Cancer Biology & Medicine最新文献

{"title":"Non-coding RNA delivery <i>via</i> lipid nanoparticles: a novel strategy for cancer immunotherapy.","authors":"Yufei Sheng, Lulu Yang, Boyang Wang, Wentao Hu, Chengwei Zhou, Zhaohui Gong","doi":"10.20892/j.issn.2095-3941.2025.0825","DOIUrl":"https://doi.org/10.20892/j.issn.2095-3941.2025.0825","url":null,"abstract":"<p><p>Non-coding RNAs (ncRNAs) regulate antitumor immunity but clinical use is hindered by instability, poor specificity, and immunogenic risk. Lipid nanoparticles (LNPs) offer a promising delivery solution due to biocompatibility, a modifiable surface, and efficient ncRNA encapsulation. Preclinical and early clinical studies have demonstrated the efficacy of LNP-ncRNA therapies in various cancers, including respiratory, digestive, and neurologic malignancies, and in cancer immunotherapy. This review evaluates engineered LNPs administered via different routes, explains the mechanisms that promote antitumor effects, and highlights advances in overcoming delivery barriers to enhance immunotherapy. Current limitations of engineered LNPs are also analyzed, essential translational challenges are identified, and future directions to facilitate the clinical application of LNP-based ncRNA therapies are suggested.</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":" ","pages":""},"PeriodicalIF":8.4,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal artificial intelligence predicts PIK3CA mutation in breast cancer from digital pathology and clinical data: a multicenter study.","authors":"Jiaxian Miao, Qi Liu, Jianing Zhao, Shishun Fan, Shenwen Wang, Feng Ye, Si Wu, Jinze Li, Huirui Zhang, Meng Zhang, Hong Bu, Xiao Han, Lianghong Teng, Yueping Liu","doi":"10.20892/j.issn.2095-3941.2025.0771","DOIUrl":"10.20892/j.issn.2095-3941.2025.0771","url":null,"abstract":"<p><strong>Objective: </strong>Accurate detection of <i>PIK3CA</i> mutations is essential for guiding PI3K-targeted therapies in breast cancer, yet sequencing is not universally accessible, and single-modality prediction models have limited performance. This study developed a multimodal deep learning framework integrating whole-slide imaging (WSI) and structured clinical data to improve mutation prediction.</p><p><strong>Methods: </strong>A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included. The histopathology model used a transformer-based pretrained encoder (H-optimus-0) and a clustering-constrained attention multiple instance learning (CLAM-SB MIL) classifier to generate WSI-level representations. The clinical model incorporated engineered clinical variables and an extreme gradient boosting (XGBoost) model. A decision-level late fusion strategy (Multimodal <i>PIK3CA</i> Model, MPM) combined probabilistic outputs from both branches. Performance was evaluated with the area under the curve (AUC) and secondary metrics. Interpretability was assessed <i>via</i> attention heatmaps and shapley additive explanations (SHAP) analysis.</p><p><strong>Results: </strong>MPM outperformed single-modality models. It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts (0.695, 0.690, and 0.680). SHAP analysis identified molecular subtype as the most influential clinical feature, whereas attention maps highlighted mutation-associated morphological regions.</p><p><strong>Conclusions: </strong>The developed multimodal framework effectively integrates complementary morphological and clinical information, and provides a robust and generalizable method for predicting <i>PIK3CA</i> mutation status. Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing.</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147669707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in TROP2-targeted antibody-drug conjugates for breast cancer therapy: into the new era.","authors":"Kuikui Jiang, Shusen Wang","doi":"10.20892/j.issn.2095-3941.2025.0504","DOIUrl":"10.20892/j.issn.2095-3941.2025.0504","url":null,"abstract":"","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol and statistical designs in classic clinical research-toripalimab series trial analysis.","authors":"Yingjian He, Li Bian, Zefei Jiang","doi":"10.20892/j.issn.2095-3941.2025.0759","DOIUrl":"10.20892/j.issn.2095-3941.2025.0759","url":null,"abstract":"","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing global standards and regional nuances in breast cancer care: the role of guidelines, clinical research, precision medicine, and artificial intelligence in advancing quality of care for patients worldwide.","authors":"Michael Gnant","doi":"10.20892/j.issn.2095-3941.2025.0674","DOIUrl":"10.20892/j.issn.2095-3941.2025.0674","url":null,"abstract":"","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in breast cancer: applications and advancements.","authors":"Jianbin Li, Zefei Jiang","doi":"10.20892/j.issn.2095-3941.2025.0704","DOIUrl":"10.20892/j.issn.2095-3941.2025.0704","url":null,"abstract":"<p><p>Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes, such as subjective interpretation biases and inefficient multi-dimensional data integration. Artificial intelligence (AI), particularly deep learning and machine learning technologies, has emerged as a transformative tool in addressing these issues. Clinically, AI has been widely applied in imaging screening to improve detection rates and reduce reading time, digital pathology for precise tumor typing and gene mutation prediction, treatment decision-support systems to enhance guideline compliance, and drug research and development to accelerate target identification and virtual screening. Despite these achievements, AI implementation faces challenges, such as data standardization issues, limited model generalization, low clinical accessibility, and unclear ethical-legal responsibilities, which require targeted solutions that include national data standards, multi-center training, hierarchical physician training, and explainable AI. Future directions involve multi-modal data integration, human-AI collaborative multidisciplinary team models, and extension to full-cycle health management from prevention-to-rehabilitation. This review provides a systematic overview of the role of AI in breast cancer care, offering insights for clinical practice and scientific research innovation, and supporting the transition toward personalized and intelligent medicine in oncology.