Phenotypic Landscape Research Articles

Genotype and phenotype landscape of MEN2 in 554 medullary thyroid cancer patients: the BrasMEN study.

Multiple endocrine neoplasia type 2 (MEN2) is an autosomal dominant genetic disease caused by RET gene germline mutations that is characterized by medullary thyroid carcinoma (MTC) associated with other endocrine tumors. Several reports have demonstrated that the RET mutation profile may vary according to the geographical area. In this study, we collected clinical and molecular data from 554 patients with surgically confirmed MTC from 176 families with MEN2 in 18 different Brazilian centers to compare the type and prevalence of RET mutations with those from other countries. The most frequent mutations, classified by the number of families affected, occur in codon 634, exon 11 (76 families), followed by codon 918, exon 16 (34 families: 26 with M918T and 8 with M918V) and codon 804, exon 14 (22 families: 15 with V804M and 7 with V804L). When compared with other major published series from Europe, there are several similarities and some differences. While the mutations in codons C618, C620, C630, E768 and S891 present a similar prevalence, some mutations have a lower prevalence in Brazil, and others are found mainly in Brazil (G533C and M918V). These results reflect the singular proportion of European, Amerindian and African ancestries in the Brazilian mosaic genome.

Minimizing the Number of Measurements Required to Predict a Phenotypic Landscape in Bacterial Folate Metabolism

Minimizing the Number of Measurements Required to Predict a Phenotypic Landscape in Bacterial Folate Metabolism

Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population.

To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data (n = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype-phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes.

Parallel likelihood calculation for phylogenetic comparative models: The SPLITT C++ library

Abstract Phylogenetic comparative models (PCMs) have been used to study macroevolutionary patterns, to characterize adaptive phenotypic landscapes, to quantify rates of evolution, to measure trait heritability, and to test various evolutionary hypotheses. A major obstacle to applying these models has been the complexity of evaluating their likelihood function. Recent works have shown that for many PCMs, the likelihood can be obtained in time proportional to the size of the tree based on post‐order tree traversal, also known as pruning. Despite this progress, inferring complex multi‐trait PCMs on large trees remains a time‐intensive task. Here, we study parallelizing the pruning algorithm as a generic technique for speeding‐up PCM‐inference. We implement several parallel traversal algorithms in the form of a generic C++ library for Serial and Parallel LIneage Traversal of Trees (SPLITT). Based on SPLITT, we provide examples of parallel likelihood evaluation for several popular PCMs, ranging from a single‐trait Brownian motion model to complex multi‐trait Ornstein‐Uhlenbeck and mixed Gaussian phylogenetic models. Using the phylogenetic Ornstein–Uhlenbeck mixed model (POUMM) as a showcase, we run benchmarks on up to 24 CPU cores, reporting up to an order of magnitude parallel speed‐up for the likelihood calculation on simulated balanced and unbalanced trees of up to 100,000 tips with up to 16 traits. Noticing that the parallel speed‐up depends on multiple factors, the SPLITT library is capable to automatically select the fastest traversal strategy for a given hardware, tree‐topology, and data. Combining SPLITT likelihood calculation with adaptive Metropolis sampling on real data, we show that the time for Bayesian POUMM inference on a tree of 10,000 tips can be reduced from several days to less than an hour. We conclude that parallel pruning effectively accelerates the likelihood calculation and, thus, the statistical inference of Gaussian phylogenetic models. For time‐intensive Bayesian inferences, we recommend combining this technique with adaptive Metropolis sampling. Beyond Gaussian models, the parallel tree traversal can be applied to numerous other models, including discrete trait and birth–death population dynamics models. Currently, SPLITT supports multi‐core shared memory architectures, but can be extended to distributed memory architectures as well as graphical processing units.

Feedback-induced self-oscillations in large interacting systems subjected to phase transitions

In this article it is shown that large systems with many interacting units endowing multiple phases display self-oscillations in the presence of linear feedback between the control and order parameters, where an Andronov–Hopf bifurcation takes over the phase transition. This is simply illustrated through the mean field Landau theory whose feedback dynamics turn out to be described by the Van der Pol equation and it is then validated for the fully connected Ising model following heat bath dynamics. Despite its simplicity, this theory accounts potentially for a rich range of phenomena: here it is applied to describe in a stylized way (i) excess demand-price cycles due to strong herding in a simple agent-based market model; (ii) congestion waves in queuing networks triggered by user feedback to delays in overloaded conditions; and (iii) metabolic network oscillations resulting from cell growth control in a bistable phenotypic landscape.

