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Fisher Biomarker Research Laboratory
THE FUTURE: A COMPUTATIONAL MULTIVARIATE BIOMARKER APPROACH TO MAKE DISEASE PREDICTIONS

What does the future hold? 

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Today we now the bioinformatic tools necessary to permit high-dimensional data analysis of all types of clinical, image and molecular biomarker data to be combined, allowing the generation of computational solutions that can be readily validated in appropriate clinical settings for appropriate Government regulatory approval. Significant challenges remain:

  1. there is as yet no strong commercial partner to engineer the hardware with the correct specifications for the microscope, camera and software;
  2. direct collaboration with urologists and pathologists is required to ensure that the appropriate clinical dilemmas are addressed;
  3. validation studies are needed to verify the individual clinical applications; and
  4. FDA-approved trials must be carried out to establish specific claims. Furthermore, to benefit patient management, these new tools will require full automation and technical and clinical validation in multisite studies for various specific outcomes of PCa, such as active surveillance, biochemical recurrence, metastasis, and survival.

PROTEOMICS IN PROSTATE CANCER

Using the 2D Electrophoresis method (Figure 14) several unique prostate cancer associated proteins can be identified. The Partin-Veltri Laboratory still utilizes this technology in addition to a specific means to analyze and quantify the proteins we identify (Western Blot and ELISA). An example is provided for 2D on bladder cancer and control extracts.

2D ELECTROPHORESIS:

FIGURE 14

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FIGURE 15

Current 2D Proteomics Experience in Bladder Cancer (Dr. Veltri): In the Figure 15 below we demonstrate preliminary results for 2D PAGE comparing normal and a TCC versus a SCC cell line solubilized extracts.

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HBdMEC-p (normal endothelial bladder cell line); the Scaber (a squamous cell carcinoma, SCC cell line) and transitional cell carcinoma superficial (TCCSUP) were analyzed.  A total of 50 μg protein was dissolved in a special buffer and then Isoelectric focusing was run with using BioRad protean IEF cell system and 2nd dimensional separation was analyzed using precast 17cm gels. By visually comparing Normal, Scaber and TCCSUP cell line 2D gel, there were several gel spots present only in Scaber cell line 2D gel and we are pursuing these as possible targets .

WESTERN BLOTS (How to identify and quantify proteins of interest?)

The laboratory uses the Mini-Trans-Blot Cell from Bio-Rad Inc. (Figure 16a).  Usually we add ~40µg of whole cell lysate protein from PC3-Epi or PC3-EMT tissue culture cell lines or human prostate cancer tissue extracts were run on a 12% TGX gel and gel electrophoresis for 35min. The proteins were transferred onto nitrocellulose membranes using TransBlot® Turbo™ Transfer System. The antibodies and dilutions used in the study are: RBM3 (1:1000, Sigma), Her2/neu (1:100, Thermo Scientific), EZH2 (1:2000, Cell signaling), β-actin (1:15,000, Sigma), PCNA (1:2000, Origene) and PBOV1 (1:2000, Epitope B, Beckman Coulter). The membranes were scanned with the Odyssey imaging system to generate the pictures. Figure 16b

FIGURE 16a

FIGURE 16b – Cell Lines

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"FIELD EFFECTS" IN PCa:  Figure 16c demonstrates the PBOV-1 protein expression in both benign-cancer adjacent and cancer areas for paired indolent vs. aggressive prostate cancer cases.  Clearly there is a "field effect" or molecular changes noted in the benign area near the cancer. Also, The biomarker in the cancer area is more over-expressed for the aggressive tumor area vs. normal adjacent areas.  No such differences are noted in the less aggressive cancer phenotype.


FIGURE 16c

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Prostate Cancer Definitions

Indolent N      = Ind. n

Indolent T      = Ind. T

Aggressive T  = Agg. T

Aggressive N   = Agg. N

CAPILLARY ELECTROPHORESIS TO CHARACTERIZE PROTEIN BIOMARKERS
Another approach to analyzing proteins in the µg (microgram) range of concentration is capillary electrophoresis shown below in Figure 17a.  The method is often used to characterize purity on antibodies, serum electrophoresis, and characterization of protein extracts of cell lines or tumors.  An example of the latter is an electrophoresis result (Figure 17b) for the Rat CaP Dunning tumor [G = indolent; AT6 = aggressive].

