Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions.

We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%.

One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease.

On a set of 21 transcriptional signatures of lung cancer, we identified 247 drugs that significantly reverse these pathological gene expression changes (at FDR < 1%).

In all nine cell lines, drugs that CMapBatch identifies as reversing the transcriptional changes seen with lung cancer are significantly better than other CMap drugs at inhibiting growth (Wilcox test P < 0.01; Fig.

One such resource, the Connectivity Map (CMap), which is the focus of our analyses, catalogues the transcriptional responses to drug treatment in human cell lines for over a thousand small molecules [3].

[8] combined two microarray data sets to create a single transcriptional signature of lung adenocarcinoma and screened it against CMap.

[9] constructed a transcriptional signature of survival in patients with lung adenocarcino-ma; CMap analysis identified several drugs that might improve outcome.

We found that eleven drugs that reverse the transcriptional changes observed in lung cancer were structurally similar to one or more drugs in TOP (Fig.

Transcriptional signatures

This work demonstrates the degree at which ge-nome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

The transcriptional profile of an organism contains clues about the environmental context in Which it has evolved and currently lives, its behavior and cellular state.

(P < 4.3 X 10—7) which is in agreement with previous studies that report the prevalence of phase-dependent transcriptional regulation in a variety of biosynthetic processes [37—39].

The major transcriptional regulator for the entry into stationary phase is RpoS and, as expected, it is present in the set of genes informative for growth phase, along with several genes belonging to its regulon like dnaK, clpx, hemL, dps, rpsK, hfq, rplA, crr, rpsE and gapA [41].

Genome-scale transcriptional profiling has become a standard and relatively inexpensive way to identify the overall cellular state and condition-specific cellular responses to external stimuli.

For instance, different sets of genes are known to be active in each growth phase and medium [1], while strain polymorphisms can result in a remarkably diverse transcriptional repertoire [2,3].

Genome-wide transcriptional profiling can be thought of as a complex representation of all cellular functions and states, with a wealth of multiplexed information that, if decoded efficiently, can provide a fast and quite accurate all-encompassing snapshot of the cell and its environment.

There have been many descriptions of binding partners for TIF2 under conditions where TIF2 reverses a transcriptional response [46—50].

Cofactors and comodulators assist or impede the transcriptional activity of DNA-associated steroid receptors [2,3].

Beyond this, it is currently not possible to predict the transcriptional outcome for any specific combination of gene, receptor, and cofactor/comodulator.

An unresolved question is Whether the underlying mechanism of each transcriptional component is the same or changes With the direction of gene eXpression output (i.e., increase in induction vs. decrease in repression).

This suggests that many transcriptional cofactors/comodulators are mono-functional and similarly modulate basic steps in gene eXpression irrespective of the directional change in gene product levels.