Biology subjects

Anderson, A. R. A.

Publications and source records attributed to Anderson, A. R. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Lymphoma response to chimeric antigen receptor T cell therapy is driven by endogenous tumor-reactive CD8⁺ T cells

CD19-directed chimeric antigen receptor T-cell (CAR-T) therapy induces durable remissions in a subset of patients with relapsed or refractory large B-cell lymphoma; however, most patients fail to respond or eventually relapse. By integrating whole-genome and transcriptome profiling of 54 patients with functional assays, syngeneic models, and mathematical modeling, we demonstrate that durable CAR-T efficacy requires cooperation between CAR-T cells and endogenous CD8 (eCD8) T cells recruited to join the attack on malignant cells. Tumors with biallelic loss of MHC class I or CD58 exhibited high genomic complexity, increased neoantigen burden, and resistance to CAR-T therapy. MHC-I-deficient lymphomas showed suboptimal response in immunocompetent hosts compared to wild type. Importantly, sustained responses in mice occurred only in the presence of eCD8 cells, whereas all CD8-depleted mice relapsed after initial CAR-T-mediated tumor regression. Patients and murine ELISPOT assays confirmed enhanced endogenous T-cell activation post-CAR-T. These findings reveal that CAR-T therapy functions not only as a cytotoxic modality but also as a catalyst for endogenous anti-tumor immunity.

cancer biology↗

bioRxiv2026

Selection for targeted therapy resistance leads to an indirect selection for higher phenotypic plasticity and enhanced evolvability to orthogonal stressors

Acquired resistance to targeted therapies is a primary barrier to durable cancer control. Resistance frequently coincides with stem-like features and EMT-associated cell-state changes (partial EMT), yet whether these programs directly cause resistance or instead enable escape by increasing phenotypic plasticity remains debated. Integrating computational modeling, functional experimental assays, and lineage tracing, we investigated how cell-state plasticity shapes acquired resistance to ALK inhibition in ALK+ lung cancer models. Our results support a model in which drug exposure selects for cells with higher phenotypic plasticity, progressively increasing their representation as resistance emerges. Consequently, resistant populations show enhanced capacity to adapt to orthogonal therapeutic and environmental stressors and exhibit heightened metastatic potential. Bulk ATAC-seq showed that highly plastic cells have increased chromatin accessibility at regulators of EMT and stemness. Consistent with this, boosting plasticity via Yamanaka-factor induction or EMT-factor expression reduced ALKi sensitivity over time. In contrast, constraining plasticity (SOX2 knockdown or epigenetic inhibition) reduced long-term resistance outgrowth and prolonged ALKi response. Together, our results indicate that targeted therapy indirectly selects for cells with increased phenotypic plasticity, providing the substrate from which multifactorial resistance and metastatic competence evolve. Further, it suggests that constraining plasticity could delay resistance and extend response durability.

cancer biology↗

bioRxiv2026

75 Years of Mathematical Oncology

Constructing a comprehensive overview of any scientific field requires accurate literature selection, yet conventional keyword-based searches are susceptible to false positives. This problem is magnified in growing or interdisciplinary fields such as mathematical modeling in oncology that contain a rich but heterogeneous body of literature. Here, a generalizable, context-enriched artificial intelligence pipeline based on large language models (LLMs) curates large scientific corpora according to a user-defined field: mathematical modeling in oncology (>35k publications). Benchmarking against expert evaluation demonstrates high accuracy (ROC AUC[~]0.95) and agreement with human judgement (correlation[~]0.68), outperforming zero-shot LLM curation Analysis of the curated corpus ([~]14k) suggests that Mathematical Oncology s distinct from either Systems Biology and Pharmacokinetics/Pharmacodynamics despite employing overlapping methods. Co-occurring citation network analysis defines nine research clusters focused on a range of applications including drug delivery, optimal control, stochastic modeling, tumor microenvironment, radiation, cancer evolution, and spatial multiscale modeling. Significance StatementA generalizable, context-enriched artificial intelligence pipeline accurately curates large scientific corpora Analysis of the curated dataset applied to the se of mathematics n oncology provides comprehensive view of mathematical modeling in oncology across 140 years, revealing its shift from fundamental cancer biology towards therapeutic modelling.

