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Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy.

ABSTRACT: Sipuleucel-T (Provenge) is the first live cell vaccine approved for advanced, hormonally refractive prostate cancer. However, survival benefit is modest and the optimal combination or schedule of sipuleucel-T with androgen depletion remains unknown. We employ a nonlinear dynamical systems approach to modeling the response of hormonally refractive prostate cancer to sipuleucel-T. Our mechanistic model incorporates the immune response to the cancer elicited by vaccination, and the effect of androgen depletion therapy. Because only a fraction of patients benefit from sipuleucel-T treatment, inter-individual heterogeneity is clearly crucial. Therefore, we introduce our novel approach, Standing Variations Modeling, which exploits inestimability of model parameters to capture heterogeneity in a deterministic model. We use data from mouse xenograft experiments to infer distributions on parameters critical to tumor growth and to the resultant immune response. Sampling model parameters from these distributions allows us to represent heterogeneity, both at the level of the tumor cells and the individual (mouse) being treated. Our model simulations explain the limited success of sipuleucel-T observed in practice, and predict an optimal combination regime that maximizes predicted efficacy. This approach will generalize to a range of emerging cancer immunotherapies.

SUBMITTER: Jain HV

PROVIDER: S-EPMC8070719 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise.

Project description:BACKGROUND: Dental microwear analyses are commonly used to deduce the diet of extinct mammals. Conventional methods rely on the user identifying features within a 2D image. However, recent interdisciplinary research has lead to the development of an advanced methodology that is free of observer error, based on the automated quantification of 3D surfaces by combining confocal microscopy with scale-sensitive fractal analysis. This method has already proved to be very efficient in detecting dietary differences between species. Focusing on a finer, intra-specific scale of analysis, the aim of this study is to test this method's ability to track such differences between individuals from a single population. METHODOLOGY/PRINCIPAL FINDINGS: For the purposes of this study, the 3D molar microwear of 78 individuals from a well-known population of extant roe deer (Capreolus caprelous) is quantified. Multivariate statistical analyses indicate significant seasonal and sexual differences in individual dental microwear design. These are probably the consequence of seasonal variations in fruit, seed and leaf availability, as well as differences in feeding preference between males and females due to distinct energy requirements during periods of rutting, gestation or giving birth. Nevertheless, further investigations using two-block Partial Least-Squares analysis show no strong relationship between individual stomach contents and microwear texture. This is an expected result, assuming that stomach contents are composed of food items ingested during the last few hours whereas dental microwear texture records the physical properties of items eaten over periods of days or weeks. CONCLUSIONS/SIGNIFICANCE: Microwear 3D scale-sensitive fractal analysis does detect differences in diet ranging from the inter-feeding styles scale to the intra-population between-season and between-sex scales. It is therefore a possible tool, to be used with caution, in the further exploration of the feeding biology and ecology of extinct mammals.

Project description:BackgroundOral bacterial communities contain species that promote health and others that have been implicated in oral and/or systemic diseases. Culture-independent approaches provide the best means to assess the diversity of oral bacteria because most of them remain uncultivable.ResultsThe salivary microbiota from five adults was analyzed at three time-points by means of the 454 pyrosequencing technology. The V1-V3 region of the bacterial 16S rRNA genes was amplified by PCR using saliva lysates and broad-range primers. The bar-coded PCR products were pooled and sequenced unidirectionally to cover the V3 hypervariable region. Of 50,708 obtained sequences, 31,860 passed the quality control. Non-bacterial sequences (2.2%) were removed leaving 31,170 reads. Samples were dominated by seven major phyla: members of Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and candidate division TM7 were identified in all samples; Fusobacteria and Spirochaetes were identified in all individuals, but not at all time-points. The dataset was represented by 3,011 distinct sequences (100%-ID phylotypes) of ~215 nucleotides and 583 phylotypes defined at ≥97% identity (97%-ID phylotypes). We compared saliva samples from different individuals in terms of the phylogeny of their microbial communities. Based on the presence and absence of phylotypes defined at 100% or 97% identity thresholds, samples from each subject formed separate clusters. Among individual taxa, phylum Bacteroidetes and order Clostridiales (Firmicutes) were the best indicators of intraindividual similarity of the salivary flora over time. Fifteen out of 81 genera constituted 73 to 94% of the total sequences present in different samples. Of these, 8 were shared by all time points of all individuals, while 15-25 genera were present in all three time-points of different individuals. Representatives of the class Sphingobacteria, order Sphingobacteriales and family Clostridiaceae were found only in one subject.ConclusionsThe salivary microbial community appeared to be stable over at least 5 days, allowing for subject-specific grouping using UniFrac. Inclusion of all available samples from more distant time points (up to 29 days) confirmed this observation. Samples taken at closer time intervals were not necessarily more similar than samples obtained across longer sampling times. These results point to the persistence of subject-specific taxa whose frequency fluctuates between the time points. Genus Gemella, identified in all time-points of all individuals, was not defined as a core-microbiome genus in previous studies of salivary bacterial communities. Human oral microbiome studies are still in their infancy and larger-scale projects are required to better define individual and universal oral microbiome core.

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Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy.

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