C-X-C chemokine receptor 4 (CXCR4) is frequently over-expressed in various types of cancer; many agents against CXCR4 are in clinical development currently despite variable data for the prognostic impact of CXCR4 expression. Here eighty-five studies with a total of 11,032 subjects were included to explore the association between CXCR4 and progression-free survival (PFS) or overall survival (OS) in subjects with cancer. Pooled analysis shows that CXCR4 over-expression is significantly associated with poorer PFS (HR 2.04; 95% CI, 1.72-2.42) and OS (HR=1.94; 95% CI, 1.71-2.20) irrespective of cancer types. Subgroup analysis indicates significant association between CXCR4 and shorter PFS in hematological malignancy, breast cancer, colorectal cancer, esophageal cancer, renal cancer, gynecologic cancer, pancreatic cancer and liver cancer; the prognostic effects remained consistent across age, risk of bias, levels of adjustment, median follow-up period, geographical area, detection methods, publication year and size of studies. CXCR4 over-expression predicts unfavorable OS in hematological malignancy, breast cancer, colorectal cancer, esophageal cancer, head and neck cancer, renal cancer, lung cancer, gynecologic cancer, liver cancer, prostate cancer and gallbladder cancer; these effects were independence of age, levels of adjustment, publication year, detection methods and follow-up period. In conclusion, CXCR4 over-expression is associated with poor prognosis in cancer.

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Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

Objectives: To investigate the potential protective effect of ischemic post-conditioning (Post-con) on ischemia-reperfusion injury of the rabbit spinal cord, and to determine if there is an additive neuroprotective effect when ischemic preconditioning (IPC) and Post-con are combined.Methods: Forty New Zealand white rabbits were randomly divided into four groups: group Control (C; n = 10), aortic occlusion (AOC; for 30 min; group IPC (n = 10) three cycles of three-minute AOC plus three-minute reperfusion before the 30-min AOC; group Post-con (n = 10), three cycles of threeminute reperfusion plus three-minute AOC immediately upon reperfusion after 30-min AOC; group IPC+Post-con (n = 10), where animals were subjected to both IPC and Post-con. At six hours, 24 hr and 48 hr following reperfusion, neurological function was assessed according to Tarlov scores, and at 48 hr, the spinal cords were procured for the histopathologic evaluation, by comparing the number of intact α-motor neurons in the anterior horn. Results:The median count (and quartiles) of intact α-motor neurons was greatest in group Post-con 73 (69-76) and group IPC+Post-con 29 (22-42) compared to the numbers of viable α-motor neurons in groups C 6 (4-9) and IPC 15 (11-18) (P < 0.001). The numbers of animals who developed paraplegia according to Tarlov criteria were 7/10 in groups Post-con and IPC+Post-con, compared to 9/10 animals in each of groups C and IPC. Conclusions:This laboratory investigation provides histological evidence that Post-con may protect the spinal cord from moderate to severe ischemia reperfusion injury. Ischemic preconditioning conferred no additional benefits in this rabbit model. The results have potential clinical implications for patients undergoing thoracoabdominal aortic reconstructive surgery. et le groupe , que le nombre de neurones moteurs-α viables dans les (P < 0,001 et déterminer s'il y a un effet neuro-protecteur lors de la combinaison du pré-conditionnement ischémique (PCI) et du Post-con. Méthode : Quarante lapins blancs de Nouvelle-Zélande ont été répartis en quatre groupes de façon aléatoire : groupe témoin (T ; n = 10), occlusion aortique (OCA) de 30 min ; groupe PCI (n = 10), trois cycles de reperfusion de trois minutes plus OCA de trois minutes immédiatement à la reperfusion après une OCA de 30 min ; groupe PCI+Post-con (n = 10), dans lequel les animaux ont été soumis à un PCI et un Post-con. Six heures, 24 h et 48 h après reperfusion, la fonction neurologique a été évaluée selon le barème de Tarlov, et à 48 h, les moelles épinières étaient soumises à une évaluation histo-pathologique, en comparant le nombre de neurones moteurs-α intacts au niveau de la corne antérieure. Résultats : Le nombre médian (et les quartiles) de neurones moteurs-α intacts fut plus élevé dans le groupe

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

Homotopy continuation is an efficient tool for solving polynomial systems. Its efficiency relies on utilizing adaptive stepsize and adaptive precision path tracking, and endgames. In this article, we apply homotopy continuation to solve steady state problems of hyperbolic conservation laws. The algorithm is based on discretization of the hyperbolic PDEs by a third order finite difference weighted essentially non-oscillatory (WENO) scheme with Lax-Friedrichs flux splitting. This new approach is free of CFL condition constraint. Extensive numerical examples in both scalar and system test problems in one and two dimensions demonstrate the efficiency and robustness of the new method.

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