A senescence-mimicking (senomimetic) VEGFR TKI side-effect primes tumor immune responses via IFN/STING signaling

Tyrosine kinase inhibitors (TKIs) that block the vascular endothelial growth factor receptors (VEGFRs) disrupt tumor angiogenesis but also have many unexpected side-effects that impact tumor cells directly. This includes the induction of molecular markers associated with senescence, a form of cellular aging that typically involves growth arrest. We have shown that VEGFR TKIs can hijack these aging programs by transiently inducting senescence-markers (SMs) in tumor cells to activate senescence-associated secretory programs that fuel drug resistance. Here we show that these same senescence-mimicking ('senomimetic') VEGFR TKI effects drive an enhanced immunogenic signaling that, in turn, can alter tumor response to immunotherapy. Using a live-cell sorting method to detect beta-galactosidase, a commonly used SM, we found that subpopulations of SM-expressing (SM+) tumor cells have heightened interferon (IFN) signaling and increased expression of IFN-stimulated genes (ISGs). These ISG increases were under the control of the STimulator of INterferon Gene (STING) signaling pathway, which we found could be directly activated by several VEGFR TKIs. TKI-induced SM+ cells could stimulate or suppress CD8 T-cell activation depending on host:tumor cell contact while tumors grown from SM+ cells were more sensitive to PD-L1 inhibition in vivo, suggesting that offsetting immune-suppressive functions of SM+ cells can improve TKI efficacy overall. Our findings may explain why some (but not all) VEGFR TKIs improve outcomes when combined with immunotherapy and suggest that exploiting senomimetic drug side-effects may help identify TKIs that uniquely 'prime' tumors for enhanced sensitivity to PD-L1 targeted agents.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Tumor growth monitoring in breast cancer xenografts: A good technique for a strong ethic

Purpose

Although recent regulations improved conditions of laboratory animals, their use remains essential in cancer research to determine treatment efficacy. In most cases, such experiments are performed on xenografted animals for which tumor volume is mostly estimated from caliper measurements. However, many formulas have been employed for this estimation and no standardization is available yet.

Methods

Using previous animal studies, we compared all formulas used by the scientific community in 2019. Data were collected from 93 mice orthotopically xenografted with human breast cancer cells. All formulas were evaluated and ranked based on correlation and lower mean relative error. They were then used in a Gompertz quantitative model of tumor growth.

Results

Seven formulas for tumor volume estimation were identified and a statistically significant difference was observed among them (ANOVA test, p < 2.10⁻¹⁶), with the ellipsoid formula (1/6 π × L × W × (L + W)/2) being the most accurate (mean relative error = 0.272 ± 0.201). This was confirmed by the mathematical modeling analysis where this formula resulted in the smallest estimated residual variability. Interestingly, such result was no longer valid for tumors over 1968 ± 425 mg, for which a cubic formula (L x W x H) should be preferred.

Main findings

When considering that tumor volume remains under 1500mm³, to limit animal stress, improve tumor growth monitoring and go toward mathematic models, the following formula 1/6 π × L × W x (L + W)/2 should be preferred.

Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma

Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α).

Understanding biological mechanisms leading to metastasis development is a major challenge in order to prevent distant relapse of cancer. Classical methods to study associations of biomarkers with subsequent metastatic relapse rely on the analysis of metastasis free survival curves by means of statistical models such as proportional hazards Cox regression. These models act as black boxes and don’t provide detailed information about the specific mechanism involved. In our study, we propose to use a method based on mechanistic modeling of the metastatic development, that is, a mathematical model that simulates the biological process. The main challenge for these models is to implement the right level of complexity, because if too many parameters are included, these cannot be precisely identified from the data. We reduced the metastatic process to two main aspects: growth and dissemination. We then proposed a theoretical study of the identifiability of the two associated parameters from metastasis-free survival curves. Eventually, we applied our method to a clinical dataset in kidney cancer and illustrated how we could gain biological insights about the role of some diagnosis markers.

