Thymoma and Thymic Carcinoma: Molecular Pathology and Targeted Therapy

Abstract:Thymomas and thymic carcinomas (TC) are rare epithelial tumors of the thymus. Although most thymomas have organotypic features (i.e., resemble the normal thymus), TC are morphologically undistinguishable from carcinomas in other organs. Apart from their different morphology, TC and thymomas differ also in functional terms (TC, in contrast to thymomas, have lost the capacity to promote the maturation of intratumorous lymphocytes), have different genetic features (discussed in this review), a different immunoprofile (most TC overexpress c-KIT, whereas thymomas are consistently negative), and different clinical features (TC, in contrast to thymomas, are not associated with paraneoplastic myasthenia gravis). Thus, although all the data suggest that the biology of thymomas and TC is different, in clinical practice, their therapeutic management up to now is identical. In the age of personalized medicine, the time may have come to think this over. We will briefly review the molecular genetics of malignant thymic tumors, summarize the current status of targeted therapies with an emphasis on the multitargeted kinase inhibitors sunitinib and sorafenib, and try to outline some future directions

A Qualitative Literature Review on Linkage Techniques for Data Integration

The data linkage techniques ”entity linking” and ”record linkage” get rising attention as they enable the integration of multiple data sources for data, web, and text mining approaches. This has resulted in the development of numerous algorithms and systems for these techniques in recent years. The goal of this publication is to provide an overview of these numerous data linkage techniques. Most papers deal with record linkage and structured data. Processing unstructured data through entity linking is rising attention with the trend Big Data. Currently, deep learning algorithms are being explored for both linkage techniques. Most publications focus their research on a single process step or the entire process of ”entity linking” or ”record linkage”. However, the papers have the limitation that the used approaches and techniques have always been optimized for only a few data sources

Pathomechanisms of Paraneoplastic Myasthenia Gravis

Thymic T cell development is characterized by sequential selection processes to ensure generation of a self-tolerant, immuncompetent mature T cell repertoire. Malfunction of any of these selection processes may potentially result in either immunodeficiency or autoimmunity. Myasthenia gravis (MG) is a typical autoimmune manifestation of thymic epithelial tumors (thymomas) and is related to the capacity of these tumors to generate and export mature T cells. Analysis of the factors that lead to autoimmunization in thymomas will help to understand the mechanisms that prevent MG under physiological conditions in humans. In a comparison of MG(+) and MG(-) thymomas, we could show that only thymomas capable of generating mature CD45RA+CD4+ T cells are associated with MG (p< 0.0001), while terminal thymopoiesis was abrogated in MG(-) thymomas. In particular, acquisition of the CD27+CD45RA+ phenotype appears to be a critical checkpoint of late T cell development in the human thymus and may play an important role in the prevention of autoimmunity. Moreover, MG(-) thymomas were virtually depleted of regulatory (CD4+CD25+) T cells (regT), while regT were readily detectable in MG(+) thymomas, albeit at significantly reduced numbers compared to control thymuses. Thus, in MG(+) thymoma patients, thymectomy apparently also results in removal of a regulatory T cell pool and may explain the frequent temporary postoperative deterioration of MG in these patients

Voltage and temperature dependence of the grain boundary tunneling magnetoresistance in manganites

We have performed a systematic analysis of the voltage and temperature dependence of the tunneling magnetoresistance (TMR) of grain boundaries (GB) in the manganites. We find a strong decrease of the TMR with increasing voltage and temperature. The decrease of the TMR with increasing voltage scales with an increase of the inelastic tunneling current due to multi-step inelastic tunneling via localized defect states in the tunneling barrier. This behavior can be described within a three-current model for magnetic tunnel junctions that extends the two-current Julliere model by adding an inelastic, spin-independent tunneling contribution. Our analysis gives strong evidence that the observed drastic decrease of the GB-TMR in manganites is caused by an imperfect tunneling barrier.Comment: to be published in Europhys. Lett., 8 pages, 4 figures (included

Anti-Factor is FPT Parameterized by Treewidth and List Size (but Counting is Hard)

In the general AntiFactor problem, a graph $G$ and, for every vertex $v$ of $G$, a set $X_\subseteq \mathbb{N}$ of forbidden degrees is given. The task is to find a set $S$ of edges such that the degree of $v$ in $S$ is \emph{not} in the set $X_v$. Standard techniques (dynamic programming plus fast convolution) can be used to show that if $M$ is the largest forbidden degree, then the problem can be solved in time $O*((M+2)^k)$ if a tree decomposition of width $k$ is given. However, significantly faster algorithms are possible if the sets $X_v$ are sparse: our main algorithmic result shows that if every vertex has at most $x$ forbidden degrees (we call this special case AntiFactor_x), then the problem can be solved in time $O*((x+1)^{O(k)})$. That is, AntiFactor_x is fixed-parameter tractable parameterized by treewidth $k$ and the maximum number $x$ of excluded degrees. Our algorithm uses the technique of representative sets, which can be generalized to the optimization version, but (as expected) not to the counting version of the problem. In fact, we show that #AntiFactor_1 is already #W[1]-hard parameterized by the width of the given decomposition. Moreover, we show that, unlike for the decision version, the standard dynamic programming algorithm is essentially optimal for the counting version. Formally, for a fixed nonempty set $X$, we denote by X-AntiFactor the special case where every vertex $v$ has the same set $X_v=X$ of forbidden degrees. We show the following lower bound for every fixed set $X$: if there is an $\epsilon>0$ such that #X-AntiFactor can be solved in time $O*((\max X+2-\epsilon)^k)$ given a tree decomposition of width $k$, then the Counting Strong Exponential-Time Hypothesis (#SETH) fails

