Survival Rate For Lung Cancer Research Articles

Predicting Lung Cancer Survival to the Future: Population-Based Cancer Survival Modeling Study.

Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025. Using the Taiwan Cancer Registry, this study uses the survivorship-period-cohort model to assess the feasibility of achieving this goal by predicting future survival rates of patients with lung cancer in Taiwan. This retrospective study analyzed data from 205,104 patients with lung cancer registered between 1997 and 2018. Survival rates were calculated using the survivorship-period-cohort model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error. The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013. Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance's goal could be within reach. The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes.

Performance characteristics of a polymerase chain reaction-based assay for the detection of EGFR mutations in plasma cell-free DNA from patients with non-small cell lung cancer using cell-free DNA collection tubes.

Survival rates in non-small cell lung cancer (NSCLC) are low. Detection of circulating tumor DNA in liquid biopsy (plasma) is increasingly used to identify targeted therapies for clinically actionable mutations, including EGFR mutations in NSCLC. The cobas® EGFR Mutation Test v2 (cobas EGFR test) is FDA-approved for EGFR mutation detection in tissue or liquid biopsy from NSCLC. Standard K2EDTA tubes require plasma separation from blood within 4 to 8 hours; however, Roche Cell-Free DNA (cfDNA) Collection Tubes (Roche cfDNA tube) enable whole blood stability for up to 7 days prior to plasma separation. This analysis assessed performance of Roche cfDNA tubes with the cobas EGFR test for the detection of EGFR mutations in plasma from healthy donors or patients with NSCLC. Overall, test performance was equally robust with either blood collection tube, eg, regarding limit of detection, linearity, and reproducibility, making Roche cfDNA tubes suitable for routine clinical laboratory use in this setting. Importantly, the Roche cfDNA tubes provided more flexibility for specimen handling versus K2EDTA tubes, eg, in terms of tube mixing, plasma separation, and sample stability, and do not require processing of blood within 8 hours thereby increasing the reach of plasma biopsies in NSCLC.

Abstract 4593: A synergy between MDM2 and AURKB pathways in driving cell cycle and cell survival in lung cancer cells

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide and non-small cell lung cancer (NSCLC) is the most prevalent type among lung cancers, contributing to the high mortality seen with this deadly disease. Despite aggressive treatments, the 5-year survival rate for lung cancer is only 23.7% in the United States. MDM2 and AURKB are two cancer-driving genes that are highly expressed in a broad range of cancer types including lung cancers. Furthermore, MDM2 overexpression can positively impact AURKB expression and contribute to oncogenesis and cancer progression. Therefore, blocking MDM2 with specific inhibitors has become a promising strategy for the restoration of p53 function that could be used for the treatment of different types of cancers including lung cancers. In this regard, both MDM2 and AURKB inhibitors have shown promising antitumor activity against NSCLC. Therefore, the main objective of this study was to understand the molecular mechanisms linking MDM2 and ARKB using RG7388, CM272, BMS-582949 (p38 inhibitor), and Ralimetinib (p38 inhibitor) in A549 and H460 lung cancer cells. We analyzed the individual or combination treatment effects of these drugs on epigenetic modifications, cell cycle arrest, and cell death mechanisms, using the in vitro experimental model. When RG7388 was used individually, or in combination with BMS-582949 or Ralimetinib, a significant increase in the cytotoxic effect was observed in A549 and H460 cells. Induction of cell cycle arrest by RG7388 in cancer cells was evidenced by elevated p21 expression levels. In addition, RG7388 and combination treatments in NSCLC cells showed significant upregulation of p53, p27, Bax, cleaved PARP, phospho-FOXO3a and down-regulation in DNMT3a, DNMT1, FOXM1, AURKB, CDC25, and CDK4 levels. It appears that during treatments with a combination of MDM2 with DNMT or p38 inhibitor, two pathways might be blocked simultaneously, which could lead to increased cancer cell death. RG7388 appears to induce cell death in a p21-dependent manner and CM272, BMS-582949, and Ralimetinib seem to induce cell death via a p21-independent manner by inhibiting a downstream pathway. However, further studies are needed to fully understand the intracellular link between MDM2 and AURKB that is disrupted by the anti-cancer agents used in our study. (This project was supported by the PFRDG Grant of Nova Southeastern University and by the Generous support from the Royal Dames of Cancer Research Ft. Lauderdale, FL) Citation Format: Umamaheswari Natarajan, Appu Rathinavelu. A synergy between MDM2 and AURKB pathways in driving cell cycle and cell survival in lung cancer cells [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 4593.