Abstract LB120: Comprehensive biomarkers analysis to explain resistances to PD1-L1 ICIs: The precision immuno-oncology for advanced non-small cell lung cancer (PIONeeR) trial

Background: Resistance to PD1/L1 immune checkpoint inhibitors (ICIs) in advanced NSCLC patients is observed in about 80% of individuals with no robust predictive biomarker yet. The PIONeeR trial (NCT03493581) aims to predict such resistances through a comprehensive multiparametric biomarkers analysis.

Methodology: Among the &gt;300 advanced NSCLC patients (pts) recruited in PIONeeR, we focused on the first 137 ≥2nd line ECOG PS0-1 pts treated with single-agent nivolumab, pembrolizumab or atezolizumab. Tumor tissue was collected at baseline and pts were re-biopsied at 6 weeks, and blood-sampled every cycle throughout the 24 weeks post C1D1. Response to PD1/L1 ICIs was assessed by RECIST 1.1 every 6 weeks. Immune contexture was characterized in tumor & blood of each pt through FACS for circulating immune cell subtypes quantification and endothelial activation, blood soluble factors dosage, dual- & multiplex IHC/digital pathology to quantify immune cells infiltrating the tumor, WES for TMB & ICI plasma dosage, leading to 331 measured biomarkers in addition to routine clinical parameters. Multivariable (MV) logistic regression was used to examine the association of each biomarker (controlled by sex, age, smoking status, histological type & PDL1+ Tumor Cells) with the risk of Early Progression (EP), i.e. within 3.5 months of treatment. Multivariable Cox regression analysis was conducted for association with PFS and OS.

Results: Overall, the 137 pts were mainly male (64%), smokers (92%) and &lt;70yrs (68%). Tumors were mainly non-squamous (79%) with &gt;1% PDL1+ TC in 36% of the cases, and 21% of pts were still on treatment at data cut-off. Archived samples were available for 80% of pts at inclusion and re-biopsy was available in 52.9% of these cases. The median follow up was 19.8 months, 22.5% of pts did not progress at data cut-off while 62% presented EP. Tumor Cytotoxic T-cells density, especially PD1+ were lower in EP (MV OR=0.45, p=0.022); conversely, higher proportions of circulating cytotoxic T-cells and activated T-cells (HLA-DR+) were observed in EP (MV OR=3.8, p&lt;0.001). Among other biomarkers, Tregs (MV OR=0.44, p=0.018), NK cell subsets (MV OR≤0.44, p&lt;0.05), albumin (MV OR=0.4, p&lt;0.01) and PDL1 TC % (MV OR=0.27, p&lt;0.01) were decreased whereas alkaline phosphatase was increased (OR=3, p=0.018). &gt;65% inter-pt variability was observed in plasma exposures for all ICIs, with 8-10% of pts displaying trough levels below the target engagement threshold. Data will be presented through unsupervised clustering algorithms & multi-modal supervised learning methods. Changes after 6 weeks of treatment will be analyzed to further investigate drugs mechanisms of action.

Conclusion: The PIONeeR trial provides with the 1st comprehensive biomarkers’ analysis to establish predictive models of resistance in advanced NSCLC pts treated with PD1/L1 ICIs and highlights how tumor and circulating biomarkers are complementary.

Citation Format: Laurent Greillier, Florence Monville, Vanina Leca, Frédéric Vely, Stephane Garcia, Joseph Ciccolini, Florence Sabatier, Gilbert Ferrani, Nawel Boudai, Lamia Ghezali, Marcellin Landri, Clémence Marin, Mourad Hamimed, Laurent Arnaud, Melanie Karlsen, Kevin Atsou, Sivan Bokobza, Pauline Fleury, Arnaud Boyer, Clarisse Audigier-Valette, Stéphanie Martinez, Hervé Pegliasco, Patrice Ray, Lionel Falchero, Antoine Serre, Nicolas Cloarec, Louisiane Lebas, Stephane Hominal, Patricia Barre, Sarah Zahi, Ahmed Frikha, Pierre Bory, Maryannick Le Ray, Lilian Laborde, Virginie Martin, Richard Malkoun, Marie Roumieux, Julien Mazieres, Maurice Perol, Eric Vivier, Sebastien Benzekry, Jacques Fieschi, Fabrice Barlesi. Comprehensive biomarkers analysis to explain resistances to PD1-L1 ICIs: The precision immuno-oncology for advanced non-small cell lung cancer (PIONeeR) trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB120.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

BACKGROUND

Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

METHODS

In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.

