the biology and therapeutic vulnerabilities of SCLC. Several other attractive targets have emerged in the past few years, including inhibitors of DNA-damage-response pathways, epigenetic modifiers, antibody-drug conjugates and chimeric antigen receptor T cells. However, the rapid development of therapeutic resistance and lack of biomarkers for effective selection of patients with SCLC are ongoing challenges. Emerging single-cell RNA sequencing data are providing insights into the plasticity and intratumoural and intertumoural heterogeneity of SCLC that might be associated with therapeutic resistance. In this Review, we provide a comprehensive overview of the latest advances in genomic and transcriptomic characterization of SCLC with a particular focus on opportunities for translation into new therapeutic approaches to improve patient outcomes.

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.

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms

OBJECTIVES

To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration.

METHODS

GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out.

RESULTS

There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration.

CONCLUSION

This study identified 2 diagnostic genes, ZWINT and NRCAM, that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BACKGROUND

The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states.

RESULTS

We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure.

CONCLUSIONS

Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Lipocalin-2 as a prognostic biomarker and its association with systemic inflammation in small cell lung cancer

BACKGROUND

Systemic inflammation is believed to contribute to small cell lung cancer (SCLC) progression, but the underlying relationship remains unclear. Lipocalin-2, a potential biomarker of inflammation, has been implicated in various cancers but its prognostic value in SCLC is underexplored.

METHODS

We retrospectively analyzed 191 patients with SCLC (72 with limited-stage [LD] and 119 with extensive-stage) treated using platinum-based chemotherapy. Lipocalin-2 expression was evaluated using immunohistochemistry. Optimal cutoff values for lipocalin-2 and neutrophil-to-lymphocyte ratio (NLR) were determined using time-dependent receiver operating characteristic curve analysis. The pectoralis muscle index was used to assess sarcopenia.

RESULTS

In LD-SCLC, high lipocalin-2 expression was associated with worse progression-free survival (PFS; median: 7.0 vs. 15.9 months, p = 0.015) and overall survival (OS; median: 12.9 vs. 30.3 months, p = 0.035) compared with low lipocalin-2 expression. Patients were stratified into three prognostic groups by combining lipocalin-2 with NLR: low lipocalin-2/low NLR, high lipocalin-2/low NLR or low lipocalin-2/high NLR, and high lipocalin-2/high NLR (median PFS: 17.3 vs. 11.0 vs. 6.3 months, p = 0.004; median OS: 30.5 vs. 17.3 vs. 8.6 months, p = 0.002). Similar trends were observed when combining lipocalin-2 with the pectoralis muscle index. High lipocalin-2 expression was also associated with lower complete response rates (18.9% vs. 34.3%, p = 0.035). No significant prognostic implications were found for lipocalin-2 in extensive-stage SCLC.

CONCLUSIONS

High lipocalin-2 expression is potentially associated with poorer survival in LD-SCLC. Combining lipocalin-2 with other inflammation-related markers could improve prognostic stratification.

[Advancements in Single-cell RNA Sequencing Technology in the Study of the Tumor Microenvironment in Lung Cancer]

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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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.

Nuclear-targeted chimeric peptide nanorods to amplify innate anti-tumor immunity through localized DNA damage and STING activation

Stimulator of the interferon genes (STING) pathway is appealing but challenging to potentiate the innate anti-tumor immunity. In this work, nuclear-targeted chimeric peptide nanorods (designated as PFPD) are constructed to amplify innate immunity through localized DNA damage and STING activation. Among which, the chimeric peptide (PpIX-FFVLKPKKKRKV) is fabricated with photosensitizer and nucleus targeting peptide sequence, which can self-assemble into nanorods and load STING agonist of DMXAA. The uniform nanosize distribution and good stability of PFPD improve the sequential targeting delivery of drugs towards tumor cells and nuclei. Under light irradiation, PFPD produce a large amount of reactive oxygen species (ROS) to destroy nuclear DNA in situ, and the released cytosolic DNA fragment will efficiently activate innate anti-tumor immunity in combination with STING agonist. In vitro and in vivo results indicate the superior ability of PFPD to activate natural killer cells and T cells, thus efficiently eradicating lung metastatic tumor without inducing unwanted side effects. This work provides a sophisticated strategy for localized activation of innate immunity for systemic tumor treatment, which may inspire the rational design of nanomedicine for tumor precision therapy.

Cell-TIMP: Cellular Trajectory Inference based on Morphological Parameter

Cellular morphology, shaped by various genetic and environmental influences, is pivotal to studying experimental cell biology, necessitating precise measurement and analysis techniques. Traditional approaches, which rely on geometric metrics derived from stained images, encounter obstacles stemming from both the imaging and analytical domains. Staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Our approach revealed a clear pattern of morphological evolution tied to the diseases' advancement, highlighting the efficacy of our method in identifying functionally significant shape changes where conventional techniques falter. This advancement offers a fresh perspective on analyzing cellular morphology and holds significant potential for the broader research community, enabling a deeper understanding of complex biological dynamics.

Cold to Hot: Tumor Immunotherapy by Promoting Vascular Normalization Based on PDGFB Nanocomposites

Immunotherapy is a promising cancer therapeutic strategy. However, the "cold" tumor immune microenvironment (TIME), characterized by insufficient immune cell infiltration and immunosuppressive status, limits the efficacy of immunotherapy. Tumor vascular abnormalities due to defective pericyte coverage are gradually recognized as a profound determinant in "cold" TIME establishment by hindering immune cell trafficking. Recently, several vascular normalization strategies by improving pericyte coverage have been reported, whereas have unsatisfactory efficacy and high rates of resistance. Herein, a combinatorial strategy to induce tumor vasculature-targeted pericyte recruitment and zinc ion-mediated immune activation with a platelet-derived growth factor B (PDGFB)-loaded, cyclo (Arg-Gly-Asp-D-Phe-Lys)-modified zeolitic imidazolate framework 8 (PDGFB@ZIF8-RGD) nanoplatform is proposed. PDGFB@ZIF8-RGD effectively induced tumor vascular normalization, which facilitated trafficking and infiltration of immune effector cells, including natural killer (NK) cells, M1-like macrophages and CD8+ T cells, into tumor microenvironment. Simultaneously, vascular normalization promoted the accumulation of zinc ions inside tumors to trigger effector cell immune activation and effector molecule production. The synergy between these two effects endowed PDGFB@ZIF8-RGD with superior capabilities in reprogramming the "cold" TIME to a "hot" TIME, thereby initiating robust antitumor immunity and suppressing tumor growth. This combinatorial strategy for improving immune effector cell infiltration and activation is a promising paradigm for solid tumor immunotherapy.