Search Results (345)

Background/Objectives: Immunotherapy has improved outcomes for selected patients with advanced non-small-cell lung cancer (NSCLC), yet the predictive value of individual biomarkers such as PD-L1 remains limited. Systemic inflammatory indices derived from routine blood tests may complement molecular and immunohistochemical features, offering a broader view of host–tumor immunobiology. Methods: We conducted a retrospective study of 298 patients with stage IIIB–IV NSCLC treated with immune checkpoint inhibitors (ICIs) at a tertiary oncology center between 2022 and 2024. Baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune–inflammation index (SII) were collected alongside PD-L1 expression and molecular alterations (EGFR, KRAS, ALK, TP53). Patients were stratified into inflammatory–molecular clusters integrating these parameters. Associations with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were evaluated using Kaplan–Meier and multivariate Cox analyses. Results: Four distinct inflammatory–molecular clusters demonstrated significantly different outcomes (p < 0.001). Patients with low NLR and high PD-L1 expression (Cluster A) showed the highest ORR (41%), longest median PFS (13.0 months), and OS (22.5 months). The EGFR/ALK-driven, inflammation-dominant cluster (Cluster C) exhibited poor response (ORR 7%) and shortest survival (PFS 4.3 months). High NLR (HR 2.12), PD-L1 < 1% (HR 1.91), and EGFR mutation (HR 2.36) independently predicted shorter PFS. A combined model incorporating NLR, PD-L1, and molecular status outperformed individual biomarkers (AUC 0.82). Conclusions: Integrating systemic inflammatory indices with PD-L1 expression and molecular alterations identifies clinically meaningful NSCLC subgroups with distinct immunotherapy outcomes. This multidimensional approach improves prediction of ICI response and may enhance real-world patient stratification, particularly in settings with limited access to extended molecular profiling. Full article

The PICO framework determines the scope of the Joint Clinical Assessment (JCA) under the EU HTA Regulation (EU HTAR), with PICO consolidation being a critical final step of the scoping process. Due to limited clarity on how consolidation works in practice, Health Technology Developers (HTDs) may simulate PICO scoping as a strategic tool to guide the development of robust JCA submissions. A review of 14 publications, representing 35 individual PICO exercises across 20 indications (74% in oncology), showed an average of 7 countries participating per exercise and 8 consolidated PICOs per analysis. A separate PICO scoping simulation focused on a first-line immuno-oncology treatment for metastatic non-small cell lung cancer (mNSCLC) generated 67 PICOs, reflecting the anticipated perspectives of 25 countries, largely driven by biomarker and histology-based sub-populations. The limited number of published examples and country participation restricts the ability to draw clear conclusions or confidently predict the output of PICO scoping in a real life JCA processes. The simulation also raised questions about whether all sub-populations should be included or consolidated further. Overall, there is a need for greater clarity in the JCA PICO scoping process, in particular the consolidation step, to facilitate high-quality evidence generation and support the EU HTAR to meet its goals of efficiency, transparency, and equity in health technology evaluation across Europe, along with more consistent patient access. Full article

The oral cavity acts as the anatomical gateway to the respiratory tract, sharing both microbiological and pathophysiological links with the lower airways. Although radiotherapy is a cornerstone treatment for lung cancer, reliable oral microbiome biomarkers for predicting patient outcomes remain lacking. We analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls across discovery and two validation cohorts via 16S rRNA sequencing. Healthy controls exhibited a significantly higher abundance of Streptococcus compared to patients (p = 0.049, p < 0.001, p < 0.001, respectively). The structure of the microbial community exhibited substantial dynamic changes during treatment. Responders showed enrichment of Rothia aeria (p = 0.027) and Prevotella salivae (p = 0.043), associated with prolonged overall survival (OS) and progression-free survival (PFS), whereas non-responders exhibited elevated Porphyromonas endodontalis (p = 0.037) correlating with shorter OS and PFS. According to Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) analysis, Akkermansia and Alistipes were nearly absent in non-responders, while Desulfovibrio and Moraxella were virtually absent in responders. A diagnostic model based on Streptococcus achieved area under the curve (AUC) values of 0.85 (95% CI: 0.78–0.91) and 0.99 (95% CI: 0.98–1) in the validation cohorts, and a response prediction model incorporating Prevotella salivae and Neisseria oralis yielded an AUC of 0.74 (95% CI: 0.58–0.90). Furthermore, in small cell lung cancer, microbiota richness and diversity were inversely correlated with Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.008, p < 0.001, respectively) and pro-gastrin-releasing peptide (ProGRP) levels (p = 0.065, p = 0.084, respectively). These results demonstrate that lung cancer-associated oral microbiota signatures dynamically reflect therapeutic response and survival outcomes, supporting their potential role as non-invasive biomarkers for diagnosis and prognosis. Full article

