Subtype Of Non-small Cell Lung Cancer Research Articles

Expression of AhR-regulated miRNAs in non-small cell lung cancer in smokers and never smokers.

Smoking is a risk factor for non-small cell lung cancer (NSCLC). The most common subtypes of NSCLC are lung adenocarcinoma (LAC) and squamous cell carcinoma (SCC). The cigarette smoke contains aryl hydrocarbon receptor (AhR) ligands, such as benzo(a)pyrene (BaP). By activating the AhR, BaP can change the expression of many genes, including miRNA-encoding genes. In this study, we have evaluated the expression of few miRNAs potentially regulated by AhR (miR-21, -342, -93, -181a, -146a), as well as CYP1A1, a known AhR target gene, in lung tumor samples from smoking (n=40) and non-smoking (n=30) patients with LAC and from smoking patients with SCC (n=40). We have also collected macroscopically normal lung tissue >5 cm from the tumor margin. We compared the obtained data on the miRNA expression in tumors with data from The Cancer Genome Atlas (TCGA). We found that in 76.7% of non-smoking LAC patients, CYP1A1 mRNA was not detected in tumor and normal lung tissues, while in smoking patients, CYP1A1 expression was detected in tumors in almost half of the cases (47.5% for SCC and 42.5% for LAC). The expression profile of AhR-regulated miRNAs differed between LAC and SCC and depended on the smoking status. In LAC patients, the expression of oncogenic miRNA-21 and miRNA-93 in tumors was higher than in normal lung tissue from the same patients. However, in SCC patients from our sample, the levels of these miRNAs in tumor and non-transformed lung tissue did not differ significantly. The results of our studies and TCGA data indicate that the expression levels of miRNA-181a and miRNA-146a in LAC are associated with smoking: expression of these miRNAs was significantly lower in tumors of smokers. It is possible that their expression is regulated by AhR and AhRR (AhR repressor), and inhibition of AhR by AhRR leads to a decrease in miRNA expression in tumors of smoking patients. Overall, these results confirm that smoking has an effect on the miRNA expression profile. This should be taken into account when searching for new diagnostic and therapeutic targets for NSCLC.

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.

This research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data. A retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann-Whitney U test, Spearman's rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied. For the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748. The constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

Vinorelbine Alters lncRNA Expression in Association with EGFR Mutational Status and Potentiates Tumor Progression Depending on NSCLC Cell Lines' Genetic Profile.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most common type. In addition, NSCLC has a high mortality rate and an overall adverse patient outcome. Although significant improvements have been made in therapeutic options, effectiveness is still limited in late stages, so the need for a better understanding of the genomics events underlying the current therapies is crucial to aid future drug development. Vinorelbine (VRB) is an anti-mitotic chemotherapy drug (third-generation vinca alkaloid) used to treat several malignancies, including NSCLC. However, despite its widespread clinical use, very little is known about VRB-associated genomic alterations in different subtypes of NSCLC. This article is an in vitro investigation of the cytotoxic effects of VRB on three different types of NSCLC cell lines, A549, Calu-6, and H1792, with a closer focus on post-treatment genetic alterations. Based on the obtained results, VRB cytotoxicity produces modifications on a cellular level, altering biological processes such as apoptosis, autophagy, cellular motility, cellular adhesion, and cell cycle, but also at a genomic level, dysregulating the expression of some coding genes, such as EGFR, and long non-coding RNAs (lncRNAs), including CCAT1, CCAT2, GAS5, MALAT1, NEAT1, NORAD, XIST, and HOTAIR, that are implicated in the mitogen-activated protein kinase (MAPK) signaling pathway. Therefore, although extensive validation is required, these results pave the way towards a better understanding of the cellular and genomic alterations underlying the cytotoxicity of VRB.

XL1R-Net: Explainable AI-driven improved [formula omitted]-regularized deep neural architecture for NSCLC biomarker identification

Background and Objective:Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes — Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data. Methods:An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results:The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug–Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K–M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients’ likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List. Conclusion:A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC’s genetic diversity, using only one omics data type may not adequately capture the tumor’s complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

Efficacy of neoadjuvant therapy for lung squamous cell carcinoma and lung adenocarcinoma: A retrospective comparative study.

