A Comprehensive Analysis of HER2 status Heterogeneity Using Matched Samples among Potential Beneficiaries of Trastuzumab Deruxtecan Therapy in Non-Small Cell Lung Cancer.

A qualitative study of patient perspectives for a mindfulness-based intervention in lung cancer.

In East-Asian predominant populations, multiple reports identify East-Asian ancestry as prognostic of improved outcome in EGFRmut+ non-small cell lung cancer (NSCLC) populations when treated with first/second generation tyrosine kinase inhibitor (TKI). Generalizability to more heterogeneous populations is less understood. To identify clinical characteristics associated with long-term survival (LTS) in patients from heterogeneous Canadian populations with advanced NSCLC with EGFRmut+ treated with first-line TKIs. De novo advanced EGFRmut+ NSCLC diagnoses receiving a first/secondgeneration epidermal growth factor receptor-TKI between 2004 and 2016 were included. Demographic, clinical, treatment, and outcome details were extracted from three sources: two province-wide, multi-center registries, the Alberta Glans-Look Lung Cancer Research Database (GLR),and the British Columbia Cancer (BCC), along with data fromPrincess Margaret Cancer Center (PM), a single-center, urban, tertiary hospital. Survival time from TKI initiation was divided into LTS, patients surviving longer than the upper quartile (34.4 months), and the remaining patients described as 'non-long-term survivors' (non-LTS). Of 577 patients (GLR: 246, PM: 112, and BCC: 219), median overall survival was 20.3 months. From initiation of TKI, LTS-median survival was 48.5 months, whereas non-LTS was 15.7 months. The LTS cohort differed significantly from non-LTS in many clinical and pathologic characteristics, including being significantly more likely to be of East-Asian ancestry (50%vs. 39%, p = 0.02), Female (74%vs. 64%, p = 0.04), and more likely to harbor exon19del-mutation (60%vs. 46%, p = 0.004). In multivariate analysis, exon19del (hazard ratio [HR] = 0.77, 95% confidence interval [CI] 0.63-0.93, p = 0.01) and East-Asian ethnicity (HR = 0.75, 95% CI 0.69-0.94, p = 0.02) were independently associated with longer survival. Male sex (HR = 1.36, 95% CI 1.12-1.66, p < 0.01) was associated with shorter survival, while smoking status, treatment site, and age > 70 years were not independent prognostic factors. This study confirmed previously known prognosticators within a real-world multicenter Canadian cohort. This included East-Asian ancestry, female sex, and exon19del, which were associated with better overall survival. Future work to better understand the impact of socioeconomic factors and biological reasons for ancestry resulting in different prognosis are needed to better guide treatment management.

Acetylation, a critical epigenetic modification, plays a central role in regulating gene expression, chromatin accessibility, protein function, and tumor progression. In lung cancer, aberrant acetylation has been implicated in processes such as metastasis, immune evasion, and therapy resistance. Despite growing scientific interest, a comprehensive bibliometric evaluation of research on acetylation in lung cancer has not yet been conducted. This study aims to systematically explore global research trends, collaboration networks, influential contributors, and emerging thematic hotspots in acetylation-related lung cancer research over the past 25 years, offering strategic insight into both foundational mechanisms and translational opportunities. A total of 917 English-language publications from 2000 to 2025 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric analyses were performed using VOSviewer, CiteSpace, Scimago Graphica, and Charticulator. Key metrics such as publication growth, country and institutional contributions, author impact, keyword co-occurrence, and citation bursts were examined to map the knowledge structure and research frontiers. The annual number of publications and citations has increased significantly, particularly after 2015. China and the United States are the leading contributors, with Chinese institutions dominating in volume and U.S. scholars exhibiting greater citation influence. Major research clusters focus on histone deacetylases, EMT, immune regulation, and non-apoptotic cell death pathways including ferroptosis and autophagy. Recent studies emphasize the potential of combining acetylation-targeting agents with immunotherapies and nanomedicine to overcome resistance and enhance therapeutic efficacy. Acetylation has become a pivotal regulatory mechanism and therapeutic target in lung cancer research. This bibliometric study reveals evolving hotspots and interdisciplinary linkages, highlighting the need for deeper mechanistic studies, international collaboration, and the integration of epigenetic strategies into precision oncology. The findings provide a valuable reference for future basic and translational research in lung cancer epigenetics.

Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality. Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines. The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines. This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

ABSTRACT Objective: Cancer, characterized by uncontrolled cell proliferation and invasion into surrounding tissues, is a leading cause of global mortality. Traditional two-dimensional (2D) cell culture systems fail to adequately replicate the tumor microenvironment (TME). In contrast, three-dimensional (3D) culture systems, which better simulate cell–cell and cell–extracellular matrix (ECM) interactions, have become powerful tools in biomedical research. This study aims to compare the spheroid formation capacity of A549 lung cancer cells using three different 3D culture methods: ultra-low attachment (ULA) plates, agarose hydrogel, and the hanging drop technique. The primary objective is to identify the most effective spheroid formation method for A549 cells and to provide findings that can guide future biomedical research, particularly in cancer modeling, drug screening studies, and investigations of the tumor microenvironment.Materials and Methods: A549 cells were cultured using three different 3D culture methods: ultra-low attachment plates, agarose hydrogel, and the hanging drop method. In the ultra-low attachment method, spheroid formation was observed at cell densities of 5,000, 10,000, and 30,000 cells/ml. In the agarose hydrogel method, agarose concentrations of 1%, 1.5%, and 2% were used to evaluate cell aggregation and spheroid stability. In the hanging drop method, cells aggregated under the influence of gravity. Spheroid diameter and area were analyzed using ImageJ software.Results: In this study, the spheroid formation capacity of A549 lung cancer cells was evaluated using three different three-dimensional (3D) culture methods. The ultra-low attachment (ULA) plate method allowed cell aggregation; however, the resulting structures were not large or compact enough to be classified as spheroids. The hanging drop method showed that cells formed small clusters by day 3 but failed to develop a compact and stable spheroid structure by day 7. The agarose hydrogel method, particularly at a 2% agarose concentration, demonstrated the highest spheroid formation capacity compared to the other methods. In this method, spheroid formation began at 72 hours depending on cell density, with significant growth observed at a density of 30,000 cells/ml (p &amp;lt; 0.0001). Trypan Blue staining results indicated that 2% agarose and cell densities of 10,000–30,000 cells/ml provided the highest cell viability. Specifically, 4,800 viable cells were counted at a density of 30,000 cells/ml, while 3,600 viable cells were observed at 10,000 cells/ml. These findings suggest that the agarose hydrogel method, especially at 2% agarose concentration and higher cell densities, offers optimal spheroid formation and cell viability for A549 lung cancer cells.Conclusion: This study demonstrated that the agarose hydrogel method effectively promoted stable and organized spheroid formation in A549 lung cancer cells. Notably, the 2% agarose concentration was identified as the most effective condition for maintaining cell viability and optimizing spheroid size. In contrast, the ultra-low attachment (ULA) plate and hanging drop methods exhibited limited spheroid formation capacity, resulting in less compact and disorganized structures. These findings emphasize the critical role of three-dimensional (3D) cell culture methods in biomedical research, particularly for experimental tumor modeling and drug screening studies. In this context, the agarose hydrogel method, with its high spheroid formation capacity and ability to support cell viability, emerges as a promising 3D culture model that warrants further exploration in cancer research.

