Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Lack of cytotoxic and genotoxic effects of mPEG-silane coated iron(III) oxide nanoparticles doped with magnesium despite cellular uptake in cancerous and noncancerous lung cells

Cytotoxic and genotoxic effects of novel mPEG-silane coated iron(III) oxide nanoparticles doped with magnesium (Mg0.1-γ-Fe2O3(mPEG-silane)0.5) have been investigated on human adenocarcinomic alveolar basal epithelial (A549) and human normal bronchial epithelial (BEAS-2B) cells. In the studies several molecular and cellular targets addressing to cell membrane, cytoplasm organelles and nucleus components were served as toxicological endpoints. The as-synthesized nanoparticles were found to be stable in the cell culture media and were examined for different concentration and exposure times. No cytotoxicity of the tested nanoparticles was found although these nanoparticles slightly increased reactive oxygen species in both cell types studied. Mg0.1-γ-Fe2O3(mPEG-silane)0.5 nanoparticles did not produce any DNA strand breaks and oxidative DNA damages in A549 and BEAS-2B cells. Different concentration of Mg0.1-γ-Fe2O3(mPEG-silane)0.5 nanoparticles and different incubation time did not affect cell migration. The lung cancer cells' uptake of the nanoparticles was more effective than in normal lung cells. Altogether, the results evidence that mPEG-silane coated iron(III) oxide nanoparticles doped with magnesium do not elucidate any deleterious effects on human normal and cancerous lung cells despite cellular uptake of these nanoparticles. Therefore, it seems reasonable to conclude that these novel biocompatible nanoparticles are promising candidates for further development towards medical applications.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.

Machine Learning Radiomics Liver Function Model for Prognostic Prediction After Radical Resection of Advanced Gastric Cancer: A Retrospective Study

PURPOSE

We aimed to establish a machine learning radiomics liver function model to explore how liver function affects the prognosis of patients with gastric cancer (GC).

METHODS

Patients with advanced GC were retrospectively enrolled in this study. Eight machine learning radiomic models were constructed by extracting radiomic features from portal-vein-phase contrast-enhanced computed tomography (CE-CT) images. Clinicopathological features were determined using univariate and multifactorial Cox regression analyses. These features were used to construct a GC survival nomogram.

RESULTS

A total of 510 patients with GC were split into training and test cohorts in an 8:2 ratio. Kaplan-Meier analysis showed that patients with type I liver function had a better prognosis. Fifteen significant features were retained to establish the machine learning model. LightBGM showed the best predictive performance in the training (area under the receiver operating characteristic curve [AUC] 0.978) and test cohorts (AUC 0.714). Multivariate analysis revealed that gender, age, liver function, Nutritional Risk Screening 2002 (NRS-2002) score, tumor-lymph node-metastasis stage, tumor size, and tumor differentiation were independent risk factors for GC prognosis. The survival nomogram based on machine learning radiomics, instead of liver biochemical indicators, still had high accuracy (C-index of 0.771 vs. 0.773).

CONCLUSION

The machine learning radiomics liver function model has high diagnostic value in predicting the influence of liver function on prognosis in patients with GC.

Engineering models of head and neck and oral cancers on-a-chip

Head and neck cancers (HNCs) rank as the sixth most common cancer globally and result in over 450 000 deaths annually. Despite considerable advancements in diagnostics and treatment, the 5-year survival rate for most types of HNCs remains below 50%. Poor prognoses are often attributed to tumor heterogeneity, drug resistance, and immunosuppression. These characteristics are difficult to replicate using in vitro or in vivo models, culminating in few effective approaches for early detection and therapeutic drug development. Organs-on-a-chip offer a promising avenue for studying HNCs, serving as microphysiological models that closely recapitulate the complexities of biological tissues within highly controllable microfluidic platforms. Such systems have gained interest as advanced experimental tools to investigate human pathophysiology and assess therapeutic efficacy, providing a deeper understanding of cancer pathophysiology. This review outlines current challenges and opportunities in replicating HNCs within microphysiological systems, focusing on mimicking the soft, glandular, and hard tissues of the head and neck. We further delve into the major applications of organ-on-a-chip models for HNCs, including fundamental research, drug discovery, translational approaches, and personalized medicine. This review emphasizes the integration of organs-on-a-chip into the repertoire of biological model systems available to researchers. This integration enables the exploration of unique aspects of HNCs, thereby accelerating discoveries with the potential to improve outcomes for HNC patients.

Targeting vimentin: a multifaceted approach to combatting cancer metastasis and drug resistance

This comprehensive review explores vimentin as a pivotal therapeutic target in cancer treatment, with a primary focus on mitigating metastasis and overcoming drug resistance. Vimentin, a key player in cancer progression, is intricately involved in processes such as epithelial-to-mesenchymal transition (EMT) and resistance mechanisms to standard cancer therapies. The review delves into diverse vimentin inhibition strategies. Precision tools, including antibodies and nanobodies, selectively neutralize vimentin's pro-tumorigenic effects. DNA and RNA aptamers disrupt vimentin-associated signaling pathways through their adaptable binding properties. Innovative approaches, such as vimentin-targeted vaccines and microRNAs (miRNAs), harness the immune system and post-transcriptional regulation to combat vimentin-expressing cancer cells. By dissecting vimentin inhibition strategies across these categories, this review provides a comprehensive overview of anti-vimentin therapeutics in cancer treatment. It underscores the growing recognition of vimentin as a pivotal therapeutic target in cancer and presents a diverse array of inhibitors, including antibodies, nanobodies, DNA and RNA aptamers, vaccines, and miRNAs. These multifaceted approaches hold substantial promise for tackling metastasis and overcoming drug resistance, collectively presenting new avenues for enhanced cancer therapy.

