Biomarkers in Cancer

Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer

BACKGROUND

The objective of this study was to discover and to validate novel noninvasive biomarkers that distinguish between benign prostate hyperplasia (BPH) and localized prostate cancer (PCa), thereby helping to solve the diagnostic dilemma confronting clinicians who treat these patients.

METHODS

Quantitative iTRAQ LC/LC/MS/MS analysis was used to identify proteins that are differentially expressed in the urine of men with BPH compared with those who have localized PCa. These proteins were validated in 173 urine samples from patients diagnosed with BPH (N = 83) and PCa (N = 90). Multivariate logistic regression analysis was used to identify the predictive biomarkers.

RESULTS

Three proteins, β2M, PGA3, and MUC3 were identified by iTRAQ and validated by immunoblot analyses. Univariate analysis demonstrated significant elevations in urinary β2M (P < 0.001), PGA3 (P = 0.006), and MUC3 (P = 0.018) levels found in the urine of PCa patients. Multivariate logistic regression analysis revealed AUC values ranging from 0.618 for MUC3 (P = 0.009), 0.625 for PGA3 (P < 0.008), and 0.668 for β2M (P < 0.001). The combination of all three demonstrated an AUC of 0.710 (95% CI: 0.631 - 0.788, P < 0.001); diagnostic accuracy improved even more when these data were combined with PSA categories (AUC = 0.812, (95% CI: 0.740 - 0.885, P < 0.001).

CONCLUSIONS

Urinary β2M, PGA3, and MUC3, when analyzed alone or when multiplexed with clinically defined categories of PSA, may be clinically useful in noninvasively resolving the dilemma of effectively discriminating between BPH and localized PCa.

Urinary TIMP-1 and MMP-2 levels detect the presence of pancreatic malignancies

Background:

A majority of patients with pancreatic malignancies, including both pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumours (pNETs), present with advanced disease due to a lack of specific symptoms and current diagnostic limitations, making this disease extremely difficult to detect. Our goal was to determine whether urinary matrix metalloproteases (uMMPs) and/or their endogenous inhibitors, urinary tissue inhibitor of metalloproteases (uTIMPs), could be detected in the urine of patients with pancreatic malignancies and whether they may serve as independent predictors of disease status.

Methods:

Retrospective analyses of urine samples (n=139) from PDAC and pNET patients as well as age- and sex-matched controls were conducted. Urinary MMP-2 and uTIMP-1 levels were determined using ELISA and zymography. Biomarker expression in tumour and normal pancreatic tissues was analysed via immunohistochemistry (IHC).

Results:

Multivariable logistic regression analyses indicated that, when controlling for age and sex, uMMP-2 (P<0.0001) and uTIMP-1 (P<0.0001) but not uMMP-9, were significant independent predictors for distinguishing between PDAC patients and healthy controls. Our data also indicated that uMMP-2 was an independent predictor of the presence of pNET. In addition, uTIMP-1 levels could differentiate the two cancer groups, PDAC and pNET, respectively. Immunohistochemistry analysis confirmed that MMP-2 and TIMP-1 protein expression is significantly upregulated in PDAC tissue compared with the normal pancreas.

Conclusions:

Taken together, our results suggest that the detection of uMMP-2 and uTIMP-1 may have diagnostic value in the detection of pancreatic malignancies and that uTIMP-1 may be useful in distinguishing between pancreatic adenocarcinoma and neuroendocrine tumours.

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

Many diseases result in specific and characteristic changes in the chemical and biochemical profiles of biological fluids and tissues prior to development of clinical symptoms. These changes are often useful diagnostic and prognostic biomarkers. Identifying biomarkers that can be used for the early detection of cancer will result in more efficient treatments, reduction in suffering, and lower mortality rates. An ideal screening test should be non-invasive with high sensitivity and specificity. Proteomic and metabolomic analyses of biological samples can reveal changes in abundance levels of metabolites and proteins that when validated and confirmed through clinical trials can function as clinical tests for early detection, diagnosis, monitoring disease progression, and predicting therapeutic response. While the past decade has seen great advancements in proteomics and metabolomics research producing potential biomarkers for cancer, most of the identified biomarkers have failed to replace existing clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific, and sensitive. A search of the scientific and medical literature indicates that many studies report the discovery of potential biomarkers without proper validation and/or they do not meet the above criteria. In this manuscript, we will discuss the successes and the pitfalls of biomarker research and comment on study and experimental design, which in most cases is lacking, resulting in suboptimal biomarkers.