  Cancer cell lines have frequently been used to link drug sensitivity and
resistance with genomic profiles. To capture genomic complexity in cancer, the
Cancer Genome Project (CGP) (Garnett et al., 2012) screened 639 human tumor
cell lines with 130 drugs ranging from known chemotherapeutic agents to
experimental compounds. Questions of interest include: i) can cancer-specific
therapeutic biomarkers be detected, ii) can drug resistance patterns be
identified along with predictive strategies to circumvent resistance using
alternate drugs, iii) can biomarkers of drug synergies be predicted ? To tackle
these questions, following statistical challenges still exist: i)biomarkers
cluster among the cell lines; ii) clusters can overlap (e.g. a cell line may
belong to multiple clusters); iii) drugs should be modeled jointly. We
introduce a multivariate regression model with a latent overlapping cluster
indicator variable to address above issues. A generalized finite mixture of
multivariate regression (FMMR) model in connection with the new model and a new
EM algorithm for fitting are proposed. Re-analysis of the dataset sheds new
light on the therapeutic inter-relationships between cancers as well existing
and novel drug behaviors for the treatment and management of cancer.
