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The current gold standard for diagnostic classification of many solid-tissue neoplasms is immunohistochemistry (IHC) performed on formalin-fixed, paraffin-embedded (FFPE) tissue. Although IHC is commonly used, there remain important issues related to preanalytic variability, nonstandard methods, and operator bias that may contribute to clinically significant error. To increase the quantitative accuracy and reliability of FFPE tissue–based diagnosis, we sought to develop a clinical proteomic method to characterize protein expression in pathologic tissue samples rapidly and quantitatively.

We subclassified FFPE tissue from 136 clinical pituitary adenoma samples according to hormone translation with IHC and then extracted tissue proteins and quantified pituitary hormones with multiplex bead-based immunoassays. Hormone concentrations were normalized and compared across diagnostic groups. We developed a quantitative classification scheme for pituitary adenomas on archived samples and validated it on prospectively collected clinical samples.

The most abundant relative hormone concentrations differentiated sensitively and specifically between IHC-classified hormone-expressing adenoma types, correctly predicting IHC-positive diagnoses in 85% of cases overall, with discrepancies found only in cases of clinically nonfunctioning adenomas. Several adenomas with clinically relevant hormone-expressing phenotypes were identified with this assay yet called "null" by IHC, suggesting that multiplex immunoassays may be more sensitive than IHC for detecting clinically meaningful protein expression.

Multiplex immunoassays performed on FFPE tissue extracts can provide diagnostically relevant information and may exceed the performance of IHC in classifying some pituitary neoplasms. This technique is simple, largely amenable to automation, and likely applicable to other diagnostic problems in molecular pathology.

Aberrantly methylated genes represent important markers for cancer diagnosis. We describe a multiplex detection approach to efficiently quantify these markers for clinical applications such as colorectal cancer screening.

Quantitative allele-specific real-time target and signal amplification (QuARTS) combines a polymerase-based target amplification with an invasive cleavage-based signal amplification. The fluorescence signal is detected in a fashion similar to real-time PCR. We measured the dynamic range and analytical sensitivity of multiplex QuARTS reactions with titrated plasmid DNA. We used the QuARTS technology to quantify methylated BMP3, NDRG4, VIM, and TFPI2 genes on 91 DNA samples extracted from colorectal tissues, including 37 cancers, 25 adenomas, and 29 healthy epithelia. The assays were designed in triplex format that incorporated ACTB as a reference gene. Percent methylation was calculated by dividing methylated strands over ACTB strands and multiplying by 100.

The QuARTS method linearly detected methylated or unmethylated VIM gene down to 10 copies. No cross-reactivity was observed when methylated assays were used to amplify 10⁵ copies of unmethylated gene and vice versa. The multiplex assay detected methylated genes spiked in unmethylated genes at a 0.01% ratio and vice versa. At a diagnostic specificity cutoff of 95%, methylated BMP3, NDRG4, VIM, and TFPI2 detected 84%, 92%, 86%, and 92% of colorectal cancers and 68%, 76%, 76%, and 88% of adenomas, respectively.

The QuARTS technology provides a promising approach for quantifying methylated markers. The markers assayed highly discriminated colorectal neoplasia from healthy epithelia.

Measurement of prostate-specific antigen (PSA) in prostate cancer patients following radical prostatectomy (RP) has been hindered by the limit of quantification of available assays. Because radical prostatectomy removes the tissue responsible for PSA production, postsurgical PSA is typically undetectable with current assay methods. Evidence suggests, however, that more sensitive determination of PSA status following RP could improve assessment of patient prognosis and response to treatment and better target secondary therapy for those who may benefit most. We developed an investigational digital immunoassay with a limit of quantification 2 logs lower than current ultrasensitive third-generation PSA assays.

We developed reagents for a bead-based ELISA for use with high-density arrays of femtoliter-volume wells. Anti-PSA capture beads with immunocomplexes and associated enzyme labels were singulated within the wells of the arrays and interrogated for the presence of enzymatic product. We characterized analytical performance, compared its accuracy with a commercially available test, and analyzed longitudinal serum samples from a pilot study of 33 RP patients.

The assay exhibited a functional sensitivity (20% interassay CV) <0.05 pg/mL, total imprecision <10% from 1 to 50 pg/mL, and excellent agreement with the comparator method. All RP samples were well within the assay measurement capability. PSA concentrations following surgery were found to be predictive of prostate cancer recurrence risk over 5 years.

The robust 2-log improvement in limit of quantification relative to current ultrasensitive assays and the validated analytical performance of the assay allow for accurate assessment of PSA status after RP.

The use of different mathematical models to support medical decisions is accompanied by increasing uncertainties when they are applied in practice. Using prostate cancer (PCa) risk models as an example, we recommend requirements for model development and draw attention to possible pitfalls so as to avoid the uncritical use of these models.

We conducted MEDLINE searches for applications of multivariate models supporting the prediction of PCa risk. We critically reviewed the methodological aspects of model development and the biological and analytical variability of the parameters used for model development. In addition, we reviewed the role of prostate biopsy as the gold standard for confirming diagnoses. In addition, we analyzed different methods of model evaluation with respect to their application to different populations. When using models in clinical practice, one must validate the results with a population from the application field. Typical model characteristics (such as discrimination performance and calibration) and methods for assessing the risk of a decision should be used when evaluating a model's output. The choice of a model should be based on these results and on the practicality of its use.

