The International Association for the Study of Lung Cancer Staging Project: Prognostic Factors and Pathologic TNM Stage in Surgically Managed Non-small Cell Lung Cancer

Validation of changes in stage by the new N category in the 9th edition of lung cancer staging for resected non-small cell lung cancer

INTRODUCTION

The reclassification following N2 subcategorization (N2a vs. N2b) in the 9th edition of lung cancer staging has not yet been externally validated. This study aimed to evaluate and compare the survival outcomes of reclassified stages in patients with resected non-small cell lung cancer.

MATERIALS AND METHODS

Patients who underwent lobectomy or pneumonectomy for non-small cell lung cancer between January 2015 and December 2021 were retrospectively analyzed. Overall survival (OS) comparison and risk stratification within the pathologic N category (pN0, 1, 2a, 2b), node-positive T1 tumors (T1N1, T1N2a, T1N2b), stage IIB tumors (T1N2a, T2N1, T3N0), and T2-3N2 tumors (stage IIIA, IIIB) were performed using the Kaplan-Meier method and multivariable Cox proportional hazards analysis.

RESULTS

A total of 3864 patients were analyzed, including 962 patients with pathologically node-positive tumors. pN2a and pN2b tumors exhibited distinct survival (p < 0.001). Survival separation between neighboring pT1N1-2b tumors was statistically marginal (p = 0.06 and 0.09); however, clear separation was observed in clinical T1 tumors (p < 0.05). pT1N2a tumors that were downstaged from stage IIIA to stage IIB showed comparable survival to other stage IIB tumors (vs. pT2N1 and pT3N0; p = 0.79 and 0.35, respectively). In pT2-3 tumors (stage IIIA and IIIB), OS risk stratification between pN2a and pN2b tumors, except for between pT3N2a and pT2N2b, was valid (p < 0.05).

CONCLUSION

The reclassification of stages in the 9th edition of lung cancer staging based on N2 subcategorization is considered reasonable.

Association between modified weight loss grading system and overall survival in adults with lung cancer: A retrospective cohort study

This multicenter retrospective cohort study evaluated the prognostic value of the modified weight loss grading system (mWLGS) in patients with lung cancer by analyzing data from 3601 individuals. The investigation demonstrated that mWLGS effectively stratified patient outcomes, with median overall survival progressively declining from 35.5 months for grade 0 to 26.0 months for grade 4. Multivariate Cox regression showed grades 1, 2, 3, and 4 were independently associated with reduced survival, showing incrementally worsening hazard ratios (HR) (HR 1.56, 95% CI: 1.03-2.37, P = 0.037; HR 1.87, 95% CI: 1.24-2.84, P = 0.003; HR 1.94, 95% CI: 1.27-2.96, P = 0.003; HR: 1.95, 95% CI: 1.27-3.00, P = 0.002, respectively). Sensitivity analyses, in which we excluded patients who died within 30 days, showed that mWLGS remained an independent prognostic indicator. The system significantly enhanced prognostic discrimination when combined with TNM staging, as evidenced by improved C-statistics, continuous net reclassification improvement (NRI), and integrated discriminant improvement (IDI). These findings establish that the mWLGS can better stratification the prognosis of patients with lung cancer and is an effective indicator to predict the survival time and quality of life of lung cancer patients.

Prevalence and Risk Factors for Pathologic N2 Disease in Resected Lung Cancers Assessed as N0 or N1 Disease on Preoperative Imaging

BACKGROUND. For certain patients with lung cancer, guidelines recommend endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) as the primary method to evaluate for metastatic mediastinal lymph nodes defining pN2 disease. EBUS-TBNA has associated costs and complications and possibly limited availability. OBJECTIVE. The purpose of the present study was to investigate the prevalence of and risk factors for pN2 disease in patients undergoing resection of lung cancer who were assessed as having radiologic N0 or N1 disease. METHODS. This retrospective study included 3581 patients (mean age, 63.8 ± 9.4 [SD] years; 1917 men and 1664 women) with lung cancer who underwent chest CT and FDG PET/CT showing radiologic N0 or N1 disease before resection between January 2015 and December 2021. Tumor characteristics were assessed on chest CT. Patients were assessed for the presence of guideline-based indications for EBUS-TBNA as evaluation for imaging-occult N2 disease. Pathologic N categories were determined from surgical specimens. Preoperative risk factors for pN2 disease were identified using logistic regression analyses. RESULTS. A total of 1936 patients had radiologic N0 disease without an EBUS-TBNA indication, 1348 had radiologic N0 disease with an EBUS-TBNA indication, and 297 had radiologic N1 disease. These groups had a prevalence of pN2a disease of 4.1%, 6.5%, and 18.5%, respectively, and a prevalence of pN2b disease of 1.2%, 2.4%, and 14.8%, respectively. In multivariable analyses, independent risk factors for pN2 disease were, in patients with radiologic N0 disease without an EBUS-TBNA indication, female sex (OR = 1.66 [95% CI, 1.08-2.54]), larger size of solid portion of the tumor (OR = 1.05 [95% CI, 1.01-1.10]), pure-solid nodule (OR = 5.53 [95% CI, 3.15-9.72]), and spiculation (OR = 2.66 [95% CI, 1.72-4.11]); in patients with radiologic N0 disease with an EBUS-TBNA indication, they were younger age (OR = 0.97 [95% CI, 0.96-0.99] per year), pure-solid nodule (OR = 1.75 [95% CI, 1.10-2.80]), and lobulation (OR = 1.96 [95% CI, 1.23-3.11]); and in patients with radiologic N1 disease, they were younger age (OR = 0.973 [95% CI, 0.948-0.999] per year), female sex (OR = 2.91 [95% CI, 1.66-5.11]), and spiculation (OR = 2.81 [95% CI, 1.66-4.76]). CONCLUSION. pN2b disease was uncommon in patients with radiologic N0 disease, regardless of indications for EBUS-TBNA, and its prevalence increased in patients with radiologic N1 disease. CLINICAL IMPACT. The identified risk factors can inform patient selection for EBUS-TBNA, to aid in the detection of occult pN2 disease.

A nomogram-based survival prediction model for non-small cell lung cancer patients based on clinical risk factors and multiregion radiomics features

AIM

This study focuses on developing a nomogram-based overall survival (OS) prediction model for non-small cell lung cancer (NSCLC) patients by integrating clinical factors with multiregion radiomics features extracted from pretreatment CT images. The proposed nomogram aims to assist clinicians in stratifying patients into high- and low-risk groups for personalised treatment strategies.

MATERIALS AND METHODS

From 2008 to 2018, 77 NSCLC patients were included. The radiomics feature was extracted from the internal and peripheral tumour region of pretreatment computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) and the univariable Cox regression model were used to select the radiomics features. The Rad-score was defined as a linear combination of the selected radiomics features and the Cox proportional hazards regression coefficients. The combined model was constructed based on the clinicopathological factors and the Rad-score. The discrimination capacity of the prediction model was evaluated by Harrell's concordance index (C-index), the calibration curve, and the Kaplan-Meier survival curve.

RESULTS

We found that nine radiomics features and histology were independent predictors. The combined model showed the best performance (C-index: 0.799 [95% CI: 0.726-0.872]) compared with the clinical model (C-index: 0.692 [95% CI: 0.625-0.759]) and Rad-score (C-index: 0.663 [95% CI: 0.580-0.746]), and could significantly stratify into high-risk and low-risk NSCLC patients. The calibration curve also showed good consistency between the observation and the prediction.

CONCLUSIONS

The multregion radiomics features have the potential for predicting OS in NSCLC patients. The nomogram-based survival prediction model demonstrates significant potential in guiding clinical decision-making, allowing for precise and personalised treatment for NSCLC patients.

Residual Volume and Total Lung Capacity at Diagnosis Predict Overall Survival in Non-Small Cell Lung Cancer Patients

BACKGROUND

Residual volume (RV) / total lung capacity (TLC) ratio has been found to better predict functional impairments than spirometry and is associated with mortality in chronic obstructive pulmonary disease; however, it is rarely studied in lung cancer. Our previous work established spirometry as a prognostic factor for lung cancer, and we aimed to further investigate the prognostic value of TLC and RV in lung cancer patients.

