Sept2016 newsample cancer_sidow

1. GiaB cancer thoughts
SNV evolution and multisample motivation
Arend Sidow
Stanford / JIMB

2. Cancer SNV Evolution
AAFs
Clones
Samples (cells)
0.5
0.5
0.50
0
0
0 0
0

3. Cancer SNV Evolution
AAFs
Clones
Samples (cells)
0.25
0.17
0.330
0
0
0 0
0

4. Cancer SNV Evolution
AAFs
Clones
Samples (cells)
0.5
0.25
0.50
0
0.125
0.125 0
0

5. Cancer SNV Evolution
AAFs
Clones
Samples (cells)
0.5
0.25
0.50
0
0.125
0.125 0
0
0.5 0.375 0

6. AAFs
Clones
Samples (cells)
0.5 0.5 0.5

7. Cancer SNV Evolution
AAFs
Clones
Samples (cells)
0.5
0.25
0.50
0
0.125
0.125 0
0
0.5 0.375 0

8. Allele Dropouts / FP Gains
• Dropouts
– Biological
• deletion
• [reversion]
– Sequencing
• not sampled in sample
prep
• not sequenced
• False positives
– Biological
• [High mutation rate (not
strictly an FP but violates
one-event assumption)]
• Consistent DNA damage
due to chromatin
– Sequencing
• Systematic sequencing
errors
• Systematic sample prep
(PCR) errorsInformatics-based dropouts
(mapping etc) should affect
all samples equally

9. Bottom Line: Cancer Multisample
• Not surprising: Multisample calling and analysis
decreases noise in somatic SNV calling ancestral
SNVs in the ‘usual way’
• In addition: Because distinct samples from a
tumor are made up of cells with shared
evolutionary histories there is a highly limited
number of possible SNV classes .. so if an SNV in
question does not have a bunch of buddies (same
class) it’s likely an FP (or germline FN).