Validation of the Eighth Edition TNM Lung Cancer Staging System

Implementing circulating tumor DNA as a prognostic biomarker in resectable non-small cell lung cancer

Systemic treatment of resectable non-small cell lung cancer (NSCLC) is evolving with emerging neoadjuvant, perioperative, and adjuvant immunotherapy approaches. Circulating tumor DNA (ctDNA) detection at clinical diagnosis, during neoadjuvant therapy, or after resection may discern high-risk patients who might benefit from therapy escalation or switch. This Review summarizes translational implications of data supporting ctDNA-based risk determination in NSCLC and outstanding questions regarding ctDNA validity/utility as a prognostic biomarker. We discuss emerging ctDNA capabilities to refine clinical tumor-node-metastasis (TNM) staging in lung adenocarcinoma, ctDNA dynamics during neoadjuvant therapy for identifying patients deriving suboptimal benefit, and postoperative molecular residual disease (MRD) detection to escalate systemic therapy. Considering differential relapse characteristics in landmark MRD-negative/MRD-positive patients, we propose how ctDNA might integrate with pathological response data for optimal postoperative risk stratification.

Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study

Objective

The purpose of this study was to construct a nomogram model based on the general characteristics, histological features, pathological and immunohistochemical results, and inflammatory and nutritional indicators of patients so as to effectively predict the overall survival (OS) and progression-free survival (PFS) of patients with non-small cell lung cancer (NSCLC) after surgery.

Methods

Patients with NSCLC who received surgical treatment in our hospital from January 2017 to June 2021 were selected as the study subjects. The predictors of OS and PFS were evaluated by univariate and multivariable Cox regression analysis using the Cox proportional risk model. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling for 1 000 times) was used to internally verify the nomogram model, and C-index was used to represent the prediction performance of the nomogram model. The calibration graph method was used to visually represent its prediction compliance, and decision curve analysis (DCA) was used to evaluate the application value of the model.

Results

Univariate and multivariate analyses were used to identify independent prognostic factors and to construct a nomogram of postoperative survival and disease progression in operable NSCLC patients, with C-index values of 0.927 (907-0.947) and 0.944 (0.922-0.966), respectively. The results showed that the model had high predictive performance. Calibration curves for 1-year, 2-year, and 3-year OS and PFS show a high degree of agreement between the predicted probability and the actual observed probability. In addition, the results of the DCA curve show that the model has good clinical application value.

Conclusion

We established a predictive model of survival prognosis and disease progression in patients with non-small cell lung cancer after surgery, which has good predictive performance and can guide clinicians to make the best clinical decision.

Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma

BACKGROUND

The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement.

PURPOSE

To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD.

METHODS

We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA).

RESULTS

In total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p < 0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C-index: 0.812 ± 0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C-index: 0.743 ± 0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C-index: 0.765 ± 0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS.

CONCLUSION

The combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early-stage LUAD.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BACKGROUND

Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy.

METHODS

We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3.

FINDINGS

A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001).

INTERPRETATION

IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

PURPOSE

This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

METHODS

Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

RESULTS

We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

CONCLUSION

We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

An Updated Review of Management of Resectable Stage III NSCLC in the Era of Neoadjuvant Immunotherapy

Immune-checkpoint inhibitors (ICIs) have an established role in the treatment of locally advanced and metastatic non-small cell lung cancer (NSCLC). ICIs have now entered the paradigm of early-stage NSCLC. The recent evidence shows that the addition of ICI to neoadjuvant chemotherapy improves the pathological complete response (pCR) rate and survival rate in early-stage resectable NSCLC and is now a standard of care option in this setting. In this regard, stage III NSCLC merits special consideration, as it is heterogenous and requires a multidisciplinary approach to management. As the neoadjuvant approach is being adopted widely, new challenges have emerged and the boundaries for resectability are being re-examined. Consequently, it is ever more important to carefully individualize the treatment strategy for each patient with resectable stage III NSCLC. In this review, we discuss the recent literature in this field with particular focus on evolving definitions of resectability, T4 disease, N2 disease (single and multi-station), and nodal downstaging. We also highlight the controversy around adjuvant treatment in this setting and discuss the selection of patients for adjuvant treatment, options of salvage, and next line treatment in cases of progression on/after neoadjuvant treatment or after R2 resection. We will conclude with a brief discussion of predictive biomarkers, predictive models, ongoing studies, and directions for future research in this space.

Combined detection of serum IL-6 and CEA contributes to the diagnosis of lung adenocarcinoma in situ

Background

Effective discrimination of lung adenocarcinoma (LUAD) in situ (AIS) from benign pulmonary nodules (BPN) is critical for the early diagnosis of AIS. Our pilot study in a small cohort of 90 serum samples has shown that serum interleukin 6 (IL-6) detection can distinguish AIS from BPN and health controls (HC). In this study, we intend to comprehensively define the diagnostic value of individual and combined detection of serum IL-6 related to the traditional tumor markers carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA21-1) for AIS.

