# Precision Therapeutic Biomarker Identification with Application to the Cancer Genome Project

## TL;DR

This paper introduces a novel multivariate regression model with overlapping clusters and an EM algorithm to identify cancer-specific therapeutic biomarkers and drug resistance patterns from the Cancer Genome Project data.

## Contribution

It proposes a generalized finite mixture of multivariate regression model with a new EM algorithm to analyze complex genomic and drug response data in cancer research.

## Key findings

- Revealed new therapeutic inter-relationships between cancers
- Identified existing and novel drug behaviors
- Enhanced understanding of drug resistance patterns

## Abstract

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.