阅读报告：肾肿瘤论文检索与阅读

rainoffallingstar 于 2021-08-24 发布

肾肿瘤论文检索与阅读

关于肾肿瘤的生物标志物、治疗、预后相关

PUBMED标注 :[i]=most important ; [i-]=less important;[i–]=little important;[u]= usual; [F]=free article; [?]=unknown; [s]=unmatched

搜索结果页a»> 搜索结果页b»>

  1. [i]Histological subtypes of renal cell carcinoma : Overview and new developments] https://dx.doi.org/10.1007/s00292-021-00937-6

  2. [i-] [F]Urinary Extracellular Vesicles as Potential Biomarkers for Urologic Cancers: An Overview of Current Methods and Advances https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33810357/

  3. [i-] [F]Molecular and Metabolic Subtypes in Sporadic and Inherited Clear Cell Renal Cell Carcinoma https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33803184/

  4. [u]Renal cell carcinoma pathology in 2021: ‘new need for renal cancer immune profiling’ https://doi.org/10.1097/MOU.0000000000000864

  5. [i]Urinary Biomarkers in Tumors: An Overview https://dx.doi.org/10.1007/978-1-0716-1354-2_1

  6. [i-] [F]Functions of circular RNAs in bladder, prostate and renal cell cancer (Review) https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33649838/

  7. [i]Papillary renal cell carcinoma: Review https://linkinghub.elsevier.com/retrieve/pii/S1078-1439(21)00170-8

  8. [i-]The role of imaging in the management of renal masses https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(21)00258-8

  9. [i]Non-clear cell renal carcinomas: Review of new molecular insights and recent clinical data https://linkinghub.elsevier.com/retrieve/pii/S0305-7372(21)00039-6

  10. [i-] [F]Long Noncoding RNA Small Nucleolar Host Gene: A Potential Therapeutic Target in Urological Cancers https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33968736/

  11. [u]Microwave ablation of cT1a renal cell carcinoma: oncologic and functional outcomes at a single centerhttps://linkinghub.elsevier.com/retrieve/pii/S0899-7071(21)00193-5

  12. [s] [F]Radiotherapy and Renal Cell Carcinoma: A Continuing Saga https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33910814/

  13. [i] [F]Biomarkers for Renal Cell Carcinoma Recurrence: State of the Art https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33886004/

    A wide variety of biomarkers have been proposed. RCC biomarkers have been individuated in tumoral tissue, blood, and urine. A variety of molecules, including proteins, DNA, and RNA, warrant a good accuracy for RCC recurrence and progression prediction. Their use in prediction models might warrant a better patients’ risk stratification.

    Future prognostic models will probably include a combination of classical features (tumor grade, stage, etc.) and novel biomarkers. Such models might allow a more accurate treatment and follow-up planification.

  14. [i-] [F]Role of neutrophil gelatinase-associated lipocalin in renal cell carcinoma https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33552266/

  15. [i] [F]Molecular diagnosis and therapy of kidney cancer https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20059341/

  16. [i-] [F]Circular RNAs in renal cell carcinoma: implications for tumorigenesis, diagnosis, and therapy https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33054773/

  17. [i-]Fumarate hydratase as a therapeutic target in renal cancer https://www.tandfonline.com/doi/full/10.1080/14728222.2020.1804862

  18. [i–] [F]Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29967214/

  19. [?]Kidney cancer: Combining targeted and immunotherapy https://doi.org/10.1038/nrurol.2018.43

  20. [i]Towards individualized therapy for metastatic renal cell carcinoma https://doi.org/10.1038/s41571-019-0209-1

    Over the past decade, the treatment landscape for patients with metastatic renal cell carcinoma (RCC) has evolved dramatically. The therapeutic options available have expanded and now include immune-checkpoint inhibitors, novel targeted agents and combination strategies, and thus optimal patient selection and treatment sequencing are increasingly pertinent for optimizing clinical outcomes. A better understanding of the underlying biology of the tumour and its microenvironment continues to drive the inception of new diagnostic and therapeutic approaches. Furthermore, many biomarkers robustly associated with treatment and disease-specific outcomes have been identified, and their integration into clinical decision-making for patients with advanced-stage disease will soon become a reality. Herein, we review relevant aspects of the molecular biology of metastatic RCC, with an emphasis on predictive and prognostic biomarkers, and suggest tailored algorithms to individualize and guide treatment approaches for specific subgroups of patients.

