Proteomic classification of cancer that actually works?

Differential exoprotease activities confer tumor-specific serum peptidome patterns. Josep Villanueva et al. Journal of Clinical Investigation, 116, 1, 2006

Using an optimized peptide extraction and MALDI-TOF MS serum peptide signatures provide accurate class discrimination between patients with 3 types of solid tumors. Despite a huge effort, few proteins have been validated as cancer biomarkers. This paper demonstrates that peptides in the serum of cancer patients that are generated as a result of tumor protease activity can be used for the detection and classification of cancer. The authors propose that the proteolytic degradation patterns in the serum peptidome might also to distinguish indolent from aggressive tumors. The data set measured was pretty substantial, 106 serum samples from patients with advanced prostate cancer, bladder cancer or breast cancer.

“…this study provides a direct link between peptide marker profiles of disease and differential protease activity, and the patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer.”

I like the way they tell their story. What this area needs is more pattern recognition papers written by critical biologists. This paper comes pretty close to the mark.

One fabulous aspect of this paper is the experimental design. By distinguishing types of cancer, they demonstrated that serum peptide signatures are not just indicators of a nonspecific inflammatory condition, such as arthritis or infection.

One area of concern is the author’s feature selection. They selected only 61 masses from the entire MALDI spectrum for the pattern recognition. Assuming each mass is associated with a single peptide, 61 seems like a reasonable number of peptides that are both detected by MALDI and differentially expressed by causers. But the thresholds use to select the 61 seemed pretty arbitrary. For this reason, I question how much user intervention would be necessary to apply this method to other diseases or even other data.