Arsenteva, P. | Search bioRxiv

bioRxiv2024

Comparing cellular response to two radiation treatments based on key features visualization

MotivationIn modern treatment by radiotherapy, different irradiation modalities can be used, potentially producing different amounts of adverse effects. The differences between these modalities are often studied via two-sample time course in vitro experiments. The resulting data may be of high complexity, in which case simple methods are unadapted for extracting all the relevant information. MethodsIn this article we introduce network-based tools for the visualization of the key statistical features, extracted from the data. For the key features extraction we utilize a statistical framework performing estimation, clustering with alignment of temporal omic fold changes originating from two-sample time course data. ResultsThe approach was applied to real transcriptomic data obtained with two different types of irradiation. The results were analyzed using biological literature and enrichment analysis, thus validating the robustness of the proposed tools as well as achieving better understanding of the differences in the impact of the treatments in question. Availability and implementationPython package freely available here: https://github.com/parsenteva/scanofc. Contactpolina.arsenteva@u-bourgogne.fr

bioinformatics↗

bioRxiv2022

Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk. We apply PhenoPop to synthetically-generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples, and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response. MotivationTumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment failure, confounds current drug screening efforts aimed at informing therapy selection. In order to optimally select treatments, understanding the frequency and drug-response profile of individual subpopulations within a tumor is critical. Furthermore, quantitative profiles of tumor drug-response heterogeneity, in combination with predictive mathematical modeling of tumor dynamics, can be used to design effective temporal drug-sequencing strategies for tumor reduction. Here, we present a method that enables the deconvolution of tumor samples into individual sub-components exhibiting differential drug-response. This method relies on standard bulk drug-screen measurements and outputs the frequencies and drug-sensitivities of tumor subpopulations. This framework can also be applied more broadly to deconvolve cellular populations with heterogeneous responses to a variety of external stimuli and environmental factors.

cancer biology↗