METHODS

We identified newly diagnosed non-small cell lung cancer (NSCLC) patients who underwent static lung function tests prior to any cancer therapy between 1992 and 2020 in a longitudinal cohort of lung cancer patients: the Boston Lung Cancer Study. Cox proportional-hazards model was used to estimate the association between each lung volume test with overall survival.

RESULTS

Among 2348 NSCLC patients, 57.2% were diagnosed at stage I and 63.8% underwent surgery, with 1352 deaths observed over a median survival of 66.9 months. Higher RV, RV%, and lower TLC, TLC% were associated with worse overall survival marginally; RV/TLC was associated with overall survival as a quantitative trait, with one standard deviation (11.24%) increase in RV/TLC associated with 19.2% higher risk of mortality (HR = 1.192 [95% CI: 1.114, 1.277]) after covariate adjustment. Statistically significant interactions were found between RV/TLC and spirometry, and higher mortality risks were found with higher RV/TLC in patients across spirometry status and cancer stages.

CONCLUSION

NSCLC patients with higher RV/TLC ratios at diagnosis had worse overall survival, even when spirometry was within the predicted range. These findings suggest that lung volume measurements provide prognostic information beyond standard spirometry, supporting the need for further mechanistic and interventional studies to determine their clinical utility.

Prognostic factors of survival in patients with lung cancer after low-dose computed tomography screening: a multivariate analysis of a lung cancer screening cohort in China

OBJECTIVE

This study aimed to evaluate the prognostic factors influencing the survival of patients with lung cancer identified from a lung cancer screening cohort in the community.

METHODS

A total of 25,310 eligible participants were enrolled in this population-based prospective cohort study, derived from a community lung cancer screening program started from 2013 to 2017. Survival analyses were conducted using the Kaplan-Meier method and the log-rank test. Cox proportional hazards regression models were utilized to identify prognostic factors, including demographic characteristics, risk factors, low-dose CT (LDCT) screening, and treatment information.

RESULTS

The screening cohort identified a total of 429 patients with lung cancer (276 men, 153 women) during the study period. The 1-year, 3-year, and 5-year survival rates were 74.4%, 59.4% and 54.5%, respectively. The prognostic factors discovered by the multivariate analysis include gender (male vs. female, HR: 2.96, 95% CI: 1.88-4.64), age (HR: 1.02, 95% CI: 1.00-1.05), personal monthly income (2000-3999 CNY vs. < 2000 CNY, HR: 0.70, 95% CI: 0.52-0.95), pathological type (small cell carcinoma vs. adenocarcinoma, HR: 2.55, 95% CI: 1.39-4.66), stage (IV vs. 0-I, HR: 5.21, 95% CI: 2.78-9.75; III vs. 0-I, HR: 3.81, 95% CI: 1.88-7.74), surgery (yes vs. no, HR: 0.36, 95% CI: 0.23-0.57), and KPS (HR: 0.98, 95% CI: 0.98-0.99) among lung cancer patients identified by the basic model. Furthermore, solid nodule (non-solid nodule vs. solid nodule, HR: 0.47, 95% CI: 0.23-0.96) and larger-sized nodule (HR: 1.02, 95% CI: 1.00-1.03) were associated with a worse prognosis for lung cancer in the LDCT screening model.

CONCLUSION

Prognostic factors of patients with lung cancer detected by LDCT screening were identified, which could potentially guide clinicians in the decision-making process for lung cancer management and treatment. Further studies with larger sample sizes and more detailed follow-up data are warranted for prognostic prediction.

The value of 18F-FDG PET/CT combined with 3D quantitative technology and clinicopathological features in predicting prognosis of NSCLC

Objective

To investigate the value of Fluorine-18 Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) combined with 3D quantitative technology and clinicopathological features in predicting the prognosis of non-small cell lung cancer (NSCLC).

Methods

A retrospective review was performed for patients who underwent PET/CT and curative resection of NSCLC between January 2016 and June 2019 in our hospital. PET/CT data, clinical features, and pathology results were collected. Gross tumor volume (GTV) was delineated on CT images by ITK-SNAP software. The prognosis was followed up, and the study endpoint was progression-free survival (PFS). Receiver operating characteristic curve (ROC) was used to initially assess the relationship between each parameter and PFS, and parameters were grouped accordingly. Cox proportional hazards regression was used to develop models based on clinicopathological features to predict prognosis of NSCLC patients. Kaplan-Meier method was used to draw the survival curves.

Results

A total of 128 patients were enrolled in the study with PFS of 8-96 months. Univariate analysis demonstrated that age, SUVindex (the ratio of SUVmax of lesion to SUVmax of liver), metabolic tumor volume (MTV), Dmax (the largest diameter), GTV, lymph node metastasis (LNM), and TNM staging are significantly related to recurrence (all p<0.05). The multivariate analysis showed that only age, SUVindex, and LNM were independent prognostic factor for PFS (all p < 0.05).

Conclusions

Although 18F-FDG PET/CT combined with 3D quantitative technique were helpful in predicting PFS in NSCLC, only age, SUVindex, and LNM were independent predictors for PFS.

Incorporating Lymph Node Size at CT as an N1 Descriptor in Clinical N Staging for Lung Cancer

Background The ninth edition of the TNM classification for lung cancer revised the N2 categorization, improving patient stratification, but prognostic heterogeneity remains for the N1 category. Purpose To define the optimal size cutoff for a bulky lymph node (LN) on CT scans and to evaluate the prognostic value of bulky LN in the clinical N staging of lung cancer. Materials and Methods This retrospective study analyzed patients who underwent lobectomy or pneumonectomy for lung cancer between January 2013 and December 2021, divided into development (2016-2021) and validation (2013-2015) cohorts. The optimal threshold for a bulky LN was defined based on the short-axis diameter of the largest clinically positive LN at CT. Prognostic differences according to presence of bulky LN in cN1 category for overall survival (OS) were evaluated using multivariable Cox analysis. Survival discrimination was assessed using the Harrell concordance index (C-index). Results A total of 3426 patients (mean age, 64.0 years ± 9.3 [SD]; 1837 male) and 1327 patients (mean age, 63.0 years ± 9.7; 813 male) were included in the development and validation cohorts, respectively. The cutoff size for a bulky LN was established at 15 mm, and the presence of bulky LN was an independent risk factor for OS (hazard ratio [HR], 1.54; 95% CI: 1.10, 2.16; P = .01). In the development and validation cohorts, the cN1-bulky group had higher mortality risk than the cN1-nonbulky group (HR, 2.82 [95% CI: 1.73, 4.58; P < .001]; 2.29 [95% CI: 1.34, 3.92; P = .002], respectively). The bulky LN descriptor improved prognostic discrimination within the cN1 category compared with the current staging (C-index from 0.50 to 0.60 and to 0.58 in the development and validation cohorts [P < .001, P = .006], respectively]). Conclusion Defining bulky LN with a size cutoff of 15 mm was an effective descriptor in the clinical staging of N1 lung cancer. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Horst in this issue.

Squamous cell carcinoma predicts worse prognosis in stage IA (≤ 2 cm) non-small cell lung cancer patients following sublobectomy: a population-based study

Recent studies recommend sublobectomy as a surgical approach for non-small cell lung cancer (NSCLC) tumors that are 2 cm or smaller. However, it remains unclear whether NSCLC patients with squamous cell carcinoma (SCC) have comparable outcomes to those with adenocarcinoma (ADC) following sublobectomy. To that end, this study aims to compare the survival outcomes between SCC and ADC in patients with stage IA NSCLC (≤ 2 cm) who have undergone sublobectomy. We identified stage IA (≤ 2 cm) NSCLC patients diagnosed with lung squamous cell carcinoma or adenocarcinoma pathology and underwent sublobectomy from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2020. Overall survival (OS) was determined using the Kaplan-Meier method, and Cox proportional hazards regression was employed to identify risk factors for OS. A total of 9,831 patients diagnosed with stage IA NSCLC (≤ 2 cm) were evaluated. Of these, 2,078 patients met the inclusion criteria, including 1,565 with adenocarcinoma (ADC) and 513 with squamous cell carcinoma (SCC). Notably, SCC was associated with worse overall survival compared to ADC (HR: 2.02, 95% CI: 1.34-3.05, P = 0.03). Subgroup analyses revealed that SCC was comparable to ADC in terms of OS for tumors ≤ 1 cm (HR: 1.22, 95% CI: 0.47-3.18, P = 0.83), while patients with SCC displayed worse OS compared to ADC for tumors > 1 to 2 cm (HR: 2.05, 95% CI: 1.31-3.23, P = 0.002). Cox proportional hazards regression analysis identified female sex (HR: 1.53, 95% CI: 1.08-2.19, P = 0.017), high tumor grade (HR: 1.76, 95% CI: 1.02-3.03, P = 0.011), and SCC (HR: 1.58, 95% CI: 1.08-2.30, P = 0.017) as independent risk factors for OS. In patients with stage IA (≤ 2 cm) NSCLC who underwent sublobectomy, SCC is associated with worse overall survival compared to ADC. Furthermore, being female, having a high tumor grade, and SCC pathology are independent risk factors for OS in these patients.

