Advances in cancer research最新文献_第3页

Advances in prostate cancer treatment: Radionuclide therapy for prostate cancer. 前列腺癌治疗的进展：前列腺癌的放射性核素治疗。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-08-05 DOI: 10.1016/bs.acr.2024.07.004

Jeffrey Zhong, Albert Jang, Jorge Garcia, Norbert Avril, Qiubai Li, Patrick Wojtylak, Neal Shore, Scott Tagawa, Pedro Barata

{"title":"Advances in prostate cancer treatment: Radionuclide therapy for prostate cancer.","authors":"Jeffrey Zhong, Albert Jang, Jorge Garcia, Norbert Avril, Qiubai Li, Patrick Wojtylak, Neal Shore, Scott Tagawa, Pedro Barata","doi":"10.1016/bs.acr.2024.07.004","DOIUrl":"https://doi.org/10.1016/bs.acr.2024.07.004","url":null,"abstract":"The optimal treatment of metastatic castration-resistant prostate cancer (mCRPC) continues to be challenging, given the multitude of life prolonging treatment options. Radionuclide therapy delivers concentrated doses of radiation via ionizing particles chelated to ligands or antibody-based molecules with specific tumor targets and is approved for patients with treatment resistant mCRPC. Variations of radionuclide therapies within the continuum of prostate cancer treatment are being investigated. Landmark phase III clinical trials of beta-emitting 177Lu-PSMA radionuclide therapy have demonstrated the utility of 177Lu-PSMA in the treatment of mCRPC. Further research into alpha-emitting radionuclide therapy and vectors may provide alternative treatments for patients with treatment resistant mCRPC. As radionuclide therapy treatment options evolve, assessing appropriate patient selection for radionuclide therapy is important and may be facilitated by advances in imaging and blood-based biomarkers. Exploration of other approved life prolonging therapies in combination with radionuclide therapy has shown increasing interest as a potential method of combatting radionuclide therapy resistance. In this chapter, we review various types of radionuclide therapies for mCRPC, patient selection for radionuclide therapy from outcome predictions, ongoing clinical trials of radiopharmaceuticals for treatment of prostate cancer, and the resistance mechanisms and challenges to radionuclide therapy.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305476","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

Deep learning-based multimodal spatial transcriptomics analysis for cancer. 基于深度学习的癌症多模态空间转录组学分析

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-08-22 DOI: 10.1016/bs.acr.2024.08.001

Pankaj Rajdeo, Bruce Aronow, V B Surya Prasath

{"title":"Deep learning-based multimodal spatial transcriptomics analysis for cancer.","authors":"Pankaj Rajdeo, Bruce Aronow, V B Surya Prasath","doi":"10.1016/bs.acr.2024.08.001","DOIUrl":"10.1016/bs.acr.2024.08.001","url":null,"abstract":"The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11431148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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

Preface. 序言

Advances in cancer research Pub Date : 2024-01-01 DOI: 10.1016/S0065-230X(24)00079-4

Esha Madan, Paul B Fisher, Rajan Gogna

引用次数: 0

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":null,"pages":null},"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":null,"pages":null},"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

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":null,"pages":null},"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