Lung Cancer Gene Research Articles

USNAP: fast unique dense region detection and its application to lung cancer.

Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

Gene-level dissection of chromosome 3q locus amplification in squamous cell carcinoma of the lung using the nCounter assay.

Amplification of the 3q region has been identified as a useful biomarker for the diagnosis and treatment of squamous cell carcinoma (SqCC). This region contains genes such as PIK3CA and YEATS2, which have been linked to the prognosis of SqCC. The NanoString nCounter assay is a powerful tool for identifying genetic alterations that affect the progression and prognosis of SqCC. The NanoString nCounter assay was used to identify a subgroup of patients with gene level gain in the 3q region. Gene level gain in the 3q region was more frequent in SqCC than in adenocarcinoma. We found that genes such as PIK3CA and YEATS2 in the 3q region were associated with the prognosis of SqCC. Therefore, identifying a subgroup of patients with gene level gain in the 3q region using the NanoString nCounter assay can aid in selecting appropriate treatment options and improving prognostic predictions for SqCC patients. Amplification of the 3q region in SqCC of lung cancer is a useful biomarker for diagnosis and treatment. The NanoString nCounter assay is a powerful tool for identifying specific genetic alterations that affect the progression and prognosis of SqCC. Our study highlights the importance 3q amplification and its associated genes in lung cancer.

Integrative Analysis of Ferroptosis-Related Genes in Small Cell Lung Cancer for the Identification of Biomarkers and Therapeutic Targets.

Ferroptosis is an iron-dependent programmed cell death mode induced by the toxic buildup of phospholipid peroxidation. Although it is known to affect the initiation and growth of tumors, the association between ferroptosis-related genes (FRGs) and small cell lung cancer (SCLC) has yet to be established. We used the gene expression omnibus (GEO) and ferroptosis database (FerrDb) to acquire information on SCLC and its associated FRGs. Marker genes were subsequently identified using Least Absolute Shrinkage and Selection Operator (LASSO) and support vector machine recursive feature eilmination (SVM-RFE) algorithms and analyzed for single-gene function and pathway enrichment. Using the drug-gene interaction database (DGIdb), we identified forty drugs targeting six marker genes. The competing endogenous RNA (ceRNA) network revealed the regulation pattern for long non-coding RNA (LncRNA)-microRNA (miRNA)-messenger RNA (mRNA) based on marker genes. Six differentially expressed FRGs (ATG3, MUC1, RRM2, IDH2, PARP1, and EZH2) were identified as marker genes with accurate diagnostic capabilities. According to single-gene function and pathway enrichment analyses, these marker genes may be involved in immunomodulation and the cell cycle, as well as numerous pathways connected to tumorigenesis, including the JAK-STAT and PPAR signal pathways. In addition, CIBERSORT analysis showed that MUC1 and PARP1 expression may affect the immune microenvironment in SCLC. We confirmed the accuracy of marker genes for the diagnosis of SCLC using a logistic regression model, thus providing further opportunities to study SCLC-related mechanisms. The accuracy of these results for the diagnosis of SCLC must now be confirmed by further research prior to clinical application.

Roles of NRF2 in DNA damage repair.

The transcription factor NF-E2-related factor 2 (NRF2) is a master regulator widely involved in essential cellular functions such as DNA repair. By clarifying the upstream and downstream links of NRF2 to DNA damage repair, we hope that attention will be drawn to the utilization of NRF2 as a target for cancer therapy. Query and summarize relevant literature on the role of NRF2 in direct repair, BER, NER, MMR, HR, and NHEJ in pubmed. Make pictures of Roles of NRF2 in DNA Damage Repair and tables of antioxidant response elements (AREs) of DNA repair genes. Analyze the mutation frequency of NFE2L2 in different types of cancer using cBioPortal online tools. By using TCGA, GTEx and GO databases, analyze the correlation between NFE2L2 mutations and DNA repair systems as well as the degree of changes in DNA repair systems as malignant tumors progress. NRF2 plays roles in maintaining the integrity of the genome by repairing DNA damage, regulating the cell cycle, and acting as an antioxidant. And, it possibly plays roles in double stranded break (DSB) pathway selection following ionizing radiation (IR) damage. Whether pathways such as RNA modification, ncRNA, and protein post-translational modification affect the regulation of NRF2 on DNA repair is still to be determined. The overall mutation frequency of the NFE2L2 gene in esophageal carcinoma, lung cancer, and penile cancer is the highest. Genes (50 of 58) that are negatively correlated with clinical staging are positively correlated with NFE2L2 mutations or NFE2L2 expression levels. NRF2 participates in a variety of DNA repair pathways and plays important roles in maintaining genome stability. NRF2 is a potential target for cancer treatment.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed

