Evaluation of Eighth AJCC TNM Sage for Lung Cancer NSCLC: A Meta-analysis

Pulmonary function test-related prognostic models in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy

Background

This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy.

Methods

Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.

Results

Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.

Conclusion

The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.

Assessing the prognostic value of KRAS mutation combined with tumor size in stage I-II non-small cell lung cancer: a retrospective analysis

Background

KRAS mutation status is a well-established independent prognostic factor in advanced non-small cell lung cancer (NSCLC), yet its role in early-stage disease is unclear. Here, we investigate the prognostic value of combining survival data on KRAS mutation status and tumor size in stage I-II NSCLC.

Methods

We studied the combined impact of KRAS mutational status and tumor size on overall survival (OS) in patients with stage I-II NSCLC. We performed a retrospective study including 310 diagnosed patients with early (stage I-II) NSCLCs. All molecularly assessed patients diagnosed with stage I-II NSCLC between 2016-2018 in the Västra Götaland Region of western Sweden were screened in this multi-center retrospective study. The primary study outcome was overall survival.

Results

Out of 310 patients with stage I-II NSCLC, 37% harbored an activating mutation in the KRAS gene. Our study confirmed staging and tumor size as prognostic factors. However, KRAS mutational status was not found to impact OS and there was no difference in the risk of death when combining KRAS mutational status and primary tumor size.

Conclusions

In our patient cohort, KRAS mutations in combination with primary tumor size did not impact prognosis in stage I-II NSCLC.

Imbalance of Circulating Follicular Regulatory and Follicular Helper T Cell Subpopulations Is Associated with Disease Progression and Serum CYFRA 21-1 Levels in Patients with Non-small Cell Lung Cancer

OBJECTIVE

This study aimed to investigate the changes of follicular helper T (TFH) and follicular regulatory T (TFR) cell subpopulations in patients with non-small cell lung cancer (NSCLC) and their significance.

METHODS

Peripheral blood was collected from 58 NSCLC patients at different stages and 38 healthy controls. Flow cytometry was used to detect TFH cell subpopulation based on programmed death 1 (PD-1) and inducible co-stimulator (ICOS), and TFR cell subpopulation based on cluster determinant 45RA (CD45RA) and forkhead box protein P3 (FoxP3). The levels of interleukin-10 (IL-10), interleukin-17a (IL-17a), interleukin-21 (IL-21), and transforming growth factor-β (TGF-β) in the plasma were measured, and changes in circulating B cell subsets and plasma IgG levels were also analyzed. The correlation between serum cytokeratin fragment antigen 21-1 (CYFRA 21-1) levels and TFH, TFR, or B cell subpopulations was further explored.

RESULTS

The TFR/TFH ratio increased significantly in NSCLC patients. The CD45RA+FoxP3int TFR subsets were increased, with their proportions increasing in stages II to III and decreasing in stage IV. PD-1+ICOS+TFH cells showed a downward trend with increasing stages. Plasma IL-21 and TGF-β concentrations were increased in NSCLC patients compared with healthy controls. Plasmablasts, plasma IgG levels, and CD45RA+FoxP3int TFR cells showed similar trends. TFH numbers and plasmablasts were positively correlated with CYFRA 21-1 in stages I-III and negatively correlated with CYFRA 21-1 in stage IV.

CONCLUSION

Circulating TFH and TFR cell subpopulations and plasmablasts dynamically change in different stages of NSCLC, which is associated with serum CYFRA 21-1 levels and reflects disease progression.

Diagnostic Workup, Treatment Patterns, and Clinical Outcomes in Early-Stage IB-IIIA Non-Small-Cell Lung Cancer Patients in Denmark

