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1.
Nucleic Acid Ther ; 2021 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-34388351

RESUMO

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.

2.
Cell ; 184(8): 2239-2254.e39, 2021 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-33831375

RESUMO

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.


Assuntos
Heterogeneidade Genética , Neoplasias/genética , Variações do Número de Cópias de DNA , DNA de Neoplasias/química , DNA de Neoplasias/metabolismo , Bases de Dados Genéticas , Resistencia a Medicamentos Antineoplásicos/genética , Humanos , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma
3.
Nat Methods ; 18(2): 144-155, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33398189

RESUMO

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.


Assuntos
DNA de Neoplasias/genética , Neoplasias/genética , Análise de Sequência de DNA/métodos , Algoritmos , Humanos , Polimorfismo de Nucleotídeo Único
4.
NPJ Genom Med ; 5: 16, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32284880

RESUMO

Wilson disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3:c.1934T > G (Met645Arg) has been reported as compound heterozygous, and is highly prevalent among Wilson disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here, we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T > G causes ~70% skipping of exon 6. Exon 6 skipping results in frameshift and stop-gain, leading to loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

5.
Nat Commun ; 11(1): 731, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-32024834

RESUMO

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.


Assuntos
Biologia Computacional/métodos , Mutação , Neoplasias/genética , Simulação por Computador , Evolução Molecular , Frequência do Gene , Genoma Humano , Humanos , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma
6.
Nature ; 578(7793): 122-128, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32025013

RESUMO

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.


Assuntos
Evolução Molecular , Genoma Humano/genética , Neoplasias/genética , Reparo do DNA/genética , Dosagem de Genes , Genes Supressores de Tumor , Variação Genética , Humanos , Mutagênese Insercional/genética
7.
Nat Biotechnol ; 38(1): 97-107, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31919445

RESUMO

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.


Assuntos
Algoritmos , Neoplasias/patologia , Células Clonais , Simulação por Computador , Variações do Número de Cópias de DNA/genética , Dosagem de Genes , Genoma , Humanos , Mutação/genética , Neoplasias/genética , Polimorfismo de Nucleotídeo Único/genética , Padrões de Referência
8.
Bioinformatics ; 34(13): i429-i437, 2018 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-29949959

RESUMO

Motivation: Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends on the strength of neighboring sites. Here, we present a new model named the competitive splice site model (COSSMO), which explicitly accounts for these competitive effects and predicts the percent selected index (PSI) distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3' acceptor site conditional on a fixed upstream 5' donor site or the choice of a 5' donor site conditional on a fixed 3' acceptor site. We build four different architectures that use convolutional layers, communication layers, long short-term memory and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model. Results: COSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 0.6 in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences and many known splicing factors with high specificity. Availability and implementation: Model predictions, our training dataset, and code are available from http://cossmo.genes.toronto.edu. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Processamento Alternativo , Aprendizado Profundo , Sítios de Splice de RNA , Análise de Sequência de RNA/métodos , Biologia Computacional/métodos , Humanos , Modelos Genéticos , Probabilidade , Software
9.
Nat Med ; 23(8): 984-989, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28714989

RESUMO

Splice-site defects account for about 10% of pathogenic mutations that cause Mendelian diseases. Prevalence is higher in neuromuscular disorders (NMDs), owing to the unusually large size and multi-exonic nature of genes encoding muscle structural proteins. Therapeutic genome editing to correct disease-causing splice-site mutations has been accomplished only through the homology-directed repair pathway, which is extremely inefficient in postmitotic tissues such as skeletal muscle. Here we describe a strategy using nonhomologous end-joining (NHEJ) to correct a pathogenic splice-site mutation. As a proof of principle, we focus on congenital muscular dystrophy type 1A (MDC1A), which is characterized by severe muscle wasting and paralysis. Specifically, we correct a splice-site mutation that causes the exclusion of exon 2 from Lama2 mRNA and the truncation of Lama2 protein in the dy2J/dy2J mouse model of MDC1A. Through systemic delivery of adeno-associated virus (AAV) carrying clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 genome-editing components, we simultaneously excise an intronic region containing the mutation and create a functional donor splice site through NHEJ. This strategy leads to the inclusion of exon 2 in the Lama2 transcript and restoration of full-length Lama2 protein. Treated dy2J/dy2J mice display substantial improvement in muscle histopathology and function without signs of paralysis.


