Single_Cell_Sequencing_YongWang_2015_V1

Yong (Tony) Wang, PhD

Nick Navin’s Lab

Department of Genetics

UT MD Anderson Cancer Center

Healthcare Seminar, January 15, 2015

Diagnosing Intratumor Heterogeneity in Breast Cancer with Single-Cell Genome

Sequencing

Standard NGS vs. Single Cell Sequencing

Owens, Nature, 2012

Applications of Single Cell Sequencing

Wang et al., Molecular Cell, 2015

Timeline of Single Cell Sequencing Milestones

Exponential Growth of Single Cell Sequencing

2009 2010 2011 2012 2013 2014 20150

5

10

15

20

25

30

Year

Nu

mb

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of

Pu

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ati

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Publications by Field and Applications

Cancer24%

Developmental18%

Computational15%

Method15%

Microbiology10%

Neurobiology6%

Immunology5%

Mosaicism4%

Misc3%

Diagnosing Intratumor Heterogeneity

Intertumor vs. Intratumor heterogeneity

Burrell et al., Nature, 2013

Subclone 2 Subclone 3

Subclone 1

Intercellular genetic and non-genetic

heterogeneity

Intertumour heterogeneity

Intratumour heterogeneity

Tumor Evolution Models

Navin and Hicks, Mol. Oncol, 2011

1. Tumor heterogeneity confounds the

clinical diagnosis and basic research of

cancer

2. The extent of clonal diversity and

models for tumor evolution are poorly

understood in human breast cancer

3. Standard sequencing methods are

limited to reporting the average signal

of a complex population of tumor cells

Resolving Intratumor Heterogeneity

Zainal et al., Cell, 20121. Deep-sequencing 2. Spatial sampling

3. Single cell sequencing: The goal of this project is to develop a

single cell sequencing method to study intratumor heterogeneity

and genome evolution in breast cancer

Gerlinger et al., NEJM, 2012

Single Nucleus Sequencing (SNS)

Navin et al. 2011 Nature

Developing Single-Cell Sequencing Methods

at Base-pair Resolution

25.58% cells have all 22

chromosomes amplified

45.43% cells have all 22

chromosomes amplified

Whole-Genome or Exome Single-Cell

Sequencing

Experimental Strategy:

(1) Doubling the input DNA (4N) to decrease

allelic dropout

(2) Minimize false-positive

events by limiting the amplification reaction

NUC-SEQ

Phi29

NEB

Library (NEB)

Wang et al. (2014) Nature

Leung et al. (2015) under review

Monoclonal Copy Number in SK-BR-3

• Copy number profiling with SNS at 220kb resolution of 50 cells shows that amplifications of MET, MYC, ERBB2, BCAS1 and a deletion in DCC were present in

all single cells

• Single cell copy number profiles show a very high correlation (R2 = 0.91)

Deep-Sequencing of the SK-BR-3 Population (SKP)

• Coverage depth: 51X, coverage

breadth: 90.4%

• SNVs, CNAs and SVs were detected

• 409 nonsynonymous SNVs were

identified including many mutations in

cancer genes (CDH1, DBC1, BCR,

ETV1, PASK, PRCC)

Coverage of Single Cells from SK-BR-3 Cell Line

• DOP-PCR WGA methods (SNS) achieve low coverage breadth, even when sequenced at high coverage depth

• Multiple-displacement-amplification (MDA) can achieve high coverage breadth in single cells, similar to standard genome

sequencing

SKP SK1 SK2 SNS

Depth 51X 66X 56X 1-2X

Breadth 90.4% 87.1% 80.3% 10%

Lorenz Curve of Coverage Depth Uniformity

• Lorenz curves show coverage uniformity (‘evenness’) in single cell data

• Nuc-Seq provides very uniform coverage compared to SNS (previous method)

• Phi29-based coverage uniformity is similar to MALBAC (Zong et al 2012, Science)

Calculation of Error Rates

Allelic Dropout Rate (ADR)

False-Positive Rate (FPR)

False-Negative Coverage (FNC)

A B A A

Pop Single Cell

A A A B

Pop Single Cell

A A X

Pop Single Cell

X

9.73%

1.24e-6

NUC-SEQ

5.6%

Single-Cell Sequencing of an ER+ Breast Tumor

1. To delineate clonal diversity of the tumor and identify subpopulations

2. To trace the evolution of copy number alteration and point mutations during tumor growth

Experimental Design

Population Single Cell Whole Genome Exome SNS

Samples BCN BCT BC1 BC2 BC3 BC4 59 cells 50 cells

Breadth 89.9% 90.0% 73.4% 78.3% 89.0% 82.5% 93.0% 10%

Depth 54X 46X 43X 35X 49X 60X 47X 1-4X

• ER+/PR+/Her2-

• 53-year old patient

• Grade II invasive ductal carcinoma

Deep-Sequencing of the ER+ Tumor Population

• High coverage breadth (90%) and depth

(46X)

• A total of 4,162 somatic SNVs in the tumor

cell population.

