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.

As a result of metabolic reprogramming, cancer cells display high rates of glycolysis, causing an excess production of lactate along with an increase in extracellular acidity. Proton-linked monocarboxylate transporters (MCTs) are crucial in the maintenance of this metabolic phenotype, by mediating the proton-coupled lactate flux across cell membranes, also contributing to cancer cell pH regulation. Among the proteins codified by the SLC16 gene family, MCT1 and MCT4 isoforms are the most explored in cancers, being overexpressed in many cancer types, from solid tumours to haematological malignancies. Similarly to what occurs in particular physiological settings, MCT1 and MCT4 are able to mediate lactate shuttles among cancer cells, and also between cancer and stromal cells in the tumour microenvironment. This form of metabolic cooperation is responsible for important cancer aggressiveness features, such as cell proliferation, survival, angiogenesis, migration, invasion, metastasis, immune tolerance and therapy resistance. The growing understanding of MCT functions and regulation is offering a new path to the design of novel inhibitors that can be foreseen in clinical practices. This review provides an overview of the role of MCT isoforms in cancer and summarizes the recent advances in their pharmacological targeting, highlighting the potential of new potent and selective MCT1 and/or MCT4 inhibitors in cancer therapeutics, and anticipating its inclusion in clinical practice.

Extracellular vesicles (EVs) function as a mode of intercellular communication and molecular transfer to elicit diverse biological/functional response. Accumulating evidence has highlighted that EVs from immune, tumour, stromal cells and even bacteria and parasites mediate the communication of various immune cell types to dynamically regulate host immune response. EVs have an innate capacity to evade recognition, transport and transfer functional components to target cells, with subsequent removal by the immune system, where the immunological activities of EVs impact immunoregulation including modulation of antigen presentation and cross-dressing, immune activation, immune suppression, and immune surveillance, impacting the tumour immune microenvironment. In this review, we outline the recent progress of EVs in immunorecognition and therapeutic intervention in cancer, including vaccine and targeted drug delivery and summarise their utility towards clinical translation. We highlight the strategies where EVs (natural and engineered) are being employed as a therapeutic approach for immunogenicity, tumoricidal function, and vaccine development, termed immuno-EVs. With seminal studies providing significant progress in the sequential development of engineered EVs as therapeutic anti-tumour platforms, we now require direct assessment to tune and improve the efficacy of resulting immune responses - essential in their translation into the clinic. We believe such a review could strengthen our understanding of the progress in EV immunobiology and facilitate advances in engineering EVs for the development of novel EV-based immunotherapeutics as a platform for cancer treatment.

Stem cells have self-renewal capacities and the ability to give rise to differentiated cells thereby sustaining tissues during homeostasis and injury. This structural hierarchy extends to tumours which harbor stem-like cells deemed cancer stem cells that propagate the tumour and drive metastasis and relapse. The process of epithelial-to-mesenchymal transition (EMT), which plays an important role in development and cancer cell migration, was shown to be correlated with stemness in both homeostasis and cancer indicating that stemness can be acquired and is not necessarily an intrinsic trait. Nowadays it is experimentally proven that the activation of an EMT program does not necessarily drive cells towards a fully mesenchymal phenotype but rather to hybrid E/M states. This review offers the latest advances in connecting the EMT status and stem-cell state of both non-transformed and cancer cells. Recent literature clearly shows that hybrid EMT states have a higher probability of acquiring stem cell traits. The position of a cell along the EMT-axis which coincides with a stem cell-like state is known as the stemness window. We show how the original EMT-state of a cell dictates the EMT/MET inducing programmes required to reach stemness. Lastly we present the mechanism of stemness regulation and the regulatory feedback loops which position cells at a certain EMT state along the EMT axis.

The addition of immune checkpoint blockade (ICB) therapy to standard chemotherapy has been shown to improve survival in patients with metastatic small cell lung cancer. However, the benefit is modest and there remains an unmet need for novel therapeutic approaches to enhance the effectiveness of immunotherapy in this disease, both in the early and late stages. Ionizing radiation, which is a standard treatment for small cell lung cancer, is known to trigger immunogenic cell death in tumor cells, making it an attractive partner for ICB therapies in multiple solid tumor types. However, the optimal radiation dosage and fractionation scheme, target sites for radiation, and sequencing of radiation in relation to ICB treatment are still unclear. In this review we discuss the molecular biology underlying radiation-induced tumor immunity as well as pre-clinical and clinical studies combining radiation with ICB treatments, with a focus on translational and clinical trials in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer, accounting for approximately 15% among all lung cancers. Despite the ability of chemotherapy, the first-line treatment for SCLC, to rapidly shrink tumors, nearly all patients experience recurrence and metastasis within a few months. Cancer stem cells (CSCs) are a small population of tumor cells responsible for tumorigenesis, metastasis, and recurrence after treatment, which play a crucial role in chemoresistance by promoting DNA repair and expression of drug resistance-associated proteins. Thus, targeting CSCs has been successful in certain malignancies. Tumor therapy has entered the era of immunotherapy and numerous preclinical trials have demonstrated the effectiveness of immunotherapeutic approaches targeting CSCs, such as tumor vaccines and chimeric antigen receptor (CAR) T cell, and the feasibility of combining them with chemotherapy. Therefore, a deeper understanding of the interaction between CSCs and immune system is essential to facilitate the advances of new immunotherapies approaches targeting CSCs as well as combination with standard drugs such as chemotherapy. This narrative review summarizes the mechanisms of chemoresistance of CSCs in SCLC and the latest advances in targeted therapies. Thereafter, we discuss the effects of CSCs on tumor immune microenvironment in SCLC and corresponding immunotherapeutic approaches. Eventually, we propose that the combination of immunotherapy targeting CSCs with standard drugs is a promising direction for SCLC therapies.