Despite recent improvements in early-stage non-small-cell lung cancer (NSCLC), disease relapse remains challenging. Moreover, real-world evidence on long-term follow-up of disease-free survival (DFS) and recurrence patterns in a large, unselected cohort of early-stage NSCLC patients is lacking. This cohort study aimed to assess clinical characteristics, diagnostic workup, treatment, survival, and risk of disease relapse among early-stage NSCLC patients. Adult patients with stage IB, II, or IIIA NSCLC diagnosed and/or treated at Aarhus University Hospital in Denmark from January 2010 to December 2020 were included and followed-up until May 2021. Comprehensive clinical data were collected from electronic medical records of eligible patients and linked to Danish register data. The study population comprised 1341 early-stage NSCLC patients: 22%, 40%, and 38% were diagnosed with stage IB, II, and IIIA disease, respectively. In total, 42% of patients were tested for epidermal growth factor receptor (EGFR), of whom 10% were EGFR-mutation-positive (EGFRm+). Half of all patients received surgery, and nine percent of patients received stereotactic body radiation therapy (SBRT). Disease-free survival 5 years post-diagnosis was 49%, 42%, and 22% for stage IB, II, and stage IIIA patients, respectively. DFS improved over time both for patients treated with surgery and SBRT. However, disease relapse remained a challenge, with approximately 40% of stage IIIA having relapsed 3 years post-diagnosis. This study contributes important knowledge that puts clinical trials on new perioperative treatment modalities for early-stage NSCLC patients into perspective. Our findings cover an essential evidence gap on real-world DFS and recurrence dynamics, confirming that despite an improvement in DFS over time and across different treatment modalities, disease relapse remains a monumental challenge. Therefore, better treatment strategies are needed.

The 8th Edition TNM Stage Reclassification of T4 Non-Small Cell Lung Cancer: A Granular Examination of Short and Long-Term Outcomes

INTRODUCTION

Whilst the American Joint Committee on Cancer 7th edition (AJCC7) classified pT4 non-small-cell lung cancers (NSCLC) as those with extra-pulmonary invasion, the revised 8th edition (AJCC8) included tumors > 7cm regardless of extra-pleural spread. We examined perioperative and long-term outcomes of classical T4 definitions with patients whose tumors were greater than 7cm without extra-pulmonary invasion.

MATERIALS AND METHODS

A retrospective single center cohort study was performed. All consecutive patients with pT4 lesions between 2011 and 2018 were identified based on either the AJCC7 or AJCC8 classification. Clinicopathological variables were extracted and compared in a univariate manner. A multivariate Cox regression analysis was performed to assess factors associated with overall survival.

RESULTS

Forty patients were allocated to AJCC7 and 118 to AJCC8. Patients in the former were more likely to have positive lymph nodes, synchronous metastasis, multifocal disease and lymphovascular invasion. AJCC7 patients were more likely to undergo pneumonectomy despite significantly more being treated with neoadjuvant therapy. Ninety-day mortality was higher in the AJCC7 group. There was no difference in long-term overall survival. On multivariate analysis male gender, squamous cell histology and increasing tumor size were associated with an increased risk of death.

CONCLUSION

Although long-term outcomes were similar, the heterogenicity within the AJCC8 classification emphasizes the need to contextualize the perioperative outcomes for patients with pT4 NSCLC. These data are important for future iterations of the TNM classification in view of emerging neoadjuvant options for patients with cT4 operable NSCLC.

The prognostic value of TMB in early-stage non-small cell lung cancer: a systematic review and meta-analysis

Background

Tumor mutation burden (TMB) has been validated as a predictive biomarker for immunotherapy response and survival in numerous cancer types. Limited data is available on the inherent prognostic role of TMB in early-stage tumors.

Objective

To evaluate the prognostic role of TMB in early-stage, resected non-small cell lung cancer (NSCLC).

Design

Systematic review and meta-analysis of pertinent prospective and retrospective studies.

Data sources and methods

Publication search was performed in PubMed, Embase, Cochrane Library, and Web of Science databases. Based on the level of heterogeneity, a random- or fixed-effects model was used to calculate pooled effects of hazard ratio (HR) for overall survival (OS) and disease-free survival (DFS). The source of heterogeneity was investigated using sensitivity analysis, subgroup analysis, and publication bias assessment.