FIGURE 17a

FIGURE 17b

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ELISA TECHNOLOGY

Figure 18aprostate cancer research

The protein immunoassay format to easily quantify analytes that meets most of the criteria for specificity, sensitivity, reproducibility, and accuracy in many situations is the Enzyme-Linked Immunosorbent Assay (ELISA). Today, most ELISA’s follow one of three strategies: Indirect ELISA, typically used to screen for antibodies; Sandwich (or Antigen Capture) ELISA, to assay the amount of target antigen which is present Our laboratory use the Sandwich (or Antigen Capture) ELISA almost routinely for urine or serum immunoassays (Figure 18a).

Read at 450 nM in an automated ELISA microplate reader. Subtract blank from all the readings to normalize for background and plot the graphs with standard deviations.  A model ELISA is shown below for Annexin-A3:

Model AnnexinA3:

  1. Capture antibody was developed by under contract with the help of SDIX (Newark, Delaware) using 2-90 AA sequences of Annexin A3 recombinant protein and then the antibody affinity purified. ELISA plates were coated with 50µl of 2µg/ml capture AnnexinA3 Abper well and incubated overnight at 4oC.
  1. Detection antibody was developed under contrct with the help of SDIX, (Newark, Delaware) using 88-249 AA sequences of Annexin A3 recombinant protein and biotinylated. We used 50µl of AnnexinA3 detection antibody at 0.2µg/ml per well concentration in our assay.
  2. Standard test solutions: Recombinant protein (Abcam, cat# ab92929) was spiked in LowCross® buffer (control) [Candor Bioscience GmbH, Wangen, Germany] and in pooled normal urine or serum.

ELISA STANDARD CURVE: Once a standard curve is established as seen in Figure 16b , one can quantify the target analyte in serum, urine, cell line or tissue extracts with appropriate controls. Depending on several factors the technical sensitivity can be in pg or ng/ml.  Below is illustrated the performance of the Annexin-A3 combined with Endoglin and IL-8 markers in CaP patient’s urine (Figure 16c).  Our laboratory is currently developing several ELISA-based singlet assays for urine and serum in CaP for detection and prognosis.  

ELISA AnnexinA3 Standard Curves for R&D Specimens Assay [Figures 18b]

FIGURE 18b
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Combined ROC for Endoglin, IL-8 and Annexin-A3 in Urine

The Partin Nomogram - 2011
Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous PSA measurement, Clinical Stage and biopsy Gleason scoreYing Huang, PhD, Sumit Isharwal, MD, Alexander Haese, MD, Felix K.H. Chun, MD, Danil V. Makarov, MD, Ziding Feng, PhD, Misop Han, MD, Elizabeth Humphreys, BS, Jonathan I. Epstein, MD, Alan W. Partin, MD, PhD, and Robert W. Veltri, PhDBJU Int. 2011 May ; 107(10): 1562–1569.

We developed a "2010 Partin Nomogram" with tPSA as a continuous biomarker and employ "predictiveness curve" to calculate the percentile risk of a patient among the cohort.

The patient cohort included 5730 and 1646 men were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE) respectively.

The "2010 Partin Nomogram" calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's pre-operative clinical stage, tPSA, and biopsy Gleason score.  Figure 19 is a validation run comparing the performance of the JHH and UCHE (German) CaP datasets with a comparison based on AUC-ROCs in Figure 20 and Table 3 below the ROC graphs showing the actual numerical results.

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The predictiveness curves (solid lines) based on the "2010 Partin Nomogram" with regression spline of continuous tPSA, clinical stage, and biopsy Gleason score, as well as their 95% percentile bootstrap confidence intervals (dashed lines).

For more details please see
http://urology.jhu.edu/ 

Figure 20: Partin Nomogram Validation statistics: ROC curves for OC, EPE, SV+, LN+ for the model with categorical PSA and the model with continuous PSA. The first row is based on JHH dataset and the second row is based on validation with the UCHE dataset.

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Table 1
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The "2011 Partin Nomogram" calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's pre-operative clinical stage, tPSA, and biopsy Gleason score. While having comparable performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients’ pathological stage compared to the 2007 Partin Tables model. Further, the use of "predictiveness curves" has made it possible to obtain" percentile risk" of a patient among the cohort and to also gauge the impact of risk thresholds for making decision regarding radical prostatectomy. The "2010 Partin Nomogram" also using total PSA as a continuous biomarker together and with the corresponding "predictiveness curve" will aid clinicians and patients to make improved treatment decisions.





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