Assuntos
Reparo do DNA por Junção de Extremidades , Terapia Genética/métodos , Laminina/genética , Distrofias Musculares/genética , Sítios de Splice de RNA/genética , RNA Mensageiro/genética , Animais , Western Blotting , Sistemas CRISPR-Cas , Modelos Animais de Doenças , Imunofluorescência , Laminina/metabolismo , Camundongos , Músculo Esquelético/metabolismo , Músculo Esquelético/patologia , Distrofias Musculares/patologia , Mutação , Reação em Cadeia da Polimerase em Tempo Real
10.
BMC Bioinformatics ; 16: 156, 2015 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-25972088

RESUMO

BACKGROUND: Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. RESULTS: To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. CONCLUSIONS: The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.


Assuntos
Algoritmos , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Neoplasias da Próstata/diagnóstico , Neoplasias da Próstata/genética , Software , Humanos , Masculino , Modelos Teóricos , Prognóstico
11.
Genome Biol ; 16: 35, 2015 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-25786235

RESUMO

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.


Assuntos
Genoma Humano , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Neoplasias/genética , Filogenia , Algoritmos , Células Clonais , Análise por Conglomerados , Simulação por Computador , Variações do Número de Cópias de DNA , Frequência do Gene , Heterogeneidade Genética , Humanos , Mutação , Padrões de Referência
12.
Pac Symp Biocomput ; : 20-31, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25592565

RESUMO

Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…


Assuntos
Neoplasias/patologia , Algoritmos , Teorema de Bayes , Biologia Computacional , Simulação por Computador , Frequência do Gene , Genótipo , Humanos , Leucemia Linfocítica Crônica de Células B/genética , Leucemia Linfocítica Crônica de Células B/patologia , Funções Verossimilhança , Aprendizado de Máquina , Modelos Biológicos , Modelos Estatísticos , Mutação , Neoplasias/genética , Células-Tronco Neoplásicas/patologia , Filogenia , Estatísticas não Paramétricas
13.
BMC Bioinformatics ; 15: 35, 2014 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-24484323

RESUMO

BACKGROUND: High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. But automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described. RESULTS: We describe the conditions under which the evolutionary history can be uniquely reconstructed from SNV frequencies from single or multiple samples from the tumor population and we introduce a new statistical model, PhyloSub, that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells. It uses a Bayesian nonparametric prior over trees that groups SNVs into major subclonal lineages and automatically estimates the number of lineages and their ancestry. We sample from the joint posterior distribution over trees to identify evolutionary histories and cell population frequencies that have the highest probability of generating the observed SNV frequency data. When multiple phylogenies are consistent with a given set of SNV frequencies, PhyloSub represents the uncertainty in the tumor phylogeny using a "partial order plot". Experiments on a simulated dataset and two real datasets comprising tumor samples from acute myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that PhyloSub can infer both linear (or chain) and branching lineages and its inferences are in good agreement with ground truth, where it is available. CONCLUSIONS: PhyloSub can be applied to frequencies of any "binary" somatic mutation, including SNVs as well as small insertions and deletions. The PhyloSub and partial order plot software is available from https://github.com/morrislab/phylosub/.


Assuntos
Evolução Clonal/genética , Biologia Computacional/métodos , Neoplasias/genética , Polimorfismo de Nucleotídeo Único/genética , Algoritmos , Teorema de Bayes , Técnicas Citológicas , Evolução Molecular , Genótipo , Humanos , Mutação , Neoplasias/classificação , Filogenia , Software
14.
Bioinformatics ; 30(7): 956-61, 2014 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-24123674

RESUMO

MOTIVATION: Gene expression data are currently collected on a wide range of platforms. Differences between platforms make it challenging to combine and compare data collected on different platforms. We propose a new method of cross-platform normalization that uses topic models to summarize the expression patterns in each dataset before normalizing the topics learned from each dataset using per-gene multiplicative weights. RESULTS: This method allows for cross-platform normalization even when samples profiled on different platforms have systematic differences, allows the simultaneous normalization of data from an arbitrary number of platforms and, after suitable training, allows for online normalization of expression data collected individually or in small batches. In addition, our method outperforms existing state-of-the-art platform normalization tools. AVAILABILITY AND IMPLEMENTATION: MATLAB code is available at http://morrislab.med.utoronto.ca/plida/.


Assuntos
Perfilação da Expressão Gênica/métodos , Expressão Gênica , Neoplasias da Mama/genética , Regulação Neoplásica da Expressão Gênica , Humanos , Modelos Genéticos , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Software
15.
PLoS One ; 8(10): e73168, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-24098326

RESUMO

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA's differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Glioblastoma/genética , MicroRNAs/genética , Neoplasias da Próstata/genética , Humanos , Modelos Lineares , Masculino
16.
Genome Med ; 5(3): 29, 2013 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-23537167

RESUMO

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, to directly address the effects of variable normal tissue contamination in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.

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