Circulating activated immune cells as a potential blood biomarkers of non-small cell lung cancer occurrence and progression

Pan-cancer scRNA-seq analysis reveals immunological and diagnostic significance of the peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) reflect systemic immune response during cancer progression. However, a comprehensive understanding of the composition and function of PBMCs in cancer patients is lacking, and the potential of these features to assist cancer diagnosis is also unclear. Here, the compositional and status differences between cancer patients and healthy donors in PBMCs were investigated by single-cell RNA sequencing (scRNA-seq), involving 262,025 PBMCs from 68 cancer samples and 14 healthy samples. We observed an enhanced activation and differentiation of most immune subsets in cancer patients, along with reduction of naïve T cells, expansion of macrophages, impairment of NK cells and myeloid cells, as well as tumor promotion and immunosuppression. Based on characteristics including differential cell type abundances and/or hub genes identified from weight gene co-expression network analysis (WGCNA) modules of each major cell type, we applied logistic regression to construct cancer diagnosis models. Furthermore, we found that the above models can distinguish cancer patients and healthy donors with high sensitivity. Our study provided new insights into using the features of PBMCs in non-invasive cancer diagnosis.

Prognostic and predictive significance of circulating biomarkers in patients with advanced upper gastrointestinal cancer undergoing systemic chemotherapy

Objective

The prognosis of patients with advanced cancers of the upper gastrointestinal (UGI) tract is poor. Systemic chemotherapy forms the basis for their treatment, with limited efficacy. Biomarkers have been introduced into clinical practice for cancer management. This study aimed to investigate the predictive and prognostic values of circulating biomarkers in patients with advanced esophageal and gastric cancers receiving chemotherapy.

Design

Overall, 92 patients with advanced esophageal squamous cell carcinoma (ESCC; n = 38) and gastric adenocarcinoma (GAC; n = 54) were enrolled. We analyzed the association of circulating lymphocyte subsets, inflammatory markers, and blood cell counts with treatment efficacy and patient survival.

Results

Significant differences were identified in peripheral blood parameters between the groups with different clinicopathological features. Hemoglobin (Hb, p = 0.014), eosinophil counts (p = 0.028), CD4⁺CD28⁺T/CD4⁺T percentage (p = 0.049), CD8⁺CD38⁺T/CD8⁺T percentage (p = 0.044), memory CD4⁺T (p = 0.007), and CD4⁺CD28⁺T (p = 0.007) were determined as predictors for achieving non-PD (progression disease) in the ESCC cohort. High levels of eosinophils (p = 0.030) and memory CD4⁺T cells (p = 0.026) and high eosinophil-to-lymphocyte ratio (ELR, p = 0.013) were predictors of non-PD in patients with GAC. The combined detection models exhibited good ability to distinguish between partial response (PR)/non-PR and PD/non-PD in patients with ESCC and GAC, respectively. Using the multivariate Cox model, the Eastern Cooperative Oncology Group (ECOG) score status (hazard ratio [HR]: 4.818, 95% confidence intervals [CI]: 2.076-11.184, p < 0.001) and eosinophil count (HR: 0.276, 95% CI: 0.120-0.636, p = 0.003) were independent prognostic factors of progression-free survival (PFS) in patients with ESCC. Metastatic sites (HR: 2.092, 95% CI: 1.307-3.351, p = 0.002) and eosinophil-to-lymphocyte ratio (ELR; HR: 0.379, 95% CI: 0.161-0.893, p = 0.027) were independent prognostic factors for overall survival (OS) in patients with ESCC. Differentiation (HR: 0.041, 95% CI: 0.200-0.803, p = 0.010), memory CD4⁺T (HR: 0.304, 95% CI: 0.137-0.675, p = 0.003), NK cells (HR: 2.302, 95% CI: 1.044-3.953, p = 0.037), and C-reactive protein-to-lymphocyte ratio (CLR; HR: 2.070, 95% CI: 1.024-4.186, p = 0.043) were independent prognostic factors for PFS in patients with GAC. Total lymphocyte counts (HR: 0.260, 95% CI: 0.086-0.783, p = 0.017), CD8⁺T (HR: 0.405, 95% CI: 0.165-0.997, p = 0.049), NK cells (HR: 3.395, 95% CI: 1.592-7.238, p = 0.002), and monocyte-to-lymphocyte ratio (MLR; HR: 3.076, 95% CI: 1.488-6.360, p = 0.002) were identified as independent prognostic factors associated with OS of GAC.

