Non-small Cell Lung Cancer Samples Research Articles

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

METTL5: A Potential Biomarker for Nonsmall Cell Lung Cancer That Promotes Cancer Cell Proliferation by Interacting with IGF2BP3.

Objective: To examine if METTL5 promotes the proliferation of nonsmall cell lung cancer (NSCLC) cells by interacting with IGF2BP3. Methods: The expression patterns of METTL5 and IGF2BP3 in NSCLC tissues, their relationship with survival rate, and their correlation were analyzed using bioinformatics and clinical sample analyses. The effects of METTL5 overexpression and IGF2BP3 knockdown, as well as those of METTL5 knockdown and IGF2BP3 overexpression, on the proliferation of NSCLC cells were analyzed by transfecting appropriate constructs. The interaction between METTL5 and IGF2BP3 was verified using the co-immunoprecipitation (Co-IP) assay. The invivo effects of METTL5 and IGF2BP3 on NSCLC growth were analyzed using the tumor-bearing nude mouse model. Results: METTL5 and IGF2BP3 expression levels were positively correlated and were associated with poor clinical prognosis. The METTL5 and IGF2BP3 expression levels were upregulated in the clinical NSCLC samples. IGF2BP3 expression did not affect METTL5 expression but was regulated by METTL5. IGF2BP3 overexpression mitigated the METTL5 knockdown-induced impaired cell proliferation. Meanwhile, IGF2BP3 knockdown suppressed METTL5-mediated NSCLC cell proliferation. The Co-IP assay results revealed the interaction between METTL5 and IGF2BP3 in NSCLC cells. IGF2BP3 knockdown suppressed tumor growth, whereas IGF2BP3 overexpression enhanced tumor volume and quality. Conclusion: METTL5 induces NSCLC cell proliferation by interacting with IGF2BP3. Thus, METTL5 is a potential biomarker and a therapeutic target for NSCLC.

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer.

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

The Comparison of Real-Time PCR and Mutation-Specific Immunohistochemistry in EGFR Mutation Analysis of Non-Small Cell Lung Carcinomas

Objective: This study aims to identify activating mutations in the epidermal growth factor receptor (EGFR) gene in patients with non-small cell lung cancer (NSCLC) and to evaluate their correlation with responses to EGFR-tyrosine kinase inhibitors (TKI) treatment. This study aims to identify activating mutations in the epidermal growth factor receptor (EGFR) gene in patients with non-small cell lung cancer (NSCLC) and to evaluate their correlation with responses to EGFR-tyrosine kinase inhibitors (TKI) treatment. We conducted a comparative analysis of Real-Time PCR and immunohistochemistry to detect EGFR mutation status in non-small cell lung cancer patients, focusing on the sensitivity, specificity, and predictive values of immunohistochemistry. Material and Method: We evaluated 788 non-small cell lung cancer samples which were analyzed for EGFR mutation status by RT-PCR. We detected 126 EGFR mutated cases among these patients. We evaluated mutation-specific EGFR immunohistochemistry directed towards the exon 19 deletions (15 bp E746-A750) and exon 21 point mutation (L858R) to the 47 EGFR mutated patients histologic material and cell blocks of cytologic specimens. Results: 32 of the 47 cases (68%) had exon 19 deletion, 14 of them (30%) had point mutation in exon 21, and one of them (2%) showed exon 18 mutation. EGFR exon 19 (15 bp E746-A750 deletion) antibody showed a sensitivity of 100%, specificity of 40%, negative predictive value of 100%, and positive predictive value of 78%. The sensitivity of the exon 21 (L858R point mutation) antibody was 93%, specificity was 91%, negative predictive value was 97% and positive predictive value was 82%. Conclusion: Our investigation indicates that mutation-specific EGFR immunohistochemistry has demonstrated a notable sensitivity and specificity for exon 21. However, while sensitive, the exon 19 (15 bp E746-A750 deletion) antibody lacked specificity. While positive immunohistochemical staining may suggest the presence of an EGFR mutation, making the patient potentially eligible for TKI treatment, it should not be the sole determinant. If immunohistochemistry results are negative, it is essential to resort to molecular tests to ensure accurate diagnosis and appropriate therapeutic guidance. With evolving diagnostic landscapes, it is crucial to harness both IHC and molecular techniques judiciously for optimal patient care.