SummaryBackgroundAs life expectancy increases, more adults aged ≥80 years are diagnosed with early-stage lung cancer. Often these patients are excluded from screening programs and clinical trials due to concerns about comorbidities and surgical risk as evidence on surgical outcomes and quality of life (QoL) remains limited. We aimed to compare postoperative outcomes, survival, and QoL between octogenarians and younger patients undergoing surgery for Stage IA NSCLC.MethodsWe included patients with stage IA non-small cell lung cancer from the Mount Sinai Health System enrolled in the prospective Initiative for Early Lung Cancer Research on Treatment (IELCART) study. Octogenarians (80 years and older) were compared to younger patients in terms of clinical presentation, type of surgery, postoperative outcomes, and survival. Quality of life was assessed using physical and mental health scores at baseline and at 1, 6, and 12 months after surgery. Lung cancer–specific and overall survival were analyzed using Kaplan–Meier methods.FindingsAmong 884 patients, 114 (12.9%) were octogenarians. Compared to 770 younger patients, octogenarians had similar comorbidities but underwent more frequently sublobar resections [90/114 (78.9%) vs. 485/770 (62.4%), p = 0.030] and had higher complication rates [46/114 (40%) vs. 168/770 (22%), p < 0.0001], particularly cardiovascular. Intensive care unit admissions and readmissions were slightly more frequent. In both age groups, physical and mental health declined at two months but improved by twelve months, with no significant differences. Five-year overall- and lung cancer-specific survival rates were similar between octogenarians and younger patients (overall: 84.2% vs. 87.3%, lung cancer–specific survival: 94.4% vs. 94.5%).InterpretationAmong octogenarians with early-stage NSCLC, surgical treatment was associated with favorable safety and long-term quality-of-life outcomes in carefully selected patients.FundingThis study was supported by generous grants from the 10.13039/100000893Simons Foundation (International, Ltd.).

Real-world evidence (RWE) plays an increasingly important role in complementing randomized controlled trials (RCTs) in lung cancer research. This study aims to map the knowledge structure, identify research trends, and highlight future directions of real-world studies (RWS) in lung cancer through bibliometric analysis. We retrieved publications on RWS in lung cancer from the Web of Science Core Collection (WOSCC) spanning 2009 to 2024. Bibliometric analyses were performed using CiteSpace, VOSviewer, and the R package Bibliometrix to examine publication trends, collaborative networks, co-citation patterns, and keyword bursts. A total of 2,137 articles were included. Annual publications increased markedly after 2017, accounting for 97.52% of the total. China contributed the most publications (n = 660), while the USA exhibited the highest centrality in international collaboration. Keyword burst detection identified “combination therapy,” “KRAS mutation,” and “deep learning” as the most current research frontiers. This analysis underscores the pivotal role of RWE in complementing RCTs and validating therapeutic strategies in oncology. Current frontiers include combination therapy, KRAS mutations, and deep learning, indicating a shift toward precision oncology and advanced data analytics.

The extent to which protocol eligibility criteria contribute to the underrepresentation of racial and ethnic minority populations — including Black, Asian, and Latino Americans — in lung cancer clinical trials remains poorly characterized. This study quantifies the likelihood of clinical trial exclusion attributable to comorbid conditions across racial and ethnic groups among patients with lung cancer. Data were drawn from 1,134 lung cancer clinical trials registered on ClinicalTrials.gov with start dates between January 2014 and December 2024, and patient comorbidity data were obtained from electronic medical records (EMR) at a large urban academic medical center in the Northeast United States. Data analysis was conducted between February and May 2025. Eligibility for trial enrollment was assessed by mapping patient comorbidity profiles against study exclusion criteria; binary logistic regression was used to estimate the likelihood of exclusion by race and ethnicity, with sex and median household income included as covariates. The analytic sample comprised 4,096 patients with lung cancer (73.6% White, 12.8% Asian or Pacific Islander, 3.3% Black or African American, and 1.8% Hispanic/Latino). Compared to White American patients, Asian American and Pacific Islander (AAPI) patients and Black or African American patients were 1.8 times (OR: 1.8, 95% CI: 1.03–3.03) and 1.6 times (OR: 1.6, 95% CI: 1.01–2.48) more likely to be excluded from clinical trials based on their comorbidities, respectively. These findings indicate that standard protocol exclusion criteria may disproportionately screen out racial and ethnic minority patients, particularly Black/African American and AAPI individuals, and may represent a structural contributor to their underrepresentation in lung cancer research. Revising eligibility criteria to better reflect real-world comorbidity burdens could improve the inclusivity and generalizability of lung cancer clinical trials.

