A novel gene expression signature-based on B-cell proportion to predict prognosis of patients with lung adenocarcinoma

XL1R-Net: Explainable AI-driven improved L1-regularized deep neural architecture for NSCLC biomarker identification

BACKGROUND AND OBJECTIVE

Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes - Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data.

METHODS

An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers.

RESULTS

The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug-Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K-M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients' likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List.

CONCLUSION

A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC's genetic diversity, using only one omics data type may not adequately capture the tumor's complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

IL15 modification enables CAR T cells to act as a dual targeting agent against tumor cells and myeloid-derived suppressor cells in GBM

INTRODUCTION

The immunosuppressive tumor microenvironment (TME) is a major barrier to the efficacy of chimeric antigen receptor T cells (CAR-T cells) in glioblastoma (GBM). Transgenic expression of IL15 is one attractive strategy to modulate the TME. However, at present, it is unclear if IL15 could be used to directly target myeloid-derived suppressor cells (MDSCs), a major cellular component of the GBM TME. Here, we explored if MDSC express IL15Rα and the feasibility of exploiting its expression as an immunotherapeutic target.

METHODS

RNA-seq, RT-qPCR, and flow cytometry were used to determine IL15Rα expression in paired peripheral and tumor-infiltrating immune cells of GBM patients and two syngeneic murine GBM models. We generated murine T cells expressing IL13Rα2-CARs and secretory IL15 (CAR.IL15s) or IL13Rα2-CARs in which IL15 was fused to the CAR to serve as an IL15Rα-targeting moiety (CAR.IL15f), and characterized their effector function in vitro and in syngeneic IL13Rα2+glioma models.

RESULTS

IL15Rα was preferentially expressed in myeloid, B, and dendritic cells in patients' and syngeneic GBMs. In vitro, CAR.IL15s and CAR.IL15f T cells depleted MDSC and decreased their secretion of immunosuppressive molecules with CAR.IL15f T cells being more efficacious. Similarly, CAR.IL15f T cells significantly improved the survival of mice in two GBM models. TME analysis showed that treatment with CAR.IL15f T cells resulted in higher frequencies of CD8+T cells, NK, and B cells, but a decrease in CD11b+cells in tumors compared with therapy with CAR T cells.

CONCLUSIONS

We demonstrate that MDSC of the glioma TME express IL15Ra and that these cells can be targeted with secretory IL15 or an IL15Rα-targeting moiety incorporated into the CAR. Thus, IL15-modified CAR T cells act as a dual targeting agent against tumor cells and MDSC in GBM, warranting their future evaluation in early-phase clinical studies.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8⁺ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.

Construction of a B cell-related gene pairs signature for predicting prognosis and immunotherapeutic response in non-small cell lung cancer

BACKGROUND

Accumulating evidence indicates that the B cells play important roles in anti-tumor immunity and shaping tumor development. This study aimed to explore the expression profiles of B cell marker genes and construct a B cell-related gene pairs (BRGPs) signature associated with the prognosis and immunotherapeutic efficiency in non-small cell lung cancer (NSCLC) patients.

METHODS

B cell-related marker genes in NSCLC were identified using single-cell RNA sequencing data. TCGA and GEO datasets were utilized to identify the prognostic BRGPs based on a novel algorithm of cyclically single pairing along with a 0-or-1 matrix. BRGPs signature was then constructed using Lasso-Cox regression model. Its prognostic value, associated immunogenomic features, putative molecular mechanism and predictive ability to immunotherapy were investigated in NSCLC patients.

RESULTS

The BRGPs signature was composed of 23 BRGPs including 28 distinct B cell-related genes. This predictive signature demonstrated remarkable power in distinguishing good or poor prognosis and can serve as an independent prognostic factor for NSCLC patients in both training and validation cohorts. Furthermore, BRGPs signature was significantly associated with immune scores, tumor purity, clinicopathological characteristics and various tumor-infiltrating immune cells. Besides, we demonstrated that the tumor mutational burden scores and TIDE scores were positively correlated with the risk score of the model implying immune checkpoint blockade therapy may be more effective in NSCLC patients with high-risk scores.

CONCLUSIONS

This novel BRGPs signature can be used to assess the prognosis of NSCLC patients and may be useful in guiding immune checkpoint inhibitor treatment in our clinical practice.

B cell deficiency promotes the initiation and progression of lung cancer

Currently commercialized CAR-T cell therapies targeting CD19 and BCMA show great efficacy to cure B cell malignancies. However, intravenous infusion of these CAR-T cells severely destroys both transformed and normal B cells in most tissues and organs, in particular lung, leading to a critical question that what the impact of normal B cell depletion on pulmonary diseases and lung cancer is. Herein, we find that B cell frequency is remarkably reduced in both smoking carcinogen-treated lung tissues and lung tumors, which is associated with advanced cancer progression and worse patient survival. B cell depletion by anti-CD20 antibody significantly accelerates the initiation and progression of lung tumors, which is mediated by repressed tumor infiltration of T cells and macrophage elimination of tumor cells. These findings unveil the overall antitumor activity of B cells in lung cancer, providing novel insights into both mechanisms underlying lung cancer pathogenesis and clinical prevention post CAR-T cell therapy.

A Comparison of Tissue Dissection Techniques for Diagnostic, Prognostic, and Theragnostic Analysis of Human Disease

Histopathology has historically been the critical technique for the diagnosis and treatment of human disease. Today, genomics, transcriptomics, and proteomics from specific cells, rather than bulk tissue, have become key to understanding underlying disease mechanisms and rendering useful diagnostic information. Extraction of desired analytes, i.e., nucleic acids or proteins, from easily accessible formalin-fixed paraffin-embedded tissues allows for clinically relevant activities, such as sequencing biomarker mutations or typing amyloidogenic proteins. Genetic profiling has become routine for cancers as varied as non-small cell lung cancer and prostatic carcinoma. The five main tissue dissection techniques that have been developed thus far include: bulk scraping, manual macrodissection, manual microdissection, laser-capture microdissection, and expression microdissection. In this review, we discuss the importance of tissue dissection in clinical practice and research, the basic methods, applications, as well as some advantages and disadvantages for each modality.