Cell Lineage Analysis of a Mouse Tumor Elena Helman November 6, 2008

Classic model of cancer cell lineage Tumor is derived from a single founder cell that has acquired a growth advantage over normal cells by genetic modification. The progeny of the founder cell expands in an evolutionary pattern.

Microsatellites (MS) Microsatellite (or Simple Sequence Repeat) : loci of DNA consisting of repeating units of 1-6 bp in length. Mutations in MS loci act as “molecular tumor clocks” that record past tumor histories Why MS mutation as a marker in cell lineage reconstruction? MS slippage mutations are coupled to cell division. high and broad mutation rates occur independently at different loci, usually without affecting phenotype as most are found in noncoding genomic sequences; highly abundant in human, mouse, and many other organisms;

Procedure (biology) 37 single MLH1 –/– mouse lymphoma cells and adjacent normal cells For each single cell : DNA extracted Amplified by WGA Genotyped at 120 MS loci by PCRs and capillary electrophoresis DNA extracted from a tail clipping of the same animal, which represented the DNA of the zygote

Procedure (computational) Input: 120 MS for 37 cells Each cell is represented by a vector of length 120 (“digital identifier”), where each element is the repeat count of the MS at that locus. Vi = count of MSi Cell v = {V1,V2,…,V120} Cell w = {W1,W2,…,W120} Distance function: Dist(v,w) = i=1Σ120 (Vi – Wi) / 120 Apply NJ using this pairwise distance matrix Output: reconstructed cell lineage tree, rooted from the zygote sample Incorporate depths to translate vertical branch distances into absolute numbers of cell division

Depth assignment Depth = number of mitotic divisions since the zygote A calibration CCT (cultured cell tree) is created and reconstructed Multiplier – number representing the ratio between the reconstructed and actual depths (256 in this case). The multiplier of the CCTs is the slope of the linear regression between actual and reconstructed depths of CCT nodes Estimated depth of each cell was its reconstructed depth multiplied by the multiplier obtained from the mouse CCT

Depth calibration

Cancer cell lineage reconstruction All cells from tumor foci clustered on a single subtree. Supports common clonal origin Statistically significant – hypergeometric test – score observed compared to random permutations 3 foci interspersed – local invasion not metastasis

Physical Distance Lineage distance among cancer cells is correlated to their physical distance  coherent growth pattern Divided cells into three categories corresponding to increasing physical distances. Lineage distance measure – sum of branch lengths that connects both cells to their most recent (i.e., deepest) common ancestor) Mean lineage distance was smallest between cells obtained from the same tissue section. Student’s t tests

Cell Depths Significant difference in depths between cancer cells and normal lung epithelial cells (236 vs 121) Kolmogorov-Smirnov test Normal T lymphocytes were not extracted so depth of tumor cells could not be directly compared. Age: Founder of tumor subtree is at depth 102 cell divisions, 134 cell divisions younger than the average cancer cell. Tumor is ~ 5 months old

Presence of TP53 mutation Amplified/sequenced a 240 bp fragment, spanning the mutational hotspot in codon 270 of exon 8 of commonly mutated TP53 gene. Identical mutation found in some, but not all, tumor cells. Why? Multiple independent events or single early event Present but not detectable (allele dropout) Mutation only in tumor subclone

Problems, future directions Is MS mutation the best indicator? Assume no selection (MS neutral) What if MS mutation confers advantage? Assume tumor and normal cells divide at same rate for age/depth. Definition of “cancer” “reversion” of (R-4) / mistaken identity MLH-/- tumors good cancer model?? Maybe that’s why monoclonal… Never reconstruct common ancestors

Graph construction: ‘triplet algorithm’ View every cell as a vertex in an auxiliary graph G. In an execution of a triplet subroutine that outputs cells A and B (say, on input A, B, and C), put an edge between A and B. As long as there are more than two connected components in the graph G, pick three vertices from three different components and execute a triplet subroutine on them, thereby adding an edge to the graph and decreasing the number of connected components by one. After m steps, two connected components remain. Each one of them necessarily corresponds to a subtree of depth at most d.The condensed version of each of the subtrees can be inferred separately by repeating the above procedure. The basic primitive of the triplet algorithm is a ‘‘triplet subroutine.’’ Given identifiers for three cells (say, A, B, and C), the triplet subroutine counts for every pair of cells the number of common mutations, namely, the number of loci in which the two cells have the same label, and moreover, this label is different from the corresponding label of the root. The pair of cells that maximize this count (say, A and B) are output by the triplet subroutine. We say that the triplet subroutine is ‘‘successful’’ if the pair of cells that it outputs is the one that has the longer common branch (or equivalently, the deeper common ancestor).