Abstract 4845: Evaluating racial disparities in immune checkpoint inhibitor survival and autoimmunity

Abstract Introduction: In recent years, immune checkpoint inhibitors (ICI) have revolutionized the landscape of treatment across a variety of cancers. Potential concerns regarding racial disparities in ICI access and therapeutic efficacy have been well documented. Recent studies have indicated improved ICI access and response outcomes in White patients compared to minority groups with head and neck cancer (HNC) and melanoma patients. In contrast, patients diagnosed with lung cancer have been mixed, with some research suggesting black patients may fare better with ICI therapy. Existing literature is largely limited to retrospective studies with small population sizes, and lacks comparisons of racial disparities across malignancies. We aim to describe and compare racial inequities in ICI outcomes in HNC, melanoma, and lung cancer. Methods: The TriNetX US Collaborative Network database was queried for patients >18 years-old with a diagnosis of head and neck cancer, melanoma or lung cancer that were treated with ICIs. Malignancy cohorts were defined by race: non-Hispanic White (NHW) and Black. Race cohorts were 1:1 propensity score matched (PSM) for comorbidities, oncologic stage, and chemoradiation treatment. Demographic differences in cohorts were evaluated prior to matching. Following PSM, overall survival and rates of autoimmunity were assessed at 5 years. Results: 19,443 NHW and 2,827 Black patients treated with ICI were identified. At a baseline, the Black patient cohorts had significantly higher rates of comorbidies (p<0.02). Across all malignancies, the geographic distribution of NHW patients was more equally distributed compared to Black patients (majority south). With PSM, black HNC patients had significantly lower survival rates compared to NHW [HR: 1.4 (1.2, 1.6), p=0.0002]. In melanoma, the same trend was present, though not significant [HR: 1.3 (0.95, 1.7), p=0.1]. In contrast, in lung cancer, Black patients had significantly higher survival rates compared to NHW patients [HR 0.78 (0.70, 0.86), p<0.0001]. In HN and lung cancer, NHW patients had significantly higher rates of autoimmune conditions after ICI therapy compared to Black [OR: 2.0 (1.4,2.8), 1.2 (1.0, 1.4), respectively]. Conclusion: This study represents the most extensive analysis of racial disparities on ICI outcomes across malignancies. This study found that black patients treated with ICIs had decreased survival rates at 5 years compared to NHW patients in HNC and melanoma, but increased survival rate in lung cancer. This disparity persisted following PSM, matching cohorts for comorbidities, oncologic stage and adjuvant therapy. Black patients also had significantly lower rates of autoimmunity in HN and lung cancer. Noted differences in survival may be a reflection of geographic practice differences, access to care and/or socioeconomic status. Further investigation is required to elucidate the underlying etiologies of these trends. Citation Format: Kathryn L. Nunes, Pablo Llerena, Praneet Kaki, Shreya Mandloi, Annie Moroco, Eric Mastrolonardo, David M. Cognetti, Adam J. Luginbuhl, Joseph M. Curry. Evaluating racial disparities in immune checkpoint inhibitor survival and autoimmunity [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 4845.

Abstract 2889: Cartilage oligomeric matrix protein as a potential biomarker and therapeutic target of radiation resistance in NSCLC