Methods

The diagnostic performance of serum IL-6 along with CEA and CYFRA21-1 were evaluated in a large cohort of 300 serum samples by a chemiluminescence immunoassay and an electrochemiluminescence immunoassay. A training set comprised of 65 AIS, 65 BPN, and 65 HC samples was used to develop the predictive model for AIS. Data obtained from an independent validation set was applied to evaluate and validate the predictive model.

Results

In the training set, the levels of serum IL-6 and CEA in the AIS group were significantly higher than those in the BPN/HC group (P < 0.05). There was no significant difference in serum CYFRA21-1 levels between the AIS group and the BPN/HC group (P> 0.05). Serum IL-6 and CEA levels for AIS patients showed an area under the curve (AUC) of 0.622 with 23.1% sensitivity at 90.7% specificity, and an AUC of 0.672 with 24.6% sensitivity at 97.6% specificity, respectively. The combination of serum IL-6 and CEA presented an AUC of 0.739, with 60.0% sensitivity at 95.4% specificity. The combination of serum IL-6 and CEA showed an AUC of 0.767 for AIS patients, with 57.1% sensitivity at 91.4% specificity in the validation set.

Conclusions

IL-6 shows potential as a prospective serum biomarker for the diagnosis of AIS, and the combination of serum IL-6 with CEA may contribute to increased accuracy in AIS diagnosis. However, it is worth noting that further research is still necessary to validate and optimize the diagnostic efficacy of these biomarkers and to address potential sensitivity limitations.

Novel metabolic biomarker for early detection and diagnosis to the patients with gastric cardia adenocarcinoma

BACKGROUND

Gastric cardia adenocarcinoma (GCA) is classified as Siewert type II adenocarcinoma at the esophagogastric junction in Western countries. The majority of GCA patients do not exhibit early warning symptoms, leading to over 90% of diagnoses at an advanced stage, resulting in a grim prognosis, with less than a 20% 5-year survival rate.

METHOD

Metabolic features of 276 GCA and 588 healthy controls were characterized through a widely-targeted metabolomics by UPLC-MS/MS analysis. This study encompasses a joint pathway analysis utilizing identified metabolites, survival analysis in both early and advanced stages, as well as high and negative and low expression of HER2 immunohistochemistry staining. Machine learning techniques and Cox regression models were employed to construct a diagnostic panel.

RESULTS

A total of 25 differential metabolites were consistently identified in both discovery and validation sets based on criteria of p < 0.05, (VIP) ≥ 1, and FC ≥ 2 or FC ≤ 0.5. Early-stage GCA patients exhibited a more favorable prognosis compared to those in advanced stages. HER2 overexpression was associated with a more positive outcome compared to the negative and low expression groups. Metabolite panel demonstrated a robust diagnostic performance with AUC of 0.869 in discovery set and 0.900 in validation set.

CONCLUSIONS

A total of 25 common and stable differential metabolites may hold promise as liquid non-invasive indicators for GCA diagnosis. HER2 may function as a tumor suppressor gene in GCA, as its overexpression is associated with improved survival. The downregulation of bile acid metabolism in GCA may offer valuable theoretical insights and innovative approaches for precision-targeted treatments in GCA patients.

A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study

OBJECTIVES

Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.

METHODS

Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups.

RESULTS

In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001).

CONCLUSIONS

The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Usefulness of baseline immature reticulocyte fraction to mature reticulocyte fraction ratio (IMR) as A prognostic predictor for patients with small cell lung cancer

Background

Small cell lung cancer (SCLC) has a strong invasive ability and a high degree of malignancy, so accurate prognosis prediction is crucial for making the most favorable treatment decision.Unfortunately, there is a scarcity of prognostic indicators specific to SCLC. Reticulocyte levels in blood parameters have been linked to the prognosis of various malignancies. Given SCLC's aggressive characteristics, identifying reliable prognostic markers, such as reticulocyte counts, becomes pivotal in enhancing prognostic accuracy and guiding effective therapeutic strategies.

Objective

This study aimed to evaluate the predictive power of the immature reticulocyte fraction (IRF) to mature reticulocyte fraction (MRF) ratio (IMR) for survival outcomes in patients with SCLC.

Materials and methods

A retrospective analysis was conducted on 192 patients with small cell lung cancer (SCLC). The median values of various prognostic indicators, such as IMR, IRF, MRF, reticulocyte count (RET), SII (systemic immune-inflammatory index), were utilized as cutoff points, categorizing patients into high and low groups. The Kaplan-Meier method, univariate, multivariate analyses Cox regression, and C-index were used to analyze the prognostic factors for overall survival (OS).

