Cold Spring Harbor perspectives in medicine最新文献

Although components of possible Parkinson's disease can be found in earlier documents, the first clear medical description was written in 1817 by James Parkinson. In the mid-1800s, Jean-Martin Charcot was particularly influential in refining and expanding this early description and in disseminating information internationally about Parkinson's disease. He separated the clinical spectrum of Parkinson's disease from multiple sclerosis and other disorders characterized by tremor, and he recognized cases that later would likely be classified among the parkinsonism-plus syndromes. Early treatments of Parkinson's disease were based on empirical observation, and anticholinergic drugs were used as early as the nineteenth century. The discovery of dopaminergic deficits in Parkinson's disease and the synthetic pathway of dopamine led to the first human trials of levodopa. Further historically important anatomical, biochemical, and physiological studies identified additional pharmacological and neurosurgical targets for Parkinson's disease and allow modern clinicians to offer an array of therapies aimed at improving function in this still incurable disease.

Metabolic reprogramming in cancer allows cells to survive in harsh environments and sustain macromolecular biosynthesis to support proliferation. In addition, metabolites play crucial roles as signaling molecules. Metabolite fluctuations are detected by various sensors in the cell to regulate gene expression, metabolism, and signal transduction. Metabolic signaling mechanisms contribute to tumorigenesis by altering the physiology of cancer cells themselves, as well as that of neighboring cells in the tumor microenvironment. In this review, we discuss principles of metabolic signaling and provide examples of how cancer cells take advantage of metabolic signals to promote cell proliferation and evade the immune system, thereby contributing to tumor growth and progression.

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of rare conditions leading to various degrees of visual handicap and to progressive blindness in more severe cases. Besides visual rehabilitation, educational, and socio-professional support, there are currently limited therapeutic options, but the approval of the first gene therapy product for RPE65-related IRDs raised hope for therapeutic innovations. Such developments are facing obstacles intrinsic to the disease and the affected tissue including the extreme phenotypic and genetic variability of IRDs and the fine tuning of visual processing through the complex architecture of the postmitotic neural retina. A precise phenotypic characterization is required prior to genetic testing, which now relies on high-throughput sequencing. Their challenges will be discussed within this article as well as their implications in clinical trial design.

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Aging can be conceptualized as the progressive disequilibrium between stochastic damage accumulation and resilience mechanisms that continuously repair that damage, which eventually cause the development of chronic disease, frailty, and death. The immune system is at the forefront of these resilience mechanisms. Indeed, aging is associated with persistent activation of the immune system, witnessed by a high circulating level of inflammatory markers and activation of immune cells in the circulation and in tissue, a condition called "inflammaging." Like aging, inflammaging is associated with increased risk of many age-related pathologies and disabilities, as well as frailty and death. Herein we discuss recent advances in the understanding of the mechanisms leading to inflammaging and the intrinsic dysregulation of the immune function that occurs with aging. We focus on the underlying mechanisms of chronic inflammation, in particular the role of NF-κB and recent studies targeting proinflammatory mediators. We further explore the dysregulation of the immune response with age and immunosenescence as an important mechanistic immune response to acute stressors. We examine the role of the gastrointestinal microbiome, age-related dysbiosis, and the integrated stress response in modulating the inflammatory "response" to damage accumulation and stress. We conclude by focusing on the seminal question of whether reducing inflammation is useful and the results of related clinical trials. In summary, we propose that inflammation may be viewed both as a clinical biomarker of the failure of resilience mechanisms and as a causal factor in the rising burden of disease and disabilities with aging. The fact that inflammation can be reduced through nonpharmacological interventions such as diet and exercise suggests that a life course approach based on education may be a successful strategy to increase the health span with few adverse consequences.

All cancers arise from normal cells whose progeny acquire the cancer-initiating mutations and epigenetic modifications leading to frank tumorigenesis. The identity of those "cells-of-origin" has historically been a source of controversy across tumor types, as it has not been possible to witness the dynamic events giving rise to human tumors. Genetically engineered mouse models (GEMMs) of cancer provide an invaluable substitute, enabling researchers to interrogate the competence of various naive cellular compartments to initiate tumors in vivo. Researchers using these models have relied on lineage-specific promoters, knowledge of preneoplastic disease states in humans, and technical advances allowing more precise manipulations of the mouse germline. These approaches have given rise to the emerging view that multiple lineages within a given organ may generate tumors with similar histopathology. Here, we review some of the key studies leading to this conclusion in solid tumors and highlight the biological and clinical ramifications.

In the last decade, organoid technology has become a cornerstone in cancer research. Organoids are long-term primary cell cultures, usually of epithelial origin, grown in a three-dimensional (3D) protein matrix and a fully defined medium. Organoids can be derived from many organs and cancer types and sites, encompassing both murine and human tissues. Importantly, they can be established from various stages during tumor evolution and recapitulate with high accuracy patient genomics and phenotypes in vitro, offering a platform for personalized medicine. Additionally, organoids are remarkably amendable for experimental manipulation. Taken together, these features make organoids a powerful tool with applications in basic cancer research and personalized medicine. Here, we will discuss the origins of organoid culture, applications in cancer research, and how cancer organoids can synergize with other models of cancer to drive basic discoveries as well as to translate these toward clinical solutions.

The quest for effective cancer therapeutics has traditionally centered on targeting mutated or overexpressed oncogenic proteins. However, challenges arise in cancers with low mutational burden or when the mutated oncogene is not conventionally targetable, which are common situations in childhood cancers. This obstacle has sparked large-scale unbiased screens to identify collateral genetic dependencies crucial for cancer cell growth. These screens have revealed promising targets for therapeutic intervention in the form of lineage-selective dependency genes, which may have an expanded therapeutic window compared to pan-lethal dependencies. Many lineage-selective dependencies regulate gene expression and are closely tied to the developmental origins of pediatric tumors. Placing lineage-selective dependencies in a transcriptional network model is helpful for understanding their roles in driving malignant cell behaviors. Here, we discuss the identification of lineage-selective dependencies and how two transcriptional models, core regulatory circuits and gene regulatory networks, can serve as frameworks for understanding their individual and collective actions, particularly in cancers affecting children and young adults.

The recent rise in effective immuno-oncology therapies has increased demand for experimental approaches to model anticancer immunity. A variety of mouse models have been developed and used to study cancer immunology. These include mutagen-induced, genetically engineered, syngeneic, and other models of cancer immunology. These models each have the potential to define mechanistic aspects of anticancer immune responses, identify potential therapeutic targets, and serve as preclinical models for further therapeutic development. Specific benefits and liabilities are characteristic of particular cancer immunology modeling approaches. The optimal choice and utilization of models depends on the cancer immunology scientific question being addressed and can serve to increase mechanistic understanding and development of human immuno-oncology therapies.

In the nearly 50 years since the original models of cancer first hit the stage, mouse models have become a major contributor to virtually all aspects of cancer research, and these have evolved well beyond simple transgenic or xenograft models to encompass a wide range of more complex models. As the sophistication of mouse models has increased, an explosion of new technologies has expanded the potential to both further develop and apply these models to address major challenges in cancer research. In the current era, cancer modeling has expanded to include nongermline genetically engineered mouse models (GEMMs), patient-derived models, organoids, and adaptations of the models better suited for cancer immunology research. New technologies that have transformed the field include the application of CRISPR-Cas9-mediated genome editing, in vivo imaging, and single-cell analysis to cancer modeling. Here, we provide a historical perspective on the evolution of mouse models of cancer, focusing on how far we have come in a relatively short time and how new technologies will shape the future development of mouse models of cancer.