Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.
2/5 보강
OpenAlex 토픽 ·
Colorectal Cancer Treatments and Studies
Cancer Genomics and Diagnostics
Radiomics and Machine Learning in Medical Imaging
Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance.
- p-value p < 0.001
- 95% CI 0.89-0.94
- HR 4.7
APA
H. Sadeghi, Fatemeh Seif (2026). Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.. Biochemical genetics. https://doi.org/10.1007/s10528-026-11368-4
MLA
H. Sadeghi, et al.. "Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.." Biochemical genetics, 2026.
PMID
41964737 ↗
Abstract 한글 요약
Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
같은 제1저자의 인용 많은 논문 (1)
🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
- LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening.
- PIBAdb: a public cohort of multimodal colonoscopy videos and images including polyps with histological information.
- Exploring the Role of Extracellular Vesicles in Pancreatic and Hepatobiliary Cancers: Advances Through Artificial Intelligence.
- Feasibility of Depth-in-Color En Face Optical Coherence Tomography for Colorectal Polyp Classification Using Ensemble Learning and Score-Level Fusion.
- Impact of CT Intensity and Contrast Variability on Deep-Learning-Based Lung-Nodule Detection: A Systematic Review of Preprocessing and Harmonization Strategies (2020-2025).
- A Transformer-Based Deep Learning Model for predicting Early Recurrence in Hepatocellular Carcinoma After Hepatectomy Using Intravoxel Incoherent Motion Images.