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.

The authors have declared no competing interest.

This work was made possible in part due to The Children's Brain Tumor Tissue Consortium (CBTTC). Research reported here was further supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under award R56 EB020527, and the National Cancer Institute (NCI) under awards: R01 CA260271, U01 CA217851 and U01 CA199241. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH).

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Datasets analyzed during the current study are available at their respective data portals: (i) Adult cohort from The Cancer Genome Atlas (TCGA) available through the Genomic Data Commons Portal (https://gdc.cancer.gov/) (ii) Pediatric cohort from the PBTA available through the Gabriella Miller Kids First Data Resource Portal (KF-DRC, https://kidsfirstdrc.org), and (iii) data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) cohort (https://wiki.cancerimagingarchive.net/display/Public/CPTAC-GBM)

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Datasets analyzed during the current study are available at their respective data portals: (i) Adult cohort from The Cancer Genome Atlas (TCGA) available through the Genomic Data Commons Portal (https://gdc.cancer.gov/) (ii) Pediatric cohort from the PBTA available through the Gabriella Miller Kids First Data Resource Portal (KF-DRC, https://kidsfirstdrc.org), and (iii) data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) cohort (https://wiki.cancerimagingarchive.net/display/Public/CPTAC-GBM)