cancer sequencing. what is cancer? definitions a class of diseases characterized by malignant growth...

Cancer Sequencing

What is Cancer?

Definitions• A class of diseases

characterized by malignant growth of a group of cells– Growth is uncontrolled– Invasive and Damaging– Often able to metastasize

• An instance of such a disease (a malignant tumor)

• A disease of the genome

http://en.wikipedia.org/wiki/Cancer http://faculty.ksu.edu.sa/tatiah/Pictures%20Library/normal%20male%20karyotyping.jpg

What is Cancer?

Definitions• A class of diseases

characterized by malignant growth of a group of cells– Growth is uncontrolled– Invasive and Damaging– Often able to metastasize

• An instance of such a disease (a malignant tumor)

• A disease of the genome

http://en.wikipedia.org/wiki/Cancer http://www.moffitt.org/CCJRoot/v2n5/artcl2img4.gif

Fundamental Changes in Cancer Cell Physiology

Evasion of anti-cancer control mechanisms• Apoptosis (e.g. p53)• Antigrowth signals (e.g. pRb)• Cell Senescence

Hanahan and Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.

Exploitation of natural pathways for cellular growth• Growth Signals (e.g. TGF family)• Angiogenesis• Tissue Invasion & Metastasis

Acceleration of Cellular Evolution Via Genome Instability• DNA Repair• DNA Polymerase

Many Paths Lead to Cancer Self-Sufficiency

Hanahan, Douglas, and Ra Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.

Cancer Heterogeneity

Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–13 (2012).

Why Sequence Cancer Genomes?

• Better understand cancer biology– Pathway information– Types of mutations found in

different cancers



different cancers

• Cancer Diagnosis– Genetic signatures of cancer types will

inform diagnosis– Non-invasive means of detecting or

confirming presence of cancer

• Improve cancer therapies– Targeted treatment of cancer subtypes

COSMIC Database, v48, July 2010http://www.sanger.ac.uk/genetics/CGP/cosmic/

Forbes et al. 2011. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Research 39: D945-D950

Samples 544809

Mutations 141212

Papers 10383

Whole Genomes 29



different cancers

• Cancer Diagnosis– Genetic signatures of cancer types will

inform diagnosis– Non-invasive means of detecting or

confirming presence of cancer

• Improve cancer therapies– Targeted treatment of cancer subtypes

COSMIC Database, v71, Oct 2014http://www.sanger.ac.uk/genetics/CGP/cosmic/

Samples 1058292

Mutations 2710449

Papers 20247

Whole Genomes 15047

How Do We Sequence Cancer Genomes?

Read Mapping

Definition of Coverage

Length of genomic segment: LNumber of reads: nLength of each read: l

Definition: Coverage C = n l / L

How much coverage is enough?

Lander-Waterman model:Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides

C

Read Mapping

BWA

Paired-End Read Mapping

ReferencePhysical Coverage: 4Sequence Coverage: 2

• Physical coverage refers to the genomic coverage including the unsequenced regions of each DNA fragment

• Sequence coverage refers to the genomic coverage counting only the sequenced part of each DNA fragment

• Increased gap length between paired reads provides higher physical coverage without incurring increased costs for sequencing, which is useful for detecting certain types of mutations

• Factors that effect mutation signal– Limited genetic material (lower depth)– Mixture of tumor and normal tissue– Cancer Heterogeneity

• Factors that introduce noise– Formalin-fixed and Paraffin-embedded samples– Increased number of mutations and unusual genomic rearrangements

• General Consideration– Each individual has many unique mutations that could be confused with

cancer causing mutations

Considerations for Cancer Sequencing

Human Genome Variation

SNP TGCTGAGATGCCGAGA Novel Sequence TGCTCGGAGA

TGC - - - GAGA

Inversion Mobile Element orPseudogene Insertion

Translocation Tandem Duplication

Microdeletion TGC - - AGATGCCGAGA Transposition

Large Deletion Novel Sequenceat Breakpoint

TGC

Variant TypesVariant Types

Single Nucleotide Variants(SNVs)

