Predictive Risk Factors for Early Recurrence of Stage pIIIA-N2 Non-Small Cell Lung Cancer

Continuation of immunotherapy beyond progression is beneficial to the survival of advanced non-small-cell lung cancer

PURPOSE

To investigate the potential clinical importance of continuing immunotherapy beyond progression in patients with advanced non-small-cell lung cancer (aNSCLC).

METHODS

The data of patients with aNSCLC who experienced progressive disease after receiving first-line immunotherapy plus chemotherapy were collected from multiple centers for the period from January 1, 2018 to May 31, 2022. According to the second-line treatment, the patients were classified into two groups: the continuation of immunotherapy beyond progression (CIBP) group and the discontinuation of immunotherapy beyond progression (DIBP) group. The efficacy and safety of the treatment were compared between the groups.

RESULTS

Overall, data from 169 patients were analyzed; 93 patients were enrolled in the CIBP group and 76 patients were in the DIBP group. The median second-line progression-free survival was 5.5 months in the CIBP group, which for the DIBP group was 3.4 (p = 0.011). The median overall survival of the CIBP group was 13.3 months, whereas that of the DIBP group was 8.8 months (p = 0.031). The disease control rate of the CIBP group (79.57%) was observably higher than that of the DIBP group (64.47%; p = 0.028). Among patients who responded better (complete or partial response) to prior therapy, the median progression-free survival was 5.5 months and 3.3 months in the CIBP and DIBP groups respectively (p = 0.022), and the median overall survival was 14.8 months and 8.8 months in the CIBP and DIBP groups respectively (p = 0.046).

CONCLUSIONS

Continuing immunotherapy as a second-line treatment could be beneficial to the survival of patients with aNSCLC with disease progression beyond initial chemotherapy combined with immunotherapy.

Lymph Node Ratio Enhances Predictive Value for Treatment Outcomes in Patients with Non-Small Cell Lung Cancer Undergoing Surgery: A Retrospective Cohort Study

Purpose: To compare the prognostic value of lymph node ratio (LNR) and pN in patients with non-small cell lung cancer (NSCLC) undergoing surgery. Materials and methods: NSCLC patients were investigated between 2004 and 2015 from the Surveillance, Epidemiology, and End Results databases. The X-tile software was used to determine LNR cut-off values. Kaplan-Meier analysis was employed to assess cancer-specific survival (CSS) and overall survival (OS). Results: The identified cut-off values of LNR were 0.19 and 0.73. Median CSS for LNR1 (LNR < 0.19), LNR2 (0.19 ≤ LNR ≤ 0.73), and LNR3 (LNR > 0.73) were 71, 41, and 17 months. Both LNR2 (HR = 1.46, 95% CI: 1.36-1.57; P < 0.001) and LNR3 (HR = 2.85, 95% CI: 2.58-3.15; P < 0.001) demonstrated poorer median CSS compared to LNR1. Similarly, median OS for LNR1, LNR2, and LNR3 were 50, 35, and 16 months. LNR2 (HR = 1.36, 95% CI: 1.27-1.45; P < 0.001) and LNR3 (HR = 2.60, 95% CI: 2.37-2.85; P < 0.001) exhibited worse median OS compared to LNR1. A revised pN (r-pN) classification incorporating LNR and pN demonstrated superior penalized goodness-of-fit and discriminative ability in predicting CSS and OS compared to both LNR and pN. Conclusion: LNR outperformed pN in predicting CSS and OS in NSCLC patients undergoing surgery, potentially leading to more precise adjuvant treatment decisions.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier

Background

Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models.

Methods

A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence.

Results

Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation.

Conclusions

Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

Prognostic impact of lymph node spreading pattern in N2 NSCLC patients

BACKGROUND

One-third of non-small cell lung cancer (NSCLC) patients are diagnosed with locally advanced disease. Long-term survival in stage IIIA/B-N2 remains poor; this may also be due to lymph node spreading pattern. Therefore, we compared the overall survival of stage IIIA/B-N2 patients with superior mediastinal lymph nodes (SML) with infracarinal- or inferior mediastinal lymph nodes (IML) and with multilevel disease (MLD).

RESEARCH DESIGN AND METHODS

One-, three-and five-year survival rates were measured. Kaplan-Meier curves and Cox proportional hazards model assessed survival and were used to identify prognostic factors.

RESULTS

We reviewed data of stage IIIA/B-N2 patients (n = 129) who underwent surgery for NSCLC between 2012 and 2020. Patients with SML (n = 62) were compared to ILM (n = 37) and MLD (n = 30). SML patients showed significantly better one- (SML: 95.2% vs. IML: 78.6% vs. MLD: 69.4%, p = 0.03), three- (78.8% vs. 27.7 vs. 13.3%; p = <0.001) and five-year (61.1% vs. 17.1 vs. 3%; p < 0.001) survival rates, than IML and MLD patients. Kaplan-Meier curves showed prolonged overall survival for SML patients (log-rank SML, ILM, MLD p < 0.0001).

CONCLUSIONS

This study showed significantly better long-term survival of SML patients than IML and MLD patients. The long-term survival of ILM and MLD patients was equally poor.

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

Purpose

We developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC).

Approach

We retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort ( n = 101 ), validated in the testing cohort ( n = 34 ) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan-Meier analysis.

Results

The model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 ( p < 0.005 ) and 0.60 ( p = 0.008 ), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training ( p < 0.005 ) and the testing ( p = 0.03 ) cohorts.

Conclusions

Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.