MALDI-TOF-MS analysis was used to generate proteomic profiles from plasma samples to identify biomarkers for prostate cancer. Samples were prepared using magnetic beads to separate proteins, then analyzed using MALDI-TOF-MS. Bioinformatics tools were used to generate classification models to distinguish prostate cancer patients from healthy controls based on differences in peak intensities. A 5-peak model achieved 87.5% sensitivity and 92.9% specificity. The study demonstrated the potential of MALDI-TOF proteomic profiling for early prostate cancer screening and diagnosis in Egypt. Proteomic biomarkers may help reduce unnecessary biopsies and stratify patients in the future.

1. MALDI-TOF-MS analysis in
identification of prostate cancer
Prof./Moustafa Rizk
Clinical pathology department
Facutly of Medicine, Alexandria University
Clinical Pathology Department
Assiut university
20/2/2019

2. Today I’ll cover
Prostate Cancer Risk Factors
Prostate cancer detection using PSA
Proteomic pattern in Prostate Cancer
Sample preparation
Sample separation
Sample identification
Model Generation
Bioinformatics
The 5- peak model used
Conclusion

3. Epidemiology

4. Epidemiology
In Egypt in the year 2008, prostate cancer diagnosis was approximately 1,661 men with a rate of 6.6 per 100,000
and 1,283 men were expected to die from PCa with a rate of 5.1 per 100,000

5. Five most frequent cancers in MENA compared with North America and rest of the World

6. Prostate Cancer Risk Factors
Established
Advancing Age
Race/ethnicity
Geography
Family history
Gene changes
Presence of androgens
Potential
High dietary fat , Obesity
Sexual transmitted diseases
Smoking
Alcohol consumption
Vitamin D or E deficiency
Selenium deficiency

8. Pathophysiology
1. Prostatic intraepithelial neoplasia [PIN]
2. Adenocarcinoma (More than 95%)
3. Other rare types:
a. Ductal carcinoma ( 1% of prostatic adenocarcinoma )
b. Transitional cell carcinoma
c. Small cell (neuroendocrine) carcinoma
d. Mesenchymal tumors (0.1% to 0.2%)
e. Urothelial carcinoma (1% to 4% )
f. Metastatic (From colorectal tumours)

9. The US preventive services task force (USPSTF) no longer recommends routine PSA screening

10. Prostate cancer detection as a function of serum PSA level and
DRE findings in a contemporary series

11. PSA Screening
Pros Cons
PSA screening may help you detect prostate cancer early. Some prostate cancers are slow growing and never spread beyond
the prostate gland.
Cancer is easier to treat and is more likely to be cured if it's
diagnosed in the early stages of the disease.
Not all prostate cancers need treatment. Treatment for prostate
cancer may have risks and side effects, including urinary
incontinence, erectile dysfunction or bowel dysfunction.
PSA testing can be done with a simple, widely available blood test. PSA tests aren't foolproof. It's possible for your PSA levels to be
elevated when cancer isn't present, and to not be elevated when
cancer is present.
For some men, knowing is better than not knowing. Having the test
can provide you with a certain amount of reassurance — either that
you probably don't have prostate cancer or that you do have it and
can now have it treated.
A diagnosis of prostate cancer can provoke anxiety and confusion.
Concern that the cancer may not be life-threatening can make
decision-making complicated.
The number of deaths from prostate cancer has gone down since
PSA testing became available.
PSA testing has lowered deaths, but the number may not be
substantial enough to justify the cost and possibility of harm to the
person undergoing the testing.

12. Schematic outline presenting the key elements of routine clinical practice in the management of patients with prostate cancer.
The context of use for novel biomarkers is indicated in each case.

13. Proteomic pattern in Prostate Cancer
Sample preparation
 Platelet-depleted EDTA plasma is preferable to serum
 The addition of protease inhibitors is recommended, but should be incorporated
early and used wisely
 Transport of samples must be on ice
 Samples to be centrifuged in a cold centrifuge at 4oC for 15 minutes at 1800 g
 Samples should be aliquoted in DNA low bind Eppendorf tubes
 Eppendorf was numbered and stored in a box in a -80oC freezer
 Further, the use of reference materials for quality control and quality assurance
is recommended
 All samples during the collection should be handled in a similar manner.