BackgroundThe pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD. MethodsThe prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR). Results and ConclusionRisk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

Single-Droplet Microsensor for Ultra-Short Circulating EFGR Mutation Detection in Lung Cancer Based on Multiplex EFIRM Liquid Biopsy.

Liquid biopsy is a rapidly emerging field that involves the minimal/non-invasive assessment of signature somatic mutations through the analysis of circulating tumor DNA (ctDNA) shed by tumor cells in bodily fluids. Broadly speaking, the unmet need in liquid biopsy lung cancer detection is the lack of a multiplex platform that can detect a mutation panel of lung cancer genes using a minimum amount of sample, especially for ultra-short ctDNA (usctDNA). Here, we developed a non-PCR and non-NGS-based single-droplet-based multiplexing microsensor technology, "Electric-Field-Induced Released and Measurement (EFIRM) Liquid Biopsy" (m-eLB), for lung cancer-associated usctDNA. The m-eLB provides a multiplexable assessment of usctDNA within a single droplet of biofluid in only one well of micro-electrodes, as each electrode is coated with different probes for the ctDNA. This m-eLB prototype demonstrates accuracy for three tyrosine-kinase-inhibitor-related EGFR target sequences in synthetic nucleotides. The accuracy of the multiplexing assay has an area under the curve (AUC) of 0.98 for L858R, 0.94 for Ex19 deletion, and 0.93 for T790M. In combination, the 3 EGFR assay has an AUC of 0.97 for the multiplexing assay.

A mathematical model for cancer risk and accumulation of mutations caused by replication errors and external factors.

Replication errors influence mutations, and thus, lifetime cancer risk can be explained by the number of stem-cell divisions. Additionally, mutagens also affect cancer risk, for instance, high-dose radiation exposure increases lifetime cancer risk. However, the influence of low-dose radiation exposure is still unclear because this influence, if any, is very slight. We can assess the minimal influence of the mutagen by virtually comparing the states with and without mutagen using a mathematical model. Here, we constructed a mathematical model to assess the influence of replication errors and mutagens on cancer risk. In our model, replication errors occur with a certain probability during cell division. Mutagens cause mutations at a constant rate. Cell division is arrested when the number of cells reaches the capacity of the cell pool. When the number of cells decreases because of cell death or other reasons, cells resume division. It was assumed that the mutations of cancer driver genes occur stochastically with each mutation and that cancer occurs when the number of cancer driver gene mutations exceeds a certain threshold. We approximated the number of mutations caused by errors and mutagens. Then, we examined whether cancer registry data on cancer risk can be explained only through replication errors. Although the risk of leukemia was not fitted to the model, the risks of esophageal, liver, thyroid, pancreatic, colon, breast, and prostate cancers were explained only by replication errors. Even if the risk was explained by replication errors, the estimated parameters did not always agree with previously reported values. For example, the estimated number of cancer driver genes in lung cancer was larger than the previously reported values. This discrepancy can be partly resolved by assuming the influence of mutagen. First, the influence of mutagens was analyzed using various parameters. The model predicted that the influence of mutagens will appear earlier, when the turnover rate of the tissue is higher and fewer mutations of cancer driver genes were necessary for carcinogenesis. Next, the parameters of lung cancer were re-estimated assuming the influence of mutagens. The estimated parameters were closer to the previously reported values. than when considering only replication errors. Although it may be useful to explain cancer risk by replication errors, it would be biologically more plausible to consider mutagens in cancers in which the effects of mutagens are apparent.

Expression Pattern and Prognostic Analysis of Branched-Chain Amino Acid Catabolism-Related Genes in Non-Small Cell Lung Cancer.

