Cytokine Networks And Immunosurveillance In Cancer

The cytokine milieu in the tumor microenvironment plays a key role in modulating the immune response either in favor of or against tumorigenesis. For many tumors, this complex network of cytokine and immune interactions represent a formidable means of escape from immune surveillance. These cytokine networks are particularly important in pancreatic ductal adenocarcinoma (PDA), where a prominent infiltration of immunosuppressive immune populations could be found. Myeloid-derived suppressor cells (MDSCs) have previously been shown to be potent suppressors of anti-tumor immunity in PDA, but the cytokine networks regulating their recruitment to the tumor microenvironment remain incompletely understood. Here, I found that CXCR2 ligand expression is specifically correlated with enrichment of the granulocytic subset of MDSCs (G-MDSCs) in human PDAs. Using a genetically engineered mouse model of PDA, I showed that CXCR2 is required for G-MDSC trafficking to the tumor microenvironment, but not necessary for their systemic differentiation and expansion. The specific lack of G-MDSCs in the tumor microenvironment led to a T cell-dependent inhibition of tumor growth. Expression of CXCR2 ligands in PDA tumor cells can be potently induced by NF-κB activation. These findings describe a cytokine network in PDA where inflammatory signals in the tumor microenvironment drive the expression of CXCR2 ligands and the recruitment of immunosuppressive G-MDSCs. To discover other potentially important cytokine networks, I developed a novel analysis pipeline to reconstruct and compare cytokine networks from whole tumor gene expression data. Using expression of cytolytic genes as a gauge for anti-tumor immune activity, I found that PDA patients with high cytolytic activity have a slight survival advantage compared to those with lower activity. While macrophages were the most influential in tumors with low cytolytic activity, tumors with high cytolytic activity were characterized by increased activity of NK cells, recruitment of B cells, and increased importance of CD8 T cells, CD4 T helper cells, and B cells, among others. I further highlighted the cytokines that might be associated with these immune populations. Therefore, my analysis identified potentially important components of the cytokine network associated with high and low cytolytic activity. Collectively, the work in this thesis suggests that cytokine networks are crucial for maintaining an immunosuppressive microenvironment in cancer. Furthermore, disrupting key components of these networks can tip the balance in favor of cancer immunosurveillance

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

Principles of forensic group therapy

Long-term monitoring of distributed, multiple plots is the key to quantify macroecological patterns and changes. Here we examine the evidence for concerted changes in the structure, dynamics and composition of old-growth Amazonian forests in the late twentieth century. In the 1980s and 1990s, mature forests gained biomass and underwent accelerated growth and dynamics, all consistent with a widespread, long-acting stimulation of growth. Because growth on average exceeded mortality, intact Amazonian forests have been a carbon sink. In the late twentieth century, biomass of trees of more than 10cm diameter increased by 0.62±0.23 t C ha-1yr-1 averaged across the basin. This implies a carbon sink in Neotropical old-growth forest of at least 0.49±0.18 Pg C yr-1. If other biomass and necromass components are also increased proportionally, then the old-growth forest sink here has been 0.79±0.29 Pg C yr-1, even before allowing for any gains in soil carbon stocks. This is approximately equal to the carbon emissions to the atmosphere by Amazon deforestation. There is also evidence for recent changes in Amazon biodiversity. In the future, the growth response of remaining old-growth mature Amazon forests will saturate, and these ecosystems may switch from sink to source driven by higher respiration (temperature), higher mortality (as outputs equilibrate to the growth inputs and periodic drought) or compositional change (disturbances). Any switch from carbon sink to source would have profound implications for global climate, biodiversity and human welfare, while the documented acceleration of tree growth and mortality may already be affecting the interactions among millions of species. © 2008 The Royal Society

The First Hypervelocity Star from the LAMOST Survey

We report the first hypervelocity star (HVS) discovered from the LAMOST spectroscopic survey. It is a B-type star with a heliocentric radial velocity about 620 km/s, which projects to a Galactocentric radial velocity component of ~477 km/s. With a heliocentric distance of ~13 kpc and an apparent magnitude of ~13 mag, it is the nearest bright HVS currently known. With a mass of ~9Msun, it is one of the three most massive HVSs discovered so far. The star is clustered on the sky with many other known HVSs, with the position suggesting a possible connection to Galactic center structures. With the current poorly-determined proper motion, a Galactic center origin of this HVS remains consistent with the data at the 1sigma level, while a disk run-away origin cannot be excluded. We discuss the potential of the LAMOST survey to discover a large statistical sample of HVSs of different types.Comment: 5 pages, 5 figures, accepted for publication in ApJL, updated contour plot for the ejection positions after correcting a mistake in the calculatio

Wall deposition experiments in a new spray dryer

[EN] Wall deposition tests have been conducted on a new spray-drying sytem. Solutions of salt and skim milk powder have been dried with different inlet temperatures (170 °C and 230 °C) and solid contents (8.8 wt% and 30 wt%). The experiment showed that increasing the temperature caused an decrease in the amount of deposition for salt solution, but an increase for skim milk. The experiments also showed that a higher solid content caused an increase in deposition. The trends agreed with the studies using a conventional spray dryer, but the amount of deposition appeared to be lower in the new spray dryer at the same operating conditionsHuang, X.; Zhong, C.; Langrish, T. (2018). Wall deposition experiments in a new spray dryer. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 1919-1926. https://doi.org/10.4995/IDS2018.2018.8270OCS1919192

A Stratified Redox Model for the Ediacaran Ocean

The Ediacaran Period (635 to 542 million years ago) was a time of fundamental environmental and evolutionary change, culminating in the first appearance of macroscopic animals. Here, we present a detailed spatial and temporal record of Ediacaran ocean chemistry for the Doushantuo Formation in the Nanhua Basin, South China. We find evidence for a metastable zone of euxinic (anoxic and sulfidic) waters impinging on the continental shelf and sandwiched within ferruginous [Fe(II)-enriched] deep waters. A stratified ocean with coeval oxic, sulfidic, and ferruginous zones, favored by overall low oceanic sulfate concentrations, was maintained dynamically throughout the Ediacaran Period. Our model reconciles seemingly conflicting geochemical redox conditions proposed previously for Ediacaran deep oceans and helps to explain the patchy temporal record of early metazoan fossils