Prognostic performance of the N category in the 9th edition of lung cancer staging

OBJECTIVES

To compare the prognostic performance of the N category of lung cancer in the 9th edition with previous editions (7th edition and 8th edition's proposal).

METHODS

Patients who underwent lobectomy or pneumonectomy for lung cancer from January 2015 to December 2021 were retrospectively analyzed. Clinical and pathologic N categories were reclassified according to the 9th edition (N0, N1, N2a, and N2b), the 8th edition's proposal (N0, N1a, N1b, N2a1, N2a2, and N2b), and the 7th edition (N0, N1, and N2). Concordance index (C-index) and calibration were assessed for each edition.

RESULTS

A total of 3864 patients were included (962 pN positive and 513 cN positive). The 9th edition demonstrated clear hazard stratification between neighboring pN categories after multivariable adjustment, whereas multiple overlaps were observed in the 8th edition's proposal. It had superior discrimination performance compared with the 7th edition in pathologic staging (all p < 0.05). Compared with the 8th edition's proposal, the 9th edition showed comparable performance in pN2 and overall patients (C-index, 0.560 vs 0.569 [p = 0.163]; 0.666 vs 0.668 [p = 0.396]), In clinical staging, there was no difference in discrimination across 7th to 9th editions (all p > 0.05). N1 dichotomization in the 8th edition's proposal showed discrimination ability (C-index, 0.539 [95% confidence interval: 0.502-0.576]) only in pathologic staging. The calibration was acceptable across the clinical 7th to 9th editions for 5-year survival.

CONCLUSION

The revision of the N category in the 9th edition appears reasonable, offering enhanced prognostic discrimination compared with the 7th edition and comparability to the 8th edition's proposal.

KEY POINTS

Question Does the revised N category in the 9th edition offer added value in discrimination over previous editions? Findings The discrimination performance of the 9th edition is comparable to that of the 8th edition's proposal, demonstrating a distinct hazard stratification between neighboring pN categories. Clinical relevance The revision of the N category in the 9th edition appears reasonable; however, survival heterogeneity within the pathologic N1 category needs to be considered in future updates.

Keratin 17 and A2ML1 are negative prognostic biomarkers in non-small cell lung cancer

Although the overall prognosis for patients with non-small cell lung cancer (NSCLC) has improved over the past several decades, there are still survival differences that are not accurately defined by clinicopathological factors. Thus, there is an unmet clinical need to develop novel approaches to enhance prognostic accuracy for these patients. Keratin 17 (K17) is a negative prognostic biomarker in a wide range of cancer types, including pancreatic ductal adenocarcinoma, head and neck squamous cell carcinoma, and pulmonary adenocarcinoma (LUAD), but has yet to be investigated as a prognostic biomarker in primary lung squamous cell carcinoma (LSCC). Based on TCGA RNA-seq data, alpha-2-macroglobulin like 1 (A2ML1), a protease inhibitor, is highly correlated with K17 in other solid tumors, including pancreatic ductal adenocarcinoma and is also a prognostic biomarker for LSCC, although the prognostic accuracy of A2ML1 for LUAD has not been tested. Thus, we hypothesized that A2ML1 expression correlates with K17 expression and that K17/A2ML1 co-testing could provide complementary prognostic data for NSCLC. The aims of this study were to explore K17 and A2ML1 as dual prognostic biomarkers, using publicly available gene expression databases [The Cancer Genome Atlas (TCGA)] LSCC (n=266), LUAD (n=271)] and multiplexed immunohistochemistry (mIHC) on representative sections of LSCC (n=104) and LUAD (n=107) from two major academic medical centers. Our results suggest that using either mRNA or mIHC-based methods, combined K17 and A2ML1 testing provides information, independent of other clinicopathologic variables, that could impact treatment decisions for patients with NSCLC.

PD-L1+ Lymphocytes Are Associated with CD4+, Foxp3+CD4+, IL17+CD4+ T Cells and Subtypes of Macrophages in Resected Early-Stage Non-Small Cell Lung Cancer

The non-canonical PD-L1 pathway revealed that programmed-death ligand 1 (PD-L1) expression in immune cells also plays a crucial role in immune response. Moreover, immune cell distribution in a tumour microenvironment (TME) is pivotal for tumour genesis. However, the results remain controversial and further research is needed. Distribution of PD-L1-positive (PD-L1+) tumour-infiltrating lymphocytes in the context of TME was assessed in 72 archival I-III stage surgically resected NSCLC tumour specimens. Predominant PD-L1+ lymphocyte distribution in the tumour stroma, compared to islets, was found (p = 0.01). Higher PD-L1+ lymphocyte infiltration was detected in smokers due to their predominance in the stroma. High PD-L1+ lymphocyte infiltration in tumour stroma was more common in tumours with higher CD4+ T cell infiltration in islets and stroma, Foxp3+CD4+ T cell infiltration in islets and lover M1 macrophage infiltration in the stroma (p = 0.034, p = 0.034, p = 0.005 and p = 0.034 respectively). Meanwhile, high PD-L1+ lymphocyte infiltration in islets was predominantly found in tumours with high levels of IL-17A+CD4+ T cells in islets and Foxp3+CD4+ T cells in islets and stroma (p = 0.032, p = 0.009 and p = 0.034, respectively). Significant correlations between PD-L1+ lymphocytes and tumour-infiltrating CD4+, Foxp3+CD4+, IL-17A+CD4+ T cells and M2 macrophages were found. An analysis of the tumour-immune phenotype revealed a significant association between PD-L1 expression and IL17+CD4+ and Foxp3+CD4+ immune phenotypes. PD-L1+ lymphocytes are associated with the distribution of CD4+, Foxp3+CD4+, IL17A+CD4+ T cells, M1 and M2 macrophages in TME of resected NSCLC.

Analysis of postoperative recurrence-free survival in non-small cell lung cancer patients based on consensus clustering

AIMS

This study aims to assess whether consensus clustering, based on computed tomography (CT) radiomics from both intratumoral and peritumoral regions, can effectively stratify the risk of non-small cell lung cancer (NSCLC) patients and predict their postoperative recurrence-free survival (RFS).

MATERIALS AND METHODS

A retrospective analysis was conducted on the data of surgical patients diagnosed with NSCLC between December 2014 and April 2020. After preprocessing CT images, radiomic features were extracted from a 9-mm region encompassing both the tumor and its peritumoral area. Consensus clustering was utilized to analyze the radiomics features and categorize patients into distinct clusters. A comparison of the differences in clinical pathological characteristics was conducted among the clusters. Kaplan-Meier survival analysis was employed to investigate differences in survival among the clusters.

RESULTS

A total of 266 patients were included in this study, and consensus clustering identified three clusters (Cluster 1: n=111, Cluster 2: n=61, Cluster 3: n=94). Multiple clinical risk factors, including pathological TNM staging, programmed cell death ligand 1 (PD-L1), and epidermal growth factor receptor (EGFR) expression status exhibit significant differences among the three clusters. Kaplan-Meier survival analysis demonstrated significant variations in RFS across the clusters (P<0.001). The 3-year cumulative recurrence-free survival rates were 76.5% (95% CI: 68.6-84.4) for Cluster 1, 45.9% (95% CI: 33.4-58.4) for Cluster 2, and 41.5% (95% CI: 31.6-51.5) for Cluster 3.

CONCLUSIONS

Consensus clustering of CT radiomics based on intratumoral and peritumoral regions can stratify the risk of postoperative recurrence in patients with NSCLC.

Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Is Associated With Recurrence and Survival of Resectable Non-Small Cell Lung Cancer (NSCLC): A Retrospective Study

INTRODUCTION

Curative lung resection remains the key therapeutic strategy for early-stage non-small cell lung cancer (NSCLC). However, a proportion of patients still experience variable outcomes and eventually develop recurrence or die from their disease. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has been identified as a deleterious factor that inhibits tumor cells apoptosis and leads to reduction of lymphocyte infiltration. However, there has been no research on the predicted role of PCSK9 as an immunohistochemical biomarker with survival in resectable NSCLC.

METHODS

One hundred sixty-three patients with resectable NSCLC were retrospectively reviewed, and PCSK9 expression of resected NSCLC was analyzed by immunohistochemistry using tissue microarrays.

RESULTS

PCSK9 was associated with recurrence (42.1% relapsed in the PCSK9lo group versus 57.9% relapsed in the PCSK9hi group, P = 0.006) and survival status (39.6% dead in PCSK9lo group versus 60.4% dead in PCSK9hi group, P = 0.004) in patients with resectable NSCLC. Moreover, resectable NSCLC patients with higher PCSK9 expression in tumor tissue experienced poorer disease-free survival (median disease-free survival: 10.5 versus 25.2 mo, hazard ratio = 1.620, 95% confidence interval: 1.124-2.334) and overall suvrival (median overall suvrival: 20.0 versus 54.1 mo, hazard ratio = 1.646, 95% confidence interval: 1.101-2.461) compared to those with lower PCSK9 expression.

CONCLUSIONS

High PCSK9 expression of tumor was correlated with recurrence and worse survival status of resectable NSCLC in our retrospective study, which indicated that PCSK9 in NSCLC may be an immunohistochemical biomarker of poor prognosis for patients with resectable NSCLC. Further large-scale prospective studies are warranted to establish these results.

Causal role of immune cells in lung cancer subtypes: Mendelian randomization study

Lung cancer, characterized by its high incidence and mortality rates, is a challenging malignancy to treat. Immunotherapy has emerged as a crucial treatment modality, yet its effectiveness varies significantly among patients due to the diverse immune microenvironment involved. Our study aims to analyze the similarities and differences in immune cell profiles across different subtypes of lung cancer. We employed a comprehensive two-sample Mendelian randomization analysis to establish causal connections between immune cells and lung cancer. We examined differential expression of 731 immune cell types and compared their profiles among various lung cancer subtypes. Our analysis revealed that 47 immune cell types exhibited differential expression in lung cancer, with 15 showing a protective effect and 32 having a tumor-promoting effect. Notably, we observed greater similarities in immune cell profile between squamous carcinoma and adenocarcinoma subtypes, while small cell lung cancerHHHH displayed less overlap with the other two types. Specifically, CD4+ naive T cells showed differential expression across all three lung cancer subtypes, whereas three other immune cell types exhibited differential expression exclusively in adenocarcinoma and squamous cell carcinoma. Our findings substantiate a causal link between immune cell dynamics and lung cancer progression. Moreover, our identification of distinct immune cell composition among histological subtypes of lung cancer may serve as a valuable reference for further investigation into immunotherapeutic strategies.

Risk prediction of multiple-station N2 metastasis in patients with upfront surgery for clinical single-station N2 non-small cell lung cancer

To investigate long-term outcomes and develop a risk model for pathological multi-station N2 (pN2b) in patients who underwent upfront surgery for clinical single-station N2 (cN2a) non-small cell lung cancer (NSCLC). From 2006 to 2018, 547 patients who had upfront surgery for suspected cN2a NSCLC underwent analysis. A risk model for predicting pN2b metastasis was developed using preoperative clinical variables via multivariable logistic analysis. Among 547 clinical cN2a NSCLC patients, 118 (21.6%), 58 (10.6%), and 371 (67.8%) had pN0, pN1, and pN2. Among 371 pN2 NSCLC patients, 77 (20.8%), 165 (44.5%), and 129 (34.7%) had pN2a1, pN2a2, and pN2b. The 5-year overall survival rates for pN2a1 and pN2a2 were significantly higher than for pN2b (p = 0.041). Histologic type (p < 0.001), age ≤ 50 years (p < 0.001), preoperatively confirmed N2 metastasis (p < 0.001), and clinical stage IIIB (vs. IIIA) (p = 0.003) were independent risk factors for pN2b metastasis. The risk scoring system based on this model demonstrated good discriminant ability for pN2b disease (area under receiver operating characteristic: 0.779). In cN2a NSCLC patients, those with multiple N2 metastases indicate worse prognosis than those with a single N2 metastasis. Our risk scoring system effectively predicts pN2b in these patients.

Current Evidence for a Lung Cancer Screening Program

Background

Lung cancer screening is still in an early phase compared to other cancer screening programs, despite its high lethality particularly when diagnosed late. Achieving early diagnosis is crucial to obtain optimal outcomes.

Summary

In this review, we will address the current evidence on lung cancer screening through low-dose computed tomography (LDCT) and its impact on mortality reduction, existing screening recommendations, patient eligibility criteria, screening frequency and duration, benefits and harms, cost-effectiveness and some insights on lung cancer screening implementation and adoption. Additionally, new non-imaging, noninvasive biomarkers with high diagnostic potential are also briefly highlighted.

Key Messages

LDCT screening in a prespecified population based on age and smoking history proved to reduce lung cancer mortality. Optimization of the target population and management of LDCT pitfalls can further improve lung cancer screening efficiency and cost-effectiveness. Novel screening technologies and biomarkers being studied can potentially be game-changers in lung cancer screening and diagnosis.

Differences in postoperative prognosis between early-stage lung adenocarcinoma and squamous cell carcinoma

BACKGROUND

Although prognosis and treatments differ between small-cell- and nonsmall-cell carcinoma, comparisons of the histological types of NSCLC are uncommon. Thus, we investigated the oncological factors associated with the prognosis of early-stage adenocarcinoma and squamous cell carcinoma.

METHODS

We retrospectively compared the clinicopathological backgrounds and postoperative outcomes of patients diagnosed with pathological stage I-IIA adenocarcinoma and squamous cell carcinoma primary lung cancer completely resected at our department from January 2007 to December 2017. Multivariable Cox regression analysis for overall survival and recurrence-free survival was performed.

RESULTS

The median follow-up duration was 55.2 months. The cohort consisted of 532 adenocarcinoma and 96 squamous cell carcinoma patients. A significant difference in survival was observed between the two groups, with a 5-year overall survival rate of 90% (95% confidence interval 86-92%) for adenocarcinoma and 77% (95% CI 66-85%) for squamous cell carcinoma (P < 0.01) patients. Squamous cell carcinoma patients had worse outcomes compared to adenocarcinoma patients in stage IA disease, but there were no significant differences between the two groups in stage IB or IIA disease. In multivariate analysis, invasion diameter was associated with overall survival in adenocarcinoma (hazard ratio 1.76, 95% confidence interval 1.36-2.28), but there was no such association in squamous cell carcinoma (hazard ratio 0.73, 95% confidence interval 0.45-1.14).

CONCLUSIONS

The importance of tumor invasion diameter in postoperative outcomes was different between adenocarcinoma and squamous cell carcinoma. Thus, it is important to consider that nonsmall-cell carcinoma may have different prognoses depending on the histological type, even for the same stage.

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.

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.

Management of stage III non-small-cell lung cancer: rays of hope

Lung cancer remains the most common cause of cancer death across the world. Non-small-cell lung cancer (NSCLC) represents the most frequent type of lung cancer and is frequently diagnosed at an advanced stage. Stage III NSCLC, which encompasses 30% of cases, refers to a state between localized and metastatic disease, and is associated with poor prognosis. As highlighted in this review, stage III represents a heterogenous group, whose complex management includes multimodal treatment, discussed below, and requires discussion in multidisciplinary teams. The goal of this approach is a maximalist attitude in these patients with locally advanced and non-metastatic disease. However, many issues remain under debate including the optimal sequences of treatment between different treatment modalities, patient selection particularly for surgery, the duration of perioperative treatments and the identification of biomarkers to determine which patients might benefit of specific treatment like immunotherapy and targeted therapies. This review describes the current landscape of management of stage III NSCLC, discussing the critical issue of resectability, and highlighting the recent advancements in the field, particularly the incorporation of immune-checkpoint inhibitors (ICIs) and targeted therapies in this setting.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

Meta-Analysis of Rates and Risk Factors for Local Recurrence in Surgically Resected Patients With NSCLC and Differences Between Asian and Non-Asian Populations

Introduction

Postoperative radiotherapy (PORT) reduces local failure in patients with NSCLC, without a clear overall survival benefit. It is unknown whether the subsets of patients benefit. Two recent large randomized controlled trials, PORT-C (People's Republic of China) and Lung ART (Europe), reported widely different locoregional recurrence (LR) rates in the control arms, at 18.3% and 28.1% (46% of which were mediastinal recurrences), respectively. We performed a meta-analysis of patients with pathologic (p) N0 to N2 disease to evaluate the risk factors for LR and to explore possible differences in recurrence risk between Asian population (AP) and non-Asian population (NAP).