Abstract Despite recent advances in lung cancer treatment, the survival rate of non-small cell lung cancer (NSCLC) is ~57% largely due to treatment resistance. Cartilage Oligomeric Matrix Protein (COMP) has previously been implicated in promoting metastasis and is associated with poor survival outcomes in breast, prostate and colorectal cancers. Patients with prostate cancer and osteoarthritis (a condition characterized by elevated circulating COMP) had both increased tumor COMP expression and rate of metastasis. Additionally, elevated COMP expression, among others, was observed in a study that investigated the gene expression of invasive NSCLC cell lines, though association with COMP was not further studied. Therefore, COMP could potentially increase the metastatic potential for NSCLC and thus serve as a therapeutic target to reduce tumor aggressiveness. We performed proliferation and migration assays on NSCLC cell lines, A549 and NCI-H1975, treated with 2 µg/mL COMP to identify COMP’s role in NSCLC progression. Plated cells in monolayer were treated with either COMP (2µg/mL) or PBS (control), and then within each group received either: 1] one of two different inhibitors of COMP signaling (either the αᵥ integrin antagonist cilengitide (1µM), or the Src inhibitor PP2 (1µM)), or 2] no additional treatment. Replicates from each group then received 2Gy Cs137 γ-radiation, or no irradiation. Proliferation and migration were monitored via the IncuCyte ZOOM live-cell imaging system over a 48hr interval. Radiation alone reduced both proliferation (-22%, p<0.0001) and migration (-40%, p<0.01) of A549 cells. However, exogenous COMP treatment attenuated the effects of radiation, maintaining both A549 proliferation (19% vs irradiated control, p<0.05) and migration (91% vs irradiated control, p<0.0001). While the overall pattern for NCI-H1975 cells with/without COMP treatment was similar, the magnitude of the responses was reduced vs A549 cells and non-significant. In contrast, radiation resistance with COMP treatment was abrogated by exogenous treatment of both COMP inhibitors. Cilengitide treatment resulted in lower proliferation rates of the COMP treated, irradiated A549 (-16%, p<0.05) and NCI-H1975 cell lines (-58%, p<0.0001) vs COMP treatment alone, while PP2 reduced proliferation and migration rates for both irradiated and non-irradiated COMP treated cell lines (p<0.0001). PP2 treatment alone did not reduce migration rates of either irradiated control cell lines. In conclusion, COMP serves to radioprotect NSCLC in an undetermined manner, and could serve as both a prognostic biomarker for and therapeutic target against radiation resistance in patients with NSCLC. Support: Milton Raben Foundation Citation Format: Kaitlyn E. Reno, Sun H. Park, Michael K. Farris, Ryan T. Hughes, Joseph E. Moore, Chirayu M. Patel, Jeffrey S. Willey. Cartilage oligomeric matrix protein as a potential biomarker and therapeutic target of radiation resistance in NSCLC [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 2889.

Abstract 2932: Computational discovery of paralog dependencies drives target identification in lung cancer

Abstract Lung cancer is the leading cause of cancer-related death in the United States, and worldwide, with the 5-year survival rate of non-small cell lung cancer at 24%. Treatment options are limited, and even in cancers that harbor druggable biomarkers, resistance is near-universal. Identifying novel treatment strategies is essential to improve the outcomes of patients with lung cancer and provide secondary strategies for those who acquire resistance to first-line therapeutics. Identifying targeted therapies based on the genotype or expression pattern of cancers is an ongoing endeavor. In lung cancer, treatment of patients that harbor activating mutations in EGFR with specific EGFR inhibitors has improved progression-free survival rates and extended the lifespan of patients diagnosed with this aggressive cancer, demonstrating the utility of targeted therapies based genetic-based vulnerabilities, or synthetic lethal interactions. A rich source of synthetic lethal genetic interactions are paralogous genes. Paralogs result from gene duplication events through evolutionary history, and many paralogs share both sequence homology and functional redundancy. Co-targeting of some paralogous genes induces a fitness defect that is synergistic compared to individual paralog loss alone. Nearly 70% of genes in the human genome are considered paralogs, and until recent efforts from the Berger lab and others, this buffering effect has caused them to be under-represented in lethality studies. Identifying frequently-inactivated paralog genes in lung cancer will provide a platform to identify new vulnerabilities in lung cancer and other cancer types that share mutational backgrounds. To identify patterns of mutation and expression of paralog genes in lung cancer, we have accessed publicly available data sets for both cell line and patient tumor samples. Using TCGA and CCLE data sources, and a set of 1030 paralog pairs identified previously by the Berger lab as potential drug targets, we have identified paralog genes that harbor high mutational frequencies in lung cancer. Using these candidates, we next asked whether genetic inactivation or transcriptional repression of one paralog influences the essentiality of its paired gene using CERES scores generated by the Cancer Dependency Map. Preliminary results identifies several paralog pairs with distinct dependency profiles in cell lines for which expression of one paralog is lost, including MAGOH/MAGOHB and TLK1/TLK2. Further work to characterize these dependencies in relevant cell line models is ongoing. Additionally, the druggability of these targets will be tested using targeted degradation using the dTAG system. This work will uncover a new set of paralog synthetic lethality targets, and further validate the utility of targeting known synthetic lethal paralogs for the treatment of highly lethal lung cancer. Citation Format: Siobhan O'Brien, Alice H. Berger. Computational discovery of paralog dependencies drives target identification in lung cancer [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 2932.

