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

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

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: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542783/ 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), 43-53. doi:10.1016/j.cels.2016.07.004 What is a copy number variant, and why are they important risk factors for ASD? (n.d.). Retrieved April 04, 2018, from http://readingroom.mindspec.org/?page_id=8221

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 https://www.slideshare.net/drmani_vet/variant-calling-workshop-glasgow-20150609 Infinites sites is related to no-homopasy Image: https://www.slideshare.net/drmani_vet/variant-calling-workshop-glasgow-20150609

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: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-35

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

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]

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 http://www.bloodjournal.org/content/120/20/4191.long?sso-checked=true 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): 4191-4196. 10.1182/blood-2012-05-433540. Schuh et al. on left, Phylosub on right

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]

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: 0.481-0.491) A TET2-T1884A 4,220 8,772 0.481 (95% CI: 0.472-0.490) B TET2-Y1649stop 7,792 16,211 0.481 (95% CI: 0.474-0.487) CXorf66 3,684 8,150 0.452 (95% CI: 0.443-0.461) CXorf36 3,523 8,060 0.437 (95% CI: 0.428-0.446) DOCK9 3,391 8,676 0.391 (95% CI: 0.382-0.400) C NCRNA00200 9,201 25,413 0.362 (95% CI: 0.357-0.367) CTCF 10,558 30,119 0.351 (95% CI: 0.346-0.355) GABARAPL1 1,648 4,992 0.330 (95% CI: 0.319-0341) SCN4B 5,113 16,386 0.312 (95% CI: 0.306-0.318) Cluster ID: given from original paper (left) Phylosub tree given on right Data given in table

Single-Cell Sequencing and PhyloSub

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

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:10.1186/1471-2105-15-35 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): 4191-4196. 10.1182/blood-2012-05-433540.

Variant allele read counts SNV Variant allele read counts Read depth Allele frequency Cluster ID CACNA1H 12,085 24,860 0.486 (95% CI: 0.481-0.491) A TET2-T1884A 4,220 8,772 0.481 (95% CI: 0.472-0.490) B TET2-Y1649stop 7,792 16,211 0.481 (95% CI: 0.474-0.487) CXorf66 3,684 8,150 0.452 (95% CI: 0.443-0.461) CXorf36 3,523 8,060 0.437 (95% CI: 0.428-0.446) DOCK9 3,391 8,676 0.391 (95% CI: 0.382-0.400) C NCRNA00200 9,201 25,413 0.362 (95% CI: 0.357-0.367) CTCF 10,558 30,119 0.351 (95% CI: 0.346-0.355) GABARAPL1 1,648 4,992 0.330 (95% CI: 0.319-0341) SCN4B 5,113 16,386 0.312 (95% CI: 0.306-0.318)