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Cdac 2018 antoniotti cancer evolution trait


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Presentation at the 2018 Workshop and School on Cancer Development and Complexity CDAC 2018

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Cdac 2018 antoniotti cancer evolution trait

  1. 1. Reconstructing Cancer Progression Models from Bulk and Single-cell Data with TRaIT Marco Antoniotti, G. Caravagna, L. De Sano, Alex Graudenzi, D. Ramazzotti
  2. 2. Outline • We have been exploring methods for reconstructing progression models: in this case of individual tumors, as opposed to ensemble models coming from several patients’ data • In the following we distinguish between – Phylogenetic trees – Clonal Trees – Mutational trees (and graphs) • … and we will present the TRONCO submodule TRaIT (Temporal oRder of Individual Tumors), which can be used to infer mutational trees from individual tumor data CDAC 2018 2
  3. 3. TRaIT and the TRONCO Library • You can look up the TRONCO R library at • TRaIT is part of the TRONCO library and you can find a description of its structure in Ramazzotti et al (2017), Learning mutational graphs of individual tumor evolution from multi-sample sequencing data, biorXiv, doi: 10.1101/132183 (submitted) CDAC 2018 3
  5. 5. Cancer Evolution CDAC 2018 5 [Davis,A.,Gao,R.,Navin,N.(2017)BiochimBiophysActa1867(2)] competition selection expansion differentiation diffusion Intra-Tumor Heterogeneity (ITH) Cancer develops via the progressive accumulation of genomic and epigenetic alterations (drivers) Modeled via phylogenetic- like models One of the most critical issues in dealing with tumor data is Intra Tumour Heterogeneity
  6. 6. Clonal Expansion and Resistance CDAC 2018 6
  7. 7. From Sequences to Mutational Information (in Cancer) We can now go back to the other two kinds of analysis described by Schwartz and Schäffer We can sequence a number of cells taken from a single tumor (bulk sequencing) or from “slices” of it In this case we can build a tree (a phylogeny) of the tumor “pieces” The evolution of tumour phylogenetics: principles and practice, R. Schwartz and A. A. Schäffer, Nature Review Genetics, 2017 CDAC 2018 7
  8. 8. From Sequences to Mutational Information (in Cancer) Again, at the most advanced (and currently expensive) frontier of sequencing technology are Single-Cell projects, where much of the effort is concentrated in isolating “single” cells In this case we can build a tree (a phylogeny) of the tumor “sub-clones” This is potentially the most precise way to build statistically well-founded progression models of a single tumor. CDAC 2018 8
  9. 9. Multiple Samples per Tumor CDAC 2018 9 Single-Cell Sequencing (SCS): highest resolution, but technical problems due to cell isolation and whole- genome amplification (WGA): data-specific errors: allelic dropouts (ADOs), false alleles, missing data, non-uniform coverage, doublets, etc. Bulk intermingled signal Phylogeny reconstruction, often via signal deconvolution (e.g., VAFs)
  10. 10. From Bulk to Single-cell Analysis CDAC 2018 Single-cell genome sequencing: current state of the science, Charles Gawad, Winston Koh & Stephen R. Quake, Nature Reviews Genetics 17, 175–188 (2016) doi:10.1038/nrg.2015.16 10
  12. 12. Clonal and Mutational Trees CDAC 2018 12 annotated in a set of cells Given a set of mutations A B C D E F Clonal Lineage Trees Mutational Trees Standard phylogenetic tree Clonal signature Prevalence Ordering Mutational Ordering
  13. 13. Clonal and Mutational Trees • “Standard” Phylogenetic Trees – Davis, A.,Navin, N. (2016) Genome Biology, 17(1):113 – … • Clonal Lineage Trees – Bitphylogeny: Yuan et al. (2015) Genome biology 16(1), 1 – OncoNem: Ross & Markowetz (2016) Genome biology 17(1), 1 – Single Cell Genotyper: Roth et al. (2016) Nat met 13(7), 573-576 – ddClone: Salehi et al (2017) Genome biology 18:44 – … • Mutational Trees – MUTTREE: Kim, & Simon (2014), BMC bioinformatics, 15(1), 27 – SCITE: Kuipers et al. (2016) Genome biology, 17(1), 86 – … – SiFit: Zafar et a. (2017), Genome Biology 18:178 (*) CDAC 2018 13
  14. 14. Clonal and Mutational Trees from SCS Most techniques rely on technical assumptions – E.g. Infinite Sites Assumption (ISA): • “each mutation occurs at most once during th evolutionary history of a tumor, and is never lost” • ⟹ possible violations, due to, e.g., convergent evolution Can be computationally expensive and require data-specific error models CDAC 2018 14 A B C D D A B C D D
  15. 15. TRaIT: Temporal oRder of Individual Tumors • Robust estimation of the mutational ordering in single tumors • Supports both multi-region and SCS data within a unified statistical framework – no data-specific noise model • Binary input data → any alteration type – SNVs, CNAs, fusions, etc. • Extends mutational trees to mutational graphs (direct acyclic graphs - DAGs) : – confounding factors – possible multiple independent trajectories – violations of the ISA, due to convergent evolution CDAC 2018 15
  16. 16. TRaIT Suite CDAC 2018 16 • Given a binary matrix that stores the presence of any alteration in a sample, • We assess (i) temporal ordering and (ii) statistical association via non-parametric Bootstrap and hypothesis testing -> direct graph G (variables = alterations). • We extract output models with algorithmic strategies based on information theoretic measures (e.g., mutual information) • Optimal polynomial-time “off-the-shelf” algorithms; e.g., Edmonds and Gabow algorithms infer trees (weighted directed MST) and Prim and Chow-Liu plus post-processing infer DAGs • The overall complexity of this step is O((nm)2 x B) where B is the cost of running bootstrap and hypothesis testing on each entry in D.
  17. 17. CAPRESE Individual Level Progression 17CDAC 2018
  18. 18. TRaIT Analisys: Multi-region data MSI- High Colorectal Cancer CDAC 2018 18 Lu, You-Wang, et al. "Colorectal cancer genetic heterogeneity delineated by multi- region sequencing." PloS one 11.3 (2016): e0152673
  19. 19. TRaIT Analisys: SCS Triple-neg Breast Cancer CDAC 2018 19 ADO rate = 9.73x10-2 FP rate = 1.24x10-6 Undetected subclone? Subclone H? Clonal group also detected in the control bulk sample Subclonal groups Uncertainty on temporal direction wild typemutated Wang, Yong, et al. "Clonal evolution in breast cancer revealed by single nucleus genome sequencing." Nature 512.7513 (2014): 155
  20. 20. Conclusions • In this talk we have seen the analysis of two kinds of data types that are produced when studying individual tumors – Region data, bulk-sequenced – Single cells sequenced • In particular we have seen a framework, based on the TRONCO library that can be used to analyze both kinds of data • Again, you are invited to use the TRaIT facilities in TRONCO to reproduce the studies presented CDAC 2018 20