To avoid possible errors in applying prediction models (the risk of PCa, for example) requires examining the possible pitfalls of the underlying mathematical models in the context of the individual case. The main tools for this purpose are discrimination, calibration, and decision curve analysis.

Human epididymis protein 4 (HE4), a precursor of human epididymis protein, has been proposed as a tumor marker for ovarian cancer. We evaluated HE4 in comparison with cancer antigen 125 (CA 125) in healthy individuals and in patients with benign and malignant diseases.

CA 125 and HE4 serum concentrations were determined in 101 healthy individuals, 535 patients with benign pathologies (292 with benign gynecologic diseases) and 423 patients with malignant diseases (127 with ovarian cancers). CA 125 and HE4 cutoffs were 35 kU/L and 140 pmol/L, respectively.

HE4 and CA 125 results were abnormal in 1.1% and 9.9% of healthy individuals and in 12.3% and 37% of patients with benign diseases, respectively. Renal failure was the most common cause of increased HE4 in patients with benign disease, who had significantly higher HE4 concentrations (P = 0.001) than patients with other benign diseases. HE4 showed a higher specificity than CA 125 in patients with benign gynecologic diseases, with abnormal concentrations in 1.3% and 33.2% of the patients, respectively. HE-4 concentrations were abnormal primarily in gynecologic cancer and lung cancer. By contrast, CA 125 was increased in many different nonovarian malignancies, including nonepithelial tumors. A significantly higher area under the ROC curve was obtained with HE4 than with CA 125 for differentiating benign from malignant diseases (0.755 vs 0.643) and in the differential diagnosis of gynecologic diseases (0.874 vs 0.722).

HE4 has significantly higher diagnostic specificity than CA 125, and the combination of CA 125 and HE4 improved the detection of ovarian cancer in all stages and histological types.

Prostate cancer is the most commonly diagnosed cancer among men in North America and is a leading cause of death. Standard treatments include androgen deprivation therapy, which leads to improved clinical outcomes. However, over time, most tumors become androgen independent and no longer respond to hormonal therapies. Several mechanisms have been implicated in the progression of prostate cancer to androgen independence.

Most tumors that have become androgen independent still rely on androgen receptor (AR) signaling. Mechanisms that enhance AR signaling in androgen-depleted conditions include: AR gene amplification, AR mutations, changes in the balance of AR cofactors, increases in steroidogenic precursors, and activation via "outlaw" pathways. Along with AR signaling, various other AR-independent "bypass" pathways have been shown to operate aberrantly during androgen independence. Changes in the epigenetic signatures and microRNA concentrations have also been implicated in the development of androgen-independent prostate cancer.

Understanding of the molecular mechanisms that lead to the development of androgen-independent prostate cancer will allow for improved therapeutic strategies that target key pathways and molecules that are essential for these cells to survive.

Circulating tumor cell (CTC) analysis is a promising new diagnostic field for estimating the risk for metastatic relapse and metastatic progression in patients with cancer.

Different analytical systems for CTC isolation and detection have been developed as immunocytochemical and molecular assays, most including separation steps by size or biological characteristics, such as expression of epithelial- or cancer-specific markers. Recent technical advancements in CTC detection and characterization include methods based on multiplex reverse-transcription quantitative PCR and approaches based on imaging and microfilter and microchip devices. New areas of research are directed toward developing novel assays for CTC molecular characterization. QC is an important issue for CTC analysis, and standardization of micrometastatic cell detection and characterization methodologies is important for the incorporation of CTCs into prospective clinical trials to test their clinical utility. The molecular characterization of CTCs can provide important information on the molecular and biological nature of these cells, such as the status of hormone receptors and epidermal and other growth factor receptor family members, and indications of stem-cell characteristics. This information is important for the identification of therapeutic targets and resistance mechanisms in CTCs as well as for the stratification of patients and real-time monitoring of systemic therapies.

CTC analysis can be used as a liquid biopsy approach for prognostic and predictive purposes in breast and other cancers. In this review we focus on state-of-the-art technology platforms for CTC isolation, imaging, and detection; QC of CTC analysis; and ongoing challenges for the molecular characterization of CTCs.

Background: Cancer has downright effects on gene expression, including a cell’s glycosylation machinery. Thus, tumors show glycoproteins that conduct oligosaccharides with structures that are markedly sundry from the after all is said protein produced by a general room. A pick protein can deliver assorted glycosylation sites that greatly detail the signals they forge compared with their protein backbones.

Content: In this article, we scan clinical tests that end carbohydrate modifications for diagnosing and treating cancer. We up to date the biological suitableness of glycosylation to contagion making by highlighting the part these structures entertainment in adhesion, signaling, and metastasis and then talk to drift methodological approaches to biomarker finding that capitalize on selectively capturing tumor-associated glycoforms to adorn and sort out disease-related seeker analytes. Finally, we examine emerging technologies—multiple revenge monitoring and lectin-antibody arrays—as capacity tools for biomarker validation studies in running down of clinically utilitarian tests.

Summary: The unborn of carbohydrate-based biomarker studies has arrived. At all stages, from ascertaining into done with verification and deployment into clinics, glycosylation should be considered a cardinal readout or a way of increasing the susceptivity and specificity of protein-based analyses.