Background: As demonstrated by the PACIFIC trial, biomarker-driven patient selection is crucial. While treatment based on programmed death ligand-1 (PD-L1) and mutational status have become routine, tests for biomarkers available from pretherapeutic blood samples are currently a topic of scientific interest. Methods: This analysis was conducted on patients from the ALLSTAR RWD study, which is a nationwide, prospective registry for inoperable non-small cell lung cancer (NSCLC) stage III. Patients were amenable if they had a full routine pre-treatment blood sample, from which the following biomarkers were extracted: neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), derived monocyte-to-lymphocyte ratio (dMLR) and lactate dehydrogenase (LDH) levels. The intention was to find a cutoff for each of these biomarkers to predict locoregional control (LRC), progression-free survival (PFS) and overall survival (OS). Results: MLR and dMLR demonstrated their predictive potential with cutoff values of 0.665 and 0.945, respectively. Stratifying the whole cohort by means of these cutoffs demonstrated significantly better locoregional control for patients below the threshold, both in the whole cohort (N = 175; 55.7% vs. 75.5%; p-value = 0.018) and in the Durvalumab subgroup (N = 106; 57.5% vs. 77.3%; p-value = 0.030). Similar findings were observed for PFS in the whole cohort (N = 175; 20.5% vs. 56.1%; p-value p < 0.001) and in the Durvalumab subgroup (N = 106; 31.2% vs. 64.6%, p-value < 0.001). dMLR could also significantly predict PFS (N = 173; 17.4% vs. 56.3%; p-value < 0.001), which was corroborated in the Durvalumab subgroup (N = 108; 23.1% vs. 64.1%; p-value = 0.003). Conclusions: This explorative analysis demonstrates the predictive potential of MLR and dMLR for LRC and PFS. These blood biomarkers can be readily integrated into clinical routines since they are easily available. Full article

Lung cancer remains a major cause of cancer-related death, highlighting the need for new molecular targets and novel therapeutics. Matrix metalloproteinases are key regulators of invasion and microenvironment remodeling, and among them, matrix metalloproteinase-12 (MMP12) is a particularly attractive candidate whose network-level effects in cancer are still poorly defined. Herein, we applied an integrative strategy that combines bioinformatics methods with experimental validation in non-small cell lung cancer (NSCLC) cells. Protein–protein interaction (PPI) and pathway analyses of MMP12-regulated genes identified 113 downstream targets enriched in the extracellular matrix, PI3K–AKT, and immune pathways, from which an eight-gene panel (MMP12, CD44, ADAM9, NFKBIA, PSME3, SPARCL1, CCL15, and APOA1) was prioritized as a biomarker signature. Guided by these predictions, we screened a 31-compound MMP12 inhibitor library and selected five leads (C1, C7, C9, C10, and C15) for testing in H1299 cells, with C9 showing the strongest antiproliferative activity. These compounds showed antimigratory activity (C1 achieving a 90% inhibition of wound closure at its IC₅₀ concentration), reduced clonogenic growth, cell cycle perturbation, and induction of apoptosis. Gene- and protein-expression analyses confirmed MMP12 suppression and modulation of the eight-gene panel. Upstream regulator predictions implicated reduced AKT signaling alongside an ADAM9-centered adaptive axis. Collectively, these findings highlight C1, C7, C9, C10, and C15 as promising MMP12 inhibitors, supporting their further development in preclinical lung cancer and nominating the eight-gene panel as a pharmacodynamic signature for MMP12-targeted therapies. Full article

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in both men and women. It is broadly classified into two main histological subtypes, with non-small cell lung cancer (NSCLC) being the most prevalent, accounting for approximately 85–90% of all cases. Liquid biopsy refers to the analysis of tumor-derived material circulating in body fluids. This minimally invasive technique can be performed repeatedly over time and enables the detection of a tumor’s genomic profile without tissue samples. Liquid biopsies have the potential to identify biomarkers across different lung cancer subtypes that may be associated with early detection, prognosis, and prediction of response to targeted therapies. In this context, bioinformatics tools play a critical role in analyzing large-scale, high-dimensional omics datasets, which can be transformed into clinically meaningful insights. This article emphasizes the significance of prognostic, predictive, and diagnostic biomarkers in lung cancer, which can be detected in various biological fluids. Furthermore, it highlights how integrating bioinformatics approaches can facilitate the development of a personalized molecular profile, ultimately supporting individualized treatment strategies for each patient. Full article

