Nothing Special   »   [go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Overcoming Observation Bias for Cancer Progression Modeling

  • Conference paper
  • First Online:
Research in Computational Molecular Biology (RECOMB 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14758))

Abstract

Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the “conditioning on a collider” bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects.

The data, code, and results are available at https://github.com/cbg-ethz/ObservationMHN.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This construction is only needed for learning the model. In order to extrapolate the progression of a tumor into the future beyond its observation, one would “unfreeze” the process again by setting the outgoing effects of the observation to 1. Ideally one would include effects from the treatment of the patient instead.

  2. 2.

    In the independence model, events occur independently of each other with rates equal to their odds in the dataset.

References

  1. Alfaro-Murillo, J.A., Townsend, J.P.: Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis, January 2022. https://doi.org/10.1101/2022.01.20.477132

  2. Beerenwinkel, N., Eriksson, N., Sturmfels, B.: Conjunctive Bayesian networks. Bernoulli 13(4), 893–909 (2007). https://doi.org/10.3150/07-BEJ6133

  3. Beerenwinkel, N., et al.: Learning multiple evolutionary pathways from cross-sectional data. J. Comput. Biol. 12(6), 584–598 (2005). https://doi.org/10.1089/cmb.2005.12.584

    Article  MathSciNet  Google Scholar 

  4. Beerenwinkel, N., Schwarz, R.F., Gerstung, M., Markowetz, F.: Cancer evolution: mathematical models and computational inference. Syst. Biol. 64(1), e1–e25 (2014). https://doi.org/10.1093/sysbio/syu081

  5. Berkson, J.: Limitations of the application of fourfold table analysis to hospital data. Biometrics Bull. 2(3), 47 (1946). https://doi.org/10.2307/3002000

    Article  Google Scholar 

  6. Bettington, M., et al.: Clinicopathological and molecular features of sessile serrated adenomas with dysplasia or carcinoma. Gut 66(1), 97–106 (2015). https://doi.org/10.1136/gutjnl-2015-310456

    Article  Google Scholar 

  7. Bleijenberg, A.G., et al.: The earliest events in BRAF-mutant colorectal cancer: exome sequencing of sessile serrated lesions with a tiny focus dysplasia or cancer reveals recurring mutations in two distinct progression pathways. J. Pathol. 257(2), 239–249 (2022). https://doi.org/10.1002/path.5881

    Article  Google Scholar 

  8. Bond, C.E., et al.: RNF43 and ZNRF3 are commonly altered in serrated pathway colorectal tumorigenesis. Oncotarget 7(43), 70589–70600 (2016). https://doi.org/10.18632/oncotarget.12130

  9. Buis, P.E., Dyksen, W.R.: Efficient vector and parallel manipulation of tensor products. ACM Trans. Math. Softw. 22(1), 18–23 (1996). https://doi.org/10.1145/225545.225548

    Article  MathSciNet  Google Scholar 

  10. Bürtin, F., Mullins, C.S., Linnebacher, M.: Mouse models of colorectal cancer: Past, present and future perspectives. World J. Gastroenterol. 26(13), 1394–1426 (2020). https://doi.org/10.3748/wjg.v26.i13.1394

    Article  Google Scholar 

  11. Chen, J.: Timed hazard networks: incorporating temporal difference for oncogenetic analysis. PLoS ONE 18(3), e0283004 (2023). https://doi.org/10.1371/journal.pone.0283004

    Article  Google Scholar 

  12. Cho, J.Y.: Risk factors for acute cholecystitis and a complicated clinical course in patients with symptomatic cholelithiasis. Arch. Surg. 145(4), 329 (2010). https://doi.org/10.1001/archsurg.2010.35

    Article  Google Scholar 

  13. Cicenas, J., et al.: KRAS, NRAS and BRAF mutations in colorectal cancer and melanoma. Med. Oncol. 34(2) (2017). https://doi.org/10.1007/s12032-016-0879-9

