Phenomenological modeling of tumor diameter growth based on a mixed effects model

TG6050, an oncolytic vaccinia virus encoding interleukin-12 and anti-CTLA-4 antibody, favors tumor regression via profound immune remodeling of the tumor microenvironment

BACKGROUND

TG6050 was designed as an improved oncolytic vector, combining the intrinsic properties of vaccinia virus to selectively replicate in tumors with the tumor-restricted expression of recombinant immune effectors to modify the tumor immune phenotype. These properties might be of particular interest for "cold" tumors, either poorly infiltrated or infiltrated with anergic T cells.

METHODS

 TG6050, an oncolytic vaccinia virus encodes single-chain human interleukin-12 (hIL-12) and full-length anti-cytotoxic T-lymphocyte-associated antigen-4 (@CTLA-4) monoclonal antibody. The relevant properties of TG6050 (replication, cytopathy, transgenes expression and functionality) were extensively characterized in vitro. The biodistribution and pharmacokinetics of the viral vector, @CTLA-4 and IL-12, as well as antitumoral activities (alone or combined with immune checkpoint inhibitors) were investigated in several "hot" (highly infiltrated) and "cold" (poorly infiltrated) syngeneic murine tumor models. The mechanism of action was deciphered by monitoring both systemic and intratumoral immune responses, and by tumor transcriptome analysis. The safety of TG6050 after repeated intravenous administrations was evaluated in cynomolgus monkeys, with a focus on the level of circulating IL-12.

RESULTS

Multiplication and propagation of TG6050 in tumor cells in vitro and in vivo were associated with local expression of functional IL-12 and @CTLA-4. This dual mechanism translated into a strong antitumoral activity in both "cold" and "hot" tumor models (B16F10, LLC1 or EMT6, CT26, respectively) that was further amplified when combined with anti-programmed cell death protein-1. Analysis of changes in the tumor microenvironment (TME) after treatment with TG6050 showed increases in interferon-gamma, of CD8+T cells, and of M1/M2 macrophages ratio, as well as a drastic decrease of regulatory T cells. These local modifications were observed alongside bolstering a systemic and specific antitumor adaptive immune response. In toxicology studies, TG6050 did not display any observable adverse effects in cynomolgus monkeys.

CONCLUSIONS

TG6050 effectively delivers functional IL-12 and @CTLA-4 into the tumor, resulting in strong antitumor activity. The shift towards an inflamed TME correlated with a boost in systemic antitumor T cells. The solid preclinical data and favorable benefit/risk ratio paved the way for the clinical evaluation of TG6050 in metastatic non-small cell lung cancer (NCT05788926 trial in progress).

Modelling Soluble Solids Content Accumulation in 'Braeburn' Apples

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e., partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked 'Braeburn' apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5% SSC over all study years. SSC modelling with longitudinal linear mixed-effect models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

Association of tumor growth rates with molecular biomarker status: a longitudinal study of high-grade glioma

To determine the association of molecular biomarkers with tumor growth in patients with high-grade gliomas (HGGs), the tumor growth rates and molecular biomarker status in 109 patients with HGGs were evaluated. Mean tumor diameter was assessed on at least two pre-surgical T₂-weighted and contrast-enhancement T_1-weighted magnetic resonance images (MRIs). Tumor growth rates were calculated based on tumor volume and diameter using various methods. The association of biomarkers with increased or decreased tumor growth was calculated using linear mixed-effects models. HGGs exhibited rapid growth rates, with an equivalent volume doubling time of 63.4 days and an equivalent velocity of diameter expansion of 51.6 mm/year. The WHO grade was an independent clinical factor of eVDEs. TERT promoter mutation C250T and MGMT promoter methylation was significantly associated with tumor growth in univariable analysis but not in multivariable analysis. Molecular groups of IDH1, TERT, and 1p/19q and IDH1 and MGMT were independently associated with tumor growth. In addition, tumor enhanced area had a faster growth rate than a tumor entity in incomplete enhanced HGGs (p = 0.006). Our findings provide crucial information for the prediction of preoperative tumor growth in HGGs, and aided in the decision making for aggressive resection and adjuvant treatment strategies.

Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.

Improving individualised dosimetry in radioiodine therapy for hyperthyroidism using population biokinetic modelling

The application of an individualised dosimetric procedure for radioiodine therapy requires the intensive use of resources in nuclear medicine facilities. In practice, the amount of data taken per patient is too limited to obtain an accurate estimate of the absorbed dose in the thyroid. The individualised absorbed dose estimates can be enhanced using statistical tools for population-based approaches. The aim of this work was to build a population biokinetic model of thyroid uptake and elimination of radioiodine using a nonlinear mixed-effects approach in patients with Graves' disease. Input data for the model development were taken from a dosimetric method based on ¹²³I imaging data. ¹²³I decay-corrected uptake values were estimated at 4, 24, and 96 h post-administration and for 58 patients. The root mean squared error (RMSE) for predicted ¹²³I uptake values by the fitted model was 4%. The root mean squared error of prediction (RMSEP) for out-of-sample ¹²³I uptake values, computed by a leave-one-out cross-validation, was 12%. We calculated ¹³¹I activity to administer from out-of-sample predicted ¹²³I uptake values and compared the result with that calculated from observed ¹²³I uptake values. RMSEP values for therapeutic activity revealed that there were measuring points with higher weight than others in the model. The mixed-effects approach can be used to enhance the accuracy of dosimetric calculations in therapies using ¹³¹I. Assessing the accuracy of the predictive model enables choosing among different time-sampling schedules of the radioiodine thyroid uptake curve. This methodology can also be applied in other areas of radiation dosimetry.

The Enhanced Tumor Specificity of TG6002, an Armed Oncolytic Vaccinia Virus Deleted in Two Genes Involved in Nucleotide Metabolism

Oncolytic vaccinia viruses are currently in clinical development. However, the safety and the tumor selectivity of these oncolytic viruses must be improved. We previously constructed a first-generation oncolytic vaccinia virus by expressing the suicide gene FCU1 inserted in the J2R locus that encodes thymidine kinase. We demonstrated that the combination of this thymidine-kinase-deleted vaccinia virus and the FCU1/5-fluocytosine system is a potent vector for cancer therapy. Here, we developed a second generation of vaccinia virus, named TG6002, expressing FCU1 and with targeted deletions of the J2R gene and the I4L gene, which encodes the large subunit of the ribonucleotide reductase. Compared to the previously used single thymidine-kinase-deleted vaccinia virus, TG6002 is highly attenuated in normal cells, yet it displays tumor-selective replication and tumor cell killing. TG6002 replication is highly dependent on cellular ribonucleotide reductase levels and is less pathogenic than the single-deleted vaccinia virus. Tumor-selective viral replication, prolonged therapeutic levels of 5-fluorouracil in tumors, and significant antitumor effects were observed in multiple human xenograft tumor models after systemic injection of TG6002 and 5-fluorocytosine. TG6002 displays a convincing safety profile and is a promising candidate for treatment of cancer in humans.

A Prediction Model of Tumor Progression and Survival in HER2-Positive Metastatic Gastric Cancer Patients Treated with Trastuzumab and Chemotherapy

The effects of different patient factors and dose levels of chemotherapeutic agents on clinical outcomes in advanced gastric cancer are not as yet fully characterized. We aimed at developing an integrative model that incorporates dose and covariate information to predict tumor growth and patient survival in advanced gastric cancer patients treated with trastuzumab (T), 5-FU(F)/capecitabine (X) (F or X), and cisplatin (P). Sixty-nine patients (training dataset) were used for model building and a separate 86 patients (test dataset) for model validation. A fraction of tumor cells sensitive to each drug was incorporated as a model parameter, and T was assumed as cytostatic and X/F and P as cytotoxic. Cox proportional hazards analyses were performed on model parameters and patient covariates. The model well described the time course of observed tumor size changes, and revealed that the pretreatment tumor growth rate constant k _g , which was formulated as a function of pretreatment disease duration and baseline tumor size, was positively correlated with baseline tumor size (p = 0.0084) and histologic grade (p = 0.034), and the efficacy of 5-FU with body weight (p < 2e-16) and that of cisplatin with histologic grade (p = 0.00013). Prior gastrectomy and Eastern Cooperative Oncology Group scores were significant prognostic factors for progression-free survival (PFS). For hazards analysis, a unit increase of k _g was associated with a relative risk of 3.19 for PFS (p = 0.00055) and 4.45 for OS (p = 2e-04) in the test dataset, with a similar trend observed in the training dataset. Dose-response simulations showed that, for small baseline tumor size or low histologic grade, a maximum cytotoxic effect was attainable with a dose smaller than the current recommended dose.

Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy

In radiotherapy for prostate cancer irradiation of neighboring organs at risk may lead to undesirable side-effects. Given this setting, the bladder presents the largest inter-fraction shape variations hampering the computation of the actual delivered dose vs. planned dose. This paper proposes a population model, based on longitudinal data, able to estimate the probability of bladder presence during treatment, using only the planning computed tomography (CT) scan as input information. As in previously-proposed principal component analysis (PCA) population-based models, we have used the data to obtain the dominant eigenmodes that describe bladder geometric variations between fractions. However, we have used a longitudinal analysis along each mode in order to properly characterize patient's variance from the total population variance. We have proposed is a mixed-effects (ME) model in order to separate intra- and inter-patient variability, in an effort to control confounding cohort effects. Other than using PCA, bladder shapes are represented by using spherical harmonics (SPHARM) that additionally enables data compression without information lost. Based on training data from repeated CT scans, the ME model was thus implemented following dimensionality reduction by means of SPHARM and PCA. We have evaluated the model in a leave-one-out cross validation framework on the training data but also using independent data. Probability maps (PMs) were thus generated with several draws from the learnt model as predicted regions where the bladder will likely move and deform. These PMs were compared with the actual regions using metrics based on mutual information distance and misestimated voxels. The prediction was also compared with two previous population PCA-based models. The proposed model was able to reduce the uncertainties in the estimation of the probable region of bladder motion and deformation. This model can thus be used for tailoring radiotherapy treatments.

Proton MR Spectroscopy and Diffusion MR Imaging Monitoring to Predict Tumor Response to Interstitial Photodynamic Therapy for Glioblastoma

Despite recent progress in conventional therapeutic approaches, the vast majority of glioblastoma recur locally, indicating that a more aggressive local therapy is required. Interstitial photodynamic therapy (iPDT) appears as a very promising and complementary approach to conventional therapies. However, an optimal fractionation scheme for iPDT remains the indispensable requirement. To achieve that major goal, we suggested following iPDT tumor response by a non-invasive imaging monitoring. Nude rats bearing intracranial glioblastoma U87MG xenografts were treated by iPDT, just after intravenous injection of AGuIX® nanoparticles, encapsulating PDT and imaging agents. Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) allowed us an original longitudinal follow-up of post-treatment effects to discriminate early predictive markers. We successfully used conventional MRI, T2 star (T2*), Diffusion Weighted Imaging (DWI) and MRS to extract relevant profiles on tissue cytoarchitectural alterations, local vascular disruption and metabolic information on brain tumor biology, achieving earlier assessment of tumor response. From one day post-iPDT, DWI and MRS allowed us to identify promising markers such as the Apparent Diffusion Coefficient (ADC) values, lipids, choline and myoInositol levels that led us to distinguish iPDT responders from non-responders. All these responses give us warning signs well before the tumor escapes and that the growth would be appreciated.

Identification of dynamical biological systems based on random effects models

System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (Expectation-Maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.

Assessment of DNA methylation status in early stages of breast cancer development

Background:

Molecular pathways determining the malignant potential of premalignant breast lesions remain unknown. In this study, alterations in DNA methylation levels were monitored during benign, premalignant and malignant stages of ductal breast cancer development.