Conclusion

Lymphocyte subsets, blood cell counts, and inflammatory parameters may predict the chemotherapeutic response and prognosis in ESCC and GAC. A combination of these markers can be used to stratify patients into risk groups, which could improve treatment strategies.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Peripheral changes in T cells predict efficacy of anti-PD-1 immunotherapy in non-small cell lung cancer

The application of programmed cell death protein 1 (PD-1) antibodies has brought great benefits to non-small cell lung cancer (NSCLC) patients. Nevertheless, not all patients respond to anti-PD-1 immunotherapy. This study aimed to find response markers to predict efficacy of anti-PD-1 immunotherapy in NSCLC patients. 80 patients with NSCLC who would accept anti-PD-1 immunotherapy were recruited, and peripheral blood was obtained before and after treatment. Flow cytometry was used to detect proportions of circulating cell subsets and expression of co-stimulatory molecules, co-inhibitory molecules and cytokines in T cells from pre- and post-treatment patients. Results showed that proportions of CD4⁺ and CD8⁺ T cells, NK, γδT and mucosal-associated invariant T (MAIT) cells were higher and regulatory T cells (Tregs) were lower in responders (n = 50) after treatment but no obvious difference was found in non-responders (n = 30). After treatment, responders showed an increase in the frequency of co-stimulatory and co-inhibitory molecules, as well as the production of cytokines in T cells. This study indicates that monitoring the alterations of immune markers in circulating cells from NSCLC patients may be helpful to discriminate responders and non-responders, which provides a potential novel way to assess efficacy of anti-PD-1 immunotherapy.

A novel nomogram to predict lymph node metastasis in cT1 non-small-cell lung cancer based on PET/CT and peripheral blood cell parameters

BACKGROUND

Accurately evaluating the lymph node status preoperatively is critical in determining the appropriate treatment plan for non-small-cell lung cancer (NSCLC) patients. This study aimed to construct a novel nomogram to predict the probability of lymph node metastasis in clinical T1 stage patients based on non-invasive and easily accessible indicators.

METHODS

From October 2019 to June 2022, the data of 84 consecutive cT1 NSCLC patients who had undergone PET/CT examination within 30 days before surgery were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify the risk factors of lymph node metastasis. A nomogram based on these predictors was constructed. The area under the receiver operating characteristic (ROC) curve and the calibration curve was used for assessment. Besides, the model was confirmed by bootstrap resampling.

RESULTS

Four predictors (tumor SUVmax value, lymph node SUVmax value, consolidation tumor ratio and platelet to lymphocyte ratio) were identified and entered into the nomogram. The model indicated certain discrimination, with an area under ROC curve of 0.921(95%CI 0.866-0.977). The calibration curve showed good concordance between the predicted and actual possibility of lymph node metastasis.

CONCLUSIONS

This nomogram was practical and effective in predicting lymph node metastasis for patients with cT1 NSCLC. It could provide treatment recommendations to clinicians.

An immune risk score predicts progression-free survival of melanoma patients in South China receiving anti-PD-1 inhibitor therapy-a retrospective cohort study examining 66 circulating immune cell subsets

Introduction

Immune checkpoint blockade inhibitor (ICI) therapy offers significant survival benefits for malignant melanoma. However, some patients were observed to be in disease progression after the first few treatment cycles. As such, it is urgent to find convenient and accessible indicators that assess whether patients can benefit from ICI therapy.

Methods

In the training cohort, flow cytometry was used to determine the absolute values of 66 immune cell subsets in the peripheral blood of melanoma patients (n=29) before treatment with anti-PD-1 inhibitors. The least absolute shrinkage and selection operator (LASSO) Cox regression model was followed for the efficacy of each subset in predicting progression-free survival. Then we validated the performance of the selected model in validation cohorts (n=20), and developed a nomogram for clinical use.

Results

A prognostic immune risk score composed of CD1c⁺ dendritic cells and three subsets of T cells (CD8⁺CD28⁺, CD3⁺TCRab⁺HLA-DR⁺, CD3⁺TCRgd⁺HLA-DR⁺) with a higher prognostic power than individual features (AUC = 0.825). Using this model, patients in the training cohort were divided into high- and low-risk groups with significant differences in mean progression-free survival (3.6 vs. 12.3 months), including disease control rate (41.2% vs. 91.7%), and objective response rate (17.6% vs. 41.6%). Integrating four-immune cell-subset based classifiers and three clinicopathologic risk factors can help to predict which patients might benefit from anti-PD-1 antibody inhibitors and remind potential non-responders to pursue effective treatment options in a timely way.

Conclusions

The prognostic immune risk score including the innate immune and adaptive immune cell populations could provide an accurate prediction efficacy in malignant melanoma patients with ICI therapy.

Development of a Molecular Blood-Based Immune Signature Classifier as Biomarker for Risks Assessment in Lung Cancer Screening

BACKGROUND

Low-dose CT (LDCT) screening trials have shown that lung cancer early detection saves lives. However, a better stratification of the screening population is still needed. In this respect, we generated and prospectively validated a plasma miRNA signature classifier (MSC) able to categorize screening participants according to lung cancer risk. Here, we aimed to deeply characterize the peripheral immune profile and develop a diagnostic immune signature classifier to further implement blood testing in lung cancer screening.

METHODS

Peripheral blood mononuclear cell (PBMC) samples collected from 20 patients with LDCT-detected lung cancer and 20 matched cancer-free screening volunteers were analyzed by flow cytometry using multiplex panels characterizing both lymphoid and myeloid immune subsets. Data were validated in PBMC from 40 patients with lung cancer and 40 matched controls and in a lung cancer specificity set including 27 subjects with suspicious lung nodules. A qPCR-based gene expression signature was generated resembling selected immune subsets.

RESULTS

Monocytic myeloid-derived suppressor cell (MDSC), polymorphonuclear MDSC, intermediate monocytes and CD8+PD-1+ T cells distinguished patients with lung cancer from controls with AUCs values of 0.94/0.72/0.88 in the training, validation, and lung cancer specificity set, respectively. AUCs raised up to 1.00/0.84/0.92 in subgroup analysis considering only MSC-negative subjects. A 14-immune genes expression signature distinguished patients from controls with AUC values of 0.76 in the validation set and 0.83 in MSC-negative subjects.

CONCLUSIONS

An immune-based classifier can enhance the accuracy of blood testing, thus supporting the contribution of systemic immunity to lung carcinogenesis.

IMPACT

Implementing LDCT screening trials with minimally invasive blood tests could help reduce unnecessary procedures and optimize cost-effectiveness.

DNA methylation-derived systemic inflammation indices and their association with oropharyngeal cancer risk and survival

BACKGROUND

Head and neck squamous cell carcinomas are associated with systemic inflammation (SI). We evaluated whether DNA methylation-derived SI (mdSI) indices are associated with oropharyngeal cancer risk and survival.

METHODS

Ninety-four oropharyngeal squamous cell carcinoma (OPSCC) cases and 57 controls with DNA methylation data were included. Logistic regression analysis and survival analysis were performed to test the association of mdSI indices with OPSCC risk and survival.

RESULTS

Higher methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) was associated with increased risk of OPSCC (OR = 1.21, 95%CI: 1.11-1.40) while no association was found with methylation-derived lymphocyte-to-monocyte ratio (mdLMR). For 5-year overall survival, higher mdLMR was significantly associated with decreased risk of death (HR = 0.25, 95%CI: 0.10-0.64) while the converse was observed for mdNLR (HR = 2.48, 95%CI: 1.04-5.92).

CONCLUSION

We observed an association between mdSI indices and OPSCC risk and 5-year overall survival. It is possible to use mdLMR as an independent prognostic factor for OPSCC.