Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody

Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts

This study aims to fill the knowledge gap in systematically mapping the evolution of omics-driven tumor immunotherapy research through a bibliometric lens. While omics technologies (genomics, transcriptomics, proteomics, metabolomics)provide multidimensional molecular profiling, their synergistic potential with immunotherapy remains underexplored in large-scale trend analyses. A comprehensive search was conducted using the Web of Science Core Collection for literature related to omics in tumor immunotherapy, up to August 2024. Bibliometric analyses, conducted using R version 4.3.3, VOSviewer 1.6.20, and Citespace 6.2, examined publication trends, country and institutional contributions, journal distributions, keyword co-occurrence, and citation bursts. This analysis of 9,494 publications demonstrates rapid growth in omics-driven tumor immunotherapy research since 2019, with China leading in output (63% of articles) yet exhibiting limited multinational collaboration (7.9% vs. the UK's 61.8%). Keyword co-occurrence and citation burst analyses reveal evolving frontiers: early emphasis on "PD-1/CTLA-4 blockade" has transitioned toward "machine learning," "multi-omics," and "lncRNA," reflecting a shift to predictive modeling and biomarker discovery. Multi-omics integration has facilitated the development of immune infiltration-based prognostic models, such as TIME subtypes, which have been validated across multiple tumor types, which inform clinical trial design (e.g. NCT06833723). Additionally, proteomic analysis of melanoma patients suggests that metabolic biomarkers, particularly oxidative phosphorylation and lipid metabolism, may stratify responders to PD-1 blockade therapy. Moreover, spatial omics has confirmed ENPP1 as a potential novel therapeutic target in Ewing sarcoma. Citation trends underscore clinical translation, particularly mutation-guided therapies. Omics technologies are transforming tumor immunotherapy by enhancing biomarker discovery and improving therapeutic predictions. Future advancements will necessitate longitudinal omics monitoring, AI-driven multi-omics integration, and international collaboration to accelerate clinical translation. This study presents a systematic framework for exploring emerging research frontiers and offers insights for optimizing precision-driven immunotherapy.

Application and future prospects of bispecific antibodies in the treatment of non-small cell lung cancer

As the leading cause of cancer-related deaths, lung cancer remains a noteworthy threat to human health. Although immunotherapies, such as immune checkpoint inhibitors (ICIs), have significantly increased the efficacy of lung cancer treatment, a significant percentage of patients are not sensitive to immunotherapies and patients who initially respond to treatment can quickly develop acquired drug resistance. Bispecific antibodies (bsAbs) bind two different antigens or epitopes simultaneously and have been shown to enhance antitumor efficacy with suitable safety profiles, thus attracting increasing attention as novel antitumor therapies. At present, in addition to the approved bsAb, amivantamab, three novel bsAbs (KN046, AK112, and SHR-1701) are being evaluated in phase 3 clinical trials and many bsAbs are being evaluated in phase 1/2 clinical trials for patients with non-small cell lung cancer (NSCLC). Herein we present the structure, classification, and mechanism of action underlying bsAbs in NSCLC and introduce related clinical trials. Finally, we discuss challenges, potential solutions, and future prospects in the context of cancer treatment with bsAbs.

Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy

BACKGROUND

Non-small cell lung cancer (NSCLC) is highly heterogeneous, leading to varied treatment responses and immune-related adverse reactions (irAEs) among patients. Habitat radiomics allows non-invasive quantitative assessment of intratumor heterogeneity (ITH). Therefore, our objective is to employ habitat radiomics techniques to develop a robust approach for predicting the efficacy of Immune checkpoint inhibitors (ICIs) and the likelihood of irAEs in advanced NSCLC patients.

METHODS

In this retrospective two center study, two independent cohorts of patients with NSCLC were used to develop (n = 248) and validate signatures (n = 95). After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. By combining habitat radiomic features with corresponding clinicopathologic information, the nomogram signature was constructed in the training cohort. Next, the internal validation cohort (n = 75) of patients, and the external validation cohort (n = 20) of patients treated with ICIs were included to evaluate the predictive value of the four signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC).