RESULTS

Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.

CONCLUSION

A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Abstract 3195: Hijacked senescence secretomes as 'immune primers' of antiangiogenic TKI efficacy

VEGF receptor tyrosine kinase inhibitors (VEGFR TKIs) and immune checkpoint inhibitors (ICIs) are approved together as a treatment regimen for multiple metastatic cancers, yet the mechanistic basis for this combinatory benefit remains unclear. Previously we have shown that resistance to VEGFR TKI treatment can transiently hijack the secretory machinery typically associated with senescence - the process of cellular aging. Here we report that these senescence-associated secretory programs can also drive immune cell activation, potentially ‘priming' the tumor for PD-1 pathway inhibition. Using a novel live-cell sorting method based on C12FDG - a substrate for senescence-associated beta-galactosidase activity - we isolated senescence-marker (SM) expressing VEGFR TKI-treated cells for transcriptomic analysis. SM+ cell populations were enriched for senescence, immune, and interferon secretory processes, with a unique gene signature validated using published preclinical and clinical datasets. Notably, SM+ cells were found to be more sensitive to CD8 T-cell mediated tumor inhibition in vivo and ex vivo and be sensitive to PD-L1 blockade and inhibitors of mTOR, a key secretory regulator. Together, these results suggest VEGFR TKI controlled secretory programs contributing to resistance can simultaneously prime the tumor microenvironment for immune cell activation, providing an explanation for improved effects of antiangiogenic and immunotherapy combinations in patients.

Citation Format: Melissa Dolan, Yuhao Shi, James W. Hill, Michalis Mastri, Cristina Vaghi, Joseph Barbi, Sebastien Benzekry, John M. Ebos. Hijacked senescence secretomes as 'immune primers' of antiangiogenic TKI efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3195.

Abstract 5493: Mathematical modeling of antibody nanoconjugates transport quantifies the impact of the tumor microenvironment on drug penetration

One of the major challenges in breast cancer treatment is the high toxicity associated with cytotoxic agents. To overcome this issue, we developed nanoparticles conjugated with cancer cell specific antibodies (trastuzumab) to improve drug delivery to the tumor, while sparing healthy tissues. These antibody nano-conjugates (ANCs) consist in docetaxel-encapsulated liposomes engrafted with trastuzumab on the surface. Intra-tumor penetration properties of these ANCs are not fully understood and could be improved. To this end, mathematical modeling can provide quantitative tools to obtain a description of the ANCs transport inside a tumor. Few models in literature characterize the nanoparticle penetration in tumors. We derived a multi-scale model to obtain a description of the fluid flow and nanoparticle transport in the tumor interstitium and vessels. Moreover, there is a lack of studies that integrate spatial data to theoretical models. Here, we aim at filling this gap.

Experimental data - carried on human MDA-MB-231 xenografts - included ex-vivo microscopy images of tumor sections obtained from different regions of the tumor (central and peripheral) using fluorescence imaging. This data allowed to recover the tumor microstucture. Our multi-scale model consisted of two Darcy equations for the fluid flow and two convection-diffusion equations governing the ANCs penetration, including the porosity of the medium. Eventually, our model was calibrated with the ex-vivo imaging data.

Simulations confirmed that the tumor microsctructure influences the macroscale flow dynamics and ANC delivery in the tumor interstitium. The model also provides quantitative predictions of the impact of the tumor microenvironment (e.g., heterogeneity, vessel leakage and interstitium permeability) on the accumulation and penetration of ANCs. This methodology could be further applied to personalize the dose of injected nanoparticles according to a patient's specific tumor histology.

