Survival Of Lung Cancer Patients Research Articles

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)—a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene’s spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer’s model.

RET-AREAL: A multi-center, real-world data analysis on the efficacy of pralsetinib in acquired RET fusion after resistance to EGFR/ALK-TKIs.

Pralsetinib demonstrated impressive improvement of survival in non-small cell lung cancer (NSCLC) patients harbored de novo RET fusion. However, the efficacy in patients with acquired RET fusion after resistance to EGFR/ALK-TKIs has only been reported on a case-by-case basis, and the strategy for overcoming the acquired RET fusion has not been fully investigated. This multicenter, real-world analysis enrolled 32 patients with unresectable NSCLC harbored acquired RET fusion after resistance to EGFR/ALK-TKIs in 23 centers across China from July 1st, 2018 to Nov 23rd, 2022. Epidemiological, clinical, genetic, and treatment data were collected. The primary outcome was time to treatment failure (TTF). Secondary outcomes were overall survival (OS), objective response rate (ORR), disease control rate (DCR) and toxicity. In real-world context, patients underwent pralsetinib-based treatment had a higher proportion of central nervous system metastasis. EGFR 19del was the predominant mutation type (62.5%) prior to acquired RET fusion. CCDC6 was the commonest RET fusion partner (40.6%). "Clonal RET fusion" (c-RET) and "subclonal RET fusion" (s-RET) were defined according to the RET fusion allele frequency. Patients with c-RET had higher proportions of undetected EGFR mutation and KIF5B-RET fusion. First-line pralsetinib-based therapy had notably superior median TTF when compared to their counterparts (8.03 versus 4.30 months, P=0.016). Notably, patients with c-RET had a better prognosis than those with s-RET (median TTF: NR versus 5.67 months, P=0.037, median OS: NR versus 9.83 months, P=0.047). In conclusion, pralsetinib-based therapy may be a potential strategy to overcome acquired RET fusion after resistance to EGFR/ALK-TKIs.

Impact of declared wildfire disasters on survival of lung cancer patients undergoing radiation.

Oncological treatments, such as radiotherapy, which requires consistent electricity, the presence of specialized clinical teams, and daily patient access to treatment facilities, are frequently disrupted by extreme weather events, posing several health hazards to patients. This study explores the association between declared wildfire disasters during radiotherapy and overall survival among patients with non-small cell lung cancer (NSCLC). The study population consisted of 202,935 adults with inoperable Stage III NSCLC, who initiated radiotherapy from 2004 through 2019. Exposure was defined as a wildfire disaster declaration in the county of the treatment facility within 12weeks of initiating radiotherapy. Overall survival was defined as the interval (months) between age at diagnosis and age at death, date of last contact, or study end. Cox proportional hazards was used to estimate crude and adjusted hazard ratios and 95% confidence intervals with inverse probability weighting. Patients exposed to a wildfire disaster declaration during radiation treatment had worse overall survival (HR, 1.03; 95% CI 1.00-1.06; p = 0.02), compared to unexposed patients in adjusted models. Exposure to a wildfire disaster during radiotherapy is associated with worse overall survival among patients with stage III non-operable NSCLC. This finding underscores the critical need for developing adaptation strategies within the healthcare sector, especially in oncology.

Toripalimab plus chemotherapy for first line treatment of advanced non-small cell lung cancer (CHOICE-01): final OS and biomarker exploration of a randomized, double-blind, phase 3 trial.

