TP53 (Tumor protein P53)

Despite these developments, the clinical translation of multi-omics findings remains limited due to the lack of standardized analytical pipelines, insufficient multi-center validation, and the high cost and technical complexity of integrating multi-omics data into routine clinical workflows. Future research integrating artificial intelligence with multi-omics data holds promise for enhancing diagnostic accuracy and enabling more precise therapeutic decision-making in ESCC.

These findings demonstrate the feasibility of targeting dysregulated tumor suppressor proteins with TCR-T cell therapeutics and identify p14ARF as a promising target for future therapies.

Compared to results from established architectures such as Inception-v3 on the same WSIs, our model demonstrated significantly improved performance for most features. With further optimization, our model could support triaging for molecular testing and inform precision treatment strategies for NS-LUAD patients.

Organized by target class (KRAS, MYC, TP53, WNT) and modality (PROTACs, ADCs, bispecifics), we explore how translational frameworks, rational trial design, and platform-specific engineering are reshaping what is now clinically feasible in drug development. Finally, we present early-phase data from the most compelling trials and compounds and explore what remains to be achieved to move beyond proof-of-concept into clinically meaningful benefits for patients with cancer.

CS4 showed the highest immune infiltration, elevated tumor mutational burden, and enhanced sensitivity to 5-fluorouracil and cetuximab. This integrative multi-omics framework refines the molecular taxonomy of COAD, revealing immunologically active and therapeutically distinct subgroups. The classification not only bridges genomic, epigenomic, and transcriptomic regulation but also provides a practical roadmap for precision oncology by linking molecular features to potential treatment strategies.

An elderly male diagnosed with Stage IV LUAD achieved sustained stable disease (SD) and symptomatic improvement through a sequential therapeutic strategy, including platinum-based chemotherapy followed by the PD-1 inhibitor sintilimab combined with anti-angiogenic agents (apatinib or anlotinib). This case demonstrates that while ICIs can provide exceptional long-term benefits in advanced NSCLC, particularly in patients with highly immunogenic mutation profiles, they may also trigger late-onset fatal irAEs. Our findings underscore the imperative for close, long-term metabolic surveillance throughout the course of immunotherapy, regardless of treatment duration or radiological stability.

Around 5-6% of MBs are associated with inherited cancer predisposition syndromes, with common genetic variants including PTCH1, SUFU, TP53, and SMO. This report describes the first pediatric patient harboring a CHEK2 germline variant of uncertain significance and developing a EA- MB localized at the trigeminal nerve and subsequent CNS and EN metastases.

NOTCH-1 expression was not associated with key clinicopathological or survival parameters in this cohort. While some molecular correlations were observed, NOTCH-1 does not appear to have strong prognostic utility in EC. Further research integrating molecular classification and long-term follow-up is needed to clarify its potential role in tumor biology.

This study provides the first single-cell landscape of de novo TP53-mutated AML, highlighting its reprogrammed leukemic hierarchy and disrupted immune-stromal ecosystem, and offering mechanistic insights and potential therapeutic targets for this high-risk subtype.

Our study characterizes the molecular and clinical distinctions between ecDNA-negative and ecDNA-positive HCC and establishes a clinically applicable gene signature for patient prognosis. These findings advance our understanding of ecDNA-driven tumor heterogeneity and provide potential strategies for personalized HCC management.

These results demonstrate that the integration of optimized nanostructured SERS substrates, Raman spectroscopy, and machine learning constitutes a robust analytical platform for the structural classification of wild-type and mutant p53 proteins. The proposed approach provides a versatile framework for studying conformational changes in biomolecules of biomedical relevance and supports its potential application in cancer biomarker detection and protein misfolding studies.