Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers.

We thank Luis Garcia-Jimeno for assistance with permutation. S.P. is supported by the Agencia Estatal de Investigacion, Ministerio de Ciencia e Innovacion (MCIN/AEI/10.13039/501100011033) through the RETOS project PID2019-109571RA-I00. This work was funded by the European Research Council (ERC) Starting grant (HYPER-INSIGHT, 757700) to F.S. and ERC Consolidator (IR-DC, 616434) and Advanced (MUTANOMICS, 883742) grants to B.L. F.S. and B.L. are funded by the ICREA Research Professor program. S.P., F.S., and B.L. acknowledge the support of the Severo Ochoa Centres of Excellence program to the CNIO, IRB Barcelona, and to the CRG (MCIN/AEI/10.13039/50110001103), respectively. B.L. and F.S. Work is funded with the grants BFU2017-89488-P and RegioMut BFU2017-89833-P (MCIN/AEI/10.13039/501100011033/FEDER "A way to make Europe"), respectively. B.L. is further supported by the Bettencourt Schueller Foundation, the Agencia de Gestio d'Ajuts Universitaris i de Recerca (2017 SGR 1322), and the Centres de Recerca de Catalunya (CERCA) program/Generalitat de Catalunya. B.L. also acknowledges the support of the Spanish Ministry of Economy, Industry, and Competitiveness to the European Molecular Biology Laboratory (EMBL) partnership. The results shown here are in whole or part based upon data generated by the TCGA Research Network.The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes in the same biological pathway. The correct genetic model for a gene thus depends on the other mutations in a genome, with a second hit in the same gene or an alteration in a different gene in the same pathway sometimes representing alternative evolutionary paths to cancer.S

Bladder-cancer-associated mutations in RXRA activate peroxisome proliferator-activated receptors to drive urothelial proliferation

RXRA regulates transcription as part of a heterodimer with 14 other nuclear receptors, including the peroxisome proliferator-activated receptors (PPARs). Analysis from TCGA raised the possibility that hyperactive PPAR signaling, either due to PPAR gamma gene amplification or RXRA hot-spot mutation (S427F/Y) drives 20–25% of human bladder cancers. Here, we characterize mutant RXRA, demonstrating it induces enhancer/promoter activity in the context of RXRA/PPAR heterodimers in human bladder cancer cells. Structure-function studies indicate that the RXRA substitution allosterically regulates the PPAR AF2 domain via an aromatic interaction with the terminal tyrosine found in PPARs. In mouse urothelial organoids, PPAR agonism is sufficient to drive growth-factor-independent growth in the context of concurrent tumor suppressor loss. Similarly, mutant RXRA stimulates growth-factor-independent growth of Trp53/Kdm6a null bladder organoids. Mutant RXRA-driven growth of urothelium is reversible by PPAR inhibition, supporting PPARs as targetable drivers of bladder cancer.</jats:p

Cancer stem cells (CSCs) : metabolic strategies for their identification and eradication

Phenotypic and functional heterogeneity is one of the most relevant features of cancer cells within different tumor types and is responsible for treatment failure. Cancer stem cells (CSCs) are a population of cells with stem cell-like properties that are considered to be the root cause of tumor heterogeneity, because of their ability to generate the full rep- ertoire of cancer cell types. Moreover, CSCs have been invoked as the main drivers of metastatic dissemination and therapeutic resistance. As such, targeting CSCs may be a useful strategy to improve the effectiveness of classical anticancer therapies. Recently, metabolism has been considered as a relevant player in CSC biology, and indeed, onco- genic alterations trigger the metabolite-driven dissemination of CSCs. More interestingly, the action of metabolic pathways in CSC maintenance might not be merely a conse- quence of genomic alterations. Indeed, certain metabotypic phenotypes may play a causative role in maintaining the stem traits, acting as an orchestrator of stemness. Here, we review the current studies on the metabolic features of CSCs, focusing on the bio- chemical energy pathways involved in CSC maintenance and propagation. We provide a detailed overview of the plastic metabolic behavior of CSCs in response to microenvironment changes, genetic aberrations, and pharmacological stressors. In addition, we describe the potential of comprehensive metabolic approaches to identify and selectively eradicate CSCs, together with the possibility to ‘force’ CSCs within certain metabolic dependences, in order to effectively target such metabolic biochemical inflexibilities. Finally, we focus on targeting mitochondria to halt CSC dissemination and effectively eradicate cancer

A framework for teaching biology using StarLogo TNG : from DNA to evolution

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 65-66).This thesis outlines a 10-unit biology curriculum implemented in StarLogo TNG. The curriculum moves through units on ecology, the DNA-protein relationship, and evolution. By combining the three topics, it aims to highlight the similarities among different scales and the relationships between them. In particular, through the curriculum, students can see how small-scale changes in molecular processes can create large-scale changes in entire populations. In addition, the curriculum encourages students to engage in problembased learning, by which they are trained to approach questions creatively and independently.by Yaa-Lirng Tu.M.Eng

Computational Approaches for Predicting Drug Targets

This thesis reports the development of several computational approaches to predict human disease proteins and to assess their value as drug targets, using in-house domain functional families (CATH FunFams). CATH-FunFams comprise evolutionary related protein domains with high structural and functional similarity. External resources were used to identify proteins associated with disease and their genetic variations. These were then mapped to the CATH-FunFams together with information on drugs bound to any relatives within the FunFam. A number of novel approaches were then used to predict the proteins likely to be driving disease and to assess whether drugs could be repurposed within the FunFams for targeting these putative driver proteins. The first work chapter of this thesis reports the mapping of drugs to CATHFunFams to identify druggable FunFams based on statistical overrepresentation of drug targets within the FunFam. 81 druggable CATH-FunFams were identified and the dispersion of their relatives on a human protein interaction network was analysed to assess their propensity to be associated with side effects. In the second work chapter, putative drug targets for bladder cancer were identified using a novel computational protocol that expands a set of known bladder cancer genes with genes highly expressed in bladder cancer and highly associated with known bladder cancer genes in a human protein interaction network. 35 new bladder cancer targets were identified in druggable FunFams, for some of which FDA approved drugs could be repurposed from other protein domains in the FunFam. In the final work chapter, protein kinases and kinase inhibitors were analysed. These are an important class of human drug targets. A novel classification protocol was applied to give a comprehensive classification of the kinases which was benchmarked and compared with other widely used kinase classifications. Druginformation from ChEMBL was mapped to the Kinase-FunFams and analyses of protein network characteristics of the kinase relatives in each FunFam used to identify those families likely to be associated with side effects