Biology subjects

Kesimoglu, Z. N.

Publications and source records attributed to Kesimoglu, Z. N..

4 recordsLinked to original sources

bioRxiv2026

Combined inference of known and novel mutational signatures with ReDeNovo

Mutational signatures represent characteristic mutational patterns imprinted on the genome by mutagenic processes. They can provide information about the impact of the environmental and endogenous cellular processes on tumor mutations and can suggest treatment. Analysis of presence and strength of mutational signatures in cancer genomes has become a cornerstone in analysis of new and legacy cancer data. However, a precise inference of novel (de novo) signatures requires a large set of genomes, and methods focusing on estimating the presence of previously defined signatures are unable to uncover potential novel signatures that might emerge in new data. Thus, reliable methods to address these challenges are needed. We formally define the Combined Mutational Signature Inference Problem (CMSI) for the identification of known signatures and the inference of novel signatures in cancer data. CMSI represents non-convex optimization, and we provide a heuristic algorithm, ReDeNovo, to solve it efficiently. We extensively validated ReDeNovo on simulated data, evaluating its ability to precisely estimate presence and exposure to known signatures and to discover of novel signatures. On both tasks ReDeNovo outperformed existing approaches. In real biological data, ReDeNovo identified signatures missed by previous analyses and defined a new signature related to UV light exposure. ReDeNovo method provides a new and powerful tool to uncover mutational signatures. ReDeNovo is available from https://github.com/ncbi/redenovo.

genomics↗

bioRxiv2025

Connecting spatial regions to clinical phenotypes by transferring knowledge from bulk patient data

Spatially resolved transcriptomics (SRT) technology has enabled a new level of knowledge about tumors. Many critical tumor properties, such as invasiveness and growth, depend on both specific transcriptomic changes in tumor cells and the tumor microenvironment. However, computational methods to study clinical phenotypes of spatial regions, such as hazard and drug response, have not yet been developed. Since clinical phenotypes are measured at the patient level and not at the level of spatial regions, such a method would require transferring knowledge from the patient level domain to the spot level domain. To overcome this challenge, we developed SpacePhenotyper. Our approach uses algebraic spectral techniques to transfer the predictive relationship between gene expression and clinical phenotypes from bulk gene expression data to SRT data. Our approach captures a gene expression pattern that is predictive of the phenotype of interest in the form of a vector, the "Eigen-Patient," which is then used to quantify the phenotype in spatial spots. After extensively validating SpacePhenotyper on simulated and real data, we utilize it to study how the spatial heterogeneity of breast cancer tumors influences residual cancer burden after treatment. By assigning relative quantities of clinical phenotypes to spatial locations, SpacePhenotyper has proven a powerful tool for the identification and interpretation of transcriptional changes over spatial regions and of spatially-regulated patterns of cellular states. SpacePhenotyper is implemented in Python. The source code and data sets used for and generated during this study are available at https://github.com/ncbi/SpacePhenotyper

cancer biology↗

bioRxiv2022

SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural Network

To pave the road towards precision medicine in cancer, patients with highly similar biology ought to be grouped into the same cancer subtypes. Utilizing high-dimensional multiomics datasets, several integrative computational approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks (GNNs) was discovered to learn node embeddings while utilizing node features and node associations at the same time on graph-structured data. Although there are some commonly used architectures such as Graph Convolutional Network (GCN) for cancer subtype prediction, the existing prediction tools have some limitations in leveraging those architectures with multiomics integration on multiple networks. Addressing them, we developed SUPREME (a subtype prediction methodology) by comprehensively analyzing multiomics data and associations between patients with graph convolutions on multiple patient similarity networks. Unlike the existing tools, SUPREME generates patient embeddings from patient similarity networks, on which it utilizes all the multiomics features. In addition, SUPREME integrates all the possible combinations of embeddings with the raw multiomics features to capture the complementary signals. Extensive evaluation of all combinations makes SUPREME interpretable in terms of utilized networks and features. On three different datasets from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and both combined, our method significantly outperformed other integrative cancer (sub)type prediction tools and baseline methods, with overall consistent results. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth (PAM50) labels, and outperformed nine cancer subtype differentiating tools and baseline methods. These results suggest that with proper utilization of multiple datatypes and patient associations, SUPREME could demystify the undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve the ground truth label, which depends mainly on a single datatype. Source code for our tool is publicly available at https://github.com/bozdaglab/SUPREME.

bioinformatics↗

bioRxiv2020

Inferring competing endogenous RNA (ceRNA) interactions in cancer

To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNAs inclusion for ceRNA inference. The source code of Crinet could be accessed on Github at https://github.com/bozdaglab/crinet.

bioinformatics↗