RESULTS

Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. The Radiomics Nomogram model exhibited superior predictive performance in the training, validation, and external validation sets, with AUCs of 0.923, 0.817, and 0.899, respectively. This model outperformed both the Whole-tumor Radiomics Signature model (AUCs of 0.870, 0.736, and 0.626) and the Habitat Signature model (AUCs of 0.900, 0.804, and 0.808). The radiomics model focusing on tumor sub-regional habitat showed better predictive performance than the model derived from the entire tumor. Decision Curve Analysis (DCA) and calibration curves confirmed the nomogram's effectiveness.

CONCLUSION

By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. This advancement will assist physicians in selecting and managing subsequent treatment strategies, thereby facilitating clinical decision-making.

Spatial immune remodeling of the liver metastases: discovering the path to antimetastatic therapy

The intrinsic characteristics of metastatic tumors are of great importance in terms of the development of antimetastatic treatment strategies. Elucidation from a spatial immune perspective has the potential to provide a more comprehensive understanding of the mechanisms underlying immune escape, effectively addressing the limitations of relying solely on the analysis of immune cell subpopulation transcriptional profiles. Advances in spatial omics technology enable researchers to precisely analyze precious liver metastasis samples in a high-throughput manner, revealing spatial alterations in immune cell distribution induced by metastasis and exploring the molecular basis of the remodeling process. The aggregation of specific cell subpopulations in distinct regions not only modifies local immune characteristics but also concurrently affects global biological behaviors of liver metastatic tumors. Identifying specific spatial immune characteristics in pretreatment or early-stage treatment tissue samples may achieve accurate clinical predictions. Moreover, developing strategies that target spatial immune remodeling is a promising avenue for future antimetastatic therapy.

Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer

Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development of precision medicine strategies. The emerging field of single-cell multi-omics in genomic medicine has improved patient outcomes. However, most clinical applications still depend on bulk genomic approaches, which fail to directly capture the genomic variations driving cellular heterogeneity. In this review, we explore the common single-cell multi-omics platforms and discuss key analytical steps for data integration. Furthermore, we highlight emerging knowledge in therapeutic resistance and immune evasion, and the potential of new therapeutic innovations informed by single-cell multi-omics. Finally, we discuss the future directions of the application of single-cell multi-omics technologies. By bridging the gap between technological advancements and clinical implementation, this review provides a roadmap for leveraging single-cell multi-omics to improve cancer treatment and patient outcomes.

Basement membranes in lung metastasis growth and progression

The lung is a highly vascularized tissue that often harbors metastases from various extrathoracic malignancies. Lung parenchyma consists of a complex network of alveolar epithelial cells and microvessels, structured within an architecture defined by basement membranes. Consequently, understanding the role of the extracellular matrix (ECM) in the growth of lung metastases is essential to uncover the biology of this pathology and developing targeted therapies. These basement membranes play a critical role in the progression of lung metastases, influencing multiple stages of the metastatic cascade, from the acquisition of an aggressive phenotype to intravasation, extravasation and colonization of secondary sites. This review examines the biological composition of basement membranes, focusing on their core components-collagens, fibronectin, and laminin-and their specific roles in cancer progression. Additionally, we discuss the function of integrins as primary mediators of cell adhesion and signaling between tumor cells, basement membranes and the extracellular matrix, as well as their implications for metastatic growth in the lung. We also explore vascular co-option (VCO) as a form of tumor growth resistance linked to basement membranes and tumor vasculature. Finally, the review covers current clinical therapies targeting tumor adhesion, extracellular matrix remodeling, and vascular development, aiming to improve the precision and effectiveness of treatments against lung metastases.

Innovative applications and future trends of multiparametric PET in the assessment of immunotherapy efficacy

Background

The integration of multiparametric PET (Positron Emission Tomography.) imaging and multi-omics data has demonstrated significant clinical potential in predicting the efficacy of cancer immunotherapies. However, the specific predictive power and underlying mechanisms remain unclear.

Objective

This review systematically evaluates the application of multiparametric PET imaging metrics (e.g., SUVmax [Maximum Standardized Uptake Value], MTV [Metabolic Tumor Volume], and TLG [Total Lesion Glycolysis]) in predicting the efficacy of immunotherapies, including PD-1/PD-L1 inhibitors and CAR-T therapy, and explores their potential role in improving predictive accuracy when integrated with multi-omics data.

