Study Releases Cache of Protein Response Data

Through large-scale profiling of protein changes in response to drug treatments, researchers at The University of Texas MD Anderson Cancer Center have generated a large dataset that includes expression changes in more than 200 clinically relevant proteins across more than 300 cell lines after treatment with 168 different compounds.

"We've seen a number of perturbation studies that look at gene expression changes following drug treatments or CRISPR-mediated changes, but there is a significant gap in terms of proteomic profiling," said Han Liang, Ph.D., senior author of a paper published in Cancer Cell today. "We hoped to fill that gap by profiling changes in major therapeutic target proteins, which provides a lot of insight in terms of drug resistance and designing drug combinations."

Perturbation biology measures how a system, such as cancer cells, responds to various stimuli. These types of experiments have proven useful in modeling cancer behaviors and understanding responses at a system level, explained Liang. To profile protein perturbations, the researchers used reverse-phase protein array (RPPA), which enables the rapid quantitative analyses of a select group of proteins. Protein levels were measured at baseline and after treatment, often at multiple time points.

The study evaluated drugs targeting a variety of signaling pathways and cellular processes across 319 commonly used, well-characterized cell lines from many cancer types, including breast, ovarian, uterine, skin, prostate and hematologic cancers.

Rather than analyzing all possible drug-cell line combinations, the researchers focused on those most likely to be relevant to the field. In total, they generated RPPA profiles of 15,492 samples, including 11,884 drug-treated samples and 3,608 control samples.

The data obtained from these analyses provides important insight into the mechanisms of drug response or resistance, highlighting signaling pathways that are activated or suppressed following treatment with a given drug. Further, having data on both baseline and post-treatment protein levels is much more useful in modeling to predict sensitivity to additional drugs, explained Liang.

The researchers also constructed a comprehensive map of protein-drug connections to visualize responses and to better study relationships between different proteins and signaling pathways. The maps showcase which proteins have significant changes from a given drug, which drugs yield similar responses and which proteins saw similar patterns of change.

The protein response data is publicly available for researchers in a data portal, which provides various methods for visualizing and downloading the data.