The extent to which protocol eligibility criteria contribute to the underrepresentation of racial and ethnic minority populations - including Black, Asian, and Latino Americans - in lung cancer clinical trials remains poorly characterized. This study quantifies the likelihood of clinical trial exclusion attributable to comorbid conditions across racial and ethnic groups among patients with lung cancer. Data were drawn from 1,134 lung cancer clinical trials registered on ClinicalTrials.gov with start dates between January 2014 and December 2024, and patient comorbidity data were obtained from electronic medical records (EMR) at a large urban academic medical center in the Northeast United States. Data analysis was conducted between February and May 2025. Eligibility for trial enrollment was assessed by mapping patient comorbidity profiles against study exclusion criteria; binary logistic regression was used to estimate the likelihood of exclusion by race and ethnicity, with sex and median household income included as covariates. The analytic sample comprised 4,096 patients with lung cancer (73.6% White, 12.8% Asian or Pacific Islander, 3.3% Black or African American, and 1.8% Hispanic/Latino). Compared to White American patients, Asian American and Pacific Islander (AAPI) patients and Black or African American patients were 1.8 times (OR: 1.8, 95% CI: 1.03-3.03) and 1.6 times (OR: 1.6, 95% CI: 1.01-2.48) more likely to be excluded from clinical trials based on their comorbidities, respectively. These findings indicate that standard protocol exclusion criteria may disproportionately screen out racial and ethnic minority patients, particularly Black/African American and AAPI individuals, and may represent a structural contributor to their underrepresentation in lung cancer research. Revising eligibility criteria to better reflect real-world comorbidity burdens could improve the inclusivity and generalizability of lung cancer clinical trials.

In 2025, lung cancer research advanced rapidly across the disease continuum, from population-level risk assessment and screening to mechanistic studies of early carcinogenesis and therapeutic innovation in perioperative and metastatic settings. A key shift moved beyond a smoking-centred paradigm toward a multidimensional risk framework reflecting the growing burden among never-smokers and the roles of air pollution, occupational exposures, and systemic metabolic-inflammatory states. This narrative review aims to synthesize influential 2025 evidence across prevention, diagnosis, treatment, and survivorship, and to identify convergent themes and translational gaps relevant to clinical practice and policy. We performed a narrative synthesis of influential lung cancer studies published in major international journals in 2025. Evidence was organized along a clinically oriented pathway spanning carcinogenesis and screening, precision diagnosis, treatment optimization in resectable and advanced disease, and survivorship, emphasizing practice-informing trials, high-impact translational research, and implementation-relevant technologies. Lineage tracing, single-cell and spatial omics, and evolutionary inference refined concepts of field cancerization, clonal selection, and copy-number-driven fitness. In small-cell lung cancer, evidence further supported neuronal coupling and synapse-like programs as potentially tractable vulnerabilities. Clinically, low-dose computed tomography (CT) strategies and data-informed nodule thresholds aimed to balance under-detection against over-surveillance harms. In diagnostics, artificial intelligence (AI) models increasingly inferred molecular features from routine histopathology ("virtual molecular testing") and should be regarded as decision support requiring prospective validation, population calibration, and explicit failure-mode reporting. Multimodal approaches integrating imaging with circulating tumor DNA (ctDNA) improved feasibility in tissue-limited settings, but clinical utility remains contingent on assay standardization and pathway-level implementation. In resectable disease, longer follow-up consolidated neoadjuvant chemo-immunotherapy for selected patients, while ctDNA kinetics emerged as a candidate biomarker for response-adaptive escalation and de-escalation. In advanced non-small cell lung cancer (NSCLC), phase III evidence for antibody-drug conjugates and bispecific antibodies began reshaping sequencing, while highlighting challenges in toxicity, access, affordability, and immature overall survival in several programs. The 2025 landscape reflects coordinated progress in risk conceptualization, biology, diagnostics, and therapeutics, yet gaps in validation, standardization, and real-world deliverability persist. Priorities include prospective evaluation of AI- and ctDNA-enabled pathways, toxicity-informed sequencing, and equitable implementation aligned with health-system capacity.

