Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes

The
quantitative structure–activity relationship (QSAR) approach
has been used to model a wide range of chemical-induced biological
responses. However, it had not been utilized to model chemical-induced
genomewide gene expression changes until very recently, owing to the
complexity of training and evaluating a very large number of models.
To address this issue, we examined the performance of a variable nearest
neighbor (v-NN) method that uses information on near
neighbors conforming to the principle that similar structures have
similar activities. Using a data set of gene expression signatures
of 13 150 compounds derived from cell-based measurements in
the NIH Library of Integrated Network-based Cellular Signatures program,
we were able to make predictions for 62% of the compounds in a 10-fold
cross validation test, with a correlation coefficient of 0.61 between
the predicted and experimentally derived signaturesa reproducibility
rivaling that of high-throughput gene expression measurements. To
evaluate the utility of the predicted gene expression signatures,
we compared the predicted and experimentally derived signatures in
their ability to identify drugs known to cause specific liver, kidney,
and heart injuries. Overall, the predicted and experimentally derived
signatures had similar receiver operating characteristics, whose areas
under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively,
across the three organ injury models. However, detailed analyses of
enrichment curves indicate that signatures predicted from multiple
near neighbors outperformed those derived from experiments, suggesting
that averaging information from near neighbors may help improve the
signal from gene expression measurements. Our results demonstrate
that the v-NN method can serve as a practical approach
for modeling large-scale, genomewide, chemical-induced, gene expression
changes.