A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers

BackgroundAs a highly heterogeneous tumor, non-small cell lung cancer (NSCLC) is famous for its high incidence and mortality worldwide. Smoking can cause genetic changes, which leading to the occurrence and progress of NSCLC. Nevertheless, the function of smoking-related genes in NSCLC needs more research.MethodsWe downloaded transcriptome data and clinicopathological parameters from Gene Expression Omnibus (GEO) databases, and screened smoking-related genes. Lasso regression were applied to establish the 7-gene signature. The associations between the 7-gene signature and immune microenvironment analysis, survival analysis, drug sensitivity analysis and enriched molecular pathways were studied. Ultimately, cell function experiments were conducted to research the function of FCGBP in NSCLC.ResultsThrough 7-gene signature, NSCLC samples were classified into high-risk group (HRG) and low-risk group (LRG). Significant difference in overall survival (OS) between HRG and LRG was found. Nomograms and ROC curves indicated that the 7-gene signature has a stable ability in predicting prognosis. Through the analysis of immune microenvironment, we found that LRG patients had better tumor immune activation. FCGBP showed the highest mutation frequency among the seven prognostic smoking related genes (LRRC31, HPGD, FCGBP, SPINK5, CYP24A1, S100P and FGG), and was notable down-regulated in NSCLC smokers compared with non-smoking NSCLC patients. The cell experiments confirmed that FCGBP knockdown promoting proliferation, migration, and invasion in NSCLC cells.ConclusionThis smoking-related prognostic signature represents a promising tool for assessing prognosis and tumor microenvironment in smokers with NSCLC. The role of FCGBP in NSCLC was found by cell experiments, which can be served as diagnostic biomarker and immunotherapy target for NSCLC.

Endobronchial Ultrasound Guided Transbronchial Needle Aspiration and Next Generation Sequencing Yields

PurposeThe use of endobronchial ultrasound (EBUS) is standard practice for lung cancer diagnosis and staging. Next generation sequencing (NGS) for detection of genetic alterations is recommended in advanced, non-squamous, non-small-cell lung cancer (NSCLC). Existing protocols for NGS testing are minimal and reported yields vary. This study aimed to determine the yield of EBUS samples obtained for NGS using a sampling protocol at our institution and assess predictive factors to form collection protocols.MethodsWe reviewed EBUS bronchoscopies from 2016 to 2021 with non-squamous NSCLC diagnoses. For target lesions suspected to be malignant, the sampling protocol was: (a) two slides for on-site evaluation, (b) three to five fine needle aspirations rinsed into saline for immunohistochemical staining and in-house molecular markers, and (c) additional three to five rinses for NGS. Sufficiency for NGS processing was determined by the pathology department.ResultsTwo hundred and seventy-eight non-squamous NSCLC samples were obtained by EBUS (205 adenocarcinoma; 73 not otherwise specified). EBUS was performed under general anesthesia in 75.5% of cases. The overall sample adequacy for NGS testing was 57.5%. Higher adequacy rates were observed when protocol was adhered to 66.0% versus 37.2% (p < 0.001). There was no statistically significant difference based on the size of the lesion or location of the sample.ConclusionWhen a protocol of three to five dedicated needle rinses for NGS was followed, we nearly doubled our sample adequacy rate for NSG as compared to standard care. Studies are needed to determine the ideal collection and processing modality to preserve tissue samples for genetic sequencing.

The value of lung function assessment and Testin expression detection in clinicopathological features and prognosis of NSCLC patients.

