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![Page 1: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/1.jpg)
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Inferring clonal composition of a breast cancer
from multiple tissue samples
Habil ZareDepartment of Genome Sciences
University of Washington19 Dec 2013
![Page 2: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/2.jpg)
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Hypothesis
Because cancer is a heterogeneous disease, synergistic medications can
treat it better than a single drug.
![Page 3: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/3.jpg)
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Traditional concept of a tumor
Schematic figure
![Page 4: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/4.jpg)
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Most tumors are heterogeneous
Schematic figure
![Page 5: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/5.jpg)
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Different clones have different genotypes and phenotypes
Clone 1
Clone 2
Clone 3Clone 4Clone 5
Clone 6
Schematic figure
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It is important to identify the clonal composition
Treatment A
Treatment B
Relapse
Relapse
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It is important to identify the clonal composition
?
![Page 8: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/8.jpg)
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It is important to identify the clonal composition
?
![Page 9: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/9.jpg)
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Our approach to analyze multiple samples from a single tumor
![Page 10: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/10.jpg)
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Our approach to analyze multiple samples from a single tumor
![Page 11: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/11.jpg)
Each sample has different information about the clonal composition
PCR
PCR
PCR
Next Gen Sequencing
Next Gen Sequencing
Next Gen Sequencing
Counting the number of reads which support each mutation
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A closer look at the Next-Gen Sequencing output
• At each locus, 2 integers are provided: total number of analyzed reads, andthe number of reads supporting the mutation.
• Because different clones have different contributions to each sample, these numbers vary across the samples.
How to use this variation to infer the clonal composition?
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The observations
The observations boils down to the number of reads which support each allele.
• M samples• Mutations on N loci
Building a generative model
Tumor
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Building a generative modelGiven the parameters, how to generate data?
Data
Parameters
Generate
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Data
Parameters
Generate
?
Building a generative modelGiven the parameters, how to generate data?
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Generate
Building a generative model
Parameters?
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The main assumption on the distribution of reads
Mutation i can be present or absent in each clone
Project on Mutation i
Building a generative model
Assumption: Reads are analyzed uniformly at random => Binomially distributed
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The main assumption on the distribution of reads
Mutation i can be present or absent in each clone
Project on Mutation i
Number of reads exhibiting the variant allele at locus i in sample j.
Total number of reads
Frequency of variant allele
Assumption
Building a generative model
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A close look at the binomial distribution
Total number of readsObserved
Frequency of variant allele ?
Number of reads exhibiting the variant
Observed
depends on:1. Which clones contain mutation i ?2. What is the frequency of those clones in sample j ?
Building a generative model
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Introducing the hidden variables
If Zi,c = 1, clone c has a variant allele at locus i. depends on:1. Which clones contain mutation i ?2. What is the frequency of those clones in sample j ?
Building a generative model
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Notation for the model parameters
depends on:1. Which clones contain mutation i ?2. What is the frequency of those clones in sample j ?
Building a generative model
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Building a generative model
ParametersC
Generate
?
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The assumptions
• Each mutation can occur at a locus independently at random.• The samples are independent from each other.
Building a generative model
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Building a generative model
ParametersC
Generate
Technical
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Overview of the generative model from parameters to the observations
C
Parameters
Observations
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InferenceGiven the observed counts, how do we infer the clonal structure?
C
Inference
Technical
EM
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We infer model parameters using expectation-maximization
Details omitted
Derived from the binomial distribution
Derived from Bernoulli distribution
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How can we evaluate whether the model works?
Inference
Two rounds of next gen sequencing
C
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We do not know the reality
~Inferred Reality
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Generating synthetic data
Inference
C`
Generate
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Inference
C
Generate
Generating synthetic data
Random parameters
compare
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• Genotype error: The frequency of false entries in the genotype matrix Z
• Clone frequency error: The average error in entries of the frequency matrix P
Defining accuracy criteria
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Simulation shows genotype error decreasing with increasing samples
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Simulation shows genotype error decreasing with increasing samples
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Clone frequency error shows a similar trend
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M1
P1
P3
P2
Experiment with real dataStudy on a primary breast cancer
• 10 breast tumor samples• 1 adjacent normal • 2 samples from the
metastatic lymph node
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Clone frequencies vary smoothly across the tumor sections
The model doesn’t know anything about the anatomic location of the samples!
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Clone frequencies vary smoothly across the tumor sections
![Page 39: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/39.jpg)
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Phylogenetic analysis tells the story of the tumor over time
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Five clone solution
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Six clone solution is consistent with five-
clone solution
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Next-Gen Sequencing Data
Oncologists
Clonal structureEM Validated by simulations
Anatomic variation of clones Phylogenetic trees
Overview of the projectInferring clonal composition of a breast cancer from multiple tissue samples
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Software publicly available
![Page 44: Inferring clonal composition of a breast cancer from multiple tissue samples Habil Zare Department of Genome Sciences University of Washington 19 Dec 2013.](https://reader038.fdocuments.us/reader038/viewer/2022110100/56649ddf5503460f94ad82e4/html5/thumbnails/44.jpg)
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Supplementary slides
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Proposed project based on former experiences:Identifying clonal decomposition using sub-tissues
SamSPECTRAL
Sort cell populations
Next Gen Sequencing
Next Gen Sequencing
Next Gen Sequencing
Next Gen Sequencing
Leukemia or lymphoma sample
Clonalanalysis