Clinical and translational discovery最新文献

Tumour heterogeneity, encompassing genetic, epigenetic, and microenvironmental diversity, remains a fundamental obstacle in precision oncology. Traditional bulk sequencing captures only averaged molecular profiles, thereby masking rare yet functionally critical subpopulations that drive malignant progression and therapeutic resistance. Recently, the emergence of single-cell sequencing technologies has overcome the limitations of bulk approaches, enabling high-resolution analyses of the genome, transcriptome, epigenome and proteome at the single-cell level. These advances have enabled detailed mapping of tumour ecosystems, identification of key cellular subtypes, reconstruction of evolutionary trajectories and elucidation of intercellular communication networks within the tumour microenvironment. Accumulating evidence demonstrates that single-cell technologies elucidate fundamental aspects of tumour biology and reveal potential diagnostic and therapeutic targets. This review systematically summarises the recent advances and applications of single-cell sequencing in the field of precision oncology, with particular emphasis on its applications in mechanistic discovery, diagnosis, therapy, and prognosis. Furthermore, we discuss current challenges related to technology, data analysis, and clinical translation, and outline future research directions. In summary, single-cell sequencing has profoundly reshaped our understanding of tumour biology and is propelling oncology into a new era of precision, prediction, and personalisation.

Breast cancer heterogeneity is still a primary concern, with a large variation in the prognosis necessitating the development of personalised treatment plans. Single biomarkers can't fully encompass the breast cancer complexity and variations, and therefore, multi-omics approaches offer a wide comprehension of the cancer-specific biology. In this study, we developed a multi-omics framework for the prediction of immune-active features framework integrating four-omics data, mainly genomic, proteomic and transcriptomic. By leveraging deep learning along with survival-based feature selection, we constructed an autoencoder that generated compressed multi-omics features to stratify the patients into two optimal immune subtypes with a significantly different overall survival (p < 0.002) and a high C-index of 0.74 (95% confidence interval: 0.62–0.83). An XGBoost classifier was trained to predict these subtypes by integrating all omics data as well as each omic individually, and was validated using both internal and external (the Cancer Genome Atlas and Gene Expression Omnibus) datasets. The integrated model achieved high predictive performance (ACC = 0.95). Omics-unique classifiers showed consistently strong validation on independent datasets, particularly the immunotherapy-treated cohort (GSE241876, p = 4.80×10⁻²). We further investigated the biological mechanisms across the clusters and discovered that the C2, low-risk cluster, exhibited an immune-active landscape, characterised by a high infiltration of cells and more immune-related pathways, making it a better candidate for a favourable immunotherapy response. On the other hand, the C1 cluster, the immune-cold group, displayed an immunosuppressive microenvironment and poor prognosis. This methodology demonstrated the promising potential of deep learning-driven multi-omics integration to support precision oncology by enhancing prognostic prediction and tailoring treatment decisions.

Computational electrophysiology models are beginning to emerge as digital-twin–oriented representations of cancer cells, offering mechanistic insights that complement traditional patch-clamp experiments. In this study, we evaluate the ability of the earliest in-silico cancer electrophysiology model, an ion channel model based on Hidden Markov state transitions, to reproduce drug-modulated current densities in A549 lung adenocarcinoma cells. Using independent experimental data from Glaser et al. (2021), we characterised Ca²⁺-activated K⁺ channels, KCa1.1 and KCa3.1, in wild-type (WT) and erlotinib-resistant (ER) A549 cells under baseline conditions, as well as after activation with 1-EBIO (3-ethyl-1H-benzimidazol-2-one) and inhibition with paxilline and senicapoc. The in-silico model reproduced the qualitative order of current responses under all pharmacological conditions, quantitatively matching the paxilline- and senicapoc-blocked states while remaining within biologically reasonable channel expression limits. Reproducing 1-EBIO activation required higher-than-physiological effective channel numbers, indicating that ligand-dependent gating is not fully represented. Nevertheless, the model captured the overall electrophysiological behaviour of both WT and ER cells and successfully distinguished their phenotypes. In summary, the in-silico model already enables mechanistic interpretation of electrophysiological phenotypes and drug-modulated responses. With continued refinement, including the incorporation of ligand-modulated gating, improved calcium-feedback dynamics, and formal uncertainty quantification, this model has the potential to evolve into a predictive digital twin platform supporting ion-channel pharmacology, therapy optimisation and precision oncology.

Despite their significant prognostic value in NPC, the clinical deployment of TLSs faces challenges, including structural and spatial heterogeneity, subjective manual assessment, lack of longitudinal data, and incomplete integration into precision immunotherapy paradigms. Future research should focus on the following three areas: (1) Standardised Evaluation Frameworks: Development of AI-assisted digital pathology tools to enable robust, automated TLS quantification across centres. (2) Investigation of dynamic evolution: collecting longitudinal samples (e.g. pre-treatment, on-treatment and post-relapse) to elucidate the spatiotemporal dynamics of TLSs across different disease phases and their relationship with clinical outcomes. (3) Exploration of precision immunotherapy strategies for NPC: investigating combination regimens involving “TLS induction + ICIs” to achieve precision immunotherapy and improve outcomes for patients with immune-desert tumour phenotypes.

TLSs represent critical immunologically active hubs within the TME of NPC. Their presence and maturity are closely associated with NPC prognosis and therapeutic responses to ICIs. The structure and function of TLSs not only facilitate the delineation of NPC immunophenotypes but also provide novel therapeutic targets for patients with immunologically desert tumours, holding considerable clinical translation potential. Future research endeavours could focus on developing standardised assessment systems, elucidating the temporal dynamics of TLSs during tumour treatment, and exploring precision immunotherapeutic strategies such as “TLS induction + ICIs,” so as to further advance the clinical application of TLSs.

Fan Liu, Lei Xiong and Xia Yu drafted the initial version of the manuscript. Mingfang Ji reviewed the manuscript. All authors approved and agreed to publish the final version of the manuscript.

The authors declare no conflict of interest.

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Data sharing is not applicable to this article as no new data were created or analysed in this study.