A randomized double-blind phase 3 trial (CHOICE-01, NCT03856411) demonstrated that combining toripalimab with chemotherapy substantially improves progression-free survival (PFS) in advanced non-small cell lung cancer (NSCLC) patients without pretreatment. This study presents the prespecified final analysis of overall survival (OS) and biomarkers utilizing circulating tumor DNA (ctDNA) and tissue-based sequencing. Additionally, the analysis revealed a higher median overall survival (OS, 23.8 months) in the toripalimab group than that in the control group (17.0 months). (HR = 0.69, 95%CI: 0.57-0.93, nominal P = 0.01). This survival benefit was particularly notable in the non-squamous subgroup. As the first phase 3 study to perform both baseline tissue whole-exome sequencing (WES) and peripheral blood ctDNA testing, we investigated efficacy predictive biomarkers based on both tissue and ctDNA, Genomic sequencing of ctDNA showed high concordance with tumor tissue independently confirmed that individuals exhibiting a high tumor mutational burden, as well as mutations in the FA-PI3K-Akt and IL-7 signaling pathways benefited more from the toripalimab treatment. Furthermore, a ctDNA response observed on cycle 3 day 1, was associated with improved clinical outcomes for patients treated with the combination therapy. In conclusion, Toripalimab plus chemotherapy yields significant improvements in OS as a first-line treatment. The study highlights the utility of ctDNA as a proxy for tumor tissue, providing novel prospects for predicting efficacy of immuno-chemotherapy through continuous ctDNA monitoring.

The systemic inflammation response index (SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model.

Inflammation and immune evasion are associated with tumorigenesis and progression. The Systemic Inflammation Response Index (SIRI) has been proposed as a pre-treatment peripheral blood biomarker. This study aims to compare the relationship between SIRI, various serum biomarkers, and the prognosis of NSCLC patients before and after treatment. A retrospective study was conducted on advanced NSCLC patients treated with anti-PD-1 drugs from December 2018 to September 2021. Peripheral blood markers were measured pre- and post-treatment, and hazard ratios were calculated to assess the association between serum biomarkers and progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and Cox proportional hazards models were employed for survival analysis. A nomogram model was built based on multivariate Cox proportional hazards regression analysis using the R survival package, with internal validation via the bootstrap method (1,000 resamples). Predictive performance was expressed using the concordance index (C-index), and calibration plots illustrated predictive accuracy.The application value of the model was evaluated by decision curve analysis (DCA). Among 148 advanced NSCLC patients treated with PD-1 inhibitors, the median PFS was 12.9 months (range: 5.4-29.2 months), and the median OS was 19.9 months (range: 9.6-35.2 months). Univariate analysis identified pre- and post-treatment SIRI, mGRIm-Score, and PNI as independent prognostic factors for both PFS and OS (p < 0.05). Multivariate analysis demonstrated that high post-SIRI and post-mGRIm-Score independently predicted poor PFS (P < 0.001, P = 0.004) and OS (P = 0.048, P = 0.001). The C-index of the nomogram model for OS was 0.720 (95% CI: 0.693-0.747) and for PFS was 0.715 (95% CI: 0.690-0.740). Internal validation via bootstrap resampling (B = 1,000) showed good agreement between predicted and observed OS and PFS at 1, 2, and 3 years, as depicted by calibration plots. SIRI is an important independent predictor of early progression in advanced NSCLC patients treated with PD-1 inhibitors and may assist in identifying patients who will benefit from PD-1 inhibitors therapy in routine clinical practice.

Persist or resist: Immune checkpoint inhibitors in EGFR-mutated NSCLC.

Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), especially third-generation TKIs, have significantly improved the progression-free survival and overall survival of non-small cell lung cancer (NSCLC) patients with EGFR mutation, TKI resistance is inevitable for most patients. Over the past few years, immune checkpoint inhibitors (ICIs) have significantly improved the survival for EGFR-wild type NSCLC patients. However, no significantly improved benefits were observed with ICI monotherapy in EGFR-mutated patients. EGFR-mutated NSCLC shows more heterogeneity in tumor mutational burden (TMB), programmed cell death-ligand 1 (PD-L1), and immune microenvironment characteristics. Whether ICIs are suitable for EGFR-mutated NSCLC patients remains to be elucidated. In this review, we summarized clinical trials of ICIs or combined therapy in EGFR-mutated NSCLC patients. We further discussed the factors determining the efficacy of ICIs in EGFR-mutated NSCLC patients, the mutation subtypes and microenvironment characteristics of potential responders. More importantly, we provided insights into areas worth further investigation in the future.

