The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Abstract: Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation

AUC scores of univariate predictors included in the BRCA and GBM models, in comparison with the AUC scores of the models themselves.

<p>(a) shows BRCA, (b) shows GBM.</p

CDF curves for individual features included in the prediction model.

<p><i>P</i>-values show Kolmogorov-Smirnov test results. Genes are separated by pathway membership; (a) shows BRCA, (b) shows GBM.</p

ROC curves for BRCA (a) and GBM (b) for single scoring methods: mutation frequency, differential expression frequency, and column means <i>μ</i>_<i>M</i> and <i>μ</i>_<i>G</i> of the matrices <i>M</i>_<i>P</i> and <i>D</i>_<i>P</i>, respectively.

<p>ROC curves for BRCA (a) and GBM (b) for single scoring methods: mutation frequency, differential expression frequency, and column means <i>μ</i>_<i>M</i> and <i>μ</i>_<i>G</i> of the matrices <i>M</i>_<i>P</i> and <i>D</i>_<i>P</i>, respectively.</p

CDF curves of prediction scores from stepped-down logistic regression models for each data set.

<p>(a) shows BRCA, (b) shows GBM.</p

ROC AUC values for predictive models fit with subsets of available features.

<p>(a) shows BRCA, (b) shows GBM.</p

AUC scores of predictive models fit with varying <i>α</i>, for <i>α</i> ∈ {0.01, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.99}.

<p>AUC scores of predictive models fit with varying <i>α</i>, for <i>α</i> ∈ {0.01, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.99}.</p

Prediction scores of highest-scoring genes that are not contained in respective pathways: BRCA in (a) and GBM in (b).

<p>Prediction scores of highest-scoring genes that are not contained in respective pathways: BRCA in (a) and GBM in (b).</p

The workflow of the proposed algorithmic pipeline that integrates mutation, gene expression, and protein-protein interaction (PPI) data to test the driving hypothesis and identify causal genes.

<p>The workflow of the proposed algorithmic pipeline that integrates mutation, gene expression, and protein-protein interaction (PPI) data to test the driving hypothesis and identify causal genes.</p