Seminars in cancer biology最新文献

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Pancreatic ductal adenocarcinoma (PDAC) is a type of cancer alarmingly expanding in our modern societies that is still proving to be very challenging to counteract. This disease constitutes a quintessential example of the multiple interactions existing between the tumour and its surrounding microenvironment. In particular, PDAC is characterized by a very immunosuppressive environment that favours cancer growth and makes this cancer type very resistant to immunotherapy. The primary tumour releases many factors that influence both the microenvironment and the immune landscape. Extracellular vesicles (EVs), recently identified as indispensable entities ensuring cell-to-cell communication in both physiological and pathological processes, seem to play a pivotal function in ensuring the delivery of these factors to the tumour-surrounding tissues. In this review, we summarize the present understanding on the crosstalk among tumour cells and the cellular immune microenvironment emphasizing the pro-malignant role played by extracellular vesicles. We also discuss how a greater knowledge of the roles of EVs in tumour immune escape could be translated into clinical applications.

Postmenopausal, obese women have a significantly higher risk of developing estrogen receptor-positive (ER+) breast tumors, that are resistant to therapies and are associated with higher recurrence and death rates. The global prevalence of overweight/obese women has reached alarming proportions and with postmenopausal ER+ breast carcinoma (BC) having the highest incidence among the three obesity-related cancers in females (i.e., breast, endometrial and ovarian), this is of significant concern. Elucidation of the precise molecular mechanisms underlying the pro-cancerous action of obesity in ER+BC is therefore critical for disease prevention and novel treatment initiatives. Interestingly, accumulating data has shown opposing relationships between obesity and cancer in either pre- or post-menopausal women. Excess body weight is associated with an increased risk of breast cancer in postmenopausal women and a decreased risk in pre-menopausal women. Moreover, excess adiposity during early life appears to be protective against postmenopausal breast cancer, including both ER+ and ER negative BC subtypes. Overall, estrogen-dependent mechanisms have been implicated as the main driving force in obesity-related breast tumorigenesis. In the present review we discuss the epidemiologic and mechanistic aspects of association between obesity and breast tumors after menopause, mainly in the context of hormone dependency. Molecular and cellular events underlying this association present as potential avenues for both therapeutic intervention as well as the prevention of BC-promoting processes linked to excess adiposity, which is proving to be vital in an increasingly obese global population.

Cancers express a large battery of genes by which they establish an immunosuppressive tumor microenvironment. Many of these genes are induced by intratumoral hypoxia through transcriptional activation mediated by hypoxia-inducible factors HIF-1 and HIF-2. This review summarizes several recent reports describing hypoxia-induced mechanisms of immune evasion in sarcoma and breast, colorectal, hepatocellular, prostate and uterine cancer. These studies point to several novel therapeutic approaches to improve anti-tumor immunity and increase responses to immunotherapy.

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Obesity results from a chronic excessive accumulation of adipose tissue due to a long-term imbalance between energy intake and expenditure. Available epidemiological and clinical data strongly support the links between obesity and certain cancers. Emerging clinical and experimental findings have improved our understanding of the roles of key players in obesity-associated carcinogenesis such as age, sex (menopause), genetic and epigenetic factors, gut microbiota and metabolic factors, body shape trajectory over life, dietary habits, and general lifestyle. It is now widely accepted that the cancer-obesity relationship depends on the site of cancer, the systemic inflammatory status, and micro environmental parameters such as levels of inflammation and oxidative stress in transforming tissues. We hereby review recent advances in our understanding of cancer risk and prognosis in obesity with respect to these players. We highlight how the lack of their consideration contributed to the controversy over the link between obesity and cancer in early epidemiological studies. Finally, the lessons and challenges of interventions for weight loss and better cancer prognosis, and the mechanisms of weight gain in survivors are also discussed.

Obesity, defined by body mass index (BMI), is an established risk factor for specific renal cell carcinoma (RCC) subtypes such as clear cell RCC, the most common RCC histology. Many studies have identified an association between obesity and improved survival after diagnosis of RCC, a potential “obesity paradox.” Clinically, there is uncertainty whether improved outcomes observed after diagnosis are driven by stage, type of treatment received, or artifacts of longitudinal changes in weight and body composition. The biological mechanisms underlying obesity’s influence on RCC are not fully established, but multiomic and mechanistic studies suggest an impact on tumor metabolism, particularly fatty acid metabolism, angiogenesis, and peritumoral inflammation, which are known to be key biological hallmarks of clear cell RCC. Conversely, high-intensity exercise associated with increased muscle mass may be a risk factor for renal medullary carcinoma, a rare RCC subtype that predominantly occurs in individuals with sickle hemoglobinopathies. Herein, we highlight methodologic challenges associated with studying the influence of obesity on RCC and review the clinical evidence and potential underlying mechanisms associating RCC with BMI and body composition.

In the recent decades, chimeric antigen receptor (CAR) therapy signaled a new revolutionary approach to cancer treatment. This method seeks to engineer immune cells expressing an artificially designed receptor, which would endue those cells with the ability to recognize and eliminate tumor cells. While some CAR therapies received FDA approval and others are subject to clinical trials, many aspects of their workings remain elusive. Techniques of systems and computational biology have been frequently employed to explain the operating principles of CAR therapy and suggest further design improvements. In this review, we sought to provide a comprehensive account of those efforts. Specifically, we discuss various computational models of CAR therapy ranging in scale from organismal to molecular. Then, we describe the molecular and functional properties of costimulatory domains frequently incorporated in CAR structure. Finally, we describe the signaling cascades by which those costimulatory domains elicit cellular response against the target. We hope that this comprehensive summary of computational and experimental studies will further motivate the use of systems approaches in advancing CAR therapy.