Biology subjects

Gaykalova, D. A.

Publications and source records attributed to Gaykalova, D. A..

6 recordsLinked to original sources

bioRxiv2021

Phenotypic shifts of tumor associated macrophages and STAT3 mediated suppression of myeloid derived suppressor cells drive sensitization of HER2+ tumor immunity.

Understanding how novel therapeutic combinations alter solid tumor microenvironments (TME) in immunosuppressive tumors such as breast cancer is essential to improve their responses to immune checkpoint inhibitors (ICIs). Entinostat, an oral histone deacetylase inhibitor (HDACi), has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs, but the precise alterations induced by entinostat and mechanisms of synergy with ICIs remain unknown. Here, we employ single-cell RNA-sequencing on HER2 overexpressing breast tumors from mice treated with entinostat + ICIs to characterize these changes across cell types in the TME. This analysis demonstrates that treatment with entinostat induces a shift from a pro-tumor to an anti-tumor TME signature characterized predominantly by changes in the myeloid cells. Notably, myeloid-derived suppressor cells (MDSCs) are shifted toward the less suppressive granulocytic phenotype in association with reduced signaling through the STAT3 pathway. In addition, tumor-associated macrophages are shifted toward an anti-tumor M1 phenotype by epigenetic reprogramming. Overall, these entinostat-induced TME changes reduce immunosuppression and increase mechanisms of tumor cell killing to improve ICI responses and broaden the population of patients who could potentially benefit from immunotherapy.

immunology↗

bioRxiv2020

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

While single-cell ATAC-seq analysis methods allow for robust clustering of cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

bioinformatics↗

bioRxiv2019

Integrated single cell and bulk multi-omics reveals heterogeneity and early changes in pathways associated with cetuximab resistance in HNSCC sensitive cell lines

Identifying potential mechanisms of resistance while tumor cells still respond to therapy is critical to delay acquired resistance. We generated the first comprehensive multi-omics, bulk and single cell data in sensitive head and neck squamous cell carcinoma (HNSCC) cells to identify immediate responses to cetuximab. Two pathways potentially associated with resistance were focus of the study: regulation of receptor tyrosine kinases through the transcription factor TFAP2A, and epithelial-to-mesenchymal transition (EMT) process. Single cell RNA-seq demonstrates heterogeneity, with cell specific TFAP2A and VIM expression profiles in response to treatment. RNA-seq and ATAC-seq reveal global changes within five days of cetuximab therapy, suggesting early onset of mechanisms of resistance; and corroborates cell line heterogeneity, with different TFAP2A targets or EMT markers affected by therapy. Lack of TFAP2A reduces HNSCC growth and is enhanced by cetuximab and JQ1. Regarding the EMT process, short term cetuximab therapy has the strongest effect on inhibiting migration. TFAP2A silencing does not affect cell migration, supporting an independent role for both mechanisms in resistance. Overall, we show that immediate adaptive transcriptional and epigenetic changes induced by cetuximab are heterogeneous and cell type dependent; and independent mechanisms of resistance arise while tumor cells are still sensitive to therapy.

cancer biology↗

bioRxiv2017

Molecular Mechanisms Of Human Papillomavirus-Related Carcinogenesis In Head And Neck Cancer

This review examines the general cellular and molecular underpinnings of human papillomavirus (HPV)-related carcinogenesis in the context of head and neck squamous cell carcinoma (HNSCC) and focuses on HPV-positive oropharyngeal squamous cell carcinoma in areas for which specific data is available. It covers the major pathways dysregulated in HPV- positive HNSCC and the genome-wide changes associated with this disease.

molecular biology↗

bioRxiv2017

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

BACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.\n\nRESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.\n\nCONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

cancer biology↗

bioRxiv2016

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

MotivationCurrent bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.\n\nResultsWe introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g., tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice, and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVAin identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery.\n\nConclusionThese results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.\n\nAvailabilitySEVA is implemented in the R/Bioconductor package GSReg.\n\nContactbahman@jhu.edu, favorov@sensi.org, ejfertig@jhmi.edu

genomics↗