Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy.

Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

The Evolutionary Landscape of Localized Prostate Cancers Drives Clinical Aggression.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

Uncovering a mammalian neural-specific poly(A) binding protein with unique properties.

The mRNA 3' poly(A) tail plays a critical role in regulating both mRNA translation and turnover. It is bound by the cytoplasmic poly(A) binding protein (PABPC), an evolutionarily conserved protein that can interact with translation factors and mRNA decay machineries to regulate gene expression. Mammalian PABPC1, the prototypical PABPC, is expressed in most tissues and interacts with eukaryotic translation initiation factor 4G (eIF4G) to stimulate translation in specific contexts. In this study, we uncovered a new mammalian PABPC, which we named neural PABP (neuPABP), as it is predominantly expressed in the brain. neuPABP maintains a unique architecture as compared with other PABPCs, containing only two RNA recognition motifs (RRMs) and maintaining a unique N-terminal domain of unknown function. neuPABP expression is activated in neurons as they mature during synaptogenesis, where neuPABP localizes to the soma and postsynaptic densities. neuPABP interacts with the noncoding RNA BC1, as well as mRNAs coding for ribosomal and mitochondrial proteins. However, in contrast to PABPC1, neuPABP does not associate with actively translating mRNAs in the brain. In keeping with this, we show that neuPABP has evolved such that it does not bind eIF4G and as a result fails to support protein synthesis in vitro. Taken together, these results indicate that mammals have expanded their PABPC repertoire in the brain and propose that neuPABP may support the translational repression of select mRNAs.

The evolutionary history of 2,658 cancers.

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies.

mRNA processing, transport, translation, and ultimately degradation involve a series of dedicated protein complexes that often assemble into large membraneless structures such as stress granules (SGs) and processing bodies (PBs). Here, systematic in vivo proximity-dependent biotinylation (BioID) analysis of 119 human proteins associated with different aspects of mRNA biology uncovers 7424 unique proximity interactions with 1,792 proteins. Classical bait-prey analysis reveals connections of hundreds of proteins to distinct mRNA-associated processes or complexes, including the splicing and transcriptional elongation machineries (protein phosphatase 4) and the CCR4-NOT deadenylase complex (CEP85, RNF219, and KIAA0355). Analysis of correlated patterns between endogenous preys uncovers the spatial organization of RNA regulatory structures and enables the definition of 144 core components of SGs and PBs. We report preexisting contacts between most core SG proteins under normal growth conditions and demonstrate that several core SG proteins (UBAP2L, CSDE1, and PRRC2C) are critical for the formation of microscopically visible SGs.

The X-linked splicing regulator MBNL3 has been co-opted to restrict placental growth in eutherians.

Understanding the regulatory interactions that control gene expression during the development of novel tissues is a key goal of evolutionary developmental biology. Here, we show that Mbnl3 has undergone a striking process of evolutionary specialization in eutherian mammals resulting in the emergence of a novel placental function for the gene. Mbnl3 belongs to a family of RNA-binding proteins whose members regulate multiple aspects of RNA metabolism. We find that, in eutherians, while both Mbnl3 and its paralog Mbnl2 are strongly expressed in placenta, Mbnl3 expression has been lost from nonplacental tissues in association with the evolution of a novel promoter. Moreover, Mbnl3 has undergone accelerated protein sequence evolution leading to changes in its RNA-binding specificities and cellular localization. While Mbnl2 and Mbnl3 share partially redundant roles in regulating alternative splicing, polyadenylation site usage and, in turn, placenta maturation, Mbnl3 has also acquired novel biological functions. Specifically, Mbnl3 knockout (M3KO) alone results in increased placental growth associated with higher Myc expression. Furthermore, Mbnl3 loss increases fetal resource allocation during limiting conditions, suggesting that location of Mbnl3 on the X chromosome has led to its role in limiting placental growth, favoring the maternal side of the parental genetic conflict.

A practical guide to cancer subclonal reconstruction from DNA sequencing.

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

PRIESSTESS: interpretable, high-performing models of the sequence and structure preferences of RNA-binding proteins.

Modelling both primary sequence and secondary structure preferences for RNA binding proteins (RBPs) remains an ongoing challenge. Current models use varied RNA structure representations and can be difficult to interpret and evaluate. To address these issues, we present a universal RNA motif-finding/scanning strategy, termed PRIESSTESS (Predictive RBP-RNA InterpretablE Sequence-Structure moTif regrESSion), that can be applied to diverse RNA binding datasets. PRIESSTESS identifies dozens of enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. Importantly, these core motifs are easily visualized and interpreted, and provide a measure of RBP secondary structure specificity. We used PRIESSTESS to interrogate new HTR-SELEX data for 23 RBPs with diverse RNA binding modes and captured known primary sequence and secondary structure preferences for each. Moreover, when applying PRIESSTESS to 144 RBPs across 202 RNA binding datasets, 75% showed an RNA secondary structure preference but only 10% had a preference besides unpaired bases, suggesting that most RBPs simply recognize the accessibility of primary sequences.

Binding specificities of human RNA-binding proteins toward structured and linear RNA sequences.

RNA-binding proteins (RBPs) regulate RNA metabolism at multiple levels by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation, and stability. Despite their central role in RNA function, the RNA-binding specificities of most RBPs remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA-binding domains (RBDs) and assessed their specificities using high-throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ∼30% preferred structured motifs folding into stem-loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins-ZC3H12A, ZC3H12B, and ZC3H12C-bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12B's PilT N terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RBDs containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high-resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs and will greatly facilitate future analysis of the various biological roles of this important class of proteins.