Results

In our cohort, 138 (71.9 %) were male, 119 (62 %) were smokers, and 82 (57.3 %) were older than 60 years old. The median survival time was 18.15 months.Higher mortality was observed in the high IMR and high IRF groups, while the high MRF group exhibited lower mortality. At the same time, mortality was lower in the high MRF group. Univariate analysis showed that smoking history (P = 0.006), tumor stage (P = 0.002), chemotherapy cycle (P = 0.014), IMR (P = 0.01), and many other factors significantly affected the prognosis of SCLC. Multivariate analysis demonstrated that elevated IMR was an independent adverse predictor of OS (P = 0.039, HR = 0.330). Spearman test confirmed that the prognostic indicators IRF, IMR, and SII were positively correlated with the overall survival rate of patients with SCLC. Kaplan-Meier analysis showed that the OS rate of patients with high IMR was significantly worse (P = 0.0096). In addition, we found that IMR was superior to IRF in distinguishing patients with different outcomes in the low and high groups (P < 0.05). Our novel integration index, combining IMR with the TNM stage system and SII index, exhibited superior prognostic value compared to the original index. Additionally, the combination of prognostic indicators IMR and SII significantly stratified stage I-II SCLC patients (P ＜0.05).

Conclusions

The prognostic index based on peripheral blood IMR stands out as an independent predictor for SCLC patients pre-treatment. Its accessibility through routine blood analysis facilitates immediate clinical application without requiring prolonged scientific research validation. The integration of IMR with the TNM score enhances survival prediction and risk stratification. Notably, when combined with the SII score, the new IMR index demonstrates significant improvements in prognostication for stage I-II small cell lung cancer.

Completeness of Resection and Long-Term Survival of Patients Undergoing Resection for Pathologic T3 NSCLC: An International Association for the Study of Lung Cancer Analysis

INTRODUCTION

Currently, tumors with different histopathologic characteristics and oncologic outcomes comprise the T3 category of the eight edition TNM classification for lung cancers. To better understand the T3 category, we evaluated completeness of resection and long-term survival in patients undergoing resection for T3 NSCLC.

METHODS

The International Association for the Study of Lung Cancer 1999 to 2010 database was queried for patients with pathologic T3N0M0 NSCLC who underwent lobectomy or pneumonectomy. The primary outcome evaluated was overall survival (OS) stratified by T3 descriptors and completeness of resection.

RESULTS

Of 1448 patients with T3N0M0 tumors, 1187 (82.0%) had a single descriptor defining them as T3. T3 tumors with chest wall infiltration (CWI) or parietal pleura infiltration (PL3) had the highest rates of incomplete resection (9.8% and 8.4%, respectively), and those classified as T3 by size only had the lowest rate of incomplete resection (2.9%). Individual T3 descriptors were associated with significant differences in OS (p = 0.005). When tumors with similar survival and complete resection rates were grouped, patients with T3 tumors characterized by size or the presence of a separate nodule (SN) in the same lobe had better 5-year OS than patients with tumors characterized by PL3 or CWI (size/SN 60% versus CWI/PL3 53%, p = 0.017) independent of completeness of resection.

CONCLUSIONS

Significant differences in 5-year OS were associated with size, SN, PL3, or CWI T3 descriptors. Subdividing pathologic T3N0M0 tumors according to the presence or absence of CWI or PL3 may increase the prognostic accuracy of tumor staging.

Characterization of TCF-1 and its relationship between CD8+ TIL densities and immune checkpoints and their joint influences on prognoses of lung adenocarcinoma patients

BACKGROUND

T cell factor-1 (TCF-1) + stem-like tumor-infiltrating lymphocytes (stem-like TILs) are important memory cells in the tumor microenvironment. However, their relationship with clinicopathological features, CD8+ TIL densities, immune checkpoint inhibitors (ICs), and prognostic values remain unknown for lung adenocarcinomas (LUADs). In this study, we aimed to characterize TCF-1+ TILs and their prognostic significance in patients with surgically resected LUADs.

METHODS

Expression of TCF-1, CD8, and ICs including programmed death-1 (PD-1), lymphocyte activating-3 (LAG-3), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3) in TILs were estimated using immunohistochemistry of resected LUADs. The association between TCF-1 expressions and clinicopathological characteristics of patient prognoses were analyzed.

RESULTS

Positive TCF-1 expression significantly correlated with advanced pathological stage, tumor grade, CD8+ TILs density, TIM-3 expression, LAG-3 expression, and PD-1 expression. TCF-1 positivity was significantly associated with a better recurrence-free survival (RFS), and overall survival (OS). Subgroup analysis revealed that the TCF-1+/CD8+ group had the best RFS and OS, while the TCF-1-/CD8- group had the worst RFS and OS. Similarly, patients with TCF-1 + PD-1- had the best prognoses and patients with TCF-1-PD-1+ had the worst prognoses.