• 12 nonsynonymous mutations, which were

validated by exome sequencing (66X).

• Several nonsynonymous mutations occurred

in cancer genes, including PIK3CA, CASP3,

FBN2 and PPP2R5E .

Neighbor-Joining Tree of 50 Single Cell CN Profiles

• Neighboring-joining tree was constructed from segmented copy number profiles of 50 single cells sequenced with SNS

• The profiles are highly similar, representing a single clonal subpopulation in the tumor (mean R2= 0.89), and a single homogeneous

population of normal diploid cells

ER+ Single Cell Whole-Genome Sequencing

• High coverage breadth (80.79%)

and depth (46.75X)

• 12 clonal nonsynonymous

mutations and 32 subclonal

mutations

• Many of the subclonal mutations

occurred in intergenic or intronic

regions

• However two subclonal mutations

(MARCH11 and CABP2) were

found in coding regions

ER+ Single Cell Exome Sequencing

• 47 single tumors cells and 12 normal cells, coverage depth 47X and coverage breadth 93%

• The 17 clonal mutations were present in many of the single tumor cells

• 22 new subclonal mutations were identified that were not detected by population sequencing

• In contrast, only a single subclonal mutation was detected between the 12 normal cells

Investigating Clonal Diversity in a Triple-Negative

Breast Tumor by Single-Cell Sequencing

Experimental Design

• ER-/PR-/Her2-

• grade III invasive ductal carcinoma

• 66 year-old woman

• no chemotherapy or hormonal therapy before lumpectomy

• no metastatic lesions detected

• Nuclei were flow-sorted from the aneuploid G2/M peak (6N), the

diploid G2/M peak (4N), the hypoploid peak and from matched

normal tissue for population sequencing, single cell CN profiling

and single cell exome sequencing

Deep-Sequencing of the TNBC Tumor Population

• We performed population sequencing of the bulk

tumor and matched normal tissue at high coverage

depths (72X and 74X) and identified 374

nonsynonymous mutations.

• A number of mutations occurred in cancer genes.

• No evidence of a TP53 mutation in this patient.

• There is a point mutation in PTEN.

• Copy number profiling identified many

chromosomal deletions, in addition to a focal

amplification on chromosome 19p13.2.

Single Cell Copy Number Profiling

• We flow-sorted 50 single cells for single-cell copy number profiling at 220kb resolution using SNS.

• Neighbor-joining was used to reconstruct a tree, revealing two distinct subpopulations of tumor cells (A and H) in addition to the

normal diploid cells (D).

Single Cell Exome Sequencing

• 16 single tumor cells and 16 single normal cells were used for exome sequencing with Nuc-Seq.

• The 374 clonal nonsynonymous mutations detected by bulk sequencing were found in the majority of the single tumor cells.

• We also identified 145 subclonal nonsynonymous mutations that were not detected by bulk tumor sequencing.

• Hierarchical clustering showed that many of the subclonal mutations occurred exclusively in one subpopulation (H, A1 or A2).

Mutation Validation With Single-Molecule Targeted

Deep-Sequencing

Single Molecule Deep Sequencing of Bulk Tumor Tissues

Schmitt et al., PNAS, 2012

12 randomized base tagFixed sequence

A

B

C

D

E

F

13 cycles of PCR amplification

Hybrid custom capture

G

H

3.4e-5

3.8e-10

Validation of ER+ Single Cell Mutations

• Raw coverage depth is 116,952X. Single molecule depth is 5,695X.

• Validated 94.44% (17/18) of the clonal mutations, 90.47% (19/21) of the subclonal mutations, and 19.40% (26/134) of the de novo mutations (p <

0.01), suggesting that many of these mutations are real biological variants in the tumor mass.

• Clonal mutations occurred at high frequencies (mean = 0.4212), while subclonal mutations were less prevalent (mean = 0.0895), and the de novo

mutations showed the lowest frequencies (mean = 0.0195) in the tumor mass.

Validation of TNBC Single Cell Mutations

• Raw coverage depth is 118,743X and single molecule depth is 6,634X.

• Validated 99.73% (374/375) of the clonal mutations, 64.83% (94/145) of the subclonal mutations and 26.99% (152/563) of the de novo mutations (p <

0.01).

• The clonal mutations showed high frequencies (mean = 0.4457), while the subclonal mutations were less prevalent (mean = 0.050) and the de novo

mutations showed the lowest frequencies (mean = 0.00047) in the tumor mass.

