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1.
Transl Oncol ; 14(12): 101220, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34521033

RESUMO

BACKGROUND: Recent literature has highlighted the role of the host in prognosis in oral squamous cell carcinoma (OSCC). Autoimmune (AI) disease represents a macroscopic depiction of host status. The goal of this study was to predict an AI "status" and to analyze the utility of this "status" as a prognostic indicator in OSCC. METHODS: From a departmental database of OSCC patients (n = 1377), 125 patients with an AI disorder were identified. PBL values were obtained and standardized for analysis. A LASSO regression model was used to determine the best predictors of AI status and an AI score was developed. The score was then analyzed across various survival endpoints. RESULTS: When AI score was divided into a binary variable, patients in the highest quartile had a significantly worse overall survival (OS), local recurrence-free (LRFP) and distant recurrence-free probability (DRFP). Survival curves showed significant differences for OS, DSS, LRFP, and DRFP. CONCLUSIONS: AI diseases are immune dysregulations that could play a role in prognosis. Therefore, development of an AI score is necessary to depict host status in a ubiquitous manner. AI score as a binary variable may be more utilitarian in a clinical setting, compared to the continuous score. This novel tool needs validation and integration into more tumor and host characteristics. This investigation showed utility of such a score, similar to PBL data in OSCC prognosis. Future studies should incorporate other relevant variables known to affect outcome and implement a more comprehensive predictive model.

2.
Nat Commun ; 12(1): 4921, 2021 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-34389724

RESUMO

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.


Assuntos
Evolução Clonal , Hematopoiese Clonal/genética , Células-Tronco Hematopoéticas/metabolismo , Leucemia Mieloide/genética , Mutação , Doença Aguda , Adulto , Idoso , Aprendizado Profundo , Genética Populacional/métodos , Genética Populacional/estatística & dados numéricos , Células-Tronco Hematopoéticas/citologia , Humanos , Estimativa de Kaplan-Meier , Leucemia Mieloide/patologia , Pessoa de Meia-Idade , Modelos Genéticos , Avaliação de Resultados em Cuidados de Saúde/métodos , Avaliação de Resultados em Cuidados de Saúde/estatística & dados numéricos
3.
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
4.
PLoS Comput Biol ; 17(1): e1008400, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33465079

RESUMO

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.


Assuntos
Algoritmos , Biologia Computacional/métodos , Mutação/genética , Neoplasias , Simulação por Computador , Evolução Molecular , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias/classificação , Neoplasias/genética , Sequenciamento Completo do Genoma
5.
Nat Commun ; 12(1): 335, 2021 01 12.
Artigo em Inglês | MEDLINE | ID: mdl-33436550

RESUMO

Previous transcriptomic profiling studies have typically focused on separately analyzing mRNA expression, alternative splicing and alternative polyadenylation differences between cell and tissue types. However, the relative contribution of these three transcriptomic regulatory layers to cell type specification is poorly understood. This question is particularly relevant to neurons, given their extensive heterogeneity associated with brain location, morphology and function. In the present study, we generated profiles for the three regulatory layers from developmentally and regionally distinct subpopulations of neurons from the mouse hippocampus and broader nervous system. Multi-omics factor analyses revealed differing contributions of each transcriptomic layer in the discrimination of neurons based on their stage of development, region, and function. Importantly, profiles of differential alternative splicing and polyadenylation better discriminated specific neuronal subtype populations than gene expression patterns. These results provide evidence for differential relative contributions of coordinated gene regulatory layers in the specification of neuronal subtypes.


Assuntos
Regulação da Expressão Gênica , Neurônios/metabolismo , Transcriptoma/genética , Regiões 3' não Traduzidas/genética , Processamento Alternativo/genética , Animais , Regulação para Baixo/genética , Hipocampo/anatomia & histologia , Hipocampo/citologia , Camundongos , Poliadenilação/genética , Proteínas Ribossômicas/genética , Proteínas Ribossômicas/metabolismo , Transcrição Genética , Regulação para Cima/genética
7.
Cell ; 184(1): 226-242.e21, 2021 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-33417860

RESUMO

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.


