OUP accepted manuscript

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Programmed Death-Ligand 1 Copy Number Alteration as an Adjunct Biomarker of Response to Immunotherapy in Advanced NSCLC

INTRODUCTION

This study aimed to evaluate the value of programmed death-ligand 1 (PD-L1) copy number (CN) alteration as an additional biomarker to standard immunohistochemistry (IHC) in predicting response to immune checkpoint inhibitor (ICI) therapy in advanced NSCLC.

METHODS

Before ICI monotherapy, tumor PD-L1 CN alteration (gain, neutral, or loss) was called using whole-exome sequencing data and compared with IHC results (tumor proportion score ≥50, 1-49, or 0). Progression-free survival (PFS) and overall survival were correlated with both biomarkers. In addition, the impact of CN alteration was further evaluated in two independent cohorts using next-generation sequencing panel.

RESULTS

A total of 291 patients with advanced-stage NSCLC met the study inclusion criteria. Although the IHC classification distinguished the best responsive group (tumor proportion score ≥ 50), the CN-based classification distinguished the worst responsive group (CN loss) from the others (PFS, p = 0.020; overall survival, p = 0.004). After adjusting for IHC results, CN loss was an independent risk factor for progression (adjusted hazard ratio = 1.32, 95% confidence interval: 1.00-1.73, p = 0.049) and death (adjusted hazard ratio = 1.39, 95% confidence interval: 1.05-1.85, p = 0.022). A risk classification system was developed on the basis of IHC and CN profiles, which outperformed the conventional IHC system. In the validation cohorts, CN loss determined by next-generation sequencing panel was independently associated with worse PFS after ICI treatment, revealing its practical value.

CONCLUSIONS

This is the first study to directly compare CN alterations with IHC results and survival outcomes after anti-PD-(L)1 therapy. Tumor PD-L1 CN loss can serve as an adjunct biomarker to predict the lack of response. Prospective studies are required to further validate this biomarker.

PD-L1 gene amplification and focality: relationship with protein expression

PD-L1 (CD274) amplification occurs in a small subset of malignancies and may predict anti-PD-1/PD-L1 immunotherapy responsiveness. We hypothesized that both copy number (CN) and focality of cancer-related PD-L1 amplifications impact protein expression, and, thus, analyzed solid tumors that underwent comprehensive genomic profiling between March 2016 and February 2022 at Foundation Medicine. PD-L1 CN alterations were detected using a comparative genomic hybridization-like method. PD-L1 CN changes were correlated with PD-L1 protein expression (DAKO 22C3 antibody) by immunohistochemistry (IHC). Overall, 60,793 samples were analyzed (most frequent histologies: lung adenocarcinoma (20%), colon adenocarcinoma (12%), lung squamous carcinoma (8%)). Using a definition of CD274 CN ≥ specimen ploidy +4 (6 copies), 1.21% of tumors (738/60,793) were PD-L1 amplified. Focality category distribution was as follows: <0.1 mB (n=18 (2.4%)), ≥0.1 to <4 mB (n=230 (31.1%)), ≥4 to <20 mB (n=310 (42%)), ≥20mB (n=180 (24.4%)). Lower levels of PD-L1 amplification (below specimen ploidy +4) were more frequently non-focal amplifications compared to higher levels. In addition, more focal amplification (<0.1 mB) correlated with higher PD-L1 IHC expression. Median tumor proportion score (TPS) for samples with PD-L1 amplification (ploidy ≥+4) according to focality were 87.5% (<0.1 mB), 80% (≥0.1 to <4 mB), 40% (≥4 to <20 mB), 1% (≥20mB). In specimens with PD-L1 ploidy less than +4, but highly focal (<0.1 mB), the 75th percentile of PD-L1 expression by TPS was 80%. Conversely, non-focal (≥20 mB) PD-L1 amplification (ploidy ≥+4) can present high PD-L1 expression (TPS≥50%), albeit infrequently (0.09% of our cohort). In conclusion, PD-L1 expression measured by IHC is influenced by PD-L1 amplification level and focality. Further correlation between amplification, focality, protein expression and therapeutic outcome for PD-L1 and other targetable genes warrants exploration.