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Class 12 DNA sequencing and cancer
DNA pol error rate ~10-9 per base copied
How many errors in a “typical” somatic cell?
Most errors don’t have detectable effects
But some errors do:
oncogenesN – dominant if “activated”
tumor suppressor genesN – recessive
2-hit hypothesis in inherited cancer
syndromes, BRCA, FAP
loss of heterozygosity in tumor DNA
Cancer nowadays viewed in molecular-genetic terms
Implications for therapy
Can inhibit some overactive oncogenes
with small molecule inhibitors (imatinib, etc)
often act intracellularly
or with antibodies to cell surface receptors
(panitumimab, etc) that act in pathways
that stimulate intracellular oncogenes
But can’t replace function of inactive suppressors
Example of pathway activating oncogenes
Extracellular ligand
(epidermal growth factor,
EGF) binds EGF receptor,
which binds another
protein, which causes
cytoplasmic tail of EGFR
to get phosphorylated,
which activates other
proteins (here including
Ras oncogene)…which turn on other genes
that stimulate cell growth. Antibody to EGFR may stop
process, but if Ras is mutated and constitutively
active, Ab to EGFR won’t work because Ras is “downstream”
Image from Google search “egfr kras signaling pathway”
Kras mutated and constitutively active in ~40% of
colon cancers
Large effort has gone into whole genome
sequencing of tumors and comparison
to non-tumor DNA from same patient
What are main results?
several hundred oncogenes
several hundred tumor suppressor genes
organized in at least tens of pathways
Tumors are “clonal” but continue to acquire mutations
When you sequence a tumor, do you get sequence of
majority of cells or of individual cells, with
unique mutations?
What are “driver” vs. “passenger” mutations?
What are some clues to identifying driver mutations?
occurrence in multiple tumors
mutated in inherited cancer syndromes
Do you think there a more tumor suppressor mutations
or oncogene mutations driving tumors? Why?
More ways to inactivate a gene (stop codon nearly
anywhere) than to make it overactive,
so suppressor mutations should exceed
activating oncogene mutations, but need to
inactivate both copies of a suppressor, so
answer not obvious
How fast do tumors grow?
cell birth rate b (# divisions/day, ~1/few days)
balanced by cell death rate d
cell doubling rate k, N(t)=N02kt
k related to b-d
Types of cancer therapy
surgery – curative intent or for palliation
radiation
chemo to kill rapidly dividing cells
-> toxicity from killing normal rapidly dividing
cells in gut, bone marrow, skin
drugs or antibodies that target oncogenes
could be more specific
but still often have major side-effects
examples – antibody to EGFR (drug names
ending in “ab” are antibodies)
small drug inhibitors (drug names
ending in “ib” are inhibitors)
Problem of “development” of resistance to chemo
Roles of DNA sequencing
Research – find what genes are involved in cancer
big challenge – interpreting changes
passenger vs driver mutations
are mutations in non-coding regions (98.5% of total)
important?
which mutations in coding regions are relevant?
Patient care
which genes are mutated in a specific tumor?
is whole genome seq. necessary or would seq. of
~hundred known oncogenes and suppressors do?
Patient care – cont’d.
diagnostics – circulating tumor DNA akin to
pre-natal dx from circ. fetal DNA
? useful for screening or just dx of already ill
? use to follow treatment – ? more sensitive
than other biomarkers, e.g. CEA, PSA
do genetic assays need to be specific for individual
patient’s mutations or are mutations
sufficiently common that “generic” tests ok?
“Beaming” assay
emulsion pcr for particular oncogenes
-> copies single templates on beads
break emulsion, hybridize flourescent oligo probes
to beads, different colors for oligos
matching wt, mutant, and common seq.
determine bead color with flow cytometry
http://openwetware.org/
wiki/Image:Flow_cytometry
Beaming assay for Kras mutations from Vogelstein
pre-op
day 3
What is plotted?
What do #s
in quadrants
indicate?
day 48
day 244
How sensitive is assay to mutations occurring
in fraction of tumor cells as tumors evolve?
What fraction of circulating DNA is from tumor?
How many beads can you assay?