The aim of this study is to investigate the clinical value and potential prognostic significance of lung function assessment and Testin expression in non-small cell lung cancer (NSCLC) patients. The NSCLC patients were classified into three groups according to lung function: group of normal lung function, group of PRISm (preserved ratio impaired spirometry) (FEV1, forced expiratory volume during the first second < 80% predicted and FEV1/FVC (forced vital capacity) ≥ 70%) and group of COPD (chronic obstructive pulmonary disease) (FEV1/FVC < 70%). The pre-operational clinicopathological characteristics of these patients were recorded and the markers of systemic inflammatory response, including neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and eosinophils (EOS), were compared between three groups. The expression of Testin in NSCLC samples was detected by IHC and we further explored the correlation between Testin expression and clinicopathological characteristics and prognosis of NSCLC patients. Finally, Cox regression analysis was conducted to study the prognostic factors of NSCLC patients. Of the 158 NSCLC patients, percentages of normal lung function, PRISm and COPD were 41.4%, 22.8% and 36.1%, respectively. Patients with tumor in the left lung were more likely to have pulmonary dysfunction (PRISm and COPD) than the right lung. The markers of systemic inflammatory response showed differences to various degree in the three groups and NSCLC patients with PRISm or COPD presented more unfavorable prognosis than patients with normal function. The expression of Testin correlated with lymph node metastasis, TNM stage and tumor invasion of NSCLC patients. Moreover, patients with low Testin expression exhibited poorer disease-free survival and overall survival than those with high Testin expression. In Cox regression analysis, we found that PRISm, COPD and Testin expression served as prognostic factors in NSCLC patients. The presence of COPD or PRISm influenced systemic inflammatory response and prognosis of NSCLC patients. Testin expression correlated with clinicopathological features and could be potentially used as a prognostic marker in NSCLC.

Molecular characteristics of non-small cell lung cancer tissue based on quantitative indicators of progesterone receptors expression

Background. Progesterone receptors (PR) are regulators of cell proliferation and therefore can be considered as an aim for targeted medications in the treatment of oncological diseases. At the same time, a quantitative assessment of PR expression in the tissue of non-small cell lung cancer (NSCLC), which has not yet been carried out in other studies, will determine the possibility of using PR modulators for the treatment of this disease and identify the potential category of patients most susceptible to these drugs.Purpose. To characterize NSCLC by quantitative indicators of PR expression and to determine the correlation of clinically significant characteristics of patients and clinical and morphological parameters of a NSCLC tumor with the PR expression to assess the possibility of using PR modulators in the treatment of this disease.Methods. The PR expression in 130 surgical samples of NSCLC was quantified using an immunofluorescence method associated with flow cytometry. Primary antibodies to PR (NBP2-46388) and secondary antibodies conjugated with DyLight650 (ab98729) were used.Results. The expression of progesterone receptors was detected in all the studied tumors; an abnormal distribution of the marker expression level was noted (P=0.01). The mean expression level was 55.3±16.2%, and the median was 57% with a range of 70%, which indicates heterogeneity of PR expression in tumors of different patients. There were no statistically significant differences in the level of PR expression depending on the histotype and stage of NSCLC, as well as on the sex of patients. At the same time, the level of expression and the frequency of overexpression of PR (&gt;67%) in tumors in non-smoking patients are higher than in smokers (P⩽0.02).Conclusion. The high frequency of occurrence and level of PR expression in NSCLC indicate the possible effectiveness of the use of their modulators in the treatment of this disease, especially in non-smoking patients.

Circ_100549 promotes tumor progression in lung adenocarcinoma through upregulation of BIRC6.

Lung adenocarcinoma (LUAD) is a subtype of lung cancer with high incidence and mortality globally. Emerging evidence suggests that circular RNAs (circRNAs) exert critical functions in human cancers, including LUAD. CircRNA_100549 (circ_100549) has been reported to be significantly upregulated in non-small cell lung cancer (NSCLC) samples, while its role in modulating LUAD progression remains to be explored. The current study aims at investigating the functional roles of circ_100549 in LUAD and its downstream molecular mechanism. First, we found that the expression of circ_100549 was higher in LUAD cell lines. Loss-of-function assays verified that depletion of circ_100549 repressed LUAD cell proliferation but accelerated cell apoptosis. Furthermore, in vivo experiments demonstrated that silencing of circ_100549 suppressed tumor growth. Subsequently, based on database analysis, we carried out a series of experiments to explore the mechanisms and effects of circ_100549 underlying LUAD progression, including RNA-binding protein immunoprecipitation (RIP), RNA/DNA pull-down, luciferase reporter, and chromatin immunoprecipitation (ChIP) assays. The results indicated that circ_100549 serves as a ceRNA by sponging miR-95-5p to upregulate BPTF expression, thus upregulating BIRC6 expression at a transcriptional level in LUAD. In summary, our study demonstrated that circ_100549 facilitates LUAD progression by upregulating BIRC6 expression.

DRD1 suppresses cell proliferation and reduces EGFR activation and PD-L1 expression in NSCLC.

