Multi-dimensional discovery of biomarker and phenotype complexes

Abstract

Background: Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.

Results: In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium.

Conclusions: Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

Figure 1

Overview of constructive induction (CI) methodology. Note that Concept 2, which is included in the ontology but does not map to the initial database construct, is used as an intermediate concept to define a triplet known as a conceptual knowledge construct (CKC).

Figure 2

Overview of study phases, data/knowledge sources, and outcomes/research products.

Figure 3

Top: Histogram of the number of nodes in the clinical attribute network created during Phase 1c. Bottom: Using a log-log scale, the histogram can be fitted by a straight line (red, R=0.93).

Figure 4

Energy-minimized graph visualization of semantically anchored union of network constructs generated in Phases 1a-1c, with significant groups of nodes annotated to indicated broad concept classes.

Figure 5

Illustration of heuristically derived conceptual model for multi-dimensional marker complex induction and aggregation.