The purpose of our study is to analyze the expression pattern and prognostic value of catabolism-related enzymes of branched-chain amino acids (BCAAs) in non-small cell lung cancer (NSCLC). Differential expression analysis, mutation, copy number variation (CNV), methylation analysis, and survival analysis of BCAAs catabolism-related enzymes in NSCLC were performed using the Cancer Genome Atlas (TCGA) database. Six and seven differentially expressed genes were obtained in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. IL4I1 was located at the core regulatory nodes in the gene co-expression networks of both LUAD and LUSC. The AOX1 mutation rate was the highest in both LUAD and LUSC. For CNV, IL4I1 was up-regulated in both LUAD and LUSC with an increase in copy number, whereas AOX1 and ALDH2 were differentially regulated in the two subtypes of lung cancer. In patients with NSCLC, high expression of IL4I1 was associated with lower overall survival (OS), and low expression of ALDH2 predicted shorter disease-free survival (DFS). ALDH2 expression was related with LUSC survival. This study explored the biomarkers of BCAAs catabolism related to the prognosis of NSCLC, which provided a theoretical foundation to guide the clinical diagnosis and treatment of NSCLC.

Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy

The immune and stromal contexture within the tumor microenvironment (TME) interact with cancer cells and jointly determine disease process and therapeutic response. We aimed at developing a risk scoring model based on TME-related genes of squamous cell lung cancer to predict patient prognosis and immunotherapeutic response. TME-related genes were identified through exploring genes that correlated with immune scores and stromal scores. LASSO-Cox regression model was used to establish the TME-related risk scoring (TMErisk) model. A TMErisk model containing six genes was established. High TMErisk correlated with unfavorable OS in LUSC patients and this association was validated in multiple NSCLC datasets. Genes involved in pathways associated with immunosuppressive microenvironment were enriched in the high TMErisk group. Tumors with high TMErisk showed elevated infiltration of immunosuppressive cells. High TMErisk predicted worse immunotherapeutic response and prognosis across multiple carcinomas. TMErisk model could serve as a robust biomarker for predicting OS and immunotherapeutic response.

Characterization of URST1 as a prognostic marker and therapeutic target for lung cancer.

e21000 Background: The improved understanding of cancer-specific oncoproteins could lead to the development of novel biomarkers or therapeutic targets for lung cancers. Methods: We screened oncoproteins as follows: i) Identification of up-regulated genes in lung cancers by cDNA microarray, ii) Validation of clinicopathologic importance of their gene product by tissue microarray, iii) Examination of the growth of cancer cells after transfection of their siRNA or expression vector, iv) Screening of their signal pathways. Results: We identified up-regulated in solid tumor 1 (URST1) as a candidate. Immunohistochemical staining of URST1 showed that URST1 expression was observed in 231 (64.5%) of 358 non-small cell lung cancers (NSCLC) and 11 (84.6%) of 13 small cell lung cancers. High level of URST1 expression was associated with poor prognosis for NSCLC patients ( P = 0.0003). Multivariate analysis confirmed that strong URST1 expression was an independent prognostic factor for NSCLC patients that had undergone radical surgery. Knockdown of URST1expression by siRNAs against URST1 significantly suppressed the growth of lung cancer cells, and also inhibited the cell invasive ability. Treatment of lung cancer cells with small molecule inhibitor against URST1 suppressed the cell growth partly through G2/M arrest and cell death. In contrast, enforced expression of URST1 significantly promoted lung cancer cell growth. Microarray analysis coupled with siRNAs for URST1 suggested various URST1 related oncogenic pathways in lung cancer cells. Conclusions: URST1 could play an essential role in malignant potential of lung cancer and it might be useful as a prognostic biomarker and therapeutic target molecule for lung cancer.