cancer biology↗

bioRxiv2025

Collagen remodeling promotes a GPCR-mediated mechanosensory immune checkpoint in ADGRG1+ CD8+ T cells and serves as a spatial biomarker of response to immunotherapy

Accessibility of immune cells to the tumor microenvironment (TME) in many solid tumors can be influenced by extracellular matrix (ECM) deposition, organization, and remodeling within tumor stroma. Specifically, lysyl hydroxylase-2 (LH2)-catalyzed lysine hydroxylation in type-I collagen telopeptides leads to formation of intermolecular collagen cross-links creating a stiffened and proteolytically resistant, stable ECM. Advanced head and neck squamous cell carcinomas (HNSCC) frequently exhibit desmoplasia with elevated LH2 expression, and Immune-checkpoint (ICI) therapy is effective in only <20% patients, suggesting that ECM remodeling mechanistically governs the composition and function of TME infiltrates. We show that elevated LH2 and collagen alignment promotes stromal accumulation of CD8 T-cells, and poor response to ICI in HPV-HNSCCs. Integration of clinical biopsies, transcriptomic datasets, and an immunocompetent syngeneic mouse model revealed that aligned collagen spatially restricts adhesion G protein-coupled receptor positive (ADGRG1) CD8 T-cells to activate a non-canonical GPCR-mediated mechanosensory program that drives dysfunction and exhaustion. Statement of significanceDesmoplasia is common in solid tumors, and immune checkpoint inhibitors benefit only some patients. Current biomarkers like PD-L1 and TMB have limited value. Our findings reveal a previously unrecognized collagen-ADGRG1 mechanosensory immune checkpoint, offering a clinically tractable, spatially resolved biomarker to better stratify patients for immunotherapy.

cancer biology↗

bioRxiv2025

The impact of normal tissue density on tumor growth and evolution in a 3D whole-tumor model of lung cancer

Serial low-dose computed tomography (LDCT) scans in patients who are diagnosed with lung cancer during screening offer a history of the densities of tumors and the tissues that surround them during carcinogenesis and cancer progression. We built a CT-scan-resolution computational model to explore how variations in lung tissue density impact tumor growth and evolution in non-small cell lung cancer (NSCLC). Our findings indicate that tumors spread more rapidly through denser tissues when they upregulate glycolysis whilst tumors spread more rapidly through sparser tissues when they upregulate angiogenesis. We used data and images from the National Lung Screening Trial to calibrate our model for untreated lung cancer growth in patients and observed consistency with model predictions in low-density environments. SignificanceOur lung lesion model supports prior studies that find tumors tend to evolve toward angiogenic or glycolytic phenotypes. We demonstrate that these evolutionary strategies may be driven by the surrounding normal tissue density and may be observable on imaging.

cancer biology↗

bioRxiv2025

Evolutionary antifragile therapy

A population of cells within a tumor can be described as antifragile (the opposite of fragile) if they derive a benefit from fluctuations or perturbations in environmental conditions induced by treatment. We hypothesized that treatment fluctuations may either promote (antifragile tumor) or inhibit (fragile tumor) the evolution of treatment resistance to targeted therapies. Analysis of the convexity of dose response curves provides a direct prediction of response to prescribed fluctuations in treatment. Theory predicts that continuous treatment protocols (i.e. zero prescribed fluctuations in treatment) will outperform uneven protocols when dose response is convex. We apply this theory to predict in vivo response to targeted therapy and validate these predictions by experimentally testing high/low intermittent dosing (uneven dosing) and continuous dosing (even dosing) schedules. Convexity (and its inverse, concavity) explains two phenomena: dose response is a convex (fragile) function but resistance onset rate is a concave (antifragile) function. Thus, we design, propose, and validate alternative treatment schedules which maximize response while maintaining prolonged sensitivity to treatment. These analyses provide supporting evidence that fluctuations alter the evolutionary trajectory of tumors in response to targeted therapy, even without altering the cumulative dose. This insight is used to design alternative, switching treatment protocols to limit the evolution of resistance, a process which we call evolutionary antifragile therapy.

cancer biology↗

bioRxiv2025

Predicting Treatment Outcomes from Adaptive Therapy -- A New Mathematical Biomarker