BackgroundOmics, encompassing genomics, transcriptomics, proteomics and metabolomics, plays a pivotal role in elucidating the molecular mechanisms underlying lung cancer and advancing precision oncology. While existing studies have primarily focused on the technical development and clinical efficacy of omics applications in cancer, there remains a notable gap in comprehensive assessments of the global research landscape. At different stages of lung cancer initiation, progression, and metastasis, genomics and transcriptomics predominantly reveal oncogenic alterations and dysregulated signaling networks, whereas proteomics and metabolomics capture functional protein dynamics and metabolic reprogramming that drive tumor growth and metastatic adaptation. Importantly, the integration of multi-omics data enables a systematic understanding of the crosstalk between genetic alterations, transcriptional regulation, protein expression, and metabolic remodeling throughout lung cancer evolution. This bibliometric analysis study aims to systematically evaluate scientific output, research trends and hotspots in omics-related lung cancer research.MethodsRelevant publications were retrieved from the Web of Science Core Collection (WoSCC) from January 1, 2004 to April 27, 2024. Bibliometric analyses and knowledge domain visualizations were conducted using VOSviewer (v1.6.20), CiteSpace (v6.3), R (v4.3.3), and Origin (2024).ResultsA total of 19,087 publications were included, demonstrating sustained growth over two decades [2004–2024]. China contributed the largest volume of publications, whereas the USA showed higher citation impact and stronger influence in collaboration networks. Keyword co-occurrence and burst analyses illustrated that “expression”, “lung cancer”, “gene expression”, “tumor microenvironment”, “mutation” and “immunotherapy” are dominant and emerging themes. These findings indicate a clear shift from single-omics approaches and gene-centric investigations toward integrative multi-omics frameworks, with increasing emphasis on the tumor microenvironment (TME) and immunotherapy. The burst analysis of keywords also highlights the rising prominence of artificial intelligence (AI) and machine learning (ML), which have emerged as rapidly growing methodological backbones in recent years.ConclusionsResearch on omics in lung cancer has rapidly evolved toward integrative, TME-focused and immunotherapy-oriented paradigms, with AI/ML serving as an enabling analytical infrastructure. This study underscores the critical role that omics in facilitating early detection, guiding personalized therapeutic strategies, and improving prognostic accuracy. The findings suggest that enhancing cross-disciplinary collaboration and accelerating the clinical translation of multi-omics data may help overcome current challenges in precision oncology.

Recent studies have demonstrated that the plasma level of circulating tumor DNA (ctDNA) has emerged as a highly effective, minimally invasive biomarker in patients with lung cancer. This study aimed to perform bibliometric analysis to systematically evaluate the development trends of ctDNA in lung cancer for future research. All articles on the role of ctDNA for detecting lung cancer from January 1, 2012, to December 31, 2022, were retrieved from the Web of Science (WoS) Core Collection database. Data analyses were performed using the R package Bibliometrix, VOS viewer 1.6.18, and online analysis in WoS. A total of 1122 publications were retrieved: 807 primary articles and 315 review articles. The annual publication volume exhibited asignificant upward trajectory, with a Compound Annual Growth Rate (CAGR) of 48.3%. China had the highest article output (379 papers), whereas the citation rate of papers from the United States was the highest (20,239 citations). The journal "Cancers" had the highest number of publications. Wang J was the most prolific author (35 papers), whereas Diehn M was the most cited author (4460 citations). The five most frequent keywords were "lung cancer" (n = 237), "plasma" (n = 227), "acquired-resistance" (n = 207), "circulating tumor DNA" (n = 177), and "gefitinib" (n = 151). This study provides a comprehensive quantitative synthesis of the ctDNA landscape in lung cancer over the past decade. By identifying key research hotspots and evolutionary trends, our findings offer valuable insights for clinicians and researchers, highlighting the transition of ctDNA from experimental validation to critical translational applications in precision oncology.

Lung cancer stands as the most prevalent form of cancer and the primary cause of cancer-related mortality on a global scale. The presence of multiple genetic mutations in lung cancer patients, coupled with a high degree of patient heterogeneity, necessitates the exploration of modern precision medicine through personalized tumor models. Traditional cancer models, both in vitro and in vivo, exhibit certain limitations. In vitro, cell line models lack spatial organization and are incapable of fully capturing the complex heterogeneity of tumors. In contrast, in vivo, patient-derived xenograft (PDX) models are characterized by extended culture durations and suboptimal transplantation success rates. In recent years, the advent of "organoid" technology, an in vitro three-dimensional (3D) culture method, has revolutionized cancer research. Organoids offer the advantage of rapid culture while preserving the fundamental characteristics of the parent tissue. With advancements in lung cancer organoids (LCOs) culture techniques, the construction of the tumor microenvironment, and integrations with other scientific domains, LCOs are now better equipped to mimic the in vivo lung cancer environment, thereby facilitating their application across various facets of lung cancer research. This paper conducted a comprehensive literature search on PubMed for articles about LCOs published within the last 5 years, integrating the most recent research developments. It subsequently provides a summary and in-depth discussion on the culture methods, tumor microenvironment construction, and applications in immunotherapy of LCOs, aiming to contribute to the ongoing advancements in the field of lung cancer research.

