Lung Cancer Survival Research Articles

Surveillance of subsolid nodules avoids unnecessary resections in lung cancer screening: long-term results of the prospective BioMILD trial.

The management of subsolid nodules (SSNs) in lung cancer screening (LCS) is still a topic of debate, with no current uniform strategy to deal with these lesions at risk of overdiagnosis and overtreatment. The BioMILD LCS trial has implemented a prospective conservative approach for SSNs, managing with annual low-dose computed tomography nonsolid nodules (NSNs) and part-solid nodules (PSNs) with a solid component <5 mm, regardless of the size of the nonsolid component. The present study aims to determine the lung cancer (LC) detection and survival in BioMILD volunteers with SSNs. Eligible participants were 758 out of 4071 (18.6%) BioMILD volunteers without baseline LC and at least one SSN detected at the baseline or further low-dose computed tomography rounds. The outcomes of the study were LC detection and long-term survival. A total of 844 NSNs and 241 PSNs were included. LC detection was 3.7% (31 out of 844) in NSNs and 7.1% (17 out of 241) in PSNs, being significantly greater in prevalent than incident nodules (8.4% versus 1.3% in NSNs; 14.1% versus 2.1% in PSNs; p-value for both nodule types p<0.01). Most LCs from SSNs were stage I (42/48, 87.5%), resectable (47/48, 97.9%), and caused no deaths. The 8-year cumulative survival of volunteers with LC derived from SSNs and not derived from SSNs was 93.8% and 74.9%, respectively. Conservative management of SSNs in LCS enables timely diagnosis and treatment of LCs arising from SSNs while ensuring the resection of more aggressive LCs detected away from SSNs.

Impact of timing and initial recurrence site on post-recurrence survival in resected non-small cell lung cancer

IntroductionHigh recurrence rate following curative surgery for non-small cell lung cancer (NSCLC) presents a major clinical challenge. Understanding the site and timing of recurrence and their impact on post-recurrence survival (PRS) is important for optimal postoperative surveillance and therapeutic intervention. In this study, we investigated the influence of the time to recurrence (TTR) and initial recurrence site on PRS. Materials and methodsThis multicentre prospective cohort study included patients who experienced recurrence after NSCLC resection between 2010 and 2015. The relationship between TTR and initial recurrence site, and their impact on PRS, was further evaluated. The hazard ratio (HR) for PRS was analysed using the Cox proportional hazards model. ResultsAmong 495 patients, the median TTR was 14 (range, 1–158) months; the mode of recurrence was 11 months. Early recurrence within 6 months was observed in 17 % of patients, and 68 % of patients showed recurrence within 2 years post-surgery. The HR for PRS was the highest in patients with a TTR within 6 months, and a noticeable decline was observed after the first 6 months. The HRs of TTRs beyond 2 years were not significantly different. The liver was a significantly unfavourable prognostic site for metastases (HR 2.2; P = 0.01), and metastases frequently recurred within 6 months after surgery. The timing of brain metastasis did not significantly impact the PRS. ConclusionEarlier recurrence after surgery was associated with shorter PRS. In contrast, recurrences occurring >2 years after surgery do not significantly affect PRS.

Artificial Intelligence: Can It Save Lives, Hospitals, and Lung Screening?

BackgroundEarly detection is essential in lung cancer survival. Lung screening or incidental detection on unrelated imaging holds the most promise for early detection. With the large volume of imaging performed today, management of incidental pulmonary nodules can be difficult. We hypothesized an artificial intelligence (AI) tool could reliably read all imaging reports, detect, and effectively triage indeterminate pulmonary nodules without adding additional personnel, helping save lives. MethodsAn incidental lung nodule clinic (ILNC) was created using AI and an existing nurse practitioner. Over 26 months, the software read all radiology reports, visualizing any lung tissue. Patients with nodules >3 mm and considered indeterminate by the nurse practitioner were referred to the ILNC. High-risk patients with benign nodules were offered entry into the lung screening program. ResultsOf 502,632 imaging reports analyzed, 22,136 (4.4%) had positive findings. Follow-up data were lacking in 11,797 (2.3%), 911 (7.7%) were verified lost, and 518 (4.4%) were referred to the ILNC. There were 393 patients with benign nodules and accepted enrollment in the lung screening program. Mean age of enrolled patients was 61 years, and 53% were men. Workup included 499 diagnostic computed tomographic scans, 39 positron emission tomographic scans, and 27 biopsy samples that identified 15 malignancies (2.9%), with 14 lung cancers (8 stage I, 4 stage III, and 2 stage IV). Treatment included 5 lobectomies, and 4 underwent stereotactic body radiation therapy. Financials were favorable. ConclusionsAI software can supplement practitioners, help diagnose lung cancer earlier, save lives, and generate value-based revenue for the hospital.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning.

