The identification of prognostic and/or predictive biomarkers has the potential to refine patient selection so that therapy can be individualized. For example, biomarkers could help determine which patients might avoid unnecessary therapy either because they already have a favorable prognosis without treatment, or because their tumor is not predicted to respond to treatment. In other patients, biomarkers may predict that therapy could prolong survival and that knowledge could outweigh the risk of treatment-related toxicity.
Because there was no therapeutic difference amongst the arms of CALGB 80303, serum from outliers on either treatment could be analyzed. For the current study we used a quantitative proteomics platform to search for prognostic and predictive biomarkers in patients with advanced pancreatic carcinoma. This was accomplished by subjecting the sera to depletion of the 14 most abundant proteins, followed by nano-LC-MS/MS. The validation phase, however, employed unaltered serum, with the goal of developing an assay that would be useable in clinical practice.
Analysis of the MS data produced several candidate markers, and we focused our validation studies on the three proteins that survived robust manual curation of the data. Our results suggested that increasing levels of AACT are associated with a poorer outcome, and that patients with a low level of the protein had longer survival. These results need to be confirmed in independent validation trials, but are similar to those of a previous study that found an association between AACT and pancreatic cancer11
. Additional studies have shown correlations between the concentration of AACT and both advanced stage and poor prognosis in gastric cancer12
and lung adenocarcinoma13
AACT is a member of the serpin (ser
hibitor) superfamily. Serine proteases expressed in the tumor microenvironment are essential to the tissue remodeling seen in malignant progression. While some protease inhibitors inhibit tumor progression (e.g.
, maspin, which is down-regulated in invasive breast carcinoma14
), others, including AACT, appear to promote tumor progression15
. It was shown that tumors from a metastatic breast cancer cell line had higher levels of expression of AACT than those produced from a non-metastatic cell line16
. Additionally, malignant breast tumor cells induced local AACT expression by host cells at primary and secondary tumor sites16
. Although these experiments indicate a role for AACT in tumor progression, the exact mechanism of action has not been elucidated.
Of the two putative predictive markers found from the MS-based discovery phase, neither retained a significant relationship between response to combined therapy and outcome when tested with whole serum by ELISA. The serum concentration of HRG did, however, display a marginal association with overall survival. Since HRG has been found to possess antiangiogenic properties in tumors, a positive association with survival is understandable17
. A more thorough elucidation of the role of HRG in pancreatic cancer will require additional investigation.
While the literature is replete with protein expression profiling studies in search of biomarkers, it is becoming apparent that there are fundamental limitations to this type of discovery approach. First, most discovery studies measure altered protein abundance, but do not evaluate post-translational modifications of proteins or alterations in protein amino acid sequence as a result of mutation. Thus, important distinguishing features of tumor protein expression may be missed. Second, there can be methodological limitations to biomarker discovery. We observed an inconsistent correlation between the peptide abundance by quantitative nano-LC-MS/MS and the protein level by ELISA. While it has been shown that label-free direct quantitation, as was used here, can be an effective method to compare relative peptide abundances among comparable samples18
, it is not guaranteed that these peptide abundances will necessarily correlate with those of the intact proteins to which the peptides map. One possible reason for this is ion suppression, which occurs when co-eluting peptides compete for ionization and, hence, detection by the mass spectrometer19, 20
. This can result in the underestimation of the concentration of one or more peptides relative to the amount of intact protein present in the original complex mixture.
In addition, we depleted the most abundant proteins from serum for the discovery phase but used whole serum for validation by ELISA. While antibody-based depletion methods, like the MARS columns used in the current study, have been shown to be robust and reproducible with respect to the targeted proteins, problems with depletion of off-target proteins remain21
. In our studies, peptides mapping to HRG exhibited a statistically significant correlation with response to bevacizumab yet the intact protein, as quantified by ELISA did not. HRG is known to bind immunoglobulins21
and could have been co-depleted to varying degrees along with the intended proteins. Likewise, the serum level of CFH could have been altered during the targeted depletion of complement C3, which is a known binding partner.
Finally, and just as importantly, it is uncertain whether differentially expressed tumor proteins will have sufficient abundance to produce a significant change in systemic levels. Thus despite significant advances in proteomic technology, discovery programs may be limited to a common set of relatively abundant host response proteins, which may be important for tumor formation or inhibition, but may lack specificity to define a tumor cell type.
While the theoretical considerations for employing biomarkers appear logical, the reality of introducing relevant diagnostics has been problematic. Since tumors are heterogeneous, it is unlikely that a single biomarker will have sufficient power to govern clinical practice. The solution has been to develop a panel of differentially expressed markers from platforms that interrogate a large number of proteins. In this discovery scenario, it is not difficult to find potential markers as there are a large number of features (i.e., peptide signals in this study) relative to the number of subjects.
Additionally, mining algorithms can be unstable and may overfit the data in this setting. This generates a number of potential leads, thus requiring significant resources for assay development and validation in independent trials. When not accounting for multiple testing, this approach is certain to yield a significant number of false positive results, which do not come to fruition on further analysis. Although proteomic techniques are becoming increasingly advanced, it still appears that novel strategies will be needed if this field is to make significant advances in clinical diagnostics.
By incorporating a detailed correlative sciences plan into the design of CALGB 80303, biological specimens could be probed and information could be gained despite the lack of a treatment effect of the experimental arm. This enabled the identification of a possible prognostic marker (AACT) for pancreatic cancer, which can now be tested in a prospective trial. If it is eventually validated, future trials could possibly use AACT in conjunction with other markers to stratify patients in the effort to enrich for patient populations more or less likely to benefit from given treatments.