Dopamine (DA) acts in various key neurological and physiological processes as both a neurotransmitter and circulating hormone. Over the past several decades, the DA signaling network has been shown to regulate the progression of several types of solid tumors, and considerable evidence has shown it is a druggable pathway in the cancer cell context. However, the specific activity and effect of these pathway components appears to be tissue-type and cell-context-dependent. In the present study, expression and methylation of dopamine receptor D1 (DRD1) were measured using RNA sequencing (RNAseq) and reverse transcription polymerase chain reaction (RT-PCR) in non-small cell lung cancer (NSCLC) samples, and validated using publicly available datasets, including The Cancer Genome Atlas (TCGA). In vitro and in vivo functional experiments were performed for cell proliferation and tumor growth, respectively. Mechanistic analyses of the transcriptome and kinome in DRD1-modulated cells informed further experiments, which characterized the effects on the epidermal growth factor receptor (EGFR) pathway and programmed cell death 1 ligand 1 (PD-L1) proteins. Through these experiments, we identified the DRD1 gene as a negative regulator of disease progression in NSCLC. We show that DRD1, as well as other DA pathway components, are expressed in normal human lung tissue, and that loss of DRD1 expression through promoter hypermethylation is a common feature in NSCLC patients and is associated with worse survival. At the cellular level, DRD1 affects proliferation by inhibiting the activation of EGFR and mitogen-activated protein kinase 1/2 (ERK1/2). Interestingly, we also found that DRD1 regulates the expression of PD-L1 in lung cancer cells. Taken together, these results suggest that DRD1 methylation may constitute a biomarker of poor prognosis in NSCLC patients while other components of this pathway could be targeted to improve response to EGFR- and PD-L1-targeted therapies.

TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Deciphering the Impact of Rare Missense Variants in EGFR-TKI-Resistant Non-Small-Cell Lung Cancer through Whole Exome Sequencing: A Computational Approach.

Targeted therapy revolutionizes the treatment of non-small-cell lung cancer (NSCLC), harboring molecular change. Epidermal growth factor receptor(EGFR) mutations play a crucial role in the development of NSCLC, serving as a pivotal factor in its pathogenesis. We elucidated the mechanisms of resistance and potential therapeutic strategies in NSCLC resistant to the EGFR-tyrosine kinase inhibitor (EGFR-TKI). This is achieved by identifying rare missense variants through whole exome sequencing (WES). The goal is to enhance our understanding, identify biomarkers, and lay the groundwork for targeted interventions, thereby offering hope for an improved NSCLC treatment landscape. We conducted WES analysis on 16 NSCLC samples with EGFR-TKI-resistant NSCLC obtained from SRA-NCBI (PRJEB50602) to reveal genomic profiles within the EGFR-TKI. Our findings showed that 48% of the variants were missense, and after filtering with the Ensembl variant effect predictor, 53 rare missense variants in 23 genes were identified as highly deleterious. Further examination using pathogenic tools like PredictSNP revealed 12 deleterious rare missense variants in 7 genes: ZNF717, PSPH, ESRRA, SEMA3G, PTPN7, CAVIN4, and MYBBP1A. Molecular dynamics simulation (MDS) suggested that the L385P variant alters the structural flexibility of ESRRA, potentially leading to unfolding of ERRα proteins. This could impact their function and alter ERRα expression. These insights from MDS enhance our understanding of the structural and dynamic consequences of the L385P ESRRA variant and provide valuable implications for subsequent therapeutic considerations and targeted interventions.

Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study.

Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.

Abstract 5064: Wild type KRAS amplification as de novo oncogenic driver in non-small cell lung cancer

