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Large-Scale Cancer Proteomics Study Provides Insights on Drug Resistance, Effective Therapy Combos

NEW YORK – A large-scale cancer proteomics study conducted by researchers from the University of Texas MD Anderson Cancer Center and elsewhere has found that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective therapeutic combinations.

In a paper published on Thursday in Cancer Cell, the researchers said they generated and compiled perturbed expression profiles of 210 clinically relevant proteins in more than 12,000 cancer cell line samples in response to 170 drug compounds, using reverse-phase protein arrays. They built a systematic map of protein-drug connectivity, and also developed a user-friendly data portal for the use of the research community.

"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. 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," senior author and MD Anderson researcher Han Liang said in a statement.

"Through this dataset, one can immediately see the consequences of a given drug, including perturbed pathways and adaptive responses, which can help to identify optimal drug combinations," Liang added. "As we continue working to expand the data, we think this will be a valuable starting place for researchers doing drug mechanism studies."

The researchers generated and compiled a large compendium of perturbed protein expression profiles of cancer cell lines in response to a diverse array of clinically relevant drugs using reverse-phase protein arrays (RPPAs), a quantitative antibody-based approach to assess protein markers in a large number of samples in a high-throughput, cost-effective, sensitive manner. This platform depends on antibodies for the detection of proteins, the researchers said, noting that there's currently a limited, but rapidly growing, number of proteins for which high-quality antibodies exist that give an analyzable signal.

To generate a high-quality resource of perturbed protein responses, they measured RPPA-based protein expression profiles of cancer cell lines in response to about 170 preclinical and clinical therapeutics, generated normalized RPPA data and protein response to perturbation profiles, and made the data public through a user-friendly data portal.

The final compendium contained quality control-passed RPPA profiles of 15,492 cancer samples (11,884 drug-treated samples and 3,608 control samples related to perturbation of 168 compounds in 319 cell lines) in total. These include breast, ovarian, uterus, skin, blood, and prostate cancers, and the drug compounds target a broad range of cancer-related processes, including PI3K/mTOR signaling, ERK/MAPK signaling, RTK signaling, EGFR signaling, TP53 pathway, genome integrity, cell cycle, antipsychotic drugs, and chromatin remodeling.

To assess whether the protein response data could provide meaningful insights into phenotypic consequences of drug treatments, the researchers conducted an in-depth analysis on the breast cancer cell line MCF7, which had the highest number of treated samples in their dataset. They extracted RPPA data from more than 1,500 MCF7 samples treated by a variety of compounds or combinations. They found that MCF7 was significantly more sensitive to two of the eight drug groups. In particular, MCF7, which is an ER-positive breast cancer cell line, showed the highest sensitivity to ER inhibitors, with PI3K/mTOR inhibitors being the second most effective group.

Using their pathway analysis method to elucidate the mechanistic underpinnings of the drug response, they determined that ER inhibitors decreased TSC/mTOR and hormone_a, but increased EMT, core reactive, DNA damage, and hormone_b pathways; and that PI3K/mTOR compounds inhibited TSC/mTOR, PI3K/AKT, and cell cycle, but activated apoptosis and RTK signaling. They further compared the sensitive drug groups with others for each pathway and revealed several differentially altered response pathways. Specifically, three pathways were inhibited in the sensitive groups, and three pathways in the sensitive groups had significantly higher pathway scores than other drugs, including apoptosis, RTK, and RAS/MAPK.

"These observations not only indicate that our RPPA-based protein response data successfully captured important phenotypic effects of drug treatments, but also demonstrate the ability to uncover molecular mechanisms underlying drug sensitivity," the authors wrote.

To systematically evaluate the utility of their protein response data, the researchers then built a protein-drug connectivity map based on the RPPA data. The map revealed some expected outcomes — for example, several MEKis and mTOR/PI3K inhibitors were highly connected, highlighting their similar downstream protein responses.

The map also identified some intriguing connections. For example, a PARP inhibitor showed both similar and opposite relationships with some drugs, suggesting potential additive or agonistic effects that could direct the development of rational drug combinations.

The investigators next studied protein-protein relationships on the map, and found that proteins co-perturbed by a drug tend to be involved in the same biological processes and to interact as part of a signaling cascade. This global assessment using prior protein interaction knowledge supported the utility of the approach to drive biological discoveries.

To facilitate the utilization of the protein response data, the researchers provided unrestricted access to the data through a user-friendly portal, called the Cancer Perturbed Proteomics Atlas.