14. Sample separation
Using magnetic beads as a method for peptide/protein capture
from complex biological samples :
 WCX (weak cation exchange) separate proteins based on charge
 RPC18 beads C8 (reversed phase) separate proteins based on strong
hydrophobic interaction
MagSi-WCX beads
Efficient and reproducible way before MALDI-TOF/MS analysis

15. 1. 1 µL of the sample elute was applied
to a target spot and left to dry at
room temperature.
2. Then 1 µL of MALDI-Matrix
HCCA was applied on the top of the
sample spot and left to dry.
3. 2-4 spots were spotted on the target
for each sample.
Polished steel target
Sample spotting on the polished steel target

16. Sample identification
Bruker Daltonics, Germany

17.  FlexControl software allows the introduction of the target into the
MS.
 Before starting the MS analysis, the FlexControl™ software was
calibrated and optimized using the ClinProt standard.
17
Spectra Acquisition
Using MALDI-TOF-MS (Bruker Daltonics, Germany)

18. Constitution of the ClinProt standard
Calibration was done using
Clinprot standard (CPA)
which was spotted on the
target and tested as the
samples.
Detection limit of mass spectrometer was set to 800-20000 Da

19. Flex control analysis program
 For each spot, 3000 shots were done by shooting 500 laser shots at 6 different spot positions
 After finishing the shooting of spots of each sample, they were gathered into one spectrum

20. MALDI ionization technique
Laser beams hit the matrix-analyte mixture, causing ionization of the analyte

21. Analyte ions are produced after
ionization in the ion source.
The ions that are produced are
separated according to their
mass-to-charge ratio (m/z)
The output, which is recorded
at the detector, is the intensity
at different m/z values.
The result is visualized as a
m/z vs intensity (mass
spectrum)
Mass Spectrum

22. Typical MALDI-TOF MS spectrum
Each ion has a single charge
(z = 1); thus, the m/z ratio is
equal to the mass, meaning
the mass is the variable that
determines the time of flight
and which affects the
separation

23. Bioinformatics is the field of science in which biology, computer science, and information
technology merge to form a single discipline. As large volumes of proteomics data are generated,
subsequent database searching must keep pace.
Bioinformatics and database utilization
3
23
Definition
http://www.openms.de/
WHAT IS OPENMS?
 OpenMS offers an open-source software library for LC/MS data management
and analyses.
 It provides an infrastructure for the rapid development of mass spectrometry
related software
 OpenMS is free software available

25.  Each group has been randomly distributed into two sets: training set for model generation, and
validation set for external validation.
 The spectra of the training sets of studied groups were loaded onto the software.
 Models were generated using Genetic Algorithm (GA), Supervised Neural Network (SNN) and
QuickClassifier (QC).
25
ClinProTools Version: 3.0 build 22
Name Algo
Validation
XVal X1 X2
Recogn
ition
Capabil
ity
Patients vs controls GA 91.5 % 88.1 % 94.8 % 100 %
Patients vs controls SNN 84 % 90.5 % 77.6 % 100 %
Patients vs controls QC 86.4 % 95.2 % 77.6 % 98.2 %
Model Generation

26. The 5- peak model used for discriminating between prostate cancer and
healthy control
26
Index Mass Start Mass End Mass
11 2485.97 2478.63 2496
3 1061.24 1057.89 1068.51
24 3295.1 3288.51 3306.45
33 4612.54 4603.15 4622.91
14 2817.28 2808.44 2825.01
This model achieved a sensitivity of 87.5 % and a specificity of 92.9 % during
external validation.

27. spectra of class I (patients) in red color against class II
(controls) in green

28. Pseudo-gel view
Control subjects
Patients
Down regulation of
peak 11 (m/z
2485.97)
Peak distribution
command for peak 11
(m/z 2485.97)
for patients and
controls

29. The peak is over expressed
.
Peak 24 with m/z ratio 3295.1
Patients (red)
Controls (grey)
Peak distribution command
Peak 24 with m/z ratio 3295.1
Patients (red)
Controls (grey)

30. 30

31. It could be concluded that:
01
02
Inthe era of “big data” and “personalized medicine”
proteomics-based biomarkers hold great promise to
provide clinically applicable tools
31
MALDI-TOF proteomic profiling represents a new
frontier for screening and early diagnosis of Prostate
cancer in Egypt.

32. 32
Proteomic biomarkers alone and or in
combination with clinical and pathological
risk calculators may be in future expected
to improve on decreasing the unnecessary
biopsies, stratify low risk patients, and
predict response to treatment

33. Questions or Comments