Biology subjects

Robertson-Tessi, M.

Publications and source records attributed to Robertson-Tessi, M..

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

Mechanistic learning to predict and understand minimal residual disease

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their key advantage lies in interpretability of relationships between input parameters and outcomes of interest. Mechanistic models may also be calibrated to a cohort of patients and scaled up to generate a simulated set of virtual patients whose aggregate behavior reproduces key characteristics of the real patient population. In contrast, machine learning techniques offer strong prediction performance, especially for high dimensional datasets that are common in oncology. Here, we employ a Mechanstic Learning framework that combines the advantages of both approaches by training machine learning models on mechanistic parameters inferred from clinical patient data. We assess the ability of virtual clinical cohorts for the purpose of 1) scaling up small cohort sizes and 2) balancing unbalanced patient subgroups in the setting of BCR::ABL1 positive lymphoblastic leukemia. Our mechanistic model (a Markov chain model) contains sixteen parameters that describe the rate of cell fate transitions that occur in patients with B-cell precursor acute lymphoblastic leukemia. The machine learning (a ridge logistic regression model) is trained on these parameters to predict two clinically-relevant features: BCR::ABL1 fusion gene status (positive or negative) and minimal residual disease status (positive or negative) post-induction chemotherapy. Model training is done in an iterative fashion to assess which (and how many) parameters are critical to maintain high predictive performance. Using machine learning models trained on the clinical flow-cytometry data, we find that the stem-like cell state alone is the most predictive feature for both BCR::ABL1-positive and MRD-positive disease, with composite scores (defined as the average of accuracy, balanced accuracy, and area under the curve) of 0.80 and 0.67, respectively. By comparison, mechanistic learning achieves comparable or improved composite scores for BCR::ABL1-positive and MRD-positive disease, with scores of 0.81 and 0.71, respectively, using only de-differentiation for BCR::ABL1 and stem-state persistence together with differentiation-directed exit for MRD. Virtual Patient (VP) expansion is informative for robustness analysis and class balancing, but full cohort expansion introduced additional heterogeneity, reduced predictive performance, and required larger models, whereas VP-based balancing yielded only a modest gain over class weighting at substantially greater computational cost. In summary, a mechanistic-learning approach not only preserves predictive performance, but also provides a biological hypothesis for why stemness is predictive of these clinically relevant outcomes.

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↗

bioRxiv2024

Evolutionary double-bind treatment using radiotherapy and NK cell-based immunotherapy in prostate cancer

Evolution-informed therapies exploit ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment-resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by the cancer cells, which is then selectively targeted by a follow-on therapy. Here we examine the combination of radiation therapy and immunotherapy as a quantifiable double-bind strategy. Radiotherapy (RT) induces lethal double-strand DNA breaks, but cancer cells can adapt by upregulating DNA damage response pathways. While this evolutionary strategy increases resistance to DNA damaging agents, it also results in enhanced expression of natural killer (NK) cell ligands potentially increasing vulnerability to an immune response. Using a radiation-resistant human prostate carcinoma cell line (22Rv1), we demonstrate that RT-resistant cells upregulate NK cell ligands, including major histocompatibility complex class I chain-related protein A/B (MICA/B), and poliovirus receptors (PVR1, PVRL2) with a 2-fold increase in sensitivity to NK cell mediated killing. We investigated this potential evolutionary double bind through in vitro studies and evolution-based mathematical models. Radiotherapy alone slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population but with a surviving population of radiation-sensitive cells. These dynamics were framed mathematically, and model simulation predicted optimal tumour control would be achieved through initial RT rapidly followed by NK-based immunotherapy. Subsequent experiments confirmed the model prediction. We conclude that radiotherapy and NK cell-based immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumours to limit RT resistance.

cancer biology↗

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↗

bioRxiv2022

The sum and the parts: dynamics of multiple and individual metastases during adaptive therapy

Evolutionary therapies, such as adaptive therapy, have shown promise in delaying treatment resistance in late-stage cancers. By alternating between drug applications and drug-free vacations, competition between sensitive and resistant cells can be exploited to maximize the time to progression. However, the optimal schedule of this dosing regimen depends on the properties of the tumor, which often are not directly measurable in clinical practice. In this work, we propose that the initial cycle of adaptive therapy can be used as a tool to probe the relevant tumor properties. We present a framework for estimating individual and collective components of a metastatic system through tumor response dynamics, which uses a system of off-lattice agent-based models to represent individual metastatic lesions within independent domains, all of which are subject to the same systemic therapy. We find that the first cycle of adaptive therapy delineates several features of the computational metastatic system: larger metastases have longer cycles; a higher proportion of drug resistant cells slows the cycles; and a faster cell turnover rate speeds up drug response time and slows the regrowth time. The number of metastases does not affect cycle times, as response dynamics are dominated by the largest tumors rather than the aggregate. In addition, the heterogeneity of the system is also a guide for therapeutic approaches: generally, systems with more between-tumor (intertumor) heterogeneity had better success with continuous therapy, while systems with more within-tumor (intratumor) heterogeneity responded better to adaptive therapy. Intertumor heterogeneity was found to correlate more with dynamics from patients with high and low Gleason scores while intratumor heterogeneity was correlated with dynamics from patients with intermediate Gleason scores.

