Tumor Growth Kinetics Research Articles

Patient with Von Hippel-Lindau disease diagnosed with Renal Cell Carcinoma

Von Hippel-Lindau disease is an autosomal dominant hereditary disorder caused by a germline mutation in the VHL gene. Those mutations lead to the development of multiple benign and malignant tumors, including renal cell carcinoma (RCC). VHL disease is the most common risk factor for hereditary RCC, presenting in 1 in 36,000 live births. Patients with VHL disease are at high risk of early and multiple clear cell RCC; approximately two-thirds will present several renal cysts and RCC during their lifetimes. RCCs are the leading cause of death in patients with VHL, and median overall survival in these patients is around 50 years. The purpose of the present study is to present a clinical case report of a 54-year-old Indonesian male patient who had a stiff neck, followed by constant tingling in the right arm. The patient was subsequently diagnosed with VHL disease based on clinical diagnostic criteria, which included central nervous system (CNS) hemangioblastoma, pancreatic cyst, and RCC. Early recognition and treatment remain the mainstay of VHL disease management. Treatment decisions for RCC in VHL depend on tumor size and growth kinetics of each lesion, as those parameters determine the risk of metastatic disease. The main therapeutic goal to deal with RCCs in patients with VHL disease is to remove as many RCCs as possible while preserving renal function.

Abstract B040: Investigating the impact of tumor EMT states on immune cell infiltration in breast cancer

Abstract Breast cancer is one of the most frequently diagnosed cancers and the second leading cause of women’s cancer death. Although the five-year survival rate for women in the United States with nonmetastatic invasive breast cancer is 90%, this rate drops to less than 30% for those with tumors that have metastasized to distant organs. Epithelial-to-Mesenchymal Transition (EMT) plays a critical role in tumor metastasis by enabling the loss of epithelial characteristics and the gain of mesenchymal traits, leading to increased cell motility, invasion, and resistance to therapy. Consequently, targeting the EMT process may prevent tumor cell migration and improve patient outcomes. Rather than being a binary switch from an epithelial to a mesenchymal state, cells undergoing EMT can assume a spectrum of intermediate states, some of which may reshape the tumor microenvironment by recruiting pro-tumor immunosuppressive cells, such as regulatory T cells, M2-like macrophages, and myeloid-derived suppressor cells. However, the impact of EMT on the immune composition of the tumor microenvironment and the causative mechanisms remain poorly understood. Using multi-omics approaches in a mouse model of triple-negative breast cancer, we assessed the interaction between tumor and immune cells in the tumor microenvironment driven by the EMT. A heterogeneous mouse breast cancer cell line (4T1) was used to isolate five distinct single-cell-derived clonal populations residing in distinct states within the EMT spectrum. These EMT clones, along with the parental 4T1 cell line, were orthotopically transplanted into the mammary fat pads of BALB/c mice. Primary tumors were subsequently harvested and dissociated for single-cell RNA-sequencing (scRNA-seq) and DNA methylation analysis. Lungs were histologically evaluated for metastases. Tumors derived from an intermediate EMT clone exhibited increased tumor growth kinetics and a higher incidence of metastasis compared to epithelial or mesenchymal clones. In scRNA-seq data, tumors derived from different EMT clones largely retained their original EMT states in vivo and recruited distinct immune cell profiles. DNA methylation analysis further revealed that these tumors displayed unique methylation patterns in EMT-related CpGs. This study introduced a model that captures the complexity of the tumor microenvironment across the EMT spectrum. This was accomplished through immunophenotyping using scRNA-seq and identifying unique epigenetic patterns through DNA methylation. Our findings highlight the potential for distinct immune cell populations to be recruited to tumors in different EMT states, thereby influencing survival and metastasis outcomes. Furthermore, our results open new opportunities for developing cancer combination treatment strategies that target specific tumor-immune cell interactions. Citation Format: Hanxu Lu, Meisam Bagheri, Todd Miller, Diwakar Pattabiraman, Brock Christensen. Investigating the impact of tumor EMT states on immune cell infiltration in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B040.

Personalizing computational models to construct medical digital twins.

To realize the promise of personalized medicine, we need to be able to integrate different types of data collected from a given patient into a computational framework that enables decision making about optimal interventions to help this patient to either maintain or regain health. Digital twins represent such a framework, once the technology is sufficiently developed. A fundamental problem that currently does not have a widely applicable solution is how to calibrate a generic computational model of human biology to a given patient at a given time. This paper presents a solution to this problem for the agent-based model framework, commonly used to capture stochastic and spatially heterogeneous biological processes, such as tumor growth or immune system dynamics.