  14. Cristea, S., Kuipers, J., Beerenwinkel, N.: pathTiMEx: joint inference of mutually exclusive cancer pathways and their progression dynamics. J. Comput. Biol. 24(6), 603–615 (2017). https://doi.org/10.1089/cmb.2016.0171

    Article  MathSciNet  Google Scholar 

  15. Desper, R., Jiang, F., Kallioniemi, O.P., Moch, H., Papadimitriou, C.H., Schäffer, A.A.: Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comput. Biol. 6(1), 37–51 (1999). https://doi.org/10.1089/cmb.1999.6.37

    Article  Google Scholar 

  16. Diaz-Colunga, J., Diaz-Uriarte, R.: Conditional prediction of consecutive tumor evolution using cancer progression models: what genotype comes next? PLoS Comput. Biol. 17(12), e1009055 (2021). https://doi.org/10.1371/journal.pcbi.1009055

    Article  Google Scholar 

  17. Farahani, H.S., Lagergren, J.: Learning oncogenetic networks by reducing to mixed integer linear programming. PLoS ONE 8(6), e65773 (2013). https://doi.org/10.1371/journal.pone.0065773

    Article  Google Scholar 

  18. Fearon, E.R., Vogelstein, B.: A genetic model for colorectal tumorigenesis. Cell 61(5), 759–767 (1990). https://doi.org/10.1016/0092-8674(90)90186-i

    Article  Google Scholar 

  19. Georg, P.: Tensor train decomposition for solving high-dimensional mutual hazard networks (2022). https://doi.org/10.5283/EPUB.53004. https://epub.uni-regensburg.de/id/eprint/53004

  20. Gerstung, M., Baudis, M., Moch, H., Beerenwinkel, N.: Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics 25(21), 2809–2815 (2009). https://doi.org/10.1093/bioinformatics/btp505

    Article  Google Scholar 

  21. Giannakis, M., et al.: RNF43 is frequently mutated in colorectal and endometrial cancers. Nat. Genet. 46(12), 1264–1266 (2014). https://doi.org/10.1038/ng.3127

    Article  Google Scholar 

  22. Gotovos, A., Burkholz, R., Quackenbush, J., Jegelka, S.: Scaling up continuous-time Markov chains helps resolve underspecification, July 2021. https://doi.org/10.48550/arXiv.2107.02911

  23. Grant, A., et al.: Molecular drivers of tumor progression in microsatellite stable APC mutation-negative colorectal cancers. Sci. Rep. 11(1) (2021). https://doi.org/10.1038/s41598-021-02806-x

  24. Greenbury, S.F., Barahona, M., Johnston, I.G.: HyperTraPS: inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways. Cell Syst. 10(1), 39–51.e10 (2020). https://doi.org/10.1016/j.cels.2019.10.009

  25. van de Haar, J., Canisius, S., Yu, M.K., Voest, E.E., Wessels, L.F., Ideker, T.: Identifying epistasis in cancer genomes: a delicate affair. Cell 177(6), 1375–1383 (2019). https://doi.org/10.1016/j.cell.2019.05.005

    Article  Google Scholar 

  26. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7

  27. Hernán MA, R.J.: Causal Inference: What If. Chapman & Hall/CRC, Boca Raton (2020)

    Google Scholar 

  28. Hjelm, M., Höglund, M., Lagergren, J.: New probabilistic network models and algorithms for oncogenesis. J. Comput. Biol. 13(4), 853–865 (2006). https://doi.org/10.1089/cmb.2006.13.853

    Article  MathSciNet  Google Scholar 

  29. Iranzo, J., Gruenhagen, G., Calle-Espinosa, J., Koonin, E.V.: Pervasive conditional selection of driver mutations and modular epistasis networks in cancer. Cell Rep. 40(8), 111272 (2022). https://doi.org/10.1016/j.celrep.2022.111272

