Download FIRST GENERATION of CONNECTIVITY MAP small molecules

The aim of my research is to establish a relation among
diseases, physiological processes and the action of small
molecules like mithramycin
Our goal is to provide a
generic solution to this problem by
attempting to
describe all biological states…in
terms of genomic signatures, create
a large public database of signatures
of drugs and
genes, and develop pattern-matching
tools to detect
similarities among these signatures
FIRST GENERATION of CONNECTIVITY MAP
small molecules: 164 perturbagens tested
(FDA approved and nondrug bioactive compounds)
cell lines:
MCF7 (breast cancer)
PC3
(prostate cancer)
HL60
(leukemia)
SKMEL5 (melanoma)
concentration and treatment 10mM (when the optimal
x 6h
control
concentration is unknown)
cells in the same plate and treated
with vehicle alone (medium, DMSO…)
OVERALL DATA
164 bioactive small molecules
and corresponding vehicle control
Affymetrix GeneChip microarrays
HG U133A
564 gene expression profiles
Traditional method: HIERARCHICAL CLUSTERING
CLUSTER is a collection of objects/data that are:
* similar to each object in the same cluster
* different to the objects in the other clusters
In hierarchical clustering the data are not partitioned into a
particular cluster in a single step. Instead, a series of partitions takes
place, which may run from a single cluster containing all objects to n
clusters each containing a single object.
Strategy already used to analyze data from yeast and rat tissues
Drawbacks of hierarchical clustering
 the structure that they obtained by this approach was related
to cell type and batch effects
 all profiles must be generated on the same microarray platform
 was necessary an analytical method that could detect multiple component
within the cellular response to a perturbation
new method based on rank and using Kolmogorov-Smirnov statistic
(like to TTest)
QUERY SIGNATURE
Gene expression profile
correlated with a biological state
comparison
EXPRESSION PROFILES
Gene expression profile for the
perturbagens tested
Query signature
with up regulated (+) and
down-regulated genes (-)
Profiles
gene expression profile
for each perturbagens
compared to its vehicle
(22.000 genes)
connection
strong positive
…
null
…
strong negative
connectivity score
+1
…
0
…
-1
Connectivity
map
SOME EXAMPLES
HDAC inhibitors
query signature: T24 (bladder), MDA435 and MDA468 (breast cancer)
treated with HDAC inhibitors: vorinostat(SAHA), MS-27-275, tricostatin A
Gene expression profile
8 up-regulated genes
CDKN1A
FUCA1
MT1X
DHRS2
GLRX
CLU
TUBA3
HIST1H2BG
cyclin-dependent kinase inhibitor 1A (p21, Cip1)
fucosidase, alpha-L- 1, tissue
metallothionein 1X
dehydrogenase/reductase (SDR family) member 2
glutaredoxin (thioltransferase)
clusterin
tubulin, alpha 3
histone 1, H2bg
5 down-regulated genes
ANP32B
TYMS
CTPS
KPNB1
---
acidic (leucine-rich) nuclear phosphoprotein
thymidylate synthetase
CTP synthase
karyopherin (importin) beta 1
Full-length cDNA clone CS0DH006YD11 of T cells
connectivity map
* Vorinostat
Thricostatin A
* HC toxin
Valproic acid
Connectivity map allows us to
identify compounds unknown for
this function
In this case the results are
independent from the used cell lines
and from the dose of the drug
Estrogens
query signature: MCF7 treated with 17b-estradiol (E2) natural ligand of ER
129 up and 89 down-regulated genes
connectivity map
• Both agonists and antagonists
can be discovered directly from
the Connectivity Map
• is very important to collect the
cells in an appropriate
physiological state or molecular
context
Gedunin
Gedunin is able to abrogate AR activity in prostate cancer cells. Mechanism???
• query signature: LNCaP treated for 6h with gedunin
35 up and 35 down-regulated genes
connectivity map
• high connectivity with HSP90
inhibitor
DESEASES
Diet-induced obesity
query signature: gene expression in rat model of diet-induced obesity
163 up and 161 down-regulated genes
• PPARg agonists and inducers
of adipogenesis
• there is connection also
between data in rat and data in
human cell lines (but only in
PC3)
Alzheimer disease
query signature: two independent studies
Comparison between hippocampus
from AD and normal brain
Comparison between cerebral
cortex from AD and agematched controls
40 genes
25 genes
Significant negative connectivity with DAPH
Dexamethasone resistance in ALL
query signature: comparison of cells from patients with sensitivity and
patients with resistance to Dexamethasone
• treatment with sirolimus
sensitize CEM-CL cell lines to
dexamethasone treatment
• sirolimus, mTOR inhibitor
Our data: SDK
The anticancer activity of MTM has been associated with its ability
to inhibit replication and transcription via cross-linking of the DNA
strands; MTM is known to bind to the minor groove of GC-rich DNA as
a Mg2+-dimer complex (MTM:Mg2+ = 2:1)
Sp1
Start site
//
transcription
//
MTM
Sp1
Start site
//
//
We tested a new MTM analog: SDK
no transcription
query signature: A2780 treated with SDK 100nM for 6 hours
48 up regulated genes
3355 down-regulated genes
900 ≥2 fold
change
240 ≥3 fold
change
DISCUSSION
encouraging results
connectivity map can be used for:
- drugs with common mechanism of action (HDAC inhibitors)
- discover unknown mechanism of action (gedunin)
- identify potential new therapeutics
the genomic signature are often conserved across different cell types
and different origins
but there are also several limitations at this pilot study
-
few number of used cell lines
few concentrations
interpretation of the results
the method for statistical analysis
Bye bye
Non-parametric models differ from parametric models in that the model structure is
not specified a priori but is instead determined from data. The term nonparametric is
not meant to imply that such models completely lack parameters but that the
number and nature of the parameters are flexible and not fixed in advance.
Nonparametric models are therefore also called distribution free.
A histogram is a simple nonparametric estimate of a probability distribution
Non-parametric (or distribution-free) inferential statistical methods are
mathematical procedures for statistical hypothesis testing which, unlike parametric
statistics, make no assumptions about the frequency distributions of the variables being
assessed. The most frequently used tests include
the Kolmogorov-Smirnov test (often called the K-S test) is used to determine whether
two underlying probability distributions differ, or whether an underlying probability
distribution differs from a hypothesized distribution, in either case based on finite
samples.
Nonparametric statistical methods allow one to analyze data without making strong assumptions
about the process that generated the data. For example, instead of assuming that the data have
a Gaussian distribution, we might assume only that the distribution has a probability density that
satisfies some weak, smoothness conditions