Lung Cancer Gene Research Articles

Deciphering the Anticancer Arsenal of Piper longum: Network Pharmacology and Molecular Docking Unveil Phytochemical Targets Against Lung Cancer.

Introduction: Lung cancer, characterized by uncontrolled cellular proliferation within the lung tissues, is the predominant cause of cancer-related fatalities worldwide. The traditional medicinal herb Piper longum has emerged as a significant contender in oncological research because of its documented anticancer attributes, suggesting its potential for novel therapeutic development. Methods: This study adopted network pharmacology and omics methodology to elucidate the anti-lung cancer potential of P. longum by identifying its bioactive constituents and their corresponding molecular targets. Results: Through a comprehensive literature review and the Integrated Medicinal Plant Phytochemistry and Therapeutics database (IMPPAT), we identified 33 bioactive molecules from P. longum. Subsequent analyses employing tools such as SwissTargetPrediction, SuperPred, and DIGEP-Pred facilitated the isolation of 676 potential targets, among which 72 intersected with 666 lung cancer-associated genetic markers identified through databases including the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards. Further validation through protein-protein interaction (PPI) networks, gene ontology, pathway analyses, boxplots, and overall survival metrics underscored the therapeutic potential of compounds such as 7-epi-eudesm-4(15)-ene-1β, demethoxypiplartine, methyl 3,4,5-trimethoxycinnamate, 6-alpha-diol, and aristolodione. Notably, our findings reaffirm the relevance of lung cancer genes, such as CTNNB1, STAT3, HIF1A, HSP90AA1, and ERBB2, integral to various cellular processes and pivotal in cancer genesis and advancement. Molecular docking assessments revealed pronounced affinity between 6-alpha-diol and HIF1A, underscoring their potential as therapeutic agents for lung cancer. Conclusion: This study not only highlights the bioactive compounds of P. longum but also reinforces the molecular underpinnings of its anticancer mechanism, paving the way for future lung cancer therapeutics.

Prognostic, immunity, stemness, and anticancer drug sensitivity characterization of pyroptosis related genes in non-small cell lung cancer.

Pyroptosis plays a pivotal role in the tumor immune microenvironment (TME) dynamics, particularly in non-small cell lung cancer (NSCLC). The aim of our study was to explore its effects on tumor progression, TME patterns, and the efficacy of therapeutic interventions in NSCLC. Our investigation encompassed a thorough analysis of pyroptosis-related genes (PRGs), integrating immunohistochemistry (IHC) data, TME characteristics, stemness indices, and anticancer drug sensitivities. We aimed to analyze mRNA expression profiles across various cancers, constructing benchmark datasets to assess the clinical significance of PRGs in NSCLC. This included evaluating their association with clinical responses and efficacy. Notably, both our and HPA IHC data demonstrated significantly elevated GSDMD-N protein levels in lung squamous cell carcinoma (LUSC) tissues. The expression of PRGs differed significantly between tumor and normal tissues across various cancers, as validated by IHC data, and was correlated with prognosis (p < 0.05). Moreover, our investigation revealed significant differences (p < 0.05) in the expression of the PRGs among distinct TME subtypes categorized as C1 (wound healing), C3 (inflammatory), C2 (IFN-gamma dominant), C5 (immunological quiet), C4 (lymphocyte deficient), and C6 (TGF-beta dominant). Additionally, our research on anticancer drug sensitivity uncovered compelling connections between specific anticancer medications and the expression of PRGs, including GSDMD, ELANE, IL18, and CHMP4A (p < 0.05). Our study provided valuable insights into the critical role of PRGs in TME modulation, tumor stemness, and anticancer drug sensitivity across diverse cancers. Our findings illuminate the intricate relationship between pyroptosis and the TME, offering new perspectives for enhancing NSCLC treatment and prognosis.