Citation Format: Cristina Vaghi, Anne Rodallec, Guillaume Sicard, Florian Correard, Raphaelle Fanciullino, Joseph Ciccolini, Clair Poignard, Sebastien Benzekry. Mathematical modeling of antibody nanoconjugates transport quantifies the impact of the tumor microenvironment on drug penetration [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5493.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."

Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC. Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1–5.3 years). Multiple metastatic models were further assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4–5.7 months and onset of BMs 14–19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non-Small Cell Lung Cancer

Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) is a first-line therapeutic for advanced nonsquamous non-small cell lung cancer. Bevacizumab potentiates PEM/CIS cytotoxicity by inducing transient tumor vasculature normalization. BEV-PEM/CIS has a narrow therapeutic window. Therefore, it is an attractive target for administration schedule optimization. The present study leverages our previous work on BEV-PEM/CIS pharmacodynamic modeling in non-small cell lung cancer-bearing mice to estimate the optimal gap in the scheduling of sequential BEV-PEM/CIS. We predicted the optimal gap in BEV-PEM/CIS dosing to be 2.0 days in mice and 1.2 days in humans. Our simulations suggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.

Is There Any Room for Pharmacometrics With Immuno-Oncology Drugs? Input from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology

As part of the Pharmacology & Molecular Mechanisms (PAMM) Group, European Organization for Research and Treatment on Cancer (EORTC) 2019 winter Meeting Educational sessions, special focus has been placed on strategies to be undertaken to reduce the attrition rate when developing immune-oncology drugs. Immune checkpoint inhibitors have been game-changing drugs in several settings over the past decade such as melanoma and lung cancer. However, during the last years a rising number of studies failing to further improve clinical outcome in patients with cancer was recorded. Extensive pharmacometrics such as pharmacokinetics/pharmacodynamics modeling support should help to overcome the current glass ceiling that has apparently been reached with immuno-oncology drugs (IOD). In particular, it should help in the issue of setting up combinatorial regimen (i.e. combining immune checkpoint inhibitors with cytotoxics, anti-angiogenesis or targeted therapies) that can no longer be addressed when following standard trial-and-error approaches, but rather by using mathematical-derived algorithms as decision-making tools by investigators for rational design. In routine clinical setting, developing therapeutic drug monitoring of immune checkpoint inhibitors with adaptive dosing strategies has been a long-neglected strategy. Still, substantial improvements might be achieved using dedicated tools for precision medicine and personalized medicine in immunotherapy.

CAR T Cell Immunotherapy in Human and Veterinary Oncology: Changing the Odds Against Hematological Malignancies

The advent of the genome editing era brings forth the promise of adoptive cell transfer using engineered chimeric antigen receptor (CAR) T cells for targeted cancer therapy. CAR T cell immunotherapy is probably one of the most encouraging developments for the treatment of hematological malignancies. In 2017, two CAR T cell therapies were approved by the US Food and Drug Administration: one for the treatment of pediatric acute lymphoblastic leukemia (ALL) and the other for adult patients with advanced lymphomas. However, despite significant progress in the area, CAR T cell therapy is still in its early days and faces significant challenges, including the complexity and costs associated with the technology. B cell lymphoma is the most common hematopoietic cancer in dogs, with an incidence approaching 0.1% and a total of 20-100 cases per 100,000 individuals. It is a widely accepted naturally occurring model for human non-Hodgkin's lymphoma. Current treatment is with combination chemotherapy protocols, which prolong life for less than a year in canines and are associated with severe dose-limiting side effects, such as gastrointestinal and bone marrow toxicity. To date, one canine study generated CAR T cells by transfection of mRNA for CAR domain expression. While this was shown to provide a transient anti-tumor activity, results were modest, indicating that stable, genomic integration of CAR modules is required in order to achieve lasting therapeutic benefit. This commentary summarizes the current state of knowledge on CAR T cell immunotherapy in human medicine and its potential applications in animal health, while discussing the potential of the canine model as a translational system for immuno-oncology research.