Results

Ten studies comprising 2520 patients were included in this analysis. There was no statistically significant difference in OS (HR, 1.18, 95% CI, 0.70, 1.33; p 0.53, I2 = 80%; phet < 0.0001) and DFS (HR, 1.18, 95% CI, 0.91, 1.52; p = 0.53, I2 = 75%; phet = 0.0001) between the high-TMB and low-TMB group. Subgroup analyses indicated that East Asian ethnicity, and TMB detected using whole exome sequencing, and studies with <100 patients had poor DFS in the high-TMB group.

Conclusion

The inherent prognostic role of TMB is limited in early-stage NSCLC. Ethnic differences in mutation burden must be considered while designing future trials on neoadjuvant immunotherapy. Further research in the harmonization and standardization of panel-based TMB is essential for its widespread clinical utility.Registration: CRD42023392846.

Diagnostic Performance of Dynamic Whole-Body Patlak [18F]FDG-PET/CT in Patients with Indeterminate Lung Lesions and Lymph Nodes

BACKGROUND

Static [¹⁸F]FDG-PET/CT is the imaging method of choice for the evaluation of indeterminate lung lesions and NSCLC staging; however, histological confirmation of PET-positive lesions is needed in most cases due to its limited specificity. Therefore, we aimed to evaluate the diagnostic performance of additional dynamic whole-body PET.

METHODS

A total of 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent static (60 min p.i.) and dynamic (0-60 min p.i.) whole-body [¹⁸F]FDG-PET/CT (300 MBq) using the multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modeling factors were calculated using a two-compartment linear Patlak model (FDG influx rate constant = Ki, metabolic rate = MR-FDG, distribution volume = DV-FDG) and compared to SUV using ROC analysis.

RESULTS

MR-FDG_mean provided the best discriminatory power between benign and malignant lung lesions with an AUC of 0.887. The AUC of DV-FDG_mean (0.818) and SUV_mean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDG_mean (0.987) and SUV_mean (0.993) were comparable. Moreover, the DV-FDG_mean in liver metastases was three times higher than in bone or lung metastases.

CONCLUSIONS

Metabolic rate quantification was shown to be a reliable method to detect malignant lung tumors, LNM, and distant metastases at least as accurately as the established SUV or dual-time-point PET scans.

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

The prognostic value of postoperative radiotherapy in right tumor for lung related death: based on SEER database and real-world data

Background

Postoperative radiotherapy (PORT) is a therapeutic strategy for patients with non-small cell lung cancer (NSCLC). Nevertheless, some studies suggesting PORT does not improve overall survival (OS) including Lung ART phase III trial. The role of PORT and high-risk groups need to be confirmed.

Methods

Patients from the Surveillance, Epidemiology, and End Results program (SEER) from 2004 to 2015 were eligible. Aged ≥18 years with stage IIIA-N2 NSCLC, accepted PORT or not were considered for the study. Cox regression analyses and multivariate competing risk model were performed. Propensity score matching (PSM) was conducted. Data from a single-center study in China were used for validation.

Results

In all patients with IIIA-N2 NSCLC, death from respiratory illness increased year by year, with right lung-related deaths accounting for the main proportion. In SEER database, PORT was detrimental for OS after PSM (hazard ratio [HR], 1.088; 95% CI, 1.088-1.174; P = 0.031), with a same trend for death from the lungs (HR, 1.13; 95% CI, 1.04-1.22; P = 0.005). Right tumor receiving PORT were prone to death from lung disease(HR, 1.14; 95% CI, 1.02-1.27; P = 0.018). In China single-center cohort, PORT was significantly correlated with deteriorated OS (HR 1.356; 95% CI 1.127-1.632; P <0.01), especially in the right laterality (HR 1.365; 95% CI 1.062-1.755; P = 0.015).

Conclusions

PORT was a risk factor for stage IIIA-N2 NSCLC patients, particularly with characters of right laterality, male sex, age ≥65 years, and advanced tumor stage. These patients are more likely to death from lung disease after PORT.

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability.

Fibroblast activation protein in the tumor microenvironment predicts outcomes of PD-1 blockade therapy in advanced non-small cell lung cancer

PURPOSE

The identification of robust predictive biomarkers of the response to programmed cell death-1 (PD-1) blockade remains a critical concern. Here, we investigated on fibroblast activation protein (FAP) as a microenvironment-derived biomarker of clinical outcomes of PD-1 blockade therapy, and the correlation between FAP expression and T cell infiltration in advanced non-small cell lung cancer (NSCLC).