Abstract 4821: Lung cancer survival disparities by ethnicity in Guam, 2001-2020

Abstract Background: Lung cancer is the leading cause of cancer-related deaths in Guam, with a significantly higher mortality rate compared to the United States (46.9 vs 40.2 per 100,000), despite a lower incidence rate (50.9 vs 52.6 respectively, for the years 2013-2017). There are significant ethnic disparities in lung cancer incidence and mortality rates, with CHamoru (indigenous people) and Micronesian (immigrants from other Micronesian Islands) populations being disproportionately affected compared to other ethnic groups. This study aims to assess lung cancer survival rates in Guam and explore how demographic and other factors impact these rates. Methods: This study included 1171 patients diagnosed with invasive lung cancer and reported to the Guam Cancer Registry from 2001-2020. Five-year and 10-year survival rates were calculated using the Kaplan-Meier method and were compared using the log-rank test across different ethnic groups: CHamoru (N = 613), Filipino (N = 245), Asian (N = 74), Micronesian (N = 110), and Caucasian (N = 82). The Cox proportional hazards regression was used to investigate the effects of sex, age, year of diagnosis, ethnicity, lung cancer staging, and cancer treatment status on the risk of all-cause mortality. Results: Mean age at diagnosis was 65.75 years. Of the cases, only 10.8% were identified at an early stage, 43.1% at a late stage, and 46.0% unstaged. Significant differences in the 5-year and 10-year survival rates were found across ethnic groups. CHamoru had the lowest 5-year (15.6%) and 10-year survival rates (13.2%) followed by Micronesian (18.7% and 17.2%, respectively). All-cause mortality was higher among CHamoru (HR = 1.54, 95% CI: 1.28, 1.85) and Micronesian (HR=1.51, 95% CI: 1.15, 1.98) compared with Filipino after adjusting for age, sex, year of diagnosis, staging, and cancer treatment status. There were no statistically significant differences in all-cause mortality between Asian and Caucasian patients and Filipino patients. Cancer staging also indicated a significantly higher hazard rate for late-stage patients (HR = 1.78, 95% CI: 1.41, 2.25) and un-staged patients (HR = 1.51, 95% CI: 1.18, 1.94) compared to early-stage patients. Additionally, patients who did not receive any treatment (HR = 1.46, 95% CI: 1.22-1.74) and those with unknown treatment status (HR = 1.40, 95% CI: 1.17-1.68) experienced higher hazard rates than those who received at least one form of treatment. Conclusion: The predominance of late-stage or unstaged diagnoses in this study highlights a significant gap in early lung cancer detection in Guam. Survival rates for the CHamoru and Micronesian populations are notably lower than those observed in the U.S. These findings underscore the critical need for expanded lung cancer screening initiatives in Guam to facilitate earlier diagnosis and potentially improve survival outcomes. Acknowledgement: This study was supported by the PIPCHE/U54 Grant (U54CA143728). Citation Format: Cruz Aurienne, Louis J. Dulana, Rodney Teria, Grazyna Badowski. Lung cancer survival disparities by ethnicity in Guam, 2001-2020 [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 4821.

The Role of miR-29a and miR-143 on the Anti-apoptotic MCL-1/cIAP-2 Genes Expression in EGFR Mutated Non-small Cell Lung Carcinoma Patients.