Patient-specific prostate tumour growth simulation: a first step towards the digital twin.

Prostate cancer (PCa) is a major world-wide health concern. Current diagnostic methods involve Prostate-Specific Antigen (PSA) blood tests, biopsies, and Magnetic Resonance Imaging (MRI) to assess cancer aggressiveness and guide treatment decisions. MRI aligns with in silico medicine, as patient-specific image biomarkers can be obtained, contributing towards the development of digital twins for clinical practice. This work presents a novel framework to create a personalized PCa model by integrating clinical MRI data, such as the prostate and tumour geometry, the initial distribution of cells and the vasculature, so a full representation of the whole prostate is obtained. On top of the personalized model construction, our approach simulates and predicts temporal tumour growth in the prostate through the Finite Element Method, coupling the dynamics of tumour growth and the transport of oxygen, and incorporating cellular processes such as proliferation, differentiation, and apoptosis. In addition, our approach includes the simulation of the PSA dynamics, which allows to evaluate tumour growth through the PSA patient's levels. To obtain the model parameters, a multi-objective optimization process is performed to adjust the best parameters for two patients simultaneously. This framework is validated by means of data from four patients with several MRI follow-ups. The diagnosis MRI allows the model creation and initialization, while subsequent MRI-based data provide additional information to validate computational predictions. The model predicts prostate and tumour volumes growth, along with serum PSA levels. This work represents a preliminary step towards the creation of digital twins for PCa patients, providing personalized insights into tumour growth.

Antitumor effect of the Newcastle disease virus strain NDV/Altai/pigeon/777/2010 on a model of solid Lewis carcinoma

Aim. To evaluate the efficacy of intratumoral administrations of the Newcastle disease virus strain NDV/Altai/pigeon/777/2010, to compare progression of the tumor nodes after virotherapy and to analyse pathomorphological changes in the tumor tissue in vitro and in vivo. Single intratumoral injections of the mesogenic strain of Newcastle disease virus NDV/Altai/pigeon/777/2010, isolated from a rock dove in Siberia, were done into outbred mice of the C57Bl/6 line into solid nodes of Lewis lung carcinoma that were grafted subcutaneously. Then the dynamics of tumor growth and pathomorphological changes in the tumor tissue were assessed and analyzed.It was shown that single intratumoral injections into immunocompetent C57Bl/6 mice with the mesogenic strain of Newcastle disease virus NDV/Altai/pigeon/777/2010 led to an increase in peculiar pathomorphological changes in the tumor tissue of subcutaneously grafted Lewis lung carcinoma and to a decrease in tumor growth compared to the control group of mice in vivo. A direct cytotoxic effect of the NDV strain on the Vero E6 cell line in vitro was noted.The results of this study indicate that the NDV strain NDV/Altai/pigeon/777/2010 has antitumor properties. This may enable clinical trials to prove its effectiveness as an antitumor drug.

Probabilistic analysis of tumor growth inhibition models to Support trial design

A large enough sample size of patients is required to statistically show that one treatment is better than another. However, too large a sample size is expensive and can also result in findings that are statistically significant, but not clinically relevant. How sample sizes should be chosen is a well-studied problem in classical statistics and analytical expressions can be derived from the appropriate test statistic. However, these expressions require information regarding the efficacy of the treatment, which may not be available, particularly for newly developed drugs. Tumor growth inhibition (TGI) models are frequently used to quantify the efficacy of newly developed anticancer drugs. In these models, the tumor growth dynamics are commonly described by a set of ordinary differential equations containing parameters that must be estimated using experimental data.One widely used endpoint in clinical trials is the proportion of patients in different response categories determined using the Response Evaluation Criteria In Solid Tumors (RECIST) framework. From the TGI model, we derive analytical expressions for the probability of patient response to combination therapy. The probabilistic expressions are used together with classical statistics to derive a parametric model for the sample size required to achieve a certain significance level and test power when comparing two treatments.Furthermore, the probabilistic expressions are used to generalize the Tumor Static Exposure concept to be more suitable for predicting clinical response. The derivatives of the probabilistic expressions are used to derive two additional expressions characterizing the exposure and its sensitivity. Finally, our results are illustrated using parameters obtained from calibrating the model to preclinical data.

