Preclinical Models for the Study of Lung Cancer Pathogenesis and Therapy Development

Nanoengineering Solutions for Cancer Therapy: Bridging the Gap between Clinical Practice and Translational Research

Nanoengineering has emerged as a progressive method in cancer treatment, offering precise and targeted delivery of therapeutic agents while concurrently reducing overall toxicity. This scholarly article delves into the innovative strategies and advancements in nanoengineering that bridge the gap between clinical practice and research in the field of cancer treatment. Various nanoengineered platforms such as nanoparticles, liposomes, and dendrimers are scrutinized for their capacity to encapsulate drugs, augment drug efficacy, and enhance pharmacokinetics. Moreover, the article investigates research breakthroughs that drive the progression and enhancement of nanoengineered remedies, encompassing the identification of biomarkers, establishment of preclinical models, and advancement of biomaterials, all of which are imperative for translating laboratory findings into practical medical interventions. Furthermore, the integration of nanotechnology with imaging modalities, which amplify cancer detection, treatment monitoring, and response assessment, is thoroughly examined. Finally, the obstacles and prospective directions in nanoengineering, including regulatory challenges and issues related to scalability, are examined. This underscores the significance of fostering collaboration among various entities in order to efficiently translate nanoengineered interventions into enhanced cancer therapies and patient management.

Pollutants to pathogens: The role of heavy metals in modulating TGF-β signaling and lung cancer risk

Lung cancer is a malignant tumor that develops in the lungs due to the uncontrolled growth of aberrant cells. Heavy metals, such as arsenic, cadmium, mercury, and lead, are metallic elements characterized by their high atomic weights and densities. Anthropogenic activities, such as industrial operations and pollution, have the potential to discharge heavy metals into the environment, hence presenting hazards to ecosystems and human well-being. The TGF-β signalling pathways have a crucial function in controlling several cellular processes, with the ability to both prevent and promote tumor growth. TGF-β regulates cellular responses by interacting in both canonical and non-canonical signalling pathways. Research employing both in vitro and in vivo models has shown that heavy metals may trigger TGF-β signalling via complex molecular pathways. Experiments conducted in a controlled laboratory environment show that heavy metals like cadmium and arsenic may directly bind to TGF-β receptors, leading to alterations in their structure that enable the receptor to be phosphorylated. Activation of this route sets in motion subsequent signalling cascades, most notably the canonical Smad pathway. The development of lung cancer has been linked to heavy metals, which are ubiquitous environmental pollutants. To grasp the underlying processes, it is necessary to comprehend their molecular effect on TGF-β pathways. With a particular emphasis on its consequences for lung cancer, this abstract delves into the complex connection between exposure to heavy metals and the stimulation of TGF-β signalling.

Hydrogel-Embedded Precision-Cut Lung Slices Model Lung Cancer Premalignancy Ex Vivo

Lung cancer is the leading global cause of cancer-related deaths. Although smoking cessation is the best prevention, 50% of lung cancer diagnoses occur in people who have quit smoking. Research into treatment options for high-risk patients is constrained to rodent models, which are time-consuming, expensive, and require large cohorts. Embedding precision-cut lung slices (PCLS) within an engineered hydrogel and exposing this tissue to vinyl carbamate, a carcinogen from cigarette smoke, creates an in vitro model of lung cancer premalignancy. Hydrogel formulations are selected to promote early lung cancer cellular phenotypes and extend PCLS viability to six weeks. Hydrogel-embedded PCLS are exposed to vinyl carbamate, which induces adenocarcinoma in mice. Analysis of proliferation, gene expression, histology, tissue stiffness, and cellular content after six weeks reveals that vinyl carbamate induces premalignant lesions with a mixed adenoma/squamous phenotype. Putative chemoprevention agents diffuse through the hydrogel and induce tissue-level changes. The design parameters selected using murine tissue are validated with hydrogel-embedded human PCLS and results show increased proliferation and premalignant lesion gene expression patterns. This tissue-engineered model of human lung cancer premalignancy is the foundation for more sophisticated ex vivo models that enable the study of carcinogenesis and chemoprevention strategies.

