Biology subjects

Leder, K.

Publications and source records attributed to Leder, K..

9 recordsLinked to original sources

bioRxiv2026

Explaining and predicting pseudoprogression in immunotherapythrough joint modeling of immune infiltration and ctDNA dynamics

Pseudoprogression in response to immune checkpoint inhibitor (ICI) therapy can be a complex challenge during treatment response evaluation. Erroneous classification of progression may result in stopping effective treatment for patients, while delayed confirmation of progression can prolong an ineffective treatment course. We develop a mechanistic mathematical model of tumor-immune dynamics under ICI treatment in which observed tumor volume includes contributions from both tumor burden and immune infiltration. We show analytically how pseudoprogression can arise from increased immune infiltration and proliferation as well as continued tumor growth during the ``ramp-up" phase. The model also incorporates circulating tumor DNA (ctDNA) dynamics, which we demonstrate are able to closely fit existing longitudinal tumor and ctDNA data and extract realistic parameter ranges to generate representative in silico data. Using model-generated cohorts, we train a random forest (RF) classifier using longitudinal tumor and ctDNA measurements to distinguish pseudoprogression and true progression, and demonstrate that it improves response classification relative to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and provides classification earlier than the immunotherapy-specific iRECIST, even in the presence of tumor measurement noise. This work predicts that paired longitudinal tumor-ctDNA measurements may enable earlier discrimination of true progression from pseudoprogression, and motivates collection of imaging and ctDNA data synchronously during ICI therapy, especially in the setting of suspected disease progression.

cancer biology↗

bioRxiv2026

LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors

Tumors contain cancer cells with diverse growth potentials that shape evolutionary trajectories, yet this fitness diversity remains difficult to quantify in cases of whole-genome duplication (WGD) and chromosomal instability. We present LeafRank, a mathematical framework that leverages single-cell DNA-seq phylogenies to infer the relative fitness of individual cells. Using a multi-type branching process model, LeafRank integrates full tree topology, including branch lengths and bifurcation patterns, to estimate marginal fitness probabilities under punctuated evolutionary regimes driven by rare driver events. To account for elevated aberration rates following WGD, we introduce a tree-rescaling strategy that adjusts for lineage-specific genomic instability. Unlike methods focused on predefined subclones, LeafRank ranks all sampled cells, enabling flexible assessment of growth heterogeneity. Simulations demonstrate high accuracy across spatial and non-spatial virtual tumors. Applied to ovarian cancer, LeafRank reveals directional and parallel selection in WGD tumors and identifies recurrent copy number events enriched in high-fitness lineages. WGD lineages do not show immediate growth advantages but acquire fitness through subsequent alterations.

bioinformatics↗

bioRxiv2026

Modeling CISH-KO TIL therapy: T-cell persistence and endogenous competition determine response in gastrointestinal cancer

BackgroundTumor-infiltrating lymphocyte (TIL) therapies have shown promise in murine models; however, early-phase clinical trials reveal substantial heterogeneity in treatment response across patients. Elucidating the biological drivers of this variability is critical for improving patient selection and therapeutic design. MethodsWe developed a mechanistic mathematical model of Cish-inactivated TIL therapy capturing interactions among tumor cells, endogenous TILs, and infused CISH-KO (Cish gene knockout) T-cells. The model was calibrated to longitudinal tumor size data from two murine studies and also fitted to clinical data from a first-in-human phase 1 trial enrolling 22 and treating 12 patients with metastatic gastrointestinal cancers (27 independently tracked tumor lesions). We used the calibrated model to identify patient-level determinants of response and to simulate combination and fractionation treatment strategies in silico. FindingsFour mechanistically distinct parameters emerged as dominant determinants of therapeutic outcome: CISH-KO T-cell persistence (r = 0.827, p < 10-4) was the single strongest predictor of therapeutic failure; other major determinants included endogenous T-cell proliferation rate (r = 0.648, p < 10-4) , CISH-KO T-cell killing rate, and intrinsic tumor growth rate. In silico simulations predicted that the addition of anti-PD-1 checkpoint blockade to standard-dose CISH-KO TIL therapy achieved comparable or superior tumor control relative to dose doubling, while maintaining lower peak T-cell level. Additionally, fractionating the same total dose across multiple delayed infusions further improved predicted tumor control by prolonging CISH-KO T-cell persistence, again without raising peak effector burden. InterpretationThe efficacy of current CISH-KO TIL protocols is influenced not only by the cytotoxic potency of infused T-cells, but also by their persistence within the tumor microenvironment and competitive pressure from the reconstituting host immune compartment. These findings support strategies that enhance the functional efficiency and prolong the availability of infused T-cells--such as concurrent PD-1 blockade or fractionated dosing of the same total cell product--rather than dose escalation alone and identify CISH-KO T-cell persistence and endogenous immune competition as actionable targets for improving TIL-based immunotherapies.