CONCLUSION

TCF-1 had relatively high positive expression and special clinicopathological features in patients with LUAD. TCF-1+ TILs were related to CD8 density, TIM-3 expression, LAG-3 expression, and PD-1 expression, and were associated with better prognoses in LUAD patients. A combination of TCF-1 and CD8 densities or PD-1 expression further stratified patients into different groups with distinct prognoses.

Reconsidering N component of cancer staging for T1-2N0-2M0 small-cell lung cancer: a retrospective study based on multicenter cohort

BACKGROUND

The current nodal (pN) classification still has limitations in stratifying the prognosis of small cell lung cancer (SCLC) patients with pathological classifications T1-2N0-2M0. Thus. This study aimed to develop and validate a modified nodal classification based on a multicenter cohort.

MATERIALS AND METHODS

We collected 1156 SCLC patients with pathological classifications T1-2N0-2M0 from the Surveillance, Epidemiology, and End Results database and a multicenter database in China. The X-tile software was conducted to determine the optimal cutoff points of the number of examined lymph nodes (ELNs) and lymph node ratio (LNR). The Kaplan-Meier method, the Log-rank test, and the Cox regression method were used in this study. We classified patients into three pathological N modification categories, new pN#1 (pN0-#ELNs > 3), new pN#2 (pN0-#ELNs ≤ 3 or pN1-2-#LNR ≤ 0.14), and new pN#3 (N1-2-#LNR > 0.14). The Akaike information criterion (AIC), Bayesian Information Criterion, and Concordance index (C-index) were used to compare the prognostic, predictive ability between the current pN classification and the new pN component.

RESULTS

The new pN classification had a satisfactory effect on survival curves (Log-rank P < 0.001). After adjusting for other confounders, the new pN classification could be an independent prognostic indicator. Besides, the new pN component had a much more accurate predictive ability in the prognostic assessment for SCLC patients of pathological classifications T1-2N0-2M0 compared with the current pN classification in the SEER database (AIC: 4705.544 vs. 4731.775; C-index: 0.654 vs. 0.617, P < 0.001). Those results were validated in the MCDB from China.

CONCLUSIONS

The multicenter cohort developed and validated a modified nodal classification for SCLC patients with pathological category T1-2N0-2M0 after surgery. Besides, we propose that an adequate lymph node dissection is essential; surgeons should perform and consider the situation of ELNs and LNR when they evaluate postoperative prognoses of SCLC patients.

Risk factors for long-term decline in post-operative pulmonary function after lung resection

OBJECTIVES

The study aimed to examine the risk factors for long-term decline in pulmonary function after anatomical resection for lung cancer and the effects of the decrease on survival.

METHODS

We retrospectively examined 489 patients who underwent anatomical resection for lung cancer between 2010 and 2020. Pulmonary function tests were performed preoperatively and at 1, 3, 6 and 12 months after surgery. The lower interquartile medians of the reduction rates of forced expiratory volume in 1 s and vital capacity at 12 months after surgery were taken as the cut-off values of risk factors for the decrease in post-operative pulmonary function.

RESULTS

Forced expiratory volume in 1 s and vital capacity decreased the most in the first month after surgery and then gradually recovered. Vital capacity continued to increase even after 6 months post-surgery, whereas forced expiratory volume in 1 s stabilized. Multivariable logistic analysis showed that the number of resected segments (odds ratio, 2.09; 95% confidence interval, 1.12-3.89; P = 0.019) was a risk factor for the decrease in forced expiratory volume in 1 s at 12 months, and the numbers of resected segments (odds ratio, 1.36; 95% confidence interval, 1.13-1.63; P < 0.001) and post-operative complications (odds ratio, 2.32; 95% confidence interval, 1.01-5.35; P = 0.047) were independent risk factors for decrease in vital capacity. Multivariate cox regression analysis showed that the decrease in vital capacity at 12 months was significantly associated with overall survival (hazard ratio, 2.02; 95% confidence interval, 1.24-3.67; P = 0.004).

CONCLUSIONS

Long-term decrease in vital capacity, which was influenced by the number of resected segments and post-operative complications, adversely affected survival.

Effectiveness of neoadjuvant immunochemotherapy compared to neoadjuvant chemotherapy in non-small cell lung cancer patients: Real-world data of a retrospective, dual-center study

Background

Studying the application of neoadjuvant immunochemotherapy (NICT) in the real world and evaluating its effectiveness and safety in comparison with neoadjuvant chemotherapy (NCT) are critically important.

Methods

This study included the II-IIIB stage non-small cell lung cancer (NSCLC) patients receiving NCT with or without PD-1 inhibitors and undergoing surgery after neoadjuvant treatments between January 2019 to August 2022. The clinical characteristics and treatment outcomes were retrospectively reviewed and analyzed.

