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Results for “Cancer Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Progesterone receptor maintains estrogen receptor gene expression by regulating DNA methylation in hormone-free breast cancer cells

Breast cancer prognosis and response to endocrine therapy strongly depends on the expression of the estrogen and progesterone receptors (ER and PR, respectively). Although much is known about ER gene (ESR1) regulation after hormonal stimulation, how it is regulated in hormone-free condition is not fully understood. We used ER-/PR-positive breast cancer cells to investigate the role of PR in ESR1 regulation in the absence of hormones. We show that PR binds to the low-methylated ESR1 promoter and maintains both gene expression and DNA methylation of the ESR1 locus in hormone-deprived breast cancer cells. Depletion of PR reduces ESR1 expression, with a concomitant increase in gene promoter methylation. The high amount of methylation in the ESR1 promoter of PR-depleted cells persists after the stable re-expression of PR and inhibits PR binding to this genomic region. As a consequence, the rescue of PR expression in PR-depleted cells is insufficient to restore ESR1 expression. Consistently, DNA methylation impedes PR binding to consensus progesterone responsive elements. These findings contribute to understanding the complex crosstalk between PR and ER and suggest that the analysis of ESR1 promoter methylation in breast cancer cells can help to design more appropriate targeted therapies for breast cancer patients.

molecular biology↗

bioRxiv2018

K-Ras G-domain binding with signaling lipid phosphoinositides: PIP2 association, orientation, function

Ras genes are potent drivers of human cancers, with mutated K-Ras4B being the most abundant isoform. Targeted inhibition of oncogenic gene products is considered the holy grail of present-day cancer therapy, and recent discoveries of small molecule inhibitors for K-Ras4B greatly benefited from a deeper understanding of the protein structure and dynamics of the GTPase. Since interactions with biological membranes are key for Ras function, the details of Ras - lipid interactions have become a major focus of study, especially since it is becoming clear that such interactions not only involve the Ras C-terminus for lipid anchoring, but also the G-protein domain. Here we investigated the interaction between K-Ras4B with the signaling lipid phosphatidyl inositol (4,5) phosphate (PIP2) using NMR spectroscopy and molecular dynamics simulations, complemented by biophysical and cell biology assays. We discovered that the {beta}2 and {beta}3 strands as well as helices 4 and 5 of the GTPase G-domain bind to PIP2, and that these secondary structural elements employ specific residues for these interactions. These likely occur in two orientation states of the protein relative to the membrane. Importantly, we found that some of these residues, which are known to be oncogenic when mutated (D47K, D92N, K104M and D126N), are critical for K-Ras-mediated transformation of fibroblast cells, while not substantially affecting basal and assisted nucleotide hydrolysis and exchange. We further showed that mutation K104M can indeed abolish localization of mutant K-Ras to the plasma membrane. These findings suggest that specific G-domain residues play an important, previously-unknown role in regulating Ras function by mediating interactions with membrane PIP2 lipids. Thus, a detailed description of the novel K-Ras-PIP2 binding surfaces is likely to inform the future design of therapeutic reagents.

biophysics↗

bioRxiv2018

TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

BackgroundTraditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes expression profiles to identify similarly altered signatures. ResultsTuBAs predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. ConclusionTuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

systems biology↗

bioRxiv2017

A Map Of Genetic Interactions In Cancer Cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/{beta}-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/{beta}-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

systems biology↗

bioRxiv2017

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction pathways and energy metabolism are hallmarks of cancer and both play a fundamental role in the process of tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we show that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers.

systems biology↗

bioRxiv2017

The activation loop residue serine 173 of S.pombe Chk1 kinase is critical for the response to DNA replication stress

Why the DNA damage checkpoint kinase Chk1 protects the genome of lower and higher eukaryotic cells differentially is still unclear. Mammalian Chk1 regulates replication origins, safeguards DNA replication forks and promotes fork progression. Conversely, yeast Chk1 acts only in G1 and G2. We report here that the mutation of serine 173 (S173A) in the activation loop of fission yeast Chk1 abolishes the G1-M and S-M checkpoints without affecting the G2-M arrest. Although Chk1-S173A is fully phosphorylated at serine 345 by the DNA damage sensor Rad3 (ATR) when DNA replication forks break, cells fail to stop the cell cycle. Mutant cells are uniquely sensitive to the DNA alkylation agent methyl- methanesulfate (MMS). This MMS sensitivity is genetically linked with the lagging strand DNA polymerase delta. Chk1-S173A is also unable to block mitosis when the G1 transcription factor Cdc10 is impaired. Serine 173 is equivalent to lysine 166 in human Chk1, an amino acid important for substrate specificity. We conclude that the removal of serine 173 impairs the phosphorylation of a Chk1 target that is important to protect cells from DNA replication stress.\n\nSummary statementMutation of serine-173 in the activation loop of Chk1 kinase may promote cancer as it abolishes the response to genetic alterations that arise while chromosomes are being copied.

