Biology subjects

Rockne, R. C.

Publications and source records attributed to Rockne, R. C..

At least 19 recordsLinked to original sources

bioRxiv2026

NEIL2 regulates RNA polymerase II dynamics to suppress transcription replication conflicts in pancreatic cancer

Transcription replication conflicts (TRCs) arise from spatiotemporal interference between replication and transcription machinery and contribute to genome instability in cancer cells. In pancreatic ductal adenocarcinoma (PDAC) cells, oncogenic KRAS drives TRC formation, and base excision repair (BER) contributes to TRC resolution as an adaptive response. Here, we show that TRCs are enriched in pancreatic tumor sections from patients and are associated with malignant progression. We identify NEIL2, a bifunctional DNA glycosylase in the BER pathway, as a critical regulator of TRCs in PDAC cells. Mechanistically, NEIL2 loss causes aberrant RNA polymerase II (RNAPII) occupancy at transcription start sites and downstream of transcription end sites. Dysregulated RNAPII dynamics promote R-loop-dependent and -independent TRCs contributing to replication stress, DNA damage, and impaired cancer cell proliferation. Notably, the DNA-binding motif of NEIL2 necessary for canonical BER function is dispensable for TRC regulation. Together, these findings reveal an unrecognized role for NEIL2 in regulating RNAPII dynamics and mitigating TRCs in pancreatic cancer cells.

molecular biology↗

bioRxiv2026

Intranasal L-MYC Neural Stem Cells Restore Neurovascular and Neuroimmune Homeostasis After TBI via CX3CL1 Upregulation and ICAM-1 Downregulation

Traumatic brain injury (TBI) triggers persistent neuroimmune and neurovascular dysfunction that contributes to secondary brain injury and limits recovery. Here, we investigated whether a single intranasal dose of LMNSC01 could reprogram neuroinflammatory and neurovascular responses post TBI and whether these effects were conserved across biological sex. Immunocompetent male and female rats underwent a controlled cortical impact (CCI) or Sham surgery and 7 days later received a single bolus dose of LMNSC01 (6 x10^6 cells/dose) or vehicle. Integrated histological and transcriptomic analyses and multiplex tissue ELISA were used to characterize injury- and treatment-associated responses. CCI induced persistent but sex-dependent transcriptional changes. Male rats exhibited prominent innate immune, adhesion, extracellular matrix, and tissue remodeling responses, whereas females demonstrated broader activation of microglial, interferon, complement, MHC/antigen presentation, and humoral/adaptive immune pathways. LMNSC01 treatment reprogrammed these distinct injury states through partially sex-specific mechanisms. In males, LMNSC01 increased endogenous immune-regulatory mediators, including Il1rn, and reduced pathways associated with adhesion and immune cell trafficking. In females, treatment decreased expression of genes related to microglial, leukocyte, MHC/antigen presentation, cytokine, and adhesion pathways, and reduced macrophage- and CD45 positive cell signatures. Despite these sex-dependent transcriptional changes, LMNSC01 produced conserved protein changes in both sexes, reducing injury associated ICAM-1 and restoring CX3CL1 proteins, proposing a novel molecular mechanism of LMNSCs modulating endothelial activation and neuron-microglia communication post injury. Our study provides further insight into sex differences in responses to injury and treatment after TBI and highlights candidate repair mechanisms, supporting further development of LMNSC01 as a minimally invasive therapy for TBI.

neuroscience↗

bioRxiv2026

Extracellular Vesicles Derived from L-MYC Neural Stem Cells Mediate Neuroprotection in 3D Models of Chemotherapy- and Radiation-Induced Neurotoxicity