  21. [i]Sequencing Therapies for Metastatic Renal Cell Carcinoma https://linkinghub.elsevier.com/retrieve/pii/S0094-0143(20)30023-9

    Gene expression models are being generated from large prospective clinical trial data sets.

  22. [i–] [F]RLIP76 Targeted Therapy for Kidney Cancerhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26021465/

  23. [u] [F]Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31527133/

  24. [i–]Immunotherapy in kidney cancer: the past, present, and future https://doi.org/10.1097/MOU.0000000000000338

    Systemic treatment options over the past decade have been dominated by targeted therapies inhibiting the vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways. With the approval of the immune checkpoint inhibitor nivolumab, a new era of potential combination therapies is about to shape the treatment landscape for kidney cancer. These include other immune checkpoint inhibitors (e.g., anti-CTLA4), modifiers of the tumor microenvironment (VEGF pathway, T cell agonists (anti-41BB and Ox40 antibodies), and novel vaccination strategies).

  25. [i–] [F]Renal cell carcinoma: new insights and challenges for a clinician scientist https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28381462/

    Keywords: molecularly targeted therapy and immune checkpoint inhibition; radical and partial nephrectomy; renal cell carcinoma genotyping.

  26. [u]Treatment of renal cell carcinoma: Current status and future directions https://doi.org/10.3322/caac.21411

  27. [u:too old]Systemic treatment of renal cell cancer: A comprehensive reviewhttps://linkinghub.elsevier.com/retrieve/pii/S0305-7372(17)30142-1

  28. [i] [F]Report From the International Society of Urological Pathology (ISUP) Consultation Conference on Molecular Pathology of Urogenital Cancers: III: Molecular Pathology of Kidney Cancer https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32251007/

    Renal cell carcinoma (RCC) subtypes are increasingly being discerned via their molecular underpinnings. Frequently this can be correlated to histologic and immunohistochemical surrogates, such that only simple targeted molecular assays, or none at all, are needed for diagnostic confirmation. In clear cell RCC, VHL mutation and 3p loss are well known; however, other genes with emerging important roles include SETD2, BAP1, and PBRM1, among others. Papillary RCC type 2 is now known to include likely several different molecular entities, such as fumarate hydratase (FH) deficient RCC. In MIT family translocation RCC, an increasing number of gene fusions are now described. Some TFE3 fusion partners, such as NONO, GRIPAP1, RBMX, and RBM10 may show a deceptive fluorescence in situ hybridization result due to the proximity of the genes on the same chromosome. FH and succinate dehydrogenase deficient RCC have implications for patient counseling due to heritable syndromes and the aggressiveness of FH-deficient RCC. Immunohistochemistry is increasingly available and helpful for recognizing both. Emerging tumor types with strong evidence for distinct diagnostic entities include eosinophilic solid and cystic RCC and TFEB/VEGFA/6p21 amplified RCC. Other emerging entities that are less clearly understood include TCEB1 mutated RCC, RCC with ALK rearrangement, renal neoplasms with mutations of TSC2 or MTOR, and RCC with fibromuscular stroma. In metastatic RCC, the role of molecular studies is not entirely defined at present, although there may be an increasing role for genomic analysis related to specific therapy pathways, such as for tyrosine kinase or MTOR inhibitors.

