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Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data [article]

Salem Malikic, Katharina Jahn, Jack Kuipers, Cenk Sahinalp, Niko Beerenwinkel
2017 bioRxiv   pre-print
In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data.  ...  Most of the current data on tumour genetics stems from short read bulk sequencing data.  ...  OncoNEM: Inferring tumour evolution from single-cell sequencing data. Genome Biology, 17:69, 2016. [30] Kyung I Kim and Richard Simon.  ... 
doi:10.1101/234914 fatcat:3vuqpifzavbw3budoaqnioe4ja

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Salem Malikic, Katharina Jahn, Jack Kuipers, S. Cenk Sahinalp, Niko Beerenwinkel
2019 Nature Communications  
Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data.  ...  Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data.  ...  In this work, we present B-SCITE, a probabilistic approach for the inference of tumour phylogenies from combined single-cell and bulk-sequencing data.  ... 
doi:10.1038/s41467-019-10737-5 pmid:31227714 pmcid:PMC6588593 fatcat:z54irlrrprh5dfkicnodblhgsy

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Daniele Ramazzotti, Alex Graudenzi, Luca De Sano, Marco Antoniotti, Giulio Caravagna
2019 BMC Bioinformatics  
Conclusions: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour  ...  A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data.  ...  All data used in this paper are available from the supplementary material of [34] and [40] . We provide the source code and the input data to reproduce the case studies at: BIMIB-DISCo/TRaIT. 2  ... 
doi:10.1186/s12859-019-2795-4 fatcat:4xgybq62h5fnhkclg3jbapladm

Advances in understanding tumour evolution through single-cell sequencing

Jack Kuipers, Katharina Jahn, Niko Beerenwinkel
2017 Biochimica et Biophysica Acta. CR. Reviews on Cancer  
Both of the more recent methods [115, 116] allow error rates to be learnt from the data and significantly outperform previous single-cell approaches and bulk data methods applied to single-cell data.  ...  New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks.  ... 
doi:10.1016/j.bbcan.2017.02.001 pmid:28193548 pmcid:PMC5813714 fatcat:qb722mzocvb4fhnxpyk2eoxkqu

Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics

Anna S. Nam, Ronan Chaligne, Dan A. Landau
2020 Nature reviews genetics  
In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer  ...  These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.  ...  Genetic heterogeneity and lineage tracing Inference of clonal architecture in bulk sequencing.  ... 
doi:10.1038/s41576-020-0265-5 pmid:32807900 pmcid:PMC8450921 fatcat:2vbmc3imlzgljfig2upiqvahnq

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning [article]

Tom W Ouellette, Philip Awadalla
2021 bioRxiv   pre-print
Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced  ...  Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours.  ...  where w equals subclone fitness, tend indicates time at tumour biopsy or final population size in tumour doublings, and ts indicates the time of subclone emergence in tumour doublings.  ... 
doi:10.1101/2021.11.22.469566 fatcat:cz63fwzfxrhzdox4ld4uz4bhtu

Inferring mutational timing and reconstructing tumour evolutionary histories

Samra Turajlic, Nicholas McGranahan, Charles Swanton
2015 Biochimica et Biophysica Acta. CR. Reviews on Cancer  
Here we review the current sampling and computational approaches for inferring mutational timing and the evidence from next generation sequencing-informed data on mutational timing across all tumour types  ...  Owing to cancers' complexity and heterogeneity the rules of tumour evolution, such as the role of selection, remain incompletely understood.  ...  Finally, a report of single cell sequencing from cases of MDS progressing to secondary AML [63] validated the clonal architecture inferred from WGS of bulk tumour material, additionally resolving the  ... 
doi:10.1016/j.bbcan.2015.03.005 pmid:25827356 fatcat:lylmenpba5ajngnrjhyjsjvpkm

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data [article]

Katevan Chkhaidze, Timon Heide, Benjamin Werner, Marc J Williams, Weini Huang, Giulio Caravagna, Trevor A Graham, Andrea Sottoriva
2019 bioRxiv   pre-print
We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data.  ...  We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data.  ...  We acknowledge funding from the National Institute of Health (NCI U54 CA217376) to A.S and T.A.G.  ... 
doi:10.1101/544536 fatcat:nr7uhmud7beqxj2my7ky2bchtm

