Computational Analysis of Drug Response in Biological Networks

National Library Of Medicine

Investigators

Linked publications & trials

Abstract

### 1. Computational Analysis of Breast Cancer Pharmacogenomics (Integrins) Treatment options for triple-negative breast cancer (TNBC) are limited, and patients face a poor prognosis. The work conducted aims to identify drugs that target TNBC vulnerabilities and understand the biology underlying these responses. With colleagues, Luna lab group members worked to conduct analyses in the following areas related to breast cancer: * The recurrent vulnerabilities in triple-negative breast cancer (TNBC) that can be therapeutically targeted using the existing Broad DepMap CRISPR dataset. * TNBC response to integrin inhibition via a pan-ITGAV inhibitor, GLPG0187, and the mechanism that underlies the response, which was assessed on a collection of TNBC cell lines through the examination of specific integrin subunits and extracellular matrix components. * Identification of a potential signature in TNBC derived from overall integrin proteomic abundances for integrin inhibition response through the examination of patient data. ### 2. Computational Analysis of Breast Cancer Pharmacogenomics (Drug Repurposing) There is significant interest in repurposing approved therapies for treating refractory cancer, as these drugs have undergone testing for safety in humans for other indications. The work conducted is meant to identify the mechanisms for specific treatments shown to have an anti-cancer effect in breast cancer. With colleagues, Luna lab group members worked to conduct analyses in the following areas related to breast cancer: * Investigation into the anti-cancer activity of oxyphenisatin acetate (acetalax) and bisacodyl in TNBC across a broad panel of cell lines in comparison to standard treatments that demonstrated the unique and distinct activity of these drugs. * Use of statistical analyses to identify TRPM4 and additional cell surface genes as predictive of acetalax and bisacodyl response. * Study of oxyphenisatin acetate (acetalax) and bisacodyl activity in patient-derived TNBC xenografts that showed induction of complete tumor regression in various TNBC patient-derived xenografts with characterization of their pharmacokinetics, including tumor and brain penetration. * Examination of the anti-cancer mechanism of these drugs, which we show involves TRPM4 rapid degradation, cell membrane disruption, increased cellular volume, and mitochondrial defects with ATP depletion. ### 3. Computational Analysis of Bimodally Expressing Genes as Prognosis Markers in Adrenocortical Carcinoma Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with a high mortality and poor prognosis. The work conducted is meant to develop a computational approach to analyze the genetic underpinnings of ACC. With colleagues, Luna lab group members worked to conduct analyses in the following areas related to ACC: * Development of an approach using Gaussian Mixture Models to identify bimodally expressed genes of potential use in the stratification of ACC patients by prognosis. * Results showed that elevated levels of SEMA7A, a glycoprotein involved in semaphorin cell surface signaling, were identified as a poor prognostic biomarker. * Study results also indicate that RNA expression patterns translate into protein-level expression using immunohistochemistry (IHC), suitable for clinical application. * Identification of pathways associated with SEMA7A expression, including steroidogenesis, aldosterone and cortisol synthesis, as well as integrin-β1, FAK (focal adhesion kinase), and MAPK/ERK (mitogen-activated protein kinase/extracellular signal-regulated kinases) signaling pathways. 
### 4. Continued Development of the Systems Biology Graphical Notation (SBGN) for Exchange of Cellular Signaling Knowledge
Systems Biology Graphical Notation project, an effort to standardize the graphical notation used in maps of biological processes from gene regulation, to metabolism, to cellular signaling. The effort enables scientists to visually represent networks of biochemical interactions in a standard, unambiguous way. The SBGN effort is a community effort that draws on ideas from numerous researchers to develop a widely usable representation formalism. Luna lab group members continued to develop SBGN based on community feedback, concentrating on remaining areas of ambiguity that can arise in some SBGN diagrams; an example of this involves duplication of visual elements and the semantics of these duplications. Additionally, Luna lab group members continued to work on tools to simplify the use of SBGN by the wider research community; these efforts focus on simplifying the generation of SBGN diagrams.