Biology subjects

Bozic, I.

Publications and source records attributed to Bozic, I..

12 recordsLinked to original sources

bioRxiv2025

A fast numerical integration scheme for clonal expansion processes on graphs

Compound birth-death processes are widely used to model the age-incidence curves of many cancers [1]. There are efficient schemes for directly computing the relevant probability distributions in the context of linear multi-stage clonal expansion (MSCE) models [2]. However, these schemes have not been generalised to models on arbitrary graphs, forcing the use of either full stochastic simulations or mean-field approximations, which can become inaccurate at late times or old ages [3, 4]. Here, we present a numerical integration scheme for directly computing survival probabilities of a first-order birth-death process on an arbitrary directed graph, without the use of stochastic simulations. As a concrete application, we show that this new numerical method can be used to infer the parameters of an example graphical model from simulated data.

cancer biology↗

bioRxiv2024

Spatiotemporal dynamics of tumor-CAR T-cell interaction following local administration in solid cancers

The success of chimeric antigen receptor (CAR) T-cell therapy in treating hematologic malignancies has generated widespread interest in translating this technology to solid cancers. However, issues like tumor infiltration, the immunosuppressive tumor microenvironment, and tumor heterogeneity limit its efficacy in the solid tumor setting. Recent experimental and clinical studies propose local administration directly into the tumor or at the tumor site to increase CAR T-cell infiltration and improve treatment outcomes. Characteristics of the types of solid tumors that may be the most receptive to this treatment approach remain unclear. In this work, we develop a spatiotemporal model for CAR T-cell treatment of solid tumors, and use numerical simulations to compare the effect of introducing CAR T cells via intratumoral injection versus intracavitary administration in diverse cancer types. We demonstrate that the model can recapitulate tumor and CAR T-cell data from imaging studies of local administration of CAR T cells in mouse models. Our results suggest that locally administered CAR T cells will be most successful against slowly proliferating, highly diffusive tumors, which have the lowest average tumor cell density. These findings affirm the clinical observation that CAR T cells will not perform equally across different types of solid tumors, and suggest that measuring tumor density may be helpful when considering the feasibility of CAR T-cell therapy and planning dosages for a particular patient. We additionally find that local delivery of CAR T cells can result in deep tumor responses, provided that the initial CAR T-cell dose does not contain a significant fraction of exhausted cells.

cancer biology↗

bioRxiv2024

Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer

Cancers typically result from sequential accumulation of driver mutations in a previously healthy cell. Some of these mutations, such as inactivation of the first copy of a tumor suppressor gene, can be neutral, and some, like those resulting in activation of oncogenes, may provide cells with a selective growth advantage. We study a multi-type branching process that starts with healthy tissue in home-ostasis and models accumulation of neutral and advantageous mutations on the way to cancer. We provide results regarding the sizes of premalignant populations and the waiting times to the first cell with a particular combination of mutations, including the waiting time to malignancy. Finally, we apply our results to two specific biological settings: initiation of colorectal cancer and age incidence of chronic myeloid leukemia. Our model allows for any order of neutral and advantageous mutations and can be applied to other evolutionary settings.

cancer biology↗

bioRxiv2024

Early-stage cancer results in a multiplicative increase in cell-free DNA originating from healthy tissue

Cell-free DNA is a promising biomarker for cancer detection. However, the sources of elevated cell-free DNA (cfDNA) in patients with early-stage cancer, and the mechanisms by which cfDNA is shed into, and subsequently cleared from the circulation are still poorly understood. Leveraging a rich dataset of cfDNA in healthy individuals and early-stage cancer patients, we find a multiplicative increase in cfDNA concentration in the presence of cancer. This increase is cancer type-specific, ranging from a [~]1.3-fold increase in lung cancer, to a [~]12-fold increase in liver cancer, and does not originate from tumor, but from healthy tissue. Employing an additional dataset reporting the tissue of origin of cfDNA, we observe a significant increase in the correlation between cfDNA originating from leukocytes and from non-leukocyte sources in cancer patients. Introducing a mathematical model for cfDNA dynamics, we find that the observed correlation can be explained by a saturation mechanism in cfDNA clearance. Saturation in clearance implies that smaller increases in cfDNA shedding may lead to proportionally larger increases in cfDNA levels. Our findings quantify cfDNA dynamics in patients with cancer, with implications for improving the accuracy of liquid biopsies for early cancer detection.

