Biology subjects

Ratushny, A. V.

Publications and source records attributed to Ratushny, A. V..

5 recordsLinked to original sources

bioRxiv2025

Mechanistic modeling suggests stroma-targeting antibody-drug conjugates as an alternative to cancer-targeting in cases of heterogeneous target expression

Antibody-drug conjugates (ADCs) are gaining increasing traction in the treatment of oncological diseases; however, many clinical failures have also been observed. One key factor limiting ADC effectiveness is the heterogeneous expression of their target antigen. While the vast majority of ADCs in clinical development target antigens on cancer cells (cancer-targeting), they can also target antigens expressed on non-cancerous stromal cells in the tumor microenvironment (stroma-targeting). It remains unclear whether targeting cancer cells or stromal cells is more effective in suppressing tumor growth. Here, we present three related mathematical models to evaluate: (1) cancer-targeting ADCs with homogeneous target antigen expression, (2) cancer-targeting ADCs with heterogeneous target antigen expression, and (3) stroma-targeting ADCs. Our simulations suggest that cancer-targeting ADCs can achieve high efficacy when their target antigen is homogenously expressed. However, in cases of heterogeneous antigen expression, cancer-targeting ADCs may lead to an initial reduction in tumor size, followed by regrowth due to the elimination of antigen-positive cells and expansion of antigen-negative cells. This limitation could potentially be overcome by stroma-targeting ADCs, as antigen-positive stromal cells may continue to be recruited into the tumor by the oncogenic factors produced by the remaining cancer cells. Furthermore, we demonstrate that ADCs with more permeable payloads and less stable linkers may offer improved efficacy in the context of heterogeneous target expression.

pharmacology and toxicology↗

bioRxiv2023

Single-cell immune multi-omics and repertoire analyses in pancreatic ductal adenocarcinoma reveal differential immunosuppressive mechanisms within different tumour microenvironments

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. Understanding the multiple mechanisms by which the tumour evades immune control, and how these mechanisms may be disrupted is critical to developing targeted immunotherapies. Previous studies have shown that higher lymphocyte infiltration is associated with better survival, and here we investigated what mediates these differences. We performed a comprehensive analysis of PDAC-associated immune cells using single cell multi-omics coupled with re-analysis of public PDAC scRNA-seq datasets. We introduce novel single-cell and repertoire analyses that have uncoupled diverse roles and contributions of various immune cell populations within different tumour microenvironments (TMEs). They revealed clear distinctions in the clonal characteristics among different patient groups, provided valuable insights into the mechanisms of immune cell migration and tissue adaptation underlying these disparities. These results point to differential CD4 polarisation of intra-tumoural T cells, differential B cell differentiation, GC reactions, antigen presentation pathways, and distinct cell-cell communication between the myeloid-enriched and adaptive-enriched groups. Overall, we identified two major distinct themes for future immune intervention within PDAC patients between those with higher adaptive versus myeloid immune cell infiltration.

immunology↗

bioRxiv2021

Quantitative systems pharmacology modeling of avadomide-induced neutropenia enables virtual clinical dose and schedule finding studies

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia, however the identification of optimal dosing schedules remains a clinical challenge. Quantitative Systems Pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low grade-neutropenia.

pharmacology and toxicology↗

bioRxiv2021

Patient-specific cell communication networks associate with disease progression in cancer

The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling axes with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

bioinformatics↗

bioRxiv2019

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

Immune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

biophysics↗