The phenotypic landscape of a Tbc1d24 mutant mouse includes convulsive seizures resembling human early infantile epileptic encephalopathy.

Epilepsy, deafness, onychodystrophy, osteodystrophy and intellectual disability are associated with a spectrum of mutations of human TBC1D24. The mechanisms underlying TBC1D24-associated disorders and the functions of TBC1D24 are not well understood. Using CRISPR-Cas9 genome editing, we engineered a mouse with a premature translation stop codon equivalent to human S324Tfs*3, a recessive mutation of TBC1D24 associated with early infantile epileptic encephalopathy (EIEE). Homozygous S324Tfs*3 mice have normal auditory and vestibular functions but show an abrupt onset of spontaneous seizures at postnatal day 15 recapitulating human EIEE. The S324Tfs*3 variant is located in an alternatively spliced micro-exon encoding six perfectly conserved amino acids incorporated postnatally into TBC1D24 protein due to a micro-exon utilization switch. During embryonic and early postnatal development, S324Tfs*3 homozygotes produce predominantly the shorter wild-type TBC1D24 protein isoform that omits the micro-exon. S324Tfs*3 homozygotes show an abrupt onset of seizures at P15 that correlates with a developmental switch to utilization of the micro-exon. A mouse deficient for alternative splice factor SRRM3 impairs incorporation of the Tbc1d24 micro-exon. Wild-type Tbc1d24 mRNA is abundantly expressed in the hippocampus using RNAscope in situ hybridization. Immunogold electron microscopy using a TBC1D24-specific antibody revealed that TBC1D24 is associated with clathrin-coated vesicles and synapses of hippocampal neurons, suggesting a crucial role of TBC1D24 in vesicle trafficking important for neuronal signal transmission. This is the first characterization of a mouse model of human TBC1D24-associated EIEE that can now be used to screen for antiepileptogenic drugs ameliorating TBCID24 seizure disorders.

Deep Behavioural Phenotyping Reveals Divergent Trajectories of Ageing and Quantifies Health in  <i>C. elegans</i>

Neurodegenerative diseases are suggested as a cause or a consequence of the acceleration of physiological ageing processes. Here, we compared the processes of physiological and pathological ageing in Caenorhabditis elegans. A multi-parametric approach precisely described the phenotypic evolution of each individual. Trajectories of ageing within the phenotypic landscape are defined and analysed. Rather than an acceleration of ageing, our model for synucleinopathy showed a divergent and accelerated trajectory of ageing, compared to control. Despite of similar lifespans, the disease-model worms displayed an early onset of decline in their phenotypic range of ability, an index of health. The pathological progression of individual animals can be predicted from early phenotypes with a high accuracy. We demonstrate the power of multi-parametric dataset to describe ageing, to quantify health and to predict specific health risks.

The Limits of Phenotypic Plasticity in the Actin Cytoskeleton Revealed by Unbiased Chemical Perturbation

Numerous proteins and pathways regulate F-actin organisation, meaning that, in combinatorial terms, an almost unlimited number of regulatory states are conceivable. Consequently, the potential for plasticity in F-actin phenotypes appears virtually unbounded. To estimate the actual limits of F-actin phenotype plasticity, we used a library of 114,400 structurally diverse compounds to induce unbiased chemical perturbations. Remarkably, just 25 distinct, recurrent F-actin phenotypes emerged. Correspondingly, select compounds with distinct molecular mechanisms induced equivalent phenotypes, suggesting that these recurring phenotypes reflect a low number of equilibrium or attractor states in actin organisation. This was supported by dynamic analyses comparing phenotype trajectories over time, showing how initially divergent phenotypes ultimately converged into equivalent end-states. We propose that infrequent attractor states in the actin phenotypic landscape reflect a channelling of high perturbative diversity into low phenotypic variety and consider how this may suppress chaotic outcomes during the evolution of this complex, functionally integral system.

