Cracking cancers code feb 2014
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1) The document summarizes research on analyzing the genetic code found in cancers. It discusses how the genetic code is being decoded through advances in sequencing technology that have allowed researchers to read the 3.2 billion letters in the human genome. 2) Mutations or "bugs" in the genetic code can cause cancers by disrupting genes involved in processes like cell growth. New sequencing methods are helping researchers precisely locate mutations and determine which may drive tumor growth. 3) Interpreting the sequencing data is an ongoing challenge but identifying recurrent mutations and comparing mutations across samples and genomes can help distinguish driver mutations from harmless changes.
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Cracking cancers code feb 2014
- 1. Cracking Cancers Code Max Salm, PhD Bioinformatics & Biostatistics Max.Salm@cancer.org.uk
- 2. The Code ♂ ♀ • A code passed from cell to cell • ~3.2 billion ‘letters’ in total • Encodes about 22,000 proteins Image Credit: National Human Genome Research Institute
- 3. ‘Bugs’ in the Code Sustain proliferative signalling Evade growth suppressors Mutations in the Genetic Code Avoid immune GACCTGGCAGCCAGGAACGTACTGGT destruction Deregulating cellular energetics Enabling replicative immortality • Vast majority have no consequence… Tumor-promoting inflammation …but occasionally… Resisting cell death GACCTGGCAGCC----ACGTACTGGT Genome instability & mutation Promoting local blood supply • Alteration causes a selective Activating growth advantage, increasing invasion & the ratio of cell birth to cell metastasis death. Hanahan D & Weinberg R (2011) Hallmarks of Cancer: The Next Generation Cell , Volume 144, Issue 5, Pages 646-674
- 4. Reading the Code: 1 $100,000,000 1400 1200 $10,000,000 1000 $1,000,000 800 $100,000 Tb 600 $10,000 400 $1,000 200 Cost/Genome Short Read Archive $100 0 2001 2002 2003 Draft Human Genome Project 2004 2005 2006 2007 ‘Massively Parralel’ sequencing 2008 2009 2010 1st Tumour 2011 2012 2013 2014 >12, 000 tumours 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
- 5. Reading the Code: 2 Challenge: Rate of data production has overtaken improvements in long-term storage capacity. Response: Novel Compression Algorithms Stein (2010) The case for cloud computing in genome informatics Genome Biology, 11:207 Bonfield JK, Mahoney MV (2013) Compression of FASTQ and SAM Format Sequencing Data. PLoS ONE 8(3): e59190.
- 6. Reading the Code: 3 ‘I would say the Human Genome Project is probably ‘I would say the Human Genome Project is probably ‘I would say the Human Genome Project is probably more significant than splitting the atom or going more significant than splitting the atom or going more significant than splitting the atom or going to the moon.’ Francis Collins, CNN. to the moon.’ Francis Collins, CNN. to the moon.’ Francis Collins, CNN. Shatter & Scan probab ing to ignifi X 300M y more ly mor obably Genome the Hu ifican Proje itting signif ing to oject roject Identify Original ‘ the Human Genome Project more significant’ probably
- 7. Placing the Code Snippets ACTGGTGAAAA CGTACTGGTGA CAGGAACGTAC CCTGGCAGCCA TGGTGAAAACA TCAAGATCACA GTCAAGATCAC TGTCAAGATCA TTTGGGCTGGC TTTTGGGCTGG ………GACCTGGCAGCCAGGAACGTACTGGTGAAAACACCGCAGCATGTCAACATCACAGATTTTGGGCTGGCCAAACT……… Reference genome Candidate variant
- 8. 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
- 9. Identifying Bugs, confidently • “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. Cibulskiset al (2013) A comparative unravels of algorithmsmutations inSNV detection in cancer Bioinformatics 2013;29:2223Caldas C (2012) Cancer sequencing analysissomaticevolution Nature Biotechnologyheterogeneous cancer samples Nature Roberts et al (2013) Sensitive detection of clonal point for somatic impure and 30, 408–410 Biotechnology 31, 213–219 2230
- 10. Interpreting the Code 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 Wheeler Mapping the hallmarks of lung for cancer diagnosis and therapy. Annu sequencing. 14;65:33-48. Wang L & M (2012)DA. (2014) Genomic sequencingadenocarcinoma with massively parallel Rev Med. JanCell. 2012 Sep 14;150(6):1107-20.
- 11. Thank you for your attention Particular thanks go to: BABS, Prof. Swanton & above all to the patients