IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v142y2021icp67-90.html
   My bibliography  Save this article

Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability

Author

Listed:
  • Gunnarsson, Einar Bjarki
  • Leder, Kevin
  • Foo, Jasmine

Abstract

The site frequency spectrum (SFS) is a popular summary statistic of genomic data. While the SFS of a constant-sized population undergoing neutral mutations has been extensively studied in population genetics, the rapidly growing amount of cancer genomic data has attracted interest in the spectrum of an exponentially growing population. Recent theoretical results have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large-time or large-population limits. In this work, we derive exact expressions for the expected SFS of a cell population that evolves according to a stochastic branching process, first for cells with an infinite line of descent and then for the total population, evaluated either at a fixed time (fixed-time spectrum) or at the stochastic time at which the population reaches a certain size (fixed-size spectrum). We find that while the rate of mutation scales the SFS of the total population linearly, the rates of cell birth and cell death change the shape of the spectrum at the small-frequency end, inducing a transition between a 1/j2 power-law spectrum and a 1/j spectrum as cell viability decreases. We show that this insight can in principle be used to estimate the ratio between the rate of cell death and cell birth, as well as the mutation rate, using the site frequency spectrum alone. Although the discussion is framed in terms of tumor dynamics, our results apply to any exponentially growing population of individuals undergoing neutral mutations.