    Article  Google Scholar 

  30. Jeong, W.J., Ro, E.J., Choi, K.Y.: Interaction between wnt/\(\beta \)-catenin and RAS-ERK pathways and an anti-cancer strategy via degradations of \(\beta \)-catenin and RAS by targeting the wnt/\(\beta \)-catenin pathway. npj Precis. Oncol. 2(1) (2018). https://doi.org/10.1038/s41698-018-0049-y

  31. Johnston, I.G., Williams, B.P.: Evolutionary inference across eukaryotes identifies specific pressures favoring mitochondrial gene retention. Cell Syst. 2(2), 101–111 (2016). https://doi.org/10.1016/j.cels.2016.01.013

    Article  Google Scholar 

  32. Klever, M., Georg, P., Grasedyck, L., Schill, R., Spang, R., Wettig, T.: Low-rank tensor methods for Markov chains with applications to tumor progression models. J. Math. Biol. 86(1) (2022). https://doi.org/10.1007/s00285-022-01846-9

  33. Lee, S.K., Hwang, J.H., Choi, K.Y.: Interaction of the wnt/\(\beta \)-catenin and RAS-ERK pathways involving co-stabilization of both \(\beta \)-catenin and RAS plays important roles in the colorectal tumorigenesis. Adv. Biol. Regul. 68, 46–54 (2018). https://doi.org/10.1016/j.jbior.2018.01.001

    Article  Google Scholar 

  34. Leggett, B., Whitehall, V.: Role of the serrated pathway in colorectal cancer pathogenesis. Gastroenterology 138(6), 2088–2100 (2010). https://doi.org/10.1053/j.gastro.2009.12.066

    Article  Google Scholar 

  35. Loohuis, L.O., et al.: Inferring tree causal models of cancer progression with probability raising. PLoS ONE 9(10), e108358 (2014). https://doi.org/10.1371/journal.pone.0108358

    Article  Google Scholar 

  36. Luo, X.G., Kuipers, J., Beerenwinkel, N.: Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat. Commun. 14(1) (2023). https://doi.org/10.1038/s41467-023-39400-w

  37. Mina, M., Iyer, A., Ciriello, G.: Epistasis and evolutionary dependencies in human cancers. Curr. Opin. Genet. Dev. 77, 101989 (2022). https://doi.org/10.1016/j.gde.2022.101989

    Article  Google Scholar 

  38. Misra, N., Szczurek, E., Vingron, M.: Inferring the paths of somatic evolution in cancer. Bioinformatics 30(17), 2456–2463 (2014). https://doi.org/10.1093/bioinformatics/btu319

    Article  Google Scholar 

  39. Moen, M.T., Johnston, I.G.: HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs. Bioinformatics 39(1) (2022). https://doi.org/10.1093/bioinformatics/btac803

  40. Montazeri, H., et al.: Large-scale inference of conjunctive Bayesian networks. Bioinformatics 32(17), i727–i735 (2016). https://doi.org/10.1093/bioinformatics/btw459

  41. Nguyen, B., Sanchez-Vega, C.F.F., Schultz, N., et al.: Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185(3), 563–575.e11 (2022). https://doi.org/10.1016/j.cell.2022.01.003

  42. Nicol, P.B., et al.: Oncogenetic network estimation with disjunctive Bayesian networks. Comput. Syst. Oncol. 1(2) (2021). https://doi.org/10.1002/cso2.1027

  43. Nowell, P.C.: The clonal evolution of tumor cell populations. Science 194(4260), 23–28 (1976). https://doi.org/10.1126/science.959840

    Article  Google Scholar 

  44. Oliveira, C., et al.: KRAS and BRAF oncogenic mutations in MSS colorectal carcinoma progression. Oncogene 26(1), 158–163 (2006). https://doi.org/10.1038/sj.onc.1209758

    Article  Google Scholar 

  45. Ortmann, C.A., et al.: Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372(7), 601–612 (2015). https://doi.org/10.1056/nejmoa1412098