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

Ketevan Chkhaidze, Timon Heide, Benjamin Werner, Marc J. Williams, Weini Huang, Giulio Caravagna, Trevor A. Graham, Andrea Sottoriva, Johannes Reiter
2019 PLoS Computational Biology  
We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data.  ...  We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic  ...  Acknowledgments We thank Daniel Nichol and Haider Tari for the fruitful discussion. Author Contributions Conceptualization: Benjamin Werner, Trevor A. Graham, Andrea Sottoriva.  ... 
doi:10.1371/journal.pcbi.1007243 pmid:31356595 pmcid:PMC6687187 fatcat:pvlze3hrkfgmzlrpnutc2f2lcu

The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data

Giulio Caravagna, Guido Sanguinetti, Trevor A. Graham, Andrea Sottoriva
2020 BMC Bioinformatics  
Conclusions We present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing  ...  Background The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer.  ...  Acknowledgements GC wishes to thank Timon Heide, Marc J Williams and Luca De Sano for helping to develop the mobster package.  ... 
doi:10.1186/s12859-020-03863-1 pmid:33203356 pmcid:PMC7672894 fatcat:fzzajss7afehpfdqri3gxhapzi

Genotyping Copy Number Alterations from single-cell RNA sequencing [article]

Salvatore Milite, Riccardo Bergamin, Giulio Caravagna
2021 bioRxiv   pre-print
In this work we follow this intuition and construct a new Bayesian method to genotype copy number alterations on single-cell RNA sequencing data, therefore integrating DNA and RNA measurements.  ...  We can resort to single-molecule assays, which are cheaper and scalable, and statistically emulate a joint assay, only if we can integrate measurements collected from independent cells of the same sample  ...  In this work we attempt this type of integration working with total Copy Number Alteration (CNA) profiles, and independent single-cell RNA sequencing (scRNAseq) data.  ... 
doi:10.1101/2021.02.02.429335 fatcat:indh72glyzcqdfd3ucglcs3swe

Using single-cell multiple omics approaches to resolve tumor heterogeneity

Michael A. Ortega, Olivier Poirion, Xun Zhu, Sijia Huang, Thomas K. Wolfgruber, Robert Sebra, Lana X. Garmire
2017 Clinical and Translational Medicine  
Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling.  ...  Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer  ...  Acknowledgements We would like to thank all the members of the Garmire Lab for their helpful discussions and manuscript review.  ... 
doi:10.1186/s40169-017-0177-y pmid:29285690 pmcid:PMC5746494 fatcat:4o55gjckdfdurcrus4ftrldzbu

Clonal evolution in breast cancer revealed by single nucleus genome sequencing

Yong Wang, Jill Waters, Marco L. Leung, Anna Unruh, Whijae Roh, Xiuqing Shi, Ken Chen, Paul Scheet, Selina Vattathil, Han Liang, Asha Multani, Hong Zhang (+4 others)
2014 Nature  
These data suggest that triple-negative breast cancers (TNBCs) may have increased clonal diversity and mutational evolution, but such inferences are difficult to make in bulk tissues 8, 9 .  ...  We combined this approach with targeted duplex 10 single-molecule sequencing to profile thousands of cells and understand the role of rare mutations in tumour evolution.  ...  These data suggest that triple-negative breast cancers (TNBCs) may have increased clonal diversity and mutational evolution, but such inferences are difficult to make in bulk tissues 8, 9 .  ... 
doi:10.1038/nature13600 pmid:25079324 pmcid:PMC4158312 fatcat:4yvcgdea55hdxe3yvkswwzb5ge

The evolution of tumour phylogenetics: principles and practice

Russell Schwartz, Alejandro A. Schäffer
2017 Nature reviews genetics  
Acknowledgements This research was supported in part by the Intramural Research Program of the National Library of Medicine (part of the US National Institutes of Health) and by a grant from the Pennsylvania  ...  Department of Health (grant number 4100070287).  ...  We might propose to use a regional bulk method, replacing our 200 single cells with bulk sequencing of 10 regions from each of 20 tumours.  ... 
doi:10.1038/nrg.2016.170 pmid:28190876 pmcid:PMC5886015 fatcat:h2rpqbaxdjfkxdbaj7nlirw6xy

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Pauline Gilson, Jean-Louis Merlin, Alexandre Harlé
2022 Cancers  
complexity of the bulk tumours and their constant evolution over time.  ...  To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and  ...  Acknowledgments: Figures 1, 3 and 4 were created with BioRender.com. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers14061384 pmid:35326534 pmcid:PMC8946040 fatcat:u6oeimdakngbji6zz5ymjgtske
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