Assuntos
Antineoplásicos/uso terapêutico , Neoplasias Colorretais/tratamento farmacológico , Diapausa , Resistencia a Medicamentos Antineoplásicos , Animais , Antineoplásicos/farmacologia , Autofagia/efeitos dos fármacos , Autofagia/genética , Linhagem Celular Tumoral , Células Clonais , Neoplasias Colorretais/genética , Neoplasias Colorretais/patologia , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Embrião de Mamíferos/efeitos dos fármacos , Embrião de Mamíferos/metabolismo , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Heterogeneidade Genética/efeitos dos fármacos , Humanos , Irinotecano/farmacologia , Irinotecano/uso terapêutico , Camundongos Endogâmicos NOD , Camundongos SCID , Modelos Biológicos , Transdução de Sinais/efeitos dos fármacos , Regulação para Cima/efeitos dos fármacos , Regulação para Cima/genética , Ensaios Antitumorais Modelo de Xenoenxerto
8.
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
9.
Nat Commun ; 11(1): 6247, 2020 12 07.
Artigo em Inglês | MEDLINE | ID: mdl-33288765

RESUMO

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.


Assuntos
Algoritmos , Heterogeneidade Genética , Mutação , Neoplasias da Próstata/genética , Sequenciamento Completo do Genoma/métodos , Biomarcadores Tumorais/genética , Evolução Clonal , Células Clonais/metabolismo , Biologia Computacional/métodos , Variações do Número de Cópias de DNA , Humanos , Masculino , Modelos Genéticos , Polimorfismo de Nucleotídeo Único , Neoplasias da Próstata/classificação , Neoplasias da Próstata/patologia
10.
Genome Biol ; 21(1): 195, 2020 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-32762776

RESUMO

BACKGROUND: RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in RBP expression and function are often observed in cancer and influence critical pathways implicated in tumor initiation and growth. Identification and characterization of oncogenic RBPs and their regulatory networks provide new opportunities for targeted therapy. RESULTS: We identify the RNA-binding protein SERBP1 as a novel regulator of glioblastoma (GBM) development. High SERBP1 expression is prevalent in GBMs and correlates with poor patient survival and poor response to chemo- and radiotherapy. SERBP1 knockdown causes delay in tumor growth and impacts cancer-relevant phenotypes in GBM and glioma stem cell lines. RNAcompete identifies a GC-rich region as SERBP1-binding motif; subsequent genomic and functional analyses establish SERBP1 regulation role in metabolic routes preferentially used by cancer cells. An important consequence of these functions is SERBP1 impact on methionine production. SERBP1 knockdown decreases methionine levels causing a subsequent reduction in histone methylation as shown for H3K27me3 and upregulation of genes associated with neurogenesis, neuronal differentiation, and function. Further analysis demonstrates that several of these genes are downregulated in GBM, potentially through epigenetic silencing as indicated by the presence of H3K27me3 sites. CONCLUSIONS: SERBP1 is the first example of an RNA-binding protein functioning as a central regulator of cancer metabolism and indirect modulator of epigenetic regulation in GBM. By bridging these two processes, SERBP1 enhances glioma stem cell phenotypes and contributes to GBM poorly differentiated state.


Assuntos
Neoplasias Encefálicas/metabolismo , Glioblastoma/metabolismo , Proteínas de Ligação a RNA/metabolismo , Animais , Neoplasias Encefálicas/etiologia , Neoplasias Encefálicas/mortalidade , Neoplasias Encefálicas/terapia , Epigênese Genética , Feminino , Glioblastoma/etiologia , Glioblastoma/mortalidade , Glioblastoma/terapia , Humanos , Masculino , Camundongos , Neurogênese , Fenótipo , Prognóstico , Estados Unidos/epidemiologia
11.
Genome Res ; 30(7): 962-973, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32703884

RESUMO

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.

