Download Cell Lineage Analysis of a Mouse Tumor

Cell Lineage Analysis of a
Mouse Tumor
Elena Helman
November 6, 2008
Classic model of cancer cell lineage
1970’s:
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.

highly abundant in human, mouse, and many other organisms;
high and broad mutation rates
occur independently at different loci, usually without affecting
phenotype as most are found in noncoding genomic sequences;
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 = {W 1,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’

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).

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.