Seminars in cancer biology最新文献

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.

Excess body weight is a global health problem due to sedentary lifestyle and unhealthy diet, affecting 2 billion population worldwide. Obesity is a major risk factor for metabolic diseases. Notably, the metabolic risk of obesity largely depends on body weight distribution, of which visceral adipose tissues but not subcutaneous fats are closely associated with obesity comorbidities, including type 2 diabetes, non-alcoholic fatty liver disease, cardiovascular disease and certain types of cancer. Latest multi-omics and mechanistical studies reported the crucial involvement of genetic and epigenetic alterations, adipokines dysregulation, immunity changes, imbalance of white and brown adipose tissues, and gut microbial dysbiosis in mediating the pathogenic association between visceral adipose tissues and comorbidities. In this review, we explore the epidemiology of excess body weight and the up-to-date mechanism of how excess body weight and obesity lead to chronic complications. We also examine the utilization of visceral fat measurement as an accurate clinical parameter for risk assessment in healthy individuals and clinical outcome prediction in obese subjects. In addition, current approaches for the prevention and treatment of excess body weight and its related metabolic comorbidities are further discussed.

Acute myeloid leukemia (AML) is a heterogeneous disease with a genetic, epigenetic, and transcriptional etiology mainly presenting somatic and germline abnormalities. AML incidence rises with age but can also occur during childhood. Pediatric AML (pAML) accounts for 15–20% of all pediatric leukemias and differs considerably from adult AML. Next-generation sequencing technologies have enabled the research community to “paint” the genomic and epigenomic landscape in order to identify pathology-associated mutations and other prognostic biomarkers in pAML. Although current treatments have improved the prognosis for pAML, chemoresistance, recurrence, and refractory disease remain major challenges. In particular, pAML relapse is commonly caused by leukemia stem cells that resist therapy. Marked patient-to-patient heterogeneity is likely the primary reason why the same treatment is successful for some patients but, at best, only partially effective for others. Accumulating evidence indicates that patient-specific clonal composition impinges significantly on cellular processes, such as gene regulation and metabolism. Although our understanding of metabolism in pAML is still in its infancy, greater insights into these processes and their (epigenetic) modulation may pave the way toward novel treatment options. In this review, we summarize current knowledge on the function of genetic and epigenetic (mis)regulation in pAML, including metabolic features observed in the disease. Specifically, we describe how (epi)genetic machinery can affect chromatin status during hematopoiesis, leading to an altered metabolic profile, and focus on the potential value of targeting epigenetic abnormalities in precision and combination therapy for pAML. We also discuss the possibility of using alternative epidrug-based therapeutic approaches that are already in clinical practice, either alone as adjuvant treatments and/or in combination with other drugs.

Cerebral ischemic stroke and glioma are the two leading causes of patient mortality globally. Despite physiological variations, 1 in 10 people who have an ischemic stroke go on to develop brain cancer, most notably gliomas. In addition, glioma treatments have also been shown to increase the risk of ischemic strokes. Stroke occurs more frequently in cancer patients than in the general population, according to traditional literature. Unbelievably, these events share multiple pathways, but the precise mechanism underlying their co-occurrence remains unknown. Transcription factors (TFs), the main components of gene expression programmes, finally determine the fate of cells and homeostasis. Both ischemic stroke and glioma exhibit aberrant expression of a large number of TFs, which are strongly linked to the pathophysiology and progression of both diseases. The precise genomic binding locations of TFs and how TF binding ultimately relates to transcriptional regulation remain elusive despite a strong interest in understanding how TFs regulate gene expression in both stroke and glioma. As a result, the importance of continuing efforts to understand TF-mediated gene regulation is highlighted in this review, along with some of the primary shared events in stroke and glioma.

Obesity is a global pandemic that has been associated with the development of breast, endometrial, large intestine, renal, esophageal, and pancreatic cancer. Obesity is also involved in the development of cardiovascular disease and type 2 diabetes mellitus. Recently, an increase in the incidence of obesity-related cancers has been reported. Multiple myeloma (MM) is the second most common hematological malignancy, after lymphoma. The aim of this review is to examine the epidemiological data on obesity and MM, assess the effect of obesity on MM outcomes, evaluate the possible mechanisms through which obesity might increase the incidence of MM and provide the effects of obesity management on MM. Current evidence indicates that obesity may have an impact on the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM and increase the prevalence of MM. However, data regarding the effect of obesity on MGUS incidence are controversial; further studies are needed to examine whether obesity affects the development of MGUS or the progression of MGUS to MM. In addition, obesity affects MM outcomes. Increased BMI is associated with decreased survival in patients with MM, while data regarding the effect of obesity on newly diagnosed MM subjects and autologous stem cell transplantation are limited. Interestingly, the obesity paradox may also apply to patients with relapsed/refractory MM who are overweight or obese, because they may have a survival advantage. The pathophysiological pathways linking obesity to MM are very complicated and include bone marrow adipose tissue; adipokines, such as adiponectin, leptin, resistin, and visfatin; inflammatory cytokines and growth factors, such as TNF-α and IL-6; hormones including insulin and the insulin-like growth factor system as well as sex hormones. In terms of the effect of pharmacological management of obesity, orlistat has been shown to alter the proliferation of MM cells, whereas no data exist on glucagon‐like peptide‐1 receptor agonists, naltrexone/bupropion, or phentermine/topiramate. Bariatric surgery may be associated with a reduction in the incidence of MM, however, further studies are needed.

