1. Florian Markowetz
CRUK Cambridge Institute
www.markowetzlab.org
Systems genetics
of cancer
Big data and all that

2. http://econ.st/XYyAyi

3. “With enough data
and the ability to
crunch it, virtually any
challenge facing
humanity today can
be solved.”
Eric Schmidt et al, How Google Works, 2014

4. Prof Atul Butte (Genomic Medicine, Stanford) at TEDMED 2012
“Who needs the scientific method?
Vast stores of available data (…)
are simply waiting for the right
questions.”

5. Chris Anderson, WIRED.com, 2008
http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory
The End of Theory: The Data
Deluge Makes the Scientific
Method Obsolete
‘Petabytes allow us to say: "Correlation
is enough.” (…)
We can throw the numbers into the
biggest computing clusters the world
has ever seen and let statistical
algorithms find patterns where science
cannot.’

7. Karl Popper
1902 - 1994
R I P, Inductionism!

8. Conclusion:
The hype about
Big Data is built
on zombie
ideas

9. The data never
speak for
themselves

10. Theories,
hypotheses and
models make
data speak

11. Bigger data
don’t
change this

12. Encyclopedia of
DNA Elements

13. www.nature.com

14. [The ENCODE] data enabled us to
assign bio-chemical functions for
80% of the genome.
Function = showed up in the data

15. Graur et al, Genome Biol Evol (2013)
“This claim flies in the face of
current estimates according to
which the fraction of the genome
that is evolutionarily conserved
(…)
is under 10%.”
Function = evolutionary conserved

16. What is biological
function?
A conceptual question
independent of
the size of your data set

17. Always the same science.
Always the same questions.
Big Data is a technical
challenge, not a conceptual
one

18. Systems Genetics of Cancer
Genetic variation
• In people
• In tumours
• In clones
Phenotypic variation
• Tumour subtypes
• Aggressiveness
• Survival

19. Cancer genome
Evolution
Cancer tissue
Context
Cancer genome
Function
Ines
Wei Edith
Geoff
Ke Anne Joe
Leon
Andy
Amanda

20. Cancer genome
Heterogeneity and evolution

21. Clonal evolution

23. Intra-patient heterogeneity in HGSOC
Schwarz et al, PLoS Comp Bio 2014
Schwarz et al, PLoS Medicine, 2015

24. Structural differences indicate
resistance
sensitiveresistant

25. Heterogeneity predicts survival

27. Mixture model
1. How many clones are there in the
sample?
Data
Nr of
clones Size of
clone
Variability
inside clone
Parameters

28. Graphical Model behind BitPhylogeny
Phylogeny prior
Prior on local parameters
Likelihood
The BitPhylogeny model

29. FUTURE
• ICGC pan-cancer analysis:
2500 genomes => 2500 trees
• Characterize the 2500 trees
• Correlate trees with clinical data
• Infer onco-genetic progression models across
the 2500 trees

30. Cancer tissue
Context

31. DNA RNA Protein ChIP
Van’t Veer et al (2002) http://ms.lbl.gov Ross-Innes et al (2012)
Tumors
are
complex
tissues

34. Automated image analysis
Supervised
classification
Spatial
smoothing
Cell types
and location
H&E

35. Quantitative analysis of
tumour composition

36. Spatial features of tumour tissue

37. FUTURE
• ICGC pan-cancer analysis: 2500 genomes
• Collect tissues for as many samples as possible.
• Correlate tissue architecture with clinical data
• Correlate tissue architecture with evolution.

38. Florian Markowetz
CRUK Cambridge Institute
www.markowetzlab.org
Big Data in Genomics
Thank you
!