Biology subjects

Acar, A.

Publications and source records attributed to Acar, A..

9 recordsLinked to original sources

bioRxiv2026

Targeting cancer-associated fibroblasts for treatment of ER+ breast cancer: A mathematical modeling perspective and optimization of treatment strategies

Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment that facilitate a supportive niche for cancer progression and metastasis. Experimental evidence suggests that CAFs can facilitate estrogen-independent tumor growth, thereby reducing the efficacy of anti-hormonal therapies. Understanding and quantifying the complex interactions between tumor cells, hormonal signalling, and the microenvironment are crucial for designing more effective and individualized treatment strategies. We propose a mathematical framework to explore the influence of CAFs on ER+ breast cancer progression and to evaluate strategies to mitigate their impact. We develop a system of nonlinear ordinary differential equations that substantiates the experimental observations by providing a mechanistic basis for the role of CAFs in regulating estrogen-independent growth dynamics. We then employ optimal control theory to evaluate distinct therapeutic approaches involving monotherapy or combinations of: (i) inhibition of tumor-to-CAF signaling, (ii) inhibition of CAF-to-tumor proliferative signaling, and (iii) endocrine therapy. Taken together, our results demonstrate that CAF-targeted strategies can enhance treatment efficacy across various estrogen dosing regimens. Our study provides new insights into the potential of CAF as a therapeutic target that could help to improve existing approaches for endocrine therapies.

systems biology↗

bioRxiv2026

Inference of Cancer Drug Cross Resistance Using Only Single-Drug Exposure Data

Combining drugs may prevent resistance in cancer treatment. However, the evolution of resistance to more than one drug simultaneously - cross resistance - remains a major obstacle to durable clinical response. Here, we show that we can identify the presence and strength of cross resistance using lineage tracing data from cell populations exposed only to each drug independently, removing the need for complex combinatorial treatment experiments. Simulations where the ground truth was known showed that the method accurately recovered the true underlying resistance dynamics. We applied the framework to breast cancer, lung cancer and lymphoma datasets, quantifying cross resistance across diverse therapy types. Our predictions align with biological expectations and reveal that even drugs with shared putative targets can exhibit variable cross resistance across pre-clinical models. Finally, we used inferred cross resistance strengths to evaluate treatment strategies, predicting that high cross resistance contributes to the limited efficacy of switching between CDK4/6 inhibitors. By enabling the identification of cross resistance without requiring multi-drug exposure experiments, our framework provides a method for prioritising drug combinations and screening for novel resistance-minimising drug targets.

cancer biology↗

bioRxiv2026

Molecular Profiling and High-Content Drug Screening of Metastatic Colorectal Cancer Organoids Reveal Evolutionary Mechanisms of Pan-KRAS Inhibitor Resistance

Metastatic colorectal cancer exhibits extensive intertumoral and intratumoral heterogeneity, which underlies variable treatment responses and the inevitable emergence of drug resistance. Preclinical models that accurately reflect this heterogeneity and enable the investigation of resistance mechanisms at the clonal level, while also providing sensitive and quantitative measurements of treatment response, remain limited. Here, we describe a living biobank of patient-derived organoids (PDOs), generated from metastatic colorectal cancer samples encompassing various genetic backgrounds, including different KRAS genotypes. These PDOs retain key phenotypic, genomic, and transcriptional features of their matched tumors, and, when coupled with a multiparametric high-content 3D drug-screening platform integrating volumetric growth, nuclear content, and proliferative readouts, enable sensitive detection of subtle responses that are not captured by conventional viability-based assays. Systematic phenotypic profiling identified KRAS mutational status as a major determinant of drug response, with KRAS wild-type organoids exhibiting predominant cytotoxic collapse, whereas KRAS-mutant PDOs displayed cytostatic growth arrest. Building on this phenotypic stratification, we investigated resistance to the pan-KRAS inhibitor BI-2865 by integrating longitudinal drug exposure with expressible DNA barcoding and single-cell RNA sequencing to resolve clonal and transcriptional dynamics. Resistance to BI-2865 arose primarily through transcriptional plasticity rather than widespread genomic alterations. Lineage tracing revealed genotype-dependent trajectories, with de novo emergent clones driving resistance in KRASWT PDO, whereas KRASG13D models were dominated by expansion of pre-existing clones. Single-cell analysis on KRASG13D demonstrated a shared transcriptional landscape comprising multiple resistant states rather than a single convergent identity. Together, our findings define resistance to pan-KRAS inhibition as a modular, state-resolved process driven by transcriptional plasticity and genotype-dependent clonal dynamics and establish PDO-based multiparametric phenotyping combined with lineage-resolved single-cell profiling as a robust framework for dissecting the evolutionary framework of therapeutic resistance in metastatic colorectal cancer.

