ArticleCancer research2022
Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.
Article in Cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Learnable frozen feature augmentation for few-shot biomarker prediction from pathology whole-slide images.Bioinformatics (Oxford, England) · 2026Article
- Tumor evolution: signaling pathways, molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- The seventh kidney cancer research summit: progress in accelerating cures.The oncologist · 2026Article
- From Genes to Proteins: The Indispensable Role of Proteogenomics in Advancing Clear Cell Renal Cell Carcinoma Research.International journal of molecular sciences · 2026Review
- Advancing cancer research: Cutting-edge insights from colorectal cancer patient-derived xenograft mouse models.Genes & diseases · 2026Review
- Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.Scientific reports · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.Genome medicine · 2025Article
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- Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.Nature communications · 2025Article
- Caveolin-1 regulates context-dependent signaling and survival in Ewing sarcoma.bioRxiv : the preprint server for biology · 2025Article
- Advances and challenges in precision imaging.The Lancet. Oncology · 2025Review
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- ITIH1 suppresses carcinogenesis in renal cell carcinoma through regulation of the NF‑κB signaling pathway.Experimental and therapeutic medicine · 2024Article
- Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.Cancer medicine · 2024Article
- A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.Nature communications · 2024Article
- Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial.ArXiv · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.Cell reports. Medicine · 2023Article
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13 authors at 3 institutions in 1 country.
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Abstract
Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)-stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87-0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77-0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. SIGNIFICANCE: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672.
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