cracking cancers code feb 2014

11
Cracking Cancers Code Max Salm, PhD Bioinformatics & Biostatistics [email protected]

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Page 1: Cracking cancers code feb 2014

Cracking Cancers Code

Max Salm, PhD

Bioinformatics & Biostatistics

[email protected]

Page 2: Cracking cancers code feb 2014

The Code

♀♂

Image Credit: National Human Genome Research Institute

• A code passed from cell to cell• ~3.2 billion ‘letters’ in total• Encodes about 22,000 proteins

Page 3: Cracking cancers code feb 2014

‘Bugs’ in the Code

Hanahan D & Weinberg R (2011) Hallmarks of Cancer: The Next Generation Cell , Volume 144, Issue 5, Pages 646-674

GACCTGGCAGCCAGGAACGTACTGGT

GACCTGGCAGCC----ACGTACTGGT

Mutations in the Genetic Code

• Vast majority have noconsequence…

…but occasionally…

• Alteration causes a selectivegrowth advantage, increasing the ratio of cell birth to cell death.

Sustain proliferative

signalling

Evade growth

suppressors

Avoid immune destruction

Enabling replicative immortality

Tumor-promoting inflammation

Activating invasion & metastasis

Promoting local blood

supply

Genome instability & mutation

Resisting cell death

Deregulating cellular energetics

Page 4: Cracking cancers code feb 2014

Reading the Code: 1

0

200

400

600

800

1000

1200

1400

$100

$1,000

$10,000

$100,000

$1,000,000

$10,000,000

$100,000,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Tb

Cost/Genome

Short Read Archive

Draft HumanGenome Project

1st Tumour >12, 000 tumours‘Massively Parralel’

sequencing

Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) [27/01/2014]Wang and Wheeler (2014) Genomic Sequencing for Cancer Diagnosis and Therapy Annu. Rev. Med. 65: 33-48

Page 5: Cracking cancers code feb 2014

Reading the Code: 2

Stein (2010) The case for cloud computing in genome informatics Genome Biology, 11:207Bonfield JK, Mahoney MV (2013) Compression of FASTQ and SAM Format Sequencing Data. PLoS ONE 8(3): e59190.

Challenge: Rate of data production has overtaken improvements in long-term storage capacity.Response: Novel Compression Algorithms

Page 6: Cracking cancers code feb 2014

Reading the Code: 3

‘I would say the Human Genome Project is probably

more significant than splitting the atom or going

to the moon.’ Francis Collins, CNN.

‘I would say the Human Genome Project is probably

more significant than splitting the atom or going

to the moon.’ Francis Collins, CNN.

‘I would say the Human Genome Project is probably

more significant than splitting the atom or going

to the moon.’ Francis Collins, CNN.

Shatter & Scan

probab

ing to

obablyy more

the Hu

ly mor

itting

ing to

ignifi ifican ojectProje

Genome

signif

roject

‘ the Human Genome Project probably

more significant’

Identify Original

X 300M

Page 7: Cracking cancers code feb 2014

Placing the Code Snippets

………GACCTGGCAGCCAGGAACGTACTGGTGAAAACACCGCAGCATGTCAACATCACAGATTTTGGGCTGGCCAAACT………

TGTCAAGATCACCTGGCAGCCA TTTTGGGCTGGTGGTGAAAACA

Reference genome

TTTGGGCTGGCGTCAAGATCACCAGGAACGTAC

TCAAGATCACACGTACTGGTGA

ACTGGTGAAAA

Candidate variant

Page 8: Cracking cancers code feb 2014

Placing the Code Snippets Encodes a protein change in

the EGFR gene This particular coding

change (‘L858R’) renders the tumour sensitive to targeted therapy (Afatinib)

‘Personalised medicine’

D Gonzalez de Castro, P A Clarke, B Al-Lazikani and P Workman (2013) Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance Clinical Pharmacology & Therapeutics; 93 3, 252–259

Page 9: Cracking cancers code feb 2014

Identifying Bugs, confidently

Cibulskis et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples Nature Biotechnology 31, 213–219

• “What are we missing?”The influence of sample heterogeneity, purity and read depth.

• ‘False’ mutations: sequencing errors & inaccurate alignment.

• Mutation calling is a work in progress.

• Crowdsourcing a solution.

Roberts et al (2013) A comparative analysis of algorithms for somatic SNV detection in cancer Bioinformatics 2013;29:2223-2230

Caldas C (2012) Cancer sequencing unravels clonal evolution Nature Biotechnology 30, 408–410

Page 10: Cracking cancers code feb 2014

Interpreting the Code

Wang L & Wheeler DA. (2014) Genomic sequencing for cancer diagnosis and therapy. Annu Rev Med. Jan 14;65:33-48.

Distinguishing between mutations that confer a selective advantage and those that are selectively neutral.

• Recurrent mutations• Account for variable background

mutation rates

• Comparative genomics

Imielinski M (2012) Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell. 2012 Sep 14;150(6):1107-20.

Page 11: Cracking cancers code feb 2014

Thank you for your attention

Particular thanks go to:

BABS, Prof. Swanton

& above all to the patients