Abstract 5230: Berg Interrogative Biology™ Informatics Suite: data driven integration of multi-omic technologies using Bayesian AI

Abstract The Berg Interrogative Biology™ Informatics Suite is an automated data-processing instrument for generation of actionable hypotheses using data generated exclusively via the Berg Interrogative Biology™ approach. The Berg Interrogative Biology™ is a platform that systematically interrogates the biological environment in proprietary in-vitro and in-vivo biological model systems. The biologically relevant data output include molecular data from multi-omics technologies such as proteomics, lipidomics, metabolomics and genomics generated within the context of Berg Interrogative Biology™ is subsequently processed by a set of mathematical algorithms within Informatics Suite. The steps include filtering of datasets with methods that allows for missing data without compromising data quality, normalization of data using technology specific methods, imputation of missing data by rigorous statistical approaches, and generation of a molecular interactome model by integrating data across experiments and technologies. Consequently, the multi-omics data is subjected to analysis using a Bayesian Network inference approach and a multi-omic cause-and-effect relationships are inferred for each analyzed condition de novo. In addition to inferring cross-molecular species interaction networks, in-silico perturbation experiments may be performed to predict cascades of molecular and phenotypic responses to a gene or protein knock-down or over-expression model. Model response is analyzed by statistical techniques and submitted to a Rich Internet Application (RIA) that allows a dynamic and interactive meta-analysis of integrated molecular interaction networks. The Informatics Suite pipeline was applied to multi-omic data set generated via the use of the Berg Interrogative Biology™ process in an in-vitro model of angiogenesis. Comprehensive implementation of the platform technology with the informatics workflow not only identified new and physiologically relevant molecular interactions, but also confirmed previously known canonical interaction pathways described in the literature. Thus, the Informatics Suite within the Berg Interrogative Biology™ platform represents a novel computational component for integrative analysis of multi-omics molecular data that is fast, accurate and leads to a rank-ordered number of actionable hypotheses positioning Berg Interrogative Biology™ as one of the most innovative and efficient approaches in drug and biomarker discovery. Citation Format: Leonardo Rodrigues, Vijetha Vemulapalli, Anthony Walshe, Min Du, Michael Keibish, Joaquin J. Jimenez, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Viatcheslav R. Akmaev, Niven R. Narain. Berg Interrogative Biology™ Informatics Suite: data driven integration of multi-omic technologies using Bayesian AI. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5230. doi:10.1158/1538-7445.AM2013-5230</jats:p

Inference of Transposable Element Ancestry

<div><p>Most common methods for inferring transposable element (TE) evolutionary relationships are based on dividing TEs into subfamilies using shared diagnostic nucleotides. Although originally justified based on the “master gene” model of TE evolution, computational and experimental work indicates that many of the subfamilies generated by these methods contain multiple source elements. This implies that subfamily-based methods give an incomplete picture of TE relationships. Studies on selection, functional exaptation, and predictions of horizontal transfer may all be affected. Here, we develop a Bayesian method for inferring TE ancestry that gives the probability that each sequence was replicative, its frequency of replication, and the probability that each extant TE sequence came from each possible ancestral sequence. Applying our method to 986 members of the newly-discovered LAVA family of TEs, we show that there were far more source elements in the history of LAVA expansion than subfamilies identified using the CoSeg subfamily-classification program. We also identify multiple replicative elements in the <i>Alu</i>Sc subfamily in humans. Our results strongly indicate that a reassessment of subfamily structures is necessary to obtain accurate estimates of mutation processes, phylogenetic relationships and historical times of activity.</p></div

Ancestral relationships among LAVA elements.

<p>The predicted network of LAVA ancestral relationships is shown. A) All sequences that replicated with probability >30% are represented as nodes in the network. Arrows are drawn between sequences if there was at least 5% probability that an ancestral relationship existed between those sequences, with the direction of the ancestor-descendant relationships indicated by the arrows. Sequences are colored based on their CoSeg subfamily assignments (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004482#pgen.1004482.s005" target="_blank">Table S2</a>). Sequences colored white do not exist in the data, but are inferred to have existed ancestrally. B) The network in A is modified by the addition of all extant TEs in the data added to the network as nodes represented by small dots. Edges are drawn between an element and an ancestral sequence if there was at least 5% probability the element descended from the ancestral sequence. Nodes are colored based on CoSeg subfamily assignment.</p

Posterior distribution of the number of replicative sequences.

<p>The Posterior distribution of the number of replicative sequences in A)LAVA and B)<i>Alu</i>Sc is given for MCMC runs with different penalties applied to each additional replicative sequence. Higher penalties indicate a prior distribution favoring fewer replicative sequences. Each distribution is an average over 10 replicates.</p

Number of replicative sequences identified for different prior penalties in LAVA and <i>Alu</i>Sc.

<p>Number of replicative sequences identified for different prior penalties in LAVA and <i>Alu</i>Sc.</p

Ancestry networks of <i>Alu</i>Sc sequences.

<p>The predicted network of <i>Alu</i>Sc ancestral relationships is shown. A) All sequences that replicated with probability >30% are represented as nodes in the network. Arrows are drawn between sequences if there was at least 5% probability that an ancestral relationship existed between those sequences, with the direction of the ancestor-descendant relationships indicated by the arrows. Sequences are colored based on their CoSeg subfamily assignments. B) The network in A is modified by the addition of all extant TEs in the data added to the network as nodes represented by small dots. Edges are drawn between an element and an ancestral sequence if there was at least 5% probability the element descended from the ancestral sequence. Nodes are colored based on CoSeg subfamily assignment.</p

New AnTE subfamily assignments for LAVA elements.

<p>The predicted network of LAVA TE ancestral relationships is shown, as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004482#pgen-1004482-g003" target="_blank">Figure 3</a>. A) All sequences that replicated with probability >30% are represented as nodes in the network, exactly as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004482#pgen-1004482-g003" target="_blank">Figure 3A</a> except that nodes are colored based on their new AnTE-based subfamily assignments. B) As in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004482#pgen-1004482-g004" target="_blank">Figure 4A</a>, all TEs in the data are added to the network as nodes, represented by small dots, and using the coloring scheme of the new AnTE-based subfamily assignments.</p

An empirical antigen selection method identifies neoantigens that either elicit broad anti-tumor T cell responses or drive tumor growth.

Neoantigens are critical targets of anti-tumor T cell responses. The ATLAS{trade mark, serif} bioassay was developed to identify neoantigens empirically by expressing each unique patient-specific tumor mutation individually in E. coli, pulsing autologous dendritic cells in an ordered array, and testing the patient\u27s T cells for recognition in an overnight assay. Profiling of T cells from lung cancer patients revealed both stimulatory and inhibitory responses to individual neoantigens. In the murine B16F10 melanoma model, therapeutic immunization with ATLAS-identified stimulatory neoantigens protected animals, whereas immunization with peptides associated with inhibitory ATLAS responses resulted in accelerated tumor growth and abolished efficacy of an otherwise-protective vaccine. A planned interim analysis of a clinical study testing a poly-ICLC adjuvanted personalized vaccine containing ATLAS-identified stimulatory neoantigens showed that it is well-tolerated. In an adjuvant setting, immunized patients generated both CD4+ and CD8+ T cell responses, with immune responses to 99% of the vaccinated peptide antigens