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 ageneric solution to this problem by attempting todescribe all biological states…in terms of genomic signatures, createa large public database of signatures of drugs andgenes, and develop pattern-matching tools to detectsimilarities among these signatures

FIRST GENERATION of CONNECTIVITY MAPFIRST 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 10M (when the optimal concentration is unknown)

x 6h

control cells in the same plate and treated with vehicle alone (medium, DMSO…)

OVERALL DATAOVERALL DATA

164 bioactive small molecules and corresponding vehicle control

Affymetrix GeneChip microarraysHG 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 SIGNATUREQUERY SIGNATUREGene expression profile correlated with a biological state

EXPRESSION PROFILESEXPRESSION PROFILESGene expression profile for the perturbagens tested

comparison

Query signaturewith up regulated (+) and down-regulated genes (-)

Profiles gene expression profile for each perturbagens compared to its vehicle

(22.000 genes)

connectionstrong positive

…null…

strong negative

connectivity score+1…0…-1

Connectivity map

SOME EXAMPLESSOME EXAMPLES

HDAC inhibitorsHDAC inhibitors

query signature: T24 (bladder), MDA435 and MDA468 (breast cancer)treated with HDAC inhibitors: vorinostat(SAHA), MS-27-275, tricostatin A

Gene expression profile

CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1)FUCA1 fucosidase, alpha-L- 1, tissueMT1X metallothionein 1XDHRS2 dehydrogenase/reductase (SDR family) member 2GLRX glutaredoxin (thioltransferase)CLU clusterinTUBA3 tubulin, alpha 3HIST1H2BG histone 1, H2bg

8 up-regulated genes

5 down-regulated genesANP32B acidic (leucine-rich) nuclear phosphoproteinTYMS thymidylate synthetaseCTPS CTP synthaseKPNB1 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 linesand from the dose of the drug

EstrogensEstrogens

query signature: MCF7 treated with 17-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

GeduninGedunin

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

DESEASESDESEASES

Diet-induced obesityDiet-induced obesity

query signature: gene expression in rat model of diet-induced obesity

163 up and 161 down-regulated genes

• PPAR agonists and inducers of adipogenesis

• there is connection also between data in rat and data in human cell lines (but only

in PC3)

Alzheimer diseaseAlzheimer disease

query signature: two independent studies

Comparison between hippocampusfrom AD and normal brain

Comparison between cerebral cortex from AD and age-

matched controls

40 genes25 genes

Significant negative connectivity with DAPH

Dexamethasone resistance in ALLDexamethasone resistance in ALL

query signature: comparison of cells from patients with sensitivity and patients with resistance to Dexamethasone

• sirolimus, mTOR inhibitor

• treatment with sirolimus sensitize CEM-CL cell lines to dexamethasone treatment

Sp1

Start site

// //

Start site

// //

Sp1 MTM

transcription

no transcription

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)

Our data: SDK Our data: SDK

We tested a new MTM analog: SDK

3355 down-regulated genes48 up regulated genes

900 ≥2 fold change

240 ≥3 fold change

query signature: A2780 treated with SDK 100nM for 6 hours

DISCUSSIONDISCUSSION

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