systems biology↗

bioRxiv2022

Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation

Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches. We present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. We model immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1) under anti-PD-L1 and sodium bicarbonate buffer therapies. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed "meta-phenotypes"): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose. Fomenting a stronger immune response leads to initial benefits but this advantage is offset by increased cell turnover that accelerates the emergence of aggressive phenotypes by inducing an evolutionary bottleneck. This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape. O_TEXTBOXSignificance statement In this work, we present a multi-scale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. To aid in understanding immune escape as a collective cellular phenomenon, we introduce the concept of metaphenotypes: immune escape mechanisms that account for cellular phenotype and surrounding context. Metaphenotypes are defined by accounting for the following: cellular phenotype, microenvironmental factors, neighboring cell types, and immune-tumor interactions. These metaphenotypes provide insight into why targeting intratumoral pH with bicarbonate buffer is a synergistic combination treatment when paired with immune checkpoint blockade. C_TEXTBOX

cancer biology↗

bioRxiv2021

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

cancer biology↗

bioRxiv2021

Mistic: an open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs. Mistic is open-source and can be downloaded at: https://github.com/MathOnco/Mistic.

cancer biology↗

bioRxiv2021

Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks

Molecular clocks record cellular ancestry. However, currently used clocks tick too slowly to measure the short-timescale dynamics of cellular renewal in adult tissues. Here we develop rapidly oscillating DNA methylation clocks where ongoing (de)methylation causes the clock to tick-tock back-and-forth between methylated and unmethylated states like a pendulum. We identify oscillators using standard methylation arrays and develop a mathematical modelling framework to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than colon (6.5 {+/-} 1.0 vs 5.8 {+/-} 1.7 stem cells/crypt) with slower stem cell replacement in small intestine (0.79 {+/-} 0.5 vs 1.1 {+/-} 0.8 replacements/stem cell/year). Germline APC mutation increased the number of replacements per crypt (13.0 {+/-} 2.4 replacements/crypt/year vs 6.9 {+/-} 4.6 for healthy colon). In blood, we measure rapid expansion of acute leukaemia and slower growth of chronic disease. Rapidly oscillating molecular clocks are a new methodology to quantitatively measure human somatic cell dynamics.

bioinformatics↗

bioRxiv2021

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance. Author summaryWe propose a simple mathematical model to understand the role of phenotypic plasticity and non-genetic inheritance in driving therapy resistance in cancer. We identify the role of non-genetic inheritance on treatment resistance and use a variety of analytical and numerical techniques to understand the impact of phenotypic plasticity on population fitness and dynamics. We further use our model to study the role of phenotypic heterogeneity in therapeutic resistance in a genetically identical non-small cell lung cancer population. Finally, we combine analytical perspectives and techniques from the theory of structured populations, renewal equations and infinite dimensional dynamical systems to derive a model informed therapeutic strategy that both drives tumour eradication while avoiding competitive release of a drug-tolerant subpopulation. These results exemplify the potential of using mathematical techniques to identify therapeutic strategies to guide the evolution of a heterogeneous tumour.

cancer biology↗

bioRxiv2020

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

(1) Background: Adaptive therapy aims to tackle cancer drug resistance by leveraging intra-tumoral competition between drug-sensitive and resistant cells. Motivated by promising results in prostate cancer there is growing interest in extending this approach to other cancers. Here we present a theoretical study of intra-tumoral competition during adaptive therapy, to identify under which circumstances it will be superior to aggressive treatment; (2) Methods: We use a 2-D, on-lattice, agent-based tumour model to examine the impact of different microenvironmental factors on the comparison between continuous drug administration and adaptive therapy. (3) Results: We show that the degree of crowding, the initial resistance fraction, the presence of resistance costs, and the rate of tumour cell turnover are key determinants of the benefit of adaptive therapy, and we study in detail how these factors alter competition between cells. We find that intra-specific competition between resistant cells plays an unexpectedly important role in the ability to control resistance. To conclude we show how differences in resistance cost and turnover change the tumours spatial organisation and may explain differences in cycling speed observed in a cohort of 67 prostate cancer patients undergoing intermittent androgen deprivation therapy; (4) Conclusion: Our work provides insights into how adaptive therapy leverages inter- and intra-specific competition to control resistance, and shows that the tumours spatial architecture will likely be an important factor in determining the quantitative benefit of adaptive therapy in patients.

