Co-existing TP53 and ARID1A mutations promote aggressive endometrial tumorigenesis

TP53 and ARID1A are frequently mutated across cancer but rarely in the same primary tumor. Endometrial cancer has the highest TP53-ARID1A mutual exclusivity rate. However, the functional relationship between TP53 and ARID1A mutations in the endometrium has not been elucidated. We used genetically engineered mice and in vivo genomic approaches to discern both unique and overlapping roles of TP53 and ARID1A in the endometrium. TP53 loss with oncogenic PIK3CAH1047R in the endometrial epithelium results in features of endometrial hyperplasia, adenocarcinoma, and intraepithelial carcinoma. Mutant endometrial epithelial cells were transcriptome profiled and compared to control cells and ARID1A/PIK3CA mutant endometrium. In the context of either TP53 or ARID1A loss, PIK3CA mutant endometrium exhibited inflammatory pathway activation, but other gene expression programs differed based on TP53 or ARID1A status, such as epithelial-to-mesenchymal transition. Gene expression patterns observed in the genetic mouse models are reflective of human tumors with each respective genetic alteration. Consistent with TP53-ARID1A mutual exclusivity, the p53 pathway is activated following ARID1A loss in the endometrial epithelium, where ARID1A normally directly represses p53 pathway genes in vivo, including the stress-inducible transcription factor, ATF3. However, co-existing TP53-ARID1A mutations led to invasive adenocarcinoma associated with mutant ARID1A-driven ATF3 induction, reduced apoptosis, TP63+ squamous differentiation and invasion. These data suggest TP53 and ARID1A mutations drive shared and distinct tumorigenic programs in the endometrium and promote invasive endometrial cancer when existing simultaneously. Hence, TP53 and ARID1A mutations may co-occur in a subset of aggressive or metastatic endometrial cancers, with ARID1A loss promoting squamous differentiation and the acquisition of invasive properties.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

One Function One Tool? A Review on Mutual Exclusivity in Tool Use Learning in Human and Non-human Species

The goal of this review is twofold: first to explore whether mutual exclusivity and functional fixedness overlap and what might be their respective specificities and second, to investigate whether mutual exclusivity as an inferential principle could be applied in other domains than language and whether it can be found in non-human species. In order to do that, we first give an overview of the representative studies of each phenomenon. We then analyze papers on tool use learning in children that studied or observed one of these phenomena. We argue that, despite their common principle -one tool one function- mutual exclusivity and functional fixedness are two distinct phenomena and need to be addressed separately in order to fully understand the mechanisms underlying social learning and cognition. In addition, mutual exclusivity appears to be applicable in other domains than language learning, namely tool use learning and is also found in non-human species when learning symbols and tools.

Where’s the Advantage? Mutual Exclusivity Promotes Children’s Initial Mapping, but Not Long-Term Memory, for Words Compared to Other Strategies

Children frequently apply a novel label to a novel object, a behavior known as the mutual exclusivity bias (MEB). This study examined how MEB affects children’s retention for word mappings. In Experiment 1, preschoolers (N = 39; Mage = 46.62 months) and adults (N = 24; Mage = 21.63 years) completed an immediate word mapping task and a delayed retention test. Both samples used MEB during referent selection, but neither group displayed higher retention for words mapped via MEB than words mapped via other referent selection strategies at test. Experiment 2 replicated Experiment 1 with preschoolers (N = 85; Mage = 47.78 months) and provided evidence against the possibility that interference from multiple words contributed to children’s faster forgetting of word mappings when using MEB. Experiment 3 presented children (N = 30; Mage = 51.13 months) with an abbreviated version of the task, providing evidence against the alternative hypothesis that cognitive load during learning caused the forgetting observed in Experiments 1 and 2. Taken together, these experiments suggest that MEB supports initial word mapping but may not provide an advantage for long-term retention.

The Promise of Oblivion

That forgetfulness constitutes a force detrimental to the ability of keeping promises is commonplace. Promises rely on a stable memory; in order to be realized they must be sheltered from the onslaught of oblivion. This article takes a closer look at the mutual exclusivity of promising and its forgetting— and discovers, at the very foundation of every promise, the unlikely expression of a promise of oblivion. Through readings of Sacher-Masoch, Nietzsche, Kafka, and others, this promise of oblivion emerges as the very condition of possibility of all promising: oblivion must be promised for promises to be. Thus, what on the surface seems mutually exclusive turns out essentially entangled: promising premised on oblivion. In a coda invoking Heidegger and Blanchot, the structure of language itself is revealed to be promissory—and, as such, forgetful.

A Network-centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.