Abstract Introduction: Oncogenic KRAS mutations define subsets of non-small cell lung cancer (NSCLC) with unique clinicopathologic and genomic features, and the recent development of direct KRAS inhibitors has changed the treatment landscape of these patients (pts). However, whether KRAS amplification is oncogenic and defines unique subset of potentially targetable NSCLCs is currently unknown. Methods: Clinicopathologic and genomic data were abstracted from multiple independent cohorts of NSCLCs which underwent tumor genomic profiling at the Dana-Farber Cancer Institute (Cohort 1), AACR-GENIE v.13, TCGA, and other 212 studies (Cohort 2). Whole transcriptome sequencing and quantitative proteomics data from the The Cancer Genome Atlas (TCGA) and the Cancer Cell Lines Encyclopedia (CCLE) were used to correlate KRAS amplification with KRAS RNA levels and protein expression. KRAS doxycycline-inducible models were generated to determine in vitro and in vivo oncogenicity of wild-type KRAS overexpression. Results: Among 15,341 pts with NSCLC identified, KRAS amplification was identified in 355 (2.4%) cases. Compared to KRAS not amplified cases, pts with KRAS amplified NSCLC were more likely to be males (52.4% vs 45.6%, p=0.01), have a history of smoking (82.1% vs 64.2%, p&amp;lt;0.01), and higher median pack-years (35 vs 23.5, p&amp;lt;0.01). KRAS amplification was also associated with higher median aneuploidy levels (as fraction of genome altered) (30.4% vs 14.3%, p&amp;lt;0.01), tumor mutational burden (p&amp;lt;0.01), and increased median PD-L1 expression (20% vs 5%, p=0.01). KRAS amplified cases were enriched with KRAS, POLE2, SLC34A2 and PALB2 mutations, while EGFR mutations were mutually exclusive (Q&amp;lt;0.1). Among NSCLC samples with transcriptomic and proteomic profiling from the TCGA and CCLE cohorts, KRAS amplification was associated with significantly increased KRAS mRNA (p&amp;lt;0.01) and protein expression (p&amp;lt;0.01), compared to KRAS diploid samples. Among pts with clinical outcomes data available (N=9,335), median overall survival (OS) from the date of diagnosis was significantly shorter in pts with KRAS amplified vs KRAS diploid genotype in both KRAS mutant (HR: 0.71, P=0.02) and KRAS WT (HR: 0.74, P=0.02) NSCLCs. In KRAS doxycycline-inducible models (BEAS-2B cells) we demonstrated increased cell proliferation and MAPK activation upon doxycycline-induced KRAS expression. In vivo, sustained expression of KRAS in animals constantly fed with doxycycline-diet triggered tumor formation and development of metastasis, indicating that WT KRAS overexpression is oncogenic. Conclusion: KRAS amplification defines a novel subset of NSCLCs characterized by distinct clinicopathologic and genomic features, and worse survival. Developing novel therapeutic strategies to target KRAS amplification may improve outcomes in pts with NSCLC. Citation Format: Biagio Ricciuti, Sandra V. Michelina, Joao Alessi, Xinan Wang, Federica Pecci, Alessandro Di Federico, Malini Gandhi, Giuseppe Lamberti, Enrico Patrucco, Lynette Sholl, Andrew Aguirre, Chiara Ambrogio, Mark Magdi Awad. Wild type KRAS amplification as de novo oncogenic driver in non-small cell 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 5064.

Abstract 878: Enhancing single-cell RNA sequencing analysis in cancer research: A machine learning framework based on LightGBM for automated cell type annotation

Abstract Single-cell RNA sequencing (scRNA-seq) has been widely used in cancer research to understand the complex gene expression diversity and cancer heterogeneity. However, manual annotation of cell types in the scRNA-seq pipeline is time-consuming and depends on the expertise of analyzers, which can significantly influence the results of downstream analyses. To address this problem, we proposed a novel machine learning framework utilizing the LightGBM model for automated and efficient cell-type annotation of scRNA-seq. Two independent scRNA-seq datasets of non-small cell lung cancer (NSCLC) downloaded from the Gene Expression Omnibus (GEO) were used to train and test our model. A standard procedure is applied to both scRNA-seq datasets for quality control and preprocessing, in which poor-quality cells with low gene expressions or high scores for cellular stress/death were excluded. In addition, Harmony is applied to mitigate batch effects in scRNA-seq that could cause variability due to non-biological factors in experiments. Nine different cell types, including endothelial, epithelial, fibroblast, macrophages, mast, plasma, pulmonary alveolar, B, and T cells, were manually labeled in the two datasets by the providers, which were also examined using gene markers corresponding to different cell types from PanglaoDB and DAVID. These manually labeled cell types were used as the ground truth for training and testing our model. In the training stage, the training dataset (containing 85,000 cells from 44 NSCLC samples) of scRNA-seq was used to train the LightGBM model with its high-variable genes. Then, the model would be evaluated using an independent test dataset (containing 8,000 cells from 18 NSCLC samples) by comparing the automatically predicted and manually labeled cell types. The training result showed that our model could successfully specify the nine different cell types, achieving an overall average accuracy, F1 score, and precision of 0.86 each respectively. In the independent dataset test, the model demonstrated good generalization, showing high predictive performance across all cell types, with an average accuracy, F1 score, and precision of 0.8, 0.78, and 0.8, respectively. Specific to the predictions in the test dataset, we found that some epithelial cells were mistakenly identified as other cell types. This might be because of the complex gene expression patterns exhibited by tumor epithelial cells, making accurate predictions challenging. The proposed machine learning framework facilitates cell labeling and unravels the intricate heterogeneity within lung cancer datasets. The combination of LightGBM and standardized preprocessing establishes a benchmark for high-throughput, accurate single-cell analysis, paving the way for discoveries that are more targeted and have significant clinical impact. Citation Format: Tsung Hsien Chuang, Liang-Chuan Lai, Tzu-Pin Lu, Mong-Hsun Tsai, Hsiang-Han Chen, Eric Y. Chuang. Enhancing single-cell RNA sequencing analysis in cancer research: A machine learning framework based on LightGBM for automated cell type annotation [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 878.