Mutation Analysis of Epidermal Growth Factor Receptor Gene in Non-Small Cell Lung Cancer for Selection of Patients Eligible for Tyrosine Kinase Inhibitor Therapy

Introduction: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor therapy is effective as a first-line treatment of advanced non-small-cell lung cancer (NSCLC). This research study investigated the distribution of EGFR mutations in patients diagnosed with NSCLC to assist in selecting patients who could benefit from tyrosine kinase inhibitor therapy. Materials and Methods: This cross-sectional study was conducted between July 2017 and November 2022. A real-time multiplex ​Polymerase Chain Reaction (PCR) assay supplied by Roche Diagnostics was used to examine DNA obtained from 682 tumor biopsies collected from NSCLC patients. DNA amplification was performed in a Cobas z 480 instrument for mutation analysis. The PCR assay was designed using specific primers and probes to detect 43 different mutations targeting exons 18-21. Results: Among the 682 samples, 466 (68.3%) were males, and 216 were females. The male-to-female ratio was 2.1. Twenty per cent of the male and 37% of the female samples were positive for EGFR mutations. The most common mutations were the in-frame deletion of exon 19, followed by L858R in exon 21, exon 20 insertion and S769I, exon 18 G719X. In addition, three mutations, namely del exon 19, T790M and exon 20 insertions were also detected in a patient, suggesting an actively progressive disease. Conclusions: This study showed that EGFR mutations are more common in Pakistani female patients than males. Secondly, in-frame deletion of exon 19 and exon 21 mutation L858R are prevalent in most of the NSCLC patients. The prevalence of common and rare EGFR mutations in Pakistani patients provides an opportunity for a subset of patients' chance of therapy.

Dual peptides-modified cationic liposomes for enhanced Lung cancer gene therapy by a gap junction regulating strategy

BackgroundGene therapy for lung cancer has emerged as a novel tumor-combating strategy for its superior tumor specificity, low systematical toxicity and huge clinical translation potential. Especially, the applications of microRNA shed led on effective tumor ablation by directly interfering with the crucial gene expression, making it one of the most promising gene therapy agents. However, for lung cancer therapy, the microRNA treatment confronted three bottlenecks, the poor tumor tissue penetration effect, the insufficient lung drug accumulation and unsatisfied gene transfection efficiency. To address these issues, an inhalable RGD-TAT dual peptides-modified cationic liposomes loaded with microRNA miR-34a and gap junction (GJ) regulation agent all-trans retinoic acid (ATRA) was proposed, which was further engineered into dry powder inhalers (DPIs).ResultsEquipped with a rough particle surface and appropriate aerodynamic size, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs were expected to deposit into the deep lung and reach lung tumor lesions guided by targeting peptide RGD. Assisted by cellular transmembrane peptides TAT, the RGD-TAT-CLPs/ARTA@miR-34a was proven to be effectively internalized by cancer cells, enhancing gene transfection efficiency. Then, the GJ between tumor cells was upregulated by ARTA, facilitating the intercellular transport of miR-34a and boosting the gene expression in the deep tumor.ConclusionOverall, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could enhance tumor tissue penetration, elevate lung drug accumulation and boost gene transfection efficiency, breaking the three bottlenecks to enhancing tumor elimination in vitro and in vivo. We believe that the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could serve as a promising pulmonary gene delivery platform for multiple lung local disease treatments.

A novel feature selection algorithm for identifying hub genes in lung cancer

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein–protein interaction analysis. Notably, our investigation reveals ten significant hub genes—ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B—each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer.

