Immune pathogenesis of idiopathic granulomatous mastitis: from etiology toward therapeutic approaches.

Idiopathic granulomatous mastitis (IGM) is a noncancerous, chronic inflammatory disorder of breast with unknown causes, posing significant challenges to the quality of life due to its high refractoriness and local aggressiveness. The typical symptoms of this disease involve skin redness, a firm and tender breast mass and mastalgia; others may include swelling, fistula, abscess (often without fever), nipple retraction, and peau d'orange appearance. IGM often mimics breast abscesses or malignancies, particularly inflammatory breast cancer, and is characterized by absent standardized treatment options, inconsistent patient response and unknown mechanism. Definite diagnosis of this disease relies on core needle biopsy and histopathological examination. The prevailing etiological theory suggests that IGM is an autoimmune disease, as some patients respond well to steroid treatment. Additionally, the presence of concurrent erythema nodosum or other autoimmune conditions supports the autoimmune nature of the disease. Based on current knowledge, this review aims to elucidate the autoimmune-favored features of IGM and explore its potential etiologies. Furthermore, we discuss the immune-mediated pathogenesis of IGM using existing research and propose immunotherapeutic strategies for managing this condition.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Overview of Artificial Intelligence in Breast Cancer Medical Imaging.

The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer.

Immune checkpoint blockade (ICB) therapy has evoked a prominent shift in anticancer therapy. Durable clinical antitumor activity to ICB has been observed in patients with ovarian cancer (OC). However, only a subset of patients derive clinical benefit, and immune-related adverse events (irAEs) caused by ICB therapy can lead to permanent tissue damage and even fatal consequences. It is thus urgent to develop predictive biomarkers to optimize patient outcomes and minimize toxicity risk. Herein, we review current predictive and prognostic biomarkers for checkpoint immunotherapy in OC and highlight emerging biomarkers to guide treatment with ICB. The prevalent biomarkers, such as PD-L1 expression status, tumor-infiltrating lymphocytes, mutational burden, and immune gene signatures, are further discussed. We provide a state-of-the-art survey on prognostic and predictive biomarkers for checkpoint immunotherapy and offer valuable information for guiding precision immunotherapy.

Tissue-specific transcriptomic analysis uncovers potential roles of natural antisense transcripts in Arabidopsis heat stress response.

Natural antisense transcripts (NATs) are an important class of non-coding ribonucleic acids (RNAs) that have been shown to regulate gene expression. Using strand-specific RNA sequencing, 36,317 NAT pairs were identified, and 5,536 were specifically expressed under heat stress. We found distinct expression patterns between vegetative and reproductive tissues for both coding genes and genes encoding NATs. Genes for heat-responsive NATs are associated with relatively high levels of H3K4me3 and low levels of H3K27me2/3. On the other hand, small RNAs are significantly enriched in sequence overlapping regions of NAT pairs, and a large number of heat-responsive NATs pairs serve as potential precursors of nat-siRNAs. Collectively, our results suggest epigenetic modifications and small RNAs play important roles in the regulation of NAT expression, and highlight the potential significance of heat-inducible NATs.

Combinatorial Strategies With PD-1/PD-L1 Immune Checkpoint Blockade for Breast Cancer Therapy: Mechanisms and Clinical Outcomes.

As an emerging antitumor strategy, immune checkpoint therapy is one of the most promising anticancer therapies due to its long response duration. Antibodies against the programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) axis have been extensively applied to various cancers and have demonstrated unprecedented efficacy. Nevertheless, a poor response to monotherapy with anti-PD-1/PD-L1 has been observed in metastatic breast cancer. Combination therapy with other standard treatments is expected to overcome this limitation of PD-1/PD-L1 blockade in the treatment of breast cancer. In the present review, we first illustrate the biological functions of PD-1/PD-L1 and their role in maintaining immune homeostasis as well as protecting against immune-mediated tissue damage in a variety of microenvironments. Several combination therapy strategies for the combination of PD-1/PD-L1 blockade with standard treatment modalities have been proposed to solve the limitations of anti-PD-1/PD-L1 treatment, including chemotherapy, radiotherapy, targeted therapy, antiangiogenic therapy, and other immunotherapies. The corresponding clinical trials provide valuable estimates of treatment effects. Notably, several combination options significantly improve the response and efficacy of PD-1/PD-L1 blockade. This review provides a PD-1/PD-L1 clinical trial landscape survey in breast cancer to guide the development of more effective and less toxic combination therapies.

