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Mathematical models of cancer growth have become increasingly more accurate both in the space and time domains. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In the present study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows for the calibration of multiscale, spatiotemporal models of cancer growth using state-of-the-art 3D cell culture data, and their validation based on the resulting experimental morphological patterns using spatial point-pattern analysis techniques. We applied this framework to the study of the development of Triple Negative Breast Cancer cells cultured in Matrigel scaffolds, and validated the hypothesis of chemotactic migration using a multiscale, hybrid Keller-Segel model. The results revealed transient, non-random spatial distributions of cancer cells that consist of clustered, and dispersion patterns. The proposed model was able to describe the general characteristics of the experimental observations and suggests that chemotactic migration together with random motion was found to be a plausible mechanism leading to accumulation, during the examined time period of development. The developed framework enabled us to pursue two goals; first, the quantitative description of the morphology of cancer growth in 3D cultures using point-pattern analysis, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth and migration.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Cancer, also known as malignant tumor or malignant neoplasm, is the name given to a collection of related diseases. In all types of cancer, some of the body’s cells begin to divide abnormally without stopping and have the potential to invade surrounding tissues. In this work, we focus on estimating the parameters of a model which tries to describe the growth of a cancer tumor based on the available measurements of the tumor volume and on comparing the effectiveness with respect to the accuracy of the estimation of the various methods we have tested. The Gompertz function is used as the model basis, and our analysis aims to compute the growth rate and the plateau size of the tumor. The methods used to estimate these parameters are based on least squares, maximum likelihood and the Extended Kalman Filter (EKF). In this work, we present five different methods. The results show that, when the process and measurement noise characteristics are known, maximizing the joint probability density function of the observations using numerical integration to compute the probability density functions yields most times the best results. The methods based on the EKF also yield satisfactory results.

This paper proposes an experimental design for the compositional classification of 177 ceramic samples deriving from domestic and tomb contexts in Cyprus dated to the Early and Middle Bronze Age. In this design, ceramic sample classification is achieved with three well-known methods, a standard statistical learning method termed k-Nearest Neighbours (k-NN), a method using Decision Trees (C4.5) and a more complex neural network based method known as Learning Vector Quantisation (LVQ). It is shown that the examination of classification patterns through confusion matrices allows the exploitation of inter-class relationships and the ability to provide extra information to the researcher about the compositional categorisation of samples; which could not be grouped (with certainty) into classes with the employment of ceramic petrography. Due to the compositional heterogeneity of ceramics, the effectiveness of classification using only chemical elements with mean concentrations lower than 0.1% is also evaluated to illustrate their potential significance. The developed design follows a systematic approach and well-established methods, such as bootstrapping with replacement and the 5×2 cross validation (paired t-test and F-test) tests, to ensure that the results are statistically significant.

The immune response against a tumor is characterized by the interplay among components of the immune system and neoplastic cells. Here, we bioprinted a model with two distinct regions containing gastric cancer patient-derived organoids (PDOs) and tumor-infiltrated lymphocytes (TILs). The initial cellular distribution allows for the longitudinal study of TIL migratory patterns concurrently with multiplexed cytokine analysis. The chemical properties of the bioink were designed to present physical barriers that immune T-cells must breech during infiltration and migration toward a tumor with the use of an alginate, gelatin, and basal membrane mix. TIL activity, degranulation, and regulation of proteolytic activity reveal insights into the time-dependent biochemical dynamics. Regulation of the sFas and sFas-ligand present on PDOs and TILs, respectively, and the perforin and granzyme longitudinal secretion confirms TIL activation when encountering PDO formations. TIL migratory profiles were used to create a deterministic reaction–advection diffusion model. The simulation provides insights that decouple passive from active cell migration mechanisms. The mechanisms used by TILs and other adoptive cell therapeutics as they infiltrate the tumor barrier are poorly understood. This study presents a pre-screening strategy for immune cells where motility and activation across ECM environments are crucial indicators of cellular fitness.

Introduction: The selection of an appropriate window size, window function and functional connectivity (FC) metric in the sliding window method, is not straightforward due to the absence of ground truth. Methods: A previously proposed wavelet-based method was accordingly adjusted for estimating time-varying functional connectivity (TVFC) and was applied on a large high-quality, low-motion dataset of 400 resting-state fMRI data. Specifically, the wavelet coherence magnitude and relative phase were averaged across wavelet (frequency) scales to yield TVFC and synchronization patterns. To assess whether the observed fluctuations in TVFC were statistically significant (dynamic FC [dFC]; the distinction between TVFC and dFC is intentional), surrogate data were generated using the multivariate Phase (MVPR) and multivariate Auto-regressive Randomization (MVAR) methods to define the null hypothesis of dFC absence. Results: By averaging across all frequencies, core regions of the Default Mode Network (DMN; medial prefrontal and posterior cingulate cortices, inferior parietal lobes, hippocampal formation) were found to exhibit dFC (test-retest reproducibility of 90%) and were also synchronized in activity (-15°≤phase≤15°). When averaging across distinct frequency bands, the same dynamic connections were identified, with the majority of them identified in the frequency range (0.01, 0.198] Hz, though with lower test-retest reproducibility (<66%). Additional analysis suggested that MVPR method better preserved properties (p<10-10), including time-averaged coherence, of the original data compared to MVAR approach. Conclusions: The wavelet-based approach identified dynamic associations between the core DMN regions with fewer choices in parameters, compared to sliding window method.