Fascin actin-bundling protein 1 regulates non-small cell lung cancer progression by influencing the transcription and splicing of tumorigenesis-related genes

Background High mortality rates are prevalent among patients with non-small-cell lung cancer (NSCLC), and effective therapeutic targets are key prognostic factors. Fascin actin-bundling protein 1 (FSCN1) promotes NSCLC; however, its role as an RNA-binding protein in NSCLC remains unexplored. Therefore, we aimed to explore FSCN1 expression and function in A549 cells. Method We screened for alternative-splicing events and differentially expressed genes (DEGs) after FSCN1 silence via RNA-sequencing (RNA-seq). FSCN1 immunoprecipitation followed by RNA-seq were used to identify target genes whose mRNA expression and pre-mRNA alternative-splicing levels might be influenced by FSCN1. Results Silencing FSCN1 in A549 cells affected malignant phenotypes; it inhibited proliferation, migration, and invasion, and promoted apoptosis. RNA-seq analysis revealed 2,851 DEGs and 3,057 alternatively spliced genes. Gene ontology-based functional enrichment analysis showed that downregulated DEGs and alternatively splicing genes were enriched for the cell-cycle. FSCN1 promoted the alternative splicing of cell-cycle-related mRNAs involved in tumorigenesis (i.e., BCCIP, DLGAP5, PRC1, RECQL5, WTAP, and SGO1). Combined analysis of FSCN1 RNA-binding targets and RNA-seq data suggested that FSCN1 might affect ACTG1, KRT7, and PDE3A expression by modulating the pre-mRNA alternative-splicing levels of NME4, NCOR2, and EEF1D, that were bound to long non-coding RNA transcripts (RNASNHG20, NEAT1, NSD2, and FTH1), which were highly abundant. Overall, extensive transcriptome analysis of gene alternative splicing and expression levels was performed in cells transfected with FSCN1 short-interfering RNA. Our data provide global insights into the regulatory mechanisms associated with the roles of FSCN1 and its target genes in lung cancer.

Integrative analyses of N6-methyladenosine-associated single-nucleotide polymorphisms (m6A-SNPs) identify tumor suppressor gene AK9 in lung cancer.

N6 -methyladenosine (m6 A) modification has been identified as one of the most important epigenetic regulation mechanisms in the development of human cancers. However, the association between m6 A-associated single-nucleotide polymorphisms (m6 A-SNPs) and lung cancer risk remains largely unknown. Here, we identified m6 A-SNPs and examined the association of these m6 A-SNPs with lung cancer risk in 13,793 lung cancer cases and 14,027 controls. In silico functional annotation was used to identify causal m6 A-SNPs and target genes. Furthermore, methylated RNA immunoprecipitation and quantitative real-time polymerase chain reaction (MeRIP-qPCR) assay was performed to assess the m6 A modification level of different genotypes of the causal SNP. In vitro assays were performed to validate the potential role of the target gene in lung cancer. A total of 8794 m6 A-SNPs were detected, among which 397 SNPs in nine susceptibility loci were associated with lung cancer risk, including six novel loci. Bioinformatics analyses indicated that rs1321328 in 6q21 was located around the m6 A modification site of AK9 and significantly reduced AK9 expression (β = -0.15, p = 2.78 × 10-8 ). Moreover, AK9 was significantly downregulated in lung cancer tissues than that in adjacent normal tissues of samples from the Cancer Genome Atlas and Nanjing Lung Cancer Cohort. MeRIP-qPCR assay suggested that C allele of rs1321328 could significantly decrease the m6 A modification level of AK9 compared with G allele. In vitro assays verified the tumor-suppressing role of AK9 in lung cancer. These findings shed light on the pathogenic mechanism of lung cancer susceptibility loci linked with m6 A modification.

Abstract B029: Targeting phosphodiesterase: A potential strategy to treat LKB1-mutant cancers