Predicting overall survival and prophylactic cranial irradiation benefit in small cell lung cancer patients: a multicenter cohort study

BackgroundTo construct a CT-based radiomics nomogram, enabling the estimation of overall survival (OS) in small cell lung cancer (SCLC) patients and facilitating the identification of prophylactic cranial irradiation (PCI) beneficiaries through risk stratification using the radiomics score (RS).MethodsA retrospective recruitment of 375 patients with pathologically confirmed SCLC was conducted across three medical centers, followed by their division into different cohorts. To generate the RS, a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Subsequently, patients were stratified into either the low RS or high RS group, determined by identifying the optimal RS cutoff value. Subsequently, a radiomics nomogram was constructed using the RS, followed by assessments of its discrimination, calibration, clinical utility and reclassification. Moreover, we evaluated the potential benefits of PCI following stratification by RS.ResultsFor the internal and external validation cohorts, the radiomics nomogram (concordance index [C-index]: 0.770, 0.763) outperformed clinical nomogram (C-index: 0.625, 0.570) in predicting OS. Besides, patients with high RS had survival benefit from PCI in both the limited and extensive stage (hazard ratio [HR]: 0.304, 95% confidence interval [CI]: 0.087–1.065, P = 0.003; HR: 0.481, 95% CI: 0.270–0.860, P = 0.019, respectively), while no significant association were observed in patients with low RS.ConclusionA radiomics nomogram based on CT shows potential in predicting OS for individuals with SCLC. The RS could assist in tailoring treatment plans to identify patients likely to benefit from PCI.

Evaluating the impact of cardiac substructure dosimetric parameters on survival in lung cancer patients undergoing postoperative radiotherapy.

The association of cardiac dosimetric parameters with survival in lung cancer patients is well established. However, most research has concentrated on patients undergoing definitive treatment. This study aims to investigate the relationship between cardiac dosimetric parameters and survival in patients receiving postoperative radiotherapy (PORT). Sixty patients who received PORT between 2011 and 2021 were retrospectively evaluated. The substructures of the heart were delineated on the simulation computed tomography scans of the patients. Univariate and multivariate Cox regression analyses were conducted to investigate the correlation between dosimetric parameters and overall survival. The Statistical Package for the Social Sciences (SPSS) version 23.0 (IBM Corp., Armonk, NY, USA) was utilized for statistical analyses. Right atrium (RA) maximum dose (Dmax) was the only variable that was significantly associated with ashorter OS. Further receiver operating characteristic (ROC) analysis revealed that the optimum cut-off value for RA Dmax was 43.6 Gy, with asensitivity of 69% and aspecificity of 62%. In addition, inclusion of the upper right paratracheal (2R), lower right paratracheal (4R), left pulmonary ligament (9L), and right hilus (10R) lymphatic stations in the treatment field led to an increase in RA Dmax. The results of this retrospective study show that RA Dmax appears to have an impact on overall survival in patients undergoing PORT. Limiting the RA Dmax dose to below 43.6 Gy and avoiding elective nodal irradiation might potentially enhance survival in this patient cohort.

Effect of direct oral anticoagulants compared to enoxaparin on objective response to immune checkpoint inhibitors in patients with lung cancer.

A hypoxic tumor microenvironment inhibits the normal functioning of immune cells. Studies have hypothesized that anticoagulants that can penetrate and bind to factor Xa in the tumor microenvironment, can enhance T-cell function and augment immunotherapy activity. This study compared objective response rate and progression-free survival of lung cancer patients on concomitant immunotherapy treated with direct-acting oral anticoagulants versus enoxaparin. This single-center retrospective study included 73 adults with stage-IV lung cancer who received at least two cycles of immunotherapy and one month of anticoagulant therapy with direct-acting oral anticoagulants (Arm A) versus enoxaparin (Arm B) between June 1, 2016, to September 30, 2022. Primary endpoint was objective response rate, and secondary endpoints were rates of complete response, progression-free survival, incidence of thrombotic events, and major bleeding. Objective response rate at 6 months was 24.5% versus 25% while progression-free survival at 6 months was 54.7% versus 45% in Arm A versus Arm B, respectively. Complete response rates at 6 months were 7.5% in Arm A versus 0% in Arm B. One patient in Arm A and two in Arm B had a recurrent deep vein thrombosis. Nine patients in Arm A and two in Arm B were diagnosed with new deep vein thrombosis. One patient in Arm B was diagnosed with new pulmonary embolism. Two major bleeding events occurred in Arm B. Our study suggests a trend toward improved progression-free survival at 6 months with no new safety concerns in lung cancer patients on concurrent immunotherapy and direct-acting oral anticoagulants.

