Advancing Thoracic Surgical Oncology in the Era of Precision Medicine

Cancers, Год журнала: 2025, Номер 17(1), С. 115 - 115

Опубликована: Янв. 2, 2025

The landscape of surgical oncology is rapidly evolving with the advent precision medicine, driven by breakthroughs in genomics and proteomics. This article explores how integrating molecular data transforming decision-making enabling personalized treatment strategies. We examine emerging technologies such as next-generation sequencing, proteomic analysis, imaging, which provide critical insights into tumor biology guide interventions. also highlights application genomic preoperative planning development resection Additionally, we will address current challenges future opportunities this field, emphasizing need for continuous education, interdisciplinary collaboration, ongoing research to fully realize potential medicine thoracic oncology, paving way more effective individualized cancer treatments.

Язык: Английский

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Reliable machine learning models in genomic medicine using conformal prediction

Frontiers in Bioinformatics, Год журнала: 2025, Номер 5

Опубликована: Фев. 24, 2025

Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, prediction adverse effects. However, potential errors can have life-threatening impact, raising reasonable skepticism about whether these applications practical benefit clinical settings. Conformal offers a versatile framework addressing concerns by quantifying uncertainty predictive models. In this perspective review, we investigate conformalized models discuss challenges towards bridging with practice. We also demonstrate impact binary transductive model regression-based inductive predicting drug response as well performance multi-class predictor distribution shifts molecular subtyping. The main conclusion is that machine increasingly infiltrating services, conformal has to overcome safety limitations current methods could be effectively integrated into uncertainty-informed within environments.

Язык: Английский

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Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome

Communications Biology, Год журнала: 2025, Номер 8(1)

Опубликована: Март 31, 2025

The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation subtle differences in drug responsiveness among distinct cell subpopulations tumors. A few methods have employed scRNA-seq data predict response individual date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring cancer cells. SSDA4Drug extracts pharmacogenomic features both bulk single-cell transcriptomic using adversarial domain adaptation. This allows us knowledge sensitivity bulk-level lines single We conduct extensive evaluation experiments across multiple independent datasets, demonstrating SSDA4Drug's superior over current state-of-the-art methods. Remarkably, with only one two labeled target-domain samples, significantly boosts predictive responses. Moreover, accurately recapitulates temporally dynamic changes responses during continuous exposure cells, successfully identifies reversible drug-responsive states lung which initially acquire resistance through later restore holidays. Also, our predicted consistently align developmental patterns observed along evolutionary trajectory oral squamous carcinoma addition, derived SHAP values integrated gradients effectively pinpoint key genes involved prostate These findings highlight exceptional determining powerful tool holds potential identifying subpopulations, paving way advancements precision medicine novel development. SDA4Drug semi-supervised, that improves predictions by transferring data. It aiding

Язык: Английский

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The clinical application of artificial intelligence in cancer precision treatment

Journal of Translational Medicine, Год журнала: 2025, Номер 23(1)

Опубликована: Янв. 27, 2025

Язык: Английский

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Tumor-infiltrating myeloid cells; mechanisms, functional significance, and targeting in cancer therapy

Cellular Oncology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 25, 2025

Tumor-infiltrating myeloid cells (TIMs), which encompass tumor-associated macrophages (TAMs), neutrophils (TANs), myeloid-derived suppressor (MDSCs), and dendritic (TADCs), are of great importance in tumor microenvironment (TME) integral to both pro- anti-tumor immunity. Nevertheless, the phenotypic heterogeneity functional plasticity TIMs have posed challenges fully understanding their complexity roles within TME. Emerging evidence suggested that presence is frequently linked prevention cancer treatment improvement patient outcomes survival. Given pivotal function TME, recently been recognized as critical targets for therapeutic approaches aimed at augmenting immunostimulatory cell populations while depleting or modifying those immunosuppressive. This review will explore important properties related immunity, angiogenesis, metastasis. We also document latest strategies targeting preclinical clinical settings. Our objective illustrate potential immunological may improve existing treatments.

Язык: Английский

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Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Nature Reviews Drug Discovery, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

Язык: Английский

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New horizons at the interface of artificial intelligence and translational cancer research

Cancer Cell, Год журнала: 2025, Номер 43(4), С. 708 - 727

Опубликована: Апрель 1, 2025

Язык: Английский

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Dual STAT3/STAT5 inhibition as a novel treatment strategy in T-prolymphocytic leukemia

Leukemia, Год журнала: 2025, Номер unknown

Опубликована: Апрель 15, 2025

Язык: Английский

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A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data

Current Opinion in Structural Biology, Год журнала: 2023, Номер 84, С. 102745 - 102745

Опубликована: Дек. 17, 2023

Язык: Английский

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Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 19, 2024

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses cohort primary metastatic tumors, incorporating single-nuclei transcriptomic chromatin accessibility sequencing, along spatial profiling. We identified tumor microenvironmental features previously described to associate therapy response. five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, cycling which were associated differential epigenetic plasticity clinical outcomes, for inferred candidate transcription factor regulons. Furthermore, revealed diverse localizations cells expressing their transcriptional programs predicted significant interactions types. validated our findings three external single-cell RNA-seq bulk studies. Altogether, advance understanding heterogeneity, progression,

Язык: Английский

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