Results

A total of 66 patients receiving NICT and 101 patients receiving NCT were included in this study. As compared to NCT, NICT showed similar safety while not increasing the surgical difficulty. The ORR in the NICT and NCT groups was 74.2% and 53.5%, respectively, P = 0.009. A total of 44 patients (66.7%) in the NICT group and 21 patients (20.8%) in the NCT group showed major pathology response (MPR) (P <0.001). The pathology complete response (pCR) rate was also significantly higher in NICT group than that in NCT group (45.5% vs. 10.9%, P <0.001). After Propensity Score Matching (PSM), 42 pairs of patients were included in the analysis. The results showed no significant difference in the ORR between the two groups (52.3% vs. 43.2%, P = 0.118), and the proportions of MPR (76.2%) and pCR (50.0%) in NICT group were significantly higher than those of MPR (11.9%) and pCR (4.7%) in the NCT group (P <0.001). The patients with driver mutations might also benefit from NICT.

Conclusions

As compared to NCT, the NICT could significantly increase the proportions of patients with pCR and MPR without increasing the operation-related bleeding and operation time.

Prognostic value of the modified advanced lung cancer inflammation index in overweight or obese patients with lung cancer: Results from a multicenter study

BACKGROUND

This study aimed to explore the relationship between the modified advanced lung cancer inflammation index (mALI) and the survival of overweight or obese patients with lung cancer (LC).

METHODS

The mALI was defined as the appendix skeletal muscle index multiplied by the serum albumin level/neutrophil-to-lymphocyte ratio. The cutoff values for males and females were assessed separately. Survival curves were estimated using the Kaplan-Meier method, and statistical differences were determined using the log-rank test. Univariate and multivariate Cox proportional hazards models were used for the survival analysis. The area under the receiver operating characteristic curve was used to compare the prognostic value of mALI with other nutrition assessment indicators.

RESULTS

The mALI cut-offs for males and females were 8.59 and 8.26, respectively. Malnutrition, high systemic inflammation, and advanced stage for overweight or obese LC patients were found to be correlated with a low mALI. The median survival of patients with a low mALI was significantly lower than patients with a high mALI by approximately 25 months. In addition, the mALI can be used as an effective supplement to the traditional pathological stage. Multivariable analysis found that mALI was an independent prognostic factor of overall survival (hazard ratio = 0.531; 95% CI, 0.402-0.700; P < 0.001). The prognostic predictive performance of mALI was superior to that of other nutrition assessment indicators.

CONCLUSIONS

The mALI was an independent risk factor for the prognosis of overweight or obese LC patients, and a useful supplement to the pathological stage.

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.

A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Clinically-meaningful improvements in therapy for unresectable NSCLC

INTRODUCTION

The ideal management of patients with unresectable non-small-cell lung cancer (NSCLC) is still developing. Unresectable NSCLC has a high mortality rate and poor prognosis, but the development of immune checkpoint inhibitors (ICIs) and molecular-targeted therapies has been a breakthrough in the treatment. The correct treatment of this patient population is crucial to maximize the clinical benefits without compromising their quality of life (QOL).

AREAS COVERED

We review the chemoradiotherapies, cytotoxic chemotherapies, immunotherapies, and molecular-targeted therapies available for unresectable NSCLC, focusing on their effects on overall survival, progression-free survival, and QOL.

EXPERT OPINION

Although cure is the ultimate goal of cancer treatment, it is often difficult to achieve in advanced NSCLC. Biomarker surveillance techniques, such as next-generation sequencing, have made it possible to provide the most appropriate treatment for each patient. This has led to clinically-meaningful improvements in therapies for unresectable NSCLC. The development of new molecular-targeted therapies and the establishment of treatment for patients who acquired drug resistance after initial treatment have a positive impact on patients' long-term survival. ICIs lead the long-term survival that can be considered a cure of some patients with advanced NSCLC, but such curative survival is difficult to achieve with cytotoxic chemotherapies and molecular-targeted therapies.

Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma

Chordin-like 1 (CHRDL1), an inhibitor of bone morphogenetic proteins(BMPs), has been recently reported to participate in the progression of numerous tumors, however, its role in lung adenocarcinoma (LUAD) remains unclear. Our study aimed to demonstrate relationship between CHRDL1 and LUAD based on data from The Cancer Genome Atlas (TCGA). Among them, CHRDL1 expression revealed promising power for distinguishing LUAD tissues form normal sample. Low CHRDL1 was correlated with poor clinicopathologic features, including high T stage (OR=0.45, P<0.001), high N stage (OR=0.57, P<0.003), bad treatment effect (OR=0.64, P=0.047), positive tumor status (OR=0.63, P=0.018), and TP53 mutation (OR=0.49, P<0.001). The survival curve illustrated that low CHRDL1 was significantly correlative with a poor overall survival (HR=0.60, P<0.001). At multivariate Cox regression analysis, CHRDL1 remained independently correlative with overall survival. GSEA identified that the CHRDL1 expression was related to cell cycle and immunoregulation. Immune infiltration analysis suggested that CHRDL1 was significantly correlative with 7 kinds of immune cells. Immunohistochemical validation showed that CHRDL1 was abnormally elevated and negatively correlated with Th2 cells in LUAD tissues. In conclusion, CHRDL1 might become a novel prognostic biomarker and therapy target in LUAD. Moreover, CHRDL1 may improve the effectiveness of immunotherapy by regulating immune infiltration.