Adult Bone Marrow Three-Dimensional Phenotypic Landscape of B-Cell Differentiation.

The different B-cell subsets in human bone marrow result from a dynamic equilibrium between endogenous production, B-cell bone marrow reentry and terminal plasma cell differentiation. Our aim was to define and quantify the different medullary B-cell subsets. A series of 32 normal adult bone marrows plus 15 normal adult blood samples was studied by nine color flow cytometry (CD10, CD19, CD24, CD27, CD34, CD38, CD45, IgM, and IgD). With the Kaluza software radar plots, two 2D triple parametric histograms (CD10/CD34/CD45 and CD27/IgM/IgD) were set-up to identify six progenitor and five mature B-cell subsets. Very early B-cell progenitors were CD19neg/CD10pos/CD34pos. B-cell progenitors were split into five subsets on the CD10/CD34/CD45 triple parametric histogram, sequentially ordered according to the loss of CD34 and CD10 and acquisition of surface IgM and IgD. CD19pos/CD38low mature B-cells were divided into four subsets on the CD27/IgM/IgD triple parametric histogram, with two stages of naïve B-cells and two CD27hi marginal zone and switched memory B-cell compartments. CD19pos/CD34neg/CD10low immature B-cells were the main bone marrow B-cell subset, accounting for one third of bone marrow B-cells. Transitional B-cells were the only immature bone marrow stage found in the blood. Compared to blood, the bone marrow was enriched in both marginal zone and switched B-cells. We provide the first analysis of 3D B-cell differentiation by multicolor flow cytometry leading to propose reference values for each bone marrow and blood B-cell compartment. This warrants further exploration of normal and pathological human B-cell maturation. © 2018 International Clinical Cytometry Society.

Single sample scoring of molecular phenotypes

BackgroundGene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore).ResultsWe use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data.ConclusionsThe singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Towards a Quantitative Understanding of Cell Identity.

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.

Robustness by intrinsically disordered C-termini and translational readthrough.

During protein synthesis genetic instructions are passed from DNA via mRNA to the ribosome to assemble a protein chain. Occasionally, stop codons in the mRNA are bypassed and translation continues into the untranslated region (3′-UTR). This process, called translational readthrough (TR), yields a protein chain that becomes longer than would be predicted from the DNA sequence alone. Protein sequences vary in propensity for translational errors, which may yield evolutionary constraints by limiting evolutionary paths. Here we investigated TR in Saccharomyces cerevisiae by analysing ribosome profiling data. We clustered proteins as either prone or non-prone to TR, and conducted comparative analyses. We find that a relatively high frequency (5%) of genes undergo TR, including ribosomal subunit proteins. Our main finding is that proteins undergoing TR are highly expressed and have a higher proportion of intrinsically disordered C-termini. We suggest that highly expressed proteins may compensate for the deleterious effects of TR by having intrinsically disordered C-termini, which may provide conformational flexibility but without distorting native function. Moreover, we discuss whether minimizing deleterious effects of TR is also enabling exploration of the phenotypic landscape of protein isoforms.

Abstract 1092: Defining the molecular phenotypes of metastatic castration-resistant prostate cancer sensitive to FGF pathway inhibition