cancer biology↗

bioRxiv2023

A simple stochastic model for cell population dynamics in colonic crypts

The questions of how healthy colonic crypts maintain their size, and how homeostasis is disrupted by driver mutations, are central to understanding colorectal tumorigenesis. We propose a three-type stochastic branching process, which accounts for stem, transit-amplifying (TA) and fully differentiated (FD) cells, to model the dynamics of cell populations residing in colonic crypts. Our model is simple in its formulation, allowing us to estimate all but one of the model parameters from the literature. Fitting the single remaining parameter, we find that model results agree well with data from healthy human colonic crypts, capturing the considerable variance in population sizes observed experimentally. Importantly, our model predicts a steady state population in healthy colonic crypts for relevant parameter values. We show that APC and KRAS mutations, the most significant early alterations leading to colorectal cancer, result in increased steady-state populations in mutated crypts, in agreement with experimental results. Finally, our model predicts a simple condition for unbounded growth of cells in a crypt, corresponding to colorectal malignancy. This is predicted to occur when the division rate of TA cells exceeds their differentiation rate, with implications for therapeutic cancer prevention strategies.

cancer biology↗

bioRxiv2022

Waiting times in a branching process model of colorectal cancer initiation

We study a multi-type branching process model for the development of colorectal cancer from initially healthy tissue. The model incorporates a complex sequence of driver gene alterations, some of which result in immediate growth advantage, while others have initially neutral effects. We derive analytic estimates for the sizes of premalignant subpopulations, and use these results to compute the waiting time distributions of novel driver mutations. The approach presented here is relevant to the study of finite-time properties of multi-type branching process models in which growth rates of consecutive types are non-decreasing.

cancer biology↗

bioRxiv2021

A mechanistic model of malignant transformation in vestibular schwannoma

A vestibular schwannoma (VS) is a relatively rare, benign tumour of the eighth cranial nerve, often involving alterations to the gene NF2. Previous mathematical models of schwannoma incidence have not attempted to account for alterations in specific genes, and could not distinguish between point mutations and loss of heterozygosity (LOH). Here, we present a mechanistic approach to modelling initiation and malignant transformation in Schwannoma. Each parameter is associated with a specific gene or mechanism operative in Schwann cells, and can be determined by combining incidence data with empirical frequencies of pathogenic variants and LOH. This results in new estimates for the base pair mutation rate u = 4.48 x 10-10 and the rate of LOH = 2.03 x 10-6/yr in Schwann cells. In addition to new parameter estimates, we extend the approach to estimate the risk of both spontaneous and radiation-induced malignant transformation. We conclude that radiotherapy is likely to have a negligible excess risk of malignancy for sporadic VS, with a possible exception of rapidly growing tumours.

cancer biology↗

bioRxiv2021

Deficient thalamo-cortical networks dynamics and sleep homeostatic processes in a redox dysregulation model relevant to schizophrenia.

A growing body of evidence implicates thalamo-cortical oscillations with the neuropathophysiology of schizophrenia (SZ) in both mice and humans. Yet, the precise mechanisms underlying sleep perturbations in SZ remain unclear. Here, we characterised the dynamics of thalamo-cortical networks across sleep-wake states in a mouse model carrying a mutation in the enzyme glutathione synthetase gene (Gclm-/-) associated with SZ in humans. We hypothesised that deficits in parvalbumin immunoreactive cells in the thalamic reticular nucleus (TRN) and the anterior cingulate cortex (ACC) - caused by oxidative stress - impact thalamocortical dynamics, thus affecting non-rapid eye movement (NREM) sleep and sleep homeostasis. Using polysomnographic recordings in mice, we showed that KO mice exhibited a fragmented sleep architecture, similar to SZ patients and altered sleep homeostasis responses revealed by an increase in NREM latency and slow wave activities during the recovery period (SR). Although NREM sleep spindle rate during spontaneous sleep was similar in Gclm-/- and Gcml +/+, KO mice lacked a proper homeostatic response during SR. Interestingly, using multisite electrophysiological recordings in freely-moving mice, we found that high order thalamic network dynamics showed increased synchronisation, that was exacerbated during the sleep recovery period subsequent to SD, possibly due to lower bursting activity in TRN-antero dorsal thalamus circuit in KO compared to WT littermates. Collectively, these findings provide a mechanism for SZ associated deficits of thalamo-cortical neuron dynamics and perturbations of sleep architecture.