Mathematical Evaluation and Dynamic Transmissions of a Tumor Growth Model Using a Generalized Singular and Non-Local Kernel

Currently, fractional calculus plays a critical role in improving control techniques, analyzing disease transmission dynamics, and solving several other real-world problems. This research investigates the time-fractional tumor growth model using an innovative approach. The new modified fractional derivative operator employs a singular and non-local kernel, based on Atangana and Baleanu's concepts with the Caputo derivative. The tumor growth model used the newly modified fractional operator, which provided numerical simulation. With the introduction of this new operator, we provide significant analysis for the tumor growth epidemic model. We have proven the uniqueness and stability conditions of the model by utilizing Banach's fixed point theory and the Picard successive approximation method. Using the Laplace-Adomian decomposition method (LADM), we found the numerical solution to the Modified Atangana-Baleanu-Caputo derivative model. We have verified the convergence analysis of the suggested scheme. We ultimately utilize the suggested method to obtain numeric outcomes and simulations for the tumor growth model. The study investigates the effect of multiple biological variables on the transmission of tumor growth dynamics.

Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth.

The observed time evolution of a population is well approximated by a logistic growth function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential, then decelerates as the population approaches its limit size, i.e., the carrying capacity. In mathematical oncology, the tumor carrying capacity has been postulated to be dynamically evolving as the tumor overcomes several evolutionary bottlenecks and, thus, to be patient specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic function's rotation symmetry can help estimate the population's growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against published pan-cancer animal and human breast cancer data, achieving a 30% to 40% reduction in the time at which subsequent data collection is necessary to estimate the logistic growth rate and carrying capacity correctly. These results could improve tumor dynamics forecasting and augment the clinical decision-making process.

A computational probabilistic procedure to quantify the time of breast cancer recurrence after chemotherapy administration

We propose a random linear differential equation with jumps to model the dynamics of breast tumor growth using real patients’ data. The model considers the effect of chemotherapy administration and tumor resection. Also, the inherent randomness of the data and the model are taken into account.The model is probabilistically solved by combining two main methods belonging to Uncertainty Quantification: the principle of maximum entropy (PME) and the random variable transformation technique (RVT). The PME is applied to assign proper probability distributions to model parameters. The RVT technique is used to determine the probability density function (PDF) of the solution of the random differential equation, which is a stochastic process.We apply computational optimization techniques to determine the PDF of model parameters so that the PDF of the solution matches the ones assigned to real patients’ data.Once random tumor dynamics has been modeled, we simulate, after surgery, different adjuvant chemotherapy strategies with the aim of delaying tumor recurrence as long as possible. Results are in concordance with medical literature, and tumor relapse is delayed as more cycles of chemotherapy are administered.Although the proposed model has a common general structure, it is shown how it can be customized to patients in order to construct projections about the tumor’s growth. To better illustrate the applicability of the proposed approach, we carefully show how our model can be tailored to two real patients treated at the Hospital Clínico de Valencia (Spain). The obtained results have been overseen by doctors from this hospital.

Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method

Background and ObjectiveBrain tumors are one of the most common diseases and causes of death in humans. Since the growth of brain tumors has irreparable risks for the patient, predicting the growth of the tumor and knowing its effect on the brain tissue will increase the efficiency of treatment strategies. MethodsThis study examines brain tumor growth using mathematical modeling based on the Reaction-Diffusion equation and the biomechanical model based on continuum mechanics principles. With the help of the image threshold technique of magnetic resonance images, a heterogeneous and close-to-reality environment of the brain has been modeled and experimental data validated the results to achieve maximum accuracy in predicting growth. ResultsThe obtained results have been compared with the reported conventional models to evaluate the presented model. In addition to incorporating the chemotherapy effects in governing equations, the real-time finite element analysis of the stress tensors of the surrounding tissue of tumor cells and considering its role in changing the shape and growth of the tumor has added to the importance and accuracy of the current model. ConclusionsThe comparison of the obtained results with conventional models shows that the heterogeneous model has higher reliability due to the consideration of the appropriate properties for the different regions of the brain. The presented model can contribute to personalized medicine, aid in understanding the dynamics of tumor growth, optimize treatment regimens, and develop adaptive therapy strategies.

