Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

Yulia Rubanova; Ruian Shi; Caitlin F. Harrigan; Roujia Li; Jeff Wintersinger; Nil Sahin; Amit G. Deshwar; Stefan C. Dentro; Ignaty Leshchiner; Moritz Gerstung; Clemency Jolly; Kerstin Haase; Maxime Tarabichi; Kaixian Yu; Santiago Gonzalez; Geoff Macintyre; David J. Adams; Pavana Anur; Rameen Beroukhim; Paul C. Boutros; David D. Bowtell; Peter J. Campbell; Shaolong Cao; Elizabeth L. Christie; Marek Cmero; Yupeng Cun; Kevin J. Dawson; Jonas Demeulemeester; Nilgun Donmez; Ruben M. Drews; Roland Eils; Yu Fan; Matthew Fittall; Dale W. Garsed; Gad Getz; Gavin Ha; Marcin Imielinski; Lara Jerman; Yuan Ji; Kortine Kleinheinz; Juhee Lee; Henry Lee-Six; Dimitri G. Livitz; Salem Malikic; Florian Markowetz; Inigo Martincorena; Thomas J. Mitchell; PCAWG Consortium; PCAWG Evolution and Heterogeneity Working Group; David Townend; Quaid D. Morris

doi:10.1038/s41467-020-14352-7

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

Yulia Rubanova
, Ruian Shi
, Caitlin F. Harrigan
, Roujia Li
, Jeff Wintersinger
, Nil Sahin
, Amit G. Deshwar
, Stefan C. Dentro
, Ignaty Leshchiner
, Moritz Gerstung
, Clemency Jolly
, Kerstin Haase
, Maxime Tarabichi
, Kaixian Yu
, Santiago Gonzalez
, Geoff Macintyre
, David J. Adams
, Pavana Anur
, Rameen Beroukhim
, Paul C. Boutros

David D. Bowtell, Peter J. Campbell, Shaolong Cao, Elizabeth L. Christie, Marek Cmero, Yupeng Cun, Kevin J. Dawson, Jonas Demeulemeester, Nilgun Donmez, Ruben M. Drews, Roland Eils, Yu Fan, Matthew Fittall, Dale W. Garsed, Gad Getz, Gavin Ha, Marcin Imielinski, Lara Jerman, Yuan Ji, Kortine Kleinheinz, Juhee Lee, Henry Lee-Six, Dimitri G. Livitz, Salem Malikic, Florian Markowetz, Inigo Martincorena, Thomas J. Mitchell, PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group, David Townend, Quaid D. Morris^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

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.

Original language	English
Article number	731
Number of pages	12
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-14352-7
Publication status	Published - 1 Dec 2020

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Author Correction: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
Rubanova, Y., Shi, R., Harrigan, C. F., Li, R., Wintersinger, J., Sahin, N., Deshwar, A. G., Dentro, S. C., Leshchiner, I., Gerstung, M., Jolly, C., Haase, K., Tarabichi, M., Yu, K., Gonzalez, S., Macintyre, G., Adams, D. J., Anur, P., Beroukhim, R. & Boutros, P. C. & 30 others, Bowtell, D. D., Campbell, P. J., Cao, S., Christie, E. L., Cmero, M., Cun, Y., Dawson, K. J., Demeulemeester, J., Donmez, N., Drews, R. M., Eils, R., Fan, Y., Fittall, M., Garsed, D. W., Getz, G., Ha, G., Imielinski, M., Jerman, L., Ji, Y., Kleinheinz, K., Lee, J., Lee-Six, H., Livitz, D. G., Malikic, S., Markowetz, F., Martincorena, I., Mitchell, T. J., PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group & Townend, D., 1 Dec 2022, In: Nature Communications. 13, 1, 7567.
Research output: Contribution to journal › Erratum / corrigendum › Academic

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Rubanova, Y., Shi, R., Harrigan, C. F., Li, R., Wintersinger, J., Sahin, N., Deshwar, A. G., Dentro, S. C., Leshchiner, I., Gerstung, M., Jolly, C., Haase, K., Tarabichi, M., Yu, K., Gonzalez, S., Macintyre, G., Adams, D. J., Anur, P., Beroukhim, R., ... Morris, Q. D. (2020). Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nature Communications, 11(1), Article 731. https://doi.org/10.1038/s41467-020-14352-7

@article{3efe6d80838e46208f31948c313db4fb,

title = "Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig",

abstract = "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.",

author = "Yulia Rubanova and Ruian Shi and Harrigan, \{Caitlin F.\} and Roujia Li and Jeff Wintersinger and Nil Sahin and Deshwar, \{Amit G.\} and Dentro, \{Stefan C.\} and Ignaty Leshchiner and Moritz Gerstung and Clemency Jolly and Kerstin Haase and Maxime Tarabichi and Kaixian Yu and Santiago Gonzalez and Geoff Macintyre and Adams, \{David J.\} and Pavana Anur and Rameen Beroukhim and Boutros, \{Paul C.\} and Bowtell, \{David D.\} and Campbell, \{Peter J.\} and Shaolong Cao and Christie, \{Elizabeth L.\} and Marek Cmero and Yupeng Cun and Dawson, \{Kevin J.\} and Jonas Demeulemeester and Nilgun Donmez and Drews, \{Ruben M.\} and Roland Eils and Yu Fan and Matthew Fittall and Garsed, \{Dale W.\} and Gad Getz and Gavin Ha and Marcin Imielinski and Lara Jerman and Yuan Ji and Kortine Kleinheinz and Juhee Lee and Henry Lee-Six and Livitz, \{Dimitri G.\} and Salem Malikic and Florian Markowetz and Inigo Martincorena and Mitchell, \{Thomas J.\} and \{PCAWG Consortium\} and \{PCAWG Evolution and Heterogeneity Working Group\} and David Townend and Morris, \{Quaid D.\}",