Methods

A systematic search of PubMed, Embase, and Web of Science databases identified studies evaluating the efficacy of immunotherapy using longitudinal PET/CT data and RECIST or iRECIST criteria. Only original prospective or retrospective studies were included for analysis. Review articles and meta-analyses were consulted for additional references but excluded from quantitative analysis. Studies lacking standardized efficacy evaluations were excluded to ensure data integrity and quality.

Results

Multiparametric PET imaging metrics exhibited high predictive capability for efficacy across various immunotherapies. Metabolic parameters such as SUVmax, MTV, and TLG were significantly correlated with treatment response rates, progression-free survival (PFS), and overall survival (OS). The integration of multi-omics data (including genomics and proteomics) with PET imaging enhanced the sensitivity and accuracy of efficacy prediction. Through integrated analysis, PET metabolic parameters demonstrated potential in predicting immune therapy response patterns, such as pseudo-progression and hyper-progression.

Conclusion

The integration of multiparametric PET imaging and multi-omics data holds broad potential for predicting the efficacy of immunotherapies and may support the development of personalized treatment strategies. Future validation using large-scale, multicenter datasets is needed to further advance precision medicine in cancer immunotherapy.

Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Daidzein Inhibits Non-small Cell Lung Cancer Growth by Pyroptosis

INTRODUCTION

Non-small cell lung cancer (NSCLC) represents the leading cause of cancer deaths in the world. We previously found that daidzein, one of the key bioactivators in soy isoflavone, can inhibit NSCLC cell proliferation and migration, while the molecular mechanisms of daidzein in NSCLC remain unclear.

METHODS

We developed an NSCLC nude mouse model using H1299 cells and treated the mice with daidzein (30 mg/kg/day). Mass spectrometry analysis of tumor tissues from daidzein-treated mice identified 601 differentially expressed proteins (DEPs) compared to the vehicle-treated group. Gene enrichment analysis revealed that these DEPs were primarily associated with immune regulatory functions, including B cell receptor and chemokine pathways, as well as natural killer cell-mediated cytotoxicity. Notably, the NOD-like receptor signaling pathway, which is closely linked to pyroptosis, was significantly enriched.

RESULTS

Further analysis of key pyroptosis-related molecules, such as ASC, CASP1, GSDMD, and IL-1β, revealed differential expression in NSCLC versus normal tissues. High levels of ASC and CASP1 were associated with a favorable prognosis in NSCLC, suggesting that they may be critical effectors of daidzein's action. In NSCLC-bearing mice treated with daidzein, RT-qPCR and Western blot analyses showed elevated mRNA and protein levels of ASC, CASP1, and IL-1β but not GSDMD, which was consistent with the proteomic data.

CONCLUSION

In summary, this study demonstrated that daidzein inhibits NSCLC growth by inducing pyroptosis. Key pathway modulators ASC, CASP1, and IL-1β were identified as primary targets of daidzein. These findings offer insights into the molecular mechanisms underlying the anti-NSCLC effects of daidzein and could offer dietary recommendations for managing NSCLC.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Novel Spatial Approaches to Dissect the Lung Cancer Immune Microenvironment

Lung cancer is a deadly disease with the highest rates of mortality. Over recent decades, a better understanding of the biological mechanisms implicated in its pathogenesis has led to the development of targeted therapies and immunotherapy, resulting in improvements in patient outcomes. To better understand lung cancer tumor biology and advance towards precision oncology, a comprehensive tumor profile is necessary. In recent years, novel in situ spatial multiomics approaches have emerged offering a more detailed view of the spatial location of tumor and tumor microenvironment cells, identifying their unique composition and functional status. In this sense, novel multiomics platforms have been developed to evaluate tumor heterogeneity, gene expression, metabolic reprogramming, signaling pathway activation, cell-cell interactions, and immune cell programs. In lung cancer research, several studies have used these spatial technologies to locate cells and associated them with histological features that are relevant to the pathogenesis of lung adenocarcinoma. These advancements may unveil further molecular and immune mechanisms in tumor biology that will lead to the discovery of biomarkers for treatment prediction and prognosis. In this review, we provide an overview of more widely used and emerging pathology-based approaches for spatial immune profiling in lung cancer and how they enhance our understanding of tumor biology and immune response.

