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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 55 records · Page 3Linked to original sources

bioRxiv2016

Rapidly evolving homing CRISPR barcodes

We present here an approach for engineering evolving DNA barcodes in living cells. The methodology entails using a homing guide RNA (hgRNA) scaffold that directs the Cas9-hgRNA complex to target the DNA locus of the hgRNA itself. We show that this homing CRISPR-Cas9 system acts as an expressed genetic barcode that diversifies its sequence and that the rate of diversification can be controlled in cultured cells. We further evaluate these barcodes in cultured cell populations and show that they can record lineage history and and that their RNA can be assayed as single molecules in situ. This integrated approach will have wide ranging applications, such as in deep lineage tracing, cellular barcoding, molecular recording, dissecting cancer biology, and connectome mapping.

Synthetic Biology↗

bioRxiv2016

Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions

The methods proposed for the detection of cancer driver mutations are based on the estimation of background mutation rate, impact on protein function, or network influence. Instead, we focus on those influencing patient survival. For this, an approximation of the log-rank test has been systematically applied even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank test independently of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results using simulations, comparisons with other methods, TCGA and ICGC cancer datasets, and validations, suggests that VALORATE is accurate, fast, and can identify known and novel gene mutations. Our proposal and results may have important implications in cancer biology, in bioinformatics analyses, and ultimately in precision medicine.

Bioinformatics↗

bioRxiv2017

Prominent Features Of The Amino Acid Mutation Landscape In Cancer

Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.

bioinformatics↗

bioRxiv2018

An annual cycle of gene regulation in the red-legged salamander mental gland: from hypertrophy to expression of rapidly evolving pheromones

Cell differentiation is mediated by synchronized waves of coordinated expression for hundreds to thousands of genes, and must be an exquisitely regulated process to produce complex tissues and phenotypes. For many animal species, sexual selection has driven the development of elaborate male ornaments, requiring sex-specific differentiation pathways. One such male ornament is the pheromone-producing mental gland of the red-legged salamander (Plethodon shermani). Mental gland development follows an annual cycle of extreme hypertrophy, production of pheromones for the ~2 month mating season, and then complete resorption before repeating the process in the following year. At the peak of the mating season, the transcriptional and translational machinery of the mental gland are almost exclusively redirected to synthesis of many rapidly evolving pheromones. Of these pheromones, Plethodontid Modulating Factor (PMF) has experienced an unusual history of disjunctive evolution: following gene duplication, positive sexual selection has diversified the protein coding region while the untranslated regions have been conserved by purifying selection. However, the molecular underpinnings that bridge the processes of gland hypertrophy, pheromone synthesis, and disjunctive evolution remain to be determined and are the focus of the present investigation. Using Illumina sequencing, we prepared a de novo transcriptome of the mental gland at six stages of development. Differential expression analysis and immunohistochemistry revealed that the mental gland initially adopts a highly proliferative, almost tumor-like phenotype, followed by a rapid increase in pheromone mRNA and protein levels. One likely player in this transition is Cold Inducible RNA Binding Protein (CIRBP), which selectively and cooperatively binds the highly conserved PMF 3' UTR. CIRBP, along with other stress response proteins, have seemingly been co-opted to aid in mental gland development by helping to regulate pheromone synthesis. The P. shermani mental gland utilizes a complex system of transcriptional and post-transcriptional gene regulation to facilitate its hypertrophication and pheromone synthesis. The data support the evolutionary interplay of both coding and noncoding segments in rapid gene evolution, and necessitate study of the co-evolution of pheromone gene products along with their transcriptional and translational regulators. Additionally, the mental gland could be a powerful emerging model of regulated proliferation and subsequent resorption of a tissue, within the dermis, thus having potential links to skin cancer biology.

molecular biology↗

bioRxiv2018

Acidic microenvironment shaped by lactate accumulation promotes pluripotency through multiple mechanisms

Enhanced glycolysis is a distinct feature associated with numerous stem cells and cancer cells. However, little is known about its regulatory roles in gene expression and cell fate determination. Here we show that acidic environment shaped by lactate accumulation promotes the self-renewal and pluripotency of both mouse and human embryonic stem cells (ESCs). Mechanistically, acidic pH reduces the tri-methylation of H3K27 globally at transcriptional start sites to partially prevent ESC differentiation. In addition, acidic pH stabilizes a large number of mRNAs including pluripotency genes. Furthermore, we found that AGO1 protein is downregulated at acidic conditions, leading to the de-repression of a subset of microRNA targets in low-pH treated ESCs. Altogether, our study provides insights into mechanisms whereby acidic microenvironment produced by enhanced glycolysis regulates gene expression to determine cell fate and has broad implications in the fields of regenerative medicine and cancer biology.

developmental biology↗

bioRxiv2018

R2C2: Improving nanopore read accuracy enables the sequencing of highly-multiplexed full-length single-cell cDNA

High-throughput short-read sequencing has revolutionized how transcriptomes are quantified and annotated. However, while Illumina short-read sequencers can be used to analyze entire transcriptomes down to the level of individual splicing events with great accuracy, they fall short of analyzing how these individual events are combined into complete RNA transcript isoforms. Because of this shortfall, long-read sequencing is required to complement short-read sequencing to analyze transcriptomes on the level of full-length RNA transcript isoforms. However, there are issues with both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) long-read sequencing technologies that prevent their widespread adoption. Briefly, PacBio sequencers produce low numbers of reads with high accuracy, while ONT sequencers produce higher numbers of reads with lower accuracy. Here we introduce and validate a new long-read ONT based sequencing method. At the same cost, our Rolling Circle Amplification to Concatemeric Consensus (R2C2) method generates more accurate reads of full-length RNA transcript isoforms than any other available long-read sequencing method. These reads can then be used to generate isoform-level transcriptomes for both genome annotation and differential expression analysis in bulk or single cell samples.\n\nSignificance StatementSubtle changes in RNA transcript isoform expression can have dramatic effects on cellular behaviors in both health and disease. As such, comprehensive and quantitative analysis of isoform-level transcriptomes would open an entirely new window into cellular diversity in fields ranging from developmental to cancer biology. The R2C2 method we are presenting here is the first method with sufficient throughput and accuracy to make the comprehensive and quantitative analysis of RNA transcript isoforms in bulk and single cell samples economically feasible.

genomics↗

bioRxiv2018

Decoupling between causal understanding and awareness during learning and inference

Causal reasoning is a principal higher-cognitive ability of humans, however, much remains unknown, including (a) the type (systematic versus intermixed) and order (inductive-then-deductive or vice versa) of experience that best achieves causal-chain extraction; (b) how inferences generalize to novel problems, especially with one-shot experience; and (c) how metacognition, reflected in uncertainty of ones knowledge, relates to actual knowledge. We tested people on a realistic cancer biology task (e.g., seroc chemicals inducing tumors with subsequent effects). Systematic experience was superior, with some evidence that the inductive-then-deductive order promoted stronger one-shot generalization. Notably, uncertainty was decoupled from actual knowledge, with the deductive-then-inductive group being overconfident, likely reflecting lack of awareness of the inductive component; while those with successful one-shot generalization held lower confidence, reflecting generalization with minimal experience, while remaining skeptical. Our findings clarify processes underlying causal reasoning, and reveal a complex relationship between causal reasoning and metacognitive awareness of it.

neuroscience↗

bioRxiv2018

A comprehensive reference transcriptome resource for the Iberian ribbed newt Pleurodeles waltl, an emerging model for developmental and regeneration biology