Methods

We identified all original studies of curative NSCLC surgical resection which reported risk of LR between January 1, 2000, and January 10, 2021, excluding studies with less than 10 LR, patients with metastatic disease, or any neoadjuvant therapy. A total of 87 studies were identified with pN0 to N2 disease; of these, 56 were of high quality (HQ) on the basis of the Newcastle-Ottawa Scale. For each risk factor, we derived pooled relative risk (RR) and 5-year rate estimates using random-effects models.

Results

Overall, the three significant highest pooled RRs (95% confidence intervals) for LR were pN2 versus pN0 (3.01, 1.39-6.55), lymphovascular invasion (1.92, 1.58-2.33), and advanced pT3-4 stage versus pT1 (1.86, 1.53-2.25). For HQ studies, the highest RRs for LR were lymphovascular invasion (1.94, 1.57-2.40), sublobar versus lobar resection (1.86, 1.46-2.36), and pN1 versus pN0 (1.84, 1.37-2.47), but pN2 versus pN0 was no longer significant (3.0, 0.57-15.61), on the basis of only two eligible studies. The RRs for LR were consistent for most factors in AP and NAP, although the RR for male versus female sex was higher in AP (1.44, 1.21-1.72) than in NAP (1.09, 0.99-1.19). Where reported, the pooled rate of LR at 5 years was lower in AP (12.0%) than in NAP (22.7%), despite similar overall 5-year recurrence rates (both LR and distal) in both populations: 38.0% in AP and 37.3% in NAP. Nevertheless, a lower 5-year mortality rate was noted in AP (24.3%) than in NAP (45.9%).

Conclusions

There is little high-quality evidence to support the hypothesis that pN2 disease is a risk factor for LR, but LR seems to be lower in Asians. Prospective evaluation of LR factors and rates may be necessary before further prospective evaluation of PORT, because it may not depend on nodal status alone. Recurrence rates may differ in Asians. The impact of mutational status and modern treatment including targeted therapies and immune checkpoint inhibitors is inadequately studied.

Glycolysis induces Th2 cell infiltration and significantly affects prognosis and immunotherapy response to lung adenocarcinoma

Glycolysis has a major role in cancer progression and can affect the tumor immune microenvironment, while its specific role in lung adenocarcinoma (LUAD) remains poorly studied. We obtained publicly available data from The Cancer Genome Atlas and Gene Expression Omnibus databases and used R software to analyze the specific role of glycolysis in LUAD. The Single Sample Gene Set Enrichment Analysis (ssGSEA) indicated a correlation between glycolysis and unfavorable clinical outcome, as well as a repression effect on the immunotherapy response of LUAD patients. Pathway enrichment analysis revealed a significant enrichment of MYC targets, epithelial-mesenchymal transition (EMT), hypoxia, G2M checkpoint, and mTORC1 signaling pathways in patients with higher activity of glycolysis. Immune infiltration analysis showed a higher infiltration of M0 and M1 macrophages in patients with elevated activity of glycolysis. Moreover, we developed a prognosis model based on six glycolysis-related genes, including DLGAP5, TOP2A, KIF20A, OIP5, HJURP, and ANLN. Both the training and validation cohorts demonstrated the high efficiency of prognostic prediction in this model, which identified that patients with high risk may have a poorer prognosis and lower sensitivity to immunotherapy. Additionally, we also found that Th2 cell infiltration may predict poorer survival and resistance to immunotherapy. The study indicated that glycolysis is significantly associated with poor prognosis in patients with LUAD and immunotherapy resistance, which might be partly dependent on the Th2 cell infiltration. Additionally, the signature comprised of six genes related to glycolysis showed promising predictive value for LUAD prognosis.

Prognosis of primary pulmonary adenocarcinoma after surgical resection in small-breed dogs: 52 cases (2005-2021)

BACKGROUND

Tumor size is an important prognostic factor in lung cancer in dogs, and the canine lung carcinoma stage classification (CLCSC) recently has been proposed to subdivide tumor sizes. It is unclear if the same classification scheme can be used for small-breed dogs.

OBJECTIVES

To investigate whether the tumor size classification of CLCS is prognostic for survival and progression outcomes in small-breed dogs with surgically resected pulmonary adenocarcinomas (PACs).

ANIMALS

Fifty-two client-owned small-breed dogs with PAC.

METHODS

Single-center retrospective cohort study conducted between 2005 and 2021. Medical records of dogs weighing <15 kg with surgically resected lung masses histologically diagnosed as PAC were examined.

RESULTS

The numbers of dogs with tumor size ≤3 cm, >3 cm to ≤5 cm, >5 cm to ≤7 cm, or >7 cm were 15, 18, 14, and 5, respectively. The median progression-free interval (PFI) and overall survival time (OST) were 754 and 716 days, respectively. In univariable analysis, clinical signs, lymph node metastasis, margin, and histologic grade were associated with PFI, and age, clinical signs, margin, and lymph node metastasis were associated with OST. Tumor size classification of CLCS was associated with PFI in all categories, and tumor size >7 cm was associated with OST. In multivariable analysis, tumor size >5 cm to ≤7 cm and margin were associated with PFI, and age was associated with OST.

CONCLUSIONS AND CLINICAL IMPORTANCE

The tumor size classification of CLCS would be an important prognostic factor in small-breed dogs with surgically resected PACs.

Prognostic impact of tumor spread through air spaces for T2aN0 stage IB non-small cell lung cancer

BACKGROUND

Spread through air spaces (STAS) is a pattern of invasion recently identified in non-small cell lung cancer (NSCLC), with a poor prognosis. However, the predictive impact of STAS in stage IB NSCLC is not well understood. This investigation aims to assess the prognostic influence of STAS in stage IB NSCLC.

METHODS

We reviewed 130 resected stage IB NSCLC between 2010 and 2015. Beyond the central tumor edge, lung parenchymal air gaps containing cancer cells were identified as STAS. In order to estimate recurrence-free survival (RFS) and overall survival (OS), Cox models and Kaplan-Meier techniques were utilized. Logistic regression analysis was employed to define the factors influencing STAS.

RESULTS

Of 130 patients, 72 (55.4%) had STAS. STAS was a significant prognosticator. Kaplan-Meier method showed that STAS-positive patients had a significantly lower OS and RFS than STAS-negative patients (5-year OS, 66.5% vs. 90.4%, p = 0.02; 5-year RFS, 59.5% vs. 89.7%, p = 0.004) In a semiquantitative assessment, the RFS and OS were shorter in survival analysis when STAS increased (5-year RFS, 89.7%, no STAS, 61.8%, low STAS, 57.2%, high STAS, p = 0.013; 5-year OS, 90.4%, no STAS, 78.3%, low STAS, 57.2%, high STAS, p = 0.002). The association between STAS and poor differentiation, adenocarcinoma, and vascular invasion (p value was <0.001, 0.047, and 0.041, respectively) was statistically significant.

CONCLUSIONS

The STAS is an aggressive pathological feature. RFS and OS could be significantly reduced by STAS, while it also serves as an independent predictor.

The independent prognostic effect of marital status on non-small cell lung cancer patients: a population-based study

Background

Previous studies had demonstrated that marital status was an independent prognostic factor in multiple cancers. However, the impact of marital status on non-small cell lung cancer (NSCLC) patients was still highly controversial.

Method

All NSCLC patients diagnosed between 2010-2016 were selected from the Surveillance, Epidemiology and End Results (SEER) database. To control the confounding effect of related clinicopathological characteristics, propensity score matching (PSM) was conducted between married and unmarried groups. In addition, independent prognostic clinicopathological factors were evaluated via Cox proportional hazard regression. Moreover, nomograms were established based on the clinicopathological characteristics, and the predictive accuracy was assessed by calibration curves. Furthermore, decision curve analysis (DCA) was used to determine the clinical benefits.

