Increased expression of RUNX3 inhibits normal human myeloid development

Menezes, Ana Catarina; Jones, Rachel; Shrestha, Alina; Nicholson, R. I.; Leckenby, Adam; Azevedo, Aleksandra; Davies, Sara; Baker, Sarah M.; Gilkes, Amanda F.; Darley, Richard Lawrence; Tonks, Alex

doi:10.1038/s41375-022-01577-2

Cited by 13 publications

(13 citation statements)

References 60 publications

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“…RUNX2 is upregulated in multiple cancers and is also found to contribute to glial tumor malignancy 60 . Elevated RUNX3 expression has also been observed in various metastatic cancers, such as leukemia 61 , but in glioma it has been shown to be a tumor suppressor 52 , and in our results, these pathways are enriched in good prognosis. Growing evidence indeed indicates that the roles of RUNX genes in carcinogenesis are cell type-specific and context-dependent.…”

Section: Discussionsupporting

confidence: 70%

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

et al. 2023

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Background The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer, different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information than when using these data sources in isolation. Here, we propose a DL framework that combines these two modalities with the aim to predict brain tumor prognosis. Methods Using two separate glioma cohorts of 783 adults and 305 pediatric patients we developed a DL framework that can fuse histopathology images with gene expression profiles. Three strategies for data fusion were implemented and compared: early, late, and joint fusion. Additional validation of the adult glioma models was done on an independent cohort of 97 adult patients. Results Here we show that the developed multimodal data models achieve better prediction results compared to the single data models, but also lead to the identification of more relevant biological pathways. When testing our adult models on a third brain tumor dataset, we show our multimodal framework is able to generalize and performs better on new data from different cohorts. Leveraging the concept of transfer learning, we demonstrate how our pediatric multimodal models can be used to predict prognosis for two more rare (less available samples) pediatric brain tumors. Conclusions Our study illustrates that a multimodal data fusion approach can be successfully implemented and customized to model clinical outcome of adult and pediatric brain tumors.

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Section: Discussionsupporting

confidence: 70%

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

et al. 2023

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“…RUNX2 is upregulated in multiple cancers and is also found to contribute to glial tumor malignancy [52]. Elevated RUNX3 expression has also been observed in various metastatic cancers, such as leukemia [53], but in glioma it has been shown to be a tumor suppressor [45], and in our results these pathways are enriched in good prognosis. Growing evidence indeed indicates that the roles of RUNX genes in carcinogenesis are cell type-specific and context-dependent.…”

Section: Discussionsupporting

confidence: 70%

Multimodal data fusion of adult and pediatric brain tumors with deep learning

Steyaert

Qiu

Zheng

et al. 2022

Preprint

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The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information then when using these data sources in isolation. Here, we propose a DL framework that by combining histopathology images with gene expression profiles can predict prognosis of brain tumors. Using two separate cohorts of 783 adult and 305 pediatric brain tumors, the developed multimodal data models achieved better prediction results compared to the single data models, but also leads to the identification of more relevant biological pathways. Importantly, when testing our adult models on a third independent brain tumor dataset, we show our multimodal framework is able to generalize and performs better on new data from different cohorts. Furthermore, leveraging the concept of transfer learning, we demonstrate how our multimodal models pre-trained on pediatric glioma can be used to predict prognosis for two more rare (less available samples) pediatric brain tumors, i.e. ependymoma and medulloblastoma. To summarize, our study illustrates that a multimodal data fusion approach can be successfully implemented and customized to model clinical outcome of adult and pediatric brain tumors.

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“…In the case of a switch from RUNX1 to RUNX2, it would promote commitment to the pDC lineage because RUNX2 is a key player in differentiation into pDCs [ 42 , 118 ]. Expression levels of the RUNX family would represent an interesting pathway to study in AML, as recently shown with RUNX3, found downregulated in AML with RUNX1::RUNX1T1 transcript and upregulated in AML with normal karyotype [ 119 ]. Of note, high levels of RUNX3 would be associated with poor outcome [ 119 , 120 ].…”

Section: Genetics Of Pdc Proliferation Associated With a Myeloid Diso...mentioning

confidence: 99%

“…Expression levels of the RUNX family would represent an interesting pathway to study in AML, as recently shown with RUNX3, found downregulated in AML with RUNX1::RUNX1T1 transcript and upregulated in AML with normal karyotype [ 119 ]. Of note, high levels of RUNX3 would be associated with poor outcome [ 119 , 120 ].…”

Section: Genetics Of Pdc Proliferation Associated With a Myeloid Diso...mentioning

confidence: 99%

Genetics and Epigenetics in Neoplasms with Plasmacytoid Dendritic Cells

Renosi

Callanan

Lefebvre

2022

Cancers

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Plasmacytoid Dendritic Cells (pDC) are type I interferon (IFN)-producing cells that play a key role in immune responses. Two major types of neoplastic counterparts for pDC are now discriminated: Blastic pDC Neoplasm (BPDCN) and Mature pDC Proliferation (MPDCP), associated with myeloid neoplasm. Two types of MPDCP are now better described: Chronic MyeloMonocytic Leukemia with pDC expansion (pDC-CMML) and Acute Myeloid Leukemia with pDC expansion (pDC-AML). Differential diagnosis between pDC-AML and BPDCN is particularly challenging, and genomic features can help for diagnosis. Here, we systematically review the cytogenetic, molecular, and transcriptional characteristics of BPDCN and pDC-AML. BPDCN are characterized by frequent complex karyotypes with recurrent MYB/MYC rearrangements as well as recurrent deletions involving ETV6, IKZF1, RB1, and TP53 loci. Epigenetic and splicing pathways are also particularly mutated, while original processes are dysregulated, such as NF-kB, TCF4, BCL2, and IFN pathways; neutrophil-specific receptors; and cholinergic signaling. In contrast, cytogenetic abnormalities are limited in pDC-AML and are quite similar to other AML. Interestingly, RUNX1 is the most frequently mutated gene (70% of cases). These typical genomic features are of potential interest for diagnosis, and also from a prognostic or therapeutic perspective.

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Increased expression of RUNX3 inhibits normal human myeloid development

Cited by 13 publications

References 60 publications

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

Multimodal data fusion of adult and pediatric brain tumors with deep learning

Genetics and Epigenetics in Neoplasms with Plasmacytoid Dendritic Cells

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