Abstract 6560: Cell-free RNA analysis for non-invasive cancer detection and characterization

Abstract Blood-based liquid biopsies enable non-invasive characterization of cancers. Cell-free RNA (cfRNA) analysis could potentially complement circulating tumor DNA (ctDNA) analysis and allow broader molecular characterization of cancers but has not been extensively explored. Here, we describe RARE-Seq, a novel cfRNA sequencing method designed to maximize sensitivity by targeting rare, tissue-specific transcripts, and simultaneously detecting both tumor-derived expression signatures and somatic mutations in circulating tumor RNA (ctRNA). To interrogate samples for specific gene expression signatures, we developed and optimized a framework to measure tumor- and tissue-specific enrichment scores (ES) within cfRNA. We then empirically determined the limit of detection (LOD95) of RARE-Seq using admixtures of tumor cell line RNA and control cfRNA across a wide range of fractions. We found RARE-Seq to have an estimated LOD95 of 0.05% for cancer-derived RNA, which was 52-fold better than whole transcriptome cfRNA profiling. To explore its potential clinical utility, we profiled 192 samples from 170 subjects with various cancers and non-cancer controls using RARE-Seq. Non-small cell lung cancer (NSCLC) expression signatures were significantly detected in 40 out of 51 NSCLC samples (78% sensitivity at 90% specificity). Detection increased with stage, ranging from 40% in stage I to 87% in stage IV. In a subset of stage IV NSCLC patients with matching ctDNA data (n=16), ctRNA levels were significantly correlated with ctDNA allele frequencies (Pearson R=0.84, P=0.005). Intriguingly, 86% of samples with undetectable ctDNA contained detectable ctRNA, suggesting potential complementarity of ctRNA and ctDNA analysis for cancer detection. We also developed and benchmarked a direct ctRNA genotyping approach, allowing noninvasive detection of driver mutations in 42% of patients with advanced NSCLC. When applied to other tumors, RARE-Seq demonstrated 80-100% sensitivity at 90% specificity for detection of pancreatic adenocarcinomas (PAAD), prostate adenocarcinomas (PRAD), and hepatocellular carcinomas (LIHC). Furthermore, when evaluating tissue of origin (TOO) classification performance, RARE-Seq achieved 85% median accuracy for the top predicted tissue and 96% median accuracy for the top two tissues. These results highlight the potential value of ultrasensitive cfRNA analysis, providing proof-of-principle for detection of multiple tumor types using RARE-Seq. Citation Format: Monica C. Nesselbush, Bogdan A. Luca, Young-Jun Jeon, Isabel Jabara, Catherine B. Meador, Michael S. Binkley, Nova Xu, Angela B. Hui, Andrea Garofalo, William Y. Shi, Kevin J. Liu, Takeshi Sugio, Noah Kastelowitz, Emily G. Hamilton, Rene F. Bonilla, Yi Peng Wang, Alice Jiang, Brianna Lau, Jordan Eichholz, Mandeep Banwait, Joseph Schroers-Martin, Jan Boegeholz, Daniel A. King, Mohammad S. Esfahani, Tyler Raclin, Robert Tibshirani, Sandy Srinivas, Viswam S. Nair, James M. Isbell, Bob T. Li, Zofia Piotrowska, Lecia V. Sequist, Aaron N. Hata, Joel W. Neal, Heather A. Wakelee, Andrew J. Gentles, Ash A. Alizadeh, Maximilian Diehn. Cell-free RNA analysis for non-invasive cancer detection and characterization [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 6560.