Mesenchymal tumours of the mediastinum—part II

This is the second part of a two-part review on soft tissue tumours which may be encountered in the mediastinum. This review is based on the 2013 WHO classification of soft tissue tumours and the 2015 WHO classification of tumours of the lung, pleura, thymus and heart and provides an updated overview of mesenchymal tumours that have been reported in the mediastinum

Anti-Factor is FPT Parameterized by Treewidth and List Size (but Counting is Hard)

In the general AntiFactor problem, a graph $G$ and, for every vertex $v$ of $G$, a set $X_\subseteq \mathbb{N}$ of forbidden degrees is given. The task is to find a set $S$ of edges such that the degree of $v$ in $S$ is \emph{not} in the set $X_v$. Standard techniques (dynamic programming plus fast convolution) can be used to show that if $M$ is the largest forbidden degree, then the problem can be solved in time $O*((M+2)^k)$ if a tree decomposition of width $k$ is given. However, significantly faster algorithms are possible if the sets $X_v$ are sparse: our main algorithmic result shows that if every vertex has at most $x$ forbidden degrees (we call this special case AntiFactor_x), then the problem can be solved in time $O*((x+1)^{O(k)})$. That is, AntiFactor_x is fixed-parameter tractable parameterized by treewidth $k$ and the maximum number $x$ of excluded degrees. Our algorithm uses the technique of representative sets, which can be generalized to the optimization version, but (as expected) not to the counting version of the problem. In fact, we show that #AntiFactor_1 is already #W[1]-hard parameterized by the width of the given decomposition. Moreover, we show that, unlike for the decision version, the standard dynamic programming algorithm is essentially optimal for the counting version. Formally, for a fixed nonempty set $X$, we denote by X-AntiFactor the special case where every vertex $v$ has the same set $X_v=X$ of forbidden degrees. We show the following lower bound for every fixed set $X$: if there is an $\epsilon>0$ such that #X-AntiFactor can be solved in time $O*((\max X+2-\epsilon)^k)$ given a tree decomposition of width $k$, then the Counting Strong Exponential-Time Hypothesis (#SETH) fails

Hitting Meets Packing: How Hard Can it Be?

We study a general family of problems that form a common generalization of classic hitting (also referred to as covering or transversal) and packing problems. An instance of X-HitPack asks: Can removing k (deletable) vertices of a graph G prevent us from packing $\ell$ vertex-disjoint objects of type X? This problem captures a spectrum of problems with standard hitting and packing on opposite ends. Our main motivating question is whether the combination X-HitPack can be significantly harder than these two base problems. Already for a particular choice of X, this question can be posed for many different complexity notions, leading to a large, so-far unexplored domain in the intersection of the areas of hitting and packing problems. On a high-level, we present two case studies: (1) X being all cycles, and (2) X being all copies of a fixed graph H. In each, we explore the classical complexity, as well as the parameterized complexity with the natural parameters k+l and treewidth. We observe that the combined problem can be drastically harder than the base problems: for cycles or for H being a connected graph with at least 3 vertices, the problem is \Sigma_2^P-complete and requires double-exponential dependence on the treewidth of the graph (assuming the Exponential-Time Hypothesis). In contrast, the combined problem admits qualitatively similar running times as the base problems in some cases, although significant novel ideas are required. For example, for X being all cycles, we establish a 2^poly(k+l)n^O(1) algorithm using an involved branching method. Also, for X being all edges (i.e., H = K_2; this combines Vertex Cover and Maximum Matching) the problem can be solved in time 2^\poly(tw)n^O(1) on graphs of treewidth tw. The key step enabling this running time relies on a combinatorial bound obtained from an algebraic (linear delta-matroid) representation of possible matchings

Tight Complexity Bounds for Counting Generalized Dominating Sets in Bounded-Treewidth Graphs

We investigate how efficiently a well-studied family of domination-type problems can be solved on bounded-treewidth graphs. For sets $\sigma,\rho$ of non-negative integers, a $(\sigma,\rho)$-set of a graph $G$ is a set $S$ of vertices such that $|N(u)\cap S|\in \sigma$ for every $u\in S$, and $|N(v)\cap S|\in \rho$ for every $v\not\in S$. The problem of finding a $(\sigma,\rho)$-set (of a certain size) unifies standard problems such as Independent Set, Dominating Set, Independent Dominating Set, and many others. For all pairs of finite or cofinite sets $(\sigma,\rho)$, we determine (under standard complexity assumptions) the best possible value $c_{\sigma,\rho}$ such that there is an algorithm that counts $(\sigma,\rho)$-sets in time $c_{\sigma,\rho}^{\sf tw}\cdot n^{O(1)}$ (if a tree decomposition of width ${\sf tw}$ is given in the input). For example, for the Exact Independent Dominating Set problem (also known as Perfect Code) corresponding to $\sigma=\{0\}$ and $\rho=\{1\}$, we improve the $3^{\sf tw}\cdot n^{O(1)}$ algorithm of [van Rooij, 2020] to $2^{\sf tw}\cdot n^{O(1)}$. Despite the unusually delicate definition of $c_{\sigma,\rho}$, we show that our algorithms are most likely optimal, i.e., for any pair $(\sigma, \rho)$ of finite or cofinite sets where the problem is non-trivial, and any $\varepsilon>0$, a $(c_{\sigma,\rho}-\varepsilon)^{\sf tw}\cdot n^{O(1)}$-algorithm counting the number of $(\sigma,\rho)$-sets would violate the Counting Strong Exponential-Time Hypothesis (#SETH). For finite sets $\sigma$ and $\rho$, our lower bounds also extend to the decision version, showing that our algorithms are optimal in this setting as well. In contrast, for many cofinite sets, we show that further significant improvements for the decision and optimization versions are possible using the technique of representative sets