Granzyme B (GZMB)-Positive Tumor-Infiltrating Lymphocytes in Lung Adenocarcinoma: Significance as a Prognostic Factor and Association with Immunosuppressive Proteins

BACKGROUND

Granzyme B (GZMB) is a serine protease produced by cytotoxic lymphocytes that reflects the activity of anti-tumor immune responses in tumor-infiltrating lymphocytes (TILs); however, the prognostic significance of GZMB+ TILs in lung adenocarcinoma is poorly understood.

METHODS

We analyzed 273 patients with pathological stage (pStage) I-IIIA lung adenocarcinoma who underwent surgery at Kyushu University from 2003 to 2012. We evaluated GZMB+ TIL counts by immunohistochemistry. We set the cut-off values at 12 cells/0.04 mm2 for GZMB+ TILs and divided the patients into GZMB-High (n = 171) and GZMB-Low (n = 102) groups. Then, we compared the clinicopathological characteristics of the two groups and clinical outcomes. Programmed cell death ligand-1 (PD-L1) and indoleamine 2,3-dioxygenase 1 (IDO1) expression in tumor cells was also evaluated, and combined prognostic analyses of GZMB+ TILs with PD-L1 or IDO1 were performed.

RESULTS

GZMB-Low was significantly associated with pStage II-III, PD-L1 positivity, and IDO1 positivity. Disease-free survival (DFS) and overall survival (OS) in the GZMB-Low group were significantly worse than in the GZMB-High group. In multivariable analysis, GZMB-Low was an independent prognostic factor for both DFS and OS. Furthermore, combined prognostic analyses of GZMB+ TILs with PD-L1 or IDO1 showed that GZMB-Low with high expression of these immunosuppressive proteins had the worst prognosis.

CONCLUSIONS

We analyzed GZMB+ TIL counts in lung adenocarcinoma and elucidated its prognostic significance and association with PD-L1 and IDO1. GZMB+ TIL counts might reflect the patient's immunity against cancer cells and could be a useful prognostic marker of lung adenocarcinoma.

Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

Impact of the pretreatment prognostic nutritional index on the survival after first-line immunotherapy in non-small-cell lung cancer patients

BACKGROUND

Immunotherapy has become a standard-of-care for patients with non-small-cell lung cancer (NSCLC). Although several biomarkers, such as programmed cell death-1, have been shown to be useful in selecting patients likely to benefit from immune checkpoint inhibitors (ICIs), more useful and reliable ones should be investigated. The prognostic nutritional index (PNI) is a marker of the immune and nutritional status of the host, and is derived from serum albumin level and peripheral lymphocyte count. Although several groups reported its prognostic role in patients with NSCLC receiving a single ICI, there exist no reports which have demonstrated its role in the first-line ICI combined with or without chemotherapy.

MATERIALS AND METHODS

Two-hundred and eighteen patients with NSCLC were included in the current study and received pembrolizumab alone or chemoimmunotherapy as the first-line therapy. Cutoff value of the pretreatment PNI was set as 42.17.

RESULTS

Among 218 patients, 123 (56.4%) had a high PNI (≥42.17), while 95 (43.6%) had a low PNI (<42.17). A significant association was observed between the PNI and both the progression-free survival (PFS; hazard ratio [HR] =  0.67, 95% confidence interval [CI]: 0.51-0.88, p =  0.0021) and overall survival (OS; HR = 0.46, 95% CI: 0.32-0.67, p < 0.0001) in the entire population, respectively. The multivariate analysis identified the pretreatment PNI as an independent prognosticator for the PFS (p =  0.0011) and OS (p  < 0.0001), and in patients receiving either pembrolizumab alone or chemoimmunotherapy, the pretreatment PNI remained an independent prognostic factor for the OS (p = 0.0270 and 0.0006, respectively).

CONCLUSION

The PNI might help clinicians appropriately identifying patients with better treatment outcomes when receiving first-line ICI therapy.

Predictive model of the prognosis in non-small cell lung cancer treated with first-line immunotherapy based on machine learning