Single-Cell Metabolomics in Hematopoiesis and Hematological Malignancies.

Aberrant metabolism contributes to tumor initiation, progression, metastasis, and drug resistance. Metabolic dysregulation has emerged as a hallmark of several hematologic malignancies. Decoding the molecular mechanism underlying metabolic rewiring in hematological malignancies would provide promising avenues for novel therapeutic interventions. Single-cell metabolic analysis can directly offer a meaningful readout of the cellular phenotype, allowing us to comprehensively dissect cellular states and access biological information unobtainable from bulk analysis. In this review, we first highlight the unique metabolic properties of hematologic malignancies and underscore potential metabolic vulnerabilities. We then emphasize the emerging single-cell metabolomics techniques, aiming to provide a guide to interrogating metabolism at single-cell resolution. Furthermore, we summarize recent studies demonstrating the power of single-cell metabolomics to uncover the roles of metabolic rewiring in tumor biology, cellular heterogeneity, immunometabolism, and therapeutic resistance. Meanwhile, we describe a practical view of the potential applications of single-cell metabolomics in hematopoiesis and hematological malignancies. Finally, we present the challenges and perspectives of single-cell metabolomics development.

Single-cell transcriptomic profiling unravels the adenoma-initiation role of protein tyrosine kinases during colorectal tumorigenesis.

The adenoma-carcinoma sequence is a well-accepted roadmap for the development of sporadic colorectal cancer. However, cellular heterogeneity in aberrant epithelial cells limits our understanding of carcinogenesis in colorectal tissues. Here, we performed a single-cell RNA sequencing survey of 54,788 cells from patient-matched tissue samples, including blood, normal tissue, para-cancer, polyp, and colorectal cancer. At each stage of carcinogenesis, we characterized cell types, transcriptional signatures, and differentially expressed genes of distinct cell populations. The molecular signatures of epithelial cells at normal, benign, and malignant stages were defined at the single-cell scale. Adenoma and carcinoma precursor cell populations were identified and characterized followed by validation with large cohort biopsies. Protein tyrosine kinases (PTKs) BMX and HCK were identified as potential drivers of adenoma initiation. Specific BMX and HCK upregulations were observed in adenoma precursor cell populations from normal and adenoma biopsies. Overexpression of BMX and HCK significantly promoted colorectal epithelial cell proliferation. Importantly, in the organoid culture system, BMX and HCK upregulations resulted in the formation of multilayered polyp-like buds protruding towards the organoid lumen, mimicking the pathological polyp morphology often observed in colorectal cancer. Molecular mechanism analysis revealed that upregulation of BMX or HCK activated the JAK-STAT pathway. In conclusion, our work improved the current knowledge regarding colorectal epithelial evolution during carcinogenesis at the single-cell resolution. These findings may lead to improvements in colorectal cancer diagnosis and treatment.

The Evolution of Acquired Resistance to BRAFV600E kinase inhibitor Is Sustained by IGF1-Driven Tumor Vascular Remodeling.