Cancer Biology↗

bioRxiv2026

Understanding heterogeneous responses to T cell engagers: Binding characteristics and dosing thresholds determine cytotoxic efficacy

Therapeutic efficacy of multivalent T cell engagers varies widely across individuals, but the basis for this heterogeneity remains poorly understood. Here, we integrate in vitro experiments of antitumor immune responses with a mechanistic modeling framework to investigate sources of response variability across T cell donors and TE constructs, focusing on a novel multivalent bispecific T cell engager currently in development. We identify parameter regimes that accurately recapitulate dose-response behaviors across T cell donors and doses, and perform cross-validation studies that demonstrate the models predictive accuracy. We find that variability in therapy efficacy is governed by the relationship between binding affinity and dose. When dose exceeds the binding affinity, responses are relatively robust across donors; when dose is below the binding affinity, responses are more donor dependent. At smaller doses, the TE-specific shape characteristics of the tumor-binding dose response, its steepness in particular, is a key marker of therapy efficacy. More generally, our integrated modeling and experimental framework offers insights and tools that are applicable to other bispecific T-cell engagers, and provides a quantitative foundation for the systematic, in silico optimization of TE design and dosing strategies.

immunology↗

bioRxiv2024

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Circulating tumor DNA assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes, and utilize them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, based on ctDNA longitudinal features, for targeted therapy, chemotherapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort dataset. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation - which we hypothesize to hold valuable prognostic information on longer-term treatment response. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biological mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment, and generates hypotheses for further clinical testing.

cancer biology↗

bioRxiv2022

Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

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↗

bioRxiv2020

TaqMan Array Cards enable monitoring of diverse enteric pathogens across environmental and host reservoirs

BackgroundMultiple bacteria, viruses, protists, and helminths cause enteric infections that greatly impact human health and wellbeing. These enteropathogens are transmitted via several pathways through human, animal, and environmental reservoirs. Individual quantitative PCR (qPCR) assays have been extensively used to detect enteropathogens within these types of samples, whereas the TaqMan Array Card (TAC) that allows simultaneous detection of multiple enteropathogens has only previously been validated in human clinical samples. MethodsHere, we performed a comprehensive double-blinded comparison of the performance of a custom TAC relative to standard qPCR for the detection of eight enteric targets, by using spiked samples, wastewater from Melbourne (Australia), and human, animal, and environmental samples from informal settlements in Suva, Fiji. FindingsBoth methods exhibited high and comparable specificity (TAC: 100%, qPCR: 94%), sensitivity (TAC: 92%; qPCR: 100%), and quantitation accuracy (TAC: 91%; qPCR: 99%) in non-inhibited sample matrices. PCR inhibitors substantially impacted detection via TAC, though this issue was alleviated by 10-fold sample dilution. Among samples from informal settlements, the two techniques were comparable for detection (89% agreement) and quantitation (R2 = 0.82). The TAC additionally included 38 other targets, enabling detection of diverse faecal pathogens and extensive environmental contamination that would be prohibitively labour intensive to assay by standard qPCR. InterpretationOverall, the two techniques produce comparable results across diverse sample types, with qPCR prioritising greater sensitivity and quantitation accuracy, and TAC trading small reductions in these for a cost-effective larger enteropathogen panel that enables a greater number of enteric pathogens to be analysed concurrently, which is beneficial given the abundance and variety of enteric pathogens in environments such as urban informal settlements. The ability to monitor multiple enteric pathogens across diverse reservoirs in turn allows better resolution of pathogen exposure pathways, and the design and monitoring of interventions to reduce pathogen load. FundingWellcome Trust Our Planet, Our Health program [OPOH grant 205222/Z/16/Z].

microbiology↗

bioRxiv2018

Glioblastoma recurrence and the role of MGMT promoter methylation

Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT has been associated with sensitivity to TMZ, while increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors. However, the evolutionary processes driving this shift, and more generally the emergence and growth of TMZ-resistant tumor subpopulations, are still poorly understood. Here we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM (surgery, chemotherapy and radiation). We first find that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for GBM patients with high and low levels of MGMT methylation at diagnosis.

cancer biology↗