Immunological profiling for short-term predictive analysis in PD-1/PD-L1 therapy for lung cancer.

BACKGROUND: Immune checkpoint inhibitors, such as anti-programmed cell death-1 (PD-1) and PD-1 ligand-1 (PD-L1) antibodies, have achieved breakthrough results in improving long-term survival rates in lung cancer. Although high levels of PD-L1 expression and tumor mutational burden have emerged as pivotal biomarkers, not all patients derive lasting benefits, and resistance to immune checkpoint blockade remains a prevalent issue. Comprehending the immunological intricacies of lung cancer is crucial for uncovering the mechanisms that govern responses and resistance to immunomodulatory treatments. This study aimed to explore the potential of peripheral immune markers in predicting treatment efficiency among lung cancer patients undergoing PD-1/PD-L1 checkpoint inhibitors. METHODS: This study enrolled 71 lung cancer patients undergoing PD-1/PD-L1 inhibitor therapy and 20 healthy controls. Immune cell subsets (CD4 + T cells, CD8 + T cells, B cells, NK cells, and NKT cells), phenotypic analysis of T cells and B cells, and PMA/Ionomycin-stimulated lymphocyte function assay were conducted. RESULTS: Lung cancer patients exhibited significant alterations in immune cell subsets, notably an increased percentage of Treg cells. Post-treatment, there were substantial increases in absolute numbers of CD3 + T cells, CD8 + T cells, and NKT cells, along with heightened HLA-DR expression on CD3 + T and CD8 + T cells. Comparison between complete remission and non-complete remission (NCR) groups showed higher Treg cell percentages and HLA-DR + CD4 + T cells in the NCR group. CONCLUSION: The study findings suggest potential predictive roles for immune cell subsets and phenotypes, particularly Treg cells, HLA-DR + CD4 + T cells, and naïve CD4 + T cells, in evaluating short-term PD-1/PD-L1 therapy efficacy for lung cancer patients. These insights offer valuable prospects for personalized treatment strategies and underscore the importance of immune profiling in lung cancer immunotherapy.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. METHODS: Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. RESULTS: For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. CONCLUSION: This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.

OBJECTIVE: Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS: A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS: Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION: This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

HLA-DR Expression Level in CD8+ T Cells Correlates With the Severity of Children With Acute Infectious Mononucleosis.

Background: This study aimed to assess the host immune signatures associated with EBV infection and its clinical value in indicating the severity of children with acute infectious mononucleosis (IM). Methods: Twenty-eight pediatric patients with IM aged 3-8 years were enrolled. The immune phenotypes and cytokine secretion capability of T cells were detected. Results: The percentages and absolute numbers of CD3+ and CD8+ T cells were significantly increased in IM patients compared with HCs. The percentages of Naïve CD4+ and CD8+ T cells were decreased but with increased percentages of memory CD4+ and CD8+ T subsets. Our results showed the upregulation of active marker HLA-DR, TCR-αß, and inhibitory receptors PD-1, TIGIT in CD8+ T cells from IM patients, which suggested that effective cytotoxic T cells were highly against EBV infection. However, EBV exposure impaired the cytokine (IFN-γ, IL-2, and TNF-α) secretion capability of CD4+ and CD8+ T cells after stimulation with PMA/ionomycin in vitro. Multivariate analysis revealed that the percentage of HLA-DR+ CD8+ T cells was an independent prognostic marker for IM. The percentage of HLA-DR+ CD8+ T cells was significantly correlated with high viral load and abnormal liver function results. Conclusion: Robust expansion and upregulation of HLA-DR in CD8+ T cells, accompanied with impaired cytokine secretion, were typical characteristics of children with acute IM. The percentage of HLA-DR+ CD8+ T cells might be used as a prominent marker not only for the early diagnosis but also for indicating the severity of IM.

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection.

Background: Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue. Methods: Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4+ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation. Results: A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α+ or IL-2+ cells had superiority over that on IFN-γ+ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α+IL-2+ cells produced an AUC of 0.901 (95% CI, 0.833-0.969), with a sensitivity of 93.75% (95% CI, 79.85-98.27%) and specificity of 72.97% (95% CI, 57.02-84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α+IL-2+ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19-97.47%) sensitivity and 68.97% (95% CI, 50.77-82.73%) specificity. Conclusions: We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.

Combination of Blood Routine Examination and T-SPOT.TB Assay for Distinguishing Between Active Tuberculosis and Latent Tuberculosis Infection.

Background: Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. Methods: Between 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression. Results: Significant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively. Conclusions: The diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI.