The Prognostic Value of Systemic Immune-Inflammation Index Supporting Age-Adjusted Charlson Comorbidity Index in Non-Small Cell Lung Cancer Patients with First-Line Platinum-Based Chemotherapy.

This study aimed to examine the association between the systemic immune-inflammation index (SII) (ie, neutrophil count × platelet count/lymphocyte count), the age-adjusted Charlson comorbidity index (ACCI), and overall survival (OS) in non-small cell lung cancer (NSCLC) patients undergoing first-line platinum-based chemotherapy (PBC), with a particular emphasis on the role of SII in supporting ACCI. This retrospective study enrolled 353 cases treated between July 2013 and November 2020. Mann-Whitney U-test and Kruskal-Wallis test were employed to compare parameters between high and low SII groups. The cut-off values for SII and ACCI were determined using the X-tile software. Prognostic significance was evaluated through the utilization of Kaplan-Meier curves and Cox regression analysis. In a univariate Cox regression analysis, sex, age, TNM, lymph node, therapy, SII, and ACCI were associated with OS. After adjusting for confounders in the multivariate analysis, TNM, SII, and ACCI remained independent prognostic factors for OS. Furthermore, within the ACCI subgroups (ACCI<5 or ACCI≥5), a high SII was significantly associated with an increased risk of death. Patients with both a high ACCI and a high SII had the highest risk of death (p < 0.001), with a loss of approximately ten months of survival during the first three years after treatment. SII was proven to be valuable in predicting OS and, when complemented by ACCI, can help tailor prognostic assessment and treatment strategies in assessing the survival of NSCLC patients with first-line PBC.

Respiratory Function as a Prognostic Factor for Lung Cancer in Screening and General Populations.

Despite advancements in screening, lung cancer remains the leading cause of cancer-related mortality globally. To investigate respiratory function as a prognostic factor for survival in the UK Biobank, a population-based cohort of over 500,000 participants, and the National Lung Screening Trial (NLST), a high-risk screening population of over 50,000 screenees. Participants with an incident lung cancer diagnosis and spirometry-assessed lung function were included. Lung cancer was measured as the ratio of forced expiratory volume in 1-second (FEV1) and forced vital capacity and percentage of predicted FEV1. Multivariable Cox proportional hazards models were fitted to estimate the impact of lung function on 5-year overall survival in populations with different baseline lung cancer risks. 2,690 and 609 patients were included in the analysis from the UK Biobank and the NLST, respectively. In the UK Biobank, a higher percentage of predicted FEV1 and ratio were associated with better survival after lung cancer diagnosis with hazard ratios of 0.97 (95% CI: 0.95 - 1.00, per 10% increase) and 0.95 (95% CI: 0.90 - 1.00, per 10% increase), respectively. No statistically significant results were found when assessing the data from the NLST study. Impaired lung function was associated with poorer survival for lung cancer patients in the general population, although this was less clear in a high risk, screening eligible population. This highlights the potential clinical importance of respiratory function as a prognostic factor in lung cancer in the general population and presents a possibility for personalized cancer management.

Prognostic Role of Inflammatory and Nutritional Biomarkers in Non-Small-Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Alone or in Combination with Chemotherapy as First-Line.