cell biology↗

bioRxiv2017

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

cell biology↗

bioRxiv2017

Integrating TARA Oceans Datasets Using Unsupervised Multiple Kernel Learning

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.\n\nWe propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.\n\nProposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

bioinformatics↗

bioRxiv2017

Clay-induced DNA double-strand breaks underlay genetic diversity, antibiotic resistance and molecular basis of asbestosis

Some natural clays and synthetic nanofibres present in the environment have a severe impact on human health. After several decades of research, the molecular mechanism of how asbestos induce cancers is not well understood. Different fibres, including asbestos, can penetrate the membrane and introduce DNA in both, bacterial and eukaryotic cells. Incubating Escherichia coli with sepiolite, a clayey material, and asbestos under friction forces, both fibres cause double-strand breaks in bacteria. Since antibiotics and clays are used together in animal husbandry, the mutagenic effect of these fibres might constitute a pathway to antibiotic resistance due to the friction provided by peristalsis of the gut from farm animals in addition to the previously proposed horizontal gene transfer. Moreover, we raise the possibility that the same mechanism could generate bacteria diversity in natural scenarios with a role in the evolution of species. Finally, we provide a new model on how asbestos may promote mutagenesis and cancer based on the observed mechanical genotoxicity.

evolutionary biology↗

bioRxiv2017

Pooled AAV-CRISPR Screen with Targeted Amplicon Sequencing

High-resolution, high-throughput direct in vivo screening of functional genetic factors in native tissues has long been challenging. Adeno-associated viruses (AAV) are powerful carriers of transgenes and have been shown to mediate efficient genome editing in various organs in mice. Here, we developed a new technological approach, Pooled AAV-CRISPR Screen with Targeted Amplicon Sequencing (PASTAS), and demonstrated its application for directly mapping functional cancer driver variants in the mouse liver in an autochthonous manner. Intravenous delivery of an AAV-CRISPR library targeting a set of the most frequently mutated tumor suppressor genes into fully immunocompetent conditional Cas9 knock-in mice consistently generated highly complex autochthonous liver tumors. The molecular landscapes of these genetically diverse tumors were mapped out by deep direct readout of Cas9-generated variants at predicted sgRNA cut sites using molecular inversion probe sequencing. Co-occurrence and correlation analyses as well as validation with lower complexity minipools further confirmed the potency of various co-mutated drivers. The PASTAS method can be applied to virtually any gene sets, any cancer types, or any type of in vivo genetic studies other than cancer.

systems biology↗

bioRxiv2017

Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations

Pseudotime algorithms can be employed to extract latent temporal information from crosssectional data sets allowing dynamic biological processes to be studied in situations where the collection of genuine time series data is challenging or prohibitive. Computational techniques have arisen from areas such as single-cell omics and in cancer modelling where pseudotime can be used to learn about cellular differentiation or tumour progression. However, methods to date typically assume homogenous genetic and environmental backgrounds, which becomes particularly limiting as datasets grow in size and complexity. As a solution to this we describe a novel statistical framework that learns pseudotime trajectories in the presence of non-homogeneous genetic, phenotypic, or environmental backgrounds. We demonstrate that this enables us to identify interactions between such factors and the underlying genomic trajectory. By applying this model to both single-cell gene expression data and population level cancer studies we show that it uncovers known and novel interaction effects between genetic and enironmental factors and the expression of genes in pathways. We provide an R implementation of our method PhenoPath at https://github.com/kieranrcampbell/phenopath

bioinformatics↗

bioRxiv2017

LEM-3 is a midbody-tethered DNA nuclease that resolves chromatin bridges during cytokinesis

Faithful chromosome segregation and genome maintenance requires the removal of all DNA bridges that physically link chromosomes before cells divide. Using C. elegans embryos we show that the LEM-3/Ankle1 nuclease defines a new genome integrity mechanism by processing DNA bridges right before cells divide. LEM-3 acts at the midbody, the structure where abscission occurs at the end of cytokinesis. LEM-3 localization depends on factors needed for midbody assembly, and LEM-3 accumulation is increased and prolonged when chromatin bridges are trapped at the cleavage plane. LEM-3 locally processes chromatin bridges that arise from incomplete DNA replication, unresolved recombination intermediates or the perturbance of chromosome structure. Proper LEM-3 midbody localization and function is regulated by AIR-2/Aurora B kinase. Strikingly, LEM-3 act cooperatively with the BRC-1/BRCA1 homologous recombination factor to promote genome integrity. These findings provide a molecular basis for the suspected role of the LEM-3 orthologue Ankle1 in human breast cancer.

