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Pre- or post-operational chemo-radiotherapy have become one of the standard adjuvant therapies in recent years. Though both chemo- and radiotherapy protocols are standardized, the question about the order in which they should be applied, or concurrency, remains an open question. In this work we attempt to answer it with mathematical modeling and optimization of two-dimensional control that represents therapy in a control-theory based approach. In order to address this problem, two issues are discussed. First, two different ways of modeling tumor growth under therapy are compared. For each of them, the necessary conditions for optimal control representing the therapy are presented and discussed. Then, Kaplan-Meier survival curves are compared for standard therapy protocols used in clinics and different approaches to model tumor growth. Finally, a framework for analysis of treatment efficacy is presented, in which optimization and survival analysis are used sequentially.

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells)., Author summary All cells in multicellular organisms obey orchestrated sequences of signals to ensure developmental and homeostatic fitness under a variety of external stimuli. However, there also exist self-perpetuating stem-cell populations, the function of which is to provide a steady supply of differentiated progenitors that in turn ensure persistence of organism functions. This “cell production engine” is an important element of biological homeostasis. A similar process, albeit distorted in many respects, plays a major role in cancer development; here the robustness of homeostasis contributes to difficulty in eradication of malignancy. An important role in homeostasis seems to be played by generation of heterogeneity among cell phenotypes, which then can be shaped by selection and other genetic forces. In the present paper, we present a model of a cultured cell population, which factors in relationships among related cells and the dynamics of cell growth and important proteins regulating cell division. We find that the model not only maintains homeostasis, but that it also responds to perturbations in a manner that is similar to that exhibited by the Wright-Fisher model of population genetics. The model-cell population can become dominated by the progeny of the fittest individuals, without invoking advantageous mutations. If confirmed, this may provide an alternative mode of evolution of cell populations.

Background Rapid changes in the expression of many messenger RNA (mRNA) species follow exposure of cells to ionizing radiation. One of the hypothetical mechanisms of this response may include microRNA (miRNA) regulation, since the amounts of miRNAs in cells also vary upon irradiation. To address this possibility, we designed experiments using cancer-derived cell lines transfected with luciferase reporter gene containing sequences targeted by different miRNA species in its 3′- untranslated region. We focus on the early time-course response (1 h past irradiation) to eliminate secondary mRNA expression waves. Results Experiments revealed that the irradiation-induced changes in the mRNA expression depend on the miRNAs which interact with mRNA. To identify the strongest interactions, we propose a mathematical model which predicts the mRNA fold expression changes, caused by perturbation of microRNA-mRNA interactions. Model was applied to experimental data including various cell lines, irradiation doses and observation times, both ours and literature-based. Comparison of modelled and experimental mRNA expression levels given miRNA level changes allows estimating how many and which miRNAs play a significant role in transcriptome response to stress conditions in different cell types. As an example, in the human melanoma cell line the comparison suggests that, globally, a major part of the irradiation-induced changes of mRNA expression can be explained by perturbed miRNA-mRNA interactions. A subset of about 30 out of a few hundred miRNAs expressed in these cells appears to account for the changes. These miRNAs play crucial roles in regulatory mechanisms observed after irradiation. In addition, these miRNAs have a higher average content of GC and a higher number of targeted transcripts, and many have been reported to play a role in the development of cancer. Conclusions Our proposed mathematical modeling approach may be used to identify miRNAs which participate in responses of cells to ionizing radiation, and other stress factors such as extremes of temperature, exposure to toxins, and drugs. Electronic supplementary material The online version of this article (10.1186/s12864-019-5464-0) contains supplementary material, which is available to authorized users.

Introduction: Elevated glucocorticoid (GC) activity, while difficult to accurately quantify, has been implicated in the pathophysiology of multiple cancer types. Approximately half of adrenocortical carcinoma (ACC) patients exhibit excess GC (GC+), which provides a unique test case to assess correlates of GC activity. The broad immunosuppressive effects of GC may limit tumor immune response and immune checkpoint inhibitor (ICI) efficacy. ACC multi-omics were analyzed to identify the molecular consequences of GC activity and assess the rationale for combining relacorilant, a glucocorticoid receptor (GR) antagonist, with an ICI in GC+ ACC. Methods: GC status, mRNA expression, DNA mutation, and DNA methylation data from distinct adrenal resections (n=71) were accessed via The Cancer Genome Atlas (TCGA). To deconvolute immune cell type abundance, xCell was applied to the mRNA data. Random forest was used to derive gene signatures. Results: The expression of 858 genes differed significantly between GC- and GC+ ACC cases. KEGG pathway analysis showed higher gene expression of 7 pathways involved in steroid synthesis and secretion in GC+ cases. Nineteen pathways showed lower expression, most of which were related to natural killer cells, T-cells, and immune activity. Hypomethylation was primarily observed in the steroid synthesis pathways. Tumor-infiltrating CD4+ memory (P=.003), CD8+ memory (P Conclusions: Reduced abundance of immune cells and immune-related transcripts in GC+ ACC provides insight into the mechanisms by which GC may limit response to ICI therapy. GR antagonism may increase immune related transcripts, thus promoting tumor immune response in GC+ ACC and other malignancies with elevated GC activity. This hypothesis will be tested in a Phase 1 trial of relacorilant + ICI. Citation Format: Andrew E. Greenstein, Mouhammed Amir Habra, Marzena Mura, Paweł Biernat, Marek J. Piatek, Andreas Grauer, Stacie Peacock Shepherd. Suppression of tumor immune activity in adrenocortical carcinoma with excess glucocorticoid [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-130.