  29. [i–:too old] [F]Aerobic glycolysis: a novel target in kidney cancer https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23773105/

  30. [i–]Targeting the HIF2-VEGF axis in renal cell carcinoma https://doi.org/10.1038/s41591-020-1093-z

  31. [i] [F]Personalized Management of Advanced Kidney Cancer https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/30231375/

    it will become increasingly important to develop a more tailored approach to treatment selection. Prognostic clinical models, such the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model, are routinely used for prognostication in clinical practice. The IMDC model has demonstrated a predictive capability in the context of these treatments including immune checkpoint inhibition. A number of promising molecular markers and gene expression signatures are being explored as prognostic and predictive biomarkers, but none are ready to be widely used for treatment selection. In this review, we will explore the current landscape of personalized care in metastatic renal cell carcinoma. This will include a focus on both prognostic and predictive factors as well as clinical applications of biology in kidney cancer.

  32. [u] [F]Metabolomics and Metabolic Reprogramming in Kidney Cancer https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29602399/

  33. [i]Kidney Cancer: An Overview of Current Therapeutic Approaches https://linkinghub.elsevier.com/retrieve/pii/S0094-0143(20)30049-5

    With rising rates of recurrence after first-line treatment, it is increasingly important to not only adopt a personalized treatment plan with minimal adverse events but also develop predictive biomarkers for response. This review discusses currently available first-line and second-line therapies in RCC and their pivotal data, with specific focus on ongoing clinical trials in the adjuvant setting, including those involving novel agents.

肿瘤预后分析与基因预测

搜索结果c »>

  1. [i] [F]The Application of Deep Learning in Cancer Prognosis Prediction https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32150991/

    Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

  2. [i–] [F]Pathology Image Analysis Using Segmentation Deep Learning Algorithms https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31199919/

    this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.

  3. [i]Radiomics and deep learning in lung cancer https://dx.doi.org/10.1007/s00066-020-01625-9

    models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype.

  4. [i]Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis https://linkinghub.elsevier.com/retrieve/pii/S2405-8033(19)30018-4

    These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment.

  5. [i-]Deep learning guided stroke management: a review of clinical applications https://jnis.bmj.com/cgi/pmidlookup?view=long&pmid=28954825

  6. [i]Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges https://linkinghub.elsevier.com/retrieve/pii/S0304-3835(19)30613-5

    Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient’s survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

  7. [i-]Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning https://linkinghub.elsevier.com/retrieve/pii/S1934-5925(18)30615-4

  8. [i-]\Application of Artificial Intelligence to Gastroenterology and Hepatology https://linkinghub.elsevier.com/retrieve/pii/S0016-5085(19)41412-1

  9. [i-] [F]Artificial intelligence in digital breast pathology: Techniques and applications https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31935669/

    The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumour

肿瘤分子分型与免疫疗法

mentioned by CJH & 他推荐的文献

  1. [i] Cancer immunoediting and resistance to T cell-based immunotherapy https://doi.org/10.1038/s41571-018-0142-8 SCIHUB

    肿瘤的免疫基因组学信息。免疫疗法如何更好被用于早期肿瘤的治疗。基于TMB+GEP 或者TILs+PD-L1，将肿瘤免疫微环境（TME）分为4个模型。

  2. [i] Conserved pan-cancer microenvironment subtypes predict response to immunotherapy https://linkinghub.elsevier.com/retrieve/pii/S1535-6108(21)00222-1 参考：https://c.m.163.com/news/a/GBNCCTDE051182D7.html?from=wap_redirect&spss=wap_refluxdl_2018&referFrom=

    分子靶向疗法的临床用途正在迅速发展，但主要集中在基因组改变上。转录组学分析为剖析肿瘤的复杂性提供了机会。肿瘤微环境（TME）是癌症进展和治疗结果的关键介质。通过对超过10,000名癌症患者的转录组分析进行TME分类，识别出20种不同癌症中保守的4种不同的TME亚型。TME亚型可预测多种癌症对免疫疗法的反应，拥有免疫有利的TME亚型的患者从免疫治疗中受益最大。TME亚型还可作为许多癌症类型的通用免疫治疗生物标志物。整合转录组学和基因组数据的可视化工具提供了完整的肿瘤绘像，描述了肿瘤框架，突变负荷，免疫组成，抗肿瘤免疫力和免疫抑制性逃逸机制。整合分析和可视化可以帮助发现生物标志物和治疗方案的个性化。