Abstract 6158: Tumor genomic heterogeneity in non-small cell lung cancer (NSCLC) patients from Latin America

Abstract Motivation: Life prospects of non-small cell lung cancer (NSCLC) patients have greatly improved over the last decades with the adoption of tumor genomic profiling. A better understanding of the mutational landscape has allowed for a more precise molecular characterization and the development of more effective treatment alternatives. However, tumors present a widespread heterogeneity, and many challenges remain to fully comprehend the influencing factors. Of particular importance is the Latin America population which i) is commonly underrepresented in clinical trials and large-scale epidemiological studies, ii) presents higher incidence and mortality rates in comparison to the developed world, and iii) is constituted by a genetically admixed population. Our research attempts to close this gap by investigating the interplay between sociodemographic, clinical, and lifestyle factors, and the effect these exert on the mutational profiles of Latin-American NSCLC patients. Methodology: The study population is derived from the NIRVANA study, a cross-sectional multicenter observational study aiming to characterize and validate molecular diagnostic technologies for NSCLC patients in Chile, Brazil, and Peru. Between July 2015 to October 2018, 5030 NSCLC patients were recruited from 37 centers, meeting inclusion criteria of age ≥18, histologically or cytological proven NSCLC diagnostic, and naïve treatment. Exclusion criteria included prior chemotherapy treatment and refusal to give informed consent. Primary or metastatic NSCLC samples were collected during standard of care biopsy and processed for the DNA and RNA isolation. Next Generation Sequencing (NSG) genomics profiles were obtained using the Oncomine Focus Assay (OFA). Covariates of interest, including sociodemographic, clinical, and lifestyle factors, were assessed at enrollment. QC-approved genomic profiles were obtained for 1864 participants. Results and Conclusions: The prevalence of mutations and fusions in the eight most relevant NSCLC genes (EGFR, KRAS, ALK, MET, RET, BRAF, ROS1 and ERBB2) varies based on sociodemographic, clinical and lifestyle characteristics. Clear distinctions emerged in the prevalence of EGFR, KRAS and ERBB2 mutations among the three countries (EGFR: 20.9%, 17.6%, 35.3%; KRAS: 21.8%, 15.6%, 10.3%; ERBB2: 2.8%, 3.3%, 4.4% for Brazil, Chile, and Peru, respectively). Furthermore, distinct association patterns were identified between the prevalence of genetic alterations and the studied factors, with attributes such as sex, tobacco use and ethnicity mostly influencing the occurrence of EGFR, ALK and ROS1 alterations. These findings provide novel insights into NSCLC studies among Latin-American patients, potentially serving as guidelines for more tailored strategies in the overall management of this underrepresented population. Citation Format: Javiera Garrido, Evelin González, Gonzalo Sepúlveda-Hermosilla, Matias Freire, Alejandro Blanco, Solange Rivas, Katherine Marcelain, Gareth I. Owen, Carolina Ibañez, Alejandro Corvalan, Marcelo Garrido, Rodrigo Assar, Rodrigo Lizana, Javier Cáceres-Molina, Diego Ampuero, Liliana Ramos, Paola Pérez, Osvaldo Aren, Sara Chernilo, Cristina Fernández, María Loreto Spencer, Jacqueline Flores, Giuliano Bernal, Mónica Ahumada, Germán Rasse, Carolina Sánchez, Maria Galli de Amorim, Gabriela Branco, Diana Noronha Nunes, Emmanuel Dias-Neto, Helano C. Freitas, NIRVANA team, Ricardo Armisén. Tumor genomic heterogeneity in non-small cell lung cancer (NSCLC) patients from Latin America [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 6158.

