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Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 1]

ABSTRACT: Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a novel therapeutic strategy. We successfully established 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring were utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlated with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and Cisplatin combination therapies identified a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies.

ORGANISM(S): Homo sapiens

PROVIDER: GSE293370 | GEO | 2025/04/02

REPOSITORIES: GEO

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Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 2]

Project description:Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a novel therapeutic strategy. We successfully established 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring were utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlated with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and Cisplatin combination therapies identified a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies.

2025-04-02 | GSE293371 | GEO

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

Project description:Two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the “Classical” and “Basal-like” subtypes. However, the “molecular” classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction. We constructed a PDO library for morphologic and RNA-seq analyses and drug response assays in vitro. PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) or densely proliferating inside (DP type). RNA-seq analysis revealed that the “morphological” subtype (GL vs. DP) roughly corresponded to the “molecular” subtype (“Classical” vs. “Basal-like”). The proposed “morphological” classification predicted clinical treatment response and prognosis; the median overall survival of patients with GL type were significantly longer than that of those with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK and autophagy inhibition. PDAC PDOs facilitated subtype determination and clinical outcome prediction, thereby advancing precision medicine for PDAC.

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Project description:Morphology-guided organoid classification reveals prognosis of oral cancer

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The epigenetic landscape of human colorectal cancer

Project description:We characterized the epigenetic landscape of human colorectal cancer (CRC). To this extent, we performed gene expression profiling using high throughput sequencing (RNA-seq) and genome wide binding/occupancy profiling (ChIP-seq) for histone modifications correlated to transcriptional activity, enhancers, elongation and repression (H3K4me3, H3K4me1, H3K27Ac, H3K36me3, H3K27me3) in patient-derived organoids (PDOs), and in normal and tumoral primary colon tissues. We also generated ChIP-seq data for transcription factors YAP/TAZ in human CRC PDOs.

2021-02-23 | E-MTAB-8416 | biostudies-arrayexpress

The epigenetic landscape of human colorectal cancer

2021-03-10 | E-MTAB-8448 | biostudies-arrayexpress

Subtypes in human pancreatic ductal adenocarcinoma based on niche factor dependency show distinct drug treatment responses

Project description:BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is characterized by abundant stroma in which microenvironmental (niche) factors promote PDAC progression. In mouse models, reduction of the stroma increased the proportion of poorly differentiated PDAC with a worse prognosis. Here, we aimed to clarify the effects of stroma on PDAC that may define the PDAC phenotype and induce distinct therapeutic responses. METHODS: The molecular features of PDAC based on differentiation grade were clarified by genome and transcriptome analysis using PDAC organoids (PDOs). We identified the dependency on niche factors that might regulate the differentiation grade. A three-dimensional co-culture model with cancer-associated fibroblasts (CAFs) was generated to determine whether CAFs provide niche factors essential for differentiated PDAC. PDOs were subtyped based on niche factor dependency, and the therapeutic responses for each subtype were compared. RESULTS: The expression profiles of PDOs differed depending on the differentiation grade. Consistent with the distinct profiles, well differentiated types showed high niche dependency, while poorly differentiated types showed low niche dependency. The three-dimensional co-culture model revealed that well differentiated PDOs were strongly dependent on CAFs for growth, and moderately differentiated PDOs showed plasticity to change morphology depending on CAFs. Differentiated PDOs upregulated the expression of mevalonate pathway-related genes correlated with the niche dependency and were significantly more sensitive to simvastatin than poorly differentiated PDOs. CONCLUSIONS: Our findings suggest that CAFs maintain the differentiated PDAC phenotype through secreting niche factors and induce distinct drug responses. These results may lead to the development of novel subtype-based therapeutic strategies.

2021-08-07 | GSE181593 | GEO

Patient and xenograft-derived organoids recapitulate pediatric brain tumors features and patient treatments [scRNA-Seq]

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2023-12-01 | GSE247380 | GEO

Patient and xenograft-derived organoids recapitulate pediatric brain tumors features and patient treatments [array]

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Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 2]

Project description:Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 2]

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Expression data for Vanderbilt triple-negative breast cancer subtype classification

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