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Pancreatic Cancer Study Shows Proteomics' Potential for Understanding Combo Therapies


NEW YORK (GenomeWeb) – State University of New York at Buffalo researchers have used a large-scale proteomic analysis to identify mechanisms underlying the effectiveness of a combination therapy for pancreatic cancer.

In a study published last month in Molecular & Cellular Proteomics, the researchers determined that the combination of the IAP-inhibitor birinapant and chemotherapeutic paclitaxel killed pancreatic cancer cells by suppressing the Warburg effect, a form of energy production used predominantly by cancer cells.

The finding suggests a potential targeted approach to cancer therapy and demonstrates the utility of high quality proteomic data for unraveling the mechanisms of drug combination therapies, said Jun Qu, professor of pharmaceutical sciences at SUNY-Buffalo and senior author on the study.

Attacking the Warburg effect "is a very effective method of killing cancer cells, and one that does not affect normal cells very much because normal cells are not reliant on the Warburg effect," he said. "So this is cancer specific, which is very exciting."

"We were only able to discover this using proteomics," he added, "because we have never considered this before. We didn't have a hypothesis about the Warburg effect going in. That is the power of discovery science."

In addition to serving as an example of the power of proteomics generally, Qu suggested that the study demonstrates specifically the usefulness of the IonStar mass spec method his lab has developed. First described in a paper published last year in the Journal of Proteome Research, the approach combines optimization of sample prep and liquid chromatography with high-resolution MS1 measurements to allow researchers to quantify thousands of proteins across hundreds of samples with high throughput and reproducibility.

Notably, the method allows researchers to gather quantitative data on thousands of proteins in large sample sets without gaps in that data.

This has been a challenge for shotgun proteomics techniques due in large part to the stochastic nature of such methods. In a typical shotgun proteomics approach, the instrument performs an initial scan of precursor ions entering the instrument and selects a sampling of those ions for fragmentation and generation of MS/MS spectra. However, because instruments can't scan quickly enough to acquire all the precursors entering at a given moment, many ions — particularly low-abundance ions — are never selected for MS/MS fragmentation and so are not detected.

This makes high-quality quantification across samples challenging for shotgun methods because a protein measured in one sample may not be measured in another sample. This is especially an issue for low-abundance molecules, which are most likely to be missed in a given run.

One way around this is to use precursor-level data, MS1, as opposed to MS/MS level data. This avoids the stochastic sampling problem associated with MS/MS-based shotgun assays. And while MS/MS typically offers greater specificity and sensitivity, the high resolution of current high-end mass spec instrument has significantly improved the depth of quantitative data that can be gathered at MS1.

Qu's IonStar workflow is an MS1-based approach. He and his colleagues presented the method in their 2017 JPR paper, and the MCP paper represents the first publication showing application of the method to an actual biological question.

In the study, the researchers profiled the proteomes of cells from the human pancreatic cancer cell line Panc-1 treated with birinapant, paclitaxel, or birinapant and paclitaxel combined at 6-, 24-, 48-, and 72-hour time points. They analyzed the treatment groups in triplicate along with four controls, making for a total of 40 samples. Running them on an Orbitrap Fusion Lumos mass spec, they quantified a total of 4,069 proteins, 4,061 of which (99.8 percent) were quantified in every sample.

That level of data quality across many samples was essential to teasing out the mechanisms underlying the effect of the combined therapy, Qu said.

Initial cell proliferation assays indicated the two drugs had a synergistic effect, he noted, but, he said, "we didn't know, for one, if it was real, and, two, what was the mechanism of action. To investigate that question requires the use of lots of samples to account for different drug combinations and different time points."

Looking at their proteomic data, the researchers noticed that proteins involved in the activation of oxidative phosphorylation, fatty acid β-oxidation, and the inactivation of aerobic glycolysis were significantly altered by the combination therapy as opposed to treatment with single drugs. This led them to hypothesize that the combination therapy acted to suppress the Warburg effect, which they then confirmed via metabolic profiling experiments.

Qu's SUNY-Buffalo colleague Xue Wang, first author on the study, noted the researchers also found that proteins involved in apoptosis and cell-cycle arrest played a role in the combination therapy's synergistic effect. She added that she believed the IonStar pipeline used in the MCP paper could be broadly useful for elucidating the molecular mechanisms of a variety of cancer combination therapies.

In addition to having high reproducibility, the approach has relatively high throughput for a discovery workflow. Qu said running the 40 samples took about a week of mass spec time followed by between two and three months for data analysis.

Next he and his colleagues will apply their approach to studying the birinapant-paclitaxel combination in patient-derived xenograft models of pancreatic cancer in hopes of "demonstrating the translational value of the finding," Qu said.

The researchers will also apply the workflow to proteomic analyses of cancer combination therapies more broadly, he said. "Our understanding of combination therapies is not so good right now, so that is something we want to investigate — why certain patients respond to certain combinations [of drugs] and some don't."