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.

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.

Urologic cancers (UC) account for 13.1% of all new cancer cases and 7.9% of all cancer-related deaths. A growing body of evidence has indicated a potential causal link between obesity and UC. The aim of the present review is to appraise in a critical and integrative manner evidence from meta-analyses and mechanistic studies on the role of obesity in four prevalent UC (kidney-KC, prostate-PC, urinary bladder-UBC, and testicular cancer-TC). Special emphasis is given on Mendelian Randomization Studies (MRS) corroborating a genetic causal association between obesity and UC, as well as on the role of classical and novel adipocytokines. Furthermore, the molecular pathways that link obesity to the development and progression of these cancers are reviewed. Available evidence indicates that obesity confers increased risk for KC, UBC, and advanced PC (20-82%, 10-19%, and 6-14%, respectively), whereas for TC adult height (5-cm increase) may increase the risk by 13%. Obese females tend to be more susceptible to UBC and KC than obese males. MRS have shown that a higher genetic-predicted BMI may be causally linked to KC and UBC but not PC and TC. Biological mechanisms that are involved in the association between excess body weight and UC include the Insulin-like Growth Factor axis, altered availability of sex hormones, chronic inflammation and oxidative stress, abnormal secretion of adipocytokines, ectopic fat deposition, dysbiosis of the gastrointestinal and urinary tract microbiomes and circadian rhythm dysregulation. Anti-hyperglycemic and non-steroidal anti-inflammatory drugs, statins, and adipokine receptor agonists/antagonists show potential as adjuvant cancer therapies. Identifying obesity as a modifiable risk factor for UC may have significant public health implications, allowing clinicians to tailor individualized prevention strategies for patients with excess body weight.

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.

The circadian rhythm is regulated by an intrinsic time-tracking system, composed both of a central and a peripheral clock, which influences the cycles of activities and sleep of an individual over 24 h. At the molecular level, the circadian rhythm begins when two basic helix-loop-helix/Per-ARNT-SIM (bHLH-PAS) proteins, BMAL-1 and CLOCK, interact with each other to produce BMAL-1/CLOCK heterodimers in the cytoplasm. The BMAL-1/CLOCK target genes encode for the repressor components of the clock, cryptochrome (Cry1 and Cry2) and the Period proteins (Per1, Per2 and Per3). It has been recently demonstrated that the disruption of circadian rhythm is associated with an increased risk of developing obesity and obesity-related diseases. In addition, it has been demonstrated that the disruption of the circadian rhythm plays a key role in tumorigenesis. Further, an association between the circadian rhythm disruptions and an increased incidence and progression of several types of cancer (e.g., breast, prostate, colorectal and thyroid cancer) has been found. As the perturbation of circadian rhythm has adverse metabolic consequences (e.g., obesity) and at the same time tumor promoter functions, this manuscript has the aim to report how the aberrant circadian rhythms affect the development and prognosis of different types of obesity-related cancers (breast, prostate, colon rectal and thyroid cancer) focusing on both human studies and on molecular aspects.

Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.

Hepatocellular carcinoma (HCC) is estimated to be the third leading cause of cancer-related mortality and is characterized by low survival rates. Nonalcoholic fatty liver disease (NAFLD) is emerging as a leading cause of HCC, whose rates are increasing, owing to the increasing prevalence of NAFLD. The pathogenesis of NAFLD-associated HCC is multifactorial: insulin resistance, obesity, diabetes and the low-grade hepatic inflammation, which characterizes NAFLD, seem to play key roles in the development and progression of HCC. The diagnosis of NAFLD-associated HCC is based on imaging in the presence of liver cirrhosis, preferably computerized tomography or magnetic resonance imaging, but liver biopsy for histological confirmation is usually required in the absence of liver cirrhosis. Some preventive measures have been recommended for NAFLD-associated HCC, including weight loss, cessation of even moderate alcohol drinking and smoking, as well as the use of metformin, statins and aspirin. However, these preventive measures are mainly based on observational studies, thus they need validation in trials of different design before introducing in clinical practice. The treatment of NAFLD should be tailored on an individual basis and should be ideally determined by a multidisciplinary team. In the last two decades, new medications, including tyrosine kinase inhibitors and immune checkpoints inhibitors, have improved the survival of patients with advanced HCC, but trials specifically designed for patients with NAFLD-associated HCC are scarce. The aim of this review was to overview evidence on the epidemiology and pathophysiology of NAFLD-associated HCC, then to comment on imaging tools for its appropriate screening and diagnosis, and finally to critically summarize the currently available options for its prevention and treatment.