Results

In total, 58,424 NSCLC patients were enrolled according to the selection criteria. After PSM, 20,148 patients were selected into each group for further analysis. The married group consistently demonstrated significantly better OS and CSS compared to unmarried group [OS median survival (95% CI): 25 (24-26) vs. 22 (21-23) months, p < 0.001; CSS median survival (95% CI): 31 (30-32) vs. 27 (26-28) months, p < 0.001]. Moreover, single patients were associated with the worst OS [median survival (95% CI): 20 (19-22) months] and CSS [median survival (95%CI): 24 (23-25) months] among unmarried subgroups. Besides, unmarried patients had a significantly worse prognosis compared to married patients in both univariate and multivariate Cox proportional hazard regressions. Furthermore, married group was associated with better survival in most subgroups. To predict the 1-, 3- and 5-year OS and CSS probabilities, nomograms were established based on age, race, sex, gender, marital status, histology, grade, TNM stage. The C-index for OS and CSS were 0.759 and 0.779. And the calibration curves showed significant agreement between predictive risk and the observed probability. DCA indicated nomograms had consistently better predict performance.

Conclusion

This study demonstrated that unmarried NSCLC patients were associated with significantly worse OS and CSS compared to married NSCLC patients. Therefore, unmarried patients need not only closer surveillance, but also more social and family support, which may improve patients' adherence and compliance, and eventually improve the survival.

A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

LINC01232 promotes lung squamous cell carcinoma progression through modulating miR-181a-5p/SMAD2 axis

BACKGROUND

LINC01232 has been implicated in the progression of multiple malignancies. Yet, the function of LINC01232 in the carcinogenesis of lung squamous cell carcinoma (LUSC) remains unclear. This study aims to examine the role LINC01232 plays in LUSC progression.

METHODS

mRNA and protein levels were assessed using qRT-PCR and western blot, respectively. Cell proliferation was assessed by CCK-8 and colony formation assays. Cell migration and invasion were evaluated by transwell assay. The interactions between LINC01232, miR-181a-5p, and SMAD2 were assessed using luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. The subcellular distribution of LINC01232 was examined by cytosolic/nuclear fractionation assay RESULTS: LINC01232 was upregulated in both LUSC tissues and cell lines. Knockdown of LINC01232 impaired cell proliferation, migration and invasion capability in H1229 and A549 cells, a phenotype that could be reversed by miR-181a-5p silencing. In addition, LINC01232 silencing reduced levels of N-cadherin, Vimentin, and Snail in H1229 and A549 cells, but increased the level of E-cadherin, which can be abrogated by miR-181a-5p inhibitors.

CONCLUSIONS

In summary, our study demonstrates that LINC01232 expression increases in LUSC tissues and cell lines and promotes LUSC progression by modulating the miR-181a-5p/SMAD2 signaling, providing new potential drug targets for LUSC treatment.

Construction and validation of a prognostic model based on ten signature cell cycle-related genes for early-stage lung squamous cell carcinoma

BACKGROUND

We performed a bioinformatics analysis to screen for cell cycle-related differentially expressed genes (DEGs) and constructed a model for the prognostic prediction of patients with early-stage lung squamous cell carcinoma (LSCC).

METHODS

From a gene expression omnibus (GEO) database, the GSE157011 dataset was randomly divided into an internal training group and an internal testing group at a 1:1 ratio, and the GSE30219, GSE37745, GSE42127, and GSE73403 datasets were merged as the external validation group. We performed single-sample gene set enrichment analysis (ssGSEA), univariate Cox analysis, and difference analysis, and identified 372 cell cycle-related genes. Additionally, we combined LASSO/Cox regression analysis to construct a prognostic model. Then, patients were divided into high-risk and low-risk groups according to risk scores. The internal testing group, discovery set, and external verification set were used to assess model reliability. We used a nomogram to predict patient prognoses based on clinical features and risk values. Clinical relevance analysis and the Human Protein Atlas (HPA) database were used to verify signature gene expression.

RESULTS

Ten cell cycle-related DEGs (EIF2B1, FSD1L, FSTL3, ORC3, HMMR, SETD6, PRELP, PIGW, HSD17B6, and GNG7) were identified and a model based on the internal training group constructed. From this, patients in the low-risk group had a higher survival rate when compared with the high-risk group. Time-dependent receiver operating characteristic (tROC) and Cox regression analyses showed the model was efficient and accurate. Clinical relevance analysis and the HPA database showed that DEGs were significantly dysregulated in LSCC tissue.

CONCLUSION

Our model predicted the prognosis of early-stage LSCC patients and demonstrated potential applications for clinical decision-making and individualized therapy.

A novel analytical approach for outcome prediction in newly diagnosed NSCLC based on [18F]FDG PET/CT metabolic parameters, inflammatory markers, and clinical variables

OBJECTIVES

To develop a novel analytical approach based on ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC.

METHODS

A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [¹⁸F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis.

RESULTS

The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model.

CONCLUSIONS

We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC.

KEY POINTS

• The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification.

Lung cancer immunotherapy: progress, pitfalls, and promises

Lung cancer is the primary cause of mortality in the United States and around the globe. Therapeutic options for lung cancer treatment include surgery, radiation therapy, chemotherapy, and targeted drug therapy. Medical management is often associated with the development of treatment resistance leading to relapse. Immunotherapy is profoundly altering the approach to cancer treatment owing to its tolerable safety profile, sustained therapeutic response due to immunological memory generation, and effectiveness across a broad patient population. Different tumor-specific vaccination strategies are gaining ground in the treatment of lung cancer. Recent advances in adoptive cell therapy (CAR T, TCR, TIL), the associated clinical trials on lung cancer, and associated hurdles are discussed in this review. Recent trials on lung cancer patients (without a targetable oncogenic driver alteration) reveal significant and sustained responses when treated with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) checkpoint blockade immunotherapies. Accumulating evidence indicates that a loss of effective anti-tumor immunity is associated with lung tumor evolution. Therapeutic cancer vaccines combined with immune checkpoint inhibitors (ICI) can achieve better therapeutic effects. To this end, the present article encompasses a detailed overview of the recent developments in the immunotherapeutic landscape in targeting small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Additionally, the review also explores the implication of nanomedicine in lung cancer immunotherapy as well as the combinatorial application of traditional therapy along with immunotherapy regimens. Finally, ongoing clinical trials, significant obstacles, and the future outlook of this treatment strategy are also highlighted to boost further research in the field.

Argentine consensus recommendations for lung cancer screening programmes: a RAND/UCLA-modified Delphi study

BACKGROUND

Lung cancer (LC) screening improves LC survival; the best screening method in terms of improving survival is low-dose CT (LDCT), outpacing chest X-ray and sputum cytology.

METHODS

A consensus of experts in Argentina was carried out to review the literature and generate recommendations for LC screening programmes. A mixed-method study was used with three phases: (1) review of the literature; (2) modified Delphi consensus panel; and (3) development of the recommendations. The Evidence to Decision (EtD) framework was used to generate 13 evaluation criteria. Nineteen experts participated in four voting rounds. Consensus among participants was defined using the RAND/UCLA method.

RESULTS

A total of 16 recommendations scored ≥7 points with no disagreement on any criteria. Screening for LC should be performed with LDCT annually in the population at high-risk, aged between 55 and 74 years, regardless of sex, without comorbidities with a risk of death higher than the risk of death from LC, smoking ≥30 pack-years or former smokers who quit smoking within 15 years. Screening will be considered positive when finding a solid nodule ≥6 mm in diameter (or ≥113 mm3) on baseline LDCT and 4 mm in diameter if a new nodule is identified on annual screening. A smoking cessation programme should be offered, and cardiovascular risk assessment should be performed. Institutions should have a multidisciplinary committee, have protocols for the management of symptomatic patients not included in the programme and distribute educational material.

CONCLUSION

The recommendations provide a basis for minimum requirements from which local institutions can develop their own protocols adapted to their needs and resources.

Clinical impact of histologic type on survival and recurrence in patients with surgically resected stage II and III non-small cell lung cancer

OBJECTIVES

This study aimed to investigate the clinical impact of histologic type on the survival and recurrence outcomes of patients with stage II and III non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A total of 2155 consecutive adult patients who underwent complete resection of stage II and III NSCLC between 2008 and 2018 were enrolled. The primary endpoints were freedom from recurrence (FFR) and overall survival (OS). The secondary endpoint was the time to lung cancer or non-lung cancer death.

