Synthetic Gene Expression Circuits for Designing Precision Tools in Oncology.

Biological network modelling and precision medicine in oncology

Towards programming-based synthetic genetic circuit enabled high-lysine maize

Over the past decade, the emergence of patient-derived tumor organoids (PDTOs) has broadened the repertoire of preclinical models and progressively revolutionized three-dimensional cell culture in oncology. PDTO can be grown from patient tumor samples with high efficiency and faithfully recapitulates the histological and molecular characteristics of the original tumor. Therefore, PDTOs can serve as invaluable tools in oncology research, and their translation to clinical practice is exciting for the future of precision medicine in oncology. In this review, we provide an overview of methods for establishing PDTOs and their various applications in cancer research, starting with basic research and ending with the identification of new targets and preclinical validation of new anticancer compounds and precision medicine. Finally, we highlight the challenges associated with the clinical implementation of PDTO, such as its representativeness, success rate, assay speed, and lack of a tumor microenvironment. Technological developments and autologous cocultures of PDTOs and stromal cells are currently ongoing to meet these challenges and optimally exploit the full potential of these models. The use of PDTOs as standard tools in clinical oncology could lead to a new era of precision oncology in the coming decade.

: Protein overexpression leads to growth inhibition and decreased expression of native proteins. This effect is mostly due to competition for RNAP and ribosomes. This competition affects also synthetic biology circuits, which may not behave as expected. The aims of this project are to: (1) characterize/quantify the effect of competition for RNAPs and ribosomes on synthetic genetic circuits; (2) determine the extent to which RNAPs or ribosomes are limiting; (3) develop models that predict the change of behavior of circuits due to competition; (4) demonstrate the predictions on the design of a synthetic cascade. We have constructed a synthetic circuit in E. coli and characterized through theory and experiments the effect of competition on the expression of a constitutively expressed gene when a different inducible gene is activated. For this system, we have also developed predictive models, both analytical and computational. Our finding quantifies the extent of competition and provides initial tools to predict this effect. Our experimental results and models will enable design methods for next-generation circuits that are robust to competition (to be investigated later). This ability will be critical for constructing large circuits reliably.

The European Society for Medical Oncology (ESMO) Precision Medicine Glossary

Synthetic genetic circuits are the core research objects in synthetic biology, and the programming of cell behavior is achieved through the combination of engineered gene elements. Mathematical modeling provides crucial support for understanding and designing synthetic genetic circuits, enabling researchers to predict the dynamic behavior of the circuits and guide experimental optimization. This study reviews the categories of synthetic genetic circuits (such as gene switches, oscillators, feedback circuits, etc.) and their biological mechanisms, with a focus on the application of ordinary differential equation (ODE) models, stochastic modeling, and network topology dynamics models in circuit modeling. We expounded on the estimation of model parameters, sensitivity analysis, and the integration methods of experimental data and models, and compared the characteristics of numerical simulation algorithms and commonly used software tools (such as MATLAB, COPASI, BioNetGen, etc.). Through the discussion of the steady-state, oscillation behavior, multiple steady-state and bifurcation analysis of system dynamics, the understanding of the influence of positive and negative feedback mechanisms on system stability is deepened. In addition, we took the classic synthetic gene oscillator Repressilator as a case to conduct modeling and simulation analysis, and compared the model predictions with the experimental data. Finally, the application prospects of synthetic genetic circuits in the fields of bioengineering and medicine were summarized, and the future directions of promoting the design of synthetic circuits with the help of model optimization and artificial intelligence-assisted design were prospected. Research shows that mathematical modeling and computational simulation have become key tools for the study and design of synthetic genetic circuits, providing a theoretical basis and practical guidance for the engineering transformation of complex biological systems.

Tumor research is a fundamental focus of medical science, yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation, progression, and metastasis. Recent advancements in single-cell transcriptomic sequencing have revolutionized the way researchers explore tumor biology by providing unprecedented resolution. However, a key limitation of single-cell sequencing is the loss of spatial information during single-cell preparation. Spatial transcriptomics (ST) emerges as a cutting-edge technology in tumor research that preserves the spatial information of RNA transcripts, thereby facilitating a deeper understanding of the tumor heterogeneity, the intricate interplay between tumor cells and the tumor microenvironment. This review systematically introduces ST technologies and summarizes their latest applications in tumor research. Furthermore, we provide a thorough overview of the bioinformatics analysis workflow for ST data and offer an online tutorial (https://github.com/SiyuanHuang1/ST_Analysis_Handbook). Lastly, we discuss the potential future directions of ST. We believe that ST will become a powerful tool in unraveling tumor biology and offer new insights for effective treatment and precision medicine in oncology.