Background/Objectives: Cancer survivors frequently experience long-term neurocognitive impairments following chemotherapy and cranial irradiation, yet experimental models that enable mechanistic investigation of therapy-induced neurotoxicity at the transcriptional level remain limited. This study aimed to develop a human three-dimensional (3D) neural tissue model derived from L-Myc immortalized neural stem cells (LMNSCs) and use transcriptomic profiling to identify molecular pathways underlying chemotherapy- and radiation-induced neural injury and extracellular vesicle (EV)-mediated recovery. Methods: LMNSCs were differentiated in a 3D, methylcellulose-based culture to generate neural tissue containing neurons, astrocytes, and oligodendrocytes. Cultures were exposed to methotrexate (MTX) or ionizing radiation to induce neural injury and subsequently treated with LMNSC-derived EVs. Neural injury and repair mechanisms were evaluated by immunocytochemistry and bulk transcriptomics. Results: MTX and irradiation induced dose-dependent injury, exhibited by loss of neuronal complexity and reduced glial populations. LMNSC-EV treatment promoted recovery of neuronal and glial populations following MTX- and irradiation-induced injury. Transcriptomic analysis of irradiated cultures revealed activation of inflammation, DNA damage, and stress-response pathways, which were attenuated after treatment with LMNSC-EVs. Conclusions: LMNSC-based 3D neural tissue provides a human-relevant platform for modeling cancer therapy-induced neurotoxicity. Furthermore, LMNSC-EVs represent a promising cell-free regenerative therapeutic that restores injury-associated inflammatory, stress, and metabol-ic transcriptional programs after radiation-induced neural injury.

cancer biology↗

bioRxiv2026

Resolving Immune Lineage and Cell-State Heterogeneity in Human PBMCs via Mass Spectrometry-Based Single-Cell Proteomics

Single-cell proteomics (SCP) currently lacks validated benchmarking standards, and cell annotation often relies on transcriptomic proxies. Unsupervised clustering offers a proxy-free alternative, but its success depends on biological signal outweighing technical variation. In homogeneous samples this is achievable, but in heterogeneous populations, where closely related cell types differ only subtly, technical variation can dominate the clustering and obscure the biology needed for annotation. To address this, we developed an integrated experimental and computational pipeline for protein-level cell annotation and applied it to human PBMCs as an immune-cell test case. We isolated T cells, B cells, monocytes, and NK cells by negative-selection sorting to build a high-fidelity reference. In parallel, unsorted PBMCs from the same donor were processed on a cellenONE and acquired using label-free DIA on an Orbitrap Astral Zoom. Using the labeled reference dataset, we systematically benchmarked normalization, imputation, and clustering methods to assess their effect on cell-type separation. Unsupervised analysis resolved functional subpopulations within each lineage, and a probabilistic SCP classifier trained on these annotations identified the corresponding cell types and states in the unsorted PBMC fraction, validating the pipeline on unenriched, heterogeneous samples. Together, this work delivers an analytically benchmarked SCP workflow that resolves immune lineage and cell-state heterogeneity in human PBMCs and provides a classifier-ready, protein-level reference for immune-cell assignment.

bioinformatics↗

bioRxiv2026

Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia

Temporal dynamics of the peripheral blood transcriptome are crucial for understanding leukemia evolution and response to therapy because they can reveal how gene expression programs drive abnormal cell states, disease heterogeneity, and treatment resistance. Using a mathematical model of state-transitions, we studied the temporal dynamics of peripheral blood messenger RNA (mRNA) and microRNA (miRNA) transcriptomes in a mouse model of acute myeloid leukemia (AML). In our state-transition model, mRNA and miRNA transcriptomes are represented as a particle undergoing Brownian motion in a two-dimensional multiomic potential landscape. Following chemotherapy, we observed a temporal desynchronization between mRNA and miRNA transcriptomic responses corresponding to an asymmetric shift in the landscape. Specifically, mRNA trajectories responded almost immediately post-treatment, whereas miRNA responses were delayed by approximately two weeks. Clustering analysis identified that the temporal delay is driven by a prominent cluster of miRNAs from the imprinted Dlk1-Dio3 region. Although previously implicated in acute promyelocytic leukemia, lymphomas, and metabolic dysregulation, this provides the first evidence linking the Dlk1-Dio3 locus to AML chemotherapy response and treatment-induced transcriptomic desynchronization. This framework offers an innovative dynamics-based strategy to identify biological drivers of therapeutic response and novel therapeutic targets across hematological malignancies. Key PointsO_LITreatment can induce desynchronization between messenger RNA and microRNA transcriptome dynamics in a murine model of AML C_LIO_LIMicroRNAs from the imprinted Dlk1-Dio3 locus are identified as key contributors to desynchronization and may serve as therapeutic targets C_LI

