1. Progress on Biomarkers of Cancer Diagnosis and Prognosis
In The Name of God
Progress on Biomarkers of Cancer Diagnosis and Prognosis
Majid Kheirollahi
William Cho
Ph.D, Medical Genetics
Department of Medical Genetics
Isfahan University of Medical Sciences 1

2. William Cho

3. Biomarker (Tumor marker / Mol marker / Signature marker)
Definitions:
National Cancer Institute: A biological molecule found in blood, other body fluids or tissue that is sign of a normal or abnormal process or disease.
NIH, “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
May be a molecule secreted by a tumor or a specific response of the body to the presence of cancer

4. Expanding Interest in Biomarkers
Correlation: a biomarker vs a disease or status of a disease
Do not need understand functions
Detection: Detection of a particular marker is important
Validation: Build statistical correlation – large number of samples
Validation: sensitivity and specificity
Validation: Stand alone vs along with other markers

5. History of Cancer Biomarker Discovery
The first cancer biomarker : the light chain of immunoglobulin in urine (identified in 75% of patients with myeloma)
From 1930 to 1960, scientists identified numerous hormones, enzymes and other proteins
The modern era of monitoring malignant disease, however, began in the 1960s with the discovery of alfa-fetoprotein and carcinoembryonic antigen (CEA).
In 1980, prostate-specific antigen (PSA), considered one of the best cancer markers, was discovered

6. Biomarkers: Examples Metals & Minerals Gases Steroids & Hormones
Viruses & Bacterias
DNA
RNA
Proteins

7. An Ideal Biomarker Must be;
According to FDA an ideal biomarker should be specific, sensitive, simple and inexpensive.
It should be used in standard biological sources such as serum and urine as the basis of measurement.
Minimally invasive, easily measurable
Used in confirming the diagnosis
Used in predicting the adverse events, and clinical outcomes that will appear in the future

8. Biomarkers and Individualized Medicine
Correlation: a biomarker vs a disease or status of a disease
Do not need understand functions
Detection: Detection of a particular marker is important
Validation: Build statistical correlation – large number of samples
Validation: sensitivity and specificity
Validation: Stand alone vs along with other markers

9. Golden Time of Biomarkers Application
Detection of biomarker
Detection of biomarker
Quantitative
Qualitative
a link between exist of a marker and disease

10. Biomarkers with Clinical Application

11. 70 prognosis genes are involved in all aspects of tumor cell biology
proliferation
angiogenesis
adhesion to extracellular matrix
local invasion
intravasation, survival, extravasation
proliferation
angiogenesis
adhesion to extracellular matrix
William Cho
Genes of unknown function (25)

12. Strategies for Biomarker Discovery
Hypothesis-driven
approach
Search for difference
approach
Mechanism based
((Grounds up))
((Top down))

13. Biomarker Development Pipeline
Should have great sensitivity, specificity, and accuracy in reflecting total disease burden. A tumor marker should also be prognostic of outcome and predictive of tumor recurrence and effectiveness of anti-cancer treatments.

14. Phases of Evaluation of Biomarkers
In 2002, the National Cancer Institute’s ‘Early Detection Research Network’ developed a five-phase approach to systematic discovery and evaluation of biomarkers
Phase I refers to preclinical studies. Biomarkers are discovered through knowledge-based gene selection, gene expression profiling or protein profiling to distinguish cancer and normal samples
Phase II To document clinical usefulness, firstly, such assays need to be validated for reproducibility and shown to be portable among different laboratories.

15. Phases of Evaluation of Biomarkers
Phase III & Phase IV, an investigator evaluates the sensitivity and specificity of the test for the detection of diseases that have yet to be detected clinically.
It is usually time-consuming and expensive to collect these samples with high quality; therefore, phase III should consist of large cohort studies
Phase V evaluates the overall benefits and risks of the new diagnostic test on the screened population. This again requires a large-scale study over a long time period and could also be prohibitively expensive.
Phases IV is necessary to evaluate benefits and risks of the use of a biomarker in screening and detection.

