Stable isotope labeling with amino acids in cell culture (SILAC) has so far been limited to cell lines and animals that technicians can label with heavy amino acids. Proteomics researchers at the Max Planck Institute in Germany have overcome this limitation using a mix of multiple SILAC-labeled cell lines, a technique dubbed “super-SILAC.” This new method produces superior accuracy compared with single isotope labeling of cell lines in linear quadrupole Orbitrap mass spectrometers.1 With this new capacity to accurately quantify several thousand proteins relatively rapidly, the team set out to show that proteomic methods accurately subtype cancer cells and can produce more useful information for drug formulation.
To test this theory, they chose to investigate diffuse large B-cell lymphoma (DLBCL), a cancer with two known subtypes identified recently using gene-expression profiling. While the germinal-center B-cell-like (GCB) subgroup has the gene signature of normal germinal center B-cells, the activated B-cell subgroup (ABC) presents a signature characteristic of tumor cells activated via their B-cell receptor. Because the specific biological differences between ABC and GCB are understood, the Max Planck team could evaluate their proteomic results in comparison to known biology as well as the gene profile.1
After running the super-SILAC mix through mass spectrometers, they found the six proteins that gene-expression testing had already identified. For ABC-like DLBCL, these included CD44, IRF4, and PTP1B. For GCB-like DLBCL, they were SPI1, CD27, and WIP — confirming that proteomic investigation can at least equal gene-expression techniques in cancer subtyping.1
In addition to validating the differentiators identified using gene-expression profiling, the proteomic signature revealed a new set of linked proteins, some of which may be clinically useful in diagnosis and chemotherapeutic development. For example, in the GBC subtype, they found the GTPase speckled pattern in the SLIP-GC protein — expressed exclusively in lymphomas derived from the germinal center, including DLBCL. Future work might show that SLIP-GC can immediately differentiate the two subtypes from one another. Another new member of the protein set for GCB-DLBCL included cell-surface marker CD81, a protein highly expressed in normal germinal B-cells.1
New proteins also emerged in the ABC proteomic signature. Ymer is significantly upregulated in the ABC lymphoma subtype and modulates NF-κB signaling. The team found that, in fact, all NF-κB regulated proteins are significantly more prevalent in ABC, relative to the GCB subtype. They included Ymer, PTP1B, ICAM, IRF4, CD44, and HLA-C. These results are consistent with high NF-κB levels associated with ABC-DLBCL, long thought of as a hallmark of this tumor type, which is associated with poorer patient outcome.
These findings are important because while DNA transcript-based profiling technology produces subtypes based on gene expression, they are often difficult to interpret with respect to actual molecular structure and cell-signaling processes. Gene-expression signatures cannot show to what extent the DNA translates into proteins and will never identify posttranslational protein modifications. The series of experiments the Max Planck team carried out showed that mass spectrometry, in combination with super-SILAC, can accurately subtype DLBCL tumor cells. The team first used a shotgun approach to validate their methodology and then ran the same tests, with the same results, using single-shot analyses. Single-shot analyses require far less cellular material and take less time to run, making them more suitable for use in clinical settings.1 This proteomic method offers an improved method for precision cancer subtyping and opens the potential for new diagnostic and molecular targets for drug discovery.
References
Deeb, S.J., et al., (2012) ‘Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles‘, Journal of Molecular and Cellular Proteomics, 11 (5), (pp. 77-89)
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This work elegantly demonstrates that MS based proteomics has the great potential to address different clinical questions in a more direct way as opposed to DNA or RNA based techniques. Although this work focuses on in vitro studies only, it is tempting to speculate that patient samples could be also separated based on the above mentioned protein signature. It remains exciting to follow the progress and impact of clinical proteomics in the future. This work is surely a promising start !