Prognostic impact of TP53 mutations in metastatic non-squamous non-small-cell lung cancer.

e21033 Background: TP53 is the most common mutated gene in lung cancer, with a wide range of different mutations. It is unclear whereas TP53 mutations impact the prognosis of patients treated in first-line for advanced or metastatic non-squamous non-small-cell lung cancer (nsNSCLC). Methods: In this retrospective study, we studied the hypothetical impact of TP53 mutations in patients treated in first-line for advanced or metastatic nsNSCLC on overall survival (OS) and progression-free survival (PFS). Secondary objectives were to explore OS and PFS according to treatment received among the two groups. Results: From January 1, 2018 to May 31, 2021, 222 patients were included, 129 in the mutTP53 groups and 93 in the wtTP53 group. Estimated median follow-up time was 26.4 months (95% CI, 24.5-33.5) for mutTP53 group and 24.2 months (95% CI, 20.3-27.5) for wtTP53 group. Median OS (mOS) was 17.5 months (95% CI, 11.3-21.5) in the mutTP53 and 9.5 months (95% CI, 7.3-13.4) in the wtTP53 group, with a significant difference ( p= 0.039). Median PFS was 6.19 months (95% CI, 5.3-7.39) in the mutTP53 and 3.16 months (95% CI, 2.26-5.23) in the wtTP53 group, with a significant difference ( p= 0.011). In multivariable analysis, after adjusting for confounders, the risk of death over the study period appears to be lower in the mutTP53 group than in the wtTP53 group, although this result is not significant (HR 0.74, 95% CI 0.51-1.07, p= 0.11). In subgroup analysis by treatment received (chemotherapy, immune checkpoint inhibitors (ICI) or combination), only patients in the mutTP53 group treated with ICI had a significant improvement in OS compared to patients in the wtTP53 group. The mOS was 24.7 (95% CI, 20.8-NR) vs 10.6 months (95% CI, 4.5-NR) (HR 0.30, 95% CI 0.14-0.64, p= 0.002) for ICI group, and mOS was 20.5 (95% CI, 8.8-NR) vs 16.8 months (95% CI, 8.1-NR) for combination group (HR 1.73; 95% CI, 0.61-4.91, p= 0.3). Conclusions: TP53 mutations is associated with a better OS and PFS than wtTP53 in patients treated with ICI, but we found no difference for patients treated with chemotherapy or combination of chemotherapy and ICI. WtTP53 patient had a short OS on ICI and improved when ICI is combined with chemotherapy. This study emphasizes the importance of evaluating in prospective study the prognostic and predictive role of TP53 mutations in advanced or metastatic nsNSCLC.

Genomics-based screening of novel therapeutic targets and biomarkers for lung cancer.

e15093 Background: Since the number of lung cancer patients who show objective response to standard treatments is still limited, development of new anti-cancer agents with minimum risk of side effects and specific cancer biomarkers is urgently required. Methods: We have been screening new therapeutic targets and their companion biomarkers for lung cancer as follows; i) To identify overexpressed genes in lung cancers by the gene expression profile analysis, ii) To verify the candidate genes for their scarce expression in normal tissues, iii) To validate the clinicopathological importance of their protein expression by tissue microarray covering hundreds of lung cancers, and iv) To characterize their function for the growth and/or survival of lung cancer cells by siRNAs. Results: We identified dozens of candidate oncoproteins and selected a lung cancer cell division-associated protein 1 (LCDAP1). Immunohistochemical analysis showed that LCDAP1 was expressed in 171 of 267 (64%) non-small cell lung cancer (NSCLC) tissues but not in normal lung epithelia. LCDAP1 expression was significantly associated with poor prognosis for NSCLC patients. To investigate its function, we screened LCDAP1-binding protein in lung cancer cells and found an interaction of LCDAP1 with a member of 5'-to-3' DNA helicase that was also up-regulated in lung cancer cells. Knockdown of either of LCDAP1 or its binding partner expression with siRNAs significantly inhibited the growth of cancer cells. Conclusions: Our genomics-based screening could be useful to develop diagnostic and prognostic biomarkers as well as therapeutic targets for small molecules, monoclonal antibodies, and nucleic acid drugs against lung cancer.

Homologous recombination repair gene-based risk model predicts prognosis and immune microenvironment for primary lung cancer after previous malignancies.