17. Risk Assessment
Some genetic mutations increase the risk of eventually developing cancer. These biomarkers are said to predispose us to cancer. Examples of biomarkers associated with an increased risk of cancer are the BRCA1 and BRCA2 genes.
Harmful mutations in these genes can increase the chance of developing breast and other cancers in both men and women.
Individuals with these mutations can obtain more frequent screenings that may detect cancer in its early stages when it is more readily treated.

18. Diagnosis
Prostate Cancer Diagnosis with PSA
Cancer of the prostate does not cause any symptoms until it is locally advanced or metastatic. PSA is also found in the cytoplasm of benign prostate cells.
There is a correlation between elevated PSA and prostate cancer.
Diagnosis of PSA for prostate cancer in the most time means measurement of the PSA in serum samples.
Based on these data, PSA testing was approved by the US FDA for the screening and early detection of prostate cancer.

19. Diagnosis
Cancer biomarkers can also be useful in establishing a specific diagnosis. This is particularly the case when there is a need to determine whether tumors are of primary or metastatic origin.
To make this distinction, researchers can screen the chromosomal alterations found on cells located in the primary tumor site against those found in the secondary site.
If the alterations match, the secondary tumor can be identified as metastatic; whereas if the alterations differ, the secondary tumor can be identified as a distinct primary tumor.

20. Prognosis
Prognosis refers to the natural course of the disease in the absence of treatment. Some cancers are more aggressive than others and knowing this can help guide treatment.
If a biomarker can help distinguish a cancer that is likely to grow rapidly from one that is likely to grow slowly, then patients with these two types of cancers might receive different treatments.
An example of a potential prognostic biomarker is Telomerase in brain tumors.

21. Prediction of Treatment Response
Approximately one fourth of all breast cancers have too many copies of the HER2 gene, which go on to produce too much HER2 protein.
Another aspect of HER2/neu overexpression is that it causes breast cancers to grow and divide more quickly.
For this reason, over-expression of this gene is
also used as a prognostic biomarker whose
presence indicates a more aggressive cancer.
HER-2/neu is an example of a biomarker with
more than one use.

22. Therapy Target Her-2 Herceptin
HER2-positive metastatic breast cancer have a more aggressive disease, greater likelihood of recurrence, poorer prognosis and decreased survival.
Herceptin
Herceptin binds to HER2-positive cancer cells and may block them from dividing and growing.
Herceptin attaches to the HER2-positive cancer cells and may signal the body's immune system to destroy the cell.
Herceptin can also conjugated with chemotherapy (paclitaxel) to destroy HER2-positive cancer cells.

23. Pharmacokinetics or Predicting Drug Doses
Decreased metabolism of a certain drug causes high levels of the drug to accumulate in the body.
This may cause the drug’s effects to be more intense and prolonged than expected, and may lead to more toxic side effects.
In other words, if we have mutations that affect drug metabolism, we may experience worse side effects than people without these mutations

24. Example of Pharmacokinetics
In 2008, three drugs (insulin, digoxin and warfarin) in the US were responsible for one in three emergency department visits related to medication among older adults.
For warfarin alone, overdoses resulted in 40/000 visits to US emergency rooms at an annual cost of USD 2 billion.
Mutations in two genes (VKORC1 and CYP2C9) account for 30-50% of individual response to warfarin.

25. Monitoring treatment response
Biomarkers can also be used to monitor how well a treatment is working.
An example of this is the use of a protein biomarker called S100-beta in monitoring the response of malignant melanoma.
Response to treatment is associated with reduced levels of S100-beta in the blood of individuals with melanoma.

26. Recurrence
Oncotype DX® is an example of a test used to predict the likelihood of breast cancer recurrence.
This test is specified for use in women with early-stage (Stage I or II), node-negative breast cancer who will be treated with hormone therapy.

27. Oncotype DX ®
Oncotype DX ® evaluates a panel of 21 genes in cells taken from a tumor biopsy.
The results of the test are given in the form of a recurrence score that indicates the likelihood of distant recurrence at 10 years: the higher the score, the more likely the tumor is to recur.
This test can also be used to help predict who will benefit from chemotherapy.