Abstract Cancer heterogeneity poses numerous challenges to treatment, underscoring the need for personalized/targeted therapies that align with each patient's specific genetic and molecular disease profile. Metabolic deregulation, often correlating with oncogenic alterations, is commonly observed in cancers. Such deregulation frequently presents metabolic vulnerabilities within the cancer, which could potentially be exploited for targeted therapy. Liver Kinase B1 (LKB1), a protein known for its diverse roles in cellular metabolism and its critical function as a tumor suppressor, ranks as the third most frequently mutated gene in non-small cell lung cancer (NSCLC). LKB1 mutations are linked to accelerated tumor progression and invasion, resulting in adverse clinical outcomes. Our research uncovers a novel role for LKB1 in cyclic nucleotide metabolism, wherein it suppresses a set of phosphodiesterase (PDE) expressions. Specifically, LKB1 represses PDE3 via the activation of the downstream salt-inducible kinase. Our data demonstrate that PDE3 modulators can selectively eliminate LKB1-deficient tumor cells while leaving LKB1-wildtype cells unharmed. However, some LKB1-deficient cells develop resistance due to the loss of SLFN12, whose expression is required for PDE3 modulator-induced cell death. Interestingly, this resistance can be overcome by inducing SLFN12 expression, through either epigenetic inhibitors or cAMP inducers. These findings not only broaden our understanding of the role of LKB1 in metabolic regulation but also suggest a promising strategy for targeted therapy against LKB1-mutant cancers. Citation Format: Catherine Rono, Xiaohu (Mark) Tang. Targeting phosphodiesterase: A potential strategy to treat LKB1-mutant cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B029.

Chinese Expert Consensus on the Clinical Practice of Non-small Cell Lung Cancer Fusion Gene Detection Based on RNA-based NGS

RNA-based next-generation sequencing (NGS) has been recommended as a method for detecting fusion genes in non-small cell lung cancer (NSCLC) according to clinical practice guidelines and expert consensus. The primary targetable alterations in NSCLC consist of gene mutations and fusions, making the detection of gene mutations and fusions indispensable for assessing the feasibility of targeted therapies. Currently, the integration of DNA-based NGS and RNA-based NGS allows for simultaneous detection of gene mutations and fusions and has been partially implemented in clinical practice. However, standardized guidelines and criteria for the significance, application scenarios, and quality control of RNA-based NGS in fusion gene detection are still lacking in China. This consensus aims to provide further clarity on the practical significance, application scenarios, and quality control measures of RNA-based NGS in fusion gene detection. Additionally, it offers guiding recommendations to facilitate the clinical implementation of RNA-based NGS in the diagnosis and treatment of NSCLC, ultimately maximizing the benefits for patients from fusion gene detection. .

Pulmonary Low Malignant Potential Adenocarcinoma: A Validation of the Proposed Criteria for This Novel Subtype.

Adenocarcinoma (ADC) is the most common histologic type of lung cancer, including in situ (lepidic), minimally invasive, and invasive forms. While the former 2 types are associated with a favorable outcome, the latter includes tumors with variable behavior, often tumor stage-related. A recent study proposed strict morphologic criteria defining a new subgroup of resected stage I invasive ADC (16% of cases) with favorable outcomes (100% disease-specific survival), named "ADC of low malignant potential (LMP-ADC)." The following criteria were met: ≤3cm size, nonmucinous histotype, ≥15% lepidic growth, and the absence of the following: high-grade patterns, >1 mitosis/2mm 2 , necrosis, and vascular/pleural invasion. The aim of the present study was to validate the performance of such criteria to identify LMP-ADC in a series of 274 stage IA resected lung ADCs from a single institution. Thirty-four tumors (12.4%) met the proposed criteria for LMP-ADC, as confirmed by additional stains for mitotic figures, Ki67 index, and elastic fibers (helpful to assess alveolar wall invasion). Minor differences between the lepidic and invasive components were observed regarding cell atypia and proliferation. p53 was normally expressed by invasive tumor cells. Mutations occurred in known lung cancer genes (mostly KRAS and EGFR). Five patients (14.7%) developed disease progression and 2 of them (5.9%) died of the disease. In our series, the disease-specific survival was 94.1%. In conclusion, in resected invasive lung ADC, a subgroup presenting low-grade morphologic features and associated with favorable prognosis does exist. Morphologic criteria for LMP-ADC supported by ancillary techniques represent a valid tool to better define this novel subgroup and to refine the stratification of invasive lung ADC, possibly suggesting modified follow-up protocols, based on the observed indolent behavior in most cases.