Progress on angiogenic and antiangiogenic agents in the tumor microenvironment

The development of tumors and their metastasis relies heavily on the process of angiogenesis. When the volume of a tumor expands, the resulting internal hypoxic conditions trigger the body to enhance the production of various angiogenic factors. These include vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), platelet-derived growth factor (PDGF), and transforming growth factor-α (TGF-α), all of which work together to stimulate the activation of endothelial cells and catalyze angiogenesis. Antiangiogenic therapy (AAT) aims to normalize tumor blood vessels by inhibiting these angiogenic signals. In this review, we will explore the molecular mechanisms of angiogenesis within the tumor microenvironment, discuss traditional antiangiogenic drugs along with their limitations, examine new antiangiogenic drugs and the advantages of combination therapy, and consider future research directions in the field of antiangiogenic drugs. This comprehensive overview aims to provide insights that may aid in the development of more effective anti-tumor treatments.

Distinctive Phenotypic and Microenvironmental Characteristics of Neuroendocrine Carcinoma and Adenocarcinoma Components in Gastric Mixed Adenoneuroendocrine Carcinoma

This study aimed to conduct an in-depth examination of gene expression and microenvironmental profiles of gastric neuroendocrine carcinoma (NEC) and mixed adeno-NEC (MANEC). Tissue microarrays from 55 patients with gastric MANEC (N = 32) or NEC (N = 23) were analyzed using digital spatial profiling (GeoMx DSP, NanoString Technologies). Representative regions of interest were selected from the adenocarcinoma (ADC) portion (ADC-MANEC) and the NEC portion (NEC-MANEC) of the MANEC cores, and pure NEC (pNEC) cores. All regions of interest were separated into epithelial components and stromal components using the masking procedure in the GeoMx platform, followed by transcriptome analysis. Comparison of gene expression between ADC-MANEC and NEC-MANEC/pNEC identified several differentially expressed genes in the epithelial (including PEG10, MAP1B, STMN3, and AKT3) and stromal (FN1, COL1A1, SPARC, and BGN) components. Gene set enrichment analysis revealed that pathways related to the E2F target and G2M checkpoint were more enriched in NEC-MANEC and pNEC than in ADC-MANEC. Deconvolution analysis showed that the microenvironmental profile varied according to histologic differentiation. In ADC-MANEC, intraepithelial infiltrating immune cells were relatively more numerous, whereas fibroblasts in the stroma were more abundant in NEC-MANEC and pNEC. This study confirmed the distinct expression profile of each histologic component of MANEC according to its tumor vs stromal compartment using the DSP platform. Although each component of MANEC shares the same genetic origin, distinctive phenotypes should not be overlooked when managing patients with MANEC. This study provides a useful validation data set for future studies.

Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases

Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Digital spatial profiling to predict recurrence in grade 3 stage I lung adenocarcinoma

OBJECTIVE

Early-stage lung adenocarcinoma is treated with local therapy alone, although patients with grade 3 stage I lung adenocarcinoma have a 50% 5-year recurrence rate. Our objective is to determine if analysis of the tumor microenvironment can create a predictive model for recurrence.

METHODS

Thirty-four patients with grade 3 stage I lung adenocarcinoma underwent surgical resection. Digital spatial profiling was used to perform genomic (n = 31) and proteomic (n = 34) analyses of pancytokeratin positive and negative tumor cells. K-means clustering was performed on the top 50 differential genes and top 20 differential proteins, with Kaplan-Meier recurrence curves based on patient clustering. External validation of high-expression genes was performed with Kaplan-Meier plotter.

RESULTS

There were no significant clinicopathologic differences between patients who did (n = 14) and did not (n = 20) have recurrence. Median time to recurrence was 806 days; median follow-up with no recurrence was 2897 days. K-means clustering of pancytokeratin positive genes resulted in a model with a Kaplan-Meier curve with concordance index of 0.75. K-means clustering for pancytokeratin negative genes was less successful at differentiating recurrence (concordance index 0.6). Genes upregulated or downregulated for recurrence were externally validated using available public databases. Proteomic data did not reach statistical significance but did internally validate the genomic data described.

CONCLUSIONS

Genomic difference in lung adenocarcinoma may be able to predict risk of recurrence. After further validation, stratifying patients by this risk may help guide who will benefit from adjuvant therapy.