28. How Do We Assess Risk in Breast Cancer Patients?
Oncotype DX®
New tools in the Genomic Era…
Classic Pathological Criteria
Age
Tumor Size
Lymph Node Status
ER/PR HER2
Tumor Grade
William Cho

29. Paik et al. N Engl J Med. 2004;351:2817-26.
Oncotype DX 21-gene recurrence score
16 cancer genes and 5 reference genes make up the Oncotype DX gene panel. The expression of these genes is used to calculate the recurrence score:
PROLIFERATION
Ki-67
STK15
Survivin
Cyclin B1
MYBL2
ESTROGEN ER PR
Bcl2
SCUBE2
BAG1
GSTM1
CD68
HER2
GRB7
INVASION
Stromelysin 3
Cathepsin L2
William Cho
RS =
x HER2 Group Score
x ER Group Score
x Proliferation Group Score
x Invasion Group Score
x CD68
x GSTM1
x BAG1
REFERENCE
Beta-actin
GAPDH
RPLPO
GUS
TFRC
Paik et al. N Engl J Med. 2004;351:

30. Non-coding RNA: the NA formerly known as “junk”
RNA Transcripts
Protein-coding mRNA
Non-coding RNA Transcripts
Regulatory RNA miRNA
siRNA piRNA Anti-sense RNA
Housekeeping RNAs
snoRNAs
tRNA
rRNA
snRNA
tmRNA
Rnase P RNA
vRNAs
gRNAs
MRP RNA
SRP RNAs
Telomerase RNA
William Cho
Transcription/chromatin structure regulators
Translational regulators
Protein function modulators
RNA/Protein localization regulators
NC-RNAs compose majority of transcription in complex genomes

31. Unique MicroRNA Profile in Lung Cancer Diagnosis and Prognosis
miRNAs are small non-coding RNAs which
play key roles in regulating the translation
and degradation of mRNAs
Genetic and epigenetic alteration may
affect miRNA expression, thereby
leading to aberrant target gene(s)
expression in cancers
Yanaihara et al, Cancer Cell, 2006:
- miRNA profiles of 104 pairs of primary
lung cancers and corresponding non-
cancerous lung tissues were analyzed by
miRNA microarrays
- 43 miRNAs showed statistical differences
William Cho

32. The role of microRNAs in cancer diagnosis
Aberrant miRNA expression offered new clues to pancreatic tumorigenesis and might provide diagnostic biomarkers for pancreatic cancer.
With the application of RT-PCR, it was shown that the aberrantly expressed miR-221, miR-301 and miR-376a were localized to pancreatic cancer cells but not to stroma or normal acini or ducts.
Lee EJ, et al. Expression profiling identifies microRNA signature in pancreatic cancer.
Int J Cancer 2007, 120:
Cho WC. MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol 2010.
Cho WC. MicroRNAs in cancer - from research to therapy. Biochim Biophys Acta - Rev Cancer 2010;1805(2):
William Cho

33. The role of microRNAs in cancer prognosis
Reduced let-7 miRNA expression in lung cancer was significantly associated with shorter postoperative survival.
Overexpression of let-7 miRNA in A549 lung adenocarcinoma cell line inhibited lung cancer cell growth in vitro.
Takamizawa J, et al. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival. Cancer Res 2004, 64:
William Cho

34. The role of microRNAs in cancer prognosis
The expression pattern of miRNAs in pancreatic cancer were compared with those of normal pancreas and chronic pancreatitis using miRNA microarrays.
Differentially expressed miRNAs were identified which could differentiate pancreatic cancer from normal pancreas, chronic pancreatitis, or both.
High expression of miR-196a-2 was found to predict poor survival of more than 24 months.
Bloomston M, et al. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA 2007, 297:
William Cho