Pan02 pancreatic tumor models carrying the GFP marker in mice

Animal tumor models are used for preclinical studies of drugs and cancer therapy. The aim of this work was to analyze the growth of murine pancreatic tumor cells Pan02, carrying GFP marker, injected subcutaneously (s. c.), intraperitoneally (i. p.) or orthotopically into the pancreas (ortho) of C57BL/6 mice. Mice were injected with 2 × 105 cells: s. c. in the right flank; i. p. with a syringe into the abdominal cavity, or ortho surgically under the pancreas capsule. The weight of mice was determined in the dynamics of tumor growth, and blood serum was taken to analyze the antibody response to the GFP reference protein. At the 2nd and 4th weeks of tumor growth, some mice were slaughtered and the expression of GFP by the tumor cells, as well as the composition of the immune cells in the tumor, were analyzed by flow cytometry and confocal microscopy. It was shown that with the different localization, the pancreatic tumors grew at different rates and lethality. When the tumor was injected i. p., mice lost weight with rapid tumor growth. In the ortho model, the mice increased their weight. Mortality in the s. c. and i. p. groups was comparable. In the s. c. model, the tumor grew slowly to a volume of 200-400 mm3 and stopped growing. There was no mortality in this group during the follow-up period (2 months). The same antibody response to GFP was formed with all injection schemes. The subpopulation composition of immune cells varied greatly in the different models of tumor cell administration. Regardless of the type of immune response, Pan02-GFP cells rapidly suppressed GFP gene expression in vivo. The data obtained showed that murine pancreatic tumor Pan02 is immunogenic and causes the formation of an adaptive immune response. Regardless of the presence or absence of an immune response and elimination of GFP+ cells, the tumor continued to grow in the i. p. and ortho models, but not in the s. c. one, and caused the death of mice. When conducting preclinical studies, it is necessary to use several ways of tumor cell injection to obtain a more objective result.

Gut microbiome influences efficacy of Endostatin combined with PD-1 blockade against colorectal cancer

The combination of anti-angiogenic drugs and immune checkpoint inhibitors (ICIs) in the treatment of tumors is emerging as a way to improve ICIs-resistant tumor therapy. In addition, gut microbes (GMs) are involved in angiogenesis in the tumor microenvironment and are also associated with the antitumor function of immune checkpoint inhibitors. However, it is unclear whether gut microbes have a role in anti-tumor function in the combination of anti-angiogenic drugs and immune checkpoint inhibitors for cancer treatment. Endostatin, an angiogenesis inhibitor, has been widely used as an antiangiogenic therapy for cancer. We showed that combined therapy with an adenovirus encoding human endostatin, named Ad-E, and PD-1 blockade dramatically abrogated MC38 tumor growth. The structure of intestinal microbes in mice was changed after combination treatment. We found that the antitumor function of combination therapy was inhibited after the elimination of intestinal microbes. In mice with depleted microbiota, oral gavage of Bacteroides fragilis salvaged the antitumor effects of combination Ad-E and αPD-1 monoclonal antibody (mAb) to a certain extent. Further, Bacteroides fragilis could improve CD3+T cells, NK cells, and IFNγ+CD8+ T cells in the tumor microenvironment to inhibit tumor growth. Besides, Bacteroides fragilis might restore antitumor function by down-regulating isobutyric acid (IBA). Our results suggested that GMs may be involved in the combination of Ad-E and αPD-1 mAb for cancer treatment, which has oncological implications for tumor growth dynamics and cancer immune surveillance.

Eftilagimod Alpha (a Soluble LAG-3 Protein) Combined With Pembrolizumab in Second-Line Metastatic NSCLC Refractory to Anti–Programmed Cell Death Protein 1/Programmed Death-Ligand 1-Based Therapy: Final Results from a Phase 2 Study

