Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Peripheral T-cell lymphomas (PTCLs), a rare and heterogeneous group of non-Hodgkin lymphomas, have a dismal prognosis.¹ The main subtypes are PTCL not otherwise specified, angioimmunoblastic T-cell lymphoma, anaplastic large-cell lymphoma, and natural killer/T-cell lymphoma and are typically treated with conventional regimens for aggressive B-cell lymphomas, resulting in poor clinical outcomes.² Furthermore, frequent relapses and initially refractory diseases are not uncommon in PTCL, making it more challenging. A study³ reported poor survival outcomes for 153 patients with refractory or relapsed PTCL (R/R-PTCL) receiving chemotherapy without hematopoietic transplantation, with a median overall survival (OS) of 13.7 months and progression-free survival (PFS) of 5 months. So far, because of the multitudinous morphologic features of the subtypes and the lack of randomized clinical trials, treatment of this disease remains a challenge, especially for R/R-PTCL.⁴

The survival rate for pediatric cancers has remarkably improved in recent years. Conventional chemotherapy plays a crucial role in treating pediatric cancers, especially in low- and middle-income countries where access to advanced treatments may be limited. The Food and Drug Administration (FDA) approved chemotherapy drugs that can be used in children have expanded, but patients still face numerous side effects from the treatment. In addition, multidrug resistance (MDR) continues to pose a major challenge in improving the survival rates for a significant number of patients. This review focuses on the severe side effects of pediatric chemotherapy, including doxorubicin-induced cardiotoxicity (DIC) and vincristine-induced peripheral neuropathy (VIPN). We also delve into the mechanisms of MDR in chemotherapy to the improve survival and reduce the toxicity of treatment. Additionally, the review focuses on various drug transporters found in common types of pediatric tumors, which could offer different therapeutic options.

The FDA granted orphan drug designation to darovasertib, a first-in-class oral, small molecular inhibitor of protein kinase C (PKC), for the treatment of uveal melanoma, on 2 May 2022. Primary uveal melanoma has a high risk of progressing to metastatic uveal melanoma, with a poor prognosis. The activation of the PKC and mitogen-activated protein kinase pathways play an essential role in the pathogenesis of uveal melanoma, and mutations in the G protein subunit alpha q (GNAQ), and G protein subunit alpha11 (GNA11) genes are considered early events in the development of uveal melanoma. Compared to other PKC inhibitors, such as sotrastaurin and enzastaurin, darovasertib is significantly more potent in inhibiting conventional (α, β) and novel (δ, ϵ, η, θ) PKC proteins and has a better tolerability and safety profile. Current Phase I/II clinical trials indicated that darovasertib, combined with the Mitogen-activated protein kinase/Extracellular (MEK) inhibitors, binimetinib or crizotinib, produced a synergistic effect of uveal melanoma. In this article, we summarize the development of drugs for treating uveal melanomas and discuss problems associated with current treatments. We also discuss the mechanism of action, pharmacokinetic profile, adverse effects, and clinical trial for darovasertib, and future research directions for treating uveal melanoma.

Background: Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more “AI + medical” application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice.Objective: This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment.Methods: Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords “artificial intelligence” and “hematological diseases.” We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field.Results: AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical–industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases.Conclusion: Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.

Background: T cell lymphoma is a complex and highly aggressive clinicopathological entity with a poor outcome. The angioimmunoblastic T-cell lymphoma (AITL) tumor immune microenvironment is poorly investigated. Methods: Here, to the best of our knowledge, spatial transcriptomics was applied for the first time to study AITL. Results: Using this method, we observed that AITL was surrounded by cells bearing immune-suppressive markers. CCL17 and CCL22, the dominant ligands for CCR4, were up-regulated, while the expression of natural killer (NK) cell and CD8+ cytotoxic T lymphocyte (CTL) markers decreased. Colocalization of Treg cells with the CD4+ TFH-GC region was also deduced from the bioinformatic analysis. The results obtained with spatial transcriptomics confirm that AITL has a suppressive immune environment. Chemotherapy based on the CHOP regimen (cyclophosphamide, doxorubicin, vincristine plus prednisone) induced complete remission (CR) in this AITL patient. However, the duration of remission (DoR) remains a concern. Conclusions: This study demonstrates that AITL has an immune suppressive environment and suggests that anti-CCR4 therapy could be a promising treatment for this lethal disease.