Disturbance of Immune Microenvironment in Androgenetic Alopecia through Spatial Transcriptomics

Androgenetic alopecia (AGA) is characterized by microinflammation and abnormal immune responses, particularly in the upper segment of hair follicles (HFs). However, the precise patterns of immune dysregulation remain unclear, partly due to limitations in current analysis techniques to preserve tissue architecture. The infundibulum, a major part of the upper segment of HFs, is associated with significant clusters of immune cells. In this study, we investigated immune cells around the infundibulum, referred to as peri-infundibular immune infiltration (PII). We employed spatial transcriptome profiling, a high-throughput analysis technology, to investigate the immunological disruptions within the PII region. Our comprehensive analysis included an evaluation of overall immune infiltrates, gene set enrichment analysis (GSEA), cellular deconvolution, differential expression analysis, over-representation analysis, protein-protein interaction (PPI) networks, and upstream regulator analysis to identify cell types and molecular dysregulation in immune cells. Our results demonstrated significant differences in immune signatures between the PII of AGA patients (PII-A) and the PII of control donors (PII-C). Specifically, PII-A exhibited an enrichment of CD4+ helper T cells, distinct immune response patterns, and a bias toward a T helper (Th) 2 response. Immunohistochemistry revealed disruptions in T cell subpopulations, with more CD4+ T cells displaying an elevated Th2 response and a reduced Th1-cytotoxic response compared to PII-C. These findings reveal the unique immune landscapes of PII-A and PII-C, suggesting potential for the development of innovative treatment approaches.

The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition

Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks.

Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths and represents a substantial disease burden worldwide. Immune checkpoint inhibitors combined with chemotherapy are the standard first-line therapy for advanced NSCLC without driver mutations. Programmed death-ligand 1 (PD-L1) is currently the only approved immunotherapy marker. PD-L1 detection methods are diverse and have developed rapidly in recent years, such as improved immunohistochemical detection methods, the application of liquid biopsy in PD-L1 detection, genetic testing, radionuclide imaging, and the use of machine learning methods to construct PD-L1 prediction models. This review focuses on the detection methods and challenges of PD-L1 from different sources, and discusses the influencing factors of PD-L1 detection and the value of combined biomarkers. Provide support for clinical screening of immunotherapy-advantage groups and formulation of personalized treatment decisions.

Advances in spatial transcriptomics and its applications in cancer research

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

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer

Objectives

To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.

Methods

This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).

Results

In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.

Conclusion

The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

BACKGROUND

Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy.

METHODS

We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP).

RESULTS

Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region.

CONCLUSIONS

These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Long‑term survival of a patient with advanced lung cancer treated with targeted therapy and anti‑PD‑1 immunotherapy as multi‑line therapy: A case report

Lung cancer is the most common type of cancer worldwide. Lung adenocarcinoma, a type of non-small cell lung cancer (NSCLC), is a common type of lung cancer. In recent years, immunotherapy has become the primary method of treatment for several solid cancers, including NSCLC. In the present study, the case of a patient with NSCLC following left superior lobectomy is reported. Different systemic therapies failed, such as a pemetrexed + carboplatin regimen, paclitaxel liposome + cisplatin and pembrolizumab, and albumin-bound paclitaxel + toripalimab, but long-term survival was achieved following targeted therapy and anti-programmed cell death protein-1 (PD-1) immunotherapy. The patient survived for >4 years following lung cancer progression, which is notably longer than expected for patients with advanced lung cancer. In conclusion, the present case demonstrated the efficacy of targeted therapy and anti-PD-1 immunotherapy for the treatment of advanced lung cancer following the occurrence of drug resistance and progressive disease.

Characterization of the tumor microenvironment and identification of spatially predictive biomarkers associated with beneficial neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). However, 20-40% of patients with LARC show little to no response to nCRT. Thus, comprehensively understanding the tumor microenvironment (TME), which might influence therapeutic efficacy, and identifying robust predictive biomarkers is urgently needed. Pre-treatment tumor biopsy specimens from patients with LARC were evaluated in detail through digital spatial profiling (DSP), public RNA sequencing datasets, and multiplex immunofluorescence (mIF). DSP analysis revealed distinct characteristics of the tumor stroma compared to the normal stroma and tumor compartments. We identified high levels of human leukocyte antigen-DR/major histocompatibility complex class II (HLA-DR/MHC-II) in the tumor compartment and B cells in the stroma as potential spatial predictors of nCRT efficacy in the Discovery cohort. Public datasets validated their predictive capacity for clinical outcomes. Using mIF in an independent nCRT cohort and/or the total cohort, we validated that a high density of HLA-DR/MHC-II+ cells in the tumor and CD20 + B cells in the stroma was associated with nCRT efficacy (all p ≤ 0.021). Spatial profiling successfully characterized the LARC TME and identified robust biomarkers with the potential to accurately predict nCRT response. These findings have important implications for individualized therapy.

Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies

Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

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

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

Phase I trial of KN046, a novel bispecific antibody targeting PD-L1 and CTLA-4 in patients with advanced solid tumors

BACKGROUND

KN046 is a novel bispecific antibody targeting programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). This multicenter phase I trial investigated the safety, tolerability, pharmacokinetics (PK), and efficacy of KN046 in patients with advanced solid tumors.

METHODS

Patients who failed standard treatment were included. KN046 was administered at doses of 1, 3, and 5 mg/kg every 2 weeks (Q2W), 5 mg/kg every 3 weeks (Q3W), and 300 mg Q3W based on the modified toxicity probability interval method in the dose-escalation phase; the recommended dose was used in the expansion phase. Primary objectives were maximum tolerated dose (MTD) and recommended phase II dose (RP2D) in escalation and preliminary efficacy in expansion. Secondary objectives included PK, pharmacodynamics, safety, and tolerability of KN046. We also explored biomarkers based on PD-L1 expression, multiplex immunofluorescence (mIF) staining, and RNAseq-derived nCounter platform.

RESULTS

Totally, 100 eligible patients were enrolled, including 59 with nasopharyngeal carcinoma (NPC), 36 with epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer (NSCLC), and those with other advanced solid tumors. The most common treatment-related adverse events (TRAEs) were rash (33.0%), pruritus (31.0%), and fatigue (20.0%). Grade ≥3 TRAEs were observed in 14.0% of participants. No dose-limiting toxicity occurred in the dose-escalation phase, and the MTD was not reached. The RP2D was determined as 5 mg/kg Q2W according to the pharmacokinetic-pharmacodynamic model, the preliminary exposure-response analysis, and the overall safety profile. Among 88 efficacy-evaluable participants, the objective response rate (ORR) was 12.5%, and the median duration of response was 16.6 months. In the NPC subgroup, the ORR was 15.4%, and the median overall survival (OS) was 24.7 (95% CI 16.3 to not estimable) months. In the EGFR-mutant NSCLC subgroup, the ORR was 6.3%. mIF analysis results showed patients with high CD8 expression showed longer median OS (27.1 vs 9.2 months, p=0.02); better prognosis was observed in patients with high CD8 and PD-L1 expression.

CONCLUSIONS

KN046 was well tolerated and showed promising antitumor efficacy in advanced solid tumors, especially in patients with NPC. The combination of both CD8 and PD-L1 expression improved the prediction of KN046 response.

TRIAL REGISTRATION NUMBERS

NCT03733951 .

Targeting Angiogenesis in the Era of Biliary Tract Cancer Immunotherapy: Biological Rationale, Clinical Implications, and Future Research Avenues

Although biliary tract cancers are traditionally considered rare in Western countries, their incidence and mortality rates are rising worldwide. A better knowledge of the genomic landscape of these tumor types has broadened the number of molecular targeted therapies, including angiogenesis inhibitors. The role of immune checkpoint inhibitors (ICIs) could potentially change the first-line therapeutic approach, but monotherapy with ICIs has shown disappointing results in CCA. Several clinical trials are evaluating combination strategies that include immunotherapy together with other anticancer agents with a synergistic activity. The tumor microenvironment (TME) composition plays a pivotal role in the prognosis of BTC patients. The accumulation of immunosuppressive cell types, such as tumor-associated macrophages (TAMs) and regulatory T-cells, together with the poor infiltration of cytotoxic CD8+ T-cells, is known to predispose to a poor prognosis owing to the establishment of resistance mechanisms. Likewise, angiogenesis is recognized as a major player in modulating the TME in an immunosuppressive manner. This is the mechanistic rationale for combination treatment schemes blocking both immunity and angiogenesis. In this scenario, this review aims to provide an overview of the most recent completed or ongoing clinical trials combining immunotherapy and angiogenesis inhibitors with/without a chemotherapy backbone.