RESULTS

Of the 2155 patients, 1436 (66.6 %) had adenocarcinoma (ADC) and 719 (33.4 %) had squamous cell carcinoma (SqCC). Patients with SqCC had better FFR than those with ADC (stage II, p < 0.001; stage III, p < 0.001). Although patients with ADC showed a slightly better OS until 5 years than those with SqCC, the difference was insignificant (stage II, p = 0.292; stage III, p = 0.196). Patients with SqCC had higher rates of non-lung cancer death than patients with ADC (stage II, p < 0.001; stage III, p = 0.039). The time from lung cancer recurrence to death was shorter in patients with SqCC than in those with ADC (stage II, median 13 vs 37 months, p < 0.001; stage III, median 11 vs 26 months, p < 0.001).

CONCLUSIONS

In stage II and III NSCLC, ADC had a higher risk of recurrence than SqCC, with no difference in OS. These results were related to significant differences in non-lung cancer mortality and recurrence-to-death time between the two histologic types.

Prognostic value of proliferation, PD-L1 and nuclear size in patients with superior sulcus tumours treated with chemoradiotherapy and surgery

AIMS

The aim of this study was to determine the relationship between proliferative activity, PD-L1 status and nuclear size changes after preoperative chemoradiotherapy (CRT) and the clinical outcome in patients with superior sulcus tumours.

METHODS

Proliferative activity (MIB-1) and PD-L1 status were estimated by immunohistochemistry in the tumour cells of resection specimen in a series of 33 patients with residual tumour after trimodality therapy for a sulcus superior tumour between 2005 and 2014. A morphometric analysis of both pretreatment and post-treatment tumour materials was also performed. Results were related to disease-free survival and overall survival.

RESULTS

Low proliferative activity (<20% MIB-1) was associated with better overall survival: 2-year overall survival of 73% compared with 43% and 25%, respectively, for moderate (MIB-1 20%-50%) and high (MIB-1 >50%) proliferative activity (p=0.016). A negative PD-L1 status (<1% positive tumour cells) was also associated with better overall survival (p=0.021). The mean nuclear size of normal lung tissue pneumocytes was significantly smaller compared with the mean nuclear size of tumour cells of the resection specimens (median difference -38.1; range -115.2 to 16.0; p<0.001). The mean nuclear size of tumour cells did not differ between pretreatment biopsies and resection specimens (median difference -4.6; range -75.2 to 86.7; p=0.14). Nuclear size was not associated with survival (p=0.82).

CONCLUSIONS

Low proliferative activity determined by MIB-1 as well as a negative PD-L1 expression are significantly associated with better overall survival in patients with residual tumour after CRT for superior sulcus tumour.

A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma

Background

The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear.

Methods

The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines.

Results

Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed.

Conclusion

The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.

A Nomogram to Predict the Overall Survival of Patients With Resected T1-2N0-1M0 Non-Small Cell Lung Cancer and to Identify the Optimal Candidates for Adjuvant Chemotherapy in Stage IB or IIA Non-Small Cell Lung Cancer Patients

BACKGROUND

The benefit of adjuvant chemotherapy for IB/IIA non-small cell lung cancer (NSCLC) patients remains uncertain. This study aimed to develop a prognostic model to predict overall survival in resected NSCLC patients with T1-2N0-1M0 stage and identify optimal candidates for postoperative chemotherapy among those with stage IB or IIA disease.

METHODS

We conducted a retrospective study using the SEER 18 database (2000-2018, November 2020 submission) of patients who underwent radical surgery for T1-2N0-1M0 NSCLC. The patients not receiving adjuvant chemotherapy were randomly divided into training and validation cohorts. A prognostic nomogram was established and evaluated using calibration and receiver operating characteristic curves. Based on the nomogram, stage IB and IIA patients were categorized into two prognostic groups, each further divided into cohorts based on adjuvant chemotherapy status. Kaplan-Meier analysis and log-rank tests were used to compare overall survival between these groups.

RESULTS

A total of 14 789 patients were enrolled and randomly assigned to the training cohort (n = 10 352) and validation cohort (n = 4437). Ten independent prognostic factors were identified and integrated into the prognostic model. The area under the receiver operating characteristic curve was .706, .699, and .705 in the training cohort, and .700, .698, and .695 in the validation cohort at 1, 3, and 5 years, respectively. Among stage IB and IIA patients, only those in the high-risk group showed a significant benefit from adjuvant chemotherapy, with a 16.4% absolute increase in 5-year overall survival.

CONCLUSIONS

The nomogram developed in the study may help physicians choose the most appropriate management strategy for each patient.

Development and validation of a nomogram for predicting survival time and making treatment decisions for clinical stage IA NSCLC based on the SEER database

Background

The aim of this study was to establish and validate a nomogram model for accurate prediction of patients' survival with T1aN0M0 none small cell lung cancer (NSCLC).

Methods

The patients, diagnosed with the stage IA NSCLC from 2004-2015, were identified from the Surveillance, Epidemiology and End Results (SEER) database. The variables with a P-value < 0.05 in a multivariate Cox regression were selected to establish the nomogram. The discriminative ability of the model was evaluated by the concordance index (C-index). The proximity of the nomogram prediction to the actual risk was depicted by a calibration plot. The clinical usefulness was estimated by the decision curve analysis (DCA). Survival curves were made with Kaplan-Meier method and compared by Log-Rank test.

Results

Eight variables, including treatment, age, sex, race, marriage, tumor size, histology, and grade were selected to develop the nomogram model by univariate and multivariate cox regression. The C-index was 0.704 (95% CI, 0.694-0.714) in the training set and 0.713 (95% CI, 0.697-0.728) in the test set, which performed significantly better than 8th edition AJCC TNM stage system (0.550, 95% CI, 0.408-0.683, P < 0.001). The calibration curve showed that the prediction ability of 3-years and 5-years survival rate demonstrated a high degree of agreement between the nomogram model and the actual observation. The DCA curves also proved that the nomogram-assisted decisions could improve patient outcomes.

Conclusion

We established and validated a prognostic nomogram to predict 3-years and 5-years overall survival in stage IA NSCLC.

Consistency and prognostic value of preoperative staging and postoperative pathological staging using 18F-FDG PET/MRI in patients with non-small cell lung cancer

OBJECTIVE

In recent years, positron emission tomography/magnetic resonance imaging (PET/MRI) has been clinically used as a method to diagnose non-small cell lung cancer (NSCLC). This study aimed to evaluate the concordance of staging and prognostic ability of NSCLC patients using thin-slice computed tomography (CT) and ¹⁸F-fluorodeoxyglucose (FDG) PET/MRI.

METHODS

This retrospective study was performed on consecutive NSCLC patients who underwent both diagnostic CT and ¹⁸F-FDG PET/MRI before surgery between November 2015 and May 2019. The cTNM staging yielded from PET/MRI was compared with CT and pathological staging, and concordance was investigated, defining pathological findings as reference. To assess the prognostic value of disease-free survival (DFS) and overall survival (OS), we dichotomized the typical prognostic factors and TNM classification staging (Stage I vs. Stage II or higher). Kaplan-Meier curves derived by the log-rank test were generated, and univariate and multivariate analyses were performed to identify the factors associated with DFS and OS.

RESULTS

A total of 82 subjects were included; PET/MRI staging was more consistent (59 of 82) with pathological staging than with CT staging. There was a total of 21 cases of CT and 11 cases of PET/MRI that were judged as cStage I, but were actually pStage II or pStage III. CT tended to judge pN1 or pN2 as cN0 compared to PET/MRI. There was a significant difference between NSCLC patients with Stage I and Stage II or higher by PET/MRI staging as well as prognosis prediction of DFS by pathological staging (P < 0.001). In univariate analysis, PET/MRI, CT, and pathological staging (Stage I or lower vs. Stage II or higher) all showed significant differences as prognostic factors of recurrence or metastases. In multivariate analysis, pathological staging was the only independent factor for recurrence (P = 0.009), and preoperative PET/MRI staging was a predictor of patient survival (P = 0.013).

CONCLUSIONS

In NSCLC, pathologic staging was better at predicting recurrence, and preoperative PET/MRI staging was better at predicting survival. Preoperative staging by PET/MRI was superior to CT in diagnosing hilar and mediastinal lymph-node metastases, which contributed to the high concordance with pathologic staging.