Investigating Mutation Rates and Clonal Evolution

Mathematical Modeling of Mutation Rates

• We used the single cell mutations frequencies and designed a mathematical stochastic birth-and-death branching tree process that uses

experimental parameters for cell birth rates (Ki-67 staining), cell death rates (caspase-3 staining), total tumor cell numbers (flow-sorting cell

counts)

• The simulation was run for a series of mutation rates, 1,000 times for each mutation rate and the average distributions were compared to the

single cell data

• Our data suggest that the ER+ breast tumor had a mutation rate similar to error rates reported for normal cells while the TNBC tumor had a

mutation rate 13.3X higher than normal cells.

Punctuated Evolution of Copy Number

The single cell copy number profiles are highly similar, suggesting that copy number rearrangements occurred early in punctuated bursts of

evolution, followed by stable clonal expansions to form the tumor masses .

Gradual Evolution of Point Mutations

• In both patients we observed a large number of intermediate tumor cells with subclonal and de novo mutations that were not detected by

sequencing the bulk tumor en masse.

• These data suggest that point mutations evolved gradually over long periods of time, generating extensive clonal diversity.

Genome Evolution Model in the Two Breast Tumors

Summary

1. Developed a single cell whole genome and exome sequencing method that can achieve high coverage data with low error rates.

2. Standard bulk sequencing of a ER tumor and a TNBC tumor revealed few somatic mutations, while single cell sequencing identified

hundreds of additional genomic mutations.

3. Single-cell sequencing can guide therapeutic targeting towards mutations that are present in the majority of tumor cells, or alternatively

towards therapies that target each subpopulation independently.

4. Single cell copy number data suggests that aneuploidy evolved early in tumor progression and remained stable as the tumor mass

expanded.

5. Point mutations evolved gradually and continuously over extended periods of time, generating extensive clonal diversity.

6. Future work will determine if measuring the amount of intratumor heterogeneity can predict patient survival or response to

chemotherapy in the clinic.

Single Cell Sequencing in the Clinic of Cancer Care

1. Non-invasive monitoring of tumor cells in the blood or bodily fluids (ex. residual disease).

2. Measuring the extent of intratumor heterogeneity and determining if it is predictive for survival or response to

chemotherapy.

3. Early detection of tumor cells in clinical samples (ex. Blood).

4. Scarce clinical sample analysis to obtain high quality genomic data

(fine-needle aspirates, circulating tumor cells).

Navin & Hicks 2011

[email protected]

Acknowledgements

Funding Agencies

T.C. Hsu and Alice-Reynolds Kleberg Foundation, Texas STARS

Center for Genetics & Genomics

Nick Navin

Marco Leung

Jill Waters

MDA Sequencing Core

Erika Thompson, Khadan Kahnov

Louis Ramagli, Hongli Tan

Clinical Collaborators

Funda Meric-Bernstam

Hong Zhang

Statistical Collaborators

Ken Chen, Han Liang

Paul Scheet, Selina Vattathil

Rui Zhao, Franziska Michor

Anna Unruh

Emi Sei

Alexander Davis

Navin Laboratory

Thank You!

Single Cell Isolation Methods

Amplification Methods

Improved Amplification Efficiency of G2/M vs. G1/0 Cells

The improved amplification efficiency

can be shown using panels of 22

chromosome-specific primer pairs for

PCR.

In G1/0 single cells we find that only

25.58% (11/43) of the cells show full

amplification of the PCR products,

In G2/M cells have 45.34% (39/86).

Protein Damaging Subclonal and De Novo Mutations

1. SIFT - based on sequence homology and the physical properties of amino acids

2. Polyphen - via analysis of multiple sequence alignments and protein 3D-structures

3. This plot shows that a lot of these DeNovo mutations are damaging to the structures of the proteins coded by the mutated genes

Duplex Sequencing Metrics

Duplex Sequencing Metrics

Clustered Heatmap of 50 ER+ Single Cell CN Profiles

Clustered heatmap of segmented single cell copy number profiles shows that all single tumor cells are highly clonal,

sharing amplifications of chromosome 1q, 5, 8, 10, 15, 16p, 19, 20, 21 and whole chromosome deletion of 1p, 6, 9, 13

and 18

Two Disparate Molecular Clocks Operate in the Tumor

Copy Number Evolution Mutational Evolution

(point mutations and indels)

Mutations Spectrum not Significantly Different

Kolmogorov Smirnov

test, p = 0.31

KS test, p = 0.14

Coverage Performance of Whole-Genome SCS

SKP SK1 SK2

Depth 51X 66X 56X

Breadth 90.4% 87.1% 80.3%

o High coverage breadth and depth

were achieved for whole-genome

single cell sequencing of two SK-BR-3

cells (SK1, SK2)

o Single cell coverage is less uniform

and correlates with GC content