Construction and Validation of Two Novel Nomograms for Predicting the Overall Survival and Cancer-Specific Survival of NSCLC Patients with Bone Metastasis

Background

Bone metastasis (BM) is the most common site of metastasis in non-small cell lung carcinoma (NSCLC). We aimed to construct and validate 2 novel nomograms predicting the 3-, 6-, and 12-months overall survival (OS) and cancer-specific survival (CSS).

Methods

The clinical data of 7480 patients between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Results database (SEER). The patients were allocated randomly to training and validation cohorts in a 7:3 ratio. Cox proportional hazards regression models were used to identify prognostic risk factors and establish 2 nomograms. The prediction accuracy of nomograms was assessed by C-index, the area under the ROC curve (AUC), and calibration curves.

Results

A total of 244998 NSCLC patients were identified between 2010 and 2016, with 7480 found with BM, accounting for 3.1%. Overall, 7480 patients were enrolled in the OS nomogram construction and were randomized to the training set (n = 5236) and the validation set (n = 2244). Age, sex, race, marital status, histology, grade, primary site, T stage, N stage, liver metastasis, surgery, radiotherapy, and chemotherapy were found to correlate with OS. A total of 7422 samples were included in the CSS nomogram construction, randomly grouped into training set (n = 5195) and the validation set (n = 2227). Age, sex, race, histology, grade, primary site, T stage, N stage, brain metastasis, liver metastasis, surgery, radiotherapy, and chemotherapy were associated with CSS. Two nomograms were conducted to predict the 3-, 6-, and 12-months OS and CSS. The ROC curves and exhibited good performance for predicting OS and CSS.

Conclusion

We established and validated 2 high-performance nomograms to assist clinical doctors in making personalized treatment decisions.

Characterization of CD66b and its relationship between immune checkpoints and their synergistic impact in the prognosis of surgically resected lung adenocarcinoma

OBJECTIVES

CD66b positive tumor-infiltrating neutrophils (TINs) are key immunity cells in the tumor microenvironment (TME). However, their relationship with clinicopathological features, immune checkpoints (ICs), and prognostic value remains undetermined in lung adenocarcinoma (LUAD). In this study, we aimed to characterize the infiltration by TINs and the prognostic significance in patients with surgically resected LUAD.

MATERIALS AND METHODS

Expression of CD66b and ICs, including PD-L1, PD-1, CTLA4, LAG3, TIM3, TIGIT, VISTA, and BTLA, in both cancer cell and tumor-infiltrating lymphocytes (TILs) were estimated by immunohistochemistry in resected LUAD. The associations between CD66b expression and clinicopathological characteristics in patient prognoses were analyzed. We also verified results in another cohort from 85 patients with untreated LUAD and further analyzed the correlation between CD66b expression and EGFR and KRAS mutation status in addition to the rearrangement of the anaplastic lymphoma receptor tyrosine kinase gene (ALK).

RESULTS

A total of 240 patients were included in this study. CD66b expression was observed in 87 (36.2%) samples. ICs including PD-L1, PD-1, CTLA4, LAG3, TIM3, TIGIT, VISTA, and BTLA were observed in percentages that ranged from 23.8% to 59.4%. Positive CD66b expression significantly correlated with smoking history (p = 0.029), pathological stage (p = 0.040), and the positive expression of LAG-3 (p < 0.001), PD-1 (p = 0.008), CTLA-4 (p = 0.013), TIM-3 (p = 0.025), TIGIT (p = 0.002), PD-L1 in TILs (p = 0.015), and PD-L1 in tumor cells (p = 0.010). CD66b positivity was significantly associated with worse recurrence-free survival (RFS) (hazard ratio, HR, 1.687; 95% confidence interval, CI, 1.058-2.690, p = 0.028) and overall survival (OS) (HR, 1.667; 95% CI, 1.097-2.534, p = 0.017). Subgroup analysis revealed that the CD66b+/LAG-3 + group had the worst RFS (5-year rate: 39.5%,) and OS (5-year rate: 53.7%,), while the CD66b-/LAG-3 - group had the best RFS (5-year rate: 65.6%) and OS (5-year rate: 78.8%). The p value in analysis of RFS and OS was 0.005 and 0.008, respectively. In the verification set, high expression of CD66b was also significantly correlated with the positive expression of LAG-3 (p < 0.001), PD-1 (p = 0.002), CTLA-4 (p = 0.034), TIM-3 (p = 0.049), PD-L1 in TILs (p = 0.003), and PD-L1 in tumor cells (p = 0.045). There was no correlation between CD66b expression and positive TIGIT expression (p = 0.077), EGFR mutation (p = 0.223), KRAS mutation (p = 0.151), and ALK fusion (p = 0.310).