e21125

Background: In recent years, there have been expectations for the application of artificial intelligence (AI) in the clinical practice of lung cancer. In the first-line setting, immune checkpoint inhibitors (ICIs) in combination with cytotoxic agents (CTx) have become a standard-of-care for patients with non-small cell lung cancer (NSCLC); however, no absolute biomarkers have been established for the prediction of the response to such therapy. In this study, we aimed to construct a new prognostic model by using AI in NSCLC patients treated with first-line ICI plus CTx. Methods: We retrospectively identified 218 NSCLC patients with advanced or postoperatively recurrent disease. They received first-line treatment with ICI alone or in combination with cytotoxic agents between January 2016 and October 2020. AI model using Gradient Boosting Decision Tree (GBDT) was used to identify predictors (explanatory variables) of the survivals (objective variables). A total of 53 factors were selected for the explanatory variables including clinical, hematological and pathological information. The objective variables were overall survival (OS) and progression-free survival (PFS). GBDT constructed the optimal predicting model, and calculated the contribution of each explanatory variable to the objective variables. Additionally, the importance of each feature was interpreted by SHapley Additive exPlanation (SHAP). Results: The median follow-up period was 14.4 months (range: 0.5-52.0) after the initiation of the therapy. The characteristics were shown below; the median age was 69 years old (range: 36-85); males and females were 165 (75.7%) and 53 (24.3%); patients with ICI alone or combination with cytotoxic anticancer agents were 91 (41.7%) or 127 (58.3%); the distributions of performance status (PS) were 107 (49.1%), 94 (43.1%), 13 (6.0%) with PS 0, 1 and 2; the Sq, non-Sq and others were found in 51 (23.4%), 165 (75.7%) and 2 (0.9%); 170 patients (78.0%) showed a PD-L1 expression of > 1% and 36 (16.5%) patients had no PD-L1 expression. The median PFS and OS were 10.2 and 21.8 months, respectively. The predicting optimal model for OS and PFS were constructed by GBDT, and the respective area under the curves (AUC) were 0.724 and 0.656. The explanatory variables indicating high contribution to OS were serum C-reactive protein (CRP), cytokeratin subunit 19 fragment (CYFRA), alkaline phosphatase (ALP), peripheral lymphocyte count and chloride ion level. As for PFS, the highest five contributions were CYFRA, CRP, immune-related adverse events, ALP and peripheral platelet count. Furthermore, all of these factors were explained their importance by SHAP. Neither PD-L1 expression nor PS were predictors of the survivals. Conclusions: Machine learning identified several novel factors possibly contributing to the prognoses in NSCLCs patients treated with the first-line immunotherapy.

Prognostic Impact of Albumin-bilirubin (ALBI) Grade on Non-small Lung Cell Carcinoma: A Propensity-score Matched Analysis

BACKGROUND/AIM

Albumin-bilirubin (ALBI) grade is an indicator of liver dysfunction and is useful for predicting postoperative prognosis of hepatocellular carcinomas. However, the significance of ALBI grade in non-small cell lung carcinoma (NSCLC) has not been elucidated.

PATIENTS AND METHODS

We analyzed 947 patients with pStage IA-IIIA NSCLC. We divided patients into ALBI grade 1 and grade 2/3 groups. We then analyzed the association of ABLI grade with clinicopathological characteristics and prognosis in NSCLC by using propensity-score matching.

RESULTS

ALBI grade 2/3 was significantly associated with older age, male sex, advanced pT status, and histological type. Even after propensity-score matching, ALBI grade 2/3 patients had significantly worse cancer-specific survival (CSS) than ALBI grade 1 patients (5-year CSS: 87.3% versus 92.8%; p=0.0247). In multivariate analysis, ALBI grade 2/3 was an independent predictor of CSS (HR=1.9; 95%CI=1.11-3.11; p=0.0177).

CONCLUSION

ALBI grade was an independent prognostic factor in surgically resected NSCLC.

Interleukin-38 promotes tumor growth through regulation of CD8+ tumor-infiltrating lymphocytes in lung cancer tumor microenvironment

BACKGROUND

Interleukin (IL)-38 was discovered in 2001 and is a member of the IL-1 family of cytokines. IL-38 shows anti-inflammatory activity in several inflammatory diseases. In lung adenocarcinoma, we previously demonstrated that high IL-38 expression in tumor cells was associated with poor prognosis. However, the role of IL-38 in the tumor microenvironment has not been clarified.

METHODS

IL-38-plasmid-transfected Lewis lung carcinoma cells (LLC-IL38) and empty vector-transfected LLC cells (LLC-vector) were established. Cell proliferation in vitro and tumor growth in vivo were examined, and immunohistochemical staining was used to assess tumor-infiltrating lymphocytes (TILs). A CD8⁺ lymphocyte depletion model was established to show the association between IL-38 and CD8⁺ lymphocytes. Moreover, we examined the association between IL-38 expression and CD8⁺ TILs in human samples, analyzing immunohistochemical staining in 226 patients with radically resected lung adenocarcinoma.

RESULTS

Tumor growth of LLC-IL38 in vivo was significantly increased compared with that of LLC-vector, although cell proliferation of LLC-IL38 in vitro was lower than that of LLC-vector. CD8⁺ TILs were significantly decreased in LLC-IL38 tumor compared with LLC-vector tumor. The difference in tumor growth between LLC-IL38 and LLC-vector became insignificant after depletion of CD8⁺ lymphocytes. In immunohistochemical staining in tissues from patients with lung adenocarcinoma, multivariate analysis showed high IL-38 expression was an independent negative predicter of high density of CD8⁺ TILs.

CONCLUSION

We demonstrated that high IL-38 expression in tumor cells was significantly associated with reduction of CD8⁺ TILs and tumor progression. These results suggest that IL-38 could be a therapeutic target for lung cancer.