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with tumor recurrence and metastasis posing significant challenges despite advances in targeted therapies and immunotherapy. Cellular dormancy, a reversible, quiescent state marked by cell cycle arrest, has emerged as a key driver of therapeutic resistance and disease relapse, particularly in small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Multiple mechanisms, including autophagy, stress-adaptive signaling, microenvironmental cues, and epigenetic dysregulation, have been implicated in the regulation of dormancy and long-term cell survival. Among these, epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNAs (ncRNAs) play pivotal roles in maintaining dormancy by repressing proliferative gene expression programs. Increasing evidence suggests that dormant tumor cells harbor distinct epigenomic signatures, which may serve as predictive biomarkers for minimal residual disease (MRD) and relapse risk. This review summarizes current advances in understanding the epigenetic regulation of cellular dormancy in lung cancer, with a particular emphasis on the interplay between epigenetic modifiers and oncogenic signaling pathways. Furthermore, emerging molecular targets and associated therapeutic agents currently under clinical evaluation are presented, emphasizing how a deeper understanding of the epigenetic landscape governing dormancy may inform the development of novel interventions to improve long-term clinical outcomes in lung cancer patients. Full article

Introduction: Current treatments for patients with stage III non-small-cell lung cancer (NSCLC) are not sufficiently personalized, resulting in suboptimal outcomes and high mortality rates. The Developing Circulating and Imaging Biomarkers Towards Personalized Radiotherapy in Lung Cancer (VIGILANCE) study employs innovative health technologies to collect a range of clinical data and features. This includes longitudinal analyses of cell-free and circulating tumor DNA from blood samples and radiomic features extracted from standard-of-care imaging. Additionally, patient-reported outcome measures will be collected to capture patients’ symptoms and quality of life. This will provide invaluable insight into the patient experience during and after radiotherapy. We aim to evaluate whether the data, including patient-reported outcomes, can serve as biomarkers to refine treatment strategies, improve post-treatment follow-up and provide patients with realistic outcome predictions. Key endpoints include the following: (1) assessing whether baseline ctDNA status and its early on-treatment dynamics can identify patients with radioresistant disease who could benefit from treatment intensification; (2) determining whether post-radiotherapy ctDNA clearance can predict benefit from consolidation durvalumab, potentially sparing ctDNA-negative patients from unnecessary immunotherapy; and (3) developing integrated models combining novel ctDNA and radiomic biomarkers to distinguish between radiation fibrosis and tumor recurrence and to predict survival. We adopt a pragmatic approach by recruiting patients receiving standard-of-care treatments in a real-world setting. In addition, most of the clinical data is already routinely collected in our center, except for the blood tests for cell-free and circulating tumor DNA analysis. Methods and analysis: This is a single-center, prospective, exploratory, longitudinal, follow-up study, recruiting patients with stage III NSCLC undergoing standard-of-care curative-intent radiotherapy (with or without systemic therapy). Data collection spans from baseline to during radiotherapy and is extended up to 1 year following radiotherapy. The longitudinal analysis aims to describe and characterize dynamic changes in the collected features and assess their utility as prognostic and response biomarkers. Trial registration number: NCT06086574. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Sarcopenia and reduced skeletal muscle radiodensity have been proposed as potential biomarkers influencing the outcomes of immunotherapy in cancer patients. This retrospective study aimed to evaluate the prognostic significance of skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD), assessed by means of computed tomography imaging at the L3 level, in 76 male patients with metastatic non-small cell lung cancer treated with PD-1/PD-L1 inhibitors. Patients were categorized into high and low SMI/SMD groups based on body mass index-adjusted cut-off values. Clinical outcomes included treatment response, overall survival, and immune-related adverse events. While no statistically significant differences in overall survival were observed between groups stratified by SMI or SMD, patients with higher SMD demonstrated a significantly greater disease control rate (56.22 ± 8.04 vs. 48.36 ± 10.34 HU; p = 0.031). Additionally, a statistically significant interaction was observed between PD-L1 expression and SMI (p = 0.027), indicating that muscle mass may influence the prognostic value of PD-L1. Neither SMI nor SMD were associated with immune-related adverse event incidence. Multivariate analysis identified PD-L1 expression ≥ 50% as the only independent predictor of longer overall survival (Hazard Ratio = 0.29; p = 0.001). In conclusion, while neither SMI nor SMD independently predicted overall survival, SMD was associated with treatment response. Notably, SMI modified the prognostic relevance of PD-L1 expression, suggesting a potential role for muscle mass in refining immunotherapy stratification. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality, with therapeutic resistance posing the primary barrier to durable outcomes. Beyond genetic and epigenetic alterations, amino acid transporter-driven metabolic reprogramming—mediated by LAT1 (SLC7A5), ASCT2 (SLC1A5), and xCT (SLC7A11)—supports tumor proliferation, redox homeostasis, and immune escape. Their preferential expression in NSCLC highlights their potential as therapeutic targets and predictive biomarkers. In parallel, α-particle therapy has gained attention for its capacity to eradicate resistant clones through densely clustered, irreparable DNA double-strand breaks. Astatine-211 (²¹¹At) combines a clinically relevant half-life, high linear energy transfer, and predictable decay scheme, positioning it as a unique candidate among α-emitters. Preclinical studies of ²¹¹At-labeled transporter ligands, particularly LAT1-targeted conjugates, demonstrate potent tumor suppression and synergy with targeted therapy, chemotherapy, radiotherapy, immunotherapy, and ferroptosis inducers. Advances in radiochemistry, delivery systems (antibodies, peptides, and nanocarriers), and PET tracers such as [¹⁸F]FAMT and [¹⁸F]FSPG collectively support a theranostic framework for patient stratification and adaptive dosing. By linking transporter biology with α-particle delivery, ²¹¹At-based theranostics offer a mechanistically orthogonal strategy to overcome resistance and heterogeneity in NSCLC. Successful translation will depend on precise dosimetry, scaffold stabilization, and biomarker-guided trial design, enabling progression toward first-in-human studies and future integration into multimodal NSCLC therapy. Full article

