Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of chemical information and modeling
ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms
Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of existing "black-box" machine learning models. To address this, we propose ProphDR, an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism. ProphDR incorporates a Criss-Cross Gene-level Multiomics Integration (CGMI) module to capture gene-level features and a cross-attention (CA) module to model drug-gene interactions. Evaluated on datasets from GDSC and CCLE, ProphDR achieves state-of-the-art performance in predicting ln(IC50) values (PCC = 0.938, RMSE = 0.978) and classifying drug sensitivity (AUC = 0.981). It also demonstrates strong generalizability in cold-start scenarios involving unseen drugs or cell lines. Crucially, ProphDR generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes such as ERBB2 (HER2), consistent with established mechanisms in NSCLC and BRCA. These insights bridge genomic features with phenotypic outcomes, offering valuable guidance for target prioritization and drug repurposing. ProphDR represents a robust and explainable AI tool for advancing precision oncology.
28 July 2026
Read appraisal →Signal transduction and targeted therapy
HDAC inhibitor tucidinostat and metronomic capecitabine plus endocrine therapy for patients with HR-positive HER2-negative advanced breast cancer after CDK4/6 inhibitors treatment: clinical findings and exploratory circulating tumor cell and ctDNA biomarker analyses of a multicenter, phase 2 study (SYSUCC-020 trial)
Treatment options for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (ABC) that progresses after CDK4/6 inhibitor (CDK4/6i) continue to evolve, and no single regimen has been established as the preferred standard of care. We conducted a phase 2 trial (SYSUCC-020, NCT05411380) to investigate the efficacy and safety of a novel combination of tucidinostat and metronomic capecitabine (mCAP) with endocrine therapy (ET) in patients following progression on CDK4/6i. This trial adopted a Simon two-stage design: eligible patients received tucidinostat plus metronomic capecitabine together with either an aromatase inhibitor (Cohort 1) or fulvestrant (Cohort 2), selected according to prior ET. Seventy-two female patients were screened and sixty-six patients were included in the efficacy and safety analyses. The median follow-up was 13.88 months (Interquartile range, 8.67-20.67) and the objective response rate was 25.8%, with 17 partial responses. The median progression-free survival (PFS) was 5.39 months (95% CI, 4.07-7.69), namely 6.93 months (95% CI, 4.76-12.22) in Cohort 1 and 3.98 months (95% CI, 2.79-7.59) in Cohort 2. Most patients (93.9%) experienced at least one adverse event. In addition, exploratory analyses of biomarkers indicated that the baseline TP53 mutation status (Wild type vs. mutated, 7.64 months vs. 3.55 months) and circulating tumor cell (CTC) status (CTC-negative vs. CTC-positive, 7.59 months vs. 3.78 months) were associated with the median PFS. Our study demonstrated that tucidinostat combined with mCAP and ET is efficacious and well-tolerated in patients with HR-positive HER2-negative ABC previously treated with CDK4/6i.
17 Apr 2026
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