1

Inferring clonal composition of a breast cancer

from multiple tissue samples

Habil ZareDepartment of Genome Sciences

University of Washington19 Dec 2013

2

Hypothesis

Because cancer is a heterogeneous disease, synergistic medications can

treat it better than a single drug.

3

Traditional concept of a tumor

Schematic figure

4

Most tumors are heterogeneous

Schematic figure

5

Different clones have different genotypes and phenotypes

Clone 1

Clone 2

Clone 3Clone 4Clone 5

Clone 6

Schematic figure

6

It is important to identify the clonal composition

Treatment A

Treatment B

Relapse

Relapse

7

It is important to identify the clonal composition

?

8

It is important to identify the clonal composition

?

9

Our approach to analyze multiple samples from a single tumor

10

Our approach to analyze multiple samples from a single tumor

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

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?

13

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

14

Building a generative modelGiven the parameters, how to generate data?

Data

Parameters

Generate

15

Data

Parameters

Generate

?

Building a generative modelGiven the parameters, how to generate data?

16

Generate

Building a generative model

Parameters?

17

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

18

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

19

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

20

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

21

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

22

Building a generative model

ParametersC

Generate

?

23

The assumptions

• Each mutation can occur at a locus independently at random.• The samples are independent from each other.

Building a generative model

24

Building a generative model

ParametersC

Generate

Technical

25

Overview of the generative model from parameters to the observations

C

Parameters

Observations

26

InferenceGiven the observed counts, how do we infer the clonal structure?

C

Inference

Technical

EM

27

We infer model parameters using expectation-maximization

Details omitted

Derived from the binomial distribution

Derived from Bernoulli distribution

28

How can we evaluate whether the model works?

Inference

Two rounds of next gen sequencing

C

29

We do not know the reality

~Inferred Reality

30

Generating synthetic data

Inference

C`

Generate

31

Inference

C

Generate

Generating synthetic data

Random parameters

compare

32

• 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

33

Simulation shows genotype error decreasing with increasing samples

34

Simulation shows genotype error decreasing with increasing samples

35

Clone frequency error shows a similar trend

36

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

37

Clone frequencies vary smoothly across the tumor sections

The model doesn’t know anything about the anatomic location of the samples!

38

Clone frequencies vary smoothly across the tumor sections

39

Phylogenetic analysis tells the story of the tumor over time

40

Five clone solution

41

Six clone solution is consistent with five-

clone solution

42

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

43

Software publicly available

44

Supplementary slides

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