Preoperative neoadjuvant therapy is widely used in cancer treatment; however, its efficacy in different subtypes of non-small cell lung cancer (NSCLC) is unknown. The present study compared the clinical efficacy of preoperative neoadjuvant therapy for two major NSCLC subtypes. Patients with NSCLC who underwent preoperative neoadjuvant therapy between January 2016 and August 2022 were reviewed. Patients were stratified according to histology and treatment strategy. Retrospective analysis was performed by comparing the basic clinical characteristics of the patients, clinicopathological characteristics of the tumors, imaging data and pathological responses to treatment. A total of 36 cases of lung squamous cell carcinoma (LUSC) and 31 cases of lung adenocarcinoma (LUAD) were included. After neoadjuvant chemotherapy combined with immunotherapy, the pathological response rates were higher for patients with LUSC than LUAD, but there was no statistically significant difference between the two subgroups (P=0.06). However, the pathological complete response rates after neoadjuvant chemotherapy combined with immunotherapy were significantly higher for LUSC than those after chemotherapy alone (P=0.01). These preliminary findings suggested that preoperative chemotherapy combined with immunotherapy could improve the pathological response of patients, particularly in those with LUSC. The present study provided new insights into the treatment of NSCLC.

Deep Learning Based on Enhanced MRI T1 Imaging to Differentiate Small-cell and Non-small-cell Primary Lung Cancers in Patients with Brain Metastases.

To differentiate the primary small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) for patients with brain metastases (BMs) based on a deep learning (DL) model using contrast-enhanced magnetic resonance imaging (MRI) T1 weighted (T1CE) images. Out of 711 patients with BMs of lung cancer origin (SCLC 232, NSCLC 479), the MRI datasets of 192 patients (lesions' widths and heights > 30 pixels) with BMs from lung cancer (73 SCLC and 119 NSCLC) confirmed pathologically were enrolled, retrospectively. A typical convolutional neural network ResNet18 was applied for the automatic classification of BMs lesions from lung cancer based on T1CE images, with training and testing groups randomized per patient to eliminate learning bias. A 5-fold cross-validation was performed to evaluate the classification of the model. The receiver operating characteristic (ROC) curve, accuracy, precision, recall and f1 score were calculated. For a 5-fold cross-validation test, the DL model achieved AUCs of 0.8019 and 0.8024 for SCLC and NSCLC patients with BMs, respectively, and a mean overall accuracy of 0.7515±0.04. The DL model performed well in differentiating the primary SCLC and NSCLC with BMs. The proposed DL model is feasible and effective in differentiating the pathological subtypes of SCLC and NSCLC causing BMs, which may be used as a new tool for oncologists to diagnose noninvasively BMs and guide therapy based on the imaging structure of tumors.

98. A bioinformatics analysis of differentially expressed genes in non-small cell lung cancer subtypes

98. A bioinformatics analysis of differentially expressed genes in non-small cell lung cancer subtypes

Spatial and Temporal Heterogeneity in Clonal Evolution of Nonsmall-cell Lung Cancer: Implications for Therapy

Abstract Introduction Tumoral heterogeneity has been associated with treatment resistance and failure in patients with cancer. Tumoral heterogeneity can be either intertumoral (static variation of cancer cells) or intratumoral (spatial and temporal variation of cancer cells). Nonsmall-cell lung cancer (NSCLC) is considered a model disease for the study of tumoral heterogeneity, given the emerging evidence about the clinical implications of genetic variations among NSCLC subtypes. This review provides an overview of the etiology, detection, and management methods of intratumoral heterogeneity in NSCLC and discusses their clinical implications. Methods The authors searched biomedical databases (Medline, Scopus, Embase) for studies reporting on intratumoral heterogeneity in NSCLC. Results Intratumoral heterogeneity occurs in single tumors, multiple tumors in the same organs, primary tumors and metastases, and among distinct metastases. Genetic (selective pressure, clonal evolution, genomic instability) and nongenetic pathways (tumor metabolism, hypoxia) precipitate heterogeneity across the spatial and temporal progression of the disease. Proposed classifications are based either on cancer subtypes or mutations detected and metastasis sites. Liquid biopsies (cell-free DNA, circulating tumor cells) combined with imaging (computed tomographies (CTs), positron emission tomography/computed tomographies (PET/CTs)) have a major potential for the continuous minimally invasive monitoring of intratumoral heterogeneity in comparison to conventional biopsies. Targeted therapies have a higher likelihood to induce heterogeneity and resistance, while PD-L1 immunotherapy represents a promising therapeutic strategy. Conclusion Spatial and temporal intratumoral heterogeneity within a single patient sets additional challenges to personalized precision medicine, calling for continuous cellular and molecular-level surveillance and adequate adjustment of the treatment plan.

