Abstract 2330: CTD2 "Connects the Dots" to capture disease genes in complex networks and its application on the Cancer Genomics Cloud
Authors: Petrosyan Varduhi , Kovacevic Vladimir , Obradovic Predrag , Fisher Cera , Worman Zelia , Sain Divya , DiGiovanna Jack , Davis-Dusenberry Brandi , Milosavljevic Aleksandar 
Published: March 2024

Abstract
We have developed CTD2 (an algorithm to "Connect the Dots") to capture biological signals and identify candidate genes in complex networks. CTD2 extends the functionality of its predecessor CTD by providing ranked lists of genes that are “guilty by association” and significantly linked to other genes of interest. With the explosion of large scale multi-omics studies in the cancer field, novel approaches are needed to interpret these valuable datasets. Both CTD and CTD2 are information-theoretic algorithms that allow identification of highly connected sets of genes in complex networks without the need for permutation testing. CTD has been previously used to interpret perturbations in different subtypes of breast cancer. Additionally CTD has been used to identify biomarkers of chemotherapy response in Triple Negative Breast Cancer (TNBC) murine PDX models to both platinum and taxane agents. These small multigene biomarkers of response were shown to be informative for the response of both patients and PDXs. CTD2 was developed to expand the utility of the CTD package by also capturing genes that are "guilty by association". These genes are significantly connected to genes of interest (such as disease genes), and are ranked by their connectedness to these informative gene sets. To demonstrate its utility, we investigated if CTD2 could identify known breast cancer genes. Using TCGA breast cancer expression data, we built case/control graphs over 5,000 variable genes for each subtype of breast cancer. Genes previously associated with breast cancer were identified with DisGeNET and split into a training set discovered pre-2015 and test set discovered post-2015. The genes that were discovered pre-2015 that overlapped with our networks (n = 680) were then used as an input for CTD2 along with the case/control graphs. We then ranked the connectedness of all the genes in these graphs to the training set and found that the test set of breast cancer genes that were discovered post-2015 were significantly enriched in these ranked lists.We have shown that CTD2 and CTD can be utilized to discover biologically informative signals in complex networks. Furthermore we have deployed these tools on the Cancer Genomics Cloud to make them easily accessible for users without a bioinformatics background. The democratization of these in silico tools will allow for their adaptation by a wider audience and aid in the interpretation of large multi-omic datasets.
Citation Format: Varduhi Petrosyan, Vladimir Kovacevic, Predrag Obradovic, Cera Fisher, Zelia Worman, Divya Sain, Jack DiGiovanna, Brandi Davis-Dusenberry, Aleksandar Milosavljevic. CTD2 "Connects the Dots" to capture disease genes in complex networks and its application on the Cancer Genomics Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2330.