Small cell lung cancer (SCLC) is a recalcitrant cancer with an urgent need for novel therapeutics, preclinical models, and elucidation of the molecular pathways responsible for its rapid resistance. Recently, there have been many significant advancements in our knowledge of SCLC that led to the development of novel treatments. This review will go over the recent attempts to provide new molecular subcategorization of SCLC, recent breakthroughs in various systemic treatments including immunotherapy, targeted therapy, cellular therapy, as well as advancements in radiation therapy.

Declining thymic functions associated either with old age (i.e., age-related thymic involution), or with acute involution as a result of stress, infectious disease, or cytoreductive therapies (e.g., chemotherapy/radiotherapy), have been associated with cancer development. A key mechanism underlying such increased cancer risk is the thymus-dependent debilitation of adaptive immunity, which is responsible for orchestrating immunoediting mechanisms and tumor immune surveillance. In the past few years, a blooming set of evidence has intriguingly linked obesity with cancer development and progression. The majority of such studies has focused on obesity-driven chronic inflammation, steroid/sex hormone and adipokine production, and hyperinsulinemia, as principal factors affecting the tumor microenvironment and driving the development of primary malignancy. However, experimental observations about the negative impact of obesity on T cell development and maturation have existed for more than half a century. Here, we critically discuss the molecular and cellular mechanisms of obesity-driven thymic involution as a previously underrepresented intermediary pathology leading to cancer development and progression. This knowledge could be especially relevant in the context of childhood obesity, because impaired thymic function in young individuals leads to immune system abnormalities, and predisposes to various pediatric cancers. A thorough understanding behind the molecular and cellular circuitries governing obesity-induced thymic involution could therefore help towards the rationalized development of targeted thymic regeneration strategies for obese individuals at high risk of cancer development.

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.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Cancer cells adapt to varying stress conditions to survive through plasticity. Stem cells exhibit a high degree of plasticity, allowing them to generate more stem cells or differentiate them into specialized cell types to contribute to tissue development, growth, and repair. Cancer cells can also exhibit plasticity and acquire properties that enhance their survival. TGF-β is an unrivaled growth factor exploited by cancer cells to gain plasticity. TGF-β-mediated signaling enables carcinoma cells to alter their epithelial and mesenchymal properties through epithelial-mesenchymal plasticity (EMP). However, TGF-β is a multifunctional cytokine; thus, the signaling by TGF-β can be detrimental or beneficial to cancer cells depending on the cellular context. Those cells that overcome the anti-tumor effect of TGF-β can induce epithelial-mesenchymal transition (EMT) to gain EMP benefits. EMP allows cancer cells to alter their cell properties and the tumor immune microenvironment (TIME), facilitating their survival. Due to the significant roles of TGF-β and EMP in carcinoma progression, it is essential to understand how TGF-β enables EMP and how cancer cells exploit this plasticity. This understanding will guide the development of effective TGF-β-targeting therapies that eliminate cancer cell plasticity.

Despite the development of cancer therapies, the success of most treatments has been impeded by drug resistance. The crucial role of tumor cell plasticity has emerged recently in cancer progression, cancer stemness and eventually drug resistance. Cell plasticity drives tumor cells to reversibly convert their cell identity, analogous to differentiation and dedifferentiation, to adapt to drug treatment. This phenotypical switch is driven by alteration of the transcriptome. Several pluripotent factors from the KLF and SOX families are closely associated with cancer pathogenesis and have been revealed to regulate tumor cell plasticity. In this review, we particularly summarize recent studies about KLF4, KLF5 and SOX factors in cancer development and evolution, focusing on their roles in cancer initiation, invasion, tumor hierarchy and heterogeneity, and lineage plasticity. In addition, we discuss the various regulation of these transcription factors and related cutting-edge drug development approaches that could be used to drug "undruggable" transcription factors, such as PROTAC and PPI targeting, for targeted cancer therapy. Advanced knowledge could pave the way for the development of novel drugs that target transcriptional regulation and could improve the outcome of cancer therapy.

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) is a member of a family of peptidyl-prolyl isomerases that specifically recognizes and binds phosphoproteins, catalyzing the rapid cis-trans isomerization of phosphorylated serine/threonine-proline motifs, which leads to changes in the structures and activities of the targeted proteins. Through this complex mechanism, PIN1 regulates many hallmarks of cancer including cell autonomous metabolism and the crosstalk with the cellular microenvironment. Many studies showed that PIN1 is largely overexpressed in cancer turning on a set of oncogenes and abrogating the function of tumor suppressor genes. Among these targets, recent evidence demonstrated that PIN1 is involved in lipid and glucose metabolism and accordingly, in the Warburg effect, a characteristic of tumor cells. As an orchestra master, PIN1 finely tunes the signaling pathways allowing cancer cells to adapt and take advantage from a poorly organized tumor microenvironment. In this review, we highlight the trilogy among PIN1, the tumor microenvironment and the metabolic program rewiring.