Clinical outcomes of advanced NSCLC patients with different EGFR exon 19 deletion subtypes treated with first-line tyrosine kinase inhibitors: A single-center ambispective cohort study.

Clinical significance of various subtypes of epidermal growth factor receptor (EGFR) exon 19 deletion (ex19del) mutation in non-small cell lung cancer (NSCLC) remains unclear. We analyzed EGFR ex19del subtypes in NSCLC patients receiving first-line tyrosine kinase inhibitor (TKI) therapy at our center (January 2018-June 2022) and correlated them with median progression-free survival (mPFS) and median overall survival (mOS). We identified 17 different EGFR ex19del variants in 101 patients. Between the classic (E746_A750del, 64.4%) and nonclassic groups (the rest variants), no significant difference was observed in mPFS (13.5 vs. 19.3 months, p = 0.18) or mOS (44.1 vs. 77.0 months, p = 0.06). mPFS showed no significant difference between ex19del subgroups classified based on the presence of insertion (ex19delins), starting position or length of deletion. However, patients with ex19delins starting at E746 showed longer mPFS than the others (29.7 vs. 12.5 months, p = 0.04), and patients with ex19del of 15 nucleotides had shorter mOS than the others (44.1 vs. 77.0 months, p = 0.03). In multivariate analysis, ex19delins independently predicted a better PFS (HR = 0.311, p = 0.03); however, 15 nucleotide deletion was no longer associated with OS (HR = 0.181, p = 0.11). Secondary T790M mutation incidence was significantly higher in the ex19del subgroup starting at E746 than the others (64.7% vs. 30.8%, p = 0.04). Our study revealed potential differences in TKI efficacy, resistance mechanism, and prognosis of various EGFR ex19del subtypes in NSCLC, underscoring the need for precise selection of first-line therapy.

The differential prognostic implications of PD-L1 expression in the outcomes of Filipinos with EGFR-mutant NSCLC treated with tyrosine kinase inhibitors.

The tumor immune microenvironment influences tumor evolution in non-small cell lung cancer (NSCLC). Yet, the prognostic value of programmed death-ligand 1 (PD-L1) in epidermal growth factor receptor (EGFR)-mutant NSCLC remains controversial. Additionally, prognostic studies in Filipinos with EGFR-mutant NSCLC remain unexplored to this day. We prospectively studied the outcomes of EGFR-mutant NSCLC in Filipino cohort, and retrospectively verified the survival trend using The Cancer Genome Atlas (TCGA) cohort. Kaplan-Meier method and generalized linear regression were used to assess survival. Expression and DNA methylation of cluster of differentiation 274 (CD274, gene that codes for PD-L1) were examined from TCGA tumor profiles. Pearson's correlation was used to correlate PD-L1 expression with outcomes associated with occurrence of EGFR mutations, tyrosine kinase inhibitor (TKI) types, and programmed cell death protein 1 (PD-1) expression. Proteome network analysis was used to examine the correlation between drug resistance and PD-L1. PD-L1 positivity was associated with significantly longer progression-free survival (PFS; P=0.0096) but had a significantly contrasting influence in the overall survival (OS; P=0.0011). PD-L1 positivity (in both protein and RNA) was associated with longer median OS (mOS) in exon21 L858R, whereas, negativity was associated with longer mOS in exon19 deletion (exon19del). Stratification (high, low, negative) of PD-L1 expression lacked significant prognostic value (all P>0.05). PD-L1/CD274 expression (P<0.05) and DNA methylation (P<0.001) vary significantly among NSCLC subtypes and in different disease stages. Erlotinib treatment produced the longest median progression-free survival (mPFS; 874 days) relative to other EGFR-TKIs (137-311 days). PD-L1 lacked a significant correlation with EGFR-TKIs. Consistent with the immune-regulation activities of PD-1, higher expression leads to relatively shorter mOS. PD-1 correlated positively with PD-L1 expression and occurrence of exon21 L858R. PD-L1 differentially influenced the outcomes of Filipinos with EGFR-mutant NSCLC. NSCLC subtypes, disease stage, and PD-1 expression may impact the collective outcomes associated with PD-L1 and EGFR-sensitizing mutations.