cancer biology↗

bioRxiv2025

Single-Cell Microwave Cytometry for Drug Resistance Detection in Cancer

Monitoring biophysical changes in single cells induced by drugs is crucial for advancing cancer therapies, especially for highly heterogeneous tumours. Emerging methods for single-cell drug sensitivity testing have largely relied on phenomenological analyses. Here, we present a novel electronic cytometry platform integrating microwave resonators with impedance cytometry, enabling simultaneous, label-free measurements of cell volume and dielectric permittivity at microwave frequencies. This approach uniquely captures intracellular differential responses indicative of drug-induced biophysical states, thus providing deeper insights beyond traditional phenomenological analyses. We first validated the platform using varying salt concentrations. We then demonstrated that the sensor platform could differentiate between drug resistant versus sensitive phenotypes in multiple isogenic cancer cell lines treated with cytostatic, cytotoxic and mixed-effect drugs. Notably, we showed that the technique performs successfully in patient-derived tumor organoids, a model system highlighting its immediate clinical relevance. Our findings underscore the potential of electronic measurements at the single cell level to provide informative and actionable biophysical signals relating to cellular drug response. The implementation of this platform could significantly advance cancer treatment by identifying the resistant cell populations in heterogenous tumours and optimizing selection of drugs for each patient, paving the way towards precision medicine.

bioengineering↗

bioRxiv2023

Epigenetic heritability of cell plasticity drives cancer drug resistance through one-to-many genotype to phenotype mapping

Cancer drug resistance is multi-factorial, driven by heritable (epi)genetic changes but also phenotypic plasticity. Here we dissect it by perturbing colorectal cancer patient-derived organoids longitudinally with drugs in sequence. Combining longitudinal tracking, single cell omics, evolutionary modelling, and machine leaning, we found that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory was encoded as a heritable epigenetic configuration, from which multiple transcriptional programmes could run, supporting a one-to-many (epi)genotype-to- phenotype map that explains how clonal expansions and plasticity manifest together. This may ensure drug resistance subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapies resistance was instead entirely driven by plasticity. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programmes. Collectively, our data show how genetic and epigenetic alterations are selected to "permissive epigenome" enabling phenotypic plasticity.

cancer biology↗

bioRxiv2020

Inhibition of Wnt signalling by Notch via two distinct mechanisms

Notch and Wnt are two essential signalling pathways that help to shape animals during development and to sustain adult tissue homeostasis. Although, they are often active at the same time within a tissue, they typically have opposing effects on cell fate decisions. In fact, crosstalk between the two pathways is important in generating the great diversity of cell types that we find in metazoans. However, several different mechanisms have been proposed that allow Notch to limit Wnt signalling, driving a Notch-ON/Wnt-OFF state. Here we explore these different mechanisms in vertebrate cells and demonstrate two distinct mechanisms by which Notch itself, can limit the transcriptional activity of {beta}-catenin. At the membrane, independently of DSL ligands, Notch1 can antagonise {beta}-catenin activity through an endocytosis mediated mechanism that is dependent upon its interaction with Deltex and sequesters {beta}-catenin into the membrane fraction. Within the nucleus, the intracellular domain of Notch1 can also limit {beta}-catenin induced transcription through the formation of a complex that requires its interaction with RBPj{kappa}. We believe these mechanisms contribute to the robustness of cell-fate decisions by sharpening the distinction between opposing Notch/Wnt responses.

cell biology↗

bioRxiv2019

Model-based tumor subclonal reconstruction

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

bioinformatics↗

bioRxiv2019

Exploiting evolutionary herding to control drug resistance in cancer

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called evolutionary herding that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 108-109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.

cancer biology↗

bioRxiv2019

Personalized Tumor Growth Prediction with Multiscale Tumor Modeling

BackgroundCancer is one of the most complex phenomena in biology and medicine. Extensive attempts have been made to work around this complexity. In this study, we try to take a selective approach; not modeling each particular facet in detail but rather only the pertinent and essential parts of the tumor system are simulated and followed by optimization, revealing specific traits. This leads us to a pellucid personalized model which is noteworthy as it closely approximates existing experimental results.\n\nMethodFor years, research has focused on modeling tumor growth but not many studies have put forward a framework for the personalization of models. In the present study, a hybrid modeling approach which consists of cellular automata for discrete cell state representation and diffusion equations to calculate distribution of relevant substances in the tumor micro-environment is favored. Moreover, naive Bayesian decision making with weighted stochastic equations and a Bayesian network to model the temporal order of mutations is presented. The model is personalized according to the evidence using Markov Chain Monte Carlo. Ultimately, this way of thinking about tumor modeling leads us to a vascular multi-scale model of tumor growth.\n\nResultsTo validate the tumor model, a data set belonging to the A549 cell line is used. The data represents the growth of a tumor for 30 days. We optimize the coefficients of the stochastic decision making equations using first half of the timeline. Then we predict next 15 days of growth without any other supervision. Results are promising with their low error margin and simulated growth data is in line with laboratory results.\n\nConclusionThere are many subsystems which have an effect in the growth of a tumor. A detailed model which includes all of them is currently virtually impossible to implement. We have therefore focused on a system that only includes fundamental components in this study, and have evaluated its predictions. We propose novel probability functions to obtain a personalized model and estimate the individual importance (weights) of each with parameter optimization. Our approach of using simulated annealing for parameter estimation and the subsequent validation of the prediction with in-vitro tumor growth data are, to our knowledge, unique in the literature.

bioinformatics↗