TREM1 is involved in the mechanism between asthma and lung cancer by regulating the Toll‑like receptor signaling pathway.

Lung cancer and asthma are both global health problems with significant economic consequences. Recent studies have demonstrated that asthma may be a risk factor for lung cancer. The present study aimed to explore the pathogenesis between these two diseases through a comprehensive analysis. Differentially expressed genes (DEGs) screened in the asthma-related GSE165934 dataset were analyzed to find relevant inflammatory pathways. Overlapping genes regulated by inflammatory pathways and lung cancer-DEGs from The Cancer Genome Atlas (TCGA) were obtained and subjected to survival and gene-wide mutation analyses, and nomogram construction to determine the hub gene. The hub gene was further analyzed through expression validation, immunoassays and functional experiments to investigate its role and mechanism in lung cancer. Functional enrichment analysis showed that 1,275 DEGs from GSE165934 were closely associated with the Toll-like receptor signaling pathway, and 8 overlapping genes were identified from 12 genes regulated by the Toll-like receptor signaling pathway and 3,134 TCGA-DEGs. After a series of bioinformatics analyses, it was found that triggering receptor expressed on myeloid cells 1 (TREM1) was the hub gene involved in the mechanism of asthma and lung cancer. TREM1 was also found to be a suppressor gene in lung cancer correlated with immune cells, immune checkpoint-related genes and tumor mutational burden score. Additionally, the results of Cell Counting Kit-8 and Transwell experiments demonstrated that overexpression of TREM1 could significantly inhibit the invasion, proliferation and migration of lung cancer cells. Reverse transcription-quantitative PCR and western blotting demonstrated that the overexpression of TREM1 could also significantly reduce the level of Toll-like receptor signaling pathway proteins. The present findings suggest that TREM1 is associated with the mechanism of asthma and lung cancer through its regulation of the Toll-like receptor signaling pathway. Furthermore, TREM1 may serve as a potential treatment target and prognostic indicator for patients with lung cancer.

RNA-Binding Protein RBM5 Plays an Essential Role in Acute Myeloid Leukemia By Activating the Oncogenic Protein HOXA9

Acute myeloid leukemia (AML) represents a type of malignant hematological disease that indicates poor outcomes in children and adults. HOXA9 overexpression is observed in 50-70% of human AML and positively correlates with poor patient outcomes. Leukemia subtypes with hallmark overexpression of HOXA9 include but are not limited to those carrying KMT2A gene rearrangements (KMT2A-r), NPM1c mutations, NUP98-fusions and EZH2 mutation. Accumulating genetic evidence also indicates that HOXA9 dysregulation is sufficient and necessary for leukemic transformation, and loss of function of HOXA9 and downstream pathways consequently impaired leukemia maintenance. Although HOXA9 is not an ideal direct candidate for therapeutic interventions, uncovering the novel regulators of HOXA9 might elucidate the HOXA9 regulation mechanism that could be exploited to identify novel therapeutic targets in HOXA9-driven leukemias. In this study, we performed genome-wide CRISPR/Cas9 screens in the HOXA9-endogenous reporter acute leukemia cells and unbiasedly identified that candidate genes are significantly enriched in RNA binding and RNA splicing regulation. An RBP protein, RBM5, was among the top essential genes. Initially, RBM5 was reported as a tumor suppressor gene in lung cancer. Thus far, the function of the encoding protein RBM5 in blood cancer remained largely unknown. To further test how RBM5 expression might correlate with AML development, we analyzed the public RNA-seq data from the TCGA clinical cancer sample datasets, RBM5 is highly expressed in AML, indicating a plausible role in AML oncogenesis. Functional validation by two individual sgRNAs against different exons of RBM5 confirmed the selectively survival essential of RBM5 in AML cell lines. Ectopic expression of sgRNA-resistant RBM5 cDNA ultimately rescued the cellular phenotype upon RBM5 loss. We utilized an alternative loss-of-function approach, RNA interference (RNAi), to further examine the functional requirement of RBM5, Two independent shRNAs targeted RBM5 mRNA in three AML cells lines. RBM5 loss significantly impaired cell growth, reduced colony numbers and triggered notable myeloid differentiation. We further transplant MOLM13 cells targeted with shRBM5 into NSG mice, and observed that RBM5 shRNAs-treated animals lagged progressively more behind with much less splenomegaly and reduced leukemia cell infiltration in bone marrow, spleen, and peripheral blood compared to the wild-type control, suggesting that RBM5 is required for AML progression in vivo. More importantly, Suppression of RBM5 in primary AML cells significantly reduced colony numbers and induced myeloid differentiation, as demonstrated by the maturation markers CD11b and CD14. In addition, the domain dropout CRISPR screen revealed that the DNA-binding domain C2H2 is required for the gene function. Integrative analysis further suggested that HOXA9 promises the direct transcriptional target of RBM5 as demonstrated by functional rescue assay and HOXA9 partially restore the gene transcriptional defect in RBM5 null leukemia cells. In summary, our innovative research reported that a new RNA-binding protein, RBM5, is required for the survival of AML through non-canonical transcriptional regulation via a complex regulatory mechanism. These data support RBM5 as a promising AML therapeutic target in future studies.

