Advances in cancer research最新文献_第4页

Current computational methods for spatial transcriptomics in cancer biology. 癌症生物学中空间转录组学的当前计算方法。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-25 DOI: 10.1016/bs.acr.2024.06.006

Jaewoo Mo, Junseong Bae, Jahanzeb Saqib, Dohyun Hwang, Yunjung Jin, Beomsu Park, Jeongbin Park, Junil Kim

引用次数: 0

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. 肿瘤与微环境之间的相互影响：空间转录组学的启示。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-15 DOI: 10.1016/bs.acr.2024.06.009

Malvika Sudhakar, Harie Vignesh, Kedar Nath Natarajan

引用次数: 0

The RAF cysteine-rich domain: Structure, function, and role in disease. RAF 富半胱氨酸结构域：结构、功能和在疾病中的作用

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-05-14 DOI: 10.1016/bs.acr.2024.04.009

Russell Spencer-Smith

{"title":"The RAF cysteine-rich domain: Structure, function, and role in disease.","authors":"Russell Spencer-Smith","doi":"10.1016/bs.acr.2024.04.009","DOIUrl":"10.1016/bs.acr.2024.04.009","url":null,"abstract":"RAF kinases, consisting of ARAF, BRAF and CRAF, are direct effectors of RAS GTPases and critical for signal transduction through the RAS-MAPK pathway. Driver mutations in BRAF are commonplace in human cancer, while germline mutations in BRAF and CRAF cause RASopathy development syndromes. However, there remains a lack of effective drugs that target RAF function, which is partially due to the complexity of the RAF activation cycle. Therefore, greater understanding of RAF regulation is required to identify new approaches that target its function in disease. A key piece of this puzzle is the RAF zinc finger, often referred to as the cysteine-rich domain (CRD). The CRD is a lipid and protein binding domain which plays complex and opposing roles in the RAF activation cycle. Firstly, it supports the RAS-RAF interaction during RAF activation by binding to phosphatidylserine (PS) in the plasma membrane and by making direct RAS contacts. Conversely, under quiescent conditions the CRD also plays a critical role in maintaining RAF in a closed, autoinhibited state. However, the interplay between these activities and their relative importance for RAF activation were not well understood. Recent structural and biochemical studies have contributed greatly to our understanding of these roles and identified functional differences between BRAF CRD and that of CRAF. This chapter provides an in-depth review of the CRDs roles in RAF regulation and how they may inform novel approaches to target RAF function.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"164 ","pages":"69-91"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-omics based artificial intelligence for cancer research. 基于多组学的人工智能用于癌症研究。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-09 DOI: 10.1016/bs.acr.2024.06.005

Lusheng Li, Mengtao Sun, Jieqiong Wang, Shibiao Wan

{"title":"Multi-omics based artificial intelligence for cancer research.","authors":"Lusheng Li, Mengtao Sun, Jieqiong Wang, Shibiao Wan","doi":"10.1016/bs.acr.2024.06.005","DOIUrl":"https://doi.org/10.1016/bs.acr.2024.06.005","url":null,"abstract":"With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"163 ","pages":"303-356"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Role of antioxidants in modulating anti-tumor T cell immune resposne. 抗氧化剂在调节抗肿瘤 T 细胞免疫反应中的作用

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-06-13 DOI: 10.1016/bs.acr.2024.05.003

Nathaniel Oberholtzer, Stephanie Mills, Shubham Mehta, Paramita Chakraborty, Shikhar Mehrotra

引用次数: 0

Molecular landscape of prostate cancer bone metastasis. 前列腺癌骨转移的分子图谱。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-05-11 DOI: 10.1016/bs.acr.2024.04.007

Santanu Maji, Amit Kumar, Luni Emdad, Paul B Fisher, Swadesh K Das

引用次数: 0

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions. 用于检测具有临床意义的前列腺癌的前列腺磁共振成像：最新进展和未来方向。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-04-25 DOI: 10.1016/bs.acr.2024.04.002

Shaun Trecarten, Abhijit G Sunnapwar, Geoffrey D Clarke, Michael A Liss

{"title":"Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions.","authors":"Shaun Trecarten, Abhijit G Sunnapwar, Geoffrey D Clarke, Michael A Liss","doi":"10.1016/bs.acr.2024.04.002","DOIUrl":"https://doi.org/10.1016/bs.acr.2024.04.002","url":null,"abstract":"Purpose of review: In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.Recent findings: In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.Summary: The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"161 ","pages":"71-118"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Membrane potential: A new hallmark of cancer. 膜电位：癌症的新标志

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-05-15 DOI: 10.1016/bs.acr.2024.04.010

Davide Delisi, Najmeh Eskandari, Saverio Gentile

引用次数: 0

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond. 计算机视觉和病理学的进步：发掘人工智能在精准诊断及其他方面的潜力。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-06-26 DOI: 10.1016/bs.acr.2024.05.006

Justin Chang, Bryce Hatfield

{"title":"Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond.","authors":"Justin Chang, Bryce Hatfield","doi":"10.1016/bs.acr.2024.05.006","DOIUrl":"https://doi.org/10.1016/bs.acr.2024.05.006","url":null,"abstract":"The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"161 ","pages":"431-478"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Redox, cysteines, and kinases-A triad sustaining myeloid leukemia. 氧化还原、半胱氨酸和激酶--维持骨髓性白血病的三要素。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-05-14 DOI: 10.1016/bs.acr.2024.04.008

Vanessa Marensi

{"title":"Redox, cysteines, and kinases-A triad sustaining myeloid leukemia.","authors":"Vanessa Marensi","doi":"10.1016/bs.acr.2024.04.008","DOIUrl":"https://doi.org/10.1016/bs.acr.2024.04.008","url":null,"abstract":"Reactive oxygen species (ROS) work as a second messenger, modulating cell response and establishing homeostasis. Abrupt changes in ROS are used to modulate transient cell response to different stimuli, from viral infection to inflammation. Chronic exposure to high ROS concentration can cause cellular damage and promote the development of diseases. Leukemogenesis is adapted to high concentrations of ROS, hijacking the ROS system, and uses kinase cascades to promote survival advantages. The oxidation-reduction (redox) machinery is composed of enzymes that orchestrate all classes of protein and use available Cys as transmitters and sensors, to disseminate stress signals through cells via kinase cascades. Myeloid leukemias (MLs) are known for being a heterogeneous disease, and clonal diversity is remarkably characterized by differences in the activation of kinase-regulated signaling cascades to provide survival advantage. Stress-activated kinase cascades and other cascades are regulated by the ROS system. Several studies present nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 2 (NOX2) and the ER-resident NOX4 as key elements of ROS activity in healthy myeloid cells and myeloid leukemia. Targeting ROS presents an attractive therapeutic strategy for (MLs) patients, but the boundaries between pro-apoptotic and anti-apoptotic ROS concentrations are not well established. Detailed understanding of the signaling switches that determine cell fate needs to be well understood. This work explores several aspects of the redox system and thiol-mediated reactions with focus on kinase signaling in myeloid cancers and highlights some of the challenges.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"164 ","pages":"1-68"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0