Abstract 7323: Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis

Abstract Tissue resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, whether TRM exerts any immunosurveillance role in the tumor immune microenvironment (TIME) and progression of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancers, remains unclear. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we could infer the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in TIME and significantly impacts the prognosis of NSCLC patients. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients, suggesting that TRM signatures are promising prognostic markers for immunotherapy in NSCLC. We then built a machine learning model to predict patient survival based on the TRM signatures and immune related genes. The accuracy of the model was validated by Kaplan-Meyer survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed random neighbor embedding. We developed a 4-gene risk score that effectively stratified patients into low-risk and high-risk categories. The patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell activation, macrophage regulation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. Altogether, this study provides valuable insights into the complex interactions between NSCLC TRM and TIME and their impact on patient prognosis, highlighting the importance of TRM in shaping the NSCLC microenvironment. The development of a simplified 4-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning Citation Format: Aidan Shen, Aliesha Garrett, Junhua Mai, Yangzhi Zhu, Chongming Jiang. Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis [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 7323.

MiR-1224-5p reverses gefitinib resistance in non-small-cell lung cancer cells by modulating RFX5/YAP1/HIF1α axis

Purpose: To investigate the molecular pathways by which miR-1224-5p modulate RFX5/YAP1/HIF1α pathway, thereby promoting gefitinib tolerance within non-small-cell lung cancer (NSCLC). &#x0D; Methods: To screen differentially expressed miR-1224-5p in NSCLC samples and predict its downstream target gene – RFX5 – by bioinformatics analysis, 60 NSCLC tissues and their corresponding paraneoplastic tissues were collected. Dual-luciferase assays were performed to verify the targeting relationship between miR-1224-5p and RFX5. Four NSCLC cell lines (A549, H1299, H2170, and H1975) and BEAS-2B normal lung epithelial cell lines were used for in vitro experiments. Co-immunoprecipitation (Co-IP) and Western blotting after cycloheximide (CHX) treatment were used to determine the regulatory interaction between YAP1 and HIF1α, with YAP1 modulating HIF1α protein stability. &#x0D; Results: In NSCLC, downregulation of miR-1224-5p was observed, which resulted in the decrease of RFX5 levels. This reduction in miR-1224-5p levels leads to a decrease in RFX5 levels. Furthermore, restoring miR-1224-5p expression in NSCLC cells made them more sensitive to gefitinib. In vitro, RFX5 elevates YAP1, which in turn boosts the stability of HIF1α. However, miR-1224-5p disrupts this mechanism by influencing the RFX5/YAP1/HIF1α pathway, thus mitigating resistance to gefitinib. &#x0D; Conclusion: The findings show that miR-1224-5p targets RFX5 to suppress YAP1 transcription, thereby diminishing HIF1α stability and overcoming gefitinib tolerance in NSCLC. These findings identify miR-1224-5p as a promising approach to address gefitinib tolerance in NSCLC.

N6-methyladenosine demethyltransferase FTO mediated m6A modification of estrogen receptor alpha in non-small cell lung cancer tumorigenesis.

Fat mass and obesity-associated protein (FTO), which is closely linked with obesity and dietary intake, plays an important role in diet-related metabolic diseases. However, the underlying mechanism of the N6-methyladenosine (m6A) demethyltransferase FTO in tumor development and progression remains largely unexplored. Here, we demonstrated that FTO expression was largely lower in non-small cell lung cancer (NSCLC) samples than in adjacent healthy tissues, and its expression negatively correlated with poor prognosis. Gain- and loss-of-function assays revealed that FTO inhibited NSCLC tumor cell growth and metastasis in vitro and in vivo. Mechanistically, estrogen receptor alpha (ESR1) is a target of FTO, and increased FTO expression significantly impaired the m6A levels of ESR1 mRNA. There were two clear m6A modification sites (5247A and 5409A) in the 3' untranslated region (3'UTR) of ESR1, and FTO could decrease their methylation. Moreover, the m6A readers YTHDF1 and IGF2BP3 recognized and bound the m6A sites in ESR1 mRNA, thereby enhancing its stability and facilitating tumor growth. We also showed that ESR1 has good diagnostic value for NSCLC. In conclusion, we uncovered an important mechanism of epitranscriptomic regulation by the FTO-YTHDF1-IGF2BP3-ESR1 axis and identified the potential of m6A-dependent therapeutic strategies for NSCLC.