cancer biology↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

cancer biology↗

bioRxiv2019

Niche engineering drives early passage through an immune bottleneck in progression to colorectal cancer

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/623959v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@1c48cf5org.highwire.dtl.DTLVardef@1026747org.highwire.dtl.DTLVardef@ca92f1org.highwire.dtl.DTLVardef@1753cd5_HPS_FORMAT_FIGEXP M_FIG C_FIG Colorectal cancer develops from its precursor lesion, the adenoma. The immune system is hypothesized to be key in modulating progression, but tumor-immune eco-evolutionary dynamics remain uncharacterized. Here, we demonstrate a key role for immune evasion in the progression of human benign disease to colorectal cancer. We constructed a mathematical model of tumor-immune eco-evolutionary dynamics that predicted ecological succession, from an \"immune-hot\" adenoma immune ecology rich in T cells to an \"immune-cold\" carcinoma ecology, deficient in T cells and rich in immunosuppressive cells. Using a cross-sectional cohort of adenomas and carcinomas, we validated this prediction by direct measurement of the tumor-immune ecology using whole-slide 10-marker immunohistochemistry (IHC), and analysis of neoantigen clonal architecture multi-region exome sequencing data. Changes in immune ecology relax selection against antigens with high recognition potentials. This study indicates that immune surveillance represents a key evolutionary bottleneck in the evolution of colon cancer.

cancer biology↗

bioRxiv2019

Macrophage-mediated immunoediting drives ductal carcinoma evolution: Space is the game changer

Under normal conditions, the immune system is capable of rapidly detecting and eliminating potentially dangerous entities, including tumor cells. Due to intense selection pressure imposed by the immune response, tumor cells often evolve strategies to avoid elimination in a process known as immunoediting. It is less known how the evolutionary response to immune predation is altered by context. We explore the evolution of immune escape strategies in ductal cancers, a natural case in which to study evolution in different contexts: inside and outside of ducts. We highlight the role of macrophages as a source of \"public goods,\" releasing diffusible factors (reactive oxygen species and growth factors). Immunohistochemistry reveals differences between macrophage densities of invasive ductal carcinomas and non-invasive ductal carcinomas in situ. For the first time, immunohistochemistry (IHC) imaging data comparing DCIS to IDC were used to initialize mechanistic agent-based models of evolutionary dynamics. By using IHC to map the initial conditions of a growing tumor, we show that spatial competition and structure influence transient dynamics during invasion. These dynamics are context-dependent, a conclusion that may be missed from interpreting imaging or non-spatial modeling alone. Before invasion, the presence of macrophages correlate with shorter ductal breach times. After invasion, tumors may employ a \"pioneer-engineer\" strategy where pioneering immunoresistant cells on the tumors edge stimulate the release of M1-macrophage-derived reactive oxygen species, degrading surrounding stroma. Behind the invasive edge, the engineering immunosuppressive cells promote the release of M2-macrophage-derived growth factors, providing a long-term immune escape strategy. Together, mathematical modeling and image analysis highlight the crucial role tumor-associated macrophages play in immune escape and invasion, both inside and outside of ducts.

cancer biology↗

bioRxiv2019

Tissue structure accelerates evolution: premalignant sweeps precede neutral expansion

Cancer has been hypothesized to be a caricature of the renewal process of the tissue of origin: arising from (and maintained by) small subpopulations capable of continuous growth1. The strong influence of the tissue structure has been convincingly demonstrated in intestinal cancers where adenomas grow by the fission of stem-cell-maintained glands influenced by early expression of abnormal cell mobility in cancer progenitors2, 3. So-called "born to be bad" tumors arise from progenitors which may already possess the necessary driver mutations for malignancy4, 5 and metastasis6. These tumors subsequently evolve neutrally, thereby maximizing intratumoral heterogeneity and increasing the probability of therapeutic resistance. These findings have been nuanced by the advent of multi-region sequencing, which uses spatial and temporal patterns of genetic variation among competing tumor cell populations to shed light on the mode of tumor evolution (neutral or Darwinian) and also the tempo4, 7-11. Using a classic, well-studied model of tumor evolution (a passenger-driver mutation model12-16) we systematically alter spatial constraints and cell mixing rates to show how tissue structure influences functional (driver) mutations and genetic heterogeneity over time. This model approach explores a key mechanism behind both inter-patient and intratumoral tumor heterogeneity: competition for space. Initial spatial constraints determine the emergent mode of evolution (neutral to Darwinian) without a change in cell-specific mutation rate or fitness effects. Transition from early Darwinian to late neutral evolution is accelerated by the combination of two factors: spatial constraints and well-timed dispersal events.

cancer biology↗