cell biology↗

bioRxiv2018

FAM35A associates with REV7 and modulates DNA damage responses of normal and BRCA1-defective cells

In order to exploit the specific vulnerabilities of tumors, it is urgent to identify the basis of associated defects in genome maintenance. One unsolved problem is the mechanism of inhibition of processing of DNA double-strand break repair by REV7 and its influence on DNA repair pathways. We searched for REV7-associated proteins in human cells and found FAM35A, a protein of previously unknown function. By analyzing the FAM35A sequence we discovered that FAM35A has an unstructured N-terminal region and a C-terminal region harboring three OB-fold domains similar to single-stranded binding protein RPA. Knockdown of FAM35A caused sensitivity to DNA damaging agents, and FAM35A re-localized in damaged cell nuclei. In a BRCA1 mutant cell line, however, depletion of FAM35A increased resistance to camptothecin, suggesting that FAM35A participates in processing of DNA ends to allow more efficient DNA repair. Moreover, we found FAM35A absent in one widely used BRCA1-mutant cancer cell line (HCC1937) with anomalous resistance to PARP inhibitors. A survey of FAM35A alterations in cancer revealed that the gene is altered at the highest frequency in prostate cancers (up to 13%) and significantly less expressed in metastatic cases. The results reveal a new DNA repair factor with promise as a therapeutically relevant cancer marker.

molecular biology↗

bioRxiv2019

Modeling the dynamics of Let-7-coupled gene regulatory networks linking cell proliferation to malignant transformation

The microRNA Let-7 controls the expression of proteins that belong to two distinct gene regulatory networks, namely a cyclin-dependent kinases (Cdks) network driving the cell cycle and a cell transformation network which can undergo an epigenetic switch between a non-transformed and a malignant transformed cell state.\n\nUsing mathematical modeling and transcriptomic data analysis, we here investigate how Let-7 controls the cdk-dependent cell cycle network, and how it couples the latter with the transformation network. We also determine whether the two networks can be combined into a larger entity that impacts on cancer progression.\n\nOur analysis shows that the switch from a quiescent to a cycling state depends on the relative levels of Let-7 and several cell cycle activators. Numerical simulations further indicate that the Let-7-coupled cell cycle and transformation networks control each other, and our model identifies key players for this mutual control. Transcriptomic data analysis from the The Cancer Genome Atlas (TCGA) suggest that the two networks are activated in cancer, in particular in gastrointestinal cancers, and that the activation levels vary significantly among patients affected with a same cancer type. Our mathematical model, when applied to a heterogeneous cell population, suggests that heterogeneity among tumors results from stochastic switches between a non-transformed cell state with low proliferative capability and a transformed cell state with high proliferative property. The model further predicts that Let-7 may reduce tumor heterogeneity by decreasing the occurrence of stochastic switches towards a transformed, proliferative cell state.\n\nIn conclusion, we identified the key components responsible for the qualitative dynamics of two GRNs interconnected by Let-7. The two GRNs are heterogeneously involved in several cancers, thereby stressing the need to consider patients specific GRN characteristics to optimize therapeutic strategies.

systems biology↗

bioRxiv2018

A cancer-associated polymorphism in ESCRT-III disrupts the abscission checkpoint and promotes genome instability

Cytokinetic abscission facilitates the irreversible separation of daughter cells. This process requires the Endosomal Sorting Complexes Required for Transport (ESCRT) machinery and is tightly regulated by Charged Multivesicular body Protein 4C (CHMP4C), an ESCRT-III subunit that engages the abscission checkpoint (NoCut) in response to mitotic problems such as persisting chromatin bridges within the midbody. Importantly, a human polymorphism in CHMP4CT232 (rs35094336), increases cancer susceptibility. Here, we explain the structural and functional basis for this cancer association: the CHMP4CT232 allele unwinds the C-terminal helix of CHMP4C, impairs binding to the early-acting ESCRT factor ALIX, and disrupts the abscission checkpoint. Cells expressing CHMP4CT232 exhibit increased levels of DNA damage and are sensitized to several conditions that increase chromosome mis-segregation, including DNA replication stress, inhibition of the mitotic checkpoint, and loss of p53. Our data demonstrate the biological importance of the abscission checkpoint, and suggest that dysregulation of abscission by CHMP4CT232 may synergize with oncogene-induced mitotic stress to promote genomic instability and tumorigenesis.\n\nSignificance StatementThe final step of cell division, abscission, is temporally regulated by the Aurora B kinase and CHMP4C in a conserved pathway called the abscission checkpoint which arrests abscission in the presence of lingering mitotic problems. Despite extensive study, the physiological importance of this pathway to human health has remained elusive. We now demonstrate that a cancer predisposing polymorphism in CHMP4C disrupts the abscission checkpoint and results in DNA damage accumulation. Moreover, deficits in this checkpoint synergize with p53 loss and generate aneuploidy under stress conditions that increase the frequency of chromosome missegregation. Therefore, cells expressing the cancer-associated polymorphism in CHMP4C are genetically unstable, thus suggesting a novel oncogenic mechanism that may involve the dysregulation of abscission.