Pharmacokinetics variability: Why nanoparticles are not just magic-bullets in oncology

Developing nanoparticles to improve the specificity of anticancer agents towards tumor tissue and to better control drug delivery is a rising strategy in oncology. An increasing number of forms (e.g., conjugated nanoparticles, liposomes, immunoliposomes…) are now available on the shelves and numerous other scaffolds (e.g., dendrimeres, nanospheres, squalenes …) are currently at various stages of development. However, as of today most nanoparticles made available remain lipidic carriers. Pharmacokinetic variability is a major, yet largely underestimated issue with liposomal nanoparticles. A wide variety of causes (e.g., tumor type and disease staging, comorbidities, patient's immune system) can explain this variability, which can in return negatively impact pharmacodynamic endpoints such as poor efficacy or severe toxicities. This review aims to cover the main causes for erratic pharmacokinetics observed with most nanoparticles, especially liposomes used in oncology. Should the main causes of such variability be identified, specific studies in non-clinical or clinical development stages could be undertaken using dedicated models (i.e., mechanistic or semi-mechanistic mathematical models such as PBPK approaches) to better describe nanoparticles pharmacokinetics and decipher PK/PD relationships. In addition, identifying relevant biomarkers or parameters likely to impact nanoparticles pharmacokinetics would allow for either the modification of their characteristics to reduce the influence of the expected variability during development phases or the development of biomarker-based adaptive dosing strategies to maintain an optimal efficacy/toxicity balance. Broadly, we call for the development of comprehensive distribution studies and state-of-the-art modeling support to better understand and anticipate nanoparticle pharmacokinetics in oncology.

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis

Bevacizumab is the first-in-class antiangiogenic drug and is almost always administrated in combination with cytotoxics. Reports have shown that bevacizumab could induce a transient phase of vascular normalization, thus ensuring a better drug delivery when cytotoxics administration is adjuvant. However, determining the best sequence remains challenging. We have developed a mathematical model describing the impact of antiangiogenics on tumor vasculature. A 3.4 days gap between bevacizumab and paclitaxel was first proposed by our model. To test its relevance, 84 mice were orthotopically xenografted with human MDA-231^Luc+ refractory breast cancer cells. Two sets of experiments were performed, based upon different bevacizumab dosing (10 or 20 mg/kg) and inter-cycle intervals (7 or 10 days), comprising several combinations with paclitaxel. Results showed that scheduling bevacizumab 3 days before paclitaxel improved antitumor efficacy (48% reduction in tumor size compared with concomitant dosing, p < 0.05) and reduced metastatic spreading. Additionally, bevacizumab alone could lead to more aggressive metastatic disease with shorter survival in animals. Our model was able to fit the experimental data and provided insights on the underlying dynamics of the vasculature's ability to deliver the cytotoxic agent. Final simulations suggested a new, data-informed optimal gap of 2.2 days. Our experimental data suggest that current concomitant dosing between bevacizumab and paclitaxel could be a sub-optimal strategy at bedside. In addition, this proof of concept study suggests that mathematical modelling could help to identify the optimal interval among a variety of possible alternate treatment modalities, thus refining the way experimental or clinical studies are conducted.

Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach

It has recently been suggested that pro-tumorigenic host-mediated processes induced in response to chemotherapy counteract the anti-tumor activity of therapy, and thereby decrease net therapeutic outcome. Here we use experimental data to formulate a mathematical model describing the host response to different doses of paclitaxel (PTX) chemotherapy as well as the duration of the response. Three previously described host-mediated effects are used as readouts for the host response to therapy. These include the levels of circulating endothelial progenitor cells in peripheral blood and the effect of plasma derived from PTX-treated mice on migratory and invasive properties of tumor cells in vitro. A first set of mathematical models, based on basic principles of pharmacokinetics/pharmacodynamics, did not appropriately describe the dose-dependence and duration of the host response regarding the effects on invasion. We therefore provide an alternative mathematical model with a dose-dependent threshold, instead of a concentration-dependent one, that describes better the data. This model is integrated into a global model defining all three host-mediated effects. It not only precisely describes the data, but also correctly predicts host-mediated effects at different doses as well as the duration of the host response. This mathematical model may serve as a tool to predict the host response to chemotherapy in cancer patients, and therefore may be used to design chemotherapy regimens with improved therapeutic outcome by minimizing host mediated effects.