Mutation status analysis of 58 patients with advanced ALK fusion gene positive non small cell lung cancer

PurposeTo analyze the characteristics and prognostic values of Anaplastic Lymphoma Kinase (ALK) fusion gene partner, gene subtype and abundance in tumor tissues of advanced Non Small Cell Lung Cancer (NSCLC) patients with positive ALK fusion gene and to explore the best treatment mode of ALK-Tyrosine Kinase Inhibitors(TKIs).MethodsCases of advanced NSCLC patients with ALK positive confirmed by both Next Generation Sequencing (NGS) and immunohistochemistry were retrospectively collected. The relationships of Overall Survival (OS)/Progression Free Survival (PFS) between different mutation subtypes, mutation abundance, clinicopathological features were analyzed. OS/PFS between different treatment mode of ALK inhibitors were compared.ResultsFifty-eight patients were enrolled. There were diverse fusion partners. Five subtypes of Echinoderm Microtubule-associated protein-Like 4 gene (EML4)-ALK fusion mutation were detected: V1,V2,V3,V5 and V7. The mutation abundance ranged from 0.13 to 27.77%, with a median of 5.34%. The abundance of V2 and V5 was higher than V1 and V3 respectively. There was no difference in OS between the low abundance group(≤ 5.34%) and the high abundance group(>5.34%) (P = 0.434). PFS of second-generation ALK inhibitors as first-line treatment was longer than that of Crizotinib as first-line (P<0.001). Never smokers had longer OS than current smokers(P = 0.001).ConclusionsThere are differences in abundance between different fusion partners and subtypes in advanced NSCLC with positive ALK. OS is not associated with subtypes, mutation abundance and first line treatment option of either generation of ALK inhibitors. Smoking is a poor prognostic factor.

MiRNA-Seq Tissue Diagnostic Signature: A Novel Model for NSCLC Subtyping.

Non-small cell lung cancer (NSCLC) encompasses distinct histopathological subtypes, namely adenocarcinoma (AC) and squamous cell lung carcinoma (SCC), which require precise differentiation for effective treatment strategies. In this study, we present a novel molecular diagnostic model that integrates tissue-specific expression profiles of microRNAs (miRNAs) obtained through next-generation sequencing (NGS) to discriminate between AC and SCC subtypes of NSCLC. This approach offers a more comprehensive and precise molecular characterization compared to conventional methods such as histopathology or immunohistochemistry. Firstly, we identified 31 miRNAs with significant differential expression between AC and SCC cases. Subsequently, we constructed a 17-miRNA signature through rigorous multistep analyses, including LASSO/elastic net regression. The signature includes both upregulated miRNAs (hsa-miR-326, hsa-miR-450a-5p, hsa-miR-1287-5p, hsa-miR-556-5p, hsa-miR-542-3p, hsa-miR-30b-5p, hsa-miR-4728-3p, hsa-miR-450a-1-3p, hsa-miR-375, hsa-miR-147b, hsa-miR-7705, and hsa-miR-653-3p) and downregulated miRNAs (hsa-miR-944, hsa-miR-205-5p, hsa-miR-205-3p, hsa-miR-149-5p, and hsa-miR-6510-3p). To assess the discriminative capability of the 17-miRNA signature, we performed receiver operating characteristic (ROC) curve analysis, which demonstrated an impressive area under the curve (AUC) value of 0.994. Our findings highlight the exceptional diagnostic performance of the miRNA signature as a stratifying biomarker for distinguishing between AC and SCC subtypes in lung cancer. The developed molecular diagnostic model holds promise for providing a more accurate and comprehensive molecular characterization of NSCLC, thereby guiding personalized treatment decisions and improving clinical management and prognosis for patients.

Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding

AimsWe aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. Materials and methodsA public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. ResultsSupport vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). ConclusionThis study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.

Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation

Aim: Many small datasets of significant value exist in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies are of great value as they can indicate leads for innovative drug discovery and development. Methods: Two small non-small cell lung cancer (NSCLC) datasets (GSE18842 and GSE10245) consisting of 58 samples of adenocarcinoma (ADC) and 45 samples of squamous cell carcinoma (SCC) were used in a machine intelligence framework to identify genetic biomarkers differentiating these two subtypes. Utilizing a set of standard machine learning (ML) methods, subpopulations of ADC and SCC were uncovered while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. A previously described interactive hypothesis-generating method designed to work with ML methods was employed to provide an alternative way of extracting the most important combination of variables to construct a new data set. Results: Several genes were uncovered that were previously implicated by other methods. This framework accurately discovered known subpopulations, such as genetic drivers associated with differing levels of aggressiveness within the SCC and ADC subtypes. Furthermore, phyosphatidylinositol glycan anchor biosynthesis, class X (PIGX) was a novel gene implicated in this study that warrants further investigation due to its role in breast cancer proliferation. Conclusions: The ability to learn from small datasets was highlighted and revealed well-established properties of NSCLC. This showcases the utility of ML techniques to reveal potential genes of interest, even from small datasets, shedding light on novel driving factors behind subpopulations of patients.

