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 10  M ( when the optimal concentration is unknown) x 6h control 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 EXPRESSION PROFILES Gene expression profile for the perturbagens tested comparison

Query signature with up regulated (+) and down-regulated genes (-) Profiles gene expression profile for each perturbagens compared to its vehicle ( genes) connection strong positive … null … strong negative connectivity score +1 … 0 … Connectivity map

SOME EXAMPLES HDAC inhibitors query signature: T24 (bladder), MDA435 and MDA468 (breast cancer) treated with HDAC inhibitors : vorinostat(SAHA), MS , tricostatin A Gene expression profile 8 up-regulated genes 5 down-regulated genes

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 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

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 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 disease query signature: two independent studies Comparison between hippocampus from AD and normal brain Comparison between cerebral cortex from AD and age- matched 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 sirolimus, mTOR inhibitor treatment with sirolimus sensitize CEM-CL cell lines to dexamethasone treatment

Sp1 Start site // Start site // Sp1MTM 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 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

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.parametric A histogram is a simple nonparametric estimate of a probability distributionhistogram 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 includeparametric statisticsfrequency distributions 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.KolmogorovSmirnovprobability distributions 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