Exploring the Significance of p53 Gene in Lung Cancer: Etiological Factors, Clinical Implications, and Therapeutic Approaches

Exploring the Significance of p53 Gene in Lung Cancer: Etiological Factors, Clinical Implications, and Therapeutic Approaches

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients' progression in lung cancer. Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer. A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer. Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.

A Novel Meta-Analysis-Based Regularized Orthogonal Matching Pursuit Algorithm to Predict Lung Cancer with Selected Biomarkers

Biomarker selection for predictive analytics encounters the problem of identifying a minimal-size subset of genes that is maximally predictive of an outcome of interest. For lung cancer gene expression datasets, it is a great challenge to handle the characteristics of small sample size, high dimensionality, high noise as well as the low reproducibility of important biomarkers in different studies. In this paper, our proposed meta-analysis-based regularized orthogonal matching pursuit (MA-ROMP) algorithm not only gains strength by using multiple datasets to identify important genomic biomarkers efficiently, but also keeps the selection flexible among datasets to take into account data heterogeneity through a hierarchical decomposition on regression coefficients. For a case study of lung cancer, we downloaded GSE10072, GSE19188 and GSE19804 from the GEO database with inconsistent experimental conditions, sample preparation methods, different study groups, etc. Compared with state-of-the-art methods, our method shows the highest accuracy, of up to 95.63%, with the best discriminative ability (AUC 0.9756) as well as a more than 15-fold decrease in its training time. The experimental results on both simulated data and several lung cancer gene expression datasets demonstrate that MA-ROMP is a more effective tool for biomarker selection and learning cancer prediction.

Molecular heterogeneity and co-altered genes in MET-amplified ALK-positive lung cancer: Implications for MET targeted therapy

ObjectivesMET amplification is a common mechanism of resistance to second- and third-generation anaplastic lymphoma kinase (ALK) inhibitors. In case series of MET-amplified ALK-rearranged (ALK + ) lung cancer, durability of responses to combinations targeting ALK and MET is variable, suggesting heterogeneity across tumors. However, little is known about the molecular composition of this subset of ALK-rearranged (ALK + ) NSCLC. Materials and methodsWe queried tissue and plasma databases to compile a group of > 50 specimens with ALK rearrangements and concurrent MET amplification. Fluorescence in-situ hybridization (FISH) and next-generation sequencing (NGS) were utilized to quantify the range of MET copies and describe the global molecular landscape of co-altered genes. ResultsBy FISH, high-level amplification (overall MET/centromere 7 probe ratio ≥ 5) was detected in 75 % of MET-amplified ALK + NSCLC tissue specimens. Intralesional heterogeneity of MET copies was observed, with high-level amplification identified even in cells from tumors with overall low-level MET amplification. Analysis of 48 MET-amplified ALK + NSCLC plasma specimens suggested that high-level amplification is rarely (17 %) detected in plasma. In both tissue and plasma, EML4-ALK variant 1 was the predominant variant (51 %) identified in MET-amplified specimens. ALK kinase domain mutations were only present in a minority of MET-amplified ALK + NSCLCs. MET-amplified ALK + NSCLC plasma specimens were enriched for TP53 mutations (81 % vs 45 %, p = 0.002), EGFR amplification (17 % vs 4 %, p < 0.001), and MYC amplification (21 % vs 3 %, p < 0.001) compared to ALK + NSCLC specimens lacking MET amplification. ConclusionsMET-amplified, ALK + NSCLC often presents with high-level and heterogeneous amplification in tissue, seldom overlaps with ALK mutations, and frequently co-occurs with alterations associated with aggressive tumor biology.