Hazard Function Analysis of Recurrence in Patients with Curatively Resected Lung Cancer: Results from the Japanese Lung Cancer Registry in 2010

Simple Summary

To optimize postoperative surveillance of lung cancer patients, we investigated the hazard function of tumor recurrence in patients with completely resected lung cancer. Using the records of the 2010 Japanese Joint Committee of Lung Cancer Registry, the risk of postoperative recurrence was analyzed using a cause-specific hazard function in patients who underwent lobectomy to completely resect pathological stage I–III lung cancer. The hazard function for recurrence exhibited a peak at approximately 9 months after surgery, followed by a tapered plateau-like tail extending to 60 months. The peak risk for intrathoracic recurrence was approximately two-fold higher compared with that of extrathoracic recurrence. When considered together with the results of the subgroup analysis, the characteristics of the postoperative tumor recurrence hazard in a large cohort of lung cancer patients may be useful for improving stage-related management of postoperative surveillance.

Abstract

To optimize postoperative surveillance of lung cancer patients, we investigated the hazard function of tumor recurrence in patients with completely resected lung cancer. We analyzed the records of 12,897 patients in the 2010 Japanese Joint Committee of Lung Cancer Registry who underwent lobectomy to completely resect pathological stage I–III lung cancer. The risk of postoperative recurrence was determined using a cause-specific hazard function. The hazard function for recurrence exhibited a peak at approximately 9 months after surgery, followed by a tapered plateau-like tail extending to 60 months. The peak risk for intrathoracic recurrence was approximately two-fold higher compared with that of extrathoracic recurrence. Subgroup analysis showed that patients with stage IIIA adenocarcinoma had a continuously higher risk of recurrence compared with patients with earlier-stage disease. However, the risk of recurrence in patients with squamous cell carcinoma was not significantly different compared with that more than 24 months after surgery, regardless of pathological stage. In conclusion, the characteristics of postoperative tumor recurrence hazard in a large cohort of lung cancer patients may be useful for determining the time after surgery at which patients are at the highest risk of tumor recurrence. This information may improve stage-related management of postoperative surveillance.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

Objective

This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).

Methods

For the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.

Results

A Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.

Conclusion

We developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10067-8.

The prognostic value of machine learning techniques versus cox regression model for head and neck cancer

BACKGROUND

Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC.

METHODS

Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC).

RESULTS

Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730 ± 0.8700, IBS: 7.680 ± 0.4800), oral cavity cancer (OCC, C-index: 84.250 ± 0.6700, IBS: 11.480 ± 0.3300) and oropharyngeal cancer (OPC, C-index: 82.510 ± 0.5400, IBS: 10.120 ± 0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6th edition of the AJCC TNM staging system cohort) and external validations (the TCGA HNC cohort).

CONCLUSIONS

The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.

Transcriptomics in Tumor and Normal Lung Tissues Identify Patients With Early-Stage Non-Small-Cell Lung Cancer With High Risk of Postsurgery Recurrence Who May Benefit From Adjuvant Therapies

PURPOSE

The prognosis of patients with non-small-cell lung cancer (NSCLC), traditionally determined by anatomic histology and TNM staging, neglects the biological features of the tumor that may be important in determining patient outcome and guiding therapeutic interventions. Identifying patients with NSCLC at increased risk of recurrence after curative-intent surgery remains an important unmet need so that known effective adjuvant treatments can be offered to those at highest risk of recurrence.

METHODS

Relative gene expression level in the primary tumor and normal bronchial tissues was used to retrospectively assess their association with disease-free survival (DFS) in a cohort of 120 patients with NSCLC who underwent curative-intent surgery.

RESULTS

Low versus high Digital Display Precision Predictor (DDPP) score (a measure of relative gene expression) was significantly associated with shorter DFS (highest recurrence risk; P = .006) in all patients and in patients with TNM stages 1-2 (P = .00051; n = 83). For patients with stages 1-2 and low DDPP score (n = 29), adjuvant chemotherapy was associated with improved DFS (P = .0041). High co-overexpression of CTLA-4, PD-L1, and ICOS in normal lung (28 of 120 patients) was also significantly associated with decreased DFS (P = .0013), suggesting an immune tolerance to tumor neoantigens in some patients. Patients with DDPP low and immunotolerant normal tissue had the shortest DFS (P = 2.12E-11).

CONCLUSION

TNM stage, DDPP score, and immune competence status of normal lung are independent prognostic factors in multivariate analysis. Our findings open new avenues for prospective prognostic assessment and treatment assignment on the basis of transcriptomic profiling of tumor and normal lung tissue in patients with NSCLC.

Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non-small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility.

SIGNIFICANCE

Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826.

Prognostic impact of the tumor volume doubling time in clinical T1 non-small cell lung cancer with solid radiological findings

BACKGROUND

The purpose of this study was to investigate better radiological prognostic factors in clinical T1 pure-solid non-small cell lung cancer (NSCLC).

METHODS

This study enrolled 284 patients with clinical T1 solid NSCLC who underwent anatomical lung resection. The Cox proportional hazard model was used to evaluate the prognostic impact of tumor volume doubling time (VDT) at disease-free survival (DFS) and cancer-specific survival (CSS).

RESULTS

The median VDT was 347 days. Age (hazard ratio (HR) = 1.04; 95% confidence interval (CI), 1.01-1.07) and standardized uptake value max (SUVmax) (>6.0) (HR = 2.61; 95% CI, 1.52-4.66) were identified as significantly independent worse prognostic factors for DFS in a multivariable analysis without VDT. Furthermore, a multivariable analysis without SUVmax identified age (HR = 1.06; 95% CI, 1.03-1.09), CEA (>5.0 ng/ml) (HR = 2.34; 95% CI, 1.30-4.02), tumor diameter on CT (>2.0 cm) (HR = 1.91; 95% CI, 1.18-3.13), and VDT (HR = 4.03; 95% CI, 2.41-6.93) as significantly independent worse prognostic factors for DFS.

CONCLUSIONS

The VDT value could be a useful prognostic factor in clinical T1 solid NSCLC.

Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer

Objective

To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC).

Patients and Methods

This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups.

Results

The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P<0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D’Agostino test showed good calibration performance (P=0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis.

Conclusions

The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.

CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes

Background A preoperative predictive model is needed that can be used to identify patients with lung adenocarcinoma (LUAD) who have a higher risk of recurrence or metastasis. Purpose To investigate associations between CT-based radiomic consensus clustering of stage I LUAD and clinical-pathologic features, genomic data, and patient outcomes. Materials and Methods Patients who underwent complete surgical resection for LUAD from April 2014 to December 2017 with preoperative CT and next-generation sequencing data were retrospectively identified. Comprehensive radiomic analysis was performed on preoperative CT images; tumors were classified as solid, ground glass, or mixed. Patients were clustered into groups based on their radiomics features using consensus clustering, and clusters were compared with tumor genomic alterations, histopathologic features, and recurrence-specific survival (Kruskal-Wallis test for continuous data, χ2 or Fisher exact test for categorical data, and log-rank test for recurrence-specific survival). Cluster analysis was performed on the entire cohort and on the solid, ground-glass, and mixed lesion subgroups. Results In total, 219 patients were included in the study (median age, 68 years; interquartile range, 63-74 years; 150 [68%] women). Four radiomic clusters were identified. Cluster 1 was associated with lepidic, acinar, and papillary subtypes (76 of 90 [84%]); clusters 2 (13 of 50 [26%]) and 4 (13 of 45 [29%]) were associated with solid and micropapillary subtypes (P < .001). The EGFR alterations were highest in cluster 1 (38 of 90 [42%], P = .004). Clusters 2, 3, and 4 were associated with lymphovascular invasion (19 of 50 [38%], 14 of 34 [41%], and 28 of 45 [62%], respectively; P < .001) and tumor spread through air spaces (32 of 50 [64%], 21 of 34 [62%], and 31 of 45 [69%], respectively; P < .001). STK11 alterations (14 of 45 [31%]; P = .006), phosphoinositide 3-kinase pathway alterations (22 of 45 [49%], P < .001), and risk of recurrence (log-rank P < .001) were highest in cluster 4. Conclusion CT-based radiomic consensus clustering enabled identification of associations between radiomic features and clinicalpathologic and genomic features and outcomes in patients with clinical stage I lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.