Targeting KRAS-Mutated NSCLC: Novel TKIs and Beyond.

KRAS-mutated non-small cell lung cancer (NSCLC) is the most common genetically altered subtype of NSCLC, yet targeted therapies remain limited. Multiple studies have investigated combinations of MEK inhibitors with chemotherapy without success. Here we discuss these studies and novel approaches to targeting KRAS-mutated NSCLC. See related article by Gadgeel et al., p. 3641.

The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs

We investigated the association of T1/T2 mapping values with programmed death-ligand 1 protein (PD-L1) expression in lung cancer and their potential in distinguishing between different histological subtypes of non-small cell lung cancers (NSCLCs). Thirty-five patients diagnosed with stage III NSCLC from April 2021 to December 2022 were included. Conventional MRI sequences were acquired with a 1.5 T system. Mean T1 and T2 mapping values were computed for six manually traced ROIs on different areas of the tumor. Data were analyzed through RStudio. Correlation between T1/T2 mapping values and PD-L1 expression was studied with a Wilcoxon–Mann–Whitney test. A Kruskal–Wallis test with a post-hoc Dunn test was used to study the correlation between T1/T2 mapping values and the histological subtypes: squamocellular carcinoma (SCC), adenocarcinoma (ADK), and poorly differentiated NSCLC (PD). There was no statistically significant correlation between T1/T2 mapping values and PD-L1 expression in NSCLC. We found statistically significant differences in T1 mapping values between ADK and SCC for the periphery ROI (p-value 0.004), the core ROI (p-value 0.01), and the whole tumor ROI (p-value 0.02). No differences were found concerning the PD NSCLCs.

Integrated Single-Cell and Transcriptome Sequencing Analyses Identify Dipeptidase 2 as an Immune-Associated Prognostic Biomarker for Lung Adenocarcinoma.

Dipeptidase 2 (DPEP2) is a dipeptidyl peptidase that plays an important role in the hydrolysis of leukotriene D4 (LTD4) to leukotriene E4 (LTE4). Previous studies have suggested that LTD4 promotes tumor progression and survival in non-small cell lung cancer (NSCLC). Therefore, we hypothesized that DPEP2 may play a pivotal role in this tumor. Given that lung adenocarcinoma (LUAD) is the most common subtype of NSCLC, our study aimed to examine the expression and function of DPEP2 in LUAD. Based on bioinformatics and the analysis of clinical samples, our findings revealed that DPEP2 is highly expressed in normal lung tissues, but downregulated in LUAD tissues, and its expression levels were significantly associated with clinical indicators of tumor grade and prognosis. Pathway enrichment analysis showed that DPEP2 is involved in biological processes such as chemokine signaling pathways, leukocyte trans-endothelial migration, and humoral immune responses in LUAD. In addition, DPEP2 expression was significantly associated with various immune cells, especially monocytes-macrophages. Single-cell transcriptome data further confirmed the expression of DPEP2 dominantly in macrophages from normal lung tissues. Analysis of the TCIA database revealed that high DPEP2 expression is associated with a stronger response to immune checkpoint inhibitors such as CTLA4 and PD1, and determines sensitivity to LUAD therapeutic agents. Furthermore, we found that DPEP2 inhibits the migration and invasion of LUAD cells. Therefore, DPEP2 may serve as a potential immune biomarker and therapeutic target for LUAD, providing novel therapeutic approaches for this disease.

