1. Inferring clonal evolution of tumors from single nucleotide somatic mutations
Wei Jiao, Shankar Vembu, Amit G Deshwar,
Lincoln Stein, and Quaid Morris

2. Problem Proposal
Cancer is characterized by rapid cell division and mutation
Leads to many heterogenous subclonal populations within a tumor
Driver mutations are causal in adaptation and spread of cancerous cells
Passenger mutations have no functional consequence
Research interest in identifying driver and passenger mutations leads to interest in tracing mutational patterns of cancer tumors
Deconvolution of cell mixture and construction of phylogeny
Unable to observe taxa directly in tumor samples due to heterogeneity
Need to deconvolve taxa from samples

3. Input and Assumptions Deep sequencing: Assumptions:
Sequence particular regions of DNA for hundreds or thousands of times
Allows detection of rare clonal types within a sample
Observe frequency of mutations within each sample
Assumptions:
Clonal evolution model: all cells in the tumor are derived from ancestors and mutations that confer advantages will proliferate
All tumor cells are derived from a single wild-type clone
Infinite sites
Copy-number of SNVs is given as input
Assume that each SNV has the same copy-number
Deep sequencing is necessary for cancer tumors due to heterogeneity
Authors suggest doing further sequencing for subclonal lineage (whole genome)
Image:
El-Kebir, M., Satas, G., Oesper, L., & Raphael, B. J. (2016). Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures. Cell Systems,3(1), doi: /j.cels
What is a copy number variant, and why are they important risk factors for ASD? (n.d.). Retrieved April 04, 2018, from

4. Approach Infinite sites assumption Topological constraints rules
Each SNV (mutation) only appears once
Different mutations do not occur in the same location
Topological constraints rules
Ancestor Condition: ancestor mutations must have equal or higher frequencies than their descendants
Sum Condition: if a branching phylogeny exists, then the ancestor mutation must have a higher frequency than the sum of its descendants
Crossing Rule: if the frequency of a mutation is not consistently greater than or equal to that of another, then it cannot be an ancestor
Mudaliar, M. (2015, June 12). Variant (SNP) calling - an introduction (with a worked example, using... Retrieved April 04, 2018, from
Infinites sites is related to no-homopasy
Image:

5. Algorithm Input: Process: Output:
Read counts for each SNV in each sample
Copy-number status for each SNV
Process:
Group SNVs into sub-lineages
Output:
“partial order plot”
Represent posterior uncertainty in phylogeny
Image:

6. Results Simulation: Comparison to real data:
Generate data without clear phylogeny
Generate data with clear phylogeny
Comparison to real data:
Chronic lymphocytic leukemia data
Acute myeloid leukemia data

7. Simulation
Simulate SNV frequencies consistent with multiple phylogenies
Process
Set parameters:
Number of nodes
Height of tree
Number of possible siblings per node
~9 SNVs
Sample read counts from each node using binomial distribution
Vary number of reads
PhyloSub works well on high read counts
Able to recover clusters (correlation > 0.99)
At lower read depths, true clusters are merged
When simulating chain phylogeny, Phylosub recovers true phylogeny
[need images]

8. Chronic Lymphocytic Leukemia
Compared predicted trees to trees constructed from whole genome sequencing
Grouped SNVs into subclonal lineages using k-means clustering
Similarities in allele frequencies
Changes in allele frequencies over time
Construct phylogenetic tree based on unknown method
Phylosub tree matched original tree structure on 100% of patients but clusters varied
No “ground truth” data available so comparison predictions may be incorrect
Schuh A, Becq J, Humphray S, Alexa A, Burns A, Clifford R, Feller SM, Grocock R, Henderson S, Khrebtukova I, Kingsbury Z, Luo S, McBride D, Murray L, Menju T, Timbs A, Ross M, Taylor J, Bentley D: Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood. 2012, 120 (20): /blood
Schuh et al. on left, Phylosub on right

9. Acute myeloid leukemia
Single cells sequencing → low coverage
Often only saw two or three snvs in a colony, but that doesn’t mean that there aren’t more
Passenger mutations
Phylosub Comparison
Single cell sequencing confirms the existence of some hypothesized mutants, but others commonly present in sample are predicted to be rare by Phylosub
Claim that error arises from biases in deep-sequencing and experimental error
[fix/ add to this slide!!!]
[need to talk about limitations of single cell sequencing]

10. Variant allele read counts
AML: Tree Comparison
SNV
Variant allele read counts
Read depth
Allele frequency
Cluster ID
CACNA1H
12,085
24,860
0.486 (95% CI: ) A
TET2-T1884A
4,220
8,772
0.481 (95% CI: ) B
TET2-Y1649stop
7,792
16,211
0.481 (95% CI: )
CXorf66
3,684
8,150
0.452 (95% CI: )
CXorf36
3,523
8,060
0.437 (95% CI: )
DOCK9
3,391
8,676
0.391 (95% CI: ) C
NCRNA00200
9,201
25,413
0.362 (95% CI: )
CTCF
10,558
30,119
0.351 (95% CI: )
GABARAPL1
1,648
4,992
0.330 (95% CI: )
SCN4B
5,113
16,386
0.312 (95% CI: )
Cluster ID: given from original paper (left)
Phylosub tree given on right
Data given in table

11. Single-Cell Sequencing and PhyloSub

12. Summary Problem Approach Results Limitations
Deconvolution of taxa from samples of heterogeneous tumor cell mixtures
Approach
Assume homoplasy-free mutation
Use topological constraints to estimate ancestor-descendant relationships based on SNV frequency
Derive evolutionary tree from likelihood of edges
Results
Able to recover some structures and some clusters
Limitations
Binary encoding
Copy-number assumptions
Scaling difficulty

13. References
Jiao, W., Vembu, S., Deshwar, A. G., Stein, L., & Morris, Q. (2014). Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinformatics,15(1), 35. doi: /
Jan M, Snyder TM, Corces-Zimmerman MR, Vyas P, Weissman IL, Quake SR, Majeti R: Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci Transl Med. 2012, 4 (149): 149ra118-
Schuh A, Becq J, Humphray S, Alexa A, Burns A, Clifford R, Feller SM, Grocock R, Henderson S, Khrebtukova I, Kingsbury Z, Luo S, McBride D, Murray L, Menju T, Timbs A, Ross M, Taylor J, Bentley D: Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood. 2012, 120 (20): /blood

16. Variant allele read counts
SNV
Variant allele read counts
Read depth
Allele frequency
Cluster ID
CACNA1H
12,085
24,860
0.486 (95% CI: ) A
TET2-T1884A
4,220
8,772
0.481 (95% CI: ) B
TET2-Y1649stop
7,792
16,211
0.481 (95% CI: )
CXorf66
3,684
8,150
0.452 (95% CI: )
CXorf36
3,523
8,060
0.437 (95% CI: )
DOCK9
3,391
8,676
0.391 (95% CI: ) C
NCRNA00200
9,201
25,413
0.362 (95% CI: )
CTCF
10,558
30,119
0.351 (95% CI: )
GABARAPL1
1,648
4,992
0.330 (95% CI: )
SCN4B
5,113
16,386
0.312 (95% CI: )