cancer biology↗

bioRxiv2026

Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response

Intra-tumor heterogeneity in proliferation rates fundamentally influences cancer progression and treatment resistance. To investigate how continuous phenotypic variation shapes eco-evolutionary dynamics, we develop a phenotype-structured partial differential equation framework that explicitly models proliferation heterogeneity as a dynamic trait. Our model integrates three key biological principles: (1) phenotypic diffusion capturing heritable variation in proliferation rates, (2) global resource competition enforcing density-dependent growth constraints, and (3) an experimentally grounded life-history trade-off linking elevated proliferation to increased mortality. Using adaptive dynamics, we derive the optimum proliferation rate in a growing tumor, showing that the optimal phenotype dynamically shifts toward slower proliferation as tumors approach carrying capacity under control condition. We perform in silico treatment simulations for four different treatment regimes (pan-proliferation, low-, mid-, and high-proliferation targeting) to show how therapeutic selective pressures reshape fitness landscapes. While all treatments slow down tumor growth, they induce divergent evolutionary trajectories. We connect these dynamics with changes in mean proliferation rates during and after treatment. Our work establishes a predictive, evolutionarily grounded framework for understanding how therapy reshapes tumor proliferation landscapes, offering a mechanistic basis for designing strategies that anticipate and counteract adaptive resistance.

bioinformatics↗

bioRxiv2025

Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia

Single cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cancer, yet identifying meaningful disease states from single cell data remains challenging. Here, we systematically explore the chronic myeloid leukemia (CML) specific information content encoded in single cell versus bulk transcriptomics to resolve this paradox and clarify how discrete disease-defining states emerge from inherently noisy single cell data. We demonstrate that, while CML single cell transcriptomes exist along continuous transcriptional microstates, clinically relevant leukemia phenotypes clearly manifest only at the pseudobulk (macrostate) level. By leveraging state-transition theory, we reveal how robust disease phenotype state-transitions are governed by cell type specific contributions. Our results establish a theoretical framework explaining why discrete disease phenotypes remain hidden at the single cell scale but emerge clearly at the aggregated macrostate level, enabling previously inaccessible biological insights into leukemia evolution. By resolving how single-cell variation aggregates into macroscopic disease states, our framework provides new insight into CML progression and offers a broadly applicable strategy for exploring disease dynamics across cancers and other complex conditions.

systems biology↗

bioRxiv2025

Lymphocytes and monocytes undergo swift suppression of IL-10R, IL-6R, and IL-2Rβγ signaling under high concentrations of different cytokines

The JAK-STAT signaling pathway is fundamental for immune system regulation. It involves phosphorylation of several types of STAT proteins in response to binding of cytokines to immune cell receptors. Traditionally, the immune signaling studies focus on measuring the levels of phosphorylated STATs (pSTATs) following individual cytokine application. We developed an experimental approach, based on multiparametric flow cytometry, to simultaneously measure the levels of five pSTATs after 15 minutes of cell treatment with high doses of individual cytokines and their paired combinations. Analysis of our experimental data involving peripheral blood mononuclear cells from healthy donors reveals systematic suppression of IL-10R, IL-6R, and IL-2R{beta}{gamma} signaling in T cells, B cells, NK cells, and monocytes. This suppression is mediated by at least all tested cytokines that do not induce relevant pSTATs by themselves. Remarkably, the cytokines with negligible own signaling do act as prominent selective signaling suppressors. In contrast, the signaling of IFNAR, IFNGR, IL-4R, and IL-2R{beta}{gamma} remains largely unaffected by co-application of other cytokines. We propose that this pattern of signaling suppression represents an evolutionary developed mechanism enhancing the promptness, specialization, and efficiency of the immune response, while increased concentration of cytokines serves as a danger signal of inefficient response. We hypothesize that selective signaling suppression arises from the differential sensitivity of conformations of cytokine-receptor complexes to the increase of cell surface tension and stiffness, which is caused by effects following the binding of cytokines to membrane-associated molecules, including glycocalyx elements. While the rewiring of immune cell signaling should represent a powerful evolutionary tool for augmentation of adaptive response, it should also lead to the prolonged suppression of counteracting signaling pathways, culminating in cytokine release syndrome and contributing to autoimmune diseases.