Urodele amphibian newts have unique biological properties, notably including prominent regeneration ability. Iberian ribbed newt, Pleurodeles waltl, is a promising model newt along with the successful development of the easy breeding system and efficient transgenic and genome editing methods. However, genetic information of P. waltl was limited. In the present study, we conducted an intensive transcriptome analysis of P. waltl using RNA-sequencing to build gene models and annotate them. We generated 1.2 billion Illumina reads from a wide variety of samples across 11 different tissues and 9 time points during embryogenesis. They were assembled into 202,788 non-redundant contigs that appear to cover nearly complete (~98%) P. waltl protein-coding genes. Using the gene set as a reference, our gene network analysis identified regeneration-, developmental-stage-, and tissue-specific co-expressed gene modules. Ortholog analyses with other vertebrates revealed the gene repertoire evolution of amphibians which includes urodele-specific loss of bmp4 and duplications of wnt11b. Our transcriptome resource will enhance future research employing this emerging model animal for regeneration research as well as other areas such as developmental biology, stem cell biology, cancer research, ethology and toxico-genomics. These data are available via our portal website, iNewt (http://www.nibb.ac.jp/imori/main/).

genomics↗

bioRxiv2016

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains co-occurrence

Just like recurrent somatic alterations characterize cancer genes, mutually exclusive or co-occurring alterations across genes suggest functional interactions. Identifying such patterns in large cancer studies thus helps the discovery of unknown interactions. Many studies use Fishers exact test or simple permutation procedures for this purpose. These tests assume identical gene alteration probabilities across tumors, which is not true for cancer. We show that violating this assumption yields many spurious co-occurrences and misses many mutual exclusivities. We present DISCOVER, a novel statistical test that addresses the limitations of existing tests. In a comparison with six published mutual exclusivity tests, DISCOVER is more sensitive while controlling its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence. Most co-occurrences previously detected do not exceed expectation by chance. In contrast, many mutual exclusivities are identified. These cover well known genes involved in the cell cycle and growth factor signaling. Interestingly, also lesser known regulators of the cell cycle and Hedgehog signaling are identified.\n\nAvailabilityR and Python implementations of DISCOVER, as well as Jupyter notebooks for reproducing all results and figures from this paper can be found at http://ccb.nki.nl/software/discover.

Bioinformatics↗

bioRxiv2014

Inference of Cancer Progression Models with Biological Noise

MotivationDriver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods cannot infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution. ResultsWe introduce PMCE, an expressive framework that leverages mutational profiles from crosssectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups. AvailabilityPMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript. Contactsdaniele.ramazzotti@unimib.it, alex.graudenzi@ibfm.cnr.it.

Bioinformatics↗

bioRxiv2017

A system-wide approach to monitor responses to synergistic BRAF and EGFR inhibition in colorectal cancer cells

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread upregulation of receptors tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally reactivating the MAPK pathway.

cancer biology↗

bioRxiv2017

HNF1A is a Novel Oncogene and Central Regulator of Pancreatic Cancer Stem Cells

The biological properties of pancreatic cancer stem cells (PCSCs) remain incompletely defined and the central regulators are unknown. By bioinformatic analysis of a PCSC-enriched gene signature, we identified the transcription factor HNF1A as a putative central regulator of PCSC function. Levels of HNF1A and its target genes were found to be elevated in PCSCs and tumorspheres, and depletion of HNF1A resulted in growth inhibition, apoptosis, impaired tumorsphere formation, PCSC depletion, and downregulation of OCT4 expression. Conversely, HNF1A overexpression increased PCSC numbers and tumorsphere formation in pancreatic cancer cells and drove PDA cell growth. Importantly, depletion of HNF1A in primary tumor xenografts impaired tumor growth and depleted PCSCs in vivo. Finally, we established an HNF1A-dependent gene signature in PDA cells that significantly correlated with reduced survivability in patients. These findings identify HNF1A as a central transcriptional regulator of the PCSC state and novel oncogene in pancreatic ductal adenocarcinoma.

