Characterization of The Growth Factor Receptor Network Oncogenes in Lung Cancer

Lung cancer remains the leading cause of cancer related deaths worldwide, reportedly contributing to 1.8 million of the 10.0 million mortalities documented in the year 2020. Although advancements have been made in therapeutics and diagnostic methods, formulation of effective treatments and development of drug resistance continues to be a challenge. These challenges arise from our lack of understanding of intricate signaling pathways, such as the Growth Factor Receptor Network (GFRN), which contributes to complex lung tumor heterogeneity allowing for drug resistance development. In this study, gene expression signatures of six GFRN oncogenes overexpressed in human mammary epithelial cells (HMECs) were generated to interrogate this pathway’s downstream crosstalk, beyond initial mutation status. Utilization of this method may reveal novel phenotypic patterns that could be used to improve targeted therapies for lung cancer. Thus, using computational analysis tools, gene expression signatures were generated of BAD (BAD), HER2 (ERBB2), IGF1R (IGF1R), RAF (RAF1), and KRAS (G12V), using the Bioconductor package, Adaptive Signature Selection and InteGratioN (ASSIGN). Gene lists of various lengths were generated ranging from 5 to 500 genes produced in 25 gene increments. Pathway activation estimates were predicted in 541 lung adenocarcinoma (LUAD) tumors acquired from The Cancer Genome Atlas (TCGA). Each gene signature underwent validation using proteomics data from The Cancer Proteome Atlas (TCPA) and gene expression. Following thorough analysis, optimal gene signatures were determined for the genes BAD, HER2, IGF1R, RAF, and KRAS. In all, the optimized GFRN pathway-specific gene signatures were able to distinguish upregulated pathway activity within TCGA patient tumor samples. With the use of drug response data, novel phenotypic patterns may be revealed identifying drug targets to improve individualized drug targeted therapy for lung cancer

SARS-CoV-2 Early Infection Signature Identified Potential Key Infection Mechanisms and Drug Targets

Background The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are now available, effective treatment options are still out of reach. Results To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P \u3c 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score \u3c − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection. Conclusions Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells

Clinical Implications of Combinatorial Pharmacogenomic Tests Based on Cytochrome P450 Variant Selection

Despite the potential to improve patient outcomes, the application of pharmacogenomics (PGx) is yet to be routine. A growing number of PGx implementers are leaning toward using combinatorial PGx (CPGx) tests (i.e., multigene tests) that are reusable over patients’ lifetimes. However, selecting a single best available CPGx test is challenging owing to many patient- and population-specific factors, including variant frequency differences across ethnic groups. The primary objective of this study was to evaluate the detection rate of currently available CPGx tests based on the cytochrome P450 (CYP) gene variants they target. The detection rate was defined as the percentage of a given population with an “altered metabolizer” genotype predicted phenotype, where a CPGx test targeted both gene variants a prospective diplotypes. A potential genotype predicted phenotype was considered an altered metabolizer when it resulted in medication therapy modification based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Targeted variant CPGx tests found in the Genetic Testing Registry (GTR), gene selection information, and diplotype frequency data from the Pharmacogenomics Knowledge Base (PharmGKB) were used to determine the detection rate of each CPGx test. Our results indicated that the detection rate of CPGx tests covering CYP2C19, CYP2C9, CYP2D6, and CYP2B6 show significant variation across ethnic groups. Specifically, the Sub-Saharan Africans have 63.9% and 77.9% average detection rates for CYP2B6 and CYP2C19 assays analyzed, respectively. In addition, East Asians (EAs) have an average detection rate of 55.1% for CYP2C9 assays. Therefore, the patient’s ethnic background should be carefully considered in selecting CPGx tests

Predicting Survival of NSCLC Patients Treated with Immune Checkpoint Inhibitors: Impact and Timing of Immune-related Adverse Events and Prior Tyrosine Kinase Inhibitor Therapy

Introduction: Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined. Objective: To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs. Methods: A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI therapy between 2014 and 2018. Survival analysis utilized overall survival (OS) and real-world progression free survival (rwPFS) outcomes. Model performance matrices for predicting 1-year OS and 6-month rwPFS using linear regression baseline, optimal, and machine learning modeling approaches. Results: Patients experiencing an irAE were found to have a significantly longer OS and rwPFS compared to patients who did not (median OS 25.1 vs. 11.1 months; hazard ratio [HR] 0.51, confidence interval [CI] 0.39- 0.68, P-value \u3c0.001, median rwPFS 5.7 months vs. 2.3; HR 0.52, CI 0.41- 0.66, P-value \u3c0.001, respectively). Patients who received TKI therapy before initiation of ICI experienced significantly shorter OS than patients without prior TKI therapy (median OS 7.6 months vs. 18.5 months; P-value \u3c 0.01). After adjusting for other variables, irAEs and prior TKI therapy significantly impacted OS and rwPFS. Lastly, the performances of models implementing logistic regression and machine learning approaches were comparable in predicting 1-year OS and 6-month rwPFS. Conclusion: The occurrence of irAEs, the timing of the events, and prior TKI therapy were significant predictors of survival in NSCLC patients on ICI therapy. Therefore, our study supports future prospective studies to investigate the impact of irAEs, and sequence of therapy on the survival of NSCLC patients taking ICIs