Download Estimating cancer survival and clinical outcome based on genetic

Estimating cancer survival and
clinical outcome based on genetic
tumor progression scores
Jörg Rahnenführer 1,*, Niko Beerenwinkel 1, , Wolfgang A.
Schulz 2, Christian Hartmann 3, Andreas von Deimling 3,
Bernd Wullich 4 and Thomas Lengauer 1
Presented by Rahul Jawa
Motivation




Prediction of time to death or relapse is important for
tumor classification and selecting appropriate
therapies
Survival Prediction based on clinical and histological
parameters
Accumulation of genetic alterations during tumor
progression can be used for the Assessment of the
genetic status of the tumor
Evolutionary tree models have been applied for
modeling dependences between the genetic events
Methods
•
Oncogenetic Tree Models




Describes order of genetic events in the
course of human tumor development
Genetic events are gains or losses of parts
of chromosomes
Oncogenetic tree T = (V, E, r, p)
Problem: Fixed pattern, therefore some
samples are assigned probability zero
Methods (contd)

Oncogenetic trees mixture models



Contains a star topology which models
spontaneous and independent occurrence of
genetic events
And arbitrary trees estimated from the observed
data
This model is learned by an EM-like fashion by
iteratively estimating the responsibilities of the
different tree components for the data and the
structure and parameters of the tree models are
inferred from the weighted data
Methods (contd)
Methods (contd)

Genetic progression scores (3)


Determines the progression status of
human tumors
They are defined for tumor samples that
are represented by binary vectors
indicating the occurrence of a list of
genetic events(x1,…,xl)
Methods (contd)
Count Statistic
A.
•
•
Measure of genetic progression = number of
events that have occurred
All events are independent and impact on
progression is cumulative
Methods (contd)
B. Weighted count statistic



All genetic events are not equally important
High frequent events occur early
Less frequent events indicates more advanced progression
Methods (contd)
C. Genetic progression score



Used via Oncogenetic trees mixed model to integrate
dependences between ordered events
A timed oncogenetic tree is obtained by assuming
independent Poisson processes for the occurrence of events
on the tree edges
Expected waiting time of a pattern is finally estimated as the
average of all waiting times at which pattern is observed
Methods (contd)

Survival analysis






Survival time starts at time of treatment and the endpoint
is the death or relapse
If patient drops out before endpoint, the Cox proportional
hazard model can be used to calculate risk of death
Hazard rate at a time t is the instantaneous rate of death
during the next instant of time among survivors to time t
Lambda0 is the baseline hazard function
B = (B1,…,Bp) is the vector of regression
coefficients
z = (z1,…zp) is a p-dimensional vector of
covariates that are potential predictors for the
survival time
Data Sets

Glioblastomas (Brain Tumor)




Survival time based on death
Contained 75 patients with 5 censored
Genetic events were chromosome changes
on the p-arm or q-arm of single
chromosomes
Selected the events that were observed in
at least 15% of the tumor samples

10q, 10p, 9p, 19q, 17p, 13q and 22q
Data Sets (contd)

Prostate cancer




Survival time based on tumor relapse
Contained 54 patients and 34 censored
Genetic events were gains and losses of
chromosome parts on the p-arm or q-arm of all
chromosomes
Selected the events that were observed in at least
10% of the tumor samples

3q+, 4q+, 6q+, 7q+, 8p-, 8q+, 10q-, 13q+ and Xq+
Results

Estimated oncogenetic tree model for
Glioblastomas
Results (contd)

Estimated oncogenetic tree model for
Prostate cancer
Results (contd)

Cox proportional hazard models


Is used to identify genetic markers that are
relevant for estimating clinical outcome
Hazard ratio quantifies the relative risk of
death
Results (contd)




GPS for Glioblastomas
Table 1 Glioblastoma data set: pattern of observed LOH
measurements for selected events, frequency of pattern and
GPS calculated from oncogenetic tree model
Table 2 Glioblastoma dataset: genetic events defined by LOH
on single chromosomes, frequencies and p-values in Cox models
(original and false discovery rate adjusted in univariate and
original in multivariate model)
Table 3 Glioblastoma dataset: GPS with hazard ratios, 95%
confidence intervals and p-values in univariate and bivariate Cox
regression model
Results (contd)
Results (contd)

GPS for Prostate cancer




Gleason score is a prostate cancer grading system on scale 1-10
Table 4 Prostate cancer dataset: pattern of observed CGH measurements
for selected events, frequency of pattern and GPS calculated from
oncogenetic tree model
Table 5 Prostate cancer dataset: genetic events defined by CGH on single
chromosomes, frequencies and p-values in Cox models (original and false
discovery rate adjusted in univariate and original in multivariate model)
Table 6 Prostate cancer dataset: genetic progression scores with
hazard ratios, 95% confidence intervals and p-values in univariate
and bivariate Cox regression model
Results (contd)
Results (contd)
Conclusion




The GPS of a tumor gave the estimated average waiting time of
its observed genetic pattern in the timed oncogenetic tree
GPS was able to differenciate patient subgroups with respect to
expected clinical outcome
GPS was applied to two different tumor types which shows that
it could become a universal approach
For Gleason score of 7, GPS was able to further identify
subgroups