gpps: an ILP-based approach for inferring cancer ... · gpps: an ILP-based approach for inferring...
Transcript of gpps: an ILP-based approach for inferring cancer ... · gpps: an ILP-based approach for inferring...
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gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell dataSimone Ciccolella, Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha and Paola Bonizzoni
ICCABS, Las Vegas 2018
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Introduction
Simone Ciccolella ICCABS 2018
• Cancer phylogeny
• Mutation losses
• gpps
• Experimental results
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Cancer evolution
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Cancer evolution
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Sequencing
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Infinite Sites Assumption
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• The most used assumption for the inference of cancer evolutions• Permits the use of the simplest phylogeny model
No two mutations can occur at the same locus (site). Kimura, Genetics, 1969.
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Infinite Sites Assumption
Simone Ciccolella ICCABS 2018
• The most used assumption for the inference of cancer evolutions• Permits the use of the simplest phylogeny model
No two mutations can occur at the same locus (site). Kimura, Genetics, 1969.
• “Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity for more effective cancer treatment.”
Kuipers et al., Genome Research, 2017.
• “In genomically unstable cancers, deletion of large chromosomal segments is common.”
Brown et al., Nature 8, 2017.
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Phylogenies: Perfect vs Dollo
Simone Ciccolella ICCABS 2018
Perfect Phylogeny
BA
FC D
E
Each mutation is acquired once in the evolutionaryhistory
H
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Phylogenies: Perfect vs Dollo
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Perfect Phylogeny Dollo-k Phylogeny
BA
FC D
E
Each mutation is acquired once in the evolutionaryhistory
BA
FC D
A1–
E
B1–
G
A2–
H
I
Each mutation is acquired once, but it can be lost at most k times in the evolutionary history
H
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Loss of a mutation
Simone Ciccolella ICCABS 2018
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……
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Loss of a mutation
Simone Ciccolella ICCABS 2018
……
……
……
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Loss of a mutation
Simone Ciccolella ICCABS 2018
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Loss of a mutation
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……
……
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Loss of a mutation
Simone Ciccolella ICCABS 2018
……
……
……
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Loss of a mutation
Simone Ciccolella ICCABS 2018
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Single Cell Sequencing
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Available methods for SCS
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SCITE:
• Markov Chain Monte Carlo (MCMC) maximum likelihood tree search
• Relies on the Perfect Phylogeny model• Produces solutions with respect to the Infinite
Site Assumption
Tree inference for single-cell data. Jahn K., Kuipers J. and Beerenwinkel N., Genome Biology, 2016.
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Available methods for SCS
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SCITE:
• Markov Chain Monte Carlo (MCMC) maximum likelihood tree search
• Relies on the Perfect Phylogeny model• Produces solutions with respect to the Infinite
Site Assumption
SiFit:
• Hidden Markov Model (HMM) maximum likelihood tree search
• Does not impose any specific phylogeny model
• Can produce solutions that violate the Infinite Site Assumption
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models.
Zafar H., Tzen A., Navin N., Chen K. and Nakhleh L., Genome Biology, 2017.
Tree inference for single-cell data. Jahn K., Kuipers J. and Beerenwinkel N., Genome Biology, 2016.
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Infinite Sites Assumption• Once a mutation arises it is not possible to lose it
A B C Ds1 0 1 1 1s2 1 0 1 0s3 0 0 1 0s4 0 0 1 1s5 0 1 1 1
E11000
For each pair of columns (mutations) there are no three rows with all the three configurations (0,1),(1,0),(1,1)
Simone Ciccolella ICCABS 2018
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Infinite Sites Assumption• Once a mutation arises it is not possible to lose it
A B C Ds1 0 1 1 1
s2 1 0 1 0
s3 0 0 1 0
s4 0 0 1 1
s5 0 1 1 1
E11000
For each pair of columns (mutations) there are no three rows with all the three configurations (0,1),(1,0),(1,1)
Simone Ciccolella ICCABS 2018
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Infinite Sites Assumption• Once a mutation arises it is not possible to lose it
A B C Ds1 0 1 1 1s2 1 0 1 0s3 0 0 1 0s4 0 0 1 1s5 0 1 1 1
E1
1
0
0
0
For each pair of columns (mutations) there are no three rows with all the three configurations (0,1),(1,0),(1,1)
Simone Ciccolella ICCABS 2018
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Infinite Sites Assumption• Once a mutation arises it is not possible to lose it
A B C Ds1 0 1 1 1s2 1 0 1 0s3 0 0 1 0s4 0 0 1 1s5 0 1 1 1
E1
1
0
0
0
For each pair of columns (mutations) there are no three rows with all the three configurations (0,1),(1,0),(1,1)
Simone Ciccolella ICCABS 2018
A binary matrix 𝑀 is a Dollo Phylogeny if the extended matrix 𝑀3 is a Perfect Phylogeny
Bonizzoni etal.,TCBB2018
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Dollo extended matrix
A B C D E
s1 0 1 1 1 1
s2 1 0 1 0 1
s3 0 0 1 0 0
s4 0 0 1 1 0
s5 0 1 1 1 0
Simone Ciccolella ICCABS 2018
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Dollo extended matrix
A A- B B- C C- D D- E E-
s1 0 1 1 1 1
s2 1 0 1 0 1
s3 0 0 1 0 0
s4 0 0 1 1 0
s5 0 1 1 1 0
Simone Ciccolella ICCABS 2018
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Dollo extended matrix
A A- B B- C C- D D- E E-
s1 0 0 1 1 1 0 1 0 1 1
s2 1 0 0 0 1 0 0 0 1 0
s3 0 0 0 0 1 1 0 0 0 0
s4 0 0 0 0 1 0 1 0 0 0
s5 0 0 1 0 1 0 1 1 0 0
Simone Ciccolella ICCABS 2018
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Dollo extended matrix
A A- B B- C C- D D- E E-
s1 0 0 1 1 1 0 1 0 1 1
s2 1 0 0 0 1 0 0 0 1 0
s3 0 0 0 0 1 1 0 0 0 0
s4 0 0 0 0 1 0 1 0 0 0
s5 0 0 1 0 1 0 1 1 0 0
Simone Ciccolella ICCABS 2018
Mutation A in s1 is never acquired and never lost
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Dollo extended matrix
A A- B B- C C- D D- E E-
s1 0 0 1 1 1 0 1 0 1 1
s2 1 0 0 0 1 0 0 0 1 0
s3 0 0 0 0 1 1 0 0 0 0
s4 0 0 0 0 1 0 1 0 0 0
s5 0 0 1 0 1 0 1 1 0 0
Simone Ciccolella ICCABS 2018
Mutation C in s5 is acquired but never lost
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Dollo extended matrix
A A- B B- C C- D D- E E-
s1 0 0 1 1 1 0 1 0 1 1
s2 1 0 0 0 1 0 0 0 1 0
s3 0 0 0 0 1 1 0 0 0 0
s4 0 0 0 0 1 0 1 0 0 0
s5 0 0 1 0 1 0 1 1 0 0
Simone Ciccolella ICCABS 2018
Mutation E in s1 is acquired and then lost
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
𝛼 = false negative rate
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
𝛼 = false negative rate𝛽 = false positive rate
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
𝛼 = false negative rate𝛽 = false positive rate𝐼 = Input matrix
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
𝛼 = false negative rate𝛽 = false positive rate𝐼 = Input matrix𝐹 = Inferred matrix
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚
if𝐼 𝑐,𝑚 = 1if𝐼 𝑐,𝑚 = 0
𝛼 = false negative rate𝛽 = false positive rate𝐼 = Input matrix𝐹 = Inferred matrix max7 7 log(𝑃 𝐼(𝑐,𝑚) 𝐹(𝑐,𝑚 )
�
?
�
B=
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gpps: ILP
Simone Ciccolella ICCABS 2018
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
• Additional set of constraints used to ensure a Dollo model on the tree
Gusfield etal.,ReCombinatorics 2014
Bonizzoni etal.,TCBB2018
Gusfield,RECOMB 2002
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process after the ILP is stopped
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚
2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚…
ILP Solver
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process after the ILP is stopped
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚
2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚…
ILP SolverTimeout
Sub-optimal solution
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process after the ILP is stopped
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚
2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚…
ILP SolverTimeout
Sub-optimal solution
Hill Climbing
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process after the ILP is stopped
max7 7 log𝑤(𝑐,𝑚)�
?∈A
�
B∈C
1)𝑤 𝑐,𝑚 = 1 − 𝛼 𝐹 𝑐,𝑚 + 𝛽 1 − 𝐹 𝑐,𝑚
2)𝑤 𝑐,𝑚 = 𝛼𝐹 𝑐,𝑚 + (1 − 𝛽) 1 − 𝐹 𝑐,𝑚…
ILP SolverTimeout
Sub-optimal solution
Hill Climbing
Final solution
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process using the Subtree Prune and Reattach operation
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process using the Subtree Prune and Reattach operation
A
B C
D E F
G HI
PruneReattach
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gpps: Hill Climbing
Simone Ciccolella ICCABS 2018
Continues the search process using the Subtree Prune and Reattach operation
A
B C
D E F
G HI
PruneReattach
A
B C
D E
F
G H
I
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Results: Simulated data
Simone Ciccolella ICCABS 2018
• Ancestor-Descendant accuracy:Pairs of mutations in Ancestor-Descendant relationship correctly inferred
• Different lineages accuracy:Pairs of mutations in different branches correctly inferred
F1 scores
Ancestor-Descendant accuracy
Different-Lineages accuracy
gpps gpps
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Real data: ER Breast Cancer
Simone Ciccolella ICCABS 2018
Data from: Wang et al. Nature, 2014
Tree inferred by gpps
Bold-faced mutations are drivers and red nodes are losses
DKEZ,DNM3,TRIM58,C15orf23,DCAF8L1,FBN2,H1ENT,TECTA,
ZNE318,CASP3,FUBP3,ITGAD,MUTHY,PPP2RE,LSG1,RABGAP1L,ROPN1B,BTLA,
DUSP12,CABP2,MARCH11,PAMK3,TCP11,CALD1,PITRM1,PRDM9,PIK3CA,GLCE,FCHSD2,SEC11A,
TRIB2,CNDP1,GPR64,FGFR2,CXXX1,c1orf223,KIAA1539,WDR16,PLXNA2
ZEHX4 CNDP1,TECTA,CXXX1
c1orf223,ZNE318,ITGAD,RABGAP1L
Manually curated clonal evolution
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Conclusions
Simone Ciccolella ICCABS 2018
• gpps is an accurate tool for inferring intra-tumor progression and subclonal composition from SCS data
• gpps infers mutation losses employing a Dollo model
• gpps provides a new progression model on Single Cell data
• Future directions: 1. Explore different heuristics (Genetic Programming)2. Include the possible presence of doublets to the model
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Thank you!