immunology↗

bioRxiv2025

Interstitial fluid transport dynamics predict glioblastoma invasion and progression

Glioblastoma is characterized by aggressive infiltration into surrounding brain tissue, hindering complete surgical resection and contributing to poor patient outcomes. Identifying tumor-specific invasion patterns is essential for advancing our understanding of glioblastoma progression and improving surgical and radiotherapeutic strategies. Here, we leverage in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively quantify interstitial fluid velocity, direction, and diffusion within and around glioblastomas. We introduce a novel vector-based pathline analysis to trace downstream accumulation of fluid flow originating from the tumor core, providing a spatially explicit perspective on local flow patterns. We find that localized fluid transport metrics predict glioblastoma invasion and progression, offering a new framework to non-invasively identify high-risk regions and guide targeted treatment approaches. One sentence summaryInvasion and progression of glioblastoma can be predicted with interstitial fluid flow patterns via magnetic resonance imaging.

bioengineering↗

bioRxiv2025

Mathematical Modeling of Neural Stem Cell Migration within Brain using Multi-Fiber Tractography

An agent-based model of therapeutic neural stem cell (NSC) migration is developed and used to predict the migration of NSCs in naive mouse brain. The model utilizes generalized q-sampling imaging which resolves white matter fibers that cross in the brain and is shown to better account for variations in NSC migration patterns as compared to diffusion tensor imaging. In calibrating the model to experimental data, we show that the model is able to reproduce the distribution of NSCs in the mouse brain. In addition, we show that the distribution of NSCs in the mouse brain is sensitive to the location of NSC injection. Persistent distribution of NSCs to the olfactory bulb, consistent with developmental pathways including the rostral migratory stream, suggests that future models of therapeutic NSCs in the naive brain may need to include other factors such as chemotaxis or blood flow to account for variations in NSC migration paths. The results highlight the usefulness of the model in predicting which injection locations may provide the best distribution of NSCs to a given target location.

systems biology↗

bioRxiv2025

Ligand Discrimination in Immune Cells: Signal Processing Insights into Immune Dysfunction in ER+ Breast Cancer

Prior studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis, and the presence of these defects predicts overall survival. Therefore, it is of interest to quantitatively characterize and measure signaling errors in immune signaling systems in these patients. Here we propose a novel approach combining communication theory and signal processing concepts to model ligand discrimination in immune cells in the peripheral blood. We use the model to measure the specificity of ligand discrimination in the presence of molecular noise by estimating the probability of error, which is the probability of making a wrong ligand identification. We apply our model to the JAK/STAT signaling pathway using high dimensional spectral flow cytometry measurements of transcription factors, including phosphorylated STATs and SMADs, in immune cells stimulated with several cytokines (IFN{gamma}, IL-2, IL-6, IL-4, and IL-10) from 19 ER+ breast cancer patients and 32 healthy controls. In addition, we apply our model to 10 healthy donor samples treated with a clinically approved JAK1/2 inhibitor. Our results show reduced ligand identification accuracy and higher levels of molecular noise in BC patients as compared to healthy controls, which may indicate altered immune signaling and the potential for immune cell dysfunction in these patients. Moreover, the inhibition of JAK1/2 produces ligand misidentification and molecular noise rates similar to, or even greater than, those observed in breast cancer. These results suggest a means to improve the use of signaling kinase inhibitor therapies by identifying patients with favorable ligand discrimination specificity profiles in their immune cells. One Sentence SummaryWe use a communication model to measure ligand discrimination errors in immune cells and molecular noise in cytokine signaling in ER+ breast cancer patients as compared to healthy controls.