Small Insertion / Deletion (indels)

Copy Number Variants (CNVs)

Structural Variants (SVs)

Novel Sequence

SNV CallingVariant Types





Novel Sequence

• A bayesian approach is the most general and common method of calling SNVs– MAQ, SOAPsnp, Genome Analyis ToolKit (GATK),

SAMtools






Novel Sequence

http://www.broadinstitute.org/gatk//events/2038/GATKwh0-BP-5-Variant_calling.pdf






Novel Sequence

• A given human genome (germline) differs from the reference genome at millions of positions.

• A cancer genome differs from the healthy genome of its host by tens of thousands of positions at most, which is several orders of magnitude fewer differences than germline versus reference

• How do we distinguish germline mutations from somatic mutations?

Somatic SNV calling

Tumor TissueNormal Tissue

Compare the alignment results

• Most naïve: use a standard SNV caller on both datasets. If there is a mutation found in the tumor sample but not the normal, it is somatic!

Somatic SNV calling

Roth, A. et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 28, 907–13 (2012).

JointSNVMix • probabilistic graphical models for joint

tumor-normal SNV calling

Short Indel CallingVariant Types


Short Insertion / Deletion (indels)



Novel Sequence

Reference

Deletion

Insertion

Short Indel CallingVariant Types





Novel Sequence

Reference

Deletion

Insertion

Reference

Read mappingin practice

Unmappable part of read (just the read end)

Unmapped read (could not be alignedanywhere)

Short Indel Calling – Discordant Reads Pairs

II) Deletion

I) Insertion

i

d

l

l - i

l + d

l

Variant Types





Novel Sequence

Reference

Reference

Short Indel Calling – Split Read Mapping

Variant Types





Novel Sequence

Reference

Reference

Deletion


Short Indel Calling – Split Read Mapping

Variant Types





Novel Sequence

Reference

Reference

Deletion


Remap each end of thesuspicious reads

Paired-end mapping can improve power to detect variants without need for more sequencing

Modified from Meyerson et al. . 2010. Advances in understanding cancer genomes through second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696

Copy Number Variants

Ref: A B C D E F G H I K

A B C D C E F G H C I K


Variant Types





Novel Sequence




C C C

C Depth of Coverage

Modified from Dalca and Brudno. 2010. Genome variation discovery with high-throughput sequencing data. Briefings in bioinformatics 11, no. 1: 3-14

Variant Types





Novel Sequence

• Problems with DOC – Very sensitive to stochastic variance in coverage– Sensitive to bias coverage (e.g. GC content).– Impossible to determine non-reference locations of CNVs

• Graph methods using paired-end reads help overcome some of these problems




C C C

C Depth of Coverage

Variant Types





Novel Sequence

Copy Number Variants - CNAnorm

Gusnanto, A., Wood, H. M., Pawitan, Y., Rabbitts, P. & Berri, S. Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data. Bioinformatics 28, 40–7 (2012).

Overall steps in CNAnorm method, a tool for detecting copy number changes in tumor samples

Data: sequence data from tumor and normal samples

Steps:

1) Count number of reads in fixed windows across the genome

2) Calculate ratio of reads in tumor vs. reads in normal for each window, correcting for sequence biases (e.g. GC)

3) Smooth ratio signal across windows

4) Normalize data

5) Estimate amount of normal contamination in tumor sample

6) Perform segmentation on tumor data

Variant Types


1 2 3 4 5 6 7 8

4 G I K1 2 3

1 2 4 3 5 6 7 8

Structural Rearrangement

Translocation

3 2 1 5 6 7 8 Inversion

1 3 5 9 6 7 8 Large Insertion / Deletion

2̂

Variant Types





Novel Sequence

Summary of Variant Types

Meyerson et al. . 2010. Advances in understanding cancer genomes through second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696

Passenger Mutations and Driver Mutations

Normal

CancerXX

Driver or Passenger?

Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–13 (2012).

Sequence


Stratton, Michael R, Peter J Campbell, and P Andrew Futreal. 2009. The cancer genome. Nature 458, no. 7239 (April): 719-24. doi:10.1038/nature07943


Distinguishing Features• Presence in many tumors• Predicted to have functional

impact on the cell– Conserved– Not seen in healthy adults

(rare)– Predicted to affect protein

structure

• In pathways known to be involved in cancer

Train Classifier using Machine Learning Approaches

Carter et al. 2009. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer research, no. 16: 6660-6667

Tracking the Evolution of Cancer

Models of Breast Cancer Progression

What we did

• Cancer phylogenetics– Lineage relationship of

neoplastic lesions with cancers using somatic SNVs as lineage markers

– Order of genomic events and drivers

Slide Courtesy of Arend Sidow

Samples

P1 P2 P3 P4 P5 P6LymphNormal

CCLFEADCISIDC

Side 1 Side 2

All samples are FFPE material


Samples

Patient 1 Evolution – SNVs

GGATAGATAGCG

GCGTCCTAGCGT

CCATGGCATGGCCATGGC

GGCAAA

Normal sample

Early neoplasia(EN) sample

EN with atypia(ENA) sample

Invasive ductal carcinoma(IDC) sample

GGATAGTGTCCATGGCAAA

Reads from sequencing patient sample

Human genome reference


Normal sample






CCC

Multisample SNV Code

1 0 0 0

Normal EN ENA IDC


Normal sample






Multisample SNV Code

0 1 1 0

Normal EN ENA IDC

C

CC

CCC


Code Normal EN ENA IDC SUM

1000 89 89

0100 147 147

0010 102 102

0001 46 46

0011 755 755 755



1000 89 89

0100 147 147

0010 102 102

0001 46 46

0011 755 755 755

75546

102

89 147

Normal EN

ENA IDC

Venn diagram view



1000 89 89

0100 147 147

0010 102 102

0001 46 46

0011 755 755 755

75546

102

89 147

Normal EN

ENA IDC

P1

Patient 6 Evolution - SNVs

Lymph CCL_CL CCL FEA DCIS IDC SUM

010000 219 219

001000 305 305

000100 345 345

000010 978 978

000001 608 608

000101 61 61 61

000110 185 185 185

000111 510 510 510 510

01XXXX 0

010101 0

000011 0

3211

LINEAGE CONCEPTSSomatic changes

Cell Divisions in One Generation

~40 celldivisions

~60 newpoint

mutations

“Germline”

?D

You

Your dad

“Germline”

You

Your dad

Mutations detected here ...

... but not here ...

?D

Somatic (Tumor) Lineages

Sampled lesion

Somatic (Tumor) Lineages

Sampled lesion 1 Sampled lesion 2

Patient 6 Evolution - SNVs

Patient 6 Evolution


Patient 6 Evolution – Copy Number Changes


ANEUPLOIDIESPutting the germline SNPs to good use (no somatic SNVs for this!)

Heterozygous Positions

G A Tm

A T Cp

50% 50% 50%

LOH (e.g., paternal chromosome)

G A Tm

A T Cp

0% 0% 0%

Fraction of “lesser allele”

Chromosome Duplication

G A Tm

A T Cp A T C

66% 66% 66%

Chromosome Duplication

G A Tm

A T Cp A T C

33% 33% 33%

Fraction of “lesser allele”

Lesser Allele Fraction Plots

Chromosome

Lesser allele fraction

Running number of germlinehet SNP (N ~ 1.7 million)

Plots are windows of 1000 SNPs, overlapping by 500

Lesser Allele Fraction Plots

Zoom-In

But ... What is the actual ploidy?

Absolute Coverage Pattern of LOH and Gain

Normal LOH Gain

Absolute Coverage in LOH vs Ploidy Gain

Prevalent allele absolute coverage

Lesser allele absolute coverage

Lesser allele FRACTION

LOH

Gain

?

