Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks

In an-effort to find gene regulatory networks and clusters of genes that af fect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer ag ents needed to inhibit growth of those same cell lines. Comprehensive pair- wise correlations were calculated between gene expression and measures of a gent susceptibility. Associations weaker than a threshold strength were rem oved, leaving networks of highly correlated genes and agents called-relevan ce networks. Hypotheses for potential single-gene determinants of anticance r agent susceptibility were constructed. The effect of random chance in the large number of calculations performed:was empirically determined by repea ted random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantage s of this methodology over alternative approaches, such as phylogenetic-typ e tree clustering and self-organizing maps.