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Presurgical invasive mediastinal staging in lung cancer, unexpected N2 and long-term survival: a registry-based study with data from the Spanish group for video-assisted thoracic surgery.

Mediastinal lymph node staging is a key element in the diagnosis of lung cancer. The combination of computed tomography (CT) and positron emission tomography (PET) has improved staging but some circumstances are known to influence their negative predictive value. The objective of this study was to assess the impact on survival of avoiding invasive mediastinal staging in surgical lung cancer patients with negative mediastinum in CT and PET and intermediate risk of unexpected pN2. Data were collected from the prospective cohort of the Spanish Group for Video-Assisted Thoracic Surgery (GEVATS), from December 2016 to March 2018. For this study, patients were selected if they had negative mediastinum in CT and PET findings but tumours >3 cm or located centrally, or with cN1 disease. Patients who did and did not undergo invasive staging [invasive group (IG) and non-invasive group (NIG)] were compared, analysing unexpected pN2 and survival with Kaplan-Meier curves and Cox regression. A total of 2,826 patients underwent surgery for primary lung cancer. We selected 1,247 patients who had tumours >3 cm, central tumours or cN1. Invasive staging was performed in 275 (22.1%) cases. The unexpected pN2 rate was 9.6% in the NIG and 13.8% in the IG, but half of them were discovered prior to surgery in the IG. Five-year overall survival (OS) was poorer in the IG (52.4% vs. 64%; P<0.001). In the Cox regression model, male sex, older age, diabetes, synchronous tumour, lower diffusing capacity for carbon monoxide, larger tumour size, higher pathological N-stage, and IG status were significant independent risk factors. Invasive staging recommended by guidelines could be reduced with an appropriate selection in mediastinal CT- and PET-negative patients with risk factors for unexpected pN2, because rates of pN2 and survival did not worsen without invasive staging.

Mogrol-mediated enhancement of radiotherapy sensitivity in non-small cell lung cancer: a mechanistic study.

This study investigated mogrol's impact on non-small cell lung cancer (NSCLC) radiosensitivity and underlying mechanisms, using various methods including assays, bioinformatics, and xenograft models. CCK-8, clonogenic, flow cytometry, TUNEL, and Western blot assays evaluated mogrol and radiation effects on NSCLC viability and apoptosis. Ubiquitin-specific protease 22 (USP22) expression in NSCLC patient tissues was determined by RT-qPCR and Western blot. A xenograft model validated mogrol's effects on tumor growth. Bioinformatics identified four ubiquitin-specific proteases, including USP22, in NSCLC. Kaplan-Meier analysis confirmed USP22's value in lung cancer survival. Human Protein Atlas (HPA) database analysis indicated higher USP22 expression in lung cancer tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis implicated ERK1/2 in NSCLC progression, and molecular docking showed stability between mogrol and ERK1/2. Further in vivo and in vitro experiments have demonstrated that mogrol enhances the inhibitory effect of radiation on NSCLC cell viability and clonogenic capacity. Cell viability and clonogenic capacity are reduced by >50%, and an increase in cellular apoptosis is observed, with apoptotic levels reaching 10%. USP22 expression was significantly elevated in NSCLC tissues, particularly in radiotherapy-resistant patients. Mogrol downregulated USP22 expression by inhibiting the ERK/CREB pathway, lowering COX2 expression. Mogrol also enhanced radiation's inhibition of tumor growth in mice. Mogrol enhances NSCLC radiosensitivity by downregulating USP22 via the ERK/CREB pathway, leading to reduced COX2 expression.NEW & NOTEWORTHY Mogrol enhances non-small cell lung cancer (NSCLC) cell sensitivity to radiotherapy by downregulating USP22 through the ERK/CREB pathway, reducing COX2 expression. These findings highlight mogrol's potential as an adjunct to improve NSCLC radiotherapy and open avenues for further research and clinical applications.