Suggested Citation

  • Gunnarsson, Einar Bjarki & Leder, Kevin & Foo, Jasmine, 2021. "Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability," Theoretical Population Biology, Elsevier, vol. 142(C), pages 67-90.
  • Handle: RePEc:eee:thpobi:v:142:y:2021:i:c:p:67-90
    DOI: 10.1016/j.tpb.2021.09.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580921000666
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tpb.2021.09.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rebecca A. Burrell & Nicholas McGranahan & Jiri Bartek & Charles Swanton, 2013. "The causes and consequences of genetic heterogeneity in cancer evolution," Nature, Nature, vol. 501(7467), pages 338-345, September.
    2. Xiaowei Wu & Marek Kimmel, 2013. "Modeling Neutral Evolution Using an Infinite-Allele Markov Branching Process," International Journal of Stochastic Analysis, Hindawi, vol. 2013, pages 1-10, March.
    3. Benjamin Werner & Jack Case & Marc J. Williams & Ketevan Chkhaidze & Daniel Temko & Javier Fernández-Mateos & George D. Cresswell & Daniel Nichol & William Cross & Inmaculada Spiteri & Weini Huang & I, 2020. "Measuring single cell divisions in human tissues from multi-region sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    4. Nicolas Champagnat & Amaury Lambert & Mathieu Richard, 2012. "Birth and Death Processes with Neutral Mutations," International Journal of Stochastic Analysis, Hindawi, vol. 2012, pages 1-20, December.
    5. Ohtsuki, Hisashi & Innan, Hideki, 2017. "Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population," Theoretical Population Biology, Elsevier, vol. 117(C), pages 43-50.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zicheng Wang & Yunong Xia & Lauren Mills & Athanasios N. Nikolakopoulos & Nicole Maeser & Scott M. Dehm & Jason M. Sheltzer & Ruping Sun, 2024. "Evolving copy number gains promote tumor expansion and bolster mutational diversification," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Spouge, John L., 2019. "An accurate approximation for the expected site frequency spectrum in a Galton–Watson process under an infinite sites mutation model," Theoretical Population Biology, Elsevier, vol. 127(C), pages 7-15.
    2. Humberto Contreras-Trujillo & Jiya Eerdeng & Samir Akre & Du Jiang & Jorge Contreras & Basia Gala & Mary C. Vergel-Rodriguez & Yeachan Lee & Aparna Jorapur & Areen Andreasian & Lisa Harton & Charles S, 2021. "Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Lianfeng Shan & Ming Li & Jianzhong Ma & Huidan Zhang, 2014. "PCR-Based Assays versus Direct Sequencing for Evaluating the Effect of KRAS Status on Anti-EGFR Treatment Response in Colorectal Cancer Patients: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-7, September.
    4. Katherine E. Link & Zane Schnurman & Chris Liu & Young Joon (Fred) Kwon & Lavender Yao Jiang & Mustafa Nasir-Moin & Sean Neifert & Juan Diego Alzate & Kenneth Bernstein & Tanxia Qu & Viola Chen & Euni, 2024. "Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Johanna Zerbib & Marica Rosaria Ippolito & Yonatan Eliezer & Giuseppina Feudis & Eli Reuveni & Anouk Savir Kadmon & Sara Martin & Sonia Viganò & Gil Leor & James Berstler & Julia Muenzner & Michael Mü, 2024. "Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    6. Marion Porcherie & Nyan Linn & Anne Roué Le Gall & Marie-Florence Thomas & Emmanuelle Faure & Stéphane Rican & Jean Simos & Nicola Cantoreggi & Zoé Vaillant & Linda Cambon & Jean-Philippe Regnaux, 2021. "Relationship between Urban Green Spaces and Cancer: A Scoping Review," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
    7. Shen Zhao & De-Pin Chen & Tong Fu & Jing-Cheng Yang & Ding Ma & Xiu-Zhi Zhu & Xiang-Xue Wang & Yi-Ping Jiao & Xi Jin & Yi Xiao & Wen-Xuan Xiao & Hu-Yunlong Zhang & Hong Lv & Anant Madabhushi & Wen-Tao, 2023. "Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    8. Li Chen & Peter L Choyke & Niya Wang & Robert Clarke & Zaver M Bhujwalla & Elizabeth M C Hillman & Ge Wang & Yue Wang, 2014. "Unsupervised Deconvolution of Dynamic Imaging Reveals Intratumor Vascular Heterogeneity and Repopulation Dynamics," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    9. Duncan Ingram & Guy-Bart Stan, 2023. "Modelling genetic stability in engineered cell populations," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. Jacob C Kimmel & Amy Y Chang & Andrew S Brack & Wallace F Marshall, 2018. "Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-29, January.
    11. Nick Henscheid & Eric Clarkson & Kyle J Myers & Harrison H Barrett, 2018. "Physiological random processes in precision cancer therapy," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-25, June.
    12. Claire Chung & Bert M. Verheijen & Zoe Navapanich & Eric G. McGann & Sarah Shemtov & Guan-Ju Lai & Payal Arora & Atif Towheed & Suraiya Haroon & Agnes Holczbauer & Sharon Chang & Zarko Manojlovic & St, 2023. "Evolutionary conservation of the fidelity of transcription," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. Henry, Benoit, 2021. "Approximation of the allelic frequency spectrum in general supercritical branching populations," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 192-225.
    14. Wiuf, Carsten, 2018. "Some properties of the conditioned reconstructed process with Bernoulli sampling," Theoretical Population Biology, Elsevier, vol. 122(C), pages 36-45.
    15. Richard Newton & Lorenz Wernisch, 2019. "A meta-analysis of multiple matched aCGH/expression cancer datasets reveals regulatory relationships and pathway enrichment of potential oncogenes," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-28, July.
    16. Shiqian Ma & Daniel Johnson & Cody Ashby & Donghai Xiong & Carole L Cramer & Jason H Moore & Shuzhong Zhang & Xiuzhen Huang, 2015. "SPARCoC: A New Framework for Molecular Pattern Discovery and Cancer Gene Identification," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.
    17. Aleksandr Ianevski & Kristen Nader & Kyriaki Driva & Wojciech Senkowski & Daria Bulanova & Lidia Moyano-Galceran & Tanja Ruokoranta & Heikki Kuusanmäki & Nemo Ikonen & Philipp Sergeev & Markus Vähä-Ko, 2024. "Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    18. Chandler D. Gatenbee & Ann-Marie Baker & Ryan O. Schenck & Maximilian Strobl & Jeffrey West & Margarida P. Neves & Sara Yakub Hasan & Eszter Lakatos & Pierre Martinez & William C. H. Cross & Marnix Ja, 2022. "Immunosuppressive niche engineering at the onset of human colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:thpobi:v:142:y:2021:i:c:p:67-90. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.