Use of sequence info in therapy
possibly to identify unexpected mutations (e.g.
uncommon in patient’s tumor type) that might
suggest use of different drug – this is hypothetical
identify drugs unlikely to be effective – e.g. Ab to EGFR
in pts with oncogenic Kras mutations
Use of sequence info in therapy
relevance to patients – avoid (often severe) toxicity
in patients in whom drug won’t work
(panitumimab has lots of toxic skin, gut effects)
relevance to payors - @$1000’s/dose, cheaper to gene
test everyone to avoid use when predictably
ineffective = “companion diagnostics”
relevance to pharmaceutical companies –
use in resistant patients weakens evidence for
efficacy, lack of efficacy is major cause of
failure to get FDA approval
Questions from this paper
How fast do tumors (cells resistant to chemo) grow?
How sensitive are tests for tumor mutations?
What is normal mutation rate?
What is probability that particular oncogene mutation
has occurred?
How many mutations -> drug resistance?
Do resistance mutations pre-exist in tumors, explaining
usual drug failure after few months?
Implications for multi-drug therapy
How would you describe
the patients in this study?
What is progression-free vs.
overall survival?
Does prior Kras mutation predict poor response?
How long before progression in those w/acquired
Kras mutations?
patient 1
patient 2
What do
panels show?
Do mutations
or CEA or
tumor size
assays predict
treatment
failure sooner?
What is
doubling rate?
If doubling time t is ~10d
and progression time T is ~150 weeks
how much has mutant cell # increased in time T?
N/N0 = 2T/t = 215 = 3*104
How much circulating DNA?
How many cell equivalents in 1ml @6pg/cell?
What fraction f is from tumor cells vs. normal cells?
What are these plots?
wt
mutant
How many dots?
What is the lowest % (or number) mutant detectable?
Suppose 1 mutant dot is reliable and 105 dots ->
min fraction of mut. tumor cells detectable = 1/(f*105)
If f = 0.1%, 1% tumor cells is min detectable
How many tumor cells in a 100mm2 (x-ray) tumor
Tumor vol = (area)3/2 = 1000mm3
Cell vol ~ (10mm)3 => 109 tumor cells
If 1% are mutant when mutation first detected, how
many were there before panatumumab was started?
107/(3*104) = 3*102
Is this consistent with expectation if DNA pol
makes 1 base error every generation and
you have 109 cells => 109 genomes copied?
-> ~1 error in every position
If 42 positions confer resistance to panitumumab
(their estimate), expect ~40 mutant cells to
pre-exist; not too far off estimate of 300 given
large variance in rates of doubling, etc.
If 40 (or 300) mutant cells are expected to be
present, on average, by chance in small tumor,
what is probability that a tumor has no such cells?
Poisson distribution pi=e-mmi/i!
pi = probability of a tumor having i
when average number/tumor = m
p0 = e-m = e-40 or e-300 = 10-18 or 10-131
What is chance that at least 1 cell in tumor with 109 cells
has oncogene mutations conferring resistance to 2 different
drugs, if the mutations do not overlap and changes at 40
positions confer resistance to each drug?
(40/109) * (40/109) * 109cells @ 10-6
Implication – multidrug therapy might avoid
outgrowth of resistant mutants
Main ideas
Mutations in cancer cells drive growth
gain of function = oncogenes
loss of function = tumor suppressor genes
Some drugs target oncogenes by binding to them
or their partners in cell signaling cascades
Mutations conferring resistance to individual
drugs likely preexist in tumors because
they contain large numbers of cells
harboring mutations just on basis
of DNA pol error rate
Multidrug therapy targeting different oncogenes/
pathways might overcome these resistance
mechanisms, but …
DNA sequencing has been important for discovery
of different mutations driving cancer
Often difficult to determine if individual mutations
are drivers or passengers
Genotyping specific genes in patient tumor DNAs to
see if most tumor cells already carry resistancecausing mutations can prevent futile use of
expensive toxic drugs
Not clear if routine sequencing of exons or
whole tumor genomes is useful clinically
at present, as opposed to targeted
genotyping or sequencing
“Beaming” is nice use of emulsion pcr and flow
cytometry to detect not too rare mutations in
tumor cells
HAPPY THANKSGIVING – work on picking a topic for
student presentations beginning 11/30