    Article  Google Scholar 

  46. Ramazzotti, D., et al.: CAPRI: efficient inference of cancer progression models from cross-sectional data. Bioinformatics 31(18), 3016–3026 (2015). https://doi.org/10.1093/bioinformatics/btv296

    Article  Google Scholar 

  47. Raphael, B.J., Vandin, F.: Simultaneous inference of cancer pathways and tumor progression from cross-sectional mutation data. J. Comput. Biol. 22(6), 510–527 (2015). https://doi.org/10.1089/cmb.2014.0161

    Article  MathSciNet  Google Scholar 

  48. Rupp, K., et al.: Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models (2021). https://doi.org/10.48550/ARXIV.2112.10971. https://arxiv.org/abs/2112.10971

  49. Schill, R.: Mutual hazard networks: Markov chain models of cancer progression (2022). https://doi.org/10.5283/EPUB.53417. https://epub.uni-regensburg.de/id/eprint/53417

  50. Schill, R., Solbrig, S., Wettig, T., Spang, R.: Modelling cancer progression using mutual hazard networks. Bioinformatics 36(1), 241–249 (2019). https://doi.org/10.1093/bioinformatics/btz513

    Article  Google Scholar 

  51. The AACR Project GENIE Consortium, et al.: AACR project genie: powering precision medicine through an international consortium. Cancer Discov. 7(8), 818–831 (2017). https://doi.org/10.1158/2159-8290.CD-17-0151

  52. Unni, A.M., Lockwood, W.W., Zejnullahu, K., Lee-Lin, S.Q., Varmus, H.: Evidence that synthetic lethality underlies the mutual exclusivity of oncogenic KRAS and EGFR mutations in lung adenocarcinoma. eLife 4 (2015). https://doi.org/10.7554/elife.06907

  53. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., Kinzler, K.W.: Cancer genome landscapes. Science 339(6127), 1546–1558 (2013). https://doi.org/10.1126/science.1235122

    Article  Google Scholar 

  54. Yamamoto, D., et al.: Characterization of RNF43 frameshift mutations that drive Wnt ligand- and RS-spondin-dependent colon cancer. J. Pathol. 257(1), 39–52 (2022). https://doi.org/10.1002/path.5868

    Article  Google Scholar 

  55. Yang, L., et al.: An enhanced genetic model of colorectal cancer progression history. Genome Biol. 20(1) (2019). https://doi.org/10.1186/s13059-019-1782-4

  56. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B Stat Methodol. 68(1), 49–67 (2005). https://doi.org/10.1111/j.1467-9868.2005.00532.x

Download references

Acknowledgements

This work was supported by the Swiss National Science Foundation grant 179518, the Swiss Cancer League grant KFS-2977-08-2012 and the German Research Foundation grants TRR-305 and GR-3179/6-1.

Author information

Authors and Affiliations

  1. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Rudolf Schill, Kevin Rupp & Niko Beerenwinkel

  2. Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany

    Andreas Lösch, Y. Linda Hu, Stefan Vocht & Rainer Spang

  3. Institute for Geometry and Applied Mathematics, RWTH Aachen, Aachen, Germany

    Maren Klever & Lars Grasedyck

Authors
  1. Rudolf Schill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maren Klever
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Lösch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. Linda Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Vocht
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kevin Rupp
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lars Grasedyck
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rainer Spang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Niko Beerenwinkel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rudolf Schill .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Jian Ma

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 213 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schill, R. et al. (2024). Overcoming Observation Bias for Cancer Progression Modeling. In: Ma, J. (eds) Research in Computational Molecular Biology. RECOMB 2024. Lecture Notes in Computer Science, vol 14758. Springer, Cham. https://doi.org/10.1007/978-1-0716-3989-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3989-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-1-0716-3988-7

  • Online ISBN: 978-1-0716-3989-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation