SignificancePredicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor in adults, with a median survival of 14.6 months. Recent efforts have focused on identifying clinically relevant subgroups to improve our understanding of pathogenetic mechanisms and patient stratification. Concurrently, the role of immune cells in the tumor microenvironment has received increasing attention, especially T cells and tumor-associated macrophages (TAM). The latter are a mixed population of activated brain-resident microglia and infiltrating monocytes/monocyte-derived macrophages, both of which express ionized calcium-binding adapter molecule 1 (IBA1). This study investigated differences in immune cell subpopulations among distinct transcriptional subtypes of GBM. Human GBM samples were molecularly characterized and assigned to Proneural, Mesenchymal or Classical subtypes as defined by NanoString nCounter Technology. Subsequently, we performed and analyzed automated immunohistochemical stainings for TAM as well as specific T cell populations. The Mesenchymal subtype of GBM showed the highest presence of TAM, CD8 + , CD3 + and FOXP3 + T cells, as compared to Proneural and Classical subtypes. High expression levels of the TAM-related gene AIF1, which encodes the TAM-specific protein IBA1, correlated with a worse prognosis in Proneural GBM, but conferred a survival benefit in Mesenchymal tumors. We used our data to construct a mathematical model that could reliably identify Mesenchymal GBM with high sensitivity using a combination of the aforementioned cell-specific IHC markers. In conclusion, we demonstrated that molecularly distinct GBM subtypes are characterized by profound differences in the composition of their immune microenvironment, which could potentially help to identify tumors amenable to immunotherapy.

Objective:We assessed whether peripheral activation of microglia by a nasal proteosome-based adjuvant (Protollin) that has been given safely to humans can prevent amyloid deposition in young mice and affect amyloid deposition and memory function in old mice with a large amyloid load. Methods: Amyloid precursor protein (APP) transgenic (Tg) J20 mice received nasal treatment with Protollin weekly for 8 months beginning at age 5 months. Twenty-four-month-old J20 mice were treated weekly for 6 weeks. Results: We found reduction in the level of fibrillar amyloid (93%), insoluble ␤-amyloid (A␤; 68%), and soluble A␤ (45%) fragments in 14-month-old mice treated with Protollin beginning at age 5 months. Twenty-four-month-old mice treated with nasal Protollin for 6 weeks had decreased soluble and insoluble A␤ (1-40) and (1-42) and improved memory function. Activated microglia (CD11b ϩ cells) colocalized with A␤ fibrils in the 24-month-old animals, and microglial activation correlated with the decrease in A␤. No microglial activation was observed in 14-month-old mice, suggesting that once A␤ is cleared, there is downregulation of microglial activation. Both groups had reduction in astrocytosis. Protollin was observed in the nasal cavity and cervical lymph node but not in the brain. Activated CD11b ϩ SRA ϩ (scavenger receptor A) cells were found in blood and cervical lymph node and increased interleukin-10 in cervical lymph node. No toxicity was associated with treatment.Interpretation: Our results demonstrate a novel antibody-independent immunotherapy for both prevention and treatment of Alzheimer's disease that is mediated by peripheral activation of microglia with no apparent toxicity.