Absolute eosinophil count predicts clinical outcomes and toxicity in non-small cell lung cancer patients treated with immunotherapy

Molecular subtype characteristics and development of prognostic model based on inflammation-related gene in lung adenocarcinoma

As one of the leading causes of death worldwide, lung adenocarcinoma (LUAD) currently lacks satisfactory treatment outcomes. The inflammatory process, closely associated with the formation of the tumor microenvironment and immune evasion, plays a crucial role in LUAD development. This study utilized data from public databases to analyze inflammation-related genes (INF) associated with prognosis in LUAD. Based on differentially expressed INF, molecular subtypes of LUAD were identified. Subsequently, a novel INF scoring system was developed to establish a prognostic model for LUAD patients, assessing its independence and reliability. Comprehensive evaluations, including immune microenvironment infiltration features, somatic mutation characteristics, and differences in immune therapy responsiveness, were conducted to characterize the prognostic model associated with INF. We further selected MMP14 from the screened INF targets for further in vitro experiments. Experiments such as western blot, qRT-PCR, colony-forming assay and Transwell assay confirmed that downregulation of MMP14 could inhibit the cloning, proliferation and invasion of lung cancer cells, thus confirming the results of bioinformatics. Our findings provide evidence from a new perspective on the role of inflammation in LUAD and offer new insights for clinical precision and personalized therapy.

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite instability (MSI), and tumor mutational burden (TMB), are used in clinical practice to predict benefits from immune checkpoint inhibitors (ICIs). It is therefore necessary to search for novel biomarkers that could be helpful to identify patients who respond to immunotherapy. In this context, research efforts are focusing on different cells and mechanisms involved in anti-tumor immune response. Herein, we provide un updated literature review on the role of eosinophils in cancer development and immune response, and the functions of some cytokines, including IL-31 and IL-33, in eosinophil activation. We discuss available data demonstrating a correlation between eosinophils and clinical outcomes of ICIs in lung cancer. In this context, we underscore the role of absolute eosinophil count (AEC) and tumor-associated tissue eosinophilia (TATE) as promising biomarkers able to predict the efficacy and toxicities from immunotherapy. The role of eosinophils and cytokines in NSCLC, treated with ICIs, is not yet fully understood, and further research may be crucial to determine their role as biomarkers of response. Artificial intelligence, through the analysis of big data, could be exploited in the future to elucidate the role of eosinophils and cytokines in lung cancer.

Harnessing myeloid cells in cancer

Cancer-associated myeloid cells due to their plasticity play dual roles in both promoting and inhibiting tumor progression. Myeloid cells with immunosuppressive properties play a critical role in anti-cancer immune regulation. Cells of different origin, such as tumor associated macrophages (TAMs), tumor associated neutrophils (TANs), myeloid derived suppressor cells (also called MDSCs) and eosinophils are often expanded in cancer patients and significantly influence their survival, but also the outcome of anti-cancer therapies. For this reason, the variety of preclinical and clinical studies to modulate the activity of these cells have been conducted, however without successful outcome to date. In this review, pro-tumor activity of myeloid cells, myeloid cell-specific therapeutic targets, in vivo studies on myeloid cell re-polarization and the impact of myeloid cells on immunotherapies/genetic engineering are addressed. This paper also summarizes ongoing clinical trials and the concept of chimeric antigen receptor macrophage (CAR-M) therapies, and suggests future research perspectives, offering new opportunities in the development of novel clinical treatment strategies.

Impact of Triple Inhaler Therapy on COPD Patients with Non-Small Cell Lung Cancer After Radical Surgery: A Single-Centre Retrospective Analysis

Background: Chronic obstructive pulmonary disease (COPD) is among the most relevant comorbidity associated with lung cancer. The advent of innovative triple treatment approaches for COPD has significantly improved patients' quality of life and outcomes. Few data are available regarding the impact of triple inhaler therapy on patients featuring COPD and lung cancer. Methods: We retrospectively evaluated the impact of triple inhale bronchodilators in a cohort of 56 patients with treated COPD who underwent lung surgery for primary cancer. Results: Triple bronchodilation can help to relieve the symptoms of the disease and improve lung function, allowing people with lung cancer to reduce the risk of serious exacerbations and improve their quality of life. Conclusions: Within the limits of the study, it should be underlined that bronchodilators can effectively affect the outcome and performance status after thoracic surgery.

Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy

Immune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), absolute eosinophil count (AEC) for severe irAEs induced by PD-1 inhibitors. Data were collected from tumor patients treated with PD-1 inhibitors. NLR, PLR, and AEC data were obtained from both the previous and the current cycles of irAEs occurrence. A predictive model was developed using elastic net logistic regression Cutoff values were determined using Youden's Index. The predicted results were compared with actual data using Bayesian survival analysis. A total of 138 patients were included, of whom 47 experienced grade 1-2 irAEs and 18 experienced grade 3-5 irAEs. The predictive model identified optimal α and λ through 10-fold cross-validation. The Shapiro-Wilk test, Kruskal-Wallis test and logistic regression showed that only current cycle data were meaningful. The NLR was statistically significant in predicting irAEs in the previous cycle. Both NLR and AEC were significant predictors of irAEs in the current cycle. The model achieved an area under the ROC curve (AUC) of 0.783, with a sensitivity of 77.8% and a specificity of 80.8%. A probability ≥ 0.1345 predicted severe irAEs. The model comprising NLR, AEC, and sex may predict the irAEs classification in the current cycle, offering a near-term predictive advantage over baseline models and potentially extending the duration of immunotherapy for patients.

Prognostic Value of Neutrophil-to-Eosinophil Ratio (NER) in Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

The identification of reliable prognostic biomarkers is crucial for optimizing cancer treatment strategies, especially in the era of personalized medicine. This systematic review and meta-analysis evaluate the prognostic significance of the neutrophil-to-eosinophil ratio (NER) in various cancer types, with a focus on its association with overall survival (OS) and progression-free survival (PFS).

METHODS

We conducted a systematic literature search across PubMed, Scopus, and Web of Science databases for studies published up to 28 July 2024. We performed the meta-analyses with the generic inverse variance method with a random effects model and reported hazard ratios (HR) with 95% confidence intervals (CI).

RESULTS

The comprehensive literature search identified 10 studies comprising 2351 patients. Pooled analyses demonstrated that elevated pretreatment NER levels were significantly correlated with poorer OS (HR: 1.74, 95% CI: 1.28-2.36, p < 0.001) and PFS (HR: 1.53, 95% CI: 1.21-1.95, p < 0.001). Subgroup analyses confirmed a consistent adverse association between high NER and OS across various tumor types and geographic locations, although results from studies conducted in the Far East did not reach statistical significance.

CONCLUSIONS

This meta-analysis demonstrates that elevated NER is associated with poorer OS and PFS in cancer patients, suggesting its potential utility as a non-invasive prognostic marker. Further validation in large, prospective studies is warranted to establish NER's role in guiding personalized treatment strategies across diverse oncologic contexts.

Prognostic Biomarkers of Systemic Inflammation in Non-Small Cell Lung Cancer: A Narrative Review of Challenges and Opportunities

Non-small cell lung cancer (NSCLC) is a common malignancy and is associated with poor survival outcomes. Biomarkers of systemic inflammation derived from blood tests collected as part of routine clinical care offer prognostic information for patients with NSCLC that may assist clinical decision making. They are an attractive tool, as they are inexpensive, easily measured, and reproducible in a variety of healthcare settings. Despite the wealth of evidence available to support them, these inflammatory biomarkers are not yet routinely used in clinical practice. In this narrative review, the key inflammatory indices reported in the literature and their prognostic significance in NSCLC are described. Key challenges limiting their clinical application are highlighted, including the need to define the optimal biomarker of systemic inflammation, a lack of understanding of the systemic inflammatory landscape of NSCLC as a heterogenous disease, and the lack of clinical relevance in reported outcomes. These challenges may be overcome with standardised recording and reporting of inflammatory biomarkers, clinicopathological factors, and survival outcomes. This will require a collaborative approach, to which this field of research lends itself. This work may be aided by the rise of data-driven research, including the potential to utilise modern electronic patient records and advanced data-analysis techniques.

Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade

The paradigm of non-small cell lung cancer (NSCLC) treatment has been profoundly influenced by the development of immune checkpoint inhibitors (ICI), but the range of clinical responses observed among patients poses significant challenges. To date, analyses of tumor biopsies are the only parameter used to guide prognosis to ICI therapy. Tumor biopsies, however, are often difficult to obtain and tissue-based biomarkers are limited by intratumoral heterogeneity and temporal variability. In response, there has been a growing emphasis on the development of "liquid biopsy"‒ derived biomarkers, which offer a minimally invasive means to dynamically monitor the immune status of NSCLC patients either before and/or during the course of treatment. Here we review studies in which multiple blood-based biomarkers encompassing circulating soluble analytes, immune cell subsets, circulating tumor DNA, blood-based tumor mutational burden, and circulating tumor cells have shown promising associations with the clinical response of NSCLC patients to ICI therapy. These investigations have unveiled compelling correlations between the peripheral immune status of patients both before and during ICI therapy and patient outcomes, which include response rates, progression-free survival, and overall survival. There is need for rigorous validation and standardization of these blood-based assays for broader clinical application. Integration of multiple blood-based biomarkers into comprehensive panels or algorithms also has the potential to enhance predictive accuracy. Further research aimed at longitudinal monitoring of circulating biomarkers is also crucial to comprehend immune dynamics and resistance mechanisms and should be used alongside tissue-based methods that interrogate the tumor microenvironment to guide treatment decisions and may inform on the development of novel therapeutic strategies. The data reviewed here reinforce the opportunity to refine patient stratification, optimize treatments, and improve outcomes not only in NSCLC but also in the wider spectrum of solid tumors undergoing immunotherapy.

Immunophenotyping of peripheral blood in NSCLC patients discriminates responders to immune checkpoint inhibitors

PURPOSE

Immune checkpoint inhibitors (ICIs) dramatically changed the prognosis of patients with NSCLC. Unfortunately, a reliable predictive biomarker is still missing. Commonly used biomarkers, such as PD-L1, MSI, or TMB, are not quite accurate in predicting ICI efficacy.

METHODS

In this prospective observational cohort study, we investigated the predictive role of erythrocytes, thrombocytes, innate and adaptive immune cells, complement proteins (C3, C4), and cytokines from peripheral blood of 224 patients with stage III/IV NSCLC treated with ICI alone (pembrolizumab, nivolumab, and atezolizumab) or in combination (nivolumab + ipilimumab) with chemotherapy. These values were analyzed for associations with the response to the treatment and survival endpoints.

RESULTS

Higher baseline Tregs, MPV, hemoglobin, and lower monocyte levels were associated with favorable PFS and OS. Moreover, increased baseline basophils and lower levels of C3 predicted significantly improved PFS. The levels of the baseline immature granulocytes, C3, and monocytes were significantly associated with the occurrence of partial regression at the first restaging. Multiple studied parameters (n = 9) were related to PFS benefit at the time of first restaging as compared to baseline values. In addition, PFS nonbenefit group showed a decrease in lymphocyte count after three months of therapy. The OS benefit was associated with higher levels of lymphocytes, erythrocytes, hemoglobin, MCV, and MPV, and a lower value of NLR after three months of treatment.

CONCLUSION

Our work suggests that parameters from peripheral venous blood may be potential biomarkers in NSCLC patients on ICI. The baseline values of Tregs, C3, monocytes, and MPV are especially recommended for further investigation.

Pretreatment eosinophil counts as a predictive biomarker in non-small cell lung cancer patients treated with immune checkpoint inhibitors

BACKGROUND

The peripheral blood eosinophil count prior to treatment has potential as a predictive biomarker for a beneficial clinical response to cancer immunotherapies. Therefore, the present study investigated the impact of the eosinophil count on overall survival (OS) in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICI).

METHODS

We retrospectively reviewed all patients diagnosed with NSCLC and treated with ICI monotherapy between March 2016 and August 2021 at National Hospital Organization Kochi Hospital and Tokushima University.