Background: Multiple precision medicine programs in oncology have been launched, leading to the collection of large amount of clinical and genomic data. Tumor heterogeneity and the accumulation of rare and of unknown significance genomic alterations require to study thousands of individuals to identify clinically relevant genomic drivers. Better the scale is or will be, better our understanding of a disease is or would be. In this context, data sharing appears as a precondition of the success of precision medicine in oncology. The work we present here attempts to describe the current stage of data sharing in precision medicine with a focus on oncology. Methods: A scientometric study of the publications indexed in the Web of Science (WoS) database was conducted by applying quantitative methods. A search string was defined by selecting relevant keywords, and specific metrics such as the research area, publication year, funding organization, and geographical localization were studied. A third-party software (VOSViewer) was used for analyzing and visualizing bibliometric networks. Results: A set of 672 documents were obtained between 1900 and 2019, year 2005 was a turning point, and the trend reached 86–113 publications per year over the last three years. Western Europe and Northern America accounted for 80% of the whole world production. From the 672 publications, diverse research areas were identified (i.e., computer science and medical informatics), as well as specific medical specialties (i.e., medical genetics and oncology). The term co-occurrences map identified the main challenges associated with data sharing. Conclusions: This area of research is relatively new with an unequal quantitative production of scientific literature across countries and institutions. The presence of non-medical scientific disciplines such as computer science was not that surprising as data sharing had to face major technical challenges. The results of term occurrences reflected the main parameters that govern data sharing in precision medicine but also its obstacles. Our study provided a picture of an emerging and interdisciplinary field that could be of interest to all stakeholders facing common challenges to promote data sharing in precision medicine.

In the field of synthetic biology, genetic networks are designed by combining well-characterized genetic parts, similar to electronic circuits. Such gene networks are called synthetic genetic circuits. The design approach for synthetic genetic circuits is based on mathematical modeling and numerical simulation. The approach allows the realization of various cellular functions. However, unavoidable differences in the initial states or fluctuations of the gene expression in cells have prevented the precise prediction and control of cellular behavior. Therefore, the design of synthetic genetic circuits is not sufficient, and the dynamic control of the circuits is also required. In this report, we provide examples of synthetic circuit designs and the control of synthetic biological systems, as well as perspectives on design and control.KeywordsSynthetic biologyControl engineeringSynthetic genetic circuits

Precision medicine in oncology is an evolving therapeutic approach that leverages genetic, clinical, and biomarker data to tailor treatments to individual patients. This review explores the three core pillars of modern precision oncology: targeted therapy, immunotherapy, and the integration of artificial intelligence (AI) into clinical practice. Targeted therapies, including monoclonal antibodies and antibody-drug conjugates, selectively inhibit molecular pathways involved in tumor growth. While conventional chemotherapy remains the backbone of treatment and has improved remission rates, its cytotoxic nature limits broader applicability and increases the risk of comorbidities. Immunotherapies, particularly immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies, have transformed treatment for hematologic malignancies and are now being adapted for solid tumors such as colorectal, pancreatic, and hepatocellular carcinomas through novel combination regimens. This review also highlights the therapeutic potential of modulating the tumor microenvironment and introduces emerging modalities such as neoantigen vaccines and microRNA-based therapies. Furthermore, we outline the expanding role of AI in enhancing cancer diagnosis, drug development, and clinical decision-making. By integrating computational tools with molecular therapies, precision medicine rapidly advances toward individualized data-driven care. This review provides an overview of established therapies in the current clinical practice, novel regimens, and emerging AI technologies. Despite ongoing challenges, such as resistance and toxicity, precision medicine demonstrates significant promise in improving oncologic outcomes and transforming cancer care.

The topic of precision medicine is increasingly more prevalent in the general medical literature, with oncology research leading the way. Many factors, such as availability of targeted drugs, advances in laboratory science, and improved information systems, converged to make precision medicine research possible on a large scale at the National Cancer Institute. The resultant big data will spur new kinds of research in the decades to come, but until then, all clinicians are challenged to make sense of an overabundance of information when managing individual patients.

