Cancer cell genomes originate from single-cell mutation with sequential clonal and subclonal expansion of somatic mutation acquisition during pathogenesis,thus exhibiting a Darwinian evolutionary process(Gerstung et a...Cancer cell genomes originate from single-cell mutation with sequential clonal and subclonal expansion of somatic mutation acquisition during pathogenesis,thus exhibiting a Darwinian evolutionary process(Gerstung et al.,2020;Nik-Zainal et al.,2012).Through next-generation sequencing of tumor tissue,this evolutionary process can be characterized by statistical modelling,which can identify the clonal state,somatic mutation order,and evolutionary process(Gerstung et al.,2020;Mcgranahan&Swanton,2017).Inference of clonal and subclonal structure from bulk or single-cell tumor genomic sequencing data has a huge impact on studying cancer evolution.Clonal state and mutation order can provide detailed insight into tumor origin and future development.In the past decade,various methods for subclonal reconstruction using bulk tumor sequencing data have been developed.However,these methods had different programming languages and data input formats,which limited their use and comparison.Therefore,we established a web server for Clonal and Subclonal Structure Inference and Evolution(COSINE)of cancer genomic data,which incorporated twelve popular subclonal reconstruction methods.We deconstructed each method to provide a detailed workflow of single processing steps with a user-friendly interface.To the best of our knowledge,this is the first web server providing online subclonal inference based on the integration of most popular subclonal reconstruction methods.COSINE is freely accessible at www.clab-cosine.net.展开更多
基金supported by the CAS Pioneer Hundred Talents Program and National Natural Science Foundation of China(32070683)to Y.P.C.the Science and Technology Planning Project of XI'AN(GXYD6.2)National Natural Science Foundation of China(61771369)to X.G.Y.
文摘Cancer cell genomes originate from single-cell mutation with sequential clonal and subclonal expansion of somatic mutation acquisition during pathogenesis,thus exhibiting a Darwinian evolutionary process(Gerstung et al.,2020;Nik-Zainal et al.,2012).Through next-generation sequencing of tumor tissue,this evolutionary process can be characterized by statistical modelling,which can identify the clonal state,somatic mutation order,and evolutionary process(Gerstung et al.,2020;Mcgranahan&Swanton,2017).Inference of clonal and subclonal structure from bulk or single-cell tumor genomic sequencing data has a huge impact on studying cancer evolution.Clonal state and mutation order can provide detailed insight into tumor origin and future development.In the past decade,various methods for subclonal reconstruction using bulk tumor sequencing data have been developed.However,these methods had different programming languages and data input formats,which limited their use and comparison.Therefore,we established a web server for Clonal and Subclonal Structure Inference and Evolution(COSINE)of cancer genomic data,which incorporated twelve popular subclonal reconstruction methods.We deconstructed each method to provide a detailed workflow of single processing steps with a user-friendly interface.To the best of our knowledge,this is the first web server providing online subclonal inference based on the integration of most popular subclonal reconstruction methods.COSINE is freely accessible at www.clab-cosine.net.
基金supported by the National Natural Science Foundation of China(91731301 and U1902201)the Ministry of Science and Technology of the People’s Republic of China(2020YFC0847000)the Light of West China Program of the Chinese Academy of Sciences.