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Evolving Strategies in NSCLC Care: Targeted Therapies, Biomarkers, Predictive Models, and Patient Management

Abstract: Lung cancer, primarily Non-Small Cell Lung Cancer (NSCLC), is a global public health concern responsible for 80-85% of cases, with over two million new cases occurring annually, and 50% of them in Asia. While there has been a gradual reduction in United States (US) cases over the last decade, with 238,340 new cases and 127,070 deaths reported in 2023, managing metastatic NSCLC remains crucial, focusing on prolonging survival and enhancing quality of life. Integration of early palliative care shows promise in this regard. International guidelines recommend personalized treatment guided by genetic mutations (Epidermal Growth Factor Receptor (EGFR), Anaplastic Lymphoma Kinase, C-Ros oncogene [1ROS1]) and systemic therapies, including chemotherapy, targeted therapy, and immunotherapy, which play pivotal roles in redefining care. Treatment effectiveness hinges on factors such as cancer stage, patient health, and therapy type, with surgery and radiation therapy common in early stages and advanced stages requiring chemotherapy, targeted therapy, or immunotherapy. Despite advancements in treatment modalities, NSCLC continues to pose a significant challenge globally, particularly in Asia, where a substantial portion of new cases arise. While there has been an uncertain reduction in the incidence of lung cancer in the US over the past decade, the burden of the disease persists, with substantial mortality rates reported annually. Additionally, upon diagnosis, many NSCLC patients present with distant metastases, necessitating effective treatment strategies to improve overall survival and quality of life. The objectives include investigating targeted treatments for NSCLC with specific genetic mutations, examining mechanisms of novel therapies under study, evaluating preclinical and clinical studies for therapy efficacy and safety, identifying genetic and epigenetic biomarkers for diagnosis, prognosis, and treatment selection, developing predictive models for lung cancer recurrence and survival, assessing the efficacy of treatment approaches for early-stage lung cancer, and enhancing patient outcomes through collaborative care and lifestyle interventions. These collective efforts promise to improve patient outcomes and quality of life in the battle against NSCLC.

Prognostic implications of STK11 with different mutation status and its relationship with tumor-infiltrating immune cells in non-small cell lung cancer.

Mutations in STK11 (STK11Mut) gene may present a negative impact on survival in Non-small Cell Lung Cancer (NSCLC) patients, however, its relationship with immune related genes remains unclear. This study is to unveil whether overexpressed- and mutated-STK11 impact survival in NSCLC and to explore whether immune related genes (IRGs) are involved in STK11 mutations. 188 NSCLC patients with intact formalin-fixed paraffin-embedded (FFPE) tissue available for detecting STK11 protein expression were included in the analysis. After immunohistochemical detection of STK11 protein, patients were divided into high STK11 expression group (STK11High) and low STK11 expression group (STK11Low), and then Kaplan-Meier survival analysis and COX proportional hazards model were used to compare the overall survival (OS) and progression-free survival (PFS) of the two groups of patients. In addition, the mutation data from the TCGA database was used to categorize the NSCLC population, namely STK11 Mutated (STK11Mut) and wild-type (STK11Wt) subgroups. The difference in OS between STK11Mut and STK11Wt was compared. Finally, bioinformatics analysis was used to compare the differences in IRGs expression between STK11Mut and STK11Wt populations. The median follow-up time was 51.0 months (range 3.0 - 120.0 months) for real-life cohort. At the end of follow-up, 64.36% (121/188) of patients experienced recurrence or metastasis. 64.89% (122/188) of patients ended up in cancer-related death. High expression of STK11 was a significant protective factor for NSCLC patients, both in terms of PFS [HR=0.42, 95% CI= (0.29-0.61), P<0.001] and OS [HR=0.36, 95% CI= (0.25, 0.53), P<0.001], which was consistent with the finding in TCGA cohorts [HR=0.76, 95%CI= (0.65, 0.88), P<0.001 HR=0.76, 95%CI= (0.65, 0.88), P<0.001]. In TCGA cohort, STK11 mutation was a significant risk factor for NSCLC in both lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) histology in terms of OS [HR=6.81, 95%CI= (2.16, 21.53), P<0.001; HR=1.50, 95%CI= (1.00, 2.26), P=0.051, respectively]. Furthermore, 7 IRGs, namely CALCA, BMP6, S100P, THPO, CGA, PCSK1 and MUC5AC, were found significantly overexpressed in STK11-mutated NSCLC in both LUSC and LUAD histology. Low STK11 expression at protein level and presence of STK11 mutation were associated with poor prognosis in NSCLC, and mutated STK11 might probably alter the expression IRGs profiling.