Abstract Background: Widespread and long-term use of first-and second-line androgen-deprivation therapy (ADT) is changing the molecular and phenotypic landscapes of prostate cancer. Observations made through our longstanding rapid autopsy and patient-derived xenograft (PDX) programs at the University of Washington support a shift in metastatic castration-resistant prostate cancer (mCRPC) towards androgen receptor (AR)-null phenotypes, such as neuroendocrine (NEPC) and double negative (DNPC). Currently, there are no effective therapies for AR-null mCRPC. We showed previously that DNPC (AR-null, NE-null) bypasses AR-dependence through fibroblast growth factor (FGF) signaling. However, the role of the FGF pathway in other molecular mCRPC subtypes remains to be determined. Methods and Results: Here, we define four mCRPC subtypes that can be categorized by the presence or absence of functioning AR or RE1-silencing transcription factor (REST). Transcriptomic analysis of mCRPC specimens showed that AR and REST activity define four emerging mCRPC phenotypes: adenocarcinoma (AR+/REST+), amphicrine (tumor cells that co-express AR and NE markers, AR+/REST-), DNPC (AR-/REST+) and NEPC (AR-/REST-). Immunohistochemistry of mCRPC and PDX models for AR, prostate specific antigen, synaptophysin, chromogranin, and other clinically relevant markers accurately reflected the AR/REST transcriptomic signature classifications. Furthermore, loss of REST activity can, at least in part, be attributed to alternative splicing of REST mRNA by serine/arginine repetitive matrix protein 4 (SRRM4), leading to the translation of a truncated REST protein. PCR analysis of mCRPC identified the REST splice variant exclusively in amphicrine and NEPC specimens. RNA sequencing/GSEA, qPCR and immunoblot analyses determined that overexpression of SRRM4 or siRNA knockdown of REST in C4-2B (AR+) and PC-3 (AR-) prostate cancer cells promotes expression of neuroendocrine markers. Finally, we are conducting preclinical testing of the FGFR inhibitor CH5183284 in multiple PDX models representing the four mCRPC subtypes described above to delineate the impact of FGF pathway inhibition in all mCRPC subtypes. Conclusions: Our data highlight the importance of AR and REST transcriptional programs in maintaining phenotypic stability in mCRPC and explain the phenotypic heterogeneity of mCRPC in the post-abiraterone/enzalutamide era. Understanding the mCRPC subtypes that depend on the FGF pathway for survival and proliferation will inform treatment and lead to the development of novel therapies for advanced disease. Citation Format: Mark P. Labrecque, Lisha G. Brown, Ilsa M. Coleman, Lawrence D. True, Lori Kollath, Bryce Lakely, Yu C. Yang, Holly M. Nguyen, Eva Corey, Peter S. Nelson, Colm Morrissey. Defining the molecular phenotypes of metastatic castration-resistant prostate cancer sensitive to FGF pathway inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1092.

Abstract 5302: Phenotypic heterogeneity of patient-derived tumor cells visualized by unsupervised analysis in cell-based personalized drug testing

Abstract Patient-derived tumor cells are highly variable in their morphology and molecular phenotypes and after exposure to a library of drugs their heterogeneity further increases as the cells respond to the perturbations. The complete phenotypic diversity of the tumor cells and drug response patterns may remain undetectable if only few simple readouts, such as individual biomarkers in pre-defined tumor cells, are considered. Therefore, novel techniques that capture and visualize the entire spectrum of heterogeneous phenotypic changes in ex-vivo patient-derived tumor cells are needed. Here, we applied unsupervised machine learning for comprehensive visualization of cell phenotypes with the aim to improve the analysis of image-based drug screening results. We performed high content screening of patient-derived renal clear cell carcinoma cells, exposed to 36 drugs in nine concentrations applied in 384-well format. The nuclei were stained with Hoechst and cell proliferation using Ki67, and images were acquired with a high-content imaging microscope (PE Operetta, 20x). To quantitatively describe the cell phenotypes, we used CellProfiler for cell segmentation and feature extraction and then applied a large-scale data embedding method (LargeVis) to cluster the cells based on their phenotypes. Using the clustering, we created an image-based phenotype-map to describe the phenotypic landscape. Overlaying the drug treatments on different concentrations on top of the map allowed for visualization of unique phenotypic fingerprints for each drug at the single cell resolution. Visual inspection of the phenotype-map displayed visually similar cells clustering together. Likewise, a comparison with an expert trained supervised classification indicated consistency with the phenotype-map. As expected, Ki67-positive and -negative cells as well as dying cells formed clusters. Interestingly, the phenotype-map identified sub-clusters within the Ki67-positive and -negative cells. Furthermore, the map also allowed us to adjust for classification errors occurring in the supervised classification. In conclusion, we find that phenotypic signatures provide an intuitive way of linking and comparing phenotypes of distinct populations of drug-treated cells at a single-cell level. This will reveal additional systematic information of the drug responses, complementing traditional readout, such as cell viability, by highlighting the phenotypic changes over concentrations. Citation Format: Riku Turkki, Lassi Paavolainen, Piia Mikkonen, Päivi Östling, Peter Horvath, Vilja Pietiäinen, Olli Kallioniemi. Phenotypic heterogeneity of patient-derived tumor cells visualized by unsupervised analysis in cell-based personalized drug testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5302.