Regional mutational signature activities in cancer genomes.

Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development.

Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences.

Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine.

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.

Prognostic impact of autoimmune disease in oral cavity squamous cell carcinoma.

BACKGROUND AND OBJECTIVES: Recent literature has highlighted the role of the host in the prognosis of oral squamous cell carcinoma (OSCC). In this study, we retrospectively examined the impact of autoimmune (AI) disorders as an aspect of the host status on survival outcomes in OSCC patients. METHODS: From a departmental database of OSCC patients (n = 1369), 123 patients with an AI disorder were identified. AI and no-AI groups were compared for survival outcomes. RESULTS: There were no significant differences in survival between groups for overall survival, disease-specific survival, local, regional, and distant recurrence-free probabilities. However, survival and recurrence-free probabilities were poorer in the AI group versus the no AI group. CONCLUSION: Patients with AI disease trended towards worse outcomes. This suggests immune dysregulation in these patients may impact oncologic outcomes.

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

Alternative splicing is important for the development and function of the nervous system, but little is known about the differences in alternative splicing between distinct types of neurons. Furthermore, the factors that control cell-type-specific splicing and the physiological roles of these alternative isoforms are unclear. By monitoring alternative splicing at single-cell resolution in Caenorhabditis elegans, we demonstrate that splicing patterns in different neurons are often distinct and highly regulated. We identify two conserved RNA-binding proteins, UNC-75/CELF and EXC-7/Hu/ELAV, which regulate overlapping networks of splicing events in GABAergic and cholinergic neurons. We use the UNC-75 exon network to discover regulators of synaptic transmission and to identify unique roles for isoforms of UNC-64/Syntaxin, a protein required for synaptic vesicle fusion. Our results indicate that combinatorial regulation of alternative splicing in distinct neurons provides a mechanism to specialize metazoan nervous systems.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

ARGLU1 is a transcriptional coactivator and splicing regulator important for stress hormone signaling and development.

Stress hormones bind and activate the glucocorticoid receptor (GR) in many tissues including the brain. We identified arginine and glutamate rich 1 (ARGLU1) in a screen for new modulators of glucocorticoid signaling in the CNS. Biochemical studies show that the glutamate rich C-terminus of ARGLU1 coactivates multiple nuclear receptors including the glucocorticoid receptor (GR) and the arginine rich N-terminus interacts with splicing factors and binds to RNA. RNA-seq of neural cells depleted of ARGLU1 revealed significant changes in the expression and alternative splicing of distinct genes involved in neurogenesis. Loss of ARGLU1 is embryonic lethal in mice, and knockdown in zebrafish causes neurodevelopmental and heart defects. Treatment with dexamethasone, a GR activator, also induces changes in the pattern of alternatively spliced genes, many of which were lost when ARGLU1 was absent. Importantly, the genes found to be alternatively spliced in response to glucocorticoid treatment were distinct from those under transcriptional control by GR, suggesting an additional mechanism of glucocorticoid action is present in neural cells. Our results thus show that ARGLU1 is a novel factor for embryonic development that modulates basal transcription and alternative splicing in neural cells with consequences for glucocorticoid signaling.

LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance.

LIN28 is a conserved RNA-binding protein implicated in pluripotency, reprogramming, and oncogenesis. It was previously shown to act primarily by blocking let-7 microRNA (miRNA) biogenesis, but here we elucidate distinct roles of LIN28 regulation via its direct messenger RNA (mRNA) targets. Through crosslinking and immunoprecipitation coupled with high-throughput sequencing (CLIP-seq) in human embryonic stem cells and somatic cells expressing exogenous LIN28, we have defined discrete LIN28-binding sites in a quarter of human transcripts. These sites revealed that LIN28 binds to GGAGA sequences enriched within loop structures in mRNAs, reminiscent of its interaction with let-7 miRNA precursors. Among LIN28 mRNA targets, we found evidence for LIN28 autoregulation and also direct but differing effects on the protein abundance of splicing regulators in somatic and pluripotent stem cells. Splicing-sensitive microarrays demonstrated that exogenous LIN28 expression causes widespread downstream alternative splicing changes. These findings identify important regulatory functions of LIN28 via direct mRNA interactions.

GeneMANIA update 2018.

GeneMANIA (http://genemania.org) is a flexible user-friendly web site for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query gene list, GeneMANIA finds functionally similar genes using a wealth of genomics and proteomics data. In this mode, it weights each functional genomic dataset according to its predictive value for the query. Another use of GeneMANIA is gene function prediction. Given a single query gene, GeneMANIA finds genes likely to share function with it based on their interactions with it. Enriched Gene Ontology categories among this set can point to the function of the gene. Nine organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Escherichia coli, Homo sapiens, Mus musculus, Rattus norvegicus and Saccharomyces cerevisiae). Hundreds of data sets and hundreds of millions of interactions have been collected from GEO, BioGRID, IRefIndex and I2D, as well as organism-specific functional genomics data sets. Users can customize their search by selecting specific data sets to query and by uploading their own data sets to analyze. We have recently updated the user interface to GeneMANIA to make it more intuitive and make more efficient use of visual space. GeneMANIA can now be used effectively on a variety of devices.