    在临床试验和标准护理中，肿瘤的基因组表征越来越普遍。虽然人们越来越多地接受基因组分析作为临床决策的一部分，但基因组表征通常需要在仅包含少量致癌性改变的有限基因中，靶向检测。转录组学分析为剖析肿瘤的复杂性和异质性以及发现可用于开发新的治疗策略的新生物标志物提供了额外的机会。全外显子测序（WES）和RNA测序（RNA-seq）以及常规的病理学，免疫组化和临床测试为肿瘤特征提供了多方面的视角，并有可能导致进一步鉴定和优化个体癌症患者的治疗方法。尽管如此，大规模的外显子组和转录组测序提供了数千个参数，对于常规治疗决策来说，过多的参数通常难以全面利用，反而让决策变得困难。目前发现肿瘤微环境（TME）对临床结果和治疗反应具有显著的作用。破解肿瘤免疫微环境的概况可以改善量身定制的免疫治疗策略的效果。但是，迄今为止，全面评估肿瘤和整个TME，整合基因组和转录组的分析仍然很少见.

    为了使用转录组分析对TME进行分类，首先使用已发表的文献搜索出TME各组分（如肿瘤主要成分、免疫、基质细胞和其他细胞群）的功能基因表达特征（FGE），构建出全面描绘TME的单一模型。最终共选择29个FGEs，涵盖TME中已知的细胞和功能特征，每个FGE都只包含与一个特定细胞类型或生物过程有关的基因。研究人员利用TCGA、ICGC或GTEx等多种数据库中数据集对FGEs分类的准确性进行验证，发现FGEs具有高度细胞类型特异性，例如与正常组织和痣相比，肿瘤增殖特征的表达（包括细胞周期和肿瘤进展相关基因）与恶性黑色素瘤具有强相关性。随后利用29个GFEs对黑色素瘤TME进行分类分析，划分出4种不同的微环境：1）免疫富集且纤维化（IE/F）；2）免疫富集，非纤维化（IE）；3）纤维化（F）；4）免疫缺乏（D）。这些TME亚型之间差异显著，且该差异在之前的分析中也能观察到。在黑色素瘤中经常发现的基因组改变在各TME亚型中也不一致。

    研究者通过对多种肿瘤的肿瘤微环境分析后发现，每一种肿瘤，甚至每一位患者的肿瘤浸润免疫细胞都存在差异。其中比较受研究者广泛认可的就是肿瘤的三大免疫分型：“免疫浸润型”、“免疫排斥型”、“免疫沙漠型”。研究者通过对肿瘤组织进行免疫组化染色后发现这三类显著的差异，在“免疫浸润型”肿瘤中，CD8+ T细胞可以浸润到肿瘤内部；“免疫排斥型”肿瘤中，虽然也有较高的CD8+ T细胞浸润程度，但都是集中在肿瘤外围；而“免疫沙漠型”肿瘤中很少有CD8+ T细胞的浸润。

  3. [i+][课题本组相关]Pazopanib in patients with metastatic renal cell carcinoma: a single-center, real-world, retrospective Chinese study [EN]https://tau.amegroups.com/article/view/65180/html [CH]https://mp.weixin.qq.com/s/nOoG__64OrsZpCZ4OXffBg