Genome evolution across 1,011 Saccharomyces cerevisiae isolates

Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single ‘out-of-China’ origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype–phenotype studies in this classic model system.

The very question of future development of extremely preterm infants

The survival rates for extremely preterm infants, born at <28 weeks of gestation, have markedly improved over the past two decades and this has raised concern over a possible increase in morbidity. Due to advances in perinatal, neonatal and paediatric care, our greatest fears have not materialised. Instead, most infants born preterm these days function in the same way as their term-born counterparts 1. The clinical challenge is to recognise those preterm infants who face a higher risk of a wide range of disabilities, and the most common issues at the moment are cognitive and behavioural problems. A systematic review that was published online in January 2018 looked at 30 studies covering a total of 10 293 preterm infants born before 32 weeks of gestation and/or with very low birthweights of <1500 g after 2006. It estimated that 20.6% had motor delays at approximately two years of corrected age, with a 95% confidence interval of 13.9–29.4, and that the figures for cognitive delays were 16.9% (10.4–26.3) on the basis of developmental scales 2. Despite extensive meta-analyses and outcome research after very preterm births, the clinician at the bedside of an extremely preterm infant in an individual neonatal intensive care unit is still uncertain about the extent to which they will face these delays. One of the major challenges of interpreting the results of any outcome studies or systematic reviews on preterm infants is the heterogeneity of research designs 2. There are wide differences in reporting active treatment policies at birth 3, background mortality rates 3, inclusion and exclusion criteria such as genetic disorders, sociodemographic characteristics, parental education, length of follow-up and the exact measures used and definitions of outcome. All of this might result in flaws with regard to the clinical impact of the study. Brain imaging protocols, magnetic resonance imaging (MRI) settings and expertise levels differ between centres, resulting in a wide variation in sensitivity and specificity of brain MRI findings, for example as predictors of cerebral palsy 4. It is also evident that the landscape of classical phenotypes of preterm morbidity and outcomes is changing. In this issue of Acta Paediatrica, Brumbaugh et al. 5 use the National Institute of Health consensus definition of bronchopulmonary dysplasia (BPD) to define modern BPD. Using this classification by severity, they report that modern BPD may not, in itself, translate to poor early cognitive outcomes that are similar to classic BPD. This is a concrete message that tells clinicians like me that we need to prepare to change our thinking about the causal connections between neonatal morbidities and outcome. The decreasing trend of cerebral palsy, even among extremely preterm infants 2, has already changed our perceptions of the outcomes of prematurity. At the same time, the severity of cerebral palsy in preterm infants has decreased 6. As a clinical observation, it has also been commented that the main type of cerebral palsy in preterm survivors has changed from bilateral spastic type to unilateral spastic cerebral palsy. There is strong demand for harmonising outcome studies to enhance the accumulation of accurate prognostic information. The 2016 paper by Rysavy et al. 7 provided recommendations for reporting outcomes of extremely preterm births to improve the dialogue about the topic and enable us to compare outcomes between different studies. This distinctive paper serves as a valuable resource for everyone carrying out outcome studies of preterm infants. When the parents of an extremely preterm infant who has a severe BPD and unilateral periventricular haemorrhagic infarction ask me about their child's future development, which is the most important factor after their survival, what valid evidence do I use as the basis for my answer? Despite the research that has been carried out to date, the question still remains under debate. We still need to know so much more.

Profiling Prostate Cancer Therapeutic Resistance.