Non-small-cell lung cancer (NSCLC) research has focused on complementary and well-established treatments with clear mechanisms and less toxicity. Immune dysregulation is vital in NSCLC progression and metastasis. Ze-qi decoction (ZQD) exhibits therapeutic effects in patients with NSCLC; however, its pharmacodynamic material basis and specific mechanisms remain unclear. In this study, we integrated UPLC-HRMS, pharmacological analysis, and transcriptomic analysis to identify the potential effective components of ZQD and elucidate its intrinsic mechanisms. ZQD exhibited potent anti-NSCLC activity in the mouse subcutaneous tumor model. A total of 297 bioactive compounds were identified in mouse plasma following ZQD administration. Pharmacological analysis revealed liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid as the potential active ingredients of ZQD and suggested that ZQD exerted anti-NSCLC effects primarily via immunomodulatory and anti-inflammatory pathways. Integrative analysis of network pharmacology and transcriptomics indicated the neutrophil extracellular trap (NET) formation as a key pathway. Further analysis showed that ZQD disrupted the neutrophil recruitment environment by decreasing hypoxia-inducible factor-1α, CD18, and intercellular adhesion molecule-1 levels and downregulating NET-related markers (citrullinated histone H3, myeloperoxidase, and neutrophil elastase). Finally, these results were confirmed in a lung metastasis model. This is the first study designed to analyze the material basis of ZQD responsible for its effect on NSCLC. Our results indicate that the mechanisms of action of ZQD involve impeding neutrophil recruitment and activation, as well as reducing the levels of NETs-related markers. These suggest the potential of ZQD in suppressing NETs formation or release, inhibiting NSCLC progression and metastasis.

APEX: A Web-Based Tool for Assessing Long-Term Outdoor PM2.5 Exposure-Brief Report.

ScRNA-Seq: A way to reveal the potential mechanisms of metastasis in small cell lung cancer.

Lung cancer is a highly malignant disease, posing a significant threat to global health. The presence of tumor heterogeneity results in substantial variations in prognosis and therapeutic responses among patients. Advances in bulk RNA sequencing and single-cell RNA sequencing have facilitated the identification of driver gene mutations and the exploration of cellular diversity within tumors. However, tumors are complex ecosystems comprising both tumor cells and their microenvironment, where interactions among different cell types give rise to specific functional structural units that collectively drive tumorigenesis and progression. The emergence of spatial omics technologies has allowed for the analysis of tumor ecosystems, providing unprecedented insights into tumor heterogeneity. This review aims to present updates on spatial omics technologies and data analysis algorithms, discuss current technical limitations, and explore potential future developments. Furthermore, we summarize the latest applications of spatial omics in elucidating lung cancer heterogeneity, investigating mechanisms of lung cancer progression and drug resistance, and identifying novel biomarkers. Based on these findings, we propose strategies for integrating spatial omics into lung cancer research, offering new perspectives for precision medicine.

Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI) represents a powerful tool to develop predictive algorithms tailored to individual patients. Thanks to its ability to process large quantities of heterogeneous, patient-level information, the AI-based approach is progressively fostering the growth of a data-driven paradigm to complement traditional, hypothesis-driven clinical research. However, the development of reliable AI models requires access to large, high-quality, and continuously updated datasets. Despite this necessity, no infrastructure currently exists to enable federated, multi-omic, standardized, prospective, and large-scale collection and analysis of real-world clinical and biological data in the context of lung cancer. We established the APOLLO11 consortium, a distributed, nationwide, updated Italian lung cancer network designed to build a decentralized, long-term, population-based, real-world data repository and a multilevel biobank, locally stored and centrally annotated. This strategy seeks to lay the foundation for the clinical implementation of data-driven research, ultimately advancing precision oncology.

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