cell biology↗

bioRxiv2018

Destabilization of Ran C-terminus promotes GTP loading and occurs in multiple Ran cancer mutations

Ran (Ras-related nuclear protein) plays several important roles in nucleo-cytoplasmic transport, mitotic spindle formation, nuclear envelope/nuclear pore complex assembly, and other diverse functions in the cytoplasm, as well as in cellular transformation when activated. Unlike other Ras superfamily proteins, Ran contains an auto-inhibitory C-terminal tail, which packs against its G domain and bias Ran towards binding GDP over GTP. The biological importance of this C-terminal tail is not well understood. By disrupting the interaction between the C-terminus and the G domain, we were able to generate Ran mutants that are innately active and potently bind to RanBP1 (Ran Binding Protein 1), nuclear export factor CRM1 and nuclear import factor KPNB1. In contrast to previously reported activated Ran mutants, the C-terminus destabilized mutants are hydrolysis competent in cells, support nuclear transport, and do not form nuclear rim staining. Crystal structures show that one of these C-terminal mutations slightly changes its mode of binding to RanBP1. Finally, a high percentage of Ran C-terminus mutations from cancer patients were found to be destabilizing and hyperactivating, suggesting that Ran C-destabilization might be an unprecedented cellular transformation mechanism in affected cancers. This study also highlights a new drug design strategy towards treating patients with hyperactivated Ras proteins including K-Ras.

biochemistry↗

bioRxiv2018

Triggering the expression of a silent gene cluster from genetically intractable bacteria results in scleric acid discovery

Herein we report a strategy for the rapid and rational characterisation of novel microbial natural products from silent gene clusters. A conserved set of five regulatory genes was used as a query to search genomic databases and identify atypical biosynthetic gene clusters (BGCs). A 20-kb BGC from the genetically intractable Streptomyces sclerotialus bacterial strain was captured using yeast-based homologous recombination and introduced into validated heterologous hosts. CRISPR/Cas9-mediated genome editing was then employed to rationally inactivate the key transcriptional repressor and trigger production of an unprecedented class of hybrid natural products exemplified by (2-(benzoyloxy)acetyl)-L-proline, named scleric acid. Subsequent rounds of CRISPR/Cas9-mediated gene deletions afforded a selection of biosynthetic gene mutant strains which led to a plausible biosynthetic pathway for scleric acid assembly. Scleric acid and a key biosynthetic intermediate were also synthesised and used as authentic standards. The assembly of scleric acid involves two unique enzymatic condensation reactions that respectively link a proline and a benzoyl residue to each end of a rare hydroxyethyl-ACP intermediate. Scleric acid was then shown to exhibit moderate activity against Mycobacterium tuberculosis, as well as modest inhibition of the cancer-associated metabolic enzyme Nicotinamide N-methyltransferase (NNMT).

synthetic biology↗

bioRxiv2018

Chimeric antigen receptors that trigger phagocytosis

Chimeric antigen receptors (CARs) are synthetic receptors that reprogram T cells to kill cancer. The success of CAR-T cell therapies highlights the promise of programmed immunity, and suggests that applying CAR strategies to other immune cell lineages may be beneficial. Here, we engineered a family of Chimeric Antigen Receptors for Phagocytosis (CAR-Ps) that direct macrophages to engulf specific targets, including cancer cells. CAR-Ps consist of an extracellular antibody fragment, which can be modified to direct CAR-P activity towards specific antigens. By screening a panel of engulfment receptor intracellular domains, we found that the cytosolic domains from Megf10 and FcR{gamma} robustly triggered engulfment independently of their native extracellular domain. We show that CAR-Ps drive specific engulfment of antigen-coated synthetic particles and whole cancer cells. Addition of a tandem PI3K recruitment domain increased cancer cell engulfment. Finally, we show that CAR-P expressing macrophages reduce cancer cell number in co-culture by over 40%.\n\nSummaryWe report the first Chimeric Antigen Receptors for Phagocytosis (CAR-Ps) that promote engulfment of antigen-coated particles and cancer cells.

cell biology↗