Mathematical Modeling of Tumor-Tumor Distant Interactions Supports a Systemic Control of Tumor Growth

Interactions between different tumors within the same organism have major clinical implications, especially in the context of surgery and metastatic disease. Three main explanatory theories (competition, angiogenesis inhibition, and proliferation inhibition) have been proposed, but precise determinants of the phenomenon remain poorly understood. Here, we formalized these theories into mathematical models and performed biological experiments to test them with empirical data. In syngeneic mice bearing two simultaneously implanted tumors, growth of only one of the tumors was significantly suppressed (61% size reduction at day 15, P < 0.05). The competition model had to be rejected, whereas the angiogenesis inhibition and proliferation inhibition models were able to describe the data. Additional models including a theory based on distant cytotoxic log-kill effects were unable to fit the data. The proliferation inhibition model was identifiable and minimal (four parameters), and its descriptive power was validated against the data, including consistency in predictions of single tumor growth when no secondary tumor was present. This theory may also shed new light on single cancer growth insofar as it offers a biologically translatable picture of how local and global action may combine to control local tumor growth and, in particular, the role of tumor-tumor inhibition. This model offers a depiction of concomitant resistance that provides an improved theoretical basis for tumor growth control and may also find utility in therapeutic planning to avoid postsurgery metastatic acceleration. Cancer Res; 77(18); 5183-93. ©2017 AACR.

Non-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas

Grade II gliomas are slowly growing primary brain tumors that affect mostly young patients. Cytotoxic therapies (radiotherapy and/or chemotherapy) are used initially only for patients having a bad prognosis. These therapies are planned following the “maximum dose in minimum time” principle, i. e. the same schedule used for high-grade brain tumors in spite of their very different behavior. These tumors transform after a variable time into high-grade gliomas, which significantly decreases the patient’s life expectancy. In this paper we study mathematical models describing the growth of grade II gliomas in response to radiotherapy. We find that protracted metronomic fractionations, i.e. therapeutical schedules enlarging the time interval between low-dose radiotherapy fractions, may lead to a better tumor control without an increase in toxicity. Other non-standard fractionations such as protracted or hypoprotracted schemes may also be beneficial. The potential survival improvement depends on the tumor’s proliferation rate and can be even of the order of years. A conservative metronomic scheme, still being a suboptimal treatment, delays the time to malignant progression by at least one year when compared to the standard scheme.

In Vivo Bioluminescence Tomography for Monitoring Breast Tumor Growth and Metastatic Spreading: Comparative Study and Mathematical Modeling

This study aimed at evaluating the reliability and precision of Diffuse Luminescent Imaging Tomography (DLIT) for monitoring primary tumor and metastatic spreading in breast cancer mice, and to develop a biomathematical model to describe the collected data. Using orthotopic mammary fat pad model of breast cancer (MDAMB231-Luc) in mice, we monitored tumor and metastatic spreading by three-dimensional (3D) bioluminescence and cross-validated it with standard bioluminescence imaging, caliper measurement and necropsy examination. DLIT imaging proved to be reproducible and reliable throughout time. It was possible to discriminate secondary lesions from the main breast cancer, without removing the primary tumor. Preferential metastatic sites were lungs, peritoneum and lymph nodes. Necropsy examinations confirmed DLIT measurements. Marked differences in growth profiles were observed, with an overestimation of the exponential phase when using a caliper as compared with bioluminescence. Our mathematical model taking into account the balance between living and necrotic cells proved to be able to reproduce the experimental data obtained with a caliper or DLIT imaging, because it could discriminate proliferative living cells from a more composite mass consisting of tumor cells, necrotic cell, or inflammatory tissues. DLIT imaging combined with mathematical modeling could be a powerful and informative tool in experimental oncology.