12.
Cell Rep ; 30(10): 3353-3367.e7, 2020 03 10.
Artigo em Inglês | MEDLINE | ID: mdl-32160542

RESUMO

G3BP RNA-binding proteins are important components of stress granules (SGs). Here, we analyze the role of the Drosophila G3BP Rasputin (RIN) in unstressed cells, where RIN is not SG associated. Immunoprecipitation followed by microarray analysis identifies over 550 mRNAs that copurify with RIN. The mRNAs found in SGs are long and translationally silent. In contrast, we find that RIN-bound mRNAs, which encode core components of the transcription, splicing, and translation machinery, are short, stable, and highly translated. We show that RIN is associated with polysomes and provide evidence for a direct role for RIN and its human homologs in stabilizing and upregulating the translation of their target mRNAs. We propose that when cells are stressed, the resulting incorporation of RIN/G3BPs into SGs sequesters them away from their short target mRNAs. This would downregulate the expression of these transcripts, even though they are not incorporated into stress granules.


Assuntos
Proteínas de Transporte/metabolismo , Proteínas de Drosophila/metabolismo , Drosophila melanogaster/genética , Biossíntese de Proteínas , Estabilidade de RNA/genética , Proteínas de Ligação a RNA/metabolismo , Animais , Sequência de Bases , Proteínas de Transporte/genética , Grânulos Citoplasmáticos/metabolismo , Proteínas de Drosophila/genética , Drosophila melanogaster/embriologia , Embrião não Mamífero/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Ontologia Genética , Humanos , Camundongos , Mitocôndrias/metabolismo , Mutação/genética , Células NIH 3T3 , Polirribossomos/metabolismo , Motivo de Reconhecimento de RNA/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Proteínas Ribossômicas/metabolismo , Transcriptoma/genética , Zigoto/metabolismo
13.
Cell Rep ; 30(12): 4179-4196.e11, 2020 03 24.
Artigo em Inglês | MEDLINE | ID: mdl-32209477

RESUMO

Regulation of translation during human development is poorly understood, and its dysregulation is associated with Rett syndrome (RTT). To discover shifts in mRNA ribosomal engagement (RE) during human neurodevelopment, we use parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNA sequencing (RNA-seq) on control and RTT human induced pluripotent stem cells, neural progenitor cells, and cortical neurons. We find that 30% of transcribed genes are translationally regulated, including key gene sets (neurodevelopment, transcription and translation factors, and glycolysis). Approximately 35% of abundant intergenic long noncoding RNAs (lncRNAs) are ribosome engaged. Neurons translate mRNAs more efficiently and have longer 3' UTRs, and RE correlates with elements for RNA-binding proteins. RTT neurons have reduced global translation and compromised mTOR signaling, and >2,100 genes are translationally dysregulated. NEDD4L E3-ubiquitin ligase is translationally impaired, ubiquitinated protein levels are reduced, and protein targets accumulate in RTT neurons. Overall, the dynamic translatome in neurodevelopment is disturbed in RTT and provides insight into altered ubiquitination that may have therapeutic implications.


Assuntos
Sistema Nervoso/crescimento & desenvolvimento , Sistema Nervoso/patologia , Síndrome de Rett/genética , Ribossomos/metabolismo , Ubiquitinação , Regiões 3' não Traduzidas/genética , Animais , Sequência de Bases , Feminino , Regulação da Expressão Gênica no Desenvolvimento , Glicólise/genética , Células-Tronco Pluripotentes Induzidas/metabolismo , Proteína 2 de Ligação a Metil-CpG/metabolismo , Camundongos , Ubiquitina-Proteína Ligases Nedd4/metabolismo , Neurônios/metabolismo , Ligação Proteica , Biossíntese de Proteínas , RNA não Traduzido/genética , RNA não Traduzido/metabolismo , Proteínas de Ligação a RNA/metabolismo , Proteínas Ribossômicas/genética , Proteínas Ribossômicas/metabolismo , Fatores de Transcrição/metabolismo , Ubiquitinação/genética
14.
Mol Syst Biol ; 16(2): e9243, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32064787

RESUMO

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.