The major challenge in the treatment of patients with advanced lethal prostate cancer is therapeutic resistance to androgen-deprivation therapy (ADT) and chemotherapy. Overriding this resistance requires understanding of the driving mechanisms of the tumor microenvironment, not just the androgen receptor (AR)-signaling cascade, that facilitate therapeutic resistance in order to identify new drug targets. The tumor microenvironment enables key signaling pathways promoting cancer cell survival and invasion via resistance to anoikis. In particular, the process of epithelial-mesenchymal-transition (EMT), directed by transforming growth factor-β (TGF-β), confers stem cell properties and acquisition of a migratory and invasive phenotype via resistance to anoikis. Our lead agent DZ-50 may have a potentially high efficacy in advanced metastatic castration resistant prostate cancer (mCRPC) by eliciting an anoikis-driven therapeutic response. The plasticity of differentiated prostate tumor gland epithelium allows cells to de-differentiate into mesenchymal cells via EMT and re-differentiate via reversal to mesenchymal epithelial transition (MET) during tumor progression. A characteristic feature of EMT landscape is loss of E-cadherin, causing adherens junction breakdown, which circumvents anoikis, promoting metastasis and chemoresistance. The targetable interactions between androgens/AR and TGF-β signaling are being pursued towards optimized therapeutic regimens for the treatment of mCRPC. In this review, we discuss the recent evidence on targeting the EMT-MET dynamic interconversions to overcome therapeutic resistance in patients with recurrent therapeutically resistant prostate cancer. Exploitation of the phenotypic landscape and metabolic changes that characterize the prostate tumor microenvironment in advanced prostate cancer and consequential impact in conferring treatment resistance are also considered in the context of biomarker discovery.

A Nonquiescent “Idling” Population State in Drug-Treated, BRAF-Mutated Melanoma

Targeted therapy is an effective standard of care in BRAF-mutated malignant melanoma. However, the duration of tumor remission varies unpredictably among patients, and relapse is almost inevitable. Here, we examine the responses of several BRAF-mutated melanoma cell lines (including isogenic subclones) to BRAF inhibitors. We observe complex response dynamics across cell lines, with short-term responses (<100 h) varying from cell line to cell line. In the long term, however, we observe equilibration of all drug-treated populations into a nonquiescent state characterized by a balanced rate of death and division, which we term the “idling” state, and to our knowledge, this state has not been previously reported. Using mathematical modeling, we propose that the observed population-level dynamics are the result of cells transitioning between basins of attraction within a drug-modified phenotypic landscape. Each basin is associated with a drug-induced proliferation rate, a recently introduced metric of an antiproliferative drug effect. The idling population state represents a new dynamic equilibrium in which cells are distributed across the landscape such that the population achieves zero net growth. By fitting our model to experimental drug-response data, we infer the phenotypic landscapes of all considered melanoma cell lines and provide a unifying view of how BRAF-mutated melanomas respond to BRAF inhibition. We hypothesize that the residual disease observed in patients after targeted therapy is composed of a significant number of idling cells. Thus, defining molecular determinants of the phenotypic landscape that idling populations occupy may lead to “targeted landscaping” therapies based on rational modification of the landscape to favor basins with greater drug susceptibility.

Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal.

MetaboSignal is an R/Bioconductor package designed to explore the relationships between genes and metabolites, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) as its primary database. It is a network-based approach that allows overlaying metabolic and signaling pathways and exploring the topological relationship between genes (signaling or metabolic genes) and metabolites. MetaboSignal is ideally suited to identify candidate genes in metabolome genome-wide association studies (mGWAS), particularly in the case of trans-acting associations. It can also be used to provide mechanistic explanations of perturbed metabolic patterns observed in genetic models, as well as to identify novel target metabolic pathways of signaling genes. © 2018 by John Wiley & Sons, Inc.

The Landscape of Somatic Genetic Alterations in Breast Cancers From ATM Germline Mutation Carriers.

Pathogenic germline variants in ataxia-telangiectasia mutated (ATM), a gene that plays a role in DNA damage response and cell cycle checkpoints, confer an increased breast cancer (BC) risk. Here, we investigated the phenotypic characteristics and landscape of somatic genetic alterations in 24 BCs from ATM germline mutation carriers by whole-exome and targeted sequencing. ATM-associated BCs were consistently hormone receptor positive and largely displayed minimal immune infiltrate. Although 79.2% of these tumors exhibited loss of heterozygosity of the ATM wild-type allele, none displayed high activity of mutational signature 3 associated with defective homologous recombination DNA (HRD) repair. No TP53 mutations were found in the ATM-associated BCs. Analysis of an independent data set confirmed that germline ATM variants and TP53 somatic mutations are mutually exclusive. Our findings indicate that ATM-associated BCs often harbor bi-allelic inactivation of ATM, are phenotypically distinct from BRCA1/2-associated BCs, lack HRD-related mutational signatures, and that TP53 and ATM genetic alterations are likely epistatic.