note = "Funding Information: We thank Pan-cancer Analysis of Whole Genomes (PCAWG) network, and in particular the PCAWG Evolution and Heterogeneity working group, for providing data, analysis and valuable input on this project. We would in particular like to highlight Peter Van Loo, Clemency Jolly, Stefan Dentro, David Wedge, Paul Boutros, Lydia Liu, and Moritz Gerstung who provided valuable feedback during the development of the TrackSig methodology. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. We would like to acknowledge SciNet as part of Compute Canada for providing computational resources. This research was partially supported by an Natural Science and Engineering Research Council operating grant; an Associate Investigator award from the Ontario Institute of Cancer Research; and a subgrant from the Canadian Centre for Computational Genomics genomics technology platform funded by Genome Canada, all to QDM. It also received funding from the University of Toronto{\textquoteright}s Medicine by Design initiative, which in part of the Canada First Research Excellence Fund (CFREF) and the Compute the Cure gift from the NVIDIA foundation. QDM is a Canada CIFAR AI chair at the Vector Institute. Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-020-14352-7",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

Rubanova, Y, Shi, R, Harrigan, CF, Li, R, Wintersinger, J, Sahin, N, Deshwar, AG, Dentro, SC, Leshchiner, I, Gerstung, M, Jolly, C, Haase, K, Tarabichi, M, Yu, K, Gonzalez, S, Macintyre, G, Adams, DJ, Anur, P, Beroukhim, R, Boutros, PC, Bowtell, DD, Campbell, PJ, Cao, S, Christie, EL, Cmero, M, Cun, Y, Dawson, KJ, Demeulemeester, J, Donmez, N, Drews, RM, Eils, R, Fan, Y, Fittall, M, Garsed, DW, Getz, G, Ha, G, Imielinski, M, Jerman, L, Ji, Y, Kleinheinz, K, Lee, J, Lee-Six, H, Livitz, DG, Malikic, S, Markowetz, F, Martincorena, I, Mitchell, TJ, PCAWG Consortium, PCAWG Evolution and Heterogeneity Working Group, Townend, D & Morris, QD 2020, 'Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig', Nature Communications, vol. 11, no. 1, 731. https://doi.org/10.1038/s41467-020-14352-7

TY - JOUR

T1 - Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

AU - Rubanova, Yulia

AU - Shi, Ruian

AU - Harrigan, Caitlin F.

AU - Li, Roujia

AU - Wintersinger, Jeff

AU - Sahin, Nil

AU - Deshwar, Amit G.

AU - Dentro, Stefan C.

AU - Leshchiner, Ignaty

AU - Gerstung, Moritz

AU - Jolly, Clemency

AU - Haase, Kerstin

AU - Tarabichi, Maxime

AU - Yu, Kaixian

AU - Gonzalez, Santiago

AU - Macintyre, Geoff

AU - Adams, David J.

AU - Anur, Pavana

AU - Beroukhim, Rameen

AU - Boutros, Paul C.

AU - Bowtell, David D.

AU - Campbell, Peter J.

AU - Cao, Shaolong

AU - Christie, Elizabeth L.

AU - Cmero, Marek

AU - Cun, Yupeng

AU - Dawson, Kevin J.

AU - Demeulemeester, Jonas

AU - Donmez, Nilgun

AU - Drews, Ruben M.

AU - Eils, Roland

AU - Fan, Yu

AU - Fittall, Matthew

AU - Garsed, Dale W.

AU - Getz, Gad

AU - Ha, Gavin

AU - Imielinski, Marcin

AU - Jerman, Lara

AU - Ji, Yuan

AU - Kleinheinz, Kortine

AU - Lee, Juhee

AU - Lee-Six, Henry

AU - Livitz, Dimitri G.

AU - Malikic, Salem

AU - Markowetz, Florian

AU - Martincorena, Inigo

AU - Mitchell, Thomas J.

AU - PCAWG Consortium

AU - PCAWG Evolution and Heterogeneity Working Group

AU - Townend, David

AU - Morris, Quaid D.

N1 - Funding Information: We thank Pan-cancer Analysis of Whole Genomes (PCAWG) network, and in particular the PCAWG Evolution and Heterogeneity working group, for providing data, analysis and valuable input on this project. We would in particular like to highlight Peter Van Loo, Clemency Jolly, Stefan Dentro, David Wedge, Paul Boutros, Lydia Liu, and Moritz Gerstung who provided valuable feedback during the development of the TrackSig methodology. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. We would like to acknowledge SciNet as part of Compute Canada for providing computational resources. This research was partially supported by an Natural Science and Engineering Research Council operating grant; an Associate Investigator award from the Ontario Institute of Cancer Research; and a subgrant from the Canadian Centre for Computational Genomics genomics technology platform funded by Genome Canada, all to QDM. It also received funding from the University of Toronto’s Medicine by Design initiative, which in part of the Canada First Research Excellence Fund (CFREF) and the Compute the Cure gift from the NVIDIA foundation. QDM is a Canada CIFAR AI chair at the Vector Institute. Publisher Copyright: © 2020, The Author(s).

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-020-14352-7

DO - 10.1038/s41467-020-14352-7

M3 - Article

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 731

ER -

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

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Author Correction: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

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