Clinical value of tumor-associated antigens and autoantibody panel combination detection in the early diagnostic of lung cancer.

BACKGROUND: This study aimed to investigate the efficiency of combining tumor-associated antigens (TAAs) and autoantibodies in the diagnosis of lung cancer. METHODS: The serum levels of TAAs and seven autoantibodies (7-AABs) were detected from patients with lung cancer, benign lung disease and healthy controls. The performance of a new panel by combing TAAs and 7-AABs was evaluated for the early diagnosis of lung cancer. RESULTS: The positive rate of 7-AABs was higher than the single detection of antibody. The positive rate of the combined detection of 7-AABs in lung cancer group (30.2%) was significantly higher than that of healthy controls (16.8%), but had no statistical difference compared with that of benign lung disease group (20.8%). The positive rate of 7-AABs showed a tendency to increase in lung cancer patients with higher tumor-node-metastasis (TNM) stages. For the pathological subtype analysis, the positive rate of 7-AABs was higher in patients with squamous cell carcinoma and small cell lung cancer than that of adenocarcinoma. The levels of carcinoembryonic antigen (CEA) and cytokeratin 19 fragment 211 (CYFRA 21-1) were significantly higher than that of benign lung disease and healthy control groups. An optimal model was established (including 7-AABs, CEA and CYFRA21-1) to distinguish lung cancer from control groups. The performance of this model was superior than that of single markers, with a sensitivity of 52.26% and specificity of 77.46% in the training group. Further assessment was studied in another validation group, with a sensitivity of 44.02% and specificity of 83%. CONCLUSIONS: The diagnostic performance was enhanced by combining 7-AABs, CEA and CYFRA21-1, which has critical value for the screening and early detection of lung cancer.

Dynamic changes in peripheral blood lymphocyte subset counts and functions in patients with diffuse large B cell lymphoma during chemotherapy.

BACKGROUND: This study aimed to analyze the lymphocyte subsets, their activities and their dynamic changes during immunochemotherapy in patients newly diagnosed with diffuse large B cell lymphoma (DLBCL). METHODS: Patients with DLBCL (n = 33) were included in the present study. Their peripheral lymphocyte subsets, phenotypes and functions were detected using flow cytometry. The dynamic results of lymphocyte activities were available for 18 patients. RESULTS: Compared with healthy controls (HCs), the counts of CD3+, CD4+, and CD8+ T cells as well as those NK cells decreased in patients newly diagnosed with DLBCL, mainly attributed to patients with high risk of prognosis assessed by International Prognostic Index (IPI) score. Lymphocyte counts didn't present significant difference between high risk (IPI scores 3-5) and low risk patients (IPI scores 0-2), but CD4+ T cells and CD8+ T cells expressed higher levels of CD28 and HLA-DR, respectively, in patients with IPI score ranging from 3 to 5. Patients at high risk harbored higher percentage of regulatory T cells (Tregs), and their CD4+ and CD8+ T cells produced lower levels of IFN-γ, reflecting an impaired cellular immune response. The dynamic changes of lymphocyte numbers and functions during treatment were further investigated. Total counts of CD3+, CD4+, CD8+ T and NK cells progressively decreased because of the cytotoxicity of chemotherapy and then gradually recovered after six cycles treatment (rituximab combined with cyclophosphamide, doxorubicin, vincristine and prednisone, R-CHOP). The functions of CD4+ and CD8+ T cells recovered by the end of two cycles R-CHOP treatment, although NK cell function was not significantly affected throughout treatment. These results suggest that the counts and functions of lymphocytes are significantly decreased in patients with DLBCL, particularly those of CD4+ and CD8+ T cells. CONCLUSIONS: The absolute counts and functions of CD4+, CD8+ T cells, which were significantly lower in patients with DLBCL, gradually recovered after effective treatment. Therefore, combined detection of T cell counts and functions are critically important for administering effective personalized immunotherapy as well as for identifying new prognostic markers or DLBCL.

Diagnostic utility of pleural fluid T-SPOT and interferon-gamma for tuberculous pleurisy: A two-center prospective cohort study in China.