Molecular characterization of nectin-4 in non-small cell lung cancer subtypes.

e21119 Background: Nectin-4 (Nectin cell adhesion molecule-4) is a tumor associated antigen overexpressed in multiple solid tumor types. Studies show that over 60% of NSCLC are nectin-4 overexpressed. As enfortumab vedotin, an antibody-drug conjugate directed against nectin-4, has been FDA approved for advanced urothelial carcinoma, this holds treatment implications. Here, we investigated nectin-4 expression in NSCLC subtypes. Methods: 27,607 NSCLC tumors were tested for whole transcriptome sequencing (WTS, NovaSeq) and NextGen DNA sequencing (592 gene panel or whole exome sequencing, WES) at Caris Life Sciences (Phoenix, AZ). Tumors were analyzed overall in quartiles by nectin-4 expression (Q4-greatest, Q1-least) and further analyzed in the adenocarcinoma (AD) and squamous carcinoma (SQ) cohorts. PD-L1 expression was tested by IHC (Dako 22C3). Immune cell populations were estimated by QuanTISeq. Statistical significance was determined using Chi-square/Fisher-Exact/Mann-Whitney U tests and adjusted for multiple comparisons (q &lt; 0.05). Results: We saw significantly greater expression of nectin-4 in SQ (median TPM- 15.68) versus AD tumors (median TPM - 9.88) (p &lt; 0.05). We saw a greater prevalence of NFE2L2, STK11, ERBB2 mutations and genomic loss of heterozygosity (LOH) but a lower percentage of PD-L1+ tumors in Q4 vs Q1 tumors (all q-value &lt; 0.05, Table 1). mRNA expression of immune checkpoints PDL1, CTLA4, CD80, CD86, HAVCR2, and IDO1 was increased in Q4 tumors (1.1-1.3-fold, all q &lt; 0.05). Q4 tumors had reduced estimates of M1 macrophages, regulatory T cells (Tregs), and CD8+ T cells and increased estimates of neutrophils and NK cells. The association of nectin-4 expression with the immune landscapes of AD and SQ was similar. Q4 tumors in AD and SQ displayed increased expression of the above-mentioned immune checkpoints (q &lt; 0.05). In AD and SQ, estimates of M1 macrophages, Tregs, and CD8+ T cells were reduced, but neutrophils and NK cells were increased in Q4 tumors (q &lt; 0.05). The pattern of molecular alterations was different in AD and SQ. While KEAP1, STK11 and ERBB2 mutations were significantly enriched in AD Q4 tumors, NFE2L2 mutations were significantly enriched in SQ Q4 tumors (Table 1). Prevalence of LOH was higher and PD-L1+ tumors was lower in Q4 tumors in both AD and SQ (Table 1). Conclusions: NFE2L2 alterations in SQ and STK11, KEAP1 mutations in AD, which are associated with poor immunotherapy (IO) response, were enriched in Q4 tumors. This suggests that the immune landscape associated with high nectin-4 expression in NSCLC may be driven by the dysregulated KEAP1-NFE2L2 pathway. Targeted therapy towards nectin-4 may benefit these NSCLC subtypes that do not respond well to IO. [Table: see text]

Factors impacting outcomes in individuals ≥80 years with non-small cell lung cancer (NSCLC): A national cancer database (NCDB) analysis.