neuroscience↗

bioRxiv2020

Inferring parameters of cancer evolution from sequencing and clinical data

As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. We apply our methodology to reconstruct the individual evolutionary histories of chronic lymphocytic leukemia patients, finding that the parental leukemic clone typically appears within the first fifteen years of life.

cancer biology↗

bioRxiv2020

Modelling CAR T-cell Therapy with Patient Preconditioning

AO_SCPLOWBSTRACTC_SCPLOWThe Federal Drug Administration (FDA) approved the first Chimeric Antigen Receptor T-cell (CAR T-cell) therapies for the treatment of several blood cancers in 2017, and efforts are underway to broaden CAR T technology to address other cancer types. Standard treatment protocols incorporate a preconditioning regimen of lymphodepleting chemotherapy prior to CAR T-cell infusion. However, the connection between preconditioning regimens and patient outcomes is still not fully understood. Optimizing patient preconditioning plans and reducing the CAR T-cell dose necessary for achieving remission could make therapy safer. In this paper, we test treatment regimens consisting of sequential administration of chemotherapy and CAR T-cell therapy on a system of differential equations that models the tumor-immune interaction. We use numerical simulations of treatment plans from within the scope of current medical practice to assess the effect of preconditioning plans on the success of CAR T-cell therapy. Model results affirm clinical observations that preconditioning can be crucial for some patients, not just to reduce side effects, but to even achieve remission at all. We demonstrate that preconditioning plans using the same CAR T-cell dose and the same total concentration of chemotherapy can lead to different patient outcomes due to different delivery schedules. Results from sensitivity analysis of the model parameters suggest that making small improvements in the effectiveness of CAR T-cells in attacking cancer cells, rather than targeting the recruitment and longevity of CAR T-cells, will significantly reduce the minimum dose required for successful treatment. Our modeling framework represents a starting point for evaluating the efficacy of patient preconditioning in the context of CAR T-cell therapy.

cancer biology↗

bioRxiv2020

Mathematical Model of Colorectal Cancer Initiation

Quantifying evolutionary dynamics of cancer initiation and progression can provide insights into more effective strategies of early detection and treatment. Here we develop a mathematical model of colorectal cancer initiation through inactivation of two tumor suppressor genes and activation of one oncogene, accounting for the well-known path to colorectal cancer through loss of tumor suppressors APC and TP53, and gain of the KRAS oncogene. In the model, we allow mutations to occur in any order, leading to a complex network of incomplete mutational genotypes on the way to colorectal cancer. We parametrize the model using experimentally measured parameter values, many of them only recently available, and compare its predictions to epidemiological data on colorectal cancer incidence. We find that the reported incidence of colorectal cancer can be recovered using a mathematical model of colorectal cancer initiation together with experimentally measured mutation rates in colorectal tissues and proliferation rates of premalignant lesions. We demonstrate that the order of driver events in colorectal cancer is determined by the combined effect of the rates at which driver genes are mutated and the fitness effects they provide. Our results imply that there may not be significant immune suppression of untreated benign and malignant colorectal lesions.

cancer biology↗

bioRxiv2019

On measuring selection in cancer from subclonal mutation frequencies

Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides precise quantification of the validity of the 1/f statistic across the entire range of all relevant parameter values. Our results are also applicable to the question of distinguishing driver and passenger mutations in a general exponentially expanding population.

cancer biology↗