Research Progress on the Relationship between Vitamin D and Lung Cancer

Vitamin D is a common steroid hormone in the human body, which is closely related to the occurrence and development of various tumors, including lung cancer. At present, a large number of in vivo and in vitro studies have confirmed that vitamin D and its analogs have a positive role in the prevention, treatment and prolongation of survival of lung cancer. Therefore this article provides a comprehensive review of research on the relationship between vitamin D and lung cancer, examining possible reasons for the discrepancies in results, particularly in clinical studies.

Area-based disparities in non-small-cell lung cancer survival.

In the Nordic countries, universal healthcare access has been effective in reducing socioeconomic disparities in non-small-cell lung cancer (NSCLC) management. However, other factors, such as proximity to healthcare facilities, may still affect access to care. This study aimed at investigating the influence of residential area on NSCLC survival. This population-based study utilized hospital records to identify NSCLC patients who underwent their initial treatment at Vaasa Central Hospital between January 1, 2016, and December 31, 2020. Patients were categorized based on their postal codes into urban areas (≤50 km from the hospital) and rural areas (>50 km from the hospital). Survival rates between these two groups were compared using Cox regression analysis. A total of 321 patients were included in the study. Patients residing in rural areas (n = 104) exhibited poorer 12-month survival rates compared to their urban counterparts (n = 217) (unadjusted Hazard Ratio [HR]: 1.38; 95% Confidence Interval [CI]: 1.01-1.89; p = 0.042). After adjusting for factors such as performance status, frailty, and stage at diagnosis in a multivariate Cox regression model, the adjusted HR increased to 1.47 (95% CI: 1.07-2.01; p = 0.017) for patients living in rural areas compared to those in urban areas. The study findings indicate that the distance to the hospital is associated with increased lung cancer mortality. This suggests that geographical proximity may play a crucial role in the disparities observed in NSCLC survival rates. Addressing these disparities should involve strategies aimed at improving healthcare accessibility, particularly for patients residing in rural areas, to enhance NSCLC outcomes and reduce mortality.

CLO24-062: Aneuploid Epcam + Disseminated Tumor Cells (DTCs) Correlated With Liver Metastasis and Independently Predict Poor Survival in Small Cell Lung Cancer.

CLO24-062: Aneuploid Epcam + Disseminated Tumor Cells (DTCs) Correlated With Liver Metastasis and Independently Predict Poor Survival in Small Cell Lung Cancer.

Timeliness of EGFR/ALK Inhibitor Treatment and Survival in Lung Cancer: A Population-Based Analysis