Cancer ‘stemness’ is fundamental to cancer existence. It defines the ability of cancer cells to indefinitely perpetuate as well as differentiate. Cancer stem cell populations within a growing tumor also help evade the inhibitory effects of chemo- as well as radiation-therapies, in addition to playing an important role in cancer metastases. NF-κB and STAT-3 are representative transcription factors (TFs) that have long been associated with cancer stemness, thus presenting as attractive targets for cancer therapy. The growing interest in non-coding RNAs (ncRNAs) in the recent years has provided further insight into the mechanisms by which TFs influence cancer stem cell characteristics. There is evidence for a direct regulation of TFs by ncRNAs, such as, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) as well as circular RNAs (circRNAs), and vice versa. Additionally, the TF-ncRNAs regulations are often indirect, involving ncRNA-target genes or the sponging of other ncRNA species by individual ncRNAs. The information is rapidly evolving and this review provides a comprehensive review of TF-ncRNAs interactions with implications on cancer stemness and in response to therapies. Such knowledge will help uncover the many levels of tight regulations that control cancer stemness, providing novel opportunities and targets for therapy in the process.

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Obesity is a chronic inflammatory condition that has been associated with different types of cancer. However, its role in melanoma incidence, progression, and response to immune-checkpoint-inhibitors (ICI) is still controversial. On the one hand, increased levels of lipids and adipokines can promote tumor proliferation and several genes associated with fatty acid metabolism have been found to be upregulated in melanomas. On the other hand, immunotherapy seems to be more effective in obese animal models, presumably due to an increase in CD8 + and subsequent decrease in PD-1 + T-cells in the tumor microenvironment. In humans, several studies have investigated the role of BMI (body mass index) and other adiposity-related parameters as potential prognostic markers of survival in advanced melanoma patients treated with ICI. The aim of this research has been to systematically review the scientific literature on studies evaluating the relationship between overweight/obesity and survival outcomes in patients with advanced melanoma treated with ICI and to perform a meta-analysis on those sharing common characteristics. After screening 1070 records identified through a literature search, 18 articles assessing the role of BMI-related exposure in relation to survival outcomes in ICI-treated patients with advanced melanoma were included in our review. In the meta-analysis of the association between overweight (defined as BMI>25 or BMI 25–30), overall survival (OS), and progression free survival (PFS), 7 studies were included, yielding a summary HR of 0.87 (95% CI: 0.74–1.03) and 0.96 (95% CI: 0.86–1.08), respectively. Our results show that, despite few suggestive findings, the use of BMI as a valuable predictor of melanoma patients’ survival in terms of PFS and OS should not be currently recommended, due to the limited evidence available.

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Cancer ranks among the five leading causes of death in almost all countries and has important repercussions for individual and public health, the healthcare system, and society in general. Obesity increases the incidence of many types of cancer, but growing evidence suggests that physical activity may decrease risk for developing a variety of obesity-related cancer types, and, in some cases, may improve cancer prognosis and mortality rates. This review summarizes recent evidence on the effect of physical activity on obesity-related cancer prevention and survival. For some cancers, including breast, colorectal, and endometrial cancer, there is strong evidence for a preventative effect of exercise, but for many others, including gallbladder and kidney cancer, and multiple myeloma, evidence is inconsistent or largely lacking. Though many potential mechanisms have been proposed to explain the onco-protective effect of exercise, including improved insulin sensitivity, alterations in sex hormone availability, improved immune function and inflammation, myokine secretion, and modulation of intracellular signaling at the level of AMP kinase, the exact mechanism(s) of action within each cancer subtype remains poorly defined. Overall, a deeper understanding of how exercise can help against cancer and of the exercise parameters that can be altered to optimize exercise prescription is necessary and should be the subject of future investigation.