The survival rate of lung cancer is low due to the high frequency of drug resistance in patients with mutations in the driver genes. Overexpression of anti-apoptotic genes is one of the most prominent features of tumor drug resistance. EGFR signaling induces the expression of anti-apoptotic genes. Also, microRNAs (miRNAs) have a critical role in regulating biological functions such as apoptosis; a process mostly eluded in cancer progression. The mutation screening was performed on one thousand non-small cell lung carcinoma patients to enroll clinical samples in this study. Bioinformatics analysis predicted that miRNAs (miR-29a, miR-143) might regulate MCL-1 and cIAP-2 expression. We investigated the expression of MCL-1, cIAP-2, miR-29a, and miR-143 encoding genes in adenocarcinoma patients with or without EGFR mutations before treatment. The potential role of miR-29a and miR-143 on gene expression was evaluated by overexpression and luciferase assays in HEK-293T cells. EGFR mutations were found in 262 patients (26.2%) with a greater incidence in females (36.23% vs. 20.37%, P = 0.001). The expression levels of MCL-1 and cIAP-2 genes in patients with mutated EGFR were higher than those of wild-type EGFR. In contrast, compared to those of patients with wild-type EGFR, the expression levels of miR-29a and miR-143 were lower in the patients carrying EGFR mutations. In cell culture, overexpression of miR-29a and miR-143 significantly downregulated the expression of MCL-1 and cIAP-2. Dual-luciferase reporter experiments confirmed that miR-29a and miR-143 target MCL-1 and cIAP-2 mRNAs, respectively. Our results suggest that upregulation of EGFR signaling in lung cancer cells may increase anti-apoptotic MCL-1 and cIAP-2 gene expression, possibly through downregulation of miR-29a-3p and miR-143-3p.

Symbiotic microbial communities in various locations of the lung cancer respiratory tract along with potential host immunological processes affected.

Lung cancer has the highest mortality rate among all cancers worldwide. The 5-year overall survival rate for non-small cell lung cancer (NSCLC) is estimated at around 26%, whereas for small cell lung cancer (SCLC), the survival rate is only approximately 7%. This disease places a significant financial and psychological burden on individuals worldwide. The symbiotic microbiota in the human body has been significantly associated with the occurrence, progression, and prognosis of various diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. Studies have demonstrated that respiratory symbiotic microorganisms and their metabolites play a crucial role in modulating immune function and contributing to the pathophysiology of lung cancer through their interactions with the host. In this review, we provide a comprehensive overview of the microbial characteristics associated with lung cancer, with a focus on the respiratory tract microbiota from different locations, including saliva, sputum, bronchoalveolar lavage fluid (BALF), bronchial brush samples, and tissue. We describe the respiratory tract microbiota's biodiversity characteristics by anatomical region, elucidating distinct pathological features, staging, metastasis, host chromosomal mutations, immune therapies, and the differentiated symbiotic microbiota under the influence of environmental factors. Our exploration investigates the intrinsic mechanisms linking the microbiota and its host. Furthermore, we have also provided a comprehensive review of the immune mechanisms by which microbiota are implicated in the development of lung cancer. Dysbiosis of the respiratory microbiota can promote or inhibit tumor progression through various mechanisms, including DNA damage and genomic instability, activation and regulation of the innate and adaptive immune systems, and stimulation of epithelial cells leading to the upregulation of carcinogenesis-related pathways.

Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Segmentation and Classification of Lung Cancer using Deep Learning Techniques

To increase the survival rates for lung cancer, early identification and diagnosis are essential. The detection becomes crucial for a number of reasons, including visual similarity, heterogeneity, and low contrast variation. The several varieties of lung cancer that can be categorised histologically are determined by the kind of cells a pathologist can detect under a microscope. 15% of lung cancer cases are caused by small cell lung cancer (SCLC), while 85% of cases are caused by non-small cell lung cancer (NSCLC). The three main types of NSCLC are lung adenocarcinoma, big cell carcinoma, and squamous cell carcinoma (SQCC). Deep learning models provides high level services to the healthcare domain. The proposed research focuses on applying deep learning models to segment and classify images of lung cancer histopathology. Images are segmented using the U-Net model. In order to categorise the different forms of lung cancer histopathology images, classification models like ResNet-50, VGG-16, and EfficientNet-B5 and Ensemble models of these three models were utilised. For the purpose of evaluating all the classifiers, performance metrics like accuracy, precision, recall, and F1 Score were computed. The U-Net model achieves 81% accuracy. ResNet-50 achieved an accuracy of 91%, VGG-16 94%, EfficientNet-B5 97%, and Ensemble model 99%, respectively. Among all models, an ensemble model is the most accurate.