Homologous recombination repair (HRR) plays an important role in cancer development, drug resistance, and immune escape, but the role of HRR genes in primary lung cancer (PLC) after previous malignancies is unclear. We used HRR-related score constrcted by HRR genes to classify patients into two groups and compared clinical progression, differential genes, and their functions between them. Then, we constructed a prognostic risk model based on HRR-related score and screened key differentially expressed genes. We evaluated the potential roles, mutational information, and immune correlations of key genes. Finally, we compared the long-term prognosis and immune correlations of different prognostic risk subgroups. We found that HRR-related score was associated with T-stage, immunotherapy sensitivity, and prognosis of PLC after previous malignancies. Differential genes between HRR-related low-score and high-score groups are mainly involved in DNA replication and repair processes, such as the cell cycle. We identified three key genes, ABO, SERPINE2, and MYC, by machine learning, and MYC had the highest amplification mutation frequency. We verified that the key gene-based prognostic model can better assess the prognosis of patients. The risk score of the prognostic model was associated with immune microenvironment and efficacy of immunotherapy. Overall, we identified three key genes ABO, SERPINE2, and MYC associated with HRR status in PLC after previous malignancies. The risk model based on key genes is associated with immune microenvironment and can well predict the prognosis for PLC after previous malignancies.

Identification of the potential ferroptosis key genes in lung cancer with bone metastasis.

To identify the potential key genes of ferroptosis in the pathogenesis of lung cancer with bone metastasis (LCBM) by bioinformatics analysis to provide new targets for treating LCBM and an indicator for early monitoring. We first obtained differentially expressed genes (DEGs) associated with ferroptosis from the Gene Expression Omnibus (GEO) database. MiRWalk 2.0 was used to predict the key microRNAs (miRNAs) and construct related gene-miRNA interaction networks. The functional enrichment analysis of key miRNAs was performed using the miEAA database. Finally, the clinical data of 105 lung cancer patients were retrospectively analyzed, and logistic regression analysis was conducted to assess the relationship between serum alkaline phosphatase (ALP), neuron-specific enolase (NSE), and bone metastasis in lung cancer patients, and a receiver operating characteristic (ROC) curve was drawn. We identified 15 ferroptosis-related genes that were differentially expressed in lung cancer bone metastasis. GO and KEGG enrichment analyses suggested that these genes may affect the oxidative stress response, hypoxia response, rough endoplasmic reticulum, mitochondrial outer membrane, iron-sulfur cluster binding, virus receptor activity, central carbon metabolism in cancer, the interleukin-17 (IL-17) signaling pathway, and other aspects to participate in the occurrence and development of lung cancer bone metastasis. Among the 105 lung cancer patients included in the study, 39 cases had bone metastasis, and the incidence rate was 37.14%. A high Eastern Cooperative Oncology Group (ECOG) score and serum ALP and NSE overexpression were associated with bone metastasis in patients with lung cancer. By assessing the risk of bone metastasis in patients with lung cancer, we found that the Area Under Curves (AUCs) of serum ALP and NSE alone and combined were >0.70. The differentially expressed ferroptosis-related genes and predicted miRNA regulatory network in lung cancer bone metastasis and the related functional enrichment analysis provide new targets for the treatment of lung cancer bone metastasis. At the same time, from a serological perspective, it was found that early monitoring of serum ALP and NSE expression in patients with lung cancer could be considered to assess the risk of bone metastasis in the future.

Blood tumor mutational burden and dynamic changes in circulating tumor DNA predict response to pembrolizumab treatment in advanced non-small cell lung cancer.

The use of immunotherapy targeting the programmed cell death protein-1 (PD-1) and its ligand (PD-L1) has provided new hope for patients with non-small cell lung cancer (NSCLC). However, good biomarkers are needed to identify which patients will benefit from the treatment. In this study, we investigated if circulating tumor DNA (ctDNA) could predict response to pembrolizumab. Plasma samples from patients with NSCLC treated with pembrolizumab were collected immediately before and after one or two cycles of treatment. ctDNA was isolated and analyzed using targeted next-generation sequencing with a lung cancer gene panel. Mutations were detected in ctDNA in 83.93% of patients before treatment initiation. High blood tumor mutational burden (bTMB), measured as the number of different mutations per Mb panel, correlated to longer progression-free survival (PFS) (10.45 vs. 2.30 months) and overall survival (OS) (21.80 vs. 12.20 months), whereas no predictive value was found in the number of mutant molecules per mL of plasma. The absence of mutations just after treatment initiation correlated with improved PFS (20.25 vs. 4.18 months) and OS (28.93 vs. 15.33 months). High bTMB before treatment was associated with a decreasing ctDNA level after treatment initiation. Importantly, a subgroup of patients experienced an increase in the ctDNA level after treatment initiation, and this correlated with inferior PFS (2.19 vs. 11.21 months) and OS (7.76 vs. 24.20 months). All patients in the subgroup with increased ctDNA level progressed within 10 months. Monitoring of ctDNA contains vital information about response to therapy, where the bTMB and the dynamics in the initial part of treatment are particularly important for response. Increasing ctDNA levels after treatment initiation are significantly correlated with inferior survival.