Assuntos
Mutação , Proteínas de Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/crescimento & desenvolvimento , Análise de Célula Única/métodos , Pleiotropia Genética , Variação Genética , Microscopia de Fluorescência , Redes Neurais de Computação , Penetrância , Fenótipo , Saccharomyces cerevisiae/genética , Biologia de Sistemas , Imagem com Lapso de Tempo
15.
Cancer Discov ; 10(4): 568-587, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32086311

RESUMO

Disease recurrence causes significant mortality in B-progenitor acute lymphoblastic leukemia (B-ALL). Genomic analysis of matched diagnosis and relapse samples shows relapse often arising from minor diagnosis subclones. However, why therapy eradicates some subclones while others survive and progress to relapse remains obscure. Elucidation of mechanisms underlying these differing fates requires functional analysis of isolated subclones. Here, large-scale limiting dilution xenografting of diagnosis and relapse samples, combined with targeted sequencing, identified and isolated minor diagnosis subclones that initiate an evolutionary trajectory toward relapse [termed diagnosis Relapse Initiating clones (dRI)]. Compared with other diagnosis subclones, dRIs were drug-tolerant with distinct engraftment and metabolic properties. Transcriptionally, dRIs displayed enrichment for chromatin remodeling, mitochondrial metabolism, proteostasis programs, and an increase in stemness pathways. The isolation and characterization of dRI subclones reveals new avenues for eradicating dRI cells by targeting their distinct metabolic and transcriptional pathways before further evolution renders them fully therapy-resistant. SIGNIFICANCE: Isolation and characterization of subclones from diagnosis samples of patients with B-ALL who relapsed showed that relapse-fated subclones had increased drug tolerance and distinct metabolic and survival transcriptional programs compared with other diagnosis subclones. This study provides strategies to identify and target clinically relevant subclones before further evolution toward relapse.


Assuntos
Leucemia Mieloide Aguda/genética , Células Clonais , Feminino , Humanos , Masculino , Recidiva
16.
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
17.
Nat Commun ; 11(1): 728, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-32024849

RESUMO

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Mutação , Neoplasias/genética , Neoplasias/patologia , Feminino , Genoma Humano , Humanos , Masculino , Metástase Neoplásica , Reprodutibilidade dos Testes , Sequenciamento Completo do Genoma
18.
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
19.
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
20.
Pac Symp Biocomput ; 25: 127-138, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31797592

RESUMO

Identification and subsequent intervention of patients at risk of becoming High Cost Users (HCUs) presents the opportunity to improve outcomes while also providing significant savings for the healthcare system. In this paper, the 2016 HCU status of patients was predicted using free-form text data from the 2015 cumulative patient profiles within the electronic medical records of family care practices in Ontario. These unstructured notes make substantial use of domain-specific spellings and abbreviations; we show that word embeddings derived from the same context provide more informative features than pre-trained ones based on Wikipedia, MIMIC, and Pubmed. We further demonstrate that a model using features derived from aggregated word embeddings (EmbEncode) provides a significant performance improvement over the bag-of-words representation (82.48±0.35% versus 81.85±0.36% held-out AUROC, p = 3.2 × 10-4), using far fewer input features (5,492 versus 214,750) and fewer non-zero coefficients (1,177 versus 4,284). The future HCUs of greatest interest are the transitional ones who are not already HCUs, because they provide the greatest scope for interventions. Predicting these new HCU is challenging because most HCUs recur. We show that removing recurrent HCUs from the training set improves the ability of EmbEncode to predict new HCUs, while only slightly decreasing its ability to predict recurrent ones.


Assuntos
Médicos de Atenção Primária , Biologia Computacional , Atenção à Saúde , Custos de Cuidados de Saúde , Humanos , Ontário
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