Abstract 2704: Radiotherapy and immunotherapy in cancer: A mathematical model

The rise of immunotherapy is a major breakthrough in oncology. Recently, the combination of radiotherapy with the blockade of immune checkpoint inhibitors such as the PD1-PDL1 axis or the CTLA4 pathway has shown a synergistic potential: in addition to the direct cell kill induced by irradiation, radiotherapy unleashes neoantigens that can further induce an anti-tumoral immune response. However, this immune response can be inhibited by the immunosuppressive nature of the tumor micro-environment (TME). Hence, radiotherapy combined with immune check-point inhibitors is a promising solution and is the subject of preclinical and clinical research. However, defining the most efficient scheduling between radiotherapy and immunotherapy is a crucial issue that cannot be properly addressed solely by empirical trial-and-error practices. Consequently, developing mathematical models that can describe the synergy between immune checkpoint inhibitors and radiotherapy is critical. Hence, we have built a pharmocodynamic model of the combination of radiotherapy with inhibitors of the PD1-PDL1 axis and/or the CTLA4 pathway. We describe a mathematical representation of how a growing tumor first elicits and then inhibits an anti-tumoral immune response. This anti-tumoral immune response is described by a primary and a secondary response. The primary immune response appears first and is down-regulated by the PD1-PDL1 axis, while the secondary immune response happens next and is down-regulated by the CTLA4 pathway. We describe the effects of irradiation by a slightly modified version of the Linear-Quadratic model. In particular, we explain the biphasic relationship between the size of a tumor and its immunogenicity, as measured by the abscopal immune response. The ability of the model to forecast pharmacodynamic endpoints was retrospectively validated by reproducing results from experimental studies investigating radiotherapy and immune checkpoint inhibitors. This model clarifies the issue of synchronisation of immunotherapy with radiotherapy and it also explains why the CTLA4 blockade often occurs with a delay. The model also explains why a sustained response to immunotherapy may or may not happen after treatment discontinuation. We believe that this mathematical model could be further used as a simulation tool to help decision-makers determine the optimal scheduling between radiotherapy and immunotherapy and could be a building block for the in-silico design of optimized multimodal strategies.

Citation Format: Raphael Serre, Xavier Muracciole, Joseph Ciccolini, Sebastien Benzekry, Dominique Barbolosi. Radiotherapy and immunotherapy in cancer: A mathematical model. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2704.

Abstract 2099: Model-riven optimization of anti-angiogenics combined with chemotherapy: application to bevacizumab + pemetrexed/cisplatin doublet in NSCLC-bearing mice