35. microRNAs Tumorigenesis Diagnosis Prognosis miR-9 Neuroblastoma
miR-10b
Breast cancer
miR-15, miR-15a
Leukemia, pituitary adenoma
miR-16, miR-16-1
miR-17-5p, miR-17-92
Lung cancer, lymphoma
miR-20a
Lymphoma, lung cancer
miR-21
Breast cancer, cholangiocarcinoma, head & neck cancer, leukemia
Pancreatic cancer
miR-29, miR-29b
Leukemia, cholangiocarcinoma
miR-31
Colorectal cancer
miR-34a
miR-96
miR-98
Head & neck cancer
miR-103
miR-107
Leukemia, pancreatic cancer
miR-125a, miR-125b
Neuroblastoma, breast cancer
miR-128
Glioblastoma
miR-133b
miR-135b
miR-143
Colon cancer
miR-145
Breast cancer, colorectal cancer
miR-146
Thyroid carcinoma
William Cho

36. microRNAs Tumorigenesis Diagnosis Prognosis
miR-155, has-miR-155
Breast cancer, leukemia, pancreatic cancer
Lung cancer
miR-181, imR-181a, imR-181b, imR-181c
Leukemia, glioblastoma, thyroid carcinoma
miR-183
Colorectal cancer
miR-184
Neuroblastoma
miR-193
Gastric cancer
miR-196a-2
Pancreatic cancer
miR-221
Glioblastoma, thyroid carcinoma
miR-222
Thyroid carcinoma
miR-223
Leukemia
miR-301
miR-376
let-7, let-7a, let-7a-1, has-let-7a-2, let-7a-3
Lung cancer, colon cancer
William Cho
Cho WC. MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy.
Int J Biochem Cell Biol 2010.
Cho WC. OncomiRs: the discovery and progress of microRNAs in cancers. Mol Cancer. 2007;6:60.

37. Functional genomics Genomics Proteomics
Characterizing proteins and DNA at the molecular level is the key to understanding their function
Functional genomics
DNA
mRNA
t-RNA
Ribosome
(....)
Protein
CHO
PO4
Post Translational
Modifications X
Active Protein
William Cho
Genomics
Proteomics 37

38. Proteomics: leading biological science in the 21st century
Proteomics represents the effort to establish the identities, quantities, structures, biochemical and cellular functions of all proteins in an organism, organ, or organelle
and how these properties vary in space, time, or physiological state.
William Cho
Cho WC. Proteomics – Leading biological science in the 21st century. Science J, 2004; 56(5):14-17.
Cho WC, Cheng CH. Oncoproteomics: current trends and future perspectives. Expert Rev Proteomics 2007;4(3): 38

39. Traditional vs High-throughput approach
William Cho 39

40. The emergence of proteomics and its application
Cho WC, Cheng CH. Oncoproteomics: current trends and future perspectives. Expert Rev Proteomics 2007;4(3):
William Cho
ESI: Electrospray ionization
MALDI: Matrix-assisted laser desorption ionization
SELDI: Surface-enhanced laser desorption ionization
TOF: Time of flight 40

41. Biomarker discovery
Markers can be easily found by comparing protein maps.
SELDI is faster and more reproducible than 2D PAGE.
Has been being used to discover protein biomarkers of diseases such as ovarian cancer, breast cancer, prostate and bladder cancers.
(Normal)
(Cancer)
William Cho
Cho WC. Contribution of oncoproteomics to cancer biomarker discovery. Mol Cancer 2007;6:25. 41

42. Measurements
The digested proteins were measured by Nano LC ESI-Orbitrap mass spectrometry.
Fifty centimeter (C18) columns in combination with three hours time were used to obtain the best possible separation

43. Analysis of data Not normally distributed Discrete values
Progenesis
Clustering
(Partek Genomics Suite 6.5) +
DATA
Parametric statistic (SPSS)
p<0.01
Non-parametric statistic (SPSS)
Mann-Whitney p<0.01
Discrete values

44. Un-Supervised Clustering of Samples (Partek Genomics Suite 6.5)

45. Proteins as biomarkers
The protein composition may be associated with disease processes in the organism and thus have potential utility as diagnostic markers.
Proteins are closer to the actual disease process, in most cases, than parent genes
Proteins are ultimate regulators of cellular function
Most cancer markers are proteins
The vast majority of drug targets are proteins
William Cho
Cho WC. Cancer biomarkers (an overview). In Hayat MA (ed): Methods of cancer diagnosis, therapy and prognosis. Volume 7. New York, NY: Springer, 5 Jan 2010. 45

46. Thanks