ATM Mutations Associate with Distinct Co-Mutational Patterns and Therapeutic Vulnerabilities in NSCLC.

Ataxia-telangiectasia mutated (ATM) is the most frequently mutated DNA damage repair gene in non-small cell lung cancer (NSCLC). However, the molecular correlates of ATM mutations and their clinical implications have not been fully elucidated. Clinicopathologic and genomic data from 26,587 patients with NSCLC from MD Anderson, public databases, and a de-identified nationwide (US-based) NSCLC clinicogenomic database (CGDB) were used to assess the co-mutation landscape, protein expression, and mutational processes in ATM-mutant tumors. We used the CGDB to evaluate ATM-associated outcomes in patients treated with immune checkpoint inhibitors (ICI) with or without chemotherapy, and assessed the effect of ATM loss on STING signaling and chemotherapy sensitivity in preclinical models. Nonsynonymous mutations in ATM were observed in 11.2% of samples (2,980/26,587) and were significantly associated with mutations in KRAS, but mutually exclusive with EGFR (q < 0.1). KRAS mutational status constrained the ATM co-mutation landscape, with strong mutual exclusivity with TP53 and KEAP1 within KRAS-mutated samples. Those ATM mutations that co-occurred with TP53 were more likely to be missense mutations and associate with high mutational burden, suggestive of non-functional passenger mutations. In the CGDB cohort, dysfunctional ATM mutations associated with improved OS only in patients treated with ICI-chemotherapy, and not ICI alone. In vitro analyses demonstrated enhanced upregulation of STING signaling in ATM knockout cells with the addition of chemotherapy. ATM mutations define a distinct subset of NSCLC associated with KRAS mutations, increased TMB, decreased TP53 and EGFR co-occurrence, and potential increased sensitivity to ICIs in the context of DNA-damaging chemotherapy.

Integrative analysis of GWAS and transcriptomics data reveal key genes for non-small lung cancer.

Lung cancer is one of the world's most common and deadly cancers. The two main types of lung cancer are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). More than 85% of lung cancers are NSCLC. Genetic factors play a significant role in the risk of NSCLC. Growing studies focus on studying risk factors at the molecular level. The aim of the study is to build a pipeline to integrate Genome-wide association analysis (GWAS) and transcriptomics data with machine learning to effectively identify genetic risk factors of NSCLC. GWAS datasets and GWAS summary data were downloaded from GWAS catalog, which include lung carcinoma genetic variants among the European population. Then, with the GWAS summary, data functional analysis of significant SNPs was performed using a webserver called FUMAGWAS. The transcriptomics data of NSCLC and non-NSCLC people were used to build a machine learning model to identify the key genes that help predict the NSCLC. The top up-regulation and down-regulation genes were identified by the BART cancer webserver, and the mechanistic roles of the genes were validated by literature review. By performing integrative analysis of GWAS and transcriptomics analysis using machine learning, we identified multiple SNPs and genes that related to NSCLC. The computational pipeline may facilitate the biomarker discovery for NSCLC and other diseases.

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.