The definition of precision medicine changed in the twenty-first century several times due to advances in molecular biology, genomics, biotechnology, and data science. Oncology is a model for other medical specialties for the early transition to precision medicine.The understanding of cancer has evolved over time: from macroscopic descriptions of localization moving to microscopic descriptions, molecules, and then interaction between molecules. Now we are witnessing the transition toward understanding cancer as a continuum by capturing spatial and temporal tumor heterogeneity. By integrating multiple data sources (multiomics), each case of cancer can be approached as if it represents a separate disease (N of 1). The notion of precision oncology takes on new meanings as new levels of data are added to the understanding of cancer, from complex biological, digital, clinical, behavioral, and environmental data.In this chapter, we followed transformations in oncology, from a historical perspective and also from the perspective of the revolution in omics sciences and data sciences, identifying four distinct stages, all part of the concept of precision oncology.Precision oncology 1.0 starts from the symptoms and everything that can be seen with the doctor’s naked eye, and the resolution increases through the development of microscopy, biochemistry, cell biology, and the birth of pharmacogenetics.In precision oncology 2.0, diseases begin to be reclassified starting from molecules to symptoms; it is the stage in which the notions of biomarkers and targeted therapies gain momentum. Precision in this period is based on deciphering the structure of DNA and the early stages of biotechnology development.Precision oncology 3.0 is dominated by the development of high-capacity genomic sequencing technologies, with comprehensive genomic profiling, shifting the gears toward genomic medicine and modern biotechnology, which today underpins the development of most drugs.In precision oncology 4.0, the characterization of cancer will be of very high resolution (single-cell multiomics); it will be dynamic (the transition from pre-cancer to cancer will be predictable), comprehensive (multiomic biomarkers, spatial genomics, functional tests, organoid-driven precision medicine, multitarget drugs, hybrid pharmacological constructs), and data-driven (network medicine, AI, machine learning-decision-making tools, human digital twins). Oncology 4.0 illustrates the Fourth Industrial Revolution, accelerated by the COVID-19 pandemic.KeywordsPrecision oncologyGenomic medicineMultispecific drugsSingle-cell multiomicsComposite biomarkersFunctional testsNext-generation biopharmaceutical productsGene and cell therapiesNetwork medicineDigital twins

The new challenge in oncology: Next-generation sequencing and its application in precision medicine

Synthetic models in biology are artifacts constructed from biological components, such as genes and proteins, to form biological parts or wholes. They have the same materiality as natural biological systems but are not the results of natural evolutionary processes. This entry focuses on synthetic genetic circuits, in particular the repressilator, which is one of the first and most famous of them. The introduction of the first synthetic genetic circuits in 2000 has often been taken as the beginning of synthetic biology proper. While they are intricate experimental systems, synthetic genetic circuits can be considered models in contrast to many other synthetic biology constructs. Being based on engineering principles and the enterprise of standardizing biological parts, synthetic models have enabled researchers to probe gene regulation by building small circuits implemented within living cells. Although synthetic modeling has been successful by many yardsticks, researchers have faced challenges due to biochemical particularities, as well as the stochasticity of biological systems and their unintuitive designs.

The advent of precision medicine in oncology emphasizes the urgent need for innovative therapeutic strategies that effectively integrate diagnosis and treatment while minimizing invasiveness. Manganese oxide nanomaterials (MONs) have emerged as a promising class of nanocarriers in biomedicine, particularly for targeted drug delivery and the therapeutic management of tumors. These nanomaterials are characterized by exceptional responsiveness to the tumor microenvironment (TME), high catalytic efficiency, favorable biodegradability, and advanced capabilities in magnetic resonance imaging. These attributes significantly enhance drug delivery, facilitate real-time bioimaging, and enable early tumor detection, thereby improving the precision and effectiveness of cancer therapies. This review highlights the significant advancements in the synthesis and therapeutic applications of MONs, beginning with a comprehensive overview of key synthetic methods, including thermal decomposition, potassium permanganate reduction, exfoliation, adsorption–oxidation, and hydro/solvothermal techniques. We delve into the preparation of MONs and H–MnO₂-based nanomaterials, emphasizing their chemical properties, surface modifications, and toxicity profiles, which are critical for their clinical application. Moreover, we discuss the notable applications of H–MnO₂-based nanomaterials in pH-responsive drug release, overcoming multidrug resistance (MDR), immunotherapy, and the development of nanovaccines for synergistic cancer treatments. By addressing the current challenges in the clinical translation of MONs, we propose future research directions for overcoming these obstacles. By underscoring the potential of MONs to transform cancer treatment paradigms, this review aims to inspire further investigations into their multifunctional applications in oncology, thus ultimately contributing to more effective and personalized therapeutic strategies.Graphical abstract