Comparative analysis of breast and lung cancer survival rates and clinical trial enrollments among rural and urban patients in Georgia

Comparative analysis of breast and lung cancer survival rates and clinical trial enrollments among rural and urban patients in Georgia

Neoadjuvant therapy in non-small cell lung cancer: basis, promise, and challenges.

Survival rates for early-stage non-small cell lung cancer (NSCLC) remain poor despite the decade-long established standard of surgical resection and systemic adjuvant therapy. Realizing this, researchers are exploring novel therapeutic targets and deploying neoadjuvant therapies to predict and improve clinical and pathological outcomes in lung cancer patients. Neoadjuvant therapy is also increasingly being used to downstage disease to allow for resection with a curative intent. In this review, we aim to summarize the current and developing landscape of using neoadjuvant therapy in the management of NSCLC. The PubMed.gov and the ClinicalTrials.gov databases were searched on 15 January 2023, to identify published research studies and trials relevant to this review. One hundred and seven published articles and seventeen ongoing clinical trials were selected, and relevant findings and information was reviewed. Neoadjuvant therapy, proven through clinical trials and meta-analyses, exhibits safety and efficacy comparable to or sometimes surpassing adjuvant therapy. By attacking micro-metastases early and reducing tumor burden, it allows for effective downstaging of disease, allowing for curative surgical resection attempts. Research into neoadjuvant therapy has necessitated the development of surrogate endpoints such as major pathologic response (MPR) and pathologic complete response (pCR) allowing for shorter duration clinical trials. Novel chemotherapy, immunotherapy, and targeted therapy agents are being tested at a furious rate, paving the way for a future of personalized systemic therapy in NSCLC. However, challenges remain that prevent further mainstream adoption of preoperative (Neoadjuvant) therapy. These include the risk of delaying curative surgical resection in scenarios of adverse events or treatment resistance. Also, the predictive value of surrogate markers of disease cure still needs robust verification. Finally, the body of published data is still limited compared to adjuvant therapy. Addressing these concerns with more large scale randomized controlled trials is needed.

ESMO 2023 Abstract Highlights

The following highlights spotlight selected abstracts presented at the European Society for Medical Oncology (ESMO) Annual Congress 2023. They cover key topics, including the importance of nutrients in lung cancer survival rates; a new, minimally invasive therapy which targets endometrial cancer stem cells; and new potential for identifying bladder cancer using genomic sequencing.

Revolutionizing Lung Cancer Diagnosis: A Comprehensive Review of Image Processing Techniques for Early Detection and Precision Medicine

According to World Health Organisation (WHO), lung cancer is the leading cause of cancer-related fatalities in both genders and has the highest fatality rate. Early detection of pulmonary nodules is essential to improving the significant survival rate of lung cancer due to the typical proliferation of lung cells. Studies on lung cancer indicate that smoking is the primary cause of this disease, which is more common in women nowadays and causes more deaths than breast cancer. Age, gender, race, socioeconomic status, exposure to the environment, air pollution, alcohol consumption, and second-hand smoking are a few more factors that could be significant in causing lung cancer. Early detection of lung cancer is achieved through a variety of image processing techniques, such as computed tomography (CT), bone scanning, magnetic resonance imaging (MRI), Positron Emission Tomography, PET-CT, and X-ray scanning. These techniques are combined with machine learning algorithms, data mining, and artificial intelligence-based detection techniques, which improve detection through efficient computing systems known as computer assisted diagnosis (CAD). Since practically all lung cancer screening and detection is dependent on image processing, this article will serve as a reference for aspiring researchers to understand the many detection strategies in effectively identifying lung cancer. Additionally, five distinct methods are evaluated and critically analysed, along with their benefits and drawbacks, taking into account the present and potential future developments in early lung cancer diagnosis for human survival.

