The Relationship between Lymphocyte Subsets and the Prognosis and Genomic Features of Lung Cancer: A Retrospective Study

Peripheral T Lymphocyte Predicts the Prognosis of Gastric Cancer Patients Undergoing Radical Gastrectomy: A Multicenter Retrospective Cohort Study

Objective

The aim of this study was to investigate the predictive value of peripheral lymphocyte subsets for prognosis of gastric cancer (GC) patients following radical gastrectomy.

Methods

Consecutive GC patients received curative resection and examined peripheral lymphocyte subsets in Hunan Cancer Hospital were enrolled as training cohort (n=231), and those from Wuhan Union Hospital and Wuhan Tongji Hospital were included as external validation cohort (n=159). The optimal cutoff values of lymphocyte subsets for overall survival (OS) in training cohort were determined by X-tile. The independent predictive factors for OS were identified using univariate and multivariate Cox regression analyses. Furthermore, the predictive value of lymphocyte subsets were evaluated in validation cohort.

Results

The optimal cutoff value of T lymphocytes for OS was 0.84×109/L in the training cohort. Decreased T lymphocyte (<0.84×109/L) were identified as an independent predictor for unfavorable prognosis both in the training and validation cohorts (HR:2.835, 95% CI:1.580-5.087, P<0.001; HR:2.470, 95% CI:1.069-5.711, P=0.034). In the entire cohort, stratified analyses revealed that lower T lymphocyte negatively affected the oncological outcomes in patients with stage II/III disease. A synergistic influence was confirmed in those with decreased T lymphocyte and not received adjuvant chemotherapy (AC). Further analyses revealed that AC significantly prolonged OS in stage II/III patients with decreased T lymphocyte, but not in those with relatively higher T lymphocyte.

Conclusion

Peripheral T lymphocyte numbers was a reliable predictor for OS in GC patients undergoing radical gastrectomy. Additionally, T lymphocyte might serve as an indicator for efficacy of AC in stage II/III GC patients.

Peripheral blood lymphocyte subpopulations as predictive biomarkers for first-line programmed death 1 inhibitors efficacy in esophageal squamous cell carcinoma: A retrospective study

Esophageal cancer (EC) poses a significant global health burden, necessitating effective treatment strategies. Immune checkpoint inhibitors have emerged as a promising therapeutic option for EC, but the identification of predictive biomarkers remains crucial for optimizing patient outcomes. We conducted a retrospective analysis of medical records from advanced esophageal squamous cell carcinoma patients treated with first-line programmed death 1 inhibitors. Peripheral blood lymphocyte subpopulations were evaluated using flow cytometry, while hematological tests provided data on neutrophil, lymphocyte, and monocyte counts. Cox regression and logistic regression analyses were employed to explore the association between lymphocyte subpopulations, baseline characteristics, and progression-free survival (PFS). Among the 100 initially included patients, 70 met eligibility criteria. Multivariate Cox regression analysis revealed a significant association between high CD16+CD56+ lymphocyte proportions and longer PFS, independent of other clinical variables. Similarly, a high CD4+/CD8+ ratio was correlated with prolonged PFS. Kaplan-Meier survival curves supported these findings. Logistic regression analysis indicated no significant differences in the CD4+/CD8+ ratio and CD16+CD56+ lymphocytes concerning baseline characteristics, suggesting their potential as independent prognostic markers. Our study highlights the predictive value of peripheral blood CD16+CD56+ lymphocytes and the CD4+/CD8+ ratio for the efficacy of programmed death 1 inhibitors in advanced esophageal squamous cell carcinoma patients. These findings underscore the importance of peripheral blood biomarkers in guiding personalized immunotherapy strategies and improving outcomes for EC patients.

The peripheral CD4+ T cells predict efficacy in non-small cell lung cancer (NSCLC) patients with the anti-PD-1 treatment

Background

Programmed cell death protein 1 (PD-1) inhibitor therapy has become a routine treatment for advanced non-small cell lung cancer (NSCLC). However, only some NSCLC patients would benefit from anti-PD-1 therapy. We urgently need to identify biomarkers associated with clinical response to change treatment strategies promptly for patients who fail to benefit from anti-PD-1 treatment. This study was aimed to explore whether circulating CD4+ T cells and CD8+ T cells could be biomarkers for predicting anti-PD-1 efficacy.

Methods

In this study, 118 NSCLC patients who received anti-PD-1 therapy were enrolled. The percentages of circulating CD4+ T cells and CD8+ T cells before and after anti-PD-1 treatment were determined by flow cytometry. The programmed cell death ligand 1 (PD-L1) expression of tumor tissues was detected by immunocytochemistry. The anti-PD-1 treatment efficacy was assessed by immune response evaluation criteria in solid tumors (iRECIST).