Standard-of-care cancer therapy regimens are characterized by continuous treatment at the maximum tolerated dose; however, this approach often fails on metastatic cancers due to the emergence of drug resistance. An evolution-based treatment paradigm known as Adaptive Therapy has been proposed to counter this, dynamically adjusting treatment to control, rather than minimize, the tumor burden, thus suppressing the growth of treatment-resistant cell populations and hence delaying patient relapse. Promising clinical results in prostate cancer indicate the potential of adaptive treatment protocols, but demonstrate broad heterogeneity in patient response. This naturally leads to the question: why does this heterogeneity occur, and is a one-size-fits-all protocol best for patients across this spectrum of responses? Using a Lotka-Volterra representation of drug-sensitive and -resistant tumor populations dynamics, we obtain a predictive expression for the expected benefit from Adaptive Therapy and propose two new mathematical biomarkers (the Delta AT Score and the eTTP) that can identify the best responders in a clinical dataset after the first cycle of treatment. Based on prior theoretical analyses, we derive personalized and clinically-feasible optimal treatment strategies, based on individual patients tumor dynamics. These strategies vary significantly between patients, and so we present a framework to generate individual treatment schedules based on a patients response to the first treatment cycle. Finally, we develop metrics to identify which patients have the greatest sensitivity to unplanned schedule changes, such as delayed appointments, allowing clinicians to identify high-risk patients that need to be monitored more closely and potentially more frequently. Overall, the proposed strategies offer personalized treatment schedules that consistently outperform clinical standard-of-care protocols.

cancer biology↗

bioRxiv2024

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

cancer biology↗

bioRxiv2024

Integrating diverse statistical methods to analyse stage-discriminatory cell interactions in colorectal neoplasia

Spatial biology has the potential to unlock information about the disrupted cellular ecosystems that define human disease. Quantitative analysis of spatially-resolved cell interactions allows mapping of tissue self-organisation and assessment of why cells interact differently in physiological and pathological contexts. However, the complexity of mammalian tissues, that occur across a spectrum of length scales, presents significant challenges for spatial analysis, increasing the gap between our capacity to generate and biologically interpret these datasets. Here, we have adapted a range of mathematical tools to develop a suite of spatial descriptors, and deployed them to determine how cell interactions change as colorectal cancer progresses from benign precursors. We demonstrate that combining mathematical analyses permits insightful examination of tissue organisational structures and identifies variable cell-interaction pathways that underpin disease progression. Mathematical tool triangulation can cross-corroborate spatial biology findings, facilitating development of analysis pipelines that are robust to individual method limitations.

cancer biology↗

bioRxiv2024

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

cancer biology↗

bioRxiv2024

Peristromal niches protect lung cancers from targeted therapies through a combined effect of multiple molecular mediators

Targeted therapies directed against oncogenic signaling addictions, such as inhibitors of ALK in ALK+ NSCLC often induce strong and durable clinical responses. However, they are not curative in metastatic cancers, as some tumor cells persist through therapy, eventually developing resistance. Therapy sensitivity can reflect not only cell-intrinsic mechanisms but also inputs from stromal microenvironment. Yet, the contribution of tumor stroma to therapeutic responses in vivo remains poorly defined. To address this gap of knowledge, we assessed the contribution of stroma-mediated resistance to therapeutic responses to the frontline ALK inhibitor alectinib in xenograft models of ALK+ NSCLC. We found that stroma-proximal tumor cells are partially protected against cytostatic effects of alectinib. This effect is observed not only in remission, but also during relapse, indicating the strong contribution of stroma-mediated resistance to both persistence and resistance. This therapy-protective effect of the stromal niche reflects a combined action of multiple mechanisms, including growth factors and extracellular matrix components. Consequently, despite improving alectinib responses, suppression of any individual resistance mechanism was insufficient to fully overcome the protective effect of stroma. Focusing on shared collateral sensitivity of persisters offered a superior therapeutic benefit, especially when using an antibody-drug conjugate with bystander effect to limit therapeutic escape. These findings indicate that stroma-mediated resistance might be the major contributor to both residual and progressing disease and highlight the limitation of focusing on suppressing a single resistance mechanism at a time.

cancer biology↗

bioRxiv2024

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.

cancer biology↗

bioRxiv2023

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer

Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043-1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

cancer biology↗

bioRxiv2023

Second-order effects of chemotherapy pharmacodynamics and pharmacokinetics on tumor regression and cachexia