Preclinical Modeling of Pathway-Targeted Therapy of Human Lung Cancer in the Mouse

Animal models, particularly genetically engineered mouse models (GEMMs), continue to have a transformative impact on our understanding of the initiation and progression of hematological malignancies and solid tumors. Furthermore, GEMMs have been employed in the design and optimization of potent anticancer therapies. Increasingly, drug responses are assessed in mouse models either prior, or in parallel, to the implementation of precision medical oncology, in which groups of patients with genetically stratified cancers are treated with drugs that target the relevant oncoprotein such that mechanisms of drug sensitivity or resistance may be identified. Subsequently, this has led to the design and preclinical testing of combination therapies designed to forestall the onset of drug resistance. Indeed, mouse models of human lung cancer represent a paradigm for how a wide variety of GEMMs, driven by a variety of oncogenic drivers, have been generated to study initiation, progression, and maintenance of this disease as well as response to drugs. These studies have now expanded beyond targeted therapy to include immunotherapy. We highlight key aspects of the relationship between mouse models and the evolution of therapeutic approaches, including oncogene-targeted therapies, immunotherapies, acquired drug resistance, and ways in which successful antitumor strategies improve on efficiently translating preclinical approaches into successful antitumor strategies in patients.

Paraoxonase 2 (PON2) plays a limited role in murine lung tumorigenesis

Paraoxonase 2 (PON2) is a multifunctional intracellular enzyme that has received growing attention for its ability to modulate various aspects of normal and malignant cellular physiology. Recent research has revealed that PON2 is upregulated in tissues from patients with various types of solid tumors and hematologic cancers, likely due to its ability to suppress oxidative stress and evade apoptosis. However, the effects of PON2 on pulmonary oncogenesis are unknown. Here, we conducted studies to investigate how PON2 influences lung cancer cell proliferation in vitro and lung tumorigenesis in vivo using a variety of cellular and animal models. It was found that PON2 expression deficiency hampered the proliferation of cultured lung cancer cells with concomitant cell cycle arrest at the G1 phase. In addition, the loss of endogenous PON2 expression impaired key aspects of oxidative metabolism in lung adenocarcinoma cells. Moreover, we investigated how the interplay between PON2 expression in lung tumors and host mice influences lung tumor initiation and progression. PON2 status in both transplanted tumor cells and mice failed to influence the development of subcutaneously grafted Lewis lung carcinoma (LLC) tumors, orthotopically implanted LLC tumors, and oncogenic Kras-driven primary lung adenocarcinoma tumors. Importantly, the frequencies of tumor-infiltrating myeloid subsets that include myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages were not impacted by PON2 expression in LLC tumor-bearing mice. Overall, our studies indicate that PON2 plays a limited role in murine lung tumorigenesis.

Tissue-engineered models of lung cancer premalignancy

Lung cancer is the leading global cause of cancer-related deaths. Although smoking cessation is the best preventive action, nearly 50% of all lung cancer diagnoses occur in people who have already quit smoking. Research into treatment options for these high-risk patients has been constrained to rodent models of chemical carcinogenesis, which are time-consuming, expensive, and require large numbers of animals. Here we show that embedding precision-cut lung slices within an engineered hydrogel and exposing this tissue to a carcinogen from cigarette smoke creates an in vitro model of lung cancer premalignancy. Hydrogel formulations were selected to promote early lung cancer cellular phenotypes and extend PCLS viability up to six weeks. In this study, hydrogel-embedded lung slices were exposed to the cigarette smoke derived carcinogen vinyl carbamate, which induces adenocarcinoma in mice. At six weeks, analysis of proliferation, gene expression, histology, tissue stiffness, and cellular content revealed that vinyl carbamate induced the formation of premalignant lesions with a mixed adenoma/squamous phenotype. Two putative chemoprevention agents were able to freely diffuse through the hydrogel and induce tissue-level changes. The design parameters selected using murine tissue were validated with hydrogel-embedded human PCLS and results showed increased proliferation and premalignant lesion gene expression patterns. This tissue-engineered model of human lung cancer premalignancy is the starting point for more sophisticated ex vivo models and a foundation for the study of carcinogenesis and chemoprevention strategies.

The state of the art for artificial intelligence in lung digital pathology

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.