Gene expression profiles (GEPs) of immuno-oncologic pathways as predictors of response to checkpoint inhibitors in advanced NSCLC

Immune checkpoint inhibitors: From friend to foe

Immune checkpoints are crucial in regulating the activation of cell-mediated and humoral immune responses. However, cancer cells hijack this mechanism to evade the immune surveillance and anti-cancer response. Typically, receptors like PD-1 and CTLA4, expressed on immune cells, prevent the activation and differentiation of T cells. They also inhibit the development of autoimmune reactions. However, ligands such as PD-L1 for the receptor PD-1 are also expressed on the surface of cancer cells that help prevent the activation of anti-cancer immune responses by blocking the signalling pathways mediated by PD-1 and CTLA4. Immune checkpoint inhibitors (ICIs) have promising therapeutic efficacy for treating several cancers by activating T cells and their differentiation into effector cells against tumours. Nonetheless, hyperactivated immune cells usually contribute to detrimental issues, also known as immune-related adverse effects (IrAE). IrAEs have been observed in multiple organs, leading to neurological issues, colitis, endocrine dysfunction, renal issues, hepatitis, pneumonitis, and dermatitis. The interplay between hyperactivated T cells and Treg cells helps in orchestrating the development of autoimmunity. Moreover, the crosstalk between proinflammatory interleukins and the development of autoantibodies also mediates the multiorgan effects of ICIs in cancer patients. IrAEs are generally managed by terminating the ICI therapy, reducing the ICI dose, and by using corticosteroids to subvert inflammation. Therefore, the present review aims to delineate the impacts of ICIs on the development of autoimmune diseases and inflammatory outcomes in cancer patients. In addition, mechanistic insight involving immune cells, cytokines, and autoantibodies for ICI-mediated IrAEs will also be discussed with updated findings in this field.

Threading the Needle: Navigating Novel Immunotherapeutics in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a poor prognosis. Currently, chemotherapy is the only option for most patients with advanced-stage PDAC. Further, conventional immunotherapies and targeted therapies improve survival outcomes only in rare PDAC patient subgroups. To date, combinatory immunotherapeutic strategies to overcome the immune-hostile PDAC tumor microenvironment (TME) have resulted in limited efficacy in clinical studies. However, efforts are ongoing to develop new treatment strategies for patients with PDAC with the evolving knowledge of the TME, molecular characterization, and immune resistance mechanisms. Further, the growing arsenal of various immunotherapeutic agents, including novel classes of immune checkpoint inhibitors and oncolytic, chimeric antigen receptor T cell, and vaccine therapies, reinforces these efforts. This review will focus on the place of immunotherapy and future possible strategies in PDAC.

Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning

OBJECTIVES

The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).

METHODS

A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed. RNA sequencing was performed on tumor samples to assess gene expression levels. ML techniques were applied to identify key gene features associated with treatment efficacy. A panel of genes was then developed and validated using the nCounter system, and the model's performance in predicting 180-day progression-free survival (PFS) was evaluated.

RESULTS

A total of 93 patients were included in the analysis. ML-based gene selection identified a gene set comprising 83 genes from the comprehensive gene expression data. An nCounter panel was developed using these genes, and an ML model was developed based on their expression levels. In the validation set, the model achieved an accuracy of 0.93, precision of 1.00, a true positive rate of 0.83, and a true negative rate of 1.00. PFS was significantly longer in the high-efficacy group than in the low-efficacy group in the validation set (P < 0.001).

CONCLUSIONS

These findings provide a foundation for biomarker development in ES-SCLC and highlight the potential of this method as a cost-effective, simple, and rapid tool for assessing treatment efficacy in clinical practice.

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

Roles of tissue-resident immune cells in immunotherapy of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common and lethal type of lung cancer, with limited treatment options and poor prognosis. Immunotherapy offers hope for improving the survival and quality of life of NSCLC patients, but its efficacy depends on the tumor immune microenvironment (TME). Tissue-resident immune cells are a subset of immune cells that reside in various tissues and organs, and play an important role in fighting tumors. In NSCLC, tissue-resident immune cells are heterogeneous in their distribution, phenotype, and function, and can either promote or inhibit tumor progression and response to immunotherapy. In this review, we summarize the current understanding on the characteristics, interactions, and roles of tissue-resident immune cells in NSCLC. We also discuss the potential applications of tissue-resident immune cells in NSCLC immunotherapy, including immune checkpoint inhibitors (ICIs), other immunomodulatory agents, and personalized cell-based therapies. We highlight the challenges and opportunities for developing targeted therapies for tissue-resident immune cells and optimizing existing immunotherapeutic approaches for NSCLC patients. We propose that tissue-resident immune cells are a key determinant of NSCLC outcome and immunotherapy response, and warrant further investigation in future research.