<i>C<b>3</b>
                  </i>: Consensus Cancer Driver Gene Caller

Zhu, Chenyu; Zhou, Chi; Chen, Yun-Qin; Shen, Albert Yuh-Jer; Guo, Zong-Ming; Yang, Zhaoyi; Ye, Xiangyun; Qu, Shen; Wei, Jia; Liu, Qi

doi:10.1016/j.gpb.2018.10.004

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Since the CMAP method only predicts a single drug response from L1000CDS [41], drug combinations are ranked by the average rank of two drugs in CMAP predictions. We evaluated the relative ranking of drug combination responses from comboSC, CMAP, and comboSC-bulk by Discounted Cumulative Gain (DCG) [63,64]. The DCG score is calculated as follows:…”

Section: Drug Combination Response Evaluation In Head and Neck Patien...mentioning

confidence: 99%

Personalized tumor combination therapy optimization using the single-cell transcriptome

Tang,

Fu,

Jin

et al. 2023

Genome Med

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Background The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. Methods Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. Results We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. Conclusions Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top. The source code is also available on GitHub at https://github.com/bm2-lab/comboSC.

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Section: Drug Combination Response Evaluation In Head and Neck Patien...mentioning

confidence: 99%

Personalized tumor combination therapy optimization using the single-cell transcriptome

Tang,

Fu,

Jin

et al. 2023

Genome Med

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“…More and more databases, such as DriverDBv3 ( 36 ) and IntOGen ( 37 ), are incorporating published prediction approaches to identify driver genes from large-scale cancer projects. Using combinational strategies, consensus-based methods like CTAT ( 30 ), ConsensusDriver ( 38 ), IntOGen ( 37 ) and C3 ( 39 ) promise to harness the strengths of different driver prediction methods and provide the best trade-off between sensitivity and specificity. However, there is no available integrated database for convenient search of annotations of driver genes from TCGA and ICGC cancer genomics projects by incorporating cancer driver predictions and prior oncology knowledge.…”

Section: Introductionmentioning

confidence: 99%

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Wang

Ruan

Zhao

et al. 2020

Nucleic Acids Research

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The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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“…Several studies of computational methods and their algorithms for the identification of significantly mutated genes in cancer have been conducted in recent years MA, 2013;RAPHAEL et al, 2014;ZHAO, 2015;DIMITRAKOPOULOS;BEEREN-WINKEL, 2017;SIMAO, 2020a), and various methods have been proposed (MILLER et al, 2011;RAPHAEL, 2011;CIRIELLO et al, 2012;RAPHAEL, 2012;DEES et al, 2012;BASHASHATI et al, 2012;HODIS et al, 2012;LAWRENCE et al, 2013;LEISERSON et al, 2013;MA, 2014;KIM et al, 2015;RAPHAEL, 2016;CHO et al, 2016;HOU et al, 2016;HRISTOV;SINGH, 2017;HORN et al, 2018;RAPHAEL, 2018;WU et al, 2019;ZHU et al, 2019;SIMAO, 2020b;YANG et al, 2021). Each method displays different characteristics, from computational and biological perspectives.…”

Section: Introduction 11 Contextualizationmentioning

confidence: 99%

Computational approaches for the discovery of significant genes in cancer

Cutigi¹

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O câncer é uma doença complexa provocada por alterações genéticas que se acumulam por toda a vida do indivíduo. A essas alterações dá-se o nome de mutação genética, as quais podem ser divididas em dois grupos: 1) Passenger mutations: mutações que não alteram o comportamento da célula; 2) Driver mutations: mutações significativas para o câncer, ou seja, que provocam a carcinogênese na célula. Células de câncer possuem um elevado número de mutações, das quais a maioria delas são passenger mutations e um pequeno número delas são driver mutations. A identificação de genes significativamente mutados, isto é, genes com mutações significativas, é essencial para a compreensão dos mecanismos de iniciação e progressão do câncer. Essa tarefa é um desafio chave na genômica do câncer, uma vez que estudos mostram que genes significativos podem sofrer mutação em uma frequência muito baixa. Com o sequenciamento de nova geração, uma extensa quantidade de conjuntos de dados genômicos foram gerados, criando o desafio de analisar e interpretar esses dados. Para identificar genes relacionados ao câncer com taxa de mutação baixa, redes de interação gênica combinadas com dados de mutação têm sindo exploradas. Neste contexto, esta pesquisa apresenta abordagens computacionais para a descoberta de genes significativos para o câncer. O genes são priorizados por um método baseado em redes que combina frequência de mutação ponderada e influência de vizinhos na rede, e possíveis falsos positivos são detectados por método baseado em aprendizado de máquina, o qual utiliza-se de dados de mutação e redes de interação gênica para induzir modelos preditivos. Um estudo experimental conduzido com seis tipos de câncer revelou o potencial das abordagens na descoberta de genes já conhecidos e de possíveis novos genes significativos para o câncer.

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C3 : Consensus Cancer Driver Gene Caller

Cited by 4 publications

References 34 publications

Personalized tumor combination therapy optimization using the single-cell transcriptome

Personalized tumor combination therapy optimization using the single-cell transcriptome

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Computational approaches for the discovery of significant genes in cancer

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