    mRCC的治疗已经进入了免疫与靶向疗法并存的时代，对于IMDC低危人群，多项免疫联合靶向的III期临床试验如Keynoter426、Keynote 581/CLEAR、Checkmate 9ER等研究均证实了低危患者采用靶向单药治疗的OS获益与联合治疗并无差异。在本篇真实世界研究中，低危患者PFS达22.1个月，ORR率达58.3%，优于中高危患者，证实真实世界里，低危人群中可从TKI单药治疗中获益最大化。除低危患者外，本真实世界研究分析中危组具有1个危险因素患者PFS达17.8个月，优于中危组合并2个危险因素（PFS为8.0个月），中危患者约占晚期肾癌约50%，这提示对于中危这一大类人群“一刀切“并不适合，需要更精细的”再分层“。除外，ECOG ＜2分、转移器官数量为1个和仅肺转移的患者预后好。这类患者耐受性好，瘤负荷低，予TKI单药获益佳。既往的涉及肾癌分子分型研究（IMmotion150、IMmotion151、BIONIKK）亦提示存在一部分血管生成相关基因表达高的肾癌患者，这部分患者适合进行抗血管生成为主要作用机制的TKI药物治疗。未来晚期肾癌如何进行一线药物的选择，可能的发展方向应是肾癌的分子分型结合临床特征（如IMDC评分等）来指导精准用药。

  4. [u]影像组学在肾肿瘤中的研究进展 https://m.medlive.cn/cms/research/180264?_wx=1&info=eyJhY2Nlc3NfdG9rZW4iOiI0OF9DU0ZKRF95dGFJYTFPN1U0QzVsRXZqUXZWTUFjZ0h3U1RDY2dxVkFCb3p5YUdEWngtRUNwbzN4NnRXZnRVdTlPRVlxMXU3RU9mVXpYczE2WEtjcVh1aEJ4VFBkSFpHZTlPd0hRRGN0YkwyMCIsInJlZnJlc2hfdG9rZW4iOiI0OF9BVVJaWE9oQVdhNkRrNGg3WFc3NlFubENNV1I3S1hqOTBHOVJDLUlacXc2dzItd3I1V3pmQXEtMURoN2lYQTdRZGJ4dFV3VklPNHBwblR4aDduWVFfVkhuRU04TXBqUlJyeFFFbnJ5M19vNCIsIm9wZW5pZCI6Im9aWGVZamxWemhMOGZtNm9BMzNweDJSSmg0YWMifQ%3D%3D
  5. [i+] EAU 2020:肾癌精准诊疗的探索之路 http://www.360doc6.net/wxarticlenew/928174706.html
  6. [i-] Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting http://clincancerres.aacrjournals.org/cgi/pmidlookup?view=long&pmid=25583177

TME分型相关免疫治疗及生物标志物

搜索结果D »>

  1. [i-][F]Tumour-Associated Macrophages (TAMs) in Colon Cancer and How to Reeducate Them https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/30931335/

    The characteristic of TAM is largely dependent on the stimuli present in its tumour microenvironment (TME).

  2. [i][F]The Role of Membrane Bound Complement Regulatory Proteins in Tumor Development and Cancer Immunotherapy https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31164885/

    This review will highlight the biomarker-related role that mCRP expression may have in the classification of tumor phenotype and predicted response to different anti-cancer treatments in the context of an emerging understanding that complement activation within the Tumor Microenvironment (TME) is actually harmful for tumor control. We will discuss what is known about complement activation and mCRPs relating to cancer and immunotherapy, and will examine the potential for combinatorial approaches of anti-mCRP therapy with other anti-tumor therapies, especially checkpoint inhibitors such as anti PD-1 and PD-L1 monoclonal antibodies (mAbs). Overall, mCRPs play an essential role in the immune response to tumors, and understanding their role in the immune response, particularly in modulating currently used cancer therapeutics may lead to better clinical outcomes in patients with diverse cancer types.

  3. [u][F]Heterogeneity of cancer-associated fibroblasts: Opportunities for precision medicine https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32573845/

    Despite marked development in cancer therapies during recent decades, the prognosis for advanced cancer remains poor. The conventional tumor-cell-centric view of cancer can only explain part of cancer progression, and thus a thorough understanding of the tumor microenvironment (TME) is crucial.

