Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs

Abstract

BackgroundDetailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple targets and pathways, traditional methods for analyzing structure-function relationships are not comprehensive enough. Therefore more advanced integrative models are needed for predicting biological effects elicited by specific chemical features. As a step towards creating such computational links we developed a data-driven chemical systems biology approach to comprehensively study the relationship of 76 structural 3D-descriptors (VolSurf, chemical space) of 1159 drugs with the microarray gene expression responses (biological space) they elicited in three cancer cell lines. The analysis covering 11350 genes was based on data from the Connectivity Map. We decomposed the biological response profiles into components, each linked to a characteristic chemical descriptor profile.ResultsIntegrated analysis of both the chemical and biological space was more informative than either dataset alone in predicting drug similarity as measured by shared protein targets. We identified ten major components that link distinct VolSurf chemical features across multiple compounds to specific cellular responses. For example, component 2 (hydrophobic properties) strongly linked to DNA damage response, while component 3 (hydrogen bonding) was associated with metabolic stress. Individual structural and biological features were often linked to one cell line only, such as leukemia cells (HL-60) specifically responding to cardiac glycosides.ConclusionsIn summary, our approach identified several novel links between specific chemical structure properties and distinct biological responses in cells incubated with these drugs. Importantly, the analysis focused on chemical-biological properties that emerge across multiple drugs. The decoding of such systematic relationships is necessary to build better models of drug effects, including unanticipated types of molecular properties having strong biological effects.

Molecular profiling and combinatorial activity of CCT068127: a potent CDK2 and CDK9 inhibitor

“…We then analyzed these signatures for similarity to known drug‐induced gene expression changes using the Connectivity Map (Lamb et al ., ). Strikingly, signatures for both compounds show a high level of correlation with those of other small‐molecule CDK inhibitors, namely 0175029‐0000 (Khan et al ., ), alsterpaullone (Schultz et al ., ), and GW8510 (Bramson et al ., ), as well as HDAC inhibitors vorinostat/SAHA (Richon et al ., ), trichostatin A (Yoshida et al ., ), valproic acid (Gottlicher et al ., ) (Tables and ).…”

Androgen receptor as potential therapeutic target in metastatic endometrial cancer

“…Compounds targeting phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathway were among the top ranked, along with HSP90 inhibitors, known to disrupt hormone binding and hormone receptor stability [14], the AR inhibitor Resveratol [15, 16] and a CDK inhibitor [17] (Supplementary Table 4). These findings suggest that drugs targeting proliferation and anti-androgen treatment could be potential treatment options for the high AR to ERα ratio patient subgroup in particular.…”

Transcriptional response networks for elucidating mechanisms of action of multitargeted agents

“…Using the expression data of the most abundant platform in CMap, HG-U133A, a total of 1154 drugs were found in both MCF7 and PC3 cell lines. Multiple concentrations of the drugs were combined to obtain a single reliable profile for each drug [84]. The gene expression of pinosylvin was processed analogously to obtain the treatment versus control differential gene expression response.…”