METHODS

A total of 135 patients with advanced NSCLC who received PD-1 blockade therapy were retrospectively analyzed. The potential associations among FAP expression, CD3 + T cell and CD8 + T cell infiltration, and clinical outcomes of immunotherapy were validated by immunohistochemistry, bioinformatic analyses, and statistical measurements.

RESULTS

FAP was widely expressed in advanced NSCLC tissues. FAP was correlated with decreased density of CD8 + T cells (Spearman's rho  - 0.32, p < 0.001) and immunosuppressive tumor microenvironment (TME) status. No correlations were detected between FAP and PD-L1 expression or with the density of CD3 + T cells. The patients with higher expression of FAP showed worse response rate (16.4% vs. 38.7%, p < 0.001) and worse progression-free survival (HR = 2.56, 95% CI 1.69-3.87, p < 0.001). In addition, FAP contributed to shortened overall survival in subgroups of the patients with squamous cell lung cancer (p = 0.020), PD-1 blockade monotherapy (p = 0.017), and first-line therapy (p = 0.028).

CONCLUSION

FAP is a potential predictive biomarker of resistance to PD-1 blockade. Further investigation is warranted to identify a strategy for targeting FAP to alleviate the immunosuppressive TME and broaden the clinical effectiveness of PD-1 blockade therapy.

Immune checkpoint inhibitor (ICI)-based treatment beyond progression with prior immunotherapy in patients with stage IV non-small cell lung cancer: a retrospective study

Background

Although immune checkpoint inhibitors (ICIs) provide unprecedented survival improvement for patients with advanced non-small cell lung cancer (NSCLC), disease progression inevitably occurs. After ICIs failure, limited data exist on whether ICI-based treatment beyond progression (TBP) may be beneficial to advanced NSCLC. This retrospective study aimed to evaluate the efficacy of this treatment approach in advanced NSCLC and identify potential beneficial factors.

Methods

Patients with stage IV NSCLC who received ICI-based treatment after the failure of prior PD-1/PD-L1 inhibitor treatments (monotherapy or combination therapy) between January 2016 and July 2020 were enrolled. Their clinical characteristics and treatment procedures were collected, and the follow-up would be performed.

Results

A total of 204 patients were included. All patients had disease progression after prior immunotherapy, with 49.5% (101/204) of patients presenting with new metastasis lesions and the rest 50.5% (103/204) of patients' progression on originate lesions. Within the entire cohort, the median progression-free survival (PFS) and median overall survival (OS) of ICI-based TBP with prior immunotherapy were 5.0 months (95% CI: 4.5-5.5 months) and 15.7 months (95% CI: 14.7-16.8 months), respectively. The objective response rate (ORR) and disease control rate (DCR) were 9.3% and 74.0%, respectively. According to the multivariate analysis, ICI-based combination therapy [PFS: hazard ratio (HR), 0.48, 95% confidence interval (CI): 0.28-0.84, P=0.011] (OS: HR, 0.44, 95% CI: 0.23-0.85, P=0.014), not having targetable gene alterations (PFS: HR, 0.56, 95% CI: 0.40-0.79, P=0.001) (OS: HR, 0.57, 95% CI: 0.37-0.87, P=0.009), and good response to prior immunotherapy (PFS: HR, 0.36, 95% CI: 0.24-0.53, P<0.0001) (OS: HR, 0.31, 95% CI: 0.19-0.52, P<0.0001) were independently associated with improved PFS and OS. Moreover, disease progression due to appearances of new metastasis (OS: HR, 0.56, 95% CI: 0.37-0.84, P=0.005) was only associated with better OS.

Conclusions

While the ORR in patients with advanced NSCLC receiving ICI-based TBP with prior immunotherapy was limited, the DCR was relatively high in our study which is encouraging. ICI-based treatment strategy may be a reasonable option for patients who progressed from prior immunotherapy. Further prospective studies on larger sample size are warranted.