As a hallmark of cancer, angiogenesis plays a pivotal role in carcinogenesis. However, the correlation between angiogenesis and the evolution of BRAFV600E kinase inhibitor‒acquired resistance is still poorly understood. In this study, we reported that the molecular signatures of angiogenesis were enriched in early on-treated biopsies but not in disease-progressed biopsies. The process of drug resistance development was accompanied by the remodeling of vascular morphology, which was potentially manipulated by tumor-secreted proangiogenic factors. Further transcriptomic dissection indicated that tumor-secreted IGF1 drove the vascular remodeling by activating the IGF1/IGF1R axis on endothelial cells and sustained the prompt regrowth of resistant tumor. Blockade of IGF1R with small molecules at an early stage of response disrupted vascular reconstruction and subsequently delayed tumor relapse. Our findings not only showed the correlation between IGF1-mediated tumor vascular remodeling and the development of acquired resistance to BRAFV600E kinase inhibitor but also provided a potential therapeutic strategy for the prevention of tumor relapse in clinical application.

Microbiota and their metabolites potentiate cancer immunotherapy: Therapeutic target or resource for small molecule drug discovery?

Increasing evidence has proved that microbiota is not only the target of small molecule drugs but also an underexplored resource for developing small molecule drugs. Meanwhile, microbiota as a critical modulator of the immune system impacts the efficacy and toxicity of cancer immunotherapy. Harnessing microbiota or developing microbiota-derived medications provide novel therapeutic strategies to overcome resistance to cancer immunotherapy and immune-related adverse events (irAEs). In this review, we elucidate how microbiota and their metabolites impact anti-tumor immunity and immunotherapy efficacy and highlight the potential of microbiota and their metabolites as a resource for small molecule drug discovery. We further overview the current landscape of clinical trials evaluating the potential effect of microbiota and their metabolites on immunotherapy outcomes, presenting future trends in the field of microbiota-based therapies. Microbiota-based therapies are promising therapeutic options to promote therapeutic efficacy and diminish the toxicity of immunotherapy.

Application of artificial intelligence to the public health education.

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Pan-Cancer Analysis Reveals Alternative Splicing Characteristics Associated With Immune-Related Adverse Events Elicited by Checkpoint Immunotherapy.

Immune-related adverse events (irAEs) can impair the effectiveness and safety of immune checkpoint inhibitors (ICIs) and restrict the clinical applications of ICIs in oncology. The predictive biomarkers of irAE are urgently required for early diagnosis and subsequent management. The exact mechanism underlying irAEs remains to be fully elucidated, and the availability of predictive biomarkers is limited. Herein, we performed data mining by combining pharmacovigilance data and pan-cancer transcriptomic information to illustrate the relationships between alternative splicing characteristics and irAE risk of ICIs. Four distinct classes of splicing characteristics considered were associated with splicing factors, neoantigens, splicing isoforms, and splicing levels. Correlation analysis confirmed that expression levels of splicing factors were predictive of irAE risk. Adding DHX16 expression to the bivariate PD-L1 protein expression-fPD1 model markedly enhanced the prediction for irAE. Furthermore, we identified 668 and 1,131 potential predictors based on the correlation of the incidence of irAEs with splicing frequency and isoform expression, respectively. The functional analysis revealed that alternative splicing might contribute to irAE pathogenesis via coordinating innate and adaptive immunity. Remarkably, autoimmune-related genes and autoantigens were preferentially over-represented in these predictors for irAE, suggesting a close link between autoimmunity and irAE occurrence. In addition, we established a trivariate model composed of CDC42EP3-206, TMEM138-211, and IRX3-202, that could better predict the risk of irAE across various cancer types, indicating a potential application as promising biomarkers for irAE. Our study not only highlights the clinical relevance of alternative splicing for irAE development during checkpoint immunotherapy but also sheds new light on the mechanisms underlying irAEs.

Comprehensive analysis of aberrant alternative splicing related to carcinogenesis and prognosis of papillary thyroid cancer.