BACKGROUND: Early and accurate diagnosis of tuberculous pleurisy (TP) remains a challenge. The aim of the present study is to evaluate the performance of the pleural fluid (PF) T-SPOT and interferon-gamma (IFN-γ) for TP diagnosis in high tuberculosis (TB) burden settings. METHODS: In total, 214 and 217 subjects suspected of TP were prospectively enrolled in the Wuhan (training) cohort and Changchun (validation) cohort, respectively. All patients were examined with PF T-SPOT, IFN-γ, and other traditional tests simultaneously. RESULTS: The receiver-operating characteristic (ROC) curve analysis showed that the area under the curve (AUC), sensitivity, and specificity of TB-specific antigen (TBAg) spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in PF T-SPOT assay) for TP diagnosis were 0.972, 92.86%, and 92.16%, respectively, with a cutoff value of 35 in the Wuhan cohort. Meanwhile, when a threshold value of 95 ng/mL was set, the AUC, sensitivity, and specificity of IFN-γ to diagnose TP were 0.951, 86.61%, and 90.20%, respectively. Moreover, the diagnostic model based on the combination of TBAg SFC and IFN-γ showed an AUC of 0.983 for differentiating TP from non-TP, with 95.54% sensitivity and 95.10% specificity when a cutoff value of 0.32 was used in the Wuhan cohort. Excellent diagnostic accuracy was also observed in the Changchun cohort. When applying the cutoff value obtained from the Wuhan cohort, the AUC, sensitivity, and specificity of the diagnostic model were 0.995, 95.08%, and 97.89%, respectively. CONCLUSIONS: The performance of PF T-SPOT was comparable to IFN-γ in diagnosing TP. However, using the diagnostic model established by the combination of these two assays can achieve a more accurate diagnosis of TP.

Combination of mean spot sizes of ESAT-6 spot-forming cells and modified tuberculosis-specific antigen/phytohemagglutinin ratio of T-SPOT.TB assay in distinguishing between active tuberculosis and latent tuberculosis infection.

OBJECTIVES: Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. METHODS: The modified T-SPOT.TB assay was performed in 499 participants (243 ATB and 256 LTBI) and another 322 participants (162 ATB and 160 LTBI) who were diagnosed in Qiaokou (training) and Caidian (validation) cohort respectively. RESULTS: The mean spot sizes (MSS) of early secreted antigenic target 6 (ESAT-6) spot-forming cells (SFC) of T-SPOT.TB assay in ATB patients was significantly higher than that in LTBI individuals. 1.0 × 105 was the optimal number of cells added to phytohaemagglutinin (PHA) well for obtaining more accurate TB-specific antigen to phytohaemagglutinin (TBAg/PHA) ratio. The area under the curve of the diagnostic model by combination of ESAT-6 SFC MSS and modified TBAg/PHA ratio in distinguishing ATB from LTBI was 0.959 in training cohort, with a sensitivity of 90.12% and a specificity of 91.02% when a cutoff value of 0.46 was used. This diagnostic model showed similar performance in the validation cohort. The area under the curve, sensitivity, and specificity were 0.962, 93.21%, and 90.00%, respectively. Further flow cytometry analysis showed that ESAT-6 stimulation induced a significantly higher mean fluorescence intensity of IFN-γ+ cells in lymphocytes compared with culture filtrate protein 10 (CFP-10) stimulation. In contrast, CFP-10 stimulation induced a significantly higher percentage of IFN-γ+ cells in lymphocytes compared with ESAT-6 stimulation. CONCLUSIONS: The combination of the MSS of ESAT-6 SFC and the modified TBAg/PHA ratio of T-SPOT.TB assay showed great value in discriminating ATB from LTBI.

Evaluation of the automated indirect immunofluorescence test for anti-dsDNA antibodies.

BACKGROUND: Anti-dsDNA antibody is a specific antibody in systemic lupus erythematosus (SLE). Indirect immunofluorescence test (IIFT) is a highly specific method in detecting anti-dsDNA antibody. The application of automated system has gained better consistency than manual operation. This study detected anti-dsDNA antibodies using EUROPattern Computer-aided immunofluorescence microscopy (EPA), and evaluated the performance of the automated system. METHODS: The sera of 96 patients with suspected SLE and 102 control patients were examined using IIFT. The consistency between the EPA and manual reading was analyzed. RESULTS: Analysis of 198 samples showed that the overall consistency of the negative/positive results between the EPA and manual reading was 94.95%. Based on the manual reading results, the sensitivity and specificity of EPA were 95.70% and 94.29%, respectively. The analysis of 57 samples with non-specific fluorescence showed that the overall consistency of the negative/positive results was 96.49%. The analysis of the antibody titer of 89 positive samples showed that the consistency between the EPA and manual reading was 97.75%. CONCLUSION: EPA was consistent with the manual reading with regard to qualitative reading and antibody titer. With low-exposure function, EPA could read samples with non-specific fluorescence. EPA was superior to manual reading in automation and standardization.