Abstract B107: Disparities in lung cancer screening rates: A retrospective observational study

Abstract Introduction: Lung cancer is a leading cause of cancer deaths worldwide. Despite advances in cancer care, the low five-year survival rate for lung cancer is due to late-stage diagnoses. Early detection through lung cancer screening is vital for improving patient outcomes. However, racial disparities persist, with Black individuals experiencing higher rates and advanced stages of the disease. This retrospective study assessed racial and gender disparities in lung cancer screening rates among different racial groups at WakeMed Health and Hospital outpatient primary care clinics. Study design: The study utilized data from the lung cancer screening registry, focusing on the period between Jan 1st 2017 and May 15th 2023. The data was obtained from the EPIC electronic medical record system and exported to Excel. We implemented filters to narrow down the patient population that met the U.S. Preventive Services Task Force inclusion criteria for lung cancer screening. Eligible patients for lung cancer screening are those aged 50 to 80 years, either current smokers or individuals who quit smoking within the past 15 years and have a smoking history of at least 20 pack years. The study focused on three racial groups: Asian, Black, and White populations. Results: Among the 2,098 patients who met the inclusion criteria, 15.5% were Black (n=326), 0.01% were Asian (n=23), and 83.4% were White (n=1,749). The screening rates for lung cancer were calculated for each racial group. The Asian population had a screening rate of 26% (6 out of 23 patients), the Black population had a rate of 32% (103 out of 326 patients), and the White population had a rate of 31% (543 out of 1,749 patients). Among the Asian population, the screening rate for Asian males was 20% (4 out of 20 patients), while for Asian females, it was relatively higher at 67% (2 out of 3 patients). For the Black male population, the screening rate was 31% (52 out of 167 patients), while among females, it was 32% (51 out of 159 patients). Among the White population, white males were screened at a rate of 33% (313 out of 962 patients), while among females, it was slightly lower at 29% (230 out of 787 patients). Conclusion: The findings reveal no significant difference in lung cancer screening rates between racial groups or genders. Caution must be exercised when interpreting the findings for the Asian population due to the small sample size. Further research using a larger and more diverse dataset is necessary to draw more robust conclusions regarding potential disparities in lung cancer screening rates. This study underscores the importance of improving overall lung cancer screening rates, as the majority of the eligible population did not receive screening. Efforts will be made to implement interventions that enhance lung cancer screening rates, ensuring timely detection and improving patient outcomes. Addressing disparities in lung cancer screening access and utilization is crucial for achieving equitable healthcare and reducing the burden of lung cancer on vulnerable populations. Citation Format: Giuseppina Jacob, Amanpreet Dhaliwal, Praveen Namireddy. Disparities in lung cancer screening rates: A retrospective observational study [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B107.

NARFL deficiency caused mitochondrial dysfunction in lung cancer cells by HIF-1α–DNMT1 axis

NARFL was reported to be a component of cytosolic iron–sulfur cluster assembly pathway and a causative gene of the diffused pulmonary arteriovenous malformations (dPAVMs). NARFL knockout dramatically impaired mitochondrial integrity in mice, which might promote mitochondrial dysfunction and lead to worse survival rate of lung cancer. However, the underlying molecular mechanism of NARFL deficiency in non-small cell lung cancer (NSCLC) is unknown. Knockdown assay was performed in A549 and H1299 cells. The protein levels of HIF-1α and DNMT1 were measured, and then Complex I activity, mtDNA copy numbers and mRNA levels of mtND genes were determined. Cisplatin resistance and cell proliferation were conducted using CCK8 assay. Cell migration and invasion were detected using wound heal assay and transwell assay. Survival analysis of lung cancer patients and KM plotter database were used for evaluating the potential value of NARFL deficiency. NARFL protein was expressed in two cell lines and knockdown assay significantly reduced its levels. Knockdown NARFL increased the protein levels of HIF-1α and DNMT1, and downregulated the mRNA levels of ND genes, mitochondrial Complex I activity, mtDNA copy number, and ATP levels. The mitochondrial dysfunction caused by NARFL deficiency were ameliorated by siHIF-1α and DNMT1 inhibitor. Knockdown NARFL increased the drug resistance and cell migration, and siHIF-1α reversed this effect. Moreover, NSCLC patients with NARFL deficiency had a poor survival rate using a tissue array and KM plotter database, and it would be a target for cancer prognosis and treatment. NARFL deficiency caused dysregulation of energy metabolism in lung cancer cells via HIF-1α–DNMT1 axis, which promoted drug resistance and cell migration. It provided a potential target for treatment and prognosis of lung cancer.

