Biology subjects

Hodzic, E.

Publications and source records attributed to Hodzic, E..

6 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↗

bioRxiv2021

Mutational Signatures as Sensors of Environmental Exposures: Role of Smoking in COVID-19 Vulnerabilities

BackgroundEnvironmental exposures such as smoking are widely recognized risk factors in the emergence of lung diseases including lung cancer and acute respiratory distress syndrome (ARDS). However, the strength of environmental exposures is difficult to measure, making it challenging to understand their impacts. On the other hand, some COVID-19 patients develop ARDS in an unfavorable disease progression and smoking has been suggested as a potential risk factor among others. Yet initial studies on COVID-19 cases reported contradictory results on the effects of smoking on the disease - some suggest that smoking might have a protective effect against it while other studies report an increased risk. A better understanding of how the exposure to smoking and other environmental factors affect biological processes relevant to SARS-CoV-2 infection and unfavorable disease progression is needed. ApproachIn this study, we utilize mutational signatures associated with environmental factors as sensors of their exposure level. Many environmental factors including smoking are mutagenic and leave characteristic patterns of mutations, called mutational signatures, in affected genomes. We postulated that analyzing mutational signatures, combined with gene expression, can shed light on the impact of the mutagenic environmental factors to the biological processes. In particular, we utilized mutational signatures from lung adenocarcinoma (LUAD) data set collected in TCGA to investigate the role of environmental factors in COVID-19 vulnerabilities. Integrating mutational signatures with gene expression in normal tissues and using a pathway level analysis, we examined how the exposure to smoking and other mutagenic environmental factors affects the infectivity of the virus and disease progression. ResultsBy delineating changes associated with smoking in pathway-level gene expression and cell type proportions, our study demonstrates that mutational signatures can be utilized to study the impact of exogenous mutagenic factors on them. Consistent with previous findings, our analysis showed that smoking mutational signature (SBS4) is associated with activation of cytokine-mediated signaling pathways, leading to inflammatory responses. Smoking related changes in cell composition were also observed, including the correlation of SBS4 with the expansion of goblet cells. On the other hand, increased basal cells and decreased ciliated cells in proportion were associated with the strength of a different mutational signature (SBS5), which is present abundantly but not exclusively in smokers. In addition, we found that smoking increases the expression levels of genes that are up-regulated in severe COVID-19 cases. Jointly, these results suggest an unfavorable impact of smoking on the disease progression and also provide novel findings on how smoking impacts biological processes in lung.

systems biology↗

bioRxiv2020

Identification of Conserved Evolutionary Trajectories in Tumors

MotivationAs multi-region, time-series, and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). ResultsIn this paper, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. AvailabilityCONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

bioinformatics↗

bioRxiv2019

The generality of post-antimicrobial treatment persistence of replicatively-attenuated Borrelia burgdorferi in a mouse model

A basic feature of infection caused by Borrelia burgdorferi, the etiological agent of Lyme borreliosis, is that persistent infection is the rule, not the norm, in its many hosts. The ability to persist and evade host immune clearance poses a challenge to effective antimicrobial treatment. A link between therapy failure and the presence of persister cells has started to emerge. There is growing experimental evidence that viable, but non-cultivable spirochetes persist following treatment with several different antimicrobial agents, then resurge after 12 months. The current study utilized the mouse model to evaluate if persistence and resurgence occur following antimicrobial treatment in a disease-susceptible (C3H/HeN) and disease-resistant (C57BL/6) mouse strain infected with B. burgdorferi strains N40 and B31, to confirm the generality of these phenomena. The status of infection was evaluated at 12 and 18-months after treatment. The results demonstrated that persistent spirochetes remain viable for up to 18 months following treatment, but divide slowly, thereby being tolerant to the effects of antimicrobial agents, as well as being non-cultivable. The phenomenon of persistence and resurgence in disease-susceptible C3H mice is equally evident in disease-resistant B6 mice, and not unique to any particular B. burgdorferi strain. The results also demonstrate that following antimicrobial treatment, both strains of B. burgdorferi, N40 and B31, lose one or more small plasmids, resulting in attenuation. The biological relevance of attenuated B. burgdorferi spirochetes is probably inconsequential. The study demonstrated that non-cultivable spirochetes can persist in a host following antimicrobial treatment for a long time but did not demonstrate their clinical relevance in a mouse model of chronic infection.

microbiology↗

bioRxiv2018

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

BackgroundAdvances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations speciflc to tumor types, shared across many patients. Unfortunately, computational identiflcation of functionally meaningful shared alteration patterns, impacting gene/protein interaction subnetworks, has proven to be challenging. FindingsWe introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration proflle conserved subnetworks. ConclusionsIn a number of The Cancer Genome Atlas (TCGA) data sets, cd-CAP identifled large biologically signiflcant subnetworks with conserved alteration patterns, shared across many tumor samples.

systems biology↗