modeling cancer evolution from genomic data · pdf filecbg mathias cardner. simona cristea ....
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Modeling cancer evolution from genomic data
Niko Beerenwinkel
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Competence Center Personalized Medicine ETH/UZH
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Cancer is an evolutionary process
First mutant
● Normal cells
Carcinoma
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Clonal expansion
Yates et al 2012
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Intra-tumor heterogeneity
Marusyk et al 2012
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Some challenges
1. Mutation calling
2. Predicting the phenotypic effects of mutations
3. Reconstructing the evolutionary history of a tumor
4. Predicting cancer evolution and progression
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Phylogenetic vs. oncogenetic models
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Phylogenetic models Oncogenetic models
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SCITE: Tree inference for single-cell data
Katharina Jahn, Jack Kuipers (RECOMB 2016)
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Intra-tumor phylogeny
infinite sites assumption
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Observation error
α = false positive rate β = false negative rate
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n mutations, m samples Tree topology, T Attachment of samples, σ Error rates, θ = (α, β) Likelihood:
Posterior:
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Likelihood
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Attachment of samples
O(nm)
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For n mutations and m samples, the search space is [(n + 1)(n ‒ 1)] × [(n + 1)m] × IR2
and [(n + 1)(n ‒ 1)] × IR2 after marginalization
MCMC:
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Inference
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WES of 58 cancer cells 18 selected mutations 45% missing data α = 6.04 × 10‒5, β = 0.43
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Single-cell sequencing of a myeloproliferative neoplasm (Hou et al., Cell 2012)
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nuc-seq of 47 cells 40 mutations 1.4% missing data α = 1.24 × 10‒6 β = 0.097
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Single-cell sequencing of an ER+ breast tumor (Wang et al., Nature 2014)
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past clonal expansions
current, co-existing subclones
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pathTiMEx: Mutually exclusive cancer pathways and their dependencies in tumor progression Simona Cristea, Jack Kuipers (RECOMB 2016)
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Partial order among pathways
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Individual progression
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Genotypes at diagnosis
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Observed genotypes
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Waiting time distribution
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TA » Exp(¸A)
TB » Exp(¸B)
TC » TA+Exp(¸C)
TD » max(TA; TB) + Exp(¸D)
TS » Exp(¸S)
B. & Sullivant (2009), Gerstung et al (2012)
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Hidden conjunctive Bayesian network (H-CBN)
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Time to occurrence
of mutations Time to diagnosis
Genotype
Observed genotype
censoring
noise
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For example, as groups of mutually exclusive genes:
Can we find pathways de novo?
tum
ors
genes
Constantinescu, Szczurek, et al. 2015
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Pathways and their dependencies
Tg = waiting time of gene g
Up = waiting time of pathway p
true hidden genotype, X
observed genotype, Y
A pathway is altered as soon as one of its genes is altered. Genes depend on upstream pathways.
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Joint inference
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Progression in colorectal cancer
Vogelstein et al., Science 2013
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Progression in glioblastoma
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SCITE, https://gitlab.com/jahnka/SCITE Single-cell sequencing data can be used to reconstruct the
evolutionary history of individual tumors. Two intra-tumor phylogenies support an evolutionary model of
successive clonal expansions in which subclones co-exist until one of them reaches fixation.
pathTiMEx, https://github.com/cbg-ethz/pathTiMEx Tumor evolution is constrained by (partial) orders of gene and
pathway alterations. Mutually exclusive gene groups and their dependencies can be
inferred jointly from observed mutation profiles.
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Conclusions
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Acknowledgements
CBG Mathias Cardner Simona Cristea Madeline Diekmann Christos Dimitrakopoulos Simon Dirmeier Monica Golumbeanu Ariane Hofmann Katharina Jahn Vinay Jethava Jack Kuipers Brian Lang Hesam Montazeri Susana Posada Cesped Hans-Joachim Ruscheweyh Fabian Schmich David Seifert Jochen Singer Ewa Szczurek Thomas Thurnherr
www.cbg.ethz.ch
Funding ETH Zurich, SNSF, SHCS, SystemsX.ch, ERASysAPP, Swiss Cancer League, Horizon 2020, ERC Synergy Collaborators (oncology) Gerhard Christofori (U Basel), Mike Hall (U Basel), Markus Heim (U Basel), Willy Krek (ETHZ), Markus Manz (USZ), Florian Markowetz (CRUK), Holger Moch (USZ), Jörg Rahnenführer (TU Dortmund), Alejandro A. Schäffer (NIH), Bert Vogelstein (Johns Hopkins), Peter Wild (USZ)