Results

The percentage of CD4+ T cells and CD4+/CD8+ ratio in the peripheral blood (PB) was significantly elevated after anti-PD-1 treatment. In contrast, the percentage of CD8+ T cells in the PB was significantly decreased after anti-PD-1 treatment. Furthermore, we found that the percentages of CD4+ T cells and CD4+/CD8+ ratios considerably increased, and the percentages of CD8+ T cells significantly reduced in the effective group. On the contrary, the patients in the ineffective group showed no significant differences in the biomarkers. Multivariate logistic revealed that the percentage of CD4+ T cells at baseline was an independent predictor of anti-PD-1 treatment. The area under the curve (AUC) of the CD4+ T cells percentage was 0.7834 with a cut-off value of 28.53% (sensitivity =82.5%, specificity =66.23%).

Conclusions

The percentage of CD4+ T cells at baseline could predict anti-PD-1 efficacy in NSCLC patients.

Feature Engineering Assessment of Tumor Infiltrating Lymphocytes in Lung Adenocarcinoma

Tumor-Infiltrating Lymphocytes (TIL) are emerging as immunotherapy prognostic markers. Currently, TIL are assessed on hematoxylin and eosin (H&E)-stained slides of tumor tissue by pathologists. This approach is time-consuming, and subjected to inter-observer variability. The aim of this study is to propose a machine learning-based algorithm, called Feature Engineering TIL Assessment (FTA), for the automatic TIL assessment by using adenocarcinoma metadata (i.e., anamnestic, clinical and pathological data). The algorithm is an Elastic Net, tuned by Bayesian Optimization and validated by Leave-One-Subject-Out cross validation. Obtained coefficients were used for feature ranking. Results confirms the goodness of performance of FTA, with an overall Mean Absolute Error of 2.1%, Concordance Correlation Coefficient equal to 0.71 and difference in the Bland- Altman plot equal to -0.001. The obtained feature ranking revealed the key role of gender, as confirmed by the clinical literature. In conclusion, FTA is the first image-independent automatic TIL assessment procedure, having the potential to address challenges associated with inter-observer variability and the time-consuming nature of classical procedures.

Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma

BACKGROUND

Identification of micropapillary and solid subtypes components in small-sized (≤ 2 cm) lung adenocarcinoma plays a crucial role in determining optimal surgical procedures. This study aims to propose a straightforward prediction method utilizing preoperative available indicators.

METHODS

From January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance.

RESULTS

According to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893.

CONCLUSIONS

The model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

BACKGROUND

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

METHODS

A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.

RESULTS

In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).

CONCLUSIONS

The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.

Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function

The main focus of this study was to compare the predictive value of coagulation, fibrinolysis, thromboelastography, stress response, and immune function in predicting the incidence of deep venous thrombosis (DVT) in lung cancer (LC) patients undergoing thoracoscopic LC resection vs thoracotomy LC resection. To do that, a prospective, single-center, case-control study involving 460 LC patients was conducted. The risk indicators affecting patients with DVT after LC resection in the testing cohort were determined using logistic regression and receiver operator characteristic (ROC) analyses. One validation cohort was used to assess the risk prediction models. DVT incidence was higher in the thoracoscopic group (18.7%) than in the thoracotomy group (11.2%) in the testing cohort (χ ² = 4.116, P = 0.042). The final model to predict the incidence of DVT after thoracoscopic LC excision (1 day after surgery) was as follows: Logit(P) = 9.378 - 0.061(R-value) - 0.109(K value) + 0.374(α angle) + 0.403(MA) + 0.298(FIB) + 0.406(D-D) + 0.190(MDA) - 0.097(CD4⁺/CD8⁺). For thoracotomy LC resection, the final model (3 days after operation) was: Logit(P) = -2.463 - 0.026(R-value) - 0.143(K value) + 0.402(α angle) + 0.198(D-D) + 0.237(MDA) + 0.409(SOD). In the validation cohort, this risk prediction model continued to demonstrate good predictive performance. As a result, the predictive accuracy of postoperative DVT in patients who underwent thoracoscopic LC resection and thoracotomy LC resection was improved by risk prediction models.

The Effect of Microwave Ablation Combined with Anti-PD-1 Monoclonal Antibody on T Cell Subsets and Long-Term Prognosis in Patients Suffering from Non-Small-Cell Lung Cancer

Objective

This research is aimed at studying the effect of microwave ablation combined with the antiprogrammed death- (PD-) 1 monoclonal antibody on T cell subsets and long-term prognosis in patients suffering from non-small-cell lung cancer (NSCLC).

