Advances in cancer research最新文献

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.

Capsaicin is the hot and pungent ingredient of chili peppers. It is a potent pain-relieving agent and is often present in over-the-counter analgesic lotions and creams. Several convergent studies reveal that capsaicin displays growth-suppressive activity in human cancers in vitro and in vivo. Apart from its growth-suppressive activity (as a single agent), capsaicin has been found to sensitize human cancer cells to the pro-apoptotic effects of chemotherapy and radiation. The first part of this book chapter discusses the anti-cancer activity of capsaicin in gynecological cancers in cell culture experiments and mouse models. Out of all gynecological cancers, the anti-cancer activity of capsaicin (and its analogs) has only been investigated in cervical cancers and ovarian cancers. The clinical development of capsaicin as a viable anti-cancer drug has remained challenging due to its poor bioavailability and aqueous solubility properties. In addition, the administration of capsaicin is associated with adverse side effects like gastrointestinal cramps, stomach pain, irritation in the gut, nausea diarrhea and vomiting. Two strategies have been investigated to overcome these drawbacks of capsaicin. The first is to encapsulate capsaicin in sustained release drug delivery systems. The second strategy is to design non-pungent capsaicin analogs which will retain the anti-tumor activity of capsaicin. The second part of this chapter provides an overview of the anti-neoplastic (and chemosensitization activity) of capsaicin analogs and capsaicin-based sustained release formulations in cervical and ovarian cancers. The design of selective non-pungent capsaicin analogs and capsaicin-based polymeric drug delivery systems may foster the hope of novel strategies for the treatment and management of gynecological cancers.

Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

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.

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.

It has been well established that in addition to oxygen's vital in cellular respiration, a disruption of oxygen balance can lead to increased stress and oxidative injury. Similarly, reduced oxygen during tumor proliferation and invasion generates a hypoxic tumor microenvironment, resulting in dysfunction of immune cells and providing a conducive milieu for tumors to adapt and grow. Strategies to improve the persistence tumor reactive T cells in the highly oxidative tumor environment are being pursued for enhancing immunotherapy outcomes. To this end, we have focused on various strategies that can help increase or maintain the antioxidant capacity of T cells, thus reducing their susceptibility to oxidative stress/damage. Herein we lay out an overview on the role of oxygen in T cell signaling and how pathways regulating oxidative stress or antioxidant signaling can be targeted to enhance immunotherapeutic approaches for cancer treatment.

Prostate cancer (PC) has a high propensity to develop bone metastases, causing severe pain and pathological fractures that profoundly impact a patients' normal functions. Current clinical intervention is mainly palliative focused on pain management, and tumor progression is refractory to standard therapeutic regimens. This limited treatment efficacy is at least partially due to a lack of comprehensive understanding of the molecular landscape of the disease pathology, along with the intensive overlapping of physiological and pathological molecular signaling. The niche is overwhelmed with diverse cell types with inter- and intra-heterogeneity, along with growth factor-enriched cells that are supportive of invading cell proliferation, providing an additional layer of complexity. This review seeks to provide molecular insights into mechanisms underlying PC bone metastasis development and progression.

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.