MD Anderson team studies how proteins respond to treatment in more than 300 cancer cell lines
HOUSTON—Proteins probably aren't the first things you'd think of in the fight against cancer, but a research team at The University of Texas MD Anderson Cancer Center has leveraged proteomic profiling to elucidate drug resistance and sensitivity in cancer—as well as identify potential treatment options. Their work was published in Cancer Cell in a paper titled “Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines.”
This research looked at expression changes in more than 200 clinically relevant proteins across more than 300 cell lines after being treated with 168 different compounds. According to MD Anderson, the result is the largest dataset available on protein responses to drug treatments in cancer cell lines.
The researchers performed perturbation biology, which is defined in the paper as “a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms.” Put another way, it looks at a given system's response to various stimuli.
“[P]erturbation experiments provide a powerful approach in which cells are modulated by perturbagens and downstream consequences are monitored,” the authors explained in their paper. “The longitudinal data thus obtained provide considerably greater information on both the basal biological network wiring and its associated changes under stress, thereby leading to a deeper understanding of mechanisms underlying cell survival under stress … large-scale resources for analysis and integration of protein responses of perturbed cancer cell lines have yet to be established. This knowledge gap is even more striking, considering that proteins comprise the basic functional units in biological processes and represent the major targets for cancer therapy.”
“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 senior author Dr. Han Liang, professor of Bioinformatics and Computational Biology. “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.”
The team used reverse-phase protein array to profile protein perturbations, a technique that enables rapid quantitative analyses of a group of proteins. Protein levels were measured before and after treatment with drugs that target a wide range of signaling pathways and cellular processes. The cancer types represented by the 319 cell lines tested included breast, ovarian, skin, prostate, uterine and hematologic cancers. RPPA profiles were generated for 15,492 samples, which broke down to 3,608 control samples and 11,884 treated samples.
With this approach, the researchers were able to identify signaling pathways that were activated or suppressed after treatment. They were also able to generate a comprehensive map of protein-drug connections, and that protein response data can be accessed in a data portal and is publicly available.
According to the authors, “The utility of our protein response dataset is severalfold. First, our dataset provides a basis for understanding cause-effect relationships that is complementary to correlation analyses and associations that can be obtained from patient cohorts. Based on these data, it will be possible to develop quantitative predictive models of how signaling networks function in intact cellular systems. Second, we show that while there is information content in biomarkers at baseline, the information content is markedly increased when baseline and response signals are combined. This is predicted by systems biology and engineering precepts, wherein perturbed systems contain more information than static analysis. Biomarkers designed to select treatment using baseline data frequently have a limited power to predict benefit, and our results suggest that adaptive protein responses after initial treatment could be highly informative in terms of treatment response and clinical benefit. Further clinical investigations are warranted to assess the potential benefit gains using such a strategy. Third, since protein responses reflect how cancer cells critically rewire their signaling pathways to survive and adapt to the stress of specific drug treatments, these protein signals provide a strong basis for the rational design of combination therapies, as we have demonstrated previously.”
“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,” said Liang. “As we continue working to expand the data, we think this will be a valuable starting place for researchers doing drug mechanism studies.”