BackgroundALK or EGFR inhibitor is an ideal frontline treatment for patients with advanced non-small cell lung cancer (NSCLC) harboring targetable alteration in ALK or EGFR. However, in the real-world setting, frontline treatment may be delayed or not ideal. For such patients, the benefit of initiating ALK or EGFR inhibitor at a later timepoint remains uninvestigated. MethodsWe utilized a nationwide electronic health record-derived, deidentified database collected from diverse oncology practices across the United States to investigate the timeliness of preferred targeted therapy (PTT). Individualized data obtained from patients with stage IV NSCLC at diagnosis treated with PTT from 2018 to 2023 were analyzed. ResultsData from 3250 patients were analyzed: 2640 patients (81%) with EGFR mutation and 610 patients (19%) with ALK rearrangement. The median time to PTT was 7 weeks from diagnosis with 26.4% of patients started PTT within 1 month. Landmark analyses using timepoints ranging from 1 to 12 months after diagnosis showed that at all timepoints, patients who had started on PTT had a significantly better survival than those who had not. In a multivariable analysis, time to PTT ≤ 1 month from diagnosis was an independent predictor of survival: HR 0.74 (95% CI: 0.62-0.89), P = .002. Time to PTT was significantly associated with age, smoking status and genomic class. ConclusionsIn this population-based analysis, an initiation of PTT occurring as late as at least 1 year from diagnosis still resulted in a significant survival benefit, though the magnitude of benefit appeared decreased as time passed.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.

Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations. We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS). CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively. CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Structure-based multitargeted docking screening, pharmacokinetics, DFT, and dynamics simulation studies reveal mitoglitazone as a potent inhibitor of cellular survival and stress response proteins of lung cancer.

Lung cancer is a disease in which lung cells grow abnormally and uncontrollably, and the cause of it is direct smoking, secondhand smoke, radon, asbestos, and certain chemicals. The worldwide leading cause of death is lung cancer, which is responsible for more than 1.8 million deaths yearly and is expected to rise to 2.2 million by 2030. The most common type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for about 80% and small cell lung cancer (SCLC), which is more aggressive than NSCLC and is often diagnosed later and accounts for 20% of cases. The global concern for lung cancer demands efficient drugs with the slightest chance of developing resistance, and the idea of multitargeted drug designing came up with the solution. In this study, we have performed multitargeted molecular docking studies of Drug Bank compounds with HTVS, SP and XP algorithms followed by MM\GBSA against the four proteins of lung cancer cellular survival and stress responses, which revealed Mitoglitazone as a multitargeted inhibitor with a docking and MM\GBSA score ranging from - 5.784 to - 7.739 kcal/mol and - 25.81 to - 47.65kcal/mol, respectively. Moreover, we performed pharmacokinetics studies and QM-based DFT analysis, showing suitable candidate and interaction pattern analysis revealed the most count of interacting residues was 4GLY, 5PHE, 6ASP, 6GLU, 6LYS, and 6THR. Further, the results were validated with SPC water model-based MD simulation for 100ns in neutralised condition, showing the cumulative deviation and fluctuation < 2Å with many intermolecular interactions. The whole analysis has suggested that Mitoglitazone can be used as a multitargeted inhibitor against lung cancer-however, experimental studies are needed before human use.

Prognostic value of pretreatment procalcitonin and neutrophil–lymphocyte ratio in extensive-stage small-cell lung cancer

ABSTRACT Background To investigate the influence of pretreatment neutrophil-to-lymphocyte ratio (NLR) and procalcitonin (PCT) on progression-free survival (PFS) in extensive-stage small-cell lung cancer (SCLC) patients. Method A total of 100 extensive-stage SCLC patients were enrolled in our study. Patients were stratified according to the median values of pretreatment NLR and PCT levels: low NLR group (NLR ≤3.17), high NLR group (NLR＞3.17), low PCT group (PCT ≤0.06; ng/ml), high PCT group (PCT＞0.06; ng/ml). The Kaplan-Meier method and multivariable Cox regression model were used to reveal the prognostic effects of pretreatment NLR and PCT on PFS. Results The median PFS of the total extensive-stage SCLC patients was 6.0 months. The median PFS of low pretreatment NLR group (NLR ≤3.17) was not significantly different from that of high pretreatment NLR group (6.2 months vs 5.8 months; p = .675). Patients with low pretreatment PCT (PCT ≤0.06; ng/ml) had significantly better PFS than patients with high pretreatment PCT (PCT＞0.06; ng/ml) (6.9 months vs 5.7 months; p = .043). With the multivariable Cox regression analysis, the response to first-line chemotherapy (p ≤ .001) and pretreatment PCT (HR = 0.516; 95%CI 0.326–0.817; p = .005) were identified as independent factors associated with PFS. Conclusion Pretreatment PCT is an independent factor associated with PFS in extensive-stage SCLC patients treated with first-line chemotherapy, but pretreatment NLR reflects no significant prognostic value in our study.

Smoking, Lung Cancer Stage, and Prognostic Factors-Findings from the National Lung Screening Trial.

Low-dose computed tomography (LDCT) increases the early detection of lung cancer. Identifying modifiable behaviors that may affect tumor progression in LDCT-detected patients increases the likelihood of long-term survival and a good quality of life. We examined cigarette smoking behaviors on lung cancer stage, progression, and survival in 299 ever-smoking patients with low-dose CT-detected tumors from the National Lung Screening Trial. Univariate and multivariate Cox models were used to estimate the hazard ratio (HR) for smoking variables on survival time. Current vs. former smokers and early morning smokers (≤5 min after waking, i.e., time to first cigarette (TTFC) ≤ 5 min) had more advanced-stage lung cancer. The adjusted HR for current vs. former smokers was 1.3 (95% confidence interval [CI] 0.911-1.98, p = 0.136) for overall survival (OS) and 1.3 (0.893-1.87, p = 0.1736) for progression-free survival (PFS). The univariate hazard ratios for TTFC ≤ 5 min vs. >5 min were 1.56 (1.1-2.2, p = 0.013) for OS and 1.53 (1.1-2.12, p = 0.01) for PFS. Among current smokers, the corresponding HRs for early TTFC were 1.78 (1.16-2.74, p = 0.0088) and 1.95 (1.29-2.95, p = 0.0016) for OS and PFS, respectively. In causal mediation analysis, the TTFC effect on survival time was mediated entirely through lung cancer stage. The current findings indicate smoking behaviors at diagnosis may affect lung cancer stage and prognosis.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

Abstract 4793: Effects of treatment delays on lung cancer survival

Abstract Background: Previous studies have established a link between treatment initiation delays and reduced overall survival in individuals diagnosed with lung cancer. These delays are often influenced by diagnostic complexities, logistical hurdles, and patient-related factors. However, the association between treatment delays and survival outcomes has yet to be extensively investigated using data from a large national cohort. This study aims to fill this research gap and provide unique insights into the relationship between treatment delays and lung cancer survival by utilizing data from a substantial national cohort. Methods: The study population consisted of 3,723 lung cancer patients participating in the NCI PLCO Cancer Screening Trial. Cox Proportional Hazards (PH) Regression analyses examined the time between cancer diagnosis and treatment initiation using several measures including intervals from tumor doubling/stage progression time models by Spratt et al. (1964) and Detterbeck et al. (2008). Analyses controlled for patient and clinical characteristics including age, sex, race/ethnicity, smoking status, histopathologic type, cancer stage, and treatment modality. Data analysis was conducted using Microsoft Excel, STATA, and IBM SPSS software. Results: PH Regression analyses demonstrated a clear association between longer time intervals from diagnosis to treatment initiation and increased risk of mortality. Patients with time intervals exceeding 20 days exhibited a hazard ratio (HR) for mortality of 1.21 (p &amp;lt; 0.001, 95% confidence interval [CI] 1.1158-1.3176). The Spratt tumor progression model, considering intervals exceeding 88 days, revealed a HR of 1.22 (p = 0.03, 95% CI 1.0146-1.4885). On the other hand, the Detterbeck model, which considered intervals exceeding 118 days, did not show a statistically significant association with increased mortality risk. Furthermore, PH analysis identified cigarette smoking status, marital status, and tumor grade as significant predictors of mortality risk. Conclusions: Data from a large national cohort study indicate that delays in lung cancer treatment initiation have detrimental impacts on survival. Treatment initiation shortly after diagnosis had the most significant differential impact on mortality risk. Timely initiation of treatment is of utmost importance and should be prioritized in lung cancer care. By addressing factors contributing to treatment delays, it is likely possible to improve the survival rates of patients with lung cancer. Citation Format: Percy Guzman Montero, Sharon Segura Cordero. Effects of treatment delays on lung cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4793.