  4. [i][F]Circular RNAs in the tumour microenvironment https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31937318/

    Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs (ncRNAs) widely expressed in eukaryotic cells. Mounting evidence has highlighted circRNAs as critical regulators of various tumours. More importantly, circRNAs have been revealed to recruit and reprogram key components involved in the tumour microenvironment (TME), and mediate various signaling pathways, thus affecting tumourigenesis, angiogenesis, immune response, and metastatic progression. In this review, we briefly introduce the biogenesis, characteristics and classification of circRNAs, and describe various mechanistic models of circRNAs. Further, we provide the first systematic overview of the interplay between circRNAs and cellular/non-cellular counterparts of the TME and highlight the potential of circRNAs as prospective biomarkers or targets in cancer clinics. Finally, we discuss the biological mechanisms through which the circRNAs drive development of resistance, revealing the mystery of circRNAs in drug resistance of tumours.

  5. [u] Immunoscore assay for the immune classification of solid tumors: Technical aspects, improvements and clinical perspectives https://linkinghub.elsevier.com/retrieve/pii/S0076-6879(19)30298-8

    IHC-based immune assay measuring the densities of CD3+ and CD8+ T cells at different tumor locations, linking them with patients’ clinical outcome.

  6. [i+][F]Checkpoint blockade-based immunotherapy in the context of tumor microenvironment: Opportunities and challenges https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/30088347/

    A dynamic and mutualistic interaction between tumor cells and tumor microenvironment (TME) promotes the progression and metastasis of solid tumors. Cancer immunotherapy is becoming a major treatment paradigm for a variety of cancers. Although immunotherapy, especially the use of immune checkpoint inhibitors, has achieved clinical success, only a minority of patients exhibits durable responses. Clinical studies directed at identifying appropriate biomarkers and immune profiles that can be used to predict immunotherapy responses are presently being conducted. Combining treatment strategies tailored to cancer-immune interactions are designed to increase the rate of durable clinical response in patients. It is essential to establish a reasonable tumor classification strategy according to TME to improve cancer immunotherapy. In the current review, a modified classification of TME is proposed, and optimization of TME classification is needed through detailed and integrated molecular characterization of large patient cohorts in the future.

  7. [i][F]Cancer Immunotherapies Targeting Tumor-Associated Regulatory T Cells https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31997881/

    we summarize the classification, immunosuppressive mechanisms, existing immunotherapies, and potential biomarkers related to tumor-infiltrating Tregs to guide the development of effective cancer immunotherapies.

  8. [s][F]Different Forms of Tumor Vascularization and Their Clinical Implications Focusing on Vessel Co-option in Colorectal Cancer Liver Metastases https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33912554/
  9. [i][F]MicroRNAs in Metastasis and the Tumour Microenvironment https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/34064331/

    Metastasis is the process whereby cancer cells migrate from the primary tumour site to colonise the surrounding or distant tissue or organ. Metastasis is the primary cause of cancer-related mortality and approximately half of all cancer patients present at diagnosis with some form of metastasis. Consequently, there is a clear need to better understand metastasis in order to develop new tools to combat this process. MicroRNAs (miRNAs) regulate gene expression and play an important role in cancer development and progression including in the metastatic process. Particularly important are the roles that miRNAs play in the interaction between tumour cells and non-tumoral cells of the tumour microenvironment (TME), a process mediated largely by circulating miRNAs contained primarily in extracellular vesicles (EVs).

