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1.  Proteomics and biomarkers in clinical trials for drug development 
Journal of proteomics  2011;74(12):2632-2641.
Proteomics allows characterization of protein structure and function, protein-protein interactions, and peptide modifications. It has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new therapeutic targets and possible indicators of response to and duration of therapy. The discovery, verification, and validation of novel biomarkers are critical in streamlining clinical development of targeted compounds, and directing rational treatments for patients whose tumors are dependent upon select signaling pathways. Studies are now underway in many diseases to examine the immune or inflammatory proteome, vascular proteome, cancer or disease proteome, and other subsets of the specific pathology microenvironment. Successful assay verification and biological validation of such biomarkers will speed development of potential agents to targetable dominant pathways and lead to selection of individuals most likely to benefit. Reconsideration of analytical and clinical trials methods for acquisition, examination, and translation of proteomics data must occur before we march further into future of drug development.
doi:10.1016/j.jprot.2011.04.023
PMCID: PMC3158266  PMID: 21570499
proteomics; biomarkers; clinical trial; drug development; cancer; targeted therapy
2.  PROGRANULIN IS A POTENTIAL PROGNOSTIC BIOMARKER IN ADVANCED EPITHELIAL OVARIAN CANCERS 
Gynecologic oncology  2010;120(1):5-10.
Purpose
There are few validated relapse prediction biomarkers for epithelial ovarian cancer (EOC). We have shown progranulin (PGRN) and secretory leukocyte protease inhibitor (SLPI) are up regulated, overexpressed survival factors in EOC. We hypothesized they would predict presence of occult EOC.
Methods
PGRN, SLPI, and the known biomarker HE4 were measured in EOC patient plasma samples, prospectively collected every 3 months from initial remission until relapse. Clinical data and CA125 results were incorporated into statistical analyses. Exploratory Kaplan-Meier estimates, dividing markers at median values, evaluated association with progression-free survival (PFS) and overall survival (OS). Area-under-the-curve (AUC) statistics were computed from receiver operating characteristic (ROC) curves to evaluate discrimination ability. A Cox proportional hazards model assessed the association between PFS, OS, and biomarkers, adjusting for clinical prognostic factors.
Results
Samples from 23 advanced stage EOC patients were evaluated. PGRN at 3 months was the only biomarker independently associated with PFS (P<0.0001) and OS (P<0.003). When used to predict progression by 18 months, sensitivity and specificity were 93% and 100%, respectively, with AUC = 0.944. The Cox model hazard ratio for PFS, divided at 59 ng/ml by ROC analysis and adjusted for clinical factors, was 23.5 (95% CI: 2.49–220). Combinations with SLPI, HE4, and/or CA125 did not improve the model.
Conclusions
We report pilot data indicating a potential independent association of PGRN on EOC patient PFS and OS. A validation study will be required to confirm this finding and to inform whether PGRN warrants evaluation as a potential screening biomarker.
doi:10.1016/j.ygyno.2010.09.006
PMCID: PMC2997933  PMID: 20950846
Progranulin (PGRN); biomarkers; progression free survival; overall survival; epithelial ovarian cancer

Results 1-2 (2)