Evolutionary theories are critical for understanding cancer development at the level of species as well as at the level of cells and tissues, and for developing effective therapies.

1. Universal Patterns of
Selection in Cancer and
Somatic Tissues
Cell, November, 2017
By: S. Mohammadpour

2. Darwinian evolution
(of species)
• Time-scale: hundreds of millions of years
•Organisms reproduce and die in an
environment with shared resources
• Inheritable germline mutations
(variability)
• Selection (survival of the fittest)

3. Somatic
evolution
• Time-scale: tens of years
•Cells reproduce and die inside an organ of one organism
•Inheritable mutations in cells’ genomes (variability)
• Selection (survival of the fittest)

4. Cancer as somatic
evolutionCells in a multicellular organism have evolved to co-operate and perform
their respective functions for the good of the whole organism
A mutant cell that “refuses” to co-operate may have a selective advantage
The offspring of such a cell may spread
This is a beginning of cancer

5. Somatic mutations types
 increasing survival or proliferation (so called ‘‘driver’’ mutations)
 selectively neutral
 disadvantageous to the cell and result in its death or senescence

6. Progression to
cancer

7. Constant population
Progression to
cancer

8. Advantageous mutant
Progression to
cancer

9. Clonal expansion
Progression to
cancer

10. Saturation
Progression to
cancer

11. Advantageous mutant
Progression to
cancer

12. Progression to
cancer
Wave of clonal expansion

13. Key words
Driver mutation: a mutation that gives a selective advantage to a clone in its microenvironment, through either increasing
its survival or reproduction. Driver mutations tend to cause clonal expansions.
Passenger mutation: a mutation that has no effect on the fitness of a clone but may be associated with a clonal expansion
because it occurs in the same genome with a driver mutation. This is known as a hitchhiker in evolutionary biology.
Negative selection
(natural selection): the selective removal of rare alleles that are deleterious
(artificial selection): when negative, rather than positive, traits of a species are selected for
(immunology): in which B-cells and T-cells that recognize MHC molecules bound to peptides of self-origin, or just MHC
molecules with high affinity are deleted from the repertoire of immune cells.
positive selection: A point mutation is under positive or directional selection if it confers a fitness benefit. Natural selection
favors its bearer and will thus increase its frequency.
Germline evolution?
Cancer evolution?
Importance of Negative selection in cancer evolution: identify genes essential for cancer growth
identify patterns of synthetic lethality
potentially yielding new therapeutic targets

15. synonymous
non-synonymous
dN/dS
A ratio greater than 1 implies positive or Darwinian selection (driving change)
less than 1 implies purifying or stabilizing selection (acting against change)
exactly 1 indicates neutral (i.e. no) selection

16. Main questions in cancer evolution
1- Which genes?
2- How many mutations?
3- Importance of negative selection?

17. Methodology
 d METHOD DETAILS
 Calling of point mutations and indels
 Quality controls and use of TCGA calls in five cancer types
 Calling of copy number changes
 List of 369 known cancer genes
 QUANTIFICATION AND STATISTICAL ANALYSIS
 dN/dS model for cancer genomics
 Screen for positive selection at gene level (driver gene discovery)
 Negative selection analyses
 Simplistic substitution models lead to biased dN/dS ratios and false inference of selection
 Impact of germline SNP contamination or SNP overfiltering
 Cohort estimation of dN/dS without patient-specific substitution models
 Estimation of the number of driver mutations
 Replication strand bias
 Performance of different dN/dS models for driver discovery
 Analyses of hypermutator tumors
 d DATA AND SOFTWARE AVAILABILITY

18. A Universal and Distinct Pattern of Selection in

19. Identification of Genes under Positive Selec

20. Identification of Genes under Positive Selec
Pancancer dN/dS values for missense and nonsense mutations for
genes with significant positive selection on missense mutations
(depicted in red) and/or truncating substitutions.

21. Negative Selection Is Largely Absent for
Coding Substitutions
Average number of selected mutations per tumor based on the inferred distributions
of dN/dS across genes, combining missense and truncating mutations from all copy
number regions
Estimated percentage of genes under different levels of positive and negative
selection based on the inferred dN/dS distribution

22. Number of Driver Mutations
per Tumor
Global dN/dS values obtained for 369 known cancer genes (Table S3). This
analysis uses a single dN/dS ratio for all non-synonymous substitutions
(missense, nonsense, and essential splice site).
Percentage of non-synonymous mutations that are drivers assuming
negligible negative selection.
Average number of driver coding substitutions per tumor. Pancancer refers
to the 24 cancer types with in-house mutation calls.

24. which mutations in a given patient
are drivers?
Left y axis: dN/dS values for missense and truncating
substitutions for a series of driver genes and for different
datasets. Right y axis: Corresponding estimates of the fraction
of driver mutations

25. In the future, such approaches could be used in the clinic to identify which few
mutations in an individual patient are driving his or her cancer, from amongst the
thousands of mutations present.
Professor Sir Mike Stratton, an author of the study and director of the Wellcome Trust Sanger Institute
“We now know of hundreds of genes, that when mutated,
drive cancer. This research shows that across cancer
types a relatively consistent small number of such
mutated genes is required to convert a single normal cell
into a cancer cell, but that the specific genes chosen differ
according to cancer type. The study also shows that we
have not yet identified many of these driver genes and
they will be the target for further searching in the future.
This increasingly precise understanding of the underlying
changes that result in cancer provides the foundation for
the discovery and use of targeted therapies that treat the
disease.”

27. Thank you