Clinicopathologic, Genomic, and Immunophenotypic Landscape of ATM Mutations in Non-Small Cell Lung Cancer.

ATM is the most commonly mutated DNA damage and repair gene in non-small cell lung cancer (NSCLC); however, limited characterization has been pursued. Clinicopathologic, genomic, and treatment data were collected for 5,172 patients with NSCLC tumors which underwent genomic profiling. ATM IHC was performed on 182 NSCLCs with ATM mutations. Multiplexed immunofluorescence was performed on a subset of 535 samples to examine tumor-infiltrating immune cell subsets. A total of 562 deleterious ATM mutations were identified in 9.7% of NSCLC samples. ATM-mutant (ATMMUT) NSCLC was significantly associated with female sex (P = 0.02), ever smoking status (P < 0.001), non-squamous histology (P = 0.004), and higher tumor mutational burden (DFCI, P < 0.0001; MSK, P < 0.0001) compared with ATM-wild-type (ATMWT) cases. Among 3,687 NSCLCs with comprehensive genomic profiling, co-occurring KRAS, STK11, and ARID2 oncogenic mutations were significantly enriched among ATMMUT NSCLCs (Q < 0.05), while TP53 and EGFR mutations were enriched in ATMWT NSCLCs. Among 182 ATMMUT samples with ATM IHC, tumors with nonsense, insertions/deletions, or splice site mutations were significantly more likely to display ATM loss by IHC (71.4% vs. 28.6%; P < 0.0001) compared with tumors with only predicted pathogenic missense mutations. Clinical outcomes to PD-(L)1 monotherapy (N = 1,522) and chemo-immunotherapy (N = 951) were similar between ATMMUT and ATMWT NSCLCs. Patients with concurrent ATM/TP53 mutations had significantly improved response rate and progression-free survival with PD-(L)1 monotherapy. Deleterious ATM mutations defined a subset of NSCLC with unique clinicopathologic, genomic, and immunophenotypic features. Our data may serve as resource to guide interpretation of specific ATM mutations in NSCLC.

Bioinformatics analysis of the genes associated with co-occurrence of heart failure and lung cancer.

Deaths of non-cardiac causes in patients with heart failure (HF) are on the rise, including lung cancer (LC). However, the common mechanisms behind the two diseases need to be further explored. This study aimed to improve understanding on the co-occurrence of LC and HF. In this study, gene expression profiles of HF (GSE57338) and LC (GSE151101) were comprehensively analyzed using the Gene Expression Omnibus database. Functional annotation, protein-protein interaction network, hub gene identification, and co-expression analysis were proceeded when the co-differentially expressed genes in HF and LC were identified. Among 44 common differentially expressed genes, 17 hub genes were identified to be associated with the co-occurrence of LC and HF; the hub genes were verified in 2 other data sets. Nine genes, including ALOX5, FPR1, ADAMTS15, ALOX5AP, ANPEP, SULF1, C1orf162, VSIG4, and LYVE1 were selected after screening. Functional analysis was performed with particular emphasis on extracellular matrix organization and regulation of leukocyte activation. Our findings suggest that disorders of the immune system could cause the co-occurrence of HF and LC. They also suggest that abnormal activation of extracellular matrix organization, inflammatory response, and other immune signaling pathways are essential in disorders of the immune system. The validated genes provide new perspectives on the common underlying pathophysiology of HF and LC, and may aid further investigation in this field.