Fractions with normal contribution

GA

Prevalent allele absolute coverage = 7

Lesser allele absolute coverage = 0

GA

Prevalent allele absolute coverage = 14

Lesser allele absolute coverage = 7

Our samples: up to 50% normal (non-tumor)

tissue contentLesser allele fraction = 7/21 = 0.33

Patient 6 Evolution – Copy Number Changes


Patient 6 Evolution


Patient 2

Lymph Normal CCL FEA IDCDCIS

Patient 2 - normal

Patient 2 – CCL and DCIS

16q: 1N (LOH)

1q: 4N (3:1)

X: 1N (LOH)

16p: 3N (2:1)

Patient 2 – IDC has same as CCL,DCIS

Patient 2 Aneuploidy Evolution

CCL,DCIS IDC

1q,16p16q, X

Patient 2 - IDC

Patient 2 Aneuploidy Evolution

CCL,DCIS IDC IDC’

1q,16p16q, X

Major CrisisInvolving all but 6 chromosomes, including 10 whole-chromosome LOHs

No aneuploidies but ...

Patient 2 – SNVs

515

5

70

0

681 80

133

Allele Freq in CCLCCL: 894

IDC: 1276

DCIS: 884

Patient 2 Evolution

1q,16p16q, X

1p245891011131415171921681

133

80 515

Patient Cancer Phylogeny Trees


Mutational Profiles

Branched tree model

Victoria Popic

Automated Inference of Multi-Sample Cancer Phylogenies

Raheleh Salari

Sample 1

Sample 3

SMutH: Somatic Mutation Hierarchies

Sample 2

VAF profiles of SNVs across samples

VAF profiles of SNVs – Clustering

Edge u v :

Cell-Lineage VAF Constraint

u

v

“Possibly mutations in u happened before those in v”

For each node u and its children C :

Tree Construction

Find all spanning trees that satisfy VAF constraints

(extension of Gabow&Myers spanning tree search algorithm)

Rank trees according to their agreement with VAFs

u

vw

Simulation Results

Pred: pairs of nodes ordered correctlyBranch: pairs of nodes correctly assigned to separate branchesShared edges: edges shared between true and reconstructed trees

u

vwu

vw z

yx

ccRCC Study of Renal Carcinoma by Gerlinger et. al

(2014)

HGSC Study of Ovarian Cancer Bashashati et. al (2013)

Reconstruction of Lineage Trees in Recent Literature

PIK3CA H1047R

PIK3CA H1047L

Expanded Breast Cancer Lineage Trees

• Fantastic Cancer Reviews– Hanahan and Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.– Hanahan and Weinberg. 2011. Hallmarks of cancer: the next generation. Cell

144, 646–74.• Reviews of Cancer Genomics

– Meyerson, Matthew, Stacey Gabriel, and Gad Getz. 2010. Advances in understanding cancer genomes through second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696. doi:10.1038/nrg2841. http://www.nature.com/doifinder/10.1038/nrg2841.

– Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).

• Variant Calling– Dalca, Adrian V, and Michael Brudno. 2010. Genome variation discovery with

high-throughput sequencing data. Briefings in bioinformatics 11, no. 1 (January): http://www.ncbi.nlm.nih.gov/pubmed/20053733.

– Medvedev, Paul, Monica Stanciu, and Michael Brudno. 2009. Computational methods for discovering structural variation with next-generation sequencing. nature methods 6, no. 11 http://www.nature.com/nmeth/journal/v6/n11s/full/nmeth.1374.html.

Further Readings for the Curious

http://www.nature.com/doifinder/10.1038/nrg2841

http://www.ncbi.nlm.nih.gov/pubmed/20053733

cancer sequencing. what is cancer? definitions a class of diseases characterized by malignant growth...

Documents

hallmarks of cancer

cancer research

cancer types

cancerous tissue

malignant tumor

tissue level

fundamental pathways

malignant growth