Liquid ImmunoBiopsy-based detection of early stage lung cancer in a volunteer subject recruited as a ‘healthy donor’ of a lung cancer diagnosis clinical trial study: A case report

Background: Developing early detection diagnostic methods, especially non-invasive methods, is a critical component in raising the overall survival rate and prognosis for lung cancer (LC) patients. Recently, we described a novel Liquid ImmunoBiopsy™ test to detect early stage LC with high accuracy. As a part of our ongoing clinical trials for early detection of LC, a blood specimen was received from a ‘healthy’ volunteer, nonsmoker male, who was recruited as a part of the trial’s control group. The Liquid ImmunoBiopsy™ test result of this subject clearly indicated a lung-related malignant lesion. The objective of this article is to provide a detailed description of the subsequent investigation of this case, up to the surgical intervention.

Ugonin P inhibits lung cancer motility by suppressing DPP-4 expression via promoting the synthesis of miR-130b-5p

Lung cancer is the leading cause of cancer-related death worldwide, and the survival rate of metastatic lung cancer is exceedingly low. Helminthostatchys Zeylanica (H. Zeylanica) is a Chinese herbal medicine renowned for its anti-inflammatory, immunomodulatory, and anti-cancer activities in various cellular and animal studies. The current study evaluated the effects of H. Zeylanica derivatives on lung cancer cells. We determined that dipeptidyl peptidase-4 (DPP-4) expression levels were higher in lung cancer tissues than in normal tissues. We also determined that DPP-4 expression levels were higher in the metastatic stage and strongly correlated with lung cancer survival rates. An H. Zeylanica derivative (ugonin P) was shown to inhibit DPP-4 mRNA and protein expression in two lung cancer cell lines in a dose-dependent manner. Ugonin P was shown to decrease migration and invasion activities in lung cancer cells while promoting the synthesis of miR-130b-5p, which was found to negatively regulate DPP-4 protein expression and cell motility in lung cancer. We determined that ugonin P suppresses the DPP-4-dependent migration and invasion of lung cancer cells by downregulating the RAF/MEK/ERK signalling pathway and enhancing the expression of miR-130b-5p. This study provides compelling evidence that ugonin P could be used to develop novel therapeutic agents for the treatment of lung cancer.

Factors associated with late-stage diagnosis and overall survival for lung cancer: An analysis of patients treated in a Brazilian hospital and a US-hospital from 2009 to 2019

BackgroundLung cancer is a major public health problem due to its high incidence and mortality rates worldwide. Histology, socioeconomic conditions, access, quality of healthcare, and regional aspects are associated with lung cancer stages at diagnosis and survival outcomes. This paper aims to examine and contrast the factors associated with late-stage diagnosis of lung cancer and overall survival rates in two different settings: a Brazilian hospital and a US hospital, spanning from 2009 to 2019. MethodsThis is a retrospective cohort study of the incidence of lung cancer cases at the institution’s cancer registry from a Brazilian and a US-based cohort. Descriptive analyses are presented using either the mean and standard deviation or the median and interquartile interval. Frequency is used to present categorical variables. Factors associated with late-stage lung cancer diagnosis were identified through bivariate and multivariable forward stepwise logistic regression. One-year overall survival and its associated factors were identified by Kaplan–Meier curves and Cox's proportional hazards model. ResultsBetween January 2009 and December 2019, a total of 5286 individuals were diagnosed with LC in the Brazilian cohort, and out of these cases, 85.6% were diagnosed with late-stage disease.The US-cohort consisted of 3594 individuals, of whom 60.3% were diagnosed with late-stage disease in lung cancer. The one-year overall survival was 8.6 months for the US cohort and 6.4 months for the Brazilian cohort. In both cohorts, late-stage diagnosis emerged as the most significant factor influencing overall survival. However, the factors associated with late-stage diagnosis differed between the US and Brazilian cohorts. In the Brazilian cohort, being male and belonging to black or brown ethnic groups, along with having a lower education level, were linked to late-stage diagnosis. On the other hand, in the US-based cohort, the factors related to late-stage diagnosis were being male, having been diagnosed before 2015, and possessing private insurance coverage. ConclusionLate-stage diagnosis was associated with the worst survival in both the US and Brazilian cohorts. This study provides valuable information on inequities and barriers to access for lung patients with cancer from upper-middle-income and high-income countries.