cancer biology↗

bioRxiv2017

A Mechanism of ATF5 Anti-Apoptotic Action that Promotes Leukemia Cells Survival

ATF5 transcription factor was found to play an essential role in hematopoietic cell survival and neuronal cell differentiation. Pro-survival activities of ATF5 are believed to be a result of its ability to inhibit apoptosis. Anti-apoptotic function of ATF5 is almost certainly carried out by regulating gene transcription, but information concerning its target genes has been missing. Here, we report the identification of cyclin D3 as an essential target of ATF5, and provide evidence that its upregulation is critical for the realization of ATF5-induced inhibition of apoptosis. Functional analysis confirmed the association of cyclin D3 with leukemia cells resistance to apoptosis and ATF5-induced splenic dysplasia in transgenic mice. These data link cyclin D3 cell cycle regulatory functions with the apoptotic machinery and reveal a potentially important facet of ATF5 biology.

cancer biology↗

bioRxiv2018

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis. Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. These shared transcriptomic alterations could help explain epidemiological observations suggesting direct and inverse comorbid associations between ASD and particular cancer types.

systems biology↗

bioRxiv2018

Multi-omic tumor data reveal diversity of molecular mechanisms underlying survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ( multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

cancer biology↗

bioRxiv2018

KH-type splicing regulatory protein controls colorectal cancer cell growth and modulates the tumor microenvironment

KH-type splicing regulatory protein (KHSRP) is a multifunctional nucleic acid binding protein implicated in key aspects of cancer cell biology: inflammation and cell-fate determination. However, the role KHSRP plays in colorectal cancer (CRC) tumorigenesis remains largely unknown. Using a combination of in silico analysis of large datasets, ex vivo analysis of protein expression in patients, and mechanistic studies using in vitro models of CRC, we investigated the oncogenic role of KHSRP. We demonstrated KHSRP expression in the epithelial and stromal compartments of both primary and metastatic tumors. Elevated expression was found in tumor versus matched normal tissue, and we validated these findings in larger independent cohorts in silico. KHSRP expression was a prognostic indicator of worse overall survival (HR=3.74, 95% CI = 1.43-22.97, p=0.0138). Mechanistic data in CRC cell line models supported a role of KHSRP in driving epithelial cell proliferation in both a primary and metastatic setting, through control of the G1/S transition. Additionally, KHSRP promoted a pro-angiogenic extracellular environment by regulating the secretion of oncogenic proteins involved in diverse cellular processes such as migration and response to cellular stress. Our study provides novel mechanistic insight into the tumor-promoting effects of KHSRP in CRC.

cancer biology↗

bioRxiv2017

Cancer Genome Interpreter Annotates The Biological And Clinical Relevance Of Tumor Alterations

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Most of the alterations observed in tumors, including those in well-known cancer genes, are of uncertain significance. Moreover, the information on tumor genomic alterations shaping the response to existing therapies is fragmented across the literature and several specialized resources. Here we present the Cancer Genome Interpreter (http://www.cancergenomeinterpreter.org), an open access tool that we have implemented to annotate genomic alterations and interpret their possible role in tumorigenesis and in the response to anti-cancer therapies.

cancer biology↗

bioRxiv2017

A Next Generation Clustering Tool Enables Identification of Functional Cancer Subtypes with Associated Biological Phenotypes

One of the major challenges faced in defining clinically applicable and homogeneous molecular tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. The conventional approach involves at least three steps: Firstly, identify subtypes using unsupervised clustering of patient tumours with molecular (etiological) profiles; secondly associate the subtypes with clinical or phenotypic information (covariates) to infer some biological meaning to the redefined subtypes; and thirdly, identify clinically relevant biomarkers associated with the subtypes. Here, we report the implementation of a tool, phenotype mapping (phenMap), which combines these three steps to define functional subtypes with associated phenotypic information and molecular signatures. phenMap models meta (unobserved) variables as a function of covariates to expose any underlying clustering structure within the data and discover associations between subtypes and phenotypes. We demonstrate how this tool can more avidly identify functional subtypes that are an improvement over already existing etiological subtypes by analysing published breast cancer gene expression data.

genomics↗