Tumor Growth Kinetics Research Articles

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.

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.

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.

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.

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.

Velocity and pattern of growth of intracranial meningiomas.

The authors' aim was to assess the velocity and pattern of growth of meningiomas and to correlate the kinetics of tumor growth with their previously reported two-item radiological risk stratification and CNS WHO grade (5th edition, 2021). The authors performed a serial volumetric analysis of meningiomas diagnosed radiologically at their institution between 2003 and 2015. The primary endpoint was velocity of diametric expansion (VDE), which represents the slope of the linear regression of the mean tumor diameter against time. For the secondary analysis, they categorized the growth patterns as linear or exponential by fitting time-volume curves to a linear and exponential function. Three radiological risk categories based on T2-weighted iso/hyperintensity and absence of calcifications were compared: low risk (T2-weighted hypointense), intermediate-risk (T2-weighted iso/hyperintense with calcifications), and high-risk (T2-weighted iso/hyperintense without calcifications) tumors. For the entire cohort of 240 meningiomas, the median (IQR) VDE was 0.33 (0.00-0.71) mm/year. Distribution of VDE differed significantly among radiological risk categories (0.49 vs 0.35 vs 0.05 mm/year, p < 0.001). High-risk and intermediate-risk tumors more frequently tended to grow exponentially compared to low-risk tumors (43.8% vs 37.0% vs 8.3%, p = 0.067). The authors found no correlation of growth velocity with CNS WHO grade in their cohort (1.30 mm/year for CNS WHO grade 1 vs 4.01 mm/year for CNS WHO grade 2, p = 0.185). A radiological risk assessment using two parameters-T2-weighted signal iso/hyperintensity and absence of calcifications-allows estimation of growth velocity and characteristics of untreated intracranial meningiomas. Only high-risk tumors exhibit the potential for rapid growth. However, rapid tumor growth does not indicate a higher CNS WHO grade per se.

Evaluation of the Immunomodulatory Effects of Radiation for Chimeric Antigen Receptor T Cell Therapy in Glioblastoma Multiforme.

Standard-of-care treatment for Glioblastoma Multiforme (GBM) is comprised of surgery and adjuvant chemoradiation. Chimeric Antigen Receptor (CAR) T cell therapy has demonstrated disease-modifying activity in GBM and holds great promise. Radiation, a standard-of-care treatment for GBM, has well-known immunomodulatory properties and may overcome the immunosuppressive tumor microenvironment (TME); however, radiation dose optimization and integration with CAR T cell therapy is not well defined. Murine immunocompetent models of GBM were treated with titrated doses of stereotactic radiosurgery (SRS) of 5, 10, and 20 Gray (Gy), and the TME was analyzed using Nanostring. A conditioning dose of 10 Gy was determined based on tumor growth kinetics and gene expression changes in the TME. We demonstrate that a conditioning dose of 10 Gy activates innate and adaptive immune cells in the TME. Mice treated with 10 Gy in combination with mCAR T cells demonstrated enhanced antitumor activity and superior memory responses to rechallenge with IL13Rα2-positive tumors. Furthermore, 10 Gy plus mCAR T cells also protected against IL13Rα2-negative tumors through a mechanism that was, in part, c-GAS-STING pathway-dependent. Together, these findings support combination conditioning with low-dose 10 Gy radiation in combination with mCAR T cells as a therapeutic strategy for GBM.

A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.

Tumor Growth Kinetics Based on Initial Tumor Volume Doubling Time in Active Surveillance of Low-Risk Papillary Thyroid Carcinoma.

Background: During active surveillance (AS) of low-risk papillary thyroid carcinomas (PTCs), the majority remain stable, while some exhibit either an increase or a decrease in tumor diameter or tumor volume (TV). We aimed to evaluate the clinical outcomes and relevant parameters influencing tumor growth kinetics of low-risk PTCs. Methods: This retrospective cohort study evaluated clinical parameters of 402 patients with low-risk PTC sized <2 cm, with a follow-up duration over 3 years. Changes in maximum tumor diameter, TV, and initial TV doubling time (i-TVDT) calculated within 3 years were assessed. A significant change in TV was defined as a change of 75% or more. Results: Of the 402 patients with low-risk PTC, 93.3% (375/402) were diagnosed with papillary thyroid microcarcinoma. During a median follow-up of 5 years, 3.4% (14/402) of patients developed new cervical lymph node (LN) metastasis, and 8.2% (33/402) experienced a maximal diameter increase of ≥3 mm. The i-TVDT of <5 years emerged as an independent risk factor for both maximal diameter growth and new LN metastasis (p < 0.001 and p = 0.04, respectively). Based on TV changes and i-TVDT during AS, we identified four statistically significant tumor kinetic patterns (p < 0.001): Stable (±75% change in TV), Rapid growth (TV increase >75% and i-TVDT <5 years), Slow growth (TV increase >75% and i-TVDT ≥5 years), and Shrinkage (TV decrease >75%). Most of the PTCs remained stable (67.7%), but 17.2% were rapidly growing, with a median onset of growth of 2.0 years. Slowly growing PTCs, comprising 10.9%, grew at a median of 4.3 years. A minority, 4.2%, exhibited shrinkage. In total, 115 (28.6%) patients underwent delayed surgery >12 months after initiating AS. The reasons for delayed surgery included patient preference (51/115, 44.3%), disease progression (31/115, 27.0%), and suspected disease progression, which was referred to as tumor growth not meeting the criteria of an increase of ≥3 mm in maximal tumor diameter (17/115, 14.8%). Conclusion: An i-TVDT of <5 years serves as an important prognostic indicator for disease progression, including tumor growth and new LN metastasis. The four tumor kinetic patterns based on TV changes and i-TVDT assist in guiding personalized decisions early in AS.