CONCLUSION

CD66b had a relatively high positive expression rate and special clinicopathological features in patients with LUAD. CD66b + TINs were related to the expression of ICs and associated with poor prognoses in LUAD. A combination of CD66b and ICs, especially LAG-3 could further stratify patients into different groups with distinct prognoses.

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Lung cancer is a serious malignancy, and lung adenocarcinoma (LUAD) is the most common pathological subtype. Immune-related factors play an important role in lymph node metastasis. In this study, we obtained gene expression profile data for LUAD and normal tissues from the TCGA database and analyzed their immune-related genes (IRGs), and observed that 459 IRGs were differentially expressed. Further analysis of the correlation between differentially expressed IRGs and lymph node metastasis revealed 18 lymph node metastasis-associated IRGs. In addition, we analyzed the mutations status, function and pathway enrichment of these IRGs, and regulatory networks established through TF genes. We then identified eight IRGs (IKBKB, LTBR, MIF, PPARD, PPIA, PSME3, S100A6, SEMA4B) as the best predictors by LASSO Logistic analysis and used these IRGs to construct a model to predict lymph node metastasis in patients with LUAD (AUC 0.75; 95% CI: 0.7064-0.7978), and survival analysis showed that the risk score independently affected patient survival. We validated the predictive effect of risk scores on lymph node metastasis and survival using the GEO database as a validation cohort and the results showed good agreement. In addition, the risk score was highly correlated with infiltration of immune cells (mast cells activated, macrophages M2, macrophages M0 and B cells naïve), immune and stromal scores, and immune checkpoint genes (LTBR, CD40LG, EDA2R, and TNFRSF19). We identified key IRGs associated with lymph node metastasis in LUAD and constructed a reliable risk score model, which may provide valuable biomarkers for LUAD patients and further reveal the mechanism of its occurrence.

Validation of the T Descriptor (TNM-8) in T3N0 Non-Small-Cell Lung Cancer Patients; a Bicentric Cohort Analysis with Arguments for Redefinition

Simple Summary

Lung cancer patients have different survival outcomes depending on tumor size and growth pattern after surgery. This study aims to optimize tumor classification, and identify patients who could benefit the most from additional chemotherapy after surgery. In a specific lung cancer cohort, we study how a new redefinition of tumor classification could lead to a more solid recommendation of which patients to offer chemotherapy after surgery.

Abstract

The current pT3N0 category represents a heterogeneous subgroup involving tumor size, separate tumor nodes in one lobe, and locoregional growth pattern. We aim to validate outcomes according to the eighth edition of the TNM staging classification. A total of 281 patients who had undergone curative lung cancer surgery staged with TNM-7 in two German centers were retrospectively analyzed. The subtypes tumor size >7 cm and multiple nodules were grouped as T3a, and the subtypes parietal pleura invasion and mixed were grouped as T3b. We stratified survival by subtype and investigated the relative benefit of adjuvant chemotherapy according to subtype. The 5-year overall survival (OS) rates differed between the different subtypes tumor diameter >7 cm (71.5%), multiple nodules in one lobe (71.0%) (grouped as T3a), parietal pleura invasion (59.%), and mixed subtype (5-year OS 50.3%) (grouped as T3b), respectively. The cohort as a whole did not gain significant OS benefit from adjuvant chemotherapy. In contrast, adjuvant chemotherapy significantly improved OS in the T3b subgroup (logrank p = 0.03). This multicenter cohort analysis of pT3N0 patients identifies a new prognostic mixed subtype. Tumors >7 cm should not be moved to pT4. Patients with T3b tumors have significantly worse survival than patients with T3a tumors.

Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma

Lung cancer is a serious malignancy, and lung adenocarcinoma (LUAD) is the most common pathological subtype. Immune-related factors play an important role in lymph node metastasis. In this study, we obtained gene expression profile data for LUAD and normal tissues from the TCGA database and analyzed their immune-related genes (IRGs), and observed that 459 IRGs were differentially expressed. Further analysis of the correlation between differentially expressed IRGs and lymph node metastasis revealed 18 lymph node metastasis-associated IRGs. In addition, we analyzed the mutations status, function and pathway enrichment of these IRGs, and regulatory networks established through TF genes. We then identified eight IRGs (IKBKB, LTBR, MIF, PPARD, PPIA, PSME3, S100A6, SEMA4B) as the best predictors by LASSO Logistic analysis and used these IRGs to construct a model to predict lymph node metastasis in patients with LUAD (AUC 0.75; 95% CI: 0.7064-0.7978), and survival analysis showed that the risk score independently affected patient survival. We validated the predictive effect of risk scores on lymph node metastasis and survival using the GEO database as a validation cohort and the results showed good agreement. In addition, the risk score was highly correlated with infiltration of immune cells (mast cells activated, macrophages M2, macrophages M0 and B cells naïve), immune and stromal scores, and immune checkpoint genes (LTBR, CD40LG, EDA2R, and TNFRSF19). We identified key IRGs associated with lymph node metastasis in LUAD and constructed a reliable risk score model, which may provide valuable biomarkers for LUAD patients and further reveal the mechanism of its occurrence.