</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.","authors":"Yilin Chen, Ye Qin, Man Yang, Wei Li, Minyi Cheng, Yuhong Huang, Teng Zhu, Kun Wang","doi":"10.20892/j.issn.2095-3941.2025.0655","DOIUrl":"10.20892/j.issn.2095-3941.2025.0655","url":null,"abstract":"<p><p>Breast cancer exhibits profound biological and spatial heterogeneity, which contributes to variable responses to neoadjuvant chemotherapy (NAC) and challenges precision treatment planning. Radiomics, an emerging discipline that converts standard medical images into high-dimensional quantitative data, offers a non-invasive and reproducible means to capture tumor phenotype, heterogeneity, and treatment-induced changes. This review provides a comprehensive overview of recent advances in radiomics for breast cancer NAC, emphasizing the roles in predicting a pathologic complete response (pCR), monitoring early therapeutic efficacy, and quantifying intratumoral heterogeneity. Among imaging modalities, magnetic resonance imaging (MRI)-based radiomics, particularly utilizing dynamic contrast-enhanced and diffusion-weighted sequences, demonstrates robust predictive performance for the pCR, with multi-center studies reporting area under the curve (AUC) values >0.80. Longitudinal and delta-radiomics approaches further enhance early response evaluation by tracking temporal alterations in imaging features that precede measurable morphologic regression. Radiomic assessment of tumor heterogeneity, especially in triple-negative breast cancer (TNBC), reveals strong associations with immune infiltration, metabolic reprogramming, and therapeutic resistance, providing mechanistic insight into radiomic biomarkers. Integrative multi-omics frameworks, combining radiomics with genomics, transcriptomics and pathomics, are increasingly elucidating the biological underpinnings of imaging phenotypes, improving both model interpretability and clinical relevance. Despite these advances, widespread clinical adoption of radiomics is limited by methodologic variability, lack of standardization, and insufficient external validation. Future efforts should focus on harmonized imaging protocols, explainable artificial intelligence, and prospective multi-center trials to translate radiomics into a clinically actionable tool. Collectively, radiomics represents a transformative approach for individualized response prediction and dynamic treatment optimization in precision breast cancer management (<b>Figure 1</b>).</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence empowering precision diagnosis and treatment of breast cancer: advancing global clinical practice with regional insights.","authors":"Zefei Jiang","doi":"10.20892/j.issn.2095-3941.2026.0018","DOIUrl":"10.20892/j.issn.2095-3941.2026.0018","url":null,"abstract":"","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision immunotherapy for breast cancer: from biomarkers to clinical practice.","authors":"Jie Mei, Kai Yang, Xinkang Zhang, Xiang Huang, Yongmei Yin","doi":"10.20892/j.issn.2095-3941.2025.0815","DOIUrl":"10.20892/j.issn.2095-3941.2025.0815","url":null,"abstract":"","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative multi-omic analysis identified <i>ERBB2</i> mutations and senescence-driven immune suppression as dual therapeutic targets in LAR triple-negative breast cancer.","authors":"Yaxin Zhao, Han Wang, Ying Wang, Yizhou Jiang, Xin Hu, Zhiming Shao","doi":"10.20892/j.issn.2095-3941.2025.0691","DOIUrl":"10.20892/j.issn.2095-3941.2025.0691","url":null,"abstract":"<p><strong>Objective: </strong>The luminal androgen receptor (LAR) subtype of triple-negative breast cancer (TNBC) differentiation displays low proliferation yet strong metastatic potential and a poor chemotherapy response. This study aimed to define the molecular basis of the LAR subtype and identify actionable therapeutic targets.</p><p><strong>Methods: </strong>Comprehensive multi-omic analyses were performed on the FUSCC-TNBC cohort, integrating whole-exome sequencing, RNA sequencing, and functional validation <i>in vitro</i> and <i>in vivo</i>. Somatic mutation profiling, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) were used to define genomic and transcriptomic signatures. A machine learning model using the Mime1 package was applied to derive a senescence-associated prognostic signature (LAR-S) and validation in external cohorts. Immune deconvolution was performed to decipher the tumor microenvironment. Functional assays, patient-derived organoids (PDOs), and TS/V mouse models were used to evaluate therapeutic responses to senescence-modulating agent and immunotherapy combinations.</p><p><strong>Results: </strong>The LAR subtype was enriched for <i>PIK3CA</i>, <i>PTEN</i>, and <i>ERBB2</i> kinase domain mutations. Functional studies confirmed <i>ERBB2</i> variants (e.g., V777L and E698_P699delinsA) as oncogenic drivers conferring sensitivity to neratinib. Transcriptomic analyses revealed a dominant cellular senescence program associated with immune suppression. The LAR-S signature stratified survival across cohorts and predicted immunotherapy resistance. Targeting cellular senescence inhibited LAR subtype organoid growth and when combined with anti-PD-1 therapy synergistically suppressed tumor growth <i>in vivo</i>.</p><p><strong>Conclusions: </strong>The LAR subtype harbors two therapeutic vulnerabilities: <i>ERBB2</i> mutation-driven kinase activation; and senescence-mediated immune evasion. The LAR-S signature enables precise patient stratification and supports senescence-targeted and immunotherapy combination strategies as promising approaches for this refractory TNBC subtype.</p>","PeriodicalId":9611,"journal":{"name":"Cancer Biology & Medicine","volume":"23 3","pages":""},"PeriodicalIF":12.4,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13059881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}