Combined Evaluation of Tumor-Infiltrating CD8 + and FoxP3 + Lymphocytes Provides Accurate Prognosis in Stage IA Lung Adenocarcinoma

BACKGROUND

Immunotherapy has become a standard treatment option for non-small cell lung cancer (NSCLC), with the tumor microenvironment attracting significant attention. CD8 + and forkhead box protein P3 + (FoxP3 +) tumor-infiltrating lymphocytes (TILs) influence the tumor microenvironment, but the clinical significance of CD8 + and FoxP3 + TILs in stage IA lung adenocarcinoma (LAD) is poorly understood.

METHODS

We analyzed 203 patients with stage IA primary LAD who had undergone surgery at Kyushu University from January 2003 to December 2012. We evaluated CD8 + and FoxP3 + TILs by immunohistochemistry. We set the cutoff values at 50 cells/0.04 mm² for CD8 + TILs and 20 cells/0.04 mm² for FoxP3 + TILs, respectively. We divided the patients into four groups: CD8-Low/FoxP3-Low; CD8-High/FoxP3-Low; CD8-Low/FoxP3-High; and CD8-High/FoxP3-High. We compared clinical outcomes among them. Programmed cell death ligand-1 (PD-L1) expression by tumor cells was also evaluated as previously reported.

RESULTS

Respectively, 104 (51.2%), 46 (22.7%), 22 (10.8%), and 31 (15.3%) patients were classified as CD8-Low/FoxP3-Low, CD8-High/FoxP3-Low, CD8-Low/FoxP3-High, and CD8-High/FoxP3-High. Both disease-free survival (DFS) and overall survival (OS) were significantly worse in the CD8-Low/FoxP3-High group than the other groups (5-year DFS: 66.3% vs. 90.5%; P = 0.0007, 5-year OS: 90.9% vs. 97.0%; P = 0.0077). In the multivariate analysis, CD8-Low/FoxP3-High and PD-L1 expression were independent prognostic factors of DFS, and lymphatic invasion, surgical procedure, and PD-L1 expression were independent prognostic factors of OS.

CONCLUSIONS

CD8-Low/FoxP3-High was an independent prognostic factor of DFS (hazard ratio: 3.22; 95% confidence interval: 1.321-7.179; P = 0.0121) in stage IA LAD. Immunosuppressive conditions were associated with poor prognosis in stage IA LAD.

18F-FDG uptake in PET/CT is a potential predictive biomarker of response to anti-PD-1 antibody therapy in non-small cell lung cancer

To examine the association between ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake in positron emission tomography/computed tomography (PET/CT) and the response to anti-programmed cell death-1 (PD-1) monoclonal antibody therapy in non-small cell lung cancer (NSCLC) patients, 89 patients with advanced or recurrent NSCLC were retrospectively analysed. Maximum standardized uptake value (SUVmax) in ¹⁸F-FDG PET/CT and the response to anti-PD-1 antibodies were recorded. A cut-off value of SUVmax was determined by receiver operating characteristic curve analysis for patient stratification. Among the 89 patients evaluated, 24 were classified as responders (all partial response), and 65 as non-responders. The average SUVmax of the responders was 15.60 (range, 6.44–51.10), which was significantly higher than that of the non-responders (11.61; range, 2.13–32.75; P = 0.0168, Student’s t-test). The cut-off SUVmax value selected for stratification was 11.16 (sensitivity and specificity, 0.792 and 0.585, respectively). The response rate of patients with SUVmax value ≥ 11.16 (41.3% [19/46]) was significantly higher than that of patients with SUVmax < 11.16 (11.6% [5/43], P = 0.0012, Chi-squared test). The SUVmax in ¹⁸F-FDG PET/CT is a potential predictive marker of response to anti-PD-1 antibody therapy in NSCLC patients. Further prospective studies of large populations are necessary to validate these results.

Pulmonary metastasis presenting as a ground glass opacity-like lesion with a thin-walled cavity: A case report

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Most of pulmonary metastasis present as a well-defined solid and round nodule.

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This is the rare case of a pulmonary metastasis presenting as a ground glass opacity-like lesion with a thin-walled cavity of tongue cancer.

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The positron emission tomography/computed tomography examination was useful to show hilar lymph node metastasis.

Introduction

Most of pulmonary metastases present as well-defined solid and round nodules. Here we report a case of a pulmonary metastasis presenting as a ground glass opacity (GGO)-like lesion with a thin-walled cavity and lymph node metastasis of tongue cancer.

Case presentaion

A 22-year-old man was referred to our department for surgical diagnosis and treatment for a GGO-like pulmonary nodule with a thin-walled cavity in the right lower lobe. He had a history of surgical resection for tongue cancer. The size of the GGO-like lesion with a thin-walled cavity in the center gradually increased. A right lower lobectomy and hilar lymphadenectomy were performed. Postoperative pathology revealed the lesion as pulmonary metastasis and hilar lymph node metastasis of tongue cancer.

Discussion

Our case report of pulmonary metastasis of tongue cancer is rare from the viewpoint of pulmonary GGO-like lesions with a following thin-walled cavity and hilar lymph node metastasis. The positron emission tomography/computed tomography (PET/CT) examination was useful to show right hilar lymph node metastasis.

Conclusion

It is important to make a differential diagnosis of from the pulmonary nodule in case of a GGO-like lesion with a thin-walled cavity.