In recent years, several inflammation-related factors and nutritional parameters have been evaluated to develop prognostic scores as potential biomarkers in non-small-cell lung cancer (NSCLC) patients receiving immune checkpoint inhibitors (ICIs). The aim of this study was to retrospectively investigate the prognostic role of the advanced lung cancer inflammation (ALI) index, lung immune prognostic index (LIPI), prognostic nutritional index (PNI) and systemic inflammation score (SIS) in metastatic NSCLC patients receiving ICI alone or in combination with chemotherapy. We retrospectively included 191 patients with advanced NSCLC who received first-line ICI with or without chemotherapy from 2017 to 2024. The association between pretreatment ALI, LIPI, PNI, and SIS and overall survival (OS) was evaluated using the Kaplan-Meier method and Cox regression models. After a median follow-up of 27.7 months, significantly longer OS was associated with an ALI score > 18 vs. ≤18 (18.0 vs. 7.3 months; p = 0.00111), LIPI score 0 vs. 1 and 2 [18.9 vs. 8.2 and 4.2 months; (p = 0.001)], PNI ≥ 45 vs. <45 (22.7 vs. 9.6 months; p = 0.002), and SIS score 0 vs. 1 and 2 (27.4 vs. 7.1 and 8.6 months, respectively; p < 0.001). The OS benefit was independent of treatment (ICI vs. ICI + chemotherapy). At multivariate analysis, pretreatment albumin was positively associated with OS, while ECOG PS 1 and liver metastases were negatively associated with OS. Inflammatory and nutritional biomarkers such as the ALI, LIPI, PNI, and SIS represent useful tools to prognosticate survival in metastatic lung cancer patients treated with ICI alone or in combination with chemotherapy as first-line.

Abstract PR004: Age-induced chronic accumulation of glucocorticoids drives therapy resistance in lung cancer

Abstract Non-small cell lung cancer (NSCLC), a highly aggressive cancer, is the leading cause of cancer- related deaths in the U.S. While chemotherapy and targeted therapy are the main treatment modalities in the clinic, many patients do not respond to these treatments. Strikingly, the median age for lung cancer diagnosis is 71. Yet, preclinical and clinical research have largely been skewed towards young mice and patients. Here, we address this gap in knowledge and evaluate if old age affects the response to standard of care NSCLC therapies in KRAS-driven NSCLC. Using human sera from young and old healthy subjects to treat NSCLC cell lines, we show that age-driven circulatory changes are sufficient to drive a state of profound resistance to both chemotherapies as well as KRASG12C targeted therapies. We confirmed the relevance of our findings in vivo, by transplanting cancer cells derived from the K-rasLSL-G12D/+; p53fl/fl genetically engineered mouse model of lung cancer into young and old C57BL/6 mice. Using this model we showed that unlike young lung tumor-bearing mice on chemotherapy, old tumor-bearing mice do not respond to treatment and have a poor prognosis. Mechanistically, we found that old age drives a transcriptional reprogramming that favors drug resistance and identified the glucocorticoid receptor as a major driver of these transcriptional changes. We traced this to an age-induced chronic accumulation of glucocorticoids in circulation and within the tumor interstitial fluid. Chronic treatment with age-relevant concentrations of cortisol alone was sufficient to drive glucocorticoid receptor activation and induce resistance to therapies mirroring the effects of old age. Further supporting a driving role for glucocorticoids in driving age- induced drug resistance, blocking the glucocorticoid receptor genetically or pharmacologically rescues the drug resistance state imposed by old age in NSCLC cells. Lastly, using TCGA patient data we show an inverse correlation between glucocorticoid receptor activation (GR score) and progression-free survival of lung cancer patients. Together, our work demonstrates a role for age-induced glucocorticoids in circulation in promoting therapy-resistance in NSCLC patients and supports the use of therapeutic agents that block the glucocorticoid receptor as a strategy to increase therapy efficacy in NSCLC patients. Citation Format: Devesh Raizada, Felicia Lazure, Stanislav Drapela, Nadir Sarigul, Joanne Tejero, Didem Ilter, Ana Da Silva Gomes. Age-induced chronic accumulation of glucocorticoids drives therapy resistance in lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr PR004.

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy. We included 661 stage III NSCLC patients from three multi-national datasets: a training set (N = 349), test-set-1 (N = 229), and test-set-2 (N = 83), all undergoing definitive RT. A total of 104 distinct radiomic features were separately extracted from the regions of interest in the lung and the tumor. We developed four predictive models using eXtreme gradient boosting and selected the top 10 features based on the Shapley additive explanations (SHAP) values. These models were the tumor radiomic model (Model-T), lung radiomic model (Model-L), a combined radiomic model (Model-LT), and an integrated model incorporating clinical parameters (Model-LTC). Model performance was evaluated through Harrell's concordance index, Kaplan-Meier survival curves, time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Interpretability was assessed using the SHAP framework. Model-LTC exhibited superior performance, with notable predictive accuracy (C-index: training set, 0.87; test-set-2, 0.76) and time-dependent AUC above 0.75. Complex nonlinear relationships and interactions were evident among the model's variables. The integration of radiomic and clinical factors within an interpretable framework significantly improved OS prediction. The SHAP analysis provided insightful interpretability, enhancing the model's clinical applicability and potential for aiding personalized treatment decisions.