Abstract LB045: Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk

Abstract Objective: Genome-wide association studies (GWAS) have identified more than 50 loci for lung cancer risk. However, susceptibility genes and the underlying mechanisms for these risk loci remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify susceptibility genes for lung cancer. Methods: Transcriptome data from normal lung tissue and whole-genome sequencing data from 444 participants of European ancestry in the Genotype-Tissue Expression (GTEx, version 8) were used to build lung-tissue models to predict gene expression levels. Lung-tissue prediction models were successfully established for 10,802 genes with a model performance r &amp;gt; 0.1 and P &amp;lt; 0.05. We also built joint-tissue models using the joint-tissue imputation (JTI) framework, which leverages transcriptome data from lung tissue and 48 other tissue types from the 444 participants in GTEx. Joint-tissue models were successfully established for 12,629 genes with a model performance r &amp;gt; 0.1 and P &amp;lt; 0.05. These prediction models were applied to the GWAS data comprised of 29,266 lung cancer cases and 56,450 controls of European ancestry, in order to evaluate genetically predicted gene expression levels in association with lung cancer risk. Results: We found 44 genes whose genetically predicted expression levels were significantly associated with overall lung cancer risk at the Bonferroni correction significance threshold. Among the 44 genes, four were located at least 500kb away from any of the leading variants identified previously in GWAS. Of these four genes, only the CCHCR1 gene has been reported in previous TWAS, and the other three genes, LY6G5B, PRSS16, and C19orf54, have never been reported in previous GWAS or TWAS. For each of these three novel genes, consistent associations were observed in both lung-tissue and joint-tissue models. For the 40 genes located in previously identified GWAS loci, after adjusting for GWAS-identified variants, the associations for the majority of them became non-significant. However, the associations did not change materially for UCKL1 or PRPF6. These results suggest that the associations for these two genes were independent from previous GWSA-identified signals. We also identified 10 genes located at least 500kb away from GWAS-identified loci that were associated with lung cancer histological subtypes, e.g., adenocarcinoma (DCBLD1 and AQP3), squamous cell carcinoma (ZSCAN26, BLOC1S2, ABCF1, and ZSCAN9), and small cell lung cancer (BTN2A2, TMA16, RP11-218F10.3, and FRS3). An additional 7 genes located in GWAS loci were associated with risk of lung cancer histological subtypes but not with overall lung cancer risk, including 4 genes for adenocarcinoma (TP63, STN1, FAM227B, and TPRG1) and 3 genes for squamous cell carcinoma (DDAH2, OR2H2, and NELFE). Conclusion: Our TWAS identified lung cancer susceptibility genes, providing new insight into the genetics of lung cancer etiology. Citation Format: Tianying Zhao, Jiajun Shi, Yaohua Yang, Jie Ping, Xiao Ou Shu, Wei Zheng, Jirong Long, Qiuyin Cai. Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB045.

Abstract 6556: A gene signature of DNA damage response pathway is predictive of patient clinical outcomes and is associated with tumor immune microenvironment of lung adenocarcinoma