IntroductionEftilagimod alpha (efti), a soluble lymphocyte activation gene-3 protein, triggers antigen-presenting cell and T-cell (CD4+ and CD8+) activation and helps overcome resistance to programmed cell death protein 1 or programmed cell death-ligand 1 (PD-(L)1) inhibitors. We assessed efti plus pembrolizumab in second-line anti–PD-(L)1-refractory metastatic patients with NSCLC. MethodsAfter confirmed progression on anti-PD-(L)1-based first-line therapy, patients received efti (30 mg subcutaneously every 2 weeks for eight 3-week cycles and then every 3 weeks for up to 54 weeks) plus pembrolizumab (200 mg intravenously every 3 weeks for up to 105 weeks). The primary endpoint was the objective response rate by modified Response Evaluation Criteria in Solid Tumors version 1.1 for immune-based therapies. Secondary endpoints included disease control rate, progression-free survival, overall survival (OS), and tolerability. Exploratory endpoints included tumor growth kinetics and predefined subgroup analyses. Programmed cell death-ligand 1 tumor proportion score was assessed centrally. ResultsThirty-six patients were enrolled from April 2019 to August 2021 using Simon’s two-stage design. Most patients (81.8%) had low or negative (<50%) PD-(L)1 tumor proportion score. First-line therapy was anti–PD-(L)1-based for all patients, combined with chemotherapy for 66.7%. The confirmed objective response and disease control rates were 8.3% and 33.3%. The median progression-free survival was 2.1 months and the median OS was 9.9 months. Patients exhibiting high PD-(L)1 expression or acquired resistance to PD-(L)1 inhibitors revealed superior response and survival outcomes, and OS was closely correlated with disease control. No treatment-emergent adverse event led to permanent discontinuation of study treatment. ConclusionsEfti plus pembrolizumab was well-tolerated and revealed signs of antitumor activity in patients with NSCLC resistant to PD-(L)1 inhibitors, warranting further investigation. Trial registration number: NCT03625323.

Uncovering therapeutic targets for macrophage-mediated T cell suppression and PD-L1 therapy sensitization

Tumor-associated macrophages (TAMs) and other myelomonocytic cells are implicated in regulating responsiveness to immunotherapies, including immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis. We have developed an exvivo high-throughput approach to discover modulators of macrophage-mediated Tcell suppression, which can improve clinical outcomes of ICIs. We screened 1,430 Food and Drug Administration (FDA)-approved small-molecule drugs using a co-culture assay employing bone-marrow-derived macrophages (BMDMs) and splenic-derived Tcells. This identified 57 compounds that disrupted macrophage-mediated Tcell suppression. Seven compounds exerted prominent synergistic Tcell expansion activity when combined with αPD-L1. These include four COX1/2 inhibitors and two myeloid cell signaling inhibitors. We demonstrate that the use of cyclooxygenase (COX)1/2 inhibitors in combination with αPD-L1 decreases tumor growth kinetics and enhances overall survival in triple-negative breast cancer (TNBC) tumor models in a CD8+ Tcell-dependent manner. Altogether, we present a rationalized approach for identifying compounds that synergize with ICI to potentially enhance therapeutic outcomes for patients with solid tumors.

Visual predictive check of longitudinal models and dropout.

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.

Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models

Mechanistic mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the most likely parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.

IRF8-driven reprogramming of the immune microenvironment enhances anti-tumor adaptive immunity and reduces immunosuppression in murine glioblastoma.

Glioblastoma (GBM) has a highly immunosuppressive tumor immune microenvironment (TIME), largely mediated by myeloid-derived suppressor cells (MDSCs). Here, we utilized a retroviral replicating vector (RRV) to deliver Interferon Regulatory Factor 8 (IRF8), a master regulator of type 1 conventional dendritic cell (cDC1) development, in a syngeneic murine GBM model. We hypothesized that RRV-mediated delivery of IRF8 could "reprogram" intratumoral MDSCs into antigen-presenting cells (APCs) and thereby restore T-cell responses. Effects of RRV-IRF8 on survival and tumor growth kinetics were examined in the SB28 murine GBM model. Immunophenotype was analyzed by flow cytometry and gene expression assays. We assayed functional immunosuppression and antigen presentation by ex vivo T-cell-myeloid co-culture. Intratumoral injection of RRV-IRF8 in mice bearing intracerebral SB28 glioma significantly suppressed the tumor growth and prolonged survival. RRV-IRF8 treated tumors exhibited significant enrichment of cDC1s and CD8+ T-cells. Additionally, myeloid cells derived from RRV-IRF8 tumors showed decreased expression of the immunosuppressive markers Arg1 and IDO1 and demonstrated reduced suppression of naïve T-cell proliferation in ex vivo co-culture, compared to controls. Furthermore, DCs from RRV-IRF8 tumors showed increased antigen presentation compared to those from control tumors. In vivo treatment with azidothymidine (AZT), a viral replication inhibitor, showed that IRF8 transduction in both tumor and non-tumor cells is necessary for survival benefit, associated with a reprogrammed, cDC1- and CD8 T-cell-enriched TIME. Our results indicate that reprogramming of glioma-infiltrating myeloid cells by in vivo expression of IRF8 may reduce immunosuppression and enhance antigen presentation, achieving improved tumor control.

