Christer Wingren

Pancreatic cancer is an aggressive disease with poor prognosis, due, in part, to the lack of disease-specific biomarkers that could afford early and accurate diagnosis. With a recombinant antibody microarray platform, targeting mainly immunoregulatory proteins, we screened sera from 148 patients with pancreatic cancer, chronic pancreatitis, autoimmune pancreatitis (AIP), and healthy controls (N). Serum biomarker signatures were derived from training cohorts and the predictive power was evaluated using independent test cohorts. The results identified serum portraits distinguishing pancreatic cancer from N [receiver operating characteristics area under the curve (AUC) of 0.95], chronic pancreatitis (0.86), and AIP (0.99). Importantly, a 25-serum biomarker signature discriminating pancreatic cancer from the combined group of N, chronic pancreatitis, and AIP was determined. This signature exhibited a high diagnostic potential (AUC of 0.88). In summary, we present the first prevalidated, multiplexed serum biomarker signature for diagnosis of pancreatic cancer that may improve diagnosis and prevention in premalignant diseases and in screening of high-risk individuals. Cancer Res; 72(10); 2481–90. ©2012 AACR.

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Antibody-based proteomic approaches play an important role in high-throughput, multiplexed protein expression profiling in health and disease. These antibody-based technologies will provide (miniaturized) set-ups capable of the simultaneously profiling of numerous proteins in a specific, sensitive, and rapid manner, targeting high- as well as low-abundant proteins, even in crude proteomes such as serum. The generated protein expression patterns, or proteomic snapshots, can then be transformed into proteomic maps, or detailed molecular fingerprints, revealing the composition of the target (sample) proteome at a molecular level. By using bioinformatics, candidate biomarker signatures can be deciphered and evaluated for clinical applicability. The approaches will provide unique opportunities for e.g. disease diagnostics, biomarker discovery, patient stratification, predicting disease recurrence, and evidence-based therapy selection. In this review, we describe the current status of the antibody-based proteomic approaches, focusing on antibody arrays. Furthermore, the current benefits and limitations of the approaches, as well as a set of selected key applications outlining the applicative potential will be discussed.