9043 Background: Older individuals with advanced/metastatic NSCLC have inferior outcomes compared to younger patients. Aside from baseline comorbidities and treatment-limiting toxicities, identifying factors that impact overall survival (OS) in this population is needed. The aim of this study is to assess characteristics impacting outcomes in patients ≥80 years with NSCLC using the NCDB. Methods: Adults ≥80 years with NSCLC, stage III/IV disease, diagnosed between 2015-2018, and available demographic data were included. Age, sex, race, insurance status, hospital subtype, Charelson-Deyvo comorbidity (CDCC) index, cancer stage (III/IV), geographic region in the United States (US), and histologic NSCLC subtypes were compared. Kaplan-Meier methodology assessed differences in median OS, and differences were compared using hazard ratios (HR) with 95% confidence intervals (CI). Pearson Chi-Squared test assessed the significance of covariates, and a p value &lt; 0.05 was considered statistically significant. Results: There were 42,356 patients included. Median age was 83 (80-90) years, 20,422 were (48.2%) female, 35,653 (84.2%) were white, 37,738 (89.1%) had medicare coverage, 11,055 (26.1%) received treatment at an academic hospital, and 14,383 (33.9%) received care in the southern United States. Treatment at an academic hospital (HR: 0.91 [95% CI: 0.87, 0.95], p &lt; 0.001), females (HR: 0.82 [95% CI 0.81, 0.84], p &lt; 0.001), Medicaid (HR: 0.77 [95% CI: 0.65, 0.93], p = 0.005) vs no insurance, and adenocarcinoma (HR: 0.79 [95% CI: 0.74, 0.84], p &lt; 0.001) or squamous cell carcinoma (HR: 0.84 [95% CI: 0.80, 0.90, p &lt; 0.001] had better outcomes. Patients in the Northeastern (HR: 0.90 [95% CI: 0.87, 0.93], p &lt; 0.001), Southern (HR: 0.92 [95% CI: 0.89, 0.94], p &lt; 0.001), and Western (HR: 0.89 [95% CI: 0.88, 0.93], p &lt; 0.001) US had better OS compared to the Midwest. Compared to whites, patients who were black (HR: 0.92 [95% CI: 0.88, 0.85], p &lt; 0.001), Asian (HR: 0.77 [95% CI: 0.72, 0.82], p &lt; 0.001), Hispanic (HR: 0.83 [95% CI: 0.78, 0.88], p &lt; 0.001), or other (HR: 0.88 [95% CI: 0.79, 0.97) had better outcomes. Per year increase in age (HR 1.009 [95% CI: 1.006, 1.013], p &lt; 0.001), patients with CDCC score of 1 (HR: 1.16 [95% CI: 1.14, 1.19], p &lt; 0.001), 2 (HR: 1.22 [95% CI: 1.18, 1.27], p &lt; 0.001), and ≥3 (HR: 1.33 [95% CI: 1.284, 1.380], p &lt; 0.001) vs 0, and stage IV NSCLC (HR: 1.83 [95% CI: 1.79, 1.88], p = 0.000) had poorer survival. Conclusions: White race, males, uninsured status, receipt of treatment in the Midwest, community hospital treatment, large cell histology, advancing age, stage IV disease, and increasing CDCC score all had poorer outcomes in adults ≥80 years with advanced NSCLC. These factors should be taken into consideration when discussing the diagnosis, management, and expectations of the disease course with patients in the geriatric age group.

Analysis of survival outcomes in patients with non–small-cell lung cancer with mutations in STK11/LKB1 receiving chemoimmunotherapy.

e15043 Background: Many patients with non-small-cell lung cancer (NSCLC) do not benefit from first-line treatment with immune checkpoint inhibitors (ICI) alone or in combination with chemotherapy due to resistance or toxicity. In NSCLC, significant research has been undertaken to provide prognostic markers for single agent immune checkpoint inhibitor therapy. Co-alterations in TP53, STK11/LKB1 and KRAS have a low reported prevalence in NSCLC, however, potentially may represent a distinct molecular subtype of NSCLC that conveys primary resistance to single-agent ICI. Data continues to accumulate regarding resistance mechanisms to ICI, but limited data exists exploring these alterations and the corresponding survival outcomes in patients that receive chemoimmunotherapy. The primary objective of this study was to assess the association between alterations and co-alterations in STK11/LKB1 and survival outcomes in patients with NSCLC receiving chemo-immunotherapy. Methods: This was a retrospective study of patients ≥18 years old who had completed NGS testing and had a diagnosis of non-small-cell lung cancer that received chemoimmunotherapy from January 2016 to November 2022. NGS reports were reviewed to assess the status of STK11/LKB1, KRAS, KEAP1, and TP53 alterations. Additional markers assessed were PD-L1 status, TPS, CPS, TMB, and MSI if available. Results: Sixty-five patients were identified who had received chemoimmunotherapy for NSCLC, had NGS testing results, and were considered to be evaluable for retrospective review. Four patients were identified with co-altered KRAS, STK11, and TP53, and this alone was not significant for PFS (p= 0.48) or OS (p = 0.52) compared to patients without the alterations, however patients with the co-alterations and a positive PD-L1 showed an improvement in PFS (p =0.053). Patients with altered STK11, including those classified as VUS, had a poorer OS (p = 0.088). Our analysis also showed that PD-L1 negative patients had poorer OS (p = 0.088). Conclusions: The addition of chemotherapy to ICI may overcome resistance previously described in NSCLC patients with co-alterations in KRAS, STK11, and TP53, who received single-agent ICI and these genomic findings should not deter from the use of chemoimmunotherapy based on our limited experience. Patients with alterations in STK11 did have poorer outcomes overall, which is in line with previously published data describing STK11 as a poor prognostic marker in NSCLC. STK11 alterations have previously been described to be associated with PD-L1 negativity which may explain, at least in part, the poorer responses in the STK11 mutated subgroup.