Biology subjects

Rockne, R.

Publications and source records attributed to Rockne, R..

7 recordsLinked to original sources

bioRxiv2026

Radioligand therapy in combination with CAR T cells overcomes the heterogeneous immunosuppressive prostate tumor microenvironment

177Lu-PSMA-617 (PluvictoTM, Lu-177 RLT) is an FDA-approved targeted radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC), but its durability of response to this singular approach poses a challenge to the field. Chimeric antigen receptor (CAR) T cell therapy has revolutionized clinical practice for hematological malignancies, but its clinical development for solid tumors, including mCRPC, has been encumbered by antigen heterogeneity and the immunosuppressive tumor microenvironment (TME). Here, we evaluate the therapeutic combination of Lu-177 RLT and PSCA-CAR T cells to overcome these barriers. In human xenograft and mouse syngeneic prostate cancer models with homogeneous or heterogeneous antigen expression, the sequential administration of Lu-177 RLT, cyclophosphamide (Cy), and PSCA-CAR T cells improves tumor control and prolongs survival compared to monotherapies. Mechanistically, Lu-177 RLT alone or with Cy remodels the TME by promoting pro-inflammatory myeloid responses and activating endogenous T cells, while enhancing CAR T cell activation and effector function. We additionally evaluated 225Ac-PSMA-617 RLT as an emerging approach in combination with CAR T cells and observed anti-tumor responses, supporting its potential as an alternative RLT partner. These findings support RLT as an immune priming strategy to enhance CAR T cell therapy and provide a rationale for clinical translation of this combination in mCRPC. One Sentence SummaryCombining 177Lu-PSMA-617 radioligand therapy with PSCA-CAR T cells improves tumor control and survival in prostate cancer models by overcoming the antigen heterogeneity and reshaping the immunosuppressive tumor microenvironment.

cancer biology↗

bioRxiv2025

Study of combination CAR T-cell treatment for glioblastoma using mathematical modeling

Glioblastoma is a highly aggressive and difficult-to-treat brain cancer that resists conventional therapies. Recent advances in chimeric antigen receptor (CAR) T-cell therapy have shown promising potential for treating glioblastoma; however, achieving optimal efficacy remains challenging due to tumor antigen heterogeneity, the tumor microenvironment, and T-cell exhaustion. In this study, we developed a mathematical model of CAR T-cell therapy for glioblastoma to explore combinations of CAR T-cell treatments that take into account the spatial heterogeneity of antigen expression. Our hybrid model, created using the multicellular modeling platform PhysiCell, couples partial differential equations that describe the tumor microenvironment with agent-based models for glioblastoma and CAR T-cells. The model captures cell-to-cell interactions between the glioblastoma cells and CAR T-cells throughout treatment, focusing on three target antigens: IL-13R2, HER2, and EGFR. We analyze tumor antigen expression heterogeneity informed by expression patterns identified from human tissues and investigate patient-specific combination CAR T-cell treatment strategies. Our model demonstrates that an early intervention is the most effective approach, especially in glioblastoma tumors characterized by mixed antigen expression. However, in tissues with clustered antigen patterns, we find that sequential administration with specific CAR T-cell types can achieve efficacy comparable to simultaneous administration. In addition, spatially targeted delivery of CAR T-cells to specific tumor regions with matching antigen is an effective strategy as well. Our model provides a valuable platform for developing patient-specific CAR T-cell treatment plans with the potential to optimize scheduling and locations of CAR T-cell injections based on individual antigen expression profiles.

systems biology↗

bioRxiv2025

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy originating in the bone marrow, frequently progressing to extramedullary sites. Despite advances in molecularly targeted therapies and hematopoietic stem cell transplantation, clinical outcomes remain suboptimal. Tyrosine kinase inhibitors (TKIs) confer benefit in a subset of AML patients harboring FLT3-ITD mutations, yet relapse and resistance are common. These failures are driven by both intrinsic properties of leukemic stem cells (LSCs)--a quiescent, self-renewing population--and extrinsic cues from the tumor microenvironment. We previously demonstrated that arteriolar endothelial cells (ECs) produce miR-126, which is transferred to LSCs, promoting quiescence, treatment resistance, and niche retention. During disease progression, TNF- secreted by expanding blasts suppresses EC miR-126 production, enabling LSCs and their progeny to proliferate. Following TKI administration, blast reduction lowers TNF- levels, restoring EC miR-126 production and enabling LSCs to re-enter quiescence--thereby escaping therapy and facilitating relapse. To investigate this dynamic, we developed an agent-based computational model of the AML bone marrow microenvironment, parameterized with in vitro and in vivo data. The model captures vascular niche remodeling and the feedback between leukemic populations and endothelial signaling. Simulations reveal that LSC protection mediated by miR-126 can be overcome by combining TKIs with miRisten, a miR-126 inhibitor. When administered on a defined schedule, this combination disrupts the protective niche and enhances LSC eradication. These findings underscore the therapeutic potential of targeting microenvironmental feedback to overcome resistance and prevent AML relapse.

cancer biology↗

bioRxiv2025

Modeling cerebral development in vitro with L-MYC-immortalized human neural stem cell-derived organoids