ZNF652 exerts a tumor suppressor role in lung cancer by transcriptionally downregulating cyclin D3.

Dysfunction of zinc finger protein 652 (ZNF652) is associated with various malignant tumors. However, the role of ZNF652 in lung cancer (LC) is poorly understood. Here, we identified that ZNF652 was downregulated in human LC tissues and cell lines. Low ZNF652 expression was associated with poor survival in LC patients. Overexpression of ZNF652 inhibited cell viability, proliferation, migration, and invasion of LC cells, whereas ZNF652 knockdown promoted these malignant phenotypes. Using RNA-seq analysis revealed that ZNF652 overexpression resulted in obvious alterations of various biological processes, especially cell cycle and cellular senescence. Subsequently, we confirmed that ZNF652 overexpression arrested the cell cycle at the G1 phase, increased ROS-mediated DNA damage, induced LC cell senescence, and enhanced cisplatin-induced apoptosis in LC cells. Mechanistically, ZNF652 directly bound to the promoter of cyclin D3 (CCND3), inhibited its transcription, thereby arresting the cell cycle at the G1 phase. Ectopic expression of cyclin D3 rescued the decreased cell viability and cell cycle arrest induced by ZNF652. In vivo studies further showed that ZNF652 overexpression suppressed the tumorigenic potential of LC. Collectively, our findings reveal that ZNF652 exerts a tumor suppressor role in lung cancer by inducing cell cycle arrest and cellular senescence via transcriptionally downregulating cyclin D3. Thus, ZNF652 may be a prognostic predictive factor for LC patients.

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to non-Hispanic White patients across the cancer care continuum. These real-world disparities are reflected in the data used to inform the decisions made to alleviate such inequities. Failing to account for inherently biased data underlying these observations could intensify racial cancer disparities and lead to misguided efforts that fail to appropriately address the real causes of health inequity. Estimate the racial/ethnic bias of machine learning models in predicting two-year survival and surgery treatment recommendation for non-small cell lung cancer (NSCLC) patients. A Cox survival model, and a LOGIT model as well as three other machine learning models for predicting surgery recommendation were trained using SEER data from NSCLC patients diagnosed from 2000-2018. Models were trained with a 70/30 train/test split (both including and excluding race/ethnicity) and evaluated using performance and fairness metrics. The effects of oversampling the training data were also evaluated. The survival models show disparate impact towards non-Hispanic Black patients regardless of whether race/ethnicity is used as a predictor. The models including race/ethnicity amplified the disparities observed in the data. The exclusion of race/ethnicity as a predictor in the survival and surgery recommendation models improved fairness metrics without degrading model performance. Stratified oversampling strategies reduced disparate impact while reducing the accuracy of the model. NSCLC disparities are complex and multifaceted. Yet, even when accounting for age and stage at diagnosis, non-Hispanic Black patients with NSCLC are less often recommended to have surgery than non-Hispanic White patients. Machine learning models amplified the racial/ethnic disparities across the cancer care continuum (which are reflected in the data used to make model decisions). Excluding race/ethnicity lowered the bias of the models but did not affect disparate impact. Developing analytical strategies to improve fairness would in turn improve the utility of machine learning approaches analyzing population-based cancer data.