RESULTS

A total of 166 patients were included. Fifty-five patients had an eosinophil count of less than 100 cells/μL (Eo < 100). Nighty-eight patients had an eosinophil count of 100 cells/μL or more, but less than 500 cells/μL (100 ≤ Eo < 500). Thirteen patients had an eosinophil count of 500 cells/μL or more (Eo ≥500). The median OS of all lung cancer patients was 476 days. The median OS of lung cancer patients with Eo <100, 100 ≤ Eo <500, and Eo ≥500 was 339, 667, and 143 days, respectively. A Kaplan-Meier univariate analysis showed a significant difference in OS between these three groups (p < 0.001). A Cox proportional regression analysis identified 100 ≤ Eo <500 (p = 0.04), ECOG PS score ≥ 2 (p = 0.02), tumor size ≥5 cm (p = 0.02), and PD-L1 ≥ 1% (p = 0.01) as independent predictors of OS.

CONCLUSION

OS was significantly longer in ICI-treated NSCLC patients with a pretreatment eosinophil count of 100 ≤ Eo <500 than in the other patients and, thus, has potential as a new predictive biomarker.

Exploring novel genetic and hematological predictors of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Introduction: The standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant chemoradiotherapy (nCRT). To select patients who would benefit the most from nCRT, there is a need for predictive biomarkers. The aim of this study was to evaluate the role of clinical, pathological, radiological, inflammation-related genetic, and hematological parameters in the prediction of post-nCRT response. Materials and methods: In silico analysis of published transcriptomics datasets was conducted to identify candidate genes, whose expression will be measured using quantitative Real Time PCR (qRT-PCR) in pretreatment formaline-fixed paraffin-embedded (FFPE) samples. In this study, 75 patients with LARC were prospectively included between June 2020-January 2022. Patients were assessed for tumor response in week 8 post-nCRT with pelvic MRI scan and rigid proctoscopy. For patients with a clinical complete response (cCR) and initially distant located tumor no immediate surgery was suggested ("watch and wait" approach). The response after surgery was assessed using histopathological tumor regression grading (TRG) categories from postoperative specimens by Mandard. Responders (R) were defined as patients with cCR without operative treatment, and those with TRG 1 and TRG 2 postoperative categories. Non-responders (NR) were patients classified as TRG 3-5. Results: Responders group comprised 35 patients (46.6%) and NR group 53.4% of patients. Analysis of published transcriptomics data identified genes that could predict response to treatment and their significance was assessed in our cohort by qRT-PCR. When comparison was made in the subgroup of patients who were operated (TRG1 vs. TRG4), the expression of IDO1 was significantly deregulated (p < 0.05). Among hematological parameters between R and NR a significant difference in the response was detected for neutrophil-to-monocyte ratio (NMR), initial basophil, eosinophil and monocyte counts (p < 0.01). According to MRI findings, non-responders more often presented with extramural vascular invasion (p < 0.05). Conclusion: Based on logistic regression model, factors associated with favorable response to nCRT were tumor morphology and hematological parameters which can be easily and routinely derived from initial laboratory results (NMR, eosinophil, basophil and monocyte counts) in a minimally invasive manner. Using various metrics, an aggregated score of the initial eosinophil, basophil, and monocyte counts demonstrated the best predictive performance.

Biomarkers for Immune Checkpoint Inhibitor Response in NSCLC: Current Developments and Applicability

Lung cancer has the highest mortality rate among all cancer types, resulting in over 1.8 million deaths annually. Immunotherapy utilizing immune checkpoint inhibitors (ICIs) has revolutionized the treatment of non-small cell lung cancer (NSCLC). ICIs, predominantly monoclonal antibodies, modulate co-stimulatory and co-inhibitory signals crucial for maintaining immune tolerance. Despite significant therapeutic advancements in NSCLC, patients still face challenges such as disease progression, recurrence, and high mortality rates. Therefore, there is a need for predictive biomarkers that can guide lung cancer treatment strategies. Currently, programmed death-ligand 1 (PD-L1) expression is the only established biomarker for predicting ICI response. However, its accuracy and robustness are not consistently reliable. This review provides an overview of potential biomarkers currently under development or in the validation stage that hold promise in improving the classification of responders and non-responders to ICI therapy in the near future.