Abstract DNA damage response (DDR) is crucial to prevent the accumulation of DNA lesions that can lead to genomic instability and promote tumorigenesis. Dysregulation of DDR pathways also provides therapeutic opportunities for new cancer therapy. Given that lung cancer displays a high level of genetic mutation and genomic instability, a comprehensive understanding of the dysregulation of DDR genes in lung cancer, especially non-small cell lung cancer (NSCLC) which accounts for 85% of all lung cancer cases, will be critical for identifying new genes contributing to tumorigenesis and novel targets for lung cancer therapy. The goal of this study is to identify novel targets for lung cancer treatment through a comprehensive analysis of DDR genes and its association with patient survival, genomic mutation/instability, tumor immune microenvironment (TIME), and tumor proliferation ability. To this end, we have comprehensively characterized the expression of 276 DDR genes using the Cancer Genome Atlas (TCGA) and other publicly available data of lung adenocarcinoma (LUAD), the most common NSCLC. We found that 36 out of 276 DDR genes are dysregulated with majority being upregulated in tumors and are associated with patient overall survival (OS) and progression free survival (PFS) based on the TCGA LUAD cohort. A gene expression signature, i.e., DDR gene expression dysregulation score (DDRDS), was constructed based on the geometric mean of the differentially expressed genes that were also associated with patient OS. Patients with a higher DDRDS showed significantly worse OS and PFS, more advanced tumor stage, a higher tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) score. In addition, tumors with higher DDRDS displayed an increase in CD4+ activated memory T-cells, CD8+ T- cells, M0 and M1 macrophage, and NK cells, but a decrease in B cells, CD4+ resting memory T-cells, monocytes, and dendritic cells in TIME. Further, these tumors also showed a higher expression of immune check point molecules PD1 and PD-L1. Lastly, gene set enrichment analysis demonstrated that these tumors had significantly increased nuclear division, DNA replication, chromosome segregation, and cell cycles, reflecting the aggressiveness of the disease. Hence, despite the previous studies demonstrating that mutation and deletion defects in DDR genes are frequent in tumors, we found that the dysregulated DDR genes are mainly upregulated, and DDRDS based on the dysregulated expression of DDR genes is associated with the alteration in TMB/HRD/TIME, tumor aggressiveness, and ultimately patient outcomes. These results suggested that patients with a higher tumor DDRDS are associated with poor prognosis but might benefit from immune checkpoint blockade-associated immunotherapy. Citation Format: Xiaoli Zhang, Junran Zhang. A gene signature of DNA damage response pathway is predictive of patient clinical outcomes and is associated with tumor immune microenvironment of lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6556.

Abstract 3823: Therapeutic potential of plant-derived nanovesicles for neuroendocrine prostate cancer

Abstract Background: Neuroendocrine prostate cancer (NEPC) is a highly lethal variant of prostate cancer (PCa) that often emerges after androgen receptor (AR)-targeted therapies such as enzalutamide and abiraterone. NEPC arises via trans-differentiation of prostate adenocarcinomas to neuronal lineage, a process referred to as ‘neuroendocrine trans-differentiation’ (NED), wherein prostate cancer cells show an altered expression of lineage markers such as decreased expression of AR and increased expression of neuronal markers. Currently, there is a lack of effective targeted therapies for NEPC. Current treatment options are limited to highly toxic platinum-based drugs. Hence, there is an urgent need to develop novel treatment strategies to treat NEPC. The focused objective of this study is to develop novel, non-toxic therapeutic intervention against NEPC. Towards this, we exploited the potential of plant-derived nanovesicles/extracellular vesicles as a novel therapeutic. Extracellular vesicles (EVs) are lipid-bilayer-delimited particles that are released from almost all types of cells, including plant cells, typically between 30-150 nm in size. Methods: EVs/nanovesicles were isolated from plant-derived sources by ultracentrifugation. Isolated nanovesicles were characterized by Nanoparticle Tracking Analyses and electron microscopy. After characterization, the effects of nanovesicles were tested in NEPC cell lines in vitro. Effects on cell viability and apoptosis were assessed by MTS assay and flow cytometry, respectively. To understand the mechanistic basis of EV induced alterations, next generation RNA sequencing was performed in control vs EV treated NCI-H660 cells on Illumina HiSeq platform. Significant targets were further validated by real time PCR and Western blot analyses. Results: Plant-derived nanovesicles reduced viability and induced apoptosis in NEPC cell lines. Genes involved in ECM-receptor interaction, focal adhesion, proteoglycans in cancer and small cell lung cancer genes were significantly impacted by these vesicles in NEPC cell line. Remarkably, nanovesicles led to significantly attenuated levels of key proneural transcription factors including ASCL1, BRN2 and SOX2. In addition, neuronal markers ENO2 and NCAM1 were downregulated suggesting that treatment with nanovesicles reverse the ‘cardinal’ genetic alterations that drive NEPC. Conclusion: Plant-derived nanovesicles can be employed as a novel treatment modality in neuroendocrine prostate cancer. Citation Format: Amritha Sreekumar, Matthew Simmons, Tae Jin Lee, Ashok Sharma, Sharanjot Saini. Therapeutic potential of plant-derived nanovesicles for neuroendocrine prostate cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3823.