Investigation on the potential of circulating tumor DNA methylation patterns as prognostic biomarkers for lung squamous cell carcinoma

Background

Aberrant epigenetic modifications play a key role in lung tumorigenesis. In our study, we aimed to explore the clinical implications of baseline circulating tumor DNA (ctDNA) somatic and methylation profiles in patients with lung squamous cell carcinoma (LUSC).

Methods

A total of 26 patients with LUSC of various stages were included in this study. Somatic mutations and methylation levels were profiled from the plasma-derived ctDNA obtained at the time of diagnosis using unique molecular identifier (UMI)-based targeted sequencing and bisulfite sequencing, respectively. The correlation between baseline ctDNA mutation and methylation profile, and overall survival (OS), were analyzed.

Results

Somatic mutations were detected in 80.8% (20/26) of the patients. Patients harboring somatic mutations with maximum allelic fraction (maxAF) of >5% had significantly shorter OS compared to those with maxAF ≤5% (7.1 vs. 54.6 months; P=0.020). ctDNA methylation level was found to be strongly correlated with maxAF (Pearson correlation =0.934; P<0.001). Consistent with maxAF, higher methylation levels were also associated with poorer OS (hazard ratio =2.377; 95% CI: 1.283–4.405; P=0.006). Moreover, a total of 1,956 ctDNA methylation blocks were differentially methylated in patients with maxAF >0 (P<0.05). Least absolute shrinkage and selection operator (LASSO) regression analysis revealed a significant correlation between methylation signatures from 5 methylation blocks and OS (hazard ratio =183.20, 95% CI: 2.74–12,243.32; P=0.015). These 5 methylation blocks could serve as an alternative to maxAF and can be explored as prognostic biomarkers.

Conclusions

Our study identified several ctDNA methylation blocks that can potentially predict the prognosis of LUSC at the time of diagnosis.

Overexpression of hsa_circ_0001715 is a Potential Diagnostic and Prognostic Biomarker in Lung Adenocarcinoma

Background

Circular RNAs (circRNAs) play important roles in tumorigenesis, including lung cancer. However, the expression profile and clinical value of circRNAs in lung adenocarcinoma remain unclear. The purpose of this study was to establish the circRNAs expression profile of lung adenocarcinoma and determine its potential diagnostic and prognostic value.

Materials and Methods

The global expression profile of circRNAs in lung adenocarcinoma tissue was determined from five paired lung adenocarcinoma tissues and adjacent normal tissues. The expression levels of selected candidate circRNA were validated by qRT-PCR. Sequence analysis was used to confirm the specificity of amplified circRNA. The candidate circRNA level was further detected in plasma samples from lung adenocarcinoma patients and healthy controls. The relationships between their levels and clinicopathological factors were explored. Receiver operating characteristic (ROC) curve was constructed to differentiate lung adenocarcinoma from healthy controls. Kaplan–Meier was performed to show survival curves and survival characteristics. The significance of different prognostic factors for overall survival (OS) was analyzed using Cox proportional hazards model.

Results

CircRNA microarray showed 394 circRNAs were differentially expressed, including 215 up-regulated and 179 down-regulated circRNAs. Hsa_circ_0001715 was the most up-regulated circRNA in lung adenocarcinoma tissues. Plasma hsa_circ_0001715 levels were significantly higher in lung adenocarcinoma patients versus healthy controls (P < 0.001). We further found that high plasma hsa_circ_0001715 was significantly correlated with TNM stage (P = 0.039) and distant metastasis (P = 0.030). Furthermore, ROC curve analysis showed that hsa_circ_0001715 had high diagnostic value, and the area under the curve (AUC) was 0.871. Lung adenocarcinoma patients with plasma hsa_circ_0001715 levels over 0.417 had significantly shorter OS than those with lower levels (P = 0.004). Univariate and multivariate survival analysis showed that plasma hsa_circ_0001715 level was an independent prognostic factor for the OS.

Conclusion

Our study revealed an aberrant circRNA expression profile in lung adenocarcinoma, and hsa_circ_0001715 is up-regulated and could act as a novel diagnostic and prognostic biomarker for lung adenocarcinoma.