Drug resistance mechanisms in cancers: Execution of pro-survival strategies

One of the quintessential challenges in cancer treatment is drug resistance. Several mechanisms of drug resistance have been described to date, and new modes of drug resistance continue to be discovered. The phenomenon of cancer drug resistance is now widespread, with approximately 90% of cancer-related deaths associated with drug resistance. Despite significant advances in the drug discovery process, the emergence of innate and acquired mechanisms of drug resistance has impeded the progress in cancer therapy. Therefore, understanding the mechanisms of drug resistance and the various pathways involved is integral to treatment modalities. In the present review, I discuss the different mechanisms of drug resistance in cancer cells, including DNA damage repair, epithelial to mesenchymal transition, inhibition of cell death, alteration of drug targets, inactivation of drugs, deregulation of cellular energetics, immune evasion, tumor-promoting inflammation, genome instability, and other contributing epigenetic factors. Furthermore, I highlight available treatment options and conclude with future directions.

Recent progress and the emerging role of lncRNAs in cancer drug resistance; focusing on signaling pathways

It is becoming more and more apparent that many of the genetic alterations associated with cancer are located in areas that do not encode proteins. lncRNAs are a class of RNAs that do not code for proteins but play a crucial role in maintaining cell function and regulating various cellular processes. By doing this, they have recently introduced what may be a brand-new and essential layer of biological control. These have more than 200 nucleotides and are linked to several diseases; as a result, they have become potential tools for therapeutic intervention. Emerging technologies suggest the presence of mutations on genomic loci that give rise to lncRNAs rather than proteins in a disease as complex as cancer. These lncRNAs play essential parts in gene regulation, which impacts several cellular homeostasis processes, including proliferation, survival, migration, and genomic stability. The leading cause of death in the world today is cancer. Delays in diagnosis and a lack of standard and efficient treatments are the leading causes of the high death rate. Clinically, surgery is frequently used successfully to remove cancers that have not spread, but it is less successful in treating metastatic cancer, which has a drastically lower chance of survival. Chemotherapeutic drugs are a typical therapy to treat the cancer that has spread to other organs. Drug resistance to chemotherapy, however, presents a significant challenge to achieving positive outcomes and is frequently the cause of treatment failure. A substantial barrier to progress in medical oncology is cancer drug resistance. Resistance can develop clinically either before or after cancer treatment. According to this study, lncRNAs influence drug resistance through several different methods. LncRNAs often impact drug resistance by controlling the expression of a few intermediary regulatory variables rather than by directly affecting drug resistance. Additionally, lncRNAs have a variety of roles in cancer medication resistance. Most lncRNAs induce drug resistance when overexpressed; however, other lncRNAs have inhibitory effects. This study provides an overview of the current understanding of lncRNAs, relevance to cancer, and potential therapeutic applications.

Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level

In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

How will the identification and therapeutic intervention of genetic targets in oncology evolve for future therapy?

INTRODUCTION

Mapping of the human genome, together with the broad understanding of new biomolecular pathways involved in cancer development, represents a huge dividing line for advances in cancer treatment. This special article aims to express the next evolution of cancer therapy, while also considering the challenges and uncertainties facing future directions.

AREA COVERED

The recent achievements of medical science in the oncology field concern both new diagnostic techniques, such as liquid biopsy, and therapeutic strategies with innovative anticancer drugs. Although several molecular characteristics of tumors are linked to the tissue of origin, some mutations are shared by multiple tumor histologies, thus paving the way for what is called 'precision oncology.' The article highlights the importance of identifying new mutations and biomolecular pathways that can be pursued with new anticancer drugs.

EXPERT OPINION

Oncology and medical science have made great progress in understanding new molecular targets; being able to early identify tumor markers that are not confined to a single organ through minimally invasive diagnostic techniques allows us to design new effective therapeutic strategies. Multidisciplinary teams now aim to evaluate the most appropriate and personalized diagnostic/therapeutic approach for the individual patient.

Preclinical Models of Neuroblastoma-Current Status and Perspectives

Preclinical in vitro and in vivo models remain indispensable tools in cancer research. These classic models, including two- and three-dimensional cell culture techniques and animal models, are crucial for basic and translational studies. However, each model has its own limitations and typically does not fully recapitulate the course of the human disease. Therefore, there is an urgent need for the development of novel, advanced systems that can allow for efficient evaluation of the mechanisms underlying cancer development and progression, more accurately reflect the disease pathophysiology and complexity, and effectively inform therapeutic decisions for patients. Preclinical models are especially important for rare cancers, such as neuroblastoma, where the availability of patient-derived specimens that could be used for potential therapy evaluation and screening is limited. Neuroblastoma modeling is further complicated by the disease heterogeneity. In this review, we present the current status of preclinical models for neuroblastoma research, discuss their development and characteristics emphasizing strengths and limitations, and describe the necessity of the development of novel, more advanced and clinically relevant approaches.