Bevacizumab-containing protocols are all based upon the concomitant administration of the drugs given in a row. Bevacizumab is expected to induce a transient normalization of the tumor neo-vasculature prior to exert its anti-angiogenic properties. The resulting increase in blood flow could lead to higher drug delivery to the tumors and therefore to higher efficacy, provided that cytotoxics are administered after bevacizumab. Determining this time-window cannot be performed by empirical practice, but mathematical modeling can help to achieve this goal. To this end, we have developed an original mathematical PK/PD model that enables the description of the effect of bevacizumab on the quality of the vasculature and the resulting effects of drugs sequential administration on tumor growth. The model was able to simulate a variety of scheduling and suggested that a 5-days lag between bevacizumab and cytotoxics should achieve higher antiproliferative efficacy as compared with standard administration. To test the predictivity of the model, a comparative study was undertaken in tumor-bearing mice. Human H460 NSCLC cells stably transfected with luciferase were ectotopically implanted in swiss nude mice. Treatment consisted in the combo bevacizumab (20 mg/kg) plus pemetrexed (100 mg/kg) and cisplatin (3 mg/kg) given as 3 cycles administered every 2 weeks following different scheduling. Mice were randomly allocated into 4 groups (n = 12 mice per group): control, concomitant (bevacizumab and chemo given the same day), sequence-1 (chemo followed by bevacizumab 5 days later) and sequence-2 (bevacizumab followed by chemo 5 days later). Tumor growth was monitored twice a week by bioluminescence imaging after i.p. injection of 150 mg/kg luciferine. As predicted by our mathematical model, results showed that the sequential administration of bevacizumab followed 5 days later by the pemetrexed/cisplatin doublet led to a better efficacy (-42% reduction in tumor growth at treatment completion and -35% at study conclusion, p&lt;0.05, One-Way Anova) whereas standard dosing or reverse sequence did not significantly reduce tumor growth. Additionally, this sequential administration of bevacizumab first and cytotoxics next led to a median survival of 75 days, whereas other treatment groups could only achieve 52 days survival and control mice 32 days, respectively. Altogether, our experimental data demonstrate that delaying the administration of the chemotherapy after that of bevacizumab leads to higher efficacy and longer survival as compared with standard dosing. Although preliminary, this study suggests that current administration of bevacizumab could be an underpowered strategy. Besides, this preclinical study confirms the accuracy of our mathematical model to identify the optimal sequencing between anti-angiogenics and cytotoxics.

Citation Format: Joseph Ciccolini, Sebastien Benzekry, Sarah Giacometti, Fabrice Barlesi, Dominique Barbolosi. Model-riven optimization of anti-angiogenics combined with chemotherapy: application to bevacizumab + pemetrexed/cisplatin doublet in NSCLC-bearing mice. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2099.

Capturing the Driving Role of Tumor-Host Crosstalk in a Dynamical Model of Tumor Growth

In 1999, Hahnfeldt et al. proposed a mathematical model for tumor growth as dictated by reciprocal communications between tumor and its associated vasculature, introducing the idea that a tumor is supported by a dynamic, rather than a static, carrying capacity. In this original paper, the carrying capacity was equated with the variable tumor vascular support resulting from the net effect of tumor-derived angiogenesis stimulators and inhibitors. This dynamic carrying capacity model was further abstracted and developed in our recent publication to depict the more general situation where there is an interaction between the tumor and its supportive host tissue; in that case, as a function of host aging (Benzekry et al., 2014). This allowed us to predict a range of host changes that may be occurring with age that impact tumor dynamics. More generally, the basic formalism described here can be (and has been), extended to the therapeutic context using additional optimization criteria (Hahnfeldt et al., 1999). The model depends on three parameters: One for the tumor cell proliferation kinetics, one for the stimulation of the stromal support, and one for its inhibition, as well as two initial conditions. We describe here the numerical method to estimate these parameters from longitudinal tumor volume measurements.

Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach

Rapid improvements in the detection and tracking of early-stage tumor progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in patients following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo tumor data that faithfully represent all stages of disease progression. Herein, we describe mathematical analysis that uses data from mouse models of spontaneous metastasis developing after surgical removal of orthotopically implanted primary tumors. Both presurgical (primary tumor) growth and postsurgical (metastatic) growth were quantified using bioluminescence and were then used to generate a mathematical formalism based on general laws of the disease (i.e., dissemination and growth). The model was able to fit and predict pre/postsurgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of primary tumor size. In these data-based models, interindividual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between primary tumor size and postsurgical survival, suggesting possible threshold limits for the utility of tumor size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize patient benefit.

Passing to the limit 2D-1D in a model for metastatic growth

We prove the convergence of a family of solutions to a two-dimensional transport equation with a non-local boundary condition modelling the evolution of a population of metastases. We show that when the data of the repartition along the boundary tend to a Dirac mass, then the solution of the associated problem converges and we derive a simple expression for the limit in terms of the solution of a 1D equation. This result permits us to improve the computational time needed to simulate the model.