cancer biology↗

bioRxiv2025

CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma

Glioblastoma is a highly aggressive and treatment-resistant primary brain cancer. While chimeric antigen receptor (CAR) T-cell therapy has demonstrated promising results in targeting these tumors, it has not yet been curative. An innovative approach to improve CAR T-cell efficacy is to combine them with other immune modulating therapies. In this study, we investigate in vitro combination of IL-13R2 targeted CAR T-cells with an oncolytic virus (OV) and study the complex interplay between tumor cells, CAR T-cells, and OV dynamics with a novel mathematical model. We fit the model to data collected from experiments with each therapy individually and in combination to reveal determinants of therapy synergy and improved efficacy. Our analysis reveals that the virus bursting size is a critical parameter in determining the net tumor infection rate and overall combination treatment efficacy. Moreover, the model predicts that administering the oncolytic virus simultaneously with, or prior to, CAR T-cells could maximize therapeutic efficacy.

immunology↗

bioRxiv2024

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

Targeted radionuclide therapy is based on injections of cancer-specific molecules conjugated with radioactive nuclides. Despite the specificity of this treatment, it is not devoid of side-effects limiting its use and is especially harmful for rapidly proliferating organs well perfused by blood, like bone marrow. Optimization of radioconjugates administration accounting for toxicity constraints can increase treatment efficacy. Based on our experiments on disseminated multiple myeloma mouse model treated by 225Ac-DOTA-daratumumab, we developed a mathematical model which investigation highlighted the following principles for optimization of targeted radionuclide therapy. 1) Nuclide to antibody ratio importance. The density of radioconjugates on cancer cells determines the density of radiation energy deposited in them. Low labeling ratio as well as accumulation of unlabeled antibodies and antibodies attached to decay products in the bloodstream can mitigate cancer radiation damage due to excessive occupation of specific receptors by antibodies devoid of radioactive nuclides. 2) Cancer binding capacity-based dosing. The rate of binding of drug to cancer cells depends on the total number of their specific receptors, which therefore can be estimated from the pharmacokinetic curve of diagnostic radioconjugates. Injection of doses significantly exceeding cancer binding capacity should be avoided since radioconjugates remaining in the bloodstream have negligible efficacy to toxicity ratio. 3) Particle range-guided multi-dosing. The use of short-range particle emitters and high-affinity antibodies allows for robust treatment optimization via initial saturation of cancer binding capacity, enabling redistribution of further injected radioconjugates and deposited dose towards still viable cells that continue expressing specific receptors. SignificanceMathematical modeling yields general principles for optimization of targeted radionuclide therapy in mouse models of multiple myeloma that can be extrapolated on another cancer models and on clinical setting.

cancer biology↗

bioRxiv2023

Structural and practical identifiability of contrast transport models for DCE-MRI

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

systems biology↗

bioRxiv2023

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokines cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer. Significance StatementWhile individual cytokine responses have previously been observed to be altered in breast cancer, cytokine signaling responses are tightly interconnected in a way that has not been previously characterized. Here, we profile cytokine signaling responses and find alterations that are shared across both pathways and cell types. The signatures across these measurements better define the alterations and point to a broad immunosuppression response. HighlightsO_LIBaseline and post-stimulation cytokine signaling profiles differ between healthy donors and breast cancer patients. C_LIO_LIChanges in cytokine response are not explained by differences in abundance of the cognate receptor C_LIO_LIFeatures of signaling response and receptor abundance dysregulation are coordinated across patients C_LIO_LIIntegrated patterns of dysregulation in breast cancer patients share features of Th17 like-response as well as regulatory-like B and CD8+ T cells C_LI

cancer biology↗

bioRxiv2023

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia (CML)

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML statespace and a three-well leukemogenic potential landscape. The potentials stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.

cancer biology↗

bioRxiv2023

Non-invasive measurement of intra-tumoral fluid dynamics with localized convolutional function regression

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response. One-Sentence SummaryA physics-informed computational method enables accurate and efficient measurement of fluid dynamics in individual patient tumors and demonstrates differences between tissues.

bioengineering↗

bioRxiv2022

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

cancer biology↗