Predicting Tumor Volume Doubling Time and Progression-Free Survival in Untreated Patients from Patient-Derived-Xenograft (PDX) Models: A Translational Model-Based Approach

Tumor volume doubling time (TVDT) has been shown to be a potential surrogate marker of biological tumor activity. However, its availability in clinics is strongly limited due to ethical and practical reasons, as its assessment requires at least two subsequent tumor volume measurements in untreated patients. Here, a translational modeling framework to predict TVDT distributions in untreated cancer patient populations from tumor growth data in patient-derived xenograft (PDX) mice is proposed. Eleven solid cancer types were considered. For each of them, a set of tumor growth studies in PDX mice was selected and analyzed through a mathematical model to characterize the distribution of the exponential tumor growth rate in mice. Then, assuming an exponential growth of the tumor mass in humans, the growth rates were scaled from PDX mice to humans through an allometric scaling approach and used to predict TVDTs in untreated patients. A very good agreement was found between model predicted and clinically observed TVDTs, with 91% of the predicted TVDT medians fell within 1.5-fold of observations. Further, exploiting the intrinsic relationship between tumor growth dynamics and progression free survival (PFS), the exponential growth rates in humans were used to generate the expected PFS curves in absence of anticancer treatment. Predicted curves were extremely close to published PFS data from studies involving patient cohorts treated with supportive care or low effective therapies. The proposed approach shows promise as a potential tool to increase knowledge about TVDT in humans without the need of directly measuring tumor dimensions in untreated patients, and to predict PFS curves in untreated patients, that could fill the absence of placebo-controlled arms against which to compare treaded arms during clinical trials. However, further validation and refinement are needed to fully assess its effectiveness in this regard.

Durable benefit and slowdown in tumor growth dynamics with erdafitinib in a FGFR3-TACC3 fusion-positive IDH-wild type glioblastoma.

FGFR3-TACC3 fusion-positive IDH-wild-type (IDH-WT) glioblastoma (GB) is a rare GB subtype occurring in approximately 3% of cases. It is clinical behavior and molecular profile is different from those of fusion-negative IDH-WT GBs. Evidence on the role of FGFR inhibitors in FGFR-altered gliomas is limited. We present the case of a patient with a FGFR3-TACC3 fusion-positive IDH-WT GB that at its second recurrence was treated with the FGFR inhibitor erdafitinib through a compassionate use program. Although no objective response was achieved, an overt deceleration in tumor growth was evidenced and the patient remained on treatment for 5.5 months.

Non-nucleoside O6-methylguanine-DNA methyltransferase inhibitors in murine spontaneous tumor experimental chemotherapy in vivo

Alkylating chemotherapy agents are well-established for inducing DNA lesions that result in apoptosis in cancer cells. However, the efficacy of these agents is often diminished due to the activity of the repair enzyme O6-methylguanine-DNA methyltransferase (MGMT), which confers resistance to chemotherapy by catalyzing dealkylation reactions. Recent studies have identified novel non-nucleoside MGMT inhibitors with promising properties. In this study, we evaluated the effectiveness of these novel non-nucleoside MGMT inhibitors in combination with alkylating chemotherapy in vivo. Our experimental model involved ICR female mice that spontaneously developed malignant tumors. These mice were treated with a combination of the alkylating agent N-methyl-N′-nitro-N-nitrosoguanidine (MNNG) and new MGMT inhibitors. We analyzed tumor growth dynamics and observed the levels of MGMT and other proteins using western blot analysis. Our findings demonstrated that the addition of MGMT inhibitors significantly improved the tumor growth-inhibiting effects of the alkylating chemotherapy. Tumor growth was more effectively suppressed in the mice receiving the combination therapy compared to those receiving the alkylating agents alone. Additionally, MGMT levels were significantly reduced following the combined treatment. Furthermore, the active form of caspase 3 was detected in treated tumors, suggesting that the reduction in tumor growth may be mediated through an apoptotic pathway. These results underscore the potential for these novel MGMT inhibitors to enhance the efficacy of alkylating agents in cancer therapy, holding substantial promise for improving therapeutic outcomes against tumors that exhibit high MGMT activity.