Methods:

To study epigenetic events during breast cancer development, four genomic biomarkers (Methylated-IN-Tumour (MINT)17, MINT31, RARβ2 and RASSF1A) shown to represent DNA hypermethylation in tumours were selected. Laser capture microdissection was employed to isolate DNA from breast lesions, including normal breast epithelia (n=52), ductal hyperplasia (n=23), atypical ductal hyperplasia (n=31), ductal carcinoma in situ (DCIS, n=95) and AJCC stage I invasive ductal carcinoma (IDC, n=34). Methylation Index (MI) for each biomarker was calculated based on methylated and unmethylated copy numbers measured by Absolute Quantitative Assessment Of Methylated Alleles (AQAMA). Trends in MI by developmental stage were analysed.

Results:

Methylation levels increased significantly during the progressive stages of breast cancer development; P-values are 0.0012, 0.0003, 0.012, <0.0001 and <0.0001 for MINT17, MINT31, RARβ2, RASSF1A and combined biomarkers, respectively. In both DCIS and IDC, hypermethylation was associated with unfavourable characteristics.

Conclusion:

DNA hypermethylation of selected biomarkers occurs early in breast cancer development, and may present a predictor of malignant potential.

Real-time monitoring of photocytotoxicity in nanoparticles-based photodynamic therapy: a model-based approach

Nanoparticles are widely suggested as targeted drug-delivery systems. In photodynamic therapy (PDT), the use of multifunctional nanoparticles as photoactivatable drug carriers is a promising approach for improving treatment efficiency and selectivity. However, the conventional cytotoxicity assays are not well adapted to characterize nanoparticles cytotoxic effects and to discriminate early and late cell responses. In this work, we evaluated a real-time label-free cell analysis system as a tool to investigate in vitro cyto- and photocyto-toxicity of nanoparticles-based photosensitizers compared with classical metabolic assays. To do so, we introduced a dynamic approach based on real-time cell impedance monitoring and a mathematical model-based analysis to characterize the measured dynamic cell response. Analysis of real-time cell responses requires indeed new modeling approaches able to describe suited use of dynamic models. In a first step, a multivariate analysis of variance associated with a canonical analysis of the obtained normalized cell index (NCI) values allowed us to identify different relevant time periods following nanoparticles exposure. After light irradiation, we evidenced discriminant profiles of cell index (CI) kinetics in a concentration- and light dose-dependent manner. In a second step, we proposed a full factorial design of experiments associated with a mixed effect kinetic model of the CI time responses. The estimated model parameters led to a new characterization of the dynamic cell responses such as the magnitude and the time constant of the transient phase in response to the photo-induced dynamic effects. These parameters allowed us to characterize totally the in vitro photodynamic response according to nanoparticle-grafted photosensitizer concentration and light dose. They also let us estimate the strength of the synergic photodynamic effect. This dynamic approach based on statistical modeling furnishes new insights for in vitro characterization of nanoparticles-mediated effects on cell proliferation with or without light irradiation.

Association of smoking with tumor size at diagnosis in non-small cell lung cancer

Tumor size at diagnosis (TSD) indirectly reflects tumor growth rate. The relationship between TSD and smoking is poorly understood. The aim of the study was to determine the relationship between smoking and TSD. We reviewed 1712 newly diagnosed and previously untreated non-small cell lung cancer (NSCLC) patients' electronic medical records and collected tumor characteristics. Demographic and epidemiologic characteristics were derived from questionnaires administered during personal interviews. Univariate and multivariate linear regression models were used to evaluate the relationship between TSD and smoking controlling for demographic and clinical factors. We also investigated the relationship between the rs1051730 SNP in an intron of the CHRNA3 gene (the polymorphism most significantly associated with lung cancer risk and smoking behavior) and TSD. We found a strong dose dependent relationship between TSD and smoking. Current smokers had largest and never smokers smallest TSD with former smokers having intermediate TSD. In the multivariate linear regression model, smoking status (never, former, and current), histological type (adenocarcinoma versus SqCC), and gender were significant predictors of TSD. Smoking duration and intensity may explain the gender effect in predicting TSD. We found that the variant allele of rs1051730 in CHRNA3 gene was associated with larger TSD of squamous cell carcinoma. In the multivariate linear regression model, both rs1051730 and smoking were significant predictors for the size of squamous carcinomas. We conclude that smoking is positively associated with lung tumor size at the moment of diagnosis.