Drug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we optimize treatment by comparing high variance schedules (e.g. high dose, low dose) with low variance schedules (constant dose). We extend a previous framework used to quantify second-order effects, known as antifragility theory, to investigate the role of drug pharmacokinetics. Using a simple one-compartment model, we find that high variance schedules are effective for a wide range of cumulative dose values. Next, using a mouse-parameterized two-compartment model of 5-fluorouracil, we show that the optimal schedule depends on initial tumor volume. Finally, we illustrate the trade-off between tumor response and lean mass preservation. Mathematical modeling indicates that high variance dose schedules provide a potential path forward in mitigating the risk of chemotherapy-associated cachexia by preserving lean mass without sacrificing tumor response.

cancer biology↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using a Single Cytotoxic or a Single Cytostatic Drug

Adaptive therapy, as per the dose modulation, dose-skipping, or intermittent treatment protocol works well for treatment using a single cytotoxic drug, under a wide range of scenarios and parameter settings. In contrast, adaptive therapy works well only under a limited number of scenarios and parameter settings when using a single cytostatic drug. In general, adaptive therapy works best under conditions of higher fitness cost, higher replacement rate, higher turnover. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected. In general, it is better to pause treatment sooner than later, when the tumor is shrinking If the amount of drug used is too low, it is unable to control the sensitive cells and the tumor grows. However, if the drug dose is too high, it quickly selects for resistant cells and eventually the tumor grows out of control. However, there appears to be intermediate levels of dosing, which we call the minimum effective dose, which is able to control the sensitive cells but is not high enough to select for the resistant cells to grow out of control.

bioinformatics↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using Two Cytotoxic or Two Cytostatic Drugs

While the dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression) involves adjusting drug dosages when the tumor burden changes, the fixed-dose (FD) protocols involves administering a specific, constant dosage of the drug only when the tumor is growing (Dose-Skipping) or when the absolute tumor burden is above the baseline level until it reduces to a certain percentage of the baseline (Intermittent). Moreover, two different drugs can be administered simultaneously (cocktail), or the drugs can be switched such that only one drug is applied at a given time (ping-pong), either every cycle (ping-pong alternate every cycle) or when the tumor grows (ping-pong on progression). The dose modulation protocols work well when treated with two cytotoxic drugs, however, the ping-pong protocols (DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression, FD Ping-Pong Intermittent) work well when treated with two cytostatic drugs. In general, adaptive therapy, using either two cytotoxic or two cytostatic drugs works best under conditions of high competition, such as high fitness cost, high replacement rates, and high turnover, although treatment using two cytostatic drugs works best under low turnover in many cases. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected, and it is best to pause treatment sooner rather than later when the tumor is shrinking. Adaptive therapy works best when an intermediate level of drug dosage is used, and both treatment with too little or too much drug leads to poor survival outcome.

bioinformatics↗

bioRxiv2023

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumors treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

cancer biology↗

bioRxiv2022

Distinct tumor-immune ecologies in NSCLC patientspredict progression and define a clinical biomarker of therapy response

We investigated multiplexed histological images from nine patients (both pre- and on-treatment) with immunotherapy-refractory Non-Small Cell Lung Cancer (NSCLC) treated with an oral HDAC inhibitor (vorinostat) combined with a PD-1 inhibitor (pembrolizumab). Patient responses comprised of either stable disease (SD) or progressive disease (PD). We built an extensive multiplexed-image analysis pipeline involving both cell segmentation and quadrats, coupled with spatial statistics, machine learning, and deep learning to analyze the spatial and temporal features that predict disease progression and identify potential clinical biomarkers. We found that distinct spatial immune ecologies exist between SD and PD patients. We also demonstrate that tumors from PD patients are already characterized by an immune-suppressive environment prior to treatment. Finally, we show that the learned spatial ecologies can predict disease progression better than PD-L1 status alone, suggesting these ecologies can be used as potential companion biomarkers with PD-L1 in NSCLC. These findings will be investigated in a larger-cohort study generated from an ongoing clinical trial (NCT02638090) that includes a wider range of responses including complete and partial responders. Additionally, the computational infrastructure developed in this study can be generalized to any cancer type.

cancer biology↗