A promising advance for ex vivo studies of human brain development and formulation of therapeutic strategies has been the adoption of brain organoids that, to a greater extent than monolayer or spheroid cultures, recapitulate to varying extents the patterns of tissue development and cell differentiation of human brain. Previously, such studies been hampered by limited access to relevant human tissue, inadequate human in vitro models, and the necessity of using rodent models that imperfectly reproduce human brain physiology. Here we present a novel organoid-based research platform utilizing L-MYC-immortalized human fetal neural stem cells (LMNSC01) grown in a physiological 4% oxygen environment. We visualized developmental processes in LMNSC01 brain organoids for over 120 days in vitro by immunofluorescence and NanoString gene expression profiling. Gene expression patterns revealed by NanoString profiling were quantitatively compared to those occurring during normal brain development (BrainSpan database) using the Singscore method. We observe similar developmental patterns in LMNSC01 organoids and developing cortex for genes characterizing neurons, astrocytes, and oligodendrocytes, and multiple pathways including those involved in apoptosis, neuronal cytoskeleton, neurotransmission, and metabolism. Notable properties of this LMNSC01 platform are its initiation with immortalized authentic human neural stem cells, growth in a physiological oxygen environment, the consistency of the organoids produced, and favorable comparison of their gene expression patterns with those reported for normal cortical development. SUMMARYEx vivo studies of human brain development has been advanced by adoption of organoids recapitulating to varying extents in utero patterns of tissue development and cell differentiation. We here present an organoid-based human cortical development platform employing immortalized fetal neural stem cells (LMNSC01) grown in a physiological (4% oxygen) environment. Characterizing LMNSC01 organoids for over 120 days in vitro by immunofluorescence and expression profiling (using NanoString), and then comparing these profiles to those of normal cortical development (BrainSpan database), revealed similar developmental patterns for neurons, astrocytes and oligodendrocytes. Notable properties of this platform are its initiation with immortalized authentic human NSCs, growth at physiological oxygen concentration, and subsequent favorable comparison of their gene expression patterns with those observed during cortical development.

neuroscience↗

bioRxiv2020

The Histone Chaperone CAF-1 Sustains Myeloid Lineage Identity

During hematopoiesis, stem and progenitor cells become progressively restricted in their differentiation potential. This process is driven by lineage-specific transcription factors and is accompanied by dynamic changes in chromatin structure. The chromatin assembly factor complex CAF-1 is a key regulator of cellular plasticity in various cell lineages in different organisms. However, whether CAF-1 sustains lineage identity during normal homeostasis is unclear. To address this question, we investigated the role of CAF-1 in myeloid progenitor cells. CAF-1 suppression in myeloid progenitors triggered their rapid commitment but incomplete differentiation toward granulocyte, megakaryocyte, and erythrocyte lineages, resulting in a mixed cellular state. Through comparison with a canonical paradigm of directed terminal myeloid differentiation, we define changes in chromatin accessibility that underlie a unique transcriptome of the aberrantly matured CAF-1 deficient cells. We further identify C/EBP and ELF1 as key transcription factors whose control of myeloid lineage commitment is kept in check by CAF-1. These findings shed new light on molecular underpinnings of hematopoiesis and suggest that manipulation of chromatin accessibility through modulating CAF-1 levels may provide a powerful strategy for controlled differentiation of blood cells.

molecular biology↗

bioRxiv2019

Mathematical modeling with single-cell sequencing data

Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two approaches for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; by solving partial differential equations on a graph representing discrete cell state relationships, and by solving the equations on a continuous cell state-space. We demonstrate how to calibrate model parameters from single or multiple time-point single-cell sequencing data, and examine the effects of data processing algorithms on the model calibration and predictions. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell types during the pathogenesis of acute myeloid leukemia. The mathematical modeling framework we present is general and can be applied to study cell state-transitions in any single-cell genome sequencing dataset.\n\nAuthor summaryHere we compare and contrast graph- and continuum-based approaches for constructing mathematical models of cell state-transitions using single-cell RNA-sequencing data. Using two publicly available datasets, we demonstrate how to calibrate mathematical models of hematopoeisis and how to use the models to predict dynamics of acute myeloid leukemia pathogenesis by mathematically perturbing the process of cellular proliferation and differentiation. We apply these modeling approaches to study the effects of perturbing individual or sets of genes in subsets of cells, or by modeling the dynamics of cell state-transitions directly in a reduced dimensional space. We examine the effects of different graph abstraction and trajectory inference algorithms on calibrating the models and the subsequent model predictions. We conclude that both the graph- and continuum-based modeling approaches can be equally well calibrated to data and discuss situations in which one method may be preferable over the other. This work presents a general mathematical modeling framework, applicable to any single-cell sequencing dataset where cell state-transitions are of interest.

cell biology↗

bioRxiv2017

Aging in a relativistic biological space-time

Here we present a theoretical and mathematical perspective on the process of aging. We extend the concepts of physical space and time to an abstract, mathematically-defined space, which we associate with a concept of \"biological space-time\" in which biological clocks operate. We hypothesize that biological dynamics, represented as trajectories in biological space-time, may be used to model and study different rates of biological aging. As a consequence of this hypothesis, we show how the dilation or contraction of time resulting from accelerated or decelerated biological dynamics may be used to study precipitous or protracted aging. We show specific examples of how these principles may be used to model different rates of aging, with an emphasis on cancer in aging. We discuss the implications of this theory, including novel concepts that may improve our interpretation of biological aging.

cancer biology↗