CT-Defined Coronary Artery Calcification as a Prognostic Marker for Overall Survival in Lung Cancer: A Systematic Review and Meta-analysis

Rationale and ObjectivesCoronary artery calcification (CAC) can be quantified by computed tomography (CT). It is an important predictive and prognostic imaging marker for cardiovascular disease. The prognostic role for CAC in oncological patients is provided in preliminary studies, especially in lung cancer patients. The aim of the present study was to establish the effect of CAC score on overall survival (OS) in lung cancer patients based on the published literature Materials and MethodsLiterature databases were screened for papers analyzing the association between CAC and overall survival in lung cancer patients up to June 2024. The primary endpoint of the present systematic review was the OS. Overall, 7 studies were suitable for the analysis and were included. ResultsThe included studies comprised 2292 patients. The pooled hazard ratio for the association between CAC score and OS was HR=1.42 (95% CI=(1.19; 1.69), p<0.0001) in the univariable analysis and HR=1.56 (95% CI=(1.25; 1.94), p<0.0001) in the multivariable analysis. The pooled odds ratio for the association between CAC score and major cardiovascular events was OR=1.97 (95% CI=(1.24; 3.13)], p=0.004. ConclusionCT-defined CAC has a meaningful impact on overall survival and prediction of major cardiovascular events in lung cancer patients undergoing curative treatment. The sole presence of CAC on staging CT should be reported as an important prognostic marker in these patients.

Co-mutation of TP53 and TTN is Correlated with the Efficacy of Immunotherapy in Lung Squamous Cell Carcinoma.

Immunotherapy has been a promising treatment in advanced lung cancer. However, only a few patients could benefit from it. Herein, we aimed to explore mutationrelated predictive biomarkers in lung squamous cell carcinoma (LUSC), which could help develop clinical immunotherapy strategies and screen beneficial populations. Co-occurrence and mutually exclusive analysis was conducted on the TCGA-LUSC cohort. Correlations between the gene mutation status and tumor mutation burden (TMB) levels, and neo-antigen levels were analyzed by Wilcoxon test. Kaplan-Meier method was employed to analyze the progression-free survival (PFS) of lung cancer patients with immunotherapy. Gene set enrichment analysis (GSEA) was used to investigate the functional changes affected by TP53mut/TTNmut. The immune cell infiltration landscape in co-mutation subgroups was analyzed using CIBERSORT. 1) TP53, TTN, CSMD3, MUC16, RYR2, LRP1B, USH2A, SYNE1, ZFHX4, FAM135B, KMT2D, and NAV3 were frequently mutated in LUSC patients. 2) TMB levels in highly mutated groups were higher than that in wild type groups. 3) There were higher neoantigen levels in mutation group compared to the wild-type group, and LUSC patients in mutation group had longer PFS. 4) TP53mut/TTNmut co-mutation group exhibited higher TMB levels and better response to immunotherapy. 5) A host of immune-related signaling pathways was inhibited in TP53mut/TTNmut subgroup. 6) There were more T follicular helper cells and NK cells were in TP53mut/TTNmut subgroup than in the WT subgroup. The LUSC patients with TP53 and TTN co-mutation had higher TMB levels and better response to immunotherapy. The TP53 and TTN co-mutation is a promising novel biomarker to assist LUSC immunotherapy evaluation.

Characterizing dynamic tumor-immune interactions in lung adenocarcinoma through orthotopic allograft modeling.

The major clinical challenge in lung cancer immunotherapy is drug resistance. Therefore, establishing efficient orthotopic lung cancer mouse models to explore the mechanisms of drug immunotherapy resistance is highly important. In this study, we generated multiple fluorescently labeled lung adenocarcinoma cell lines from agenetically engineered KPZ mice model. Orthotopic transplantation of the primary 1F3 cell line induced a strong immune response, causing many small tumors to disappear, but some tumors evaded the immune attack and eventually formed large tumors. Tumor microenvironment analysis demonstrated that M2 macrophages play key roles in the immune response. Further mechanistic studies revealed that the chemokine CCL7 promoted the infiltration of M2 macrophages to facilitate immune escape, thereby promoting tumor growth in the orthotopic mouse model. Moreover, CCL7 levels were elevated in human lung cancer biopsies and positively correlated with M2 macrophage infiltration, and high CCL7 levels predicted advanced pathological stage and poor survival in lung cancer patients. Overall, we established a visualized and orthotopic mouse model with fluorescently labeled cells to better dissect the tumor microenvironment of lung cancer and define the critical role of CCL7 in promoting M2 macrophage polarization and tumorigenesis, providing new preclinical tools and potential targets for lung cancer immunotherapy.