  10. [i+]Immune Contexture, Immunoscore, and Malignant Cell Molecular Subgroups for Prognostic and Theranostic Classifications of Cancers https://linkinghub.elsevier.com/retrieve/pii/S0065-2776(15)30002-X

    The outcome of tumors results from genetic and epigenetic modifications of the transformed cells and also from the interactions of the malignant cells with their tumor microenvironment (TME), which includes immune and inflammatory cells. For a given cancer type, the composition of the immunological TME is not homogeneous. Heterogeneity is found between different cancer types and also between tumors from patients with the same type of cancer. Some tumors exhibit a poor infiltration by immune cells, and others are highly infiltrated by lymphocytes. Among the latter, the architecture of the TME, with the localization of immune cells in the invasive front and the center of the tumor, the presence of tumor-adjacent organized lymphoid aggregates, and the type of inflammatory context, determines the prognostic impact of the infiltrating cells. The description and the understanding of the immune and inflammatory landscape in human tumors are of paramount importance at different levels of patient’s care. It completes the mutational, transcriptional, and epigenetic patterns of the malignant cells and open paths to understand how tumor cells shape their immune microenvironment and are shaped by the immune reaction. It provides prognostic and theranostic markers, as well as novel targets for immunotherapies.

  11. [i]Targeting CXCR1/2: The medicinal potential as cancer immunotherapy agents, antagonists research highlights and challenges ahead https://linkinghub.elsevier.com/retrieve/pii/S0223-5234(19)31005-0

    Immune suppression in the tumor microenvironment (TME) is an intractable issue in anti-cancer immunotherapy. The chemokine receptors CXCR1 and CXCR2 recruit immune suppressive cells such as the myeloid derived suppressor cells (MDSCs) to the TME. Therefore, CXCR1/2 antagonists have aroused pharmaceutical interest in recent years. In this review, the medicinal chemistry of CXCR1/2 antagonists and their relevance in cancer immunotherapy have been summarized.

  12. [u][F]Immunoepigenetics Combination Therapies: An Overview of the Role of HDACs in Cancer Immunotherapy https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31067680/

    Given the critical role of the tumor microenvironment (TME) towards the outcome of anticancer therapies, we have also discussed the effect of HDACis on different components of the TME.

  13. [s][F]Roles of Lysyl Oxidase Family Members in the Tumor Microenvironment and Progression of Liver Cancer
  14. [i+]The tumour microenvironment as an integrated framework to understand cancer biology https://linkinghub.elsevier.com/retrieve/pii/S0304-3835(19)30406-9

    Cancer cells all share the feature of being immersed in a complex environment with altered cell-cell/cell-extracellular element communication, physicochemical information, and tissue functions. The so-called tumour microenvironment (TME) is becoming recognised as a key factor in the genesis, progression and treatment of cancer lesions. Beyond genetic mutations, the existence of a malignant microenvironment forms the basis for a new perspective in cancer biology where connections at the system level are fundamental. From this standpoint, different aspects of tumour lesions such as morphology, aggressiveness, prognosis and treatment response can be considered under an integrated vision, giving rise to a new field of study and clinical management. Nowadays, somatic mutation theory is complemented with study of TME components such as the extracellular matrix, immune compartment, stromal cells, metabolism and biophysical forces. In this review we examine recent studies in this area and complement them with our own research data to propose a classification of stromal changes. Exploring these avenues and gaining insight into malignant phenotype remodelling, could reveal better ways to characterize this disease and its potential treatment.

  15. [u][F]Location, function and role of stromal cell‑derived factors and possible implications in cancer (Review) https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33416125/
  16. [i+][F]The Immunoscore in the Clinical Practice of Patients with Colon and Rectal Cancers http://revistachirurgia.ro/pdfs/2019-2-152.pdf

    we herein retrace through the example of colorectal cancer, how an effective immune test, namely the “Immunoscore”, has been developed. We also provide up to date data demonstrating the capacity of the Immunoscore to prognosticate with a better accuracy than the TME classification clinical outcomes and to guide therapeutic strategies.

  17. [i++]Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies https://dx.doi.org/10.1007/s00262-017-2058-z

    We also compared established molecular classifications of colorectal cancer and clear-cell renal cell carcinoma with the output of MCP-counter, and show that molecular subgroups have different TME profiles, and that these profiles are consistent within a given subgroup. Finally, we provide insights as to how knowing the TME composition may shape patient care in the near future.