Non-small-cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, demanding improved biomarkers and therapeutic approaches. This review synthesizes the extensive evidence positioning the miR-200 family as a master regulator of NSCLC progression. We detail the core molecular circuitry centered on the bistable, double-negative feedback loop between miR-200 and the ZEB1/ZEB2 transcription factors, which governs epithelial–mesenchymal transition (EMT). This review connects this central mechanism to critical clinical challenges, including the development of resistance to EGFR-targeted therapies and the regulation of immune evasion through PD-L1 expression and CD8+ T cell infiltration. We evaluate the strong clinical evidence for the miR-200 family’s utility as a diagnostic, prognostic, and predictive biomarker. Finally, we explore emerging therapeutic strategies that target this network, including miRNA replacement, epigenetic reactivation, and rational combinations with immunotherapy and targeted agents. We synthesize evidence positioning the miR-200/ZEB feedback circuit as a central regulatory node in NSCLC that links EMT with therapeutic resistance and immune evasion. Beyond summarizing associations, we interpret how this circuitry could inform biomarker development and rational combinations with targeted and immune therapies. Given heterogeneous study designs and non-standardized assays, translational claims remain provisional; we outline immediate priorities for assay harmonization and biomarker-stratified trials. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally. The advent of immune checkpoint inhibitors (ICIs) has significantly improved outcomes for a subset of patients; however, predictive biomarkers to identify responders are still lacking. Peripheral blood mononuclear cells (PBMCs) offer a minimally invasive means to assess systemic immune status and have emerged as a potential source of predictive biomarkers. Recent studies have highlighted the role of chemokines and their receptors in modulating immune responses against tumors. In particular, the expression levels of chemokine receptors such as CXCR4 on PBMCs have been associated with patient responses to ICIs. The differences in expression of these receptors correlates with enhanced T cell trafficking and infiltration into the tumor microenvironment, leading to improved antitumor activity. This review consolidates current evidence on the prognostic and predictive value of chemokine receptor expression in PBMCs, like T cells, for NSCLC patients treated with ICIs. Understanding these associations can aid in the development of non-invasive biomarkers to guide treatment decisions and improve patient stratification in immunotherapy. Full article

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling—by capturing the metabolic phenotype of tumors—has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring. Full article

Objectives: This study evaluated the utility of complex morphometric analyses for predicting radiation pneumonitis (RP) and proposed a quantitative prognostic framework for patients with non-small cell lung cancer (NSCLC) undergoing curative-intent radiotherapy (RT). Imaging biomarkers, including box-counting fractal dimension (BoxFD), lacunarity, and minimum spanning tree fractal dimension (MSTFD), were assessed for their prognostic significance. Materials and Methods: We retrospectively analyzed 166 NSCLC patients who received curative-intent RT and had both pre-treatment and follow-up chest CT scans. Among them, 85 received RT alone and 81 underwent concurrent chemoradiotherapy (CCRT). Fractal features were measured to build a Random Forest model (RFM) predicting RP of grade ≥ 2, and the most important features were used to construct a decision tree model. Results: RP of grade ≥ 2 occurred in 19 patients (22.3%) in the RT alone group and 44 patients (54.3%) in the CCRT group. Lacunarity increased significantly post-RT in both groups, while BoxFD and MSTFD showed no significant changes. In the RFM, pre-RT MSTFD and lung dose parameters (V10 in RT alone; V5–V20 in CCRT) were identified as key predictors. Decision tree models based on these features achieved high predictive performance, with AUROC of 0.83 and 0.85, and F1 scores of 0.92 and 0.76 for RT alone and CCRT groups, respectively. Conclusions: Fractal imaging biomarkers demonstrated promising prognostic value for predicting grade ≥ 2 RP in NSCLC patients. The proposed decision tree model may serve as a practical tool for early identification of high-risk patients, facilitating personalized treatment strategies and informing future research. Full article