As a key mechanism, alternative splicing (AS) plays a role in the cancer initiation and development. However, in papillary thyroid cancer (PTC), data for the comprehensive AS event profile and its clinical implications are lacking. Herein, a genome-wide AS event profiling using RNA-Seq data and its correlation with matched clinical information was performed using a 389 PTC patient cohort from the project of The Cancer Genome Atlas (TCGA). We identified 1,925 cancer-associated AS events (CASEs) by comparing paired tumors and neighboring healthy tissues. Parent genes with CASEs remarkably enriched in the pathways were linked with carcinogenesis, such as P53, KRAS, IL6-JAK-STAT3, apoptosis, and MYC signaling. The regulatory networks of AS implied an obvious correlation between the expression of splicing factor and CASE. We identified eight CASEs as predictors for overall survival (OS) and disease-free survival (DFS). The established risk score model based on DFS-associated CASEs successfully predicted the prognosis of PTC patients. From the unsupervised clustering analysis results, it is found that different clusters based on AS correlated with prognosis, molecular features, and immune characteristics. Taken together, the comprehensive genome-wide AS landscape analysis in PTC showed new AS events linked with tumorigenesis and prognosis, which provide new insights for clinical monitoring and therapy for PTC.

Genome assembly of the Chinese maize elite inbred line RP125 and its EMS mutant collection provide new resources for maize genetics research and crop improvement.

Maize is an important crop worldwide, as well as a valuable model with vast genetic diversity. Accurate genome and annotation information for a wide range of inbred lines would provide valuable resources for crop improvement and pan-genome characterization. In this study, we generated a high-quality de novo genome assembly (contig N50 of 15.43 Mb) of the Chinese elite inbred line RP125 using Nanopore long-read sequencing and Hi-C scaffolding, which yield highly contiguous, chromosome-length scaffolds. Global comparison of the RP125 genome with those of B73, W22, and Mo17 revealed a large number of structural variations. To create new germplasm for maize research and crop improvement, we carried out an EMS mutagenesis screen on RP125. In total, we obtained 5818 independent M2 families, with 946 mutants showing heritable phenotypes. Taking advantage of the high-quality RP125 genome, we successfully cloned 10 mutants from the EMS library, including the novel kernel mutant qk1 (quekou: "missing a small part" in Chinese), which exhibited partial loss of endosperm and a starch accumulation defect. QK1 encodes a predicted metal tolerance protein, which is specifically required for Fe transport. Increased accumulation of Fe and reactive oxygen species as well as ferroptosis-like cell death were detected in qk1 endosperm. Our study provides the community with a high-quality genome sequence and a large collection of mutant germplasm.

Management of Adverse Events in Cancer Patients Treated With PD-1/PD-L1 Blockade: Focus on Asian Populations.

The interaction between programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) induces exhaustions of cytotoxic lymphocytes in the tumor microenvironment, which facilitates tumor immune evasion. PD-1/PD-L1 blockade therapy, which prevents the receptors and ligands from binding to each other, disrupts the T-cell exhaustion signaling, thereby increasing antitumor immunity. Inspiringly, it has revolutionized the treatment of many different types of cancers including non-small-cell lung carcinoma, melanoma, lymphoma, and so on. However, with the intention of generating an antitumor immune response, PD-1/PD-L1 blockade may also lead to a spectrum of side effects. The profile of adverse events (AEs) of PD-1/PD-L1 blockade is not exactly the same with other immune checkpoint blockades, such as blockade of cytotoxic T-lymphocyte-associated protein 4. Although cutaneous, gastrointestinal, and pulmonary systems are common victims, AEs of PD-1/PD-L1 blockade might occur in any other organ system of the human body. These toxicities can be life-threatening if not managed promptly, and proper treatment intervention is imperative for optimal control and prevention of severe damage. Currently, clinical practice for the management of AEs in PD-1/PD-L1 blockade remains sporadic and variable. The majority of initial clinical trials were carried out in Caucasians. The trials of multiple races usually included a small portion of Asian participants, and results were calculated and interpreted for the entire included subjects without any race-specific conclusions. Therefore, the information on PD-1/PD-L1 blockade in Asians is far from systematic or comprehensive. Recently, as the results of clinical trials of anti-PD-1/PD-L1 agents in Asian populations have been gradually released, we summarized current evidence with a specific focus on the Asian population, hoping to outline strategies and offer guidance on the management of AEs in cancer patients treated with PD-1/PD-L1 blockade in the Asian world.