Methods

Employing the random number table technique, a total of 122 NSCLC patients who received treatment at our hospital between May 2015 and June 2019 were selected and assigned to the observation group and the control group, and each group comprised 61 patients (n = 61). While the control group received only anti-PD-1 monoclonal antibody treatment, the observation group received microwave ablation in combination with anti-PD-1 monoclonal antibody. The clinical efficacy was observed for both groups. The levels of T cell subsets (CD3+, CD4+, and CD8+), serum tumor markers (squamous cell carcinoma antigen (SCCA), cytokeratin Ig fragment (CYFRA21-1), and serum carcinoembryonic antigen (CEA)), nuclear factor kappa B (NF-κB), protease C (PKC), and mitogen-activated protein kinase (MAPK) mRNA expression between the two groups were compared. The frequency of adverse reactions was observed in both groups. The survival time of both the groups was recorded over the course of three years of follow-up. The Kaplan-Meier method was employed for analyzing the survival of both the control and the observation group.

Results

The response rate (RR) of the observation group (80.33%) was considerably greater in comparison to that of the control group (62.30%) (P < 0.05). Following treatment, the observation group's levels of CD3+, CD4+, CD8+, SCCA, CyFRA21-1, and CEA and the mRNA expressions of NF-κB, PKC, and MAPK were superior to those of the control group, with statistical significances (all P < 0.05). Between the two groups, there was no significant difference in the occurrence of adverse reactions (P > 0.05). The observation group had greater 1-, 2-, and 3-year survival rates (57.38%, 39.34%, and 29.51%) than the control group (32.79%, 18.03%, and 8.20%), with statistically significant differences (all P < 0.05).

Conclusion

Microwave ablation in combination with an anti-PD-1 monoclonal antibody could effectively improve the level of T cell subsets and serum tumor markers in NSCLC patients, resulting in a long-term prognosis of patients with good therapeutic effect and safety.

Association of lymphocyte subsets with efficacy and prognosis of immune checkpoint inhibitor therapy in advanced non-small cell lung carcinoma: a retrospective study

Background

Immune checkpoint inhibitors (ICIs) have achieved promising effects in patients with non-small cell lung cancer (NSCLC). However, not all patients with NSCLC benefit from immunotherapy. There is an urgent need to explore biomarkers that could predict the survival outcomes and therapeutic efficacy in advanced NSCLC patients treated with immunotherapy. In this study, we aimed to assess the changes in peripheral blood lymphocyte subsets and their association with the therapeutic efficacy and clinical prognosis of advanced NSCLC patients treated with immunotherapy.

Methods

A total of 276 patients with advanced NSCLC were enrolled. Peripheral blood lymphocyte subsets including CD4⁺ T cells, CD8⁺ T cells, CD4⁺/CD8⁺ ratio, NK cells, Tregs and B cells were collected before any treatment, before immunotherapy or chemotherapy, and after 4 cycles of immunotherapy or chemotherapy. T-test was used to analyze the factors influencing lymphocyte subsets and their changes before and after therapy. Logistic regression was used to plot ROC curves and analyze the relationship between lymphocyte subsets and therapeutic efficacy. Log-rank test and Cox regression model were used to evaluate the relationship between lymphocyte subsets and progression-free survival (PFS).

Results

Gender, distant metastasis, and EGFR mutation status are known to affect the proportion of peripheral blood lymphocyte subsets in patients with advanced NSCLC. The proportions of CD4⁺ T cells, CD8⁺ T cells, Tregs and B cells were found to decrease after chemotherapy as compared to the baseline. The proportion of CD4⁺ T cells, CD8⁺ T cells, CD4⁺/CD8⁺ ratio, NK cells and Tregs were higher after immunotherapy than after chemotherapy. Compared to the baseline, the effective group showed significant increase in the proportions of CD4⁺ T cells, CD4⁺/CD8⁺ ratio, NK cells and Tregs, and the number of CD8⁺ T cells was significantly lower in the peripheral blood after 4 cycles of immunotherapy. On the contrary, the ineffective group did not show any significant differences in the above parameters. Baseline CD4⁺ T cells and NK cells were independent predictors of immunotherapy efficacy and PFS. Baseline Tregs were independent predictor of immunotherapy efficacy.

Conclusion

Immune checkpoint inhibitors induced changes in the proportion of peripheral blood lymphocyte subsets in patients that responded well to immunotherapy. The levels of the different lymphocyte subsets could serve as valuable predictive biomarkers of efficacy and clinical prognosis for NSCLC patients treated with immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01951-x.