Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5-7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5-10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5-7 cm H2O and 53.6% more frequently PEEP levels of 7-9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50-55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients

CD133+ Anaplastic Thyroid Cancer Cells Initiate Tumors in Immunodeficient Mice and Are Regulated by Thyrotropin

Anaplastic thyroid cancer (ATC) is one of the most lethal human malignancies. Its rapid onset and resistance to conventional therapeutics contribute to a mean survival of six months after diagnosis and make the identification of thyroid-cancer-initiating cells increasingly important.In prior studies of ATC cell lines, CD133(+) cells exhibited stem-cell-like features such as high proliferation, self-renewal and colony-forming ability in vitro. Here we show that transplantation of CD133(+) cells, but not CD133(-) cells, into immunodeficient NOD/SCID mice is sufficient to induce growth of tumors in vivo. We also describe how the proportion of ATC cells that are CD133(+) increases dramatically over three months of culture, from 7% to more than 80% of the total. This CD133(+) cell pool can be further separated by flow cytometry into two distinct populations: CD133(+/high) and CD133(+/low). Although both subsets are capable of long-term tumorigenesis, the rapidly proliferating CD133(+/high) cells are by far the most efficient. They also express high levels of the stem cell antigen Oct4 and the receptor for thyroid stimulating hormone, TSHR. Treating ATC cells with TSH causes a three-fold increase in the numbers of CD133(+) cells and elicits a dose-dependent up-regulation of the expression of TSHR and Oct4 in these cells. More importantly, immunohistochemical analysis of tissue specimens from ATC patients indicates that CD133 is highly expressed on tumor cells but not on neighboring normal thyroid cells.To our knowledge, this is the first report indicating that CD133(+) ATC cells are solely responsible for tumor growth in immunodeficient mice. Our data also give a unique insight into the regulation of CD133 by TSH. These highly tumorigenic CD133(+) cells and the activated TSH signaling pathway may be useful targets for future ATC therapies

Approachability in Stackelberg Stochastic Games with Vector Costs

The notion of approachability was introduced by Blackwell [1] in the context of vector-valued repeated games. The famous Blackwell's approachability theorem prescribes a strategy for approachability, i.e., for `steering' the average cost of a given agent towards a given target set, irrespective of the strategies of the other agents. In this paper, motivated by the multi-objective optimization/decision making problems in dynamically changing environments, we address the approachability problem in Stackelberg stochastic games with vector valued cost functions. We make two main contributions. Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell's. Secondly, we give a reinforcement learning algorithm for learning the approachable strategy when the transition kernel is unknown. We also recover as a by-product Blackwell's necessary and sufficient condition for approachability for convex sets in this set up and thus a complete characterization. We also give sufficient conditions for non-convex sets.Comment: 18 Pages, Submitted to Dynamic Games and Application

Oleic acid variation and marker-assisted detection of Pervenets mutation in high- and low-oleic sunflower cross

High-oleic sunflower oil is in high demand on the market due to its heart-healthy properties and richness in monounsaturated fatty acids that makes it more stable in processing than standard sunflower oil. Consequently, one of sunflower breeder's tasks is to develop stable high-oleic sunflower genotypes that will produce high quality oil. We analyzed variability and inheritance of oleic acid content (OAC) in sunflower, developed at the Institute of Field and Vegetable Crops, by analyzing F-1 and F-2 progeny obtained by crossing a standard linoleic and high-oleic inbred line. F-2 individuals were classified in two groups: low-oleic with OAC of 15.24-31.28% and high-oleic with OAC of 62.49-93.82%. Monogenic dominant inheritance was observed. Additionally, several molecular markers were tested for the use in marker-assisted selection in order to shorten the period of detecting high-oleic genotypes. Marker F4-R1 was proven to be the most efficient in detection of genotypes with Pervenets (high-oleic acid) mutation