0. Progress on Biomarkers of Cancer Diagnosis and Prognosis
William CS CHO
William Cho
Queen Elizabeth Hospital, Hong Kong
May 22, 2010

1. William Cho

2. William Cho 2

3. among patients with NSCLC.
Dual-specificity phosphatase 6 (DUSP6),
monocyte-to-macrophage differentiation associated protein (MMD),
signal transducer and activator of transcription 1 (STAT1),
v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 3 (ERBB3), lymphocyte-specific protein tyrosine kinase (LCK).
William Cho
Conclusions
Our five-gene signature is closely associated with relapse-free and overall survival
among patients with NSCLC.

4. Kaplan–Meier Estimates of Survival of Patients
William Cho
Kaplan–Meier Estimates of Survival of Patients
with NSCLC According to the Five-Gene Signatures as
Measured by RT-PCR.
Overall survival and relapse-free survival are shown for the 101 patients with NSCLC (Panel A and Panel B, respectively) and for the 59 patients with stage I or II disease (Panel C and Panel D, respectively). Overall survival is also shown for the independent cohort of 60 patients (Panel E), for the 42 patients in this cohort
who had stage I or II disease (Panel F), and for the 86 patients described in an independent set of published NSCLC microarray data10 (Panel G).

5. 70 Gene Prognosis Profile
Supervised analysis
van´t Veer et al., Nature 415, p , 2002
70 significant prognosis genes
Tumor samples
William Cho
threshold set with 10% false negatives
91 % sensitivity, 73% specificity 5

6. 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)

7. Independent validation:
Buyse et al. (2006)
JNCI. 98,
307 patients
William Cho 7

8. High reproducibility of microarray experiments (99%)
Reproducibility; repeat of the experiment
William Cho
Glas et al, BMC Genomics 2007. 8

9. No Recurrences in the Good Prognosis Group
MammaPrint:
Good Prognosis
(N=23)
Poor Prognosis
(N=144)
William Cho
Marieke Straver et al.,
Br Cancer Res and Treat.
2009

10. Clinical Development of Oncotype Dx
Development of a high-throughput, real time, RT-PCR method to quantify gene expression from fixed tumor tissue samples
Selection of 250 candidate genes
Testing the relationship between the 250 candidate genes and risk of recurrence in a series of 447 pts from three clinical studies
Published literature
Genomic databases
DNA array-based experiments
William Cho
16 cancer-related genes + 5 reference genes → Oncotype DX (recurrence score)
Paik et al. NEJM

11. 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
Adjuvant! Computer-based model
William Cho

12. 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:

13. Rate of Distant Recurrence at 10 years
Recurrence Score
Low
RS < 18
Rec. Rate = 6.8%
C.I. = 4.0% - 9.6%
Intermediate RS
Rec. Rate = 14.3%
C.I. = 8.3% %
High
RS  31
Rec. Rate = 30.5%
C.I. = 23.6% % 40 35 30 25 20 15 10 5 45 50
Recurrence Score
Rate of Distant Recurrence at 10 years
William Cho
Recurrence Rate
95% C.I.
Paik S. et al. N Engl J Med 2004;351:

14. Oncotype DXTM Low RS associated with minimal chemotherapy benefit;
High RS associated with large chemotherapy benefit.
The Oncotype DX Recurrence Score provides precise, quantitative information for individual patients on prognosis across and statistically independent of information on patient age, tumor size, and tumor grade.
William Cho

15. Nobel Prize in Physiology or Medicine 2006
Andrew Z. Fire
Craig C. Mello
William Cho
Cho WC. MicroRNAs in cancer - from research to therapy.
Biochim Biophys Acta - Rev Cancer 2010;1805(2):
C. elegans

16. 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

17. 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

18. Unique MicroRNA Profile in Lung Cancer Diagnosis and Prognosis
A univariate Cox proportional hazard
regression model with a global permutation
test indicated that expression of the miRNAs
hsa-mir-155 and hsa-let-7a-2 was related to
adenocarcinoma patient outcome
Lung adenocarcinoma patients with
either high hsa-mir-155 or reduced
hsa-let-7a-2 expression had poor survival
William Cho
Yanaihara N, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9:

19. William Cho

20. The role of microRNAs in cancer diagnosis
With the application of in situ 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.
Aberrant miRNA expression offered new clues to pancreatic tumorigenesis and might provide diagnostic biomarkers for pancreatic cancer.
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

21. The role of microRNAs in cancer prognosis
Expression of let-7 miRNA was frequently reduced in human lung cancers, and that reduced let-7 miRNA expression 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

22. 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

23. 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

24. 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.

25. Beyond the genome
William Cho
Same genome
Different proteome 25

26. 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
Genomics
William Cho
Proteomics 26

27. 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): 27

28. Traditional vs High-throughput approach
William Cho 28

29. 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 29

30. Surface-enhanced laser desorption/ionization (SELDI)
Chemical Surfaces – Protein Expression Profiling:
Hydrophobic
H50 – C9 chains
H4 – C16 chains
Cationic
WCX2 - Carboxylate
IMAC
Chelates metals
(Cu, Ni, Zn, Ga, Mn, …)
Normal Phase
NP20 –
SiO2
Anionic
SAX2 –
4O Ammonium
PS-10 or PS-20
Protein conjugation
Antibody - Antigen
Receptor - Ligand
DNA - Protein
Biological Surfaces – Protein Interaction Assays:
William Cho
Cho WC. Proteinchip. In: Encyclopedia of Cancer: 2nd Edition Springer. 30

31. HTP automation
Biomek 2000 (Beckman)
Programmed protocols for highly reproducible sample processing
William Cho
Proteinchip System PCS4000
Aquarius (Tecan) 31

32. Sample fractionation, chip binding and data acquisition in SELDI-TOF MS
Cho WC, et al. Clin Cancer Res 2004;10:43-52.
Cho WC. Chin J Biotech 2006;22(6):
Cho WC, et al. J Cell Biochem 2006;99(1):
Cho WC, et al. Dis Markers 2006;22(3):
Cho WC, et al. J Ethnopharmacol 2006;108(2):272-9.
Cho WC, et al. Clin Chem 2007;53(2):
William Cho 32

33. 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. 33

34. 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. 34

35. Nasopharyngeal cancer (NPC)
Normal nasopharynx
7th most prevalent cancer in Hong Kong.
Problems in clinical management of NPC:-
1. Diagnosis at late stage (at stage 3/4)
2. Frequent relapse (>50% for CR patients)
Nasopharynx with tumor
William Cho
Tumor on the right
eustachian cushion
Cho WC. Most common cancers in Asia-Pacific region: nasopharyngeal carcinoma.
In: Cancer report of Asian-Pacific region 35

36. Proteinchip application: nasopharyngeal carcinoma biomarkers discovery
Serum samples from 149 NPC patients (undifferentiated carcinoma of the nasopharyngeal type or poorly differentiated squamous cell type)
35 normal individuals
William Cho 36

37. William Cho

38. William Cho 38

39. Mass data collection for protein identification
1021
1386
1524
854
600
1600
sample
tryptic digestion
2-D gel purification
mass spectrometry
(peptide mapping information)
William Cho
identification
database
Protein search 39

40. Identification of marker by MS/MS
34/37 ions matched with
Serum Amyloid A
William Cho 40

41. Longitudinal follow up of biomarker, 11,695 Da in 3 relapsed NPC patients & 11 remission patients
William Cho
Cho WC, et al. Clin Cancer Res 2004, 10(1):43-52 41

42. Serum biomarkers with changes before and after chemotherapy in relapsed NPC patients
EP: Biomarker: 7,659 Da
GC: Biomarker: 7,765 Da
William Cho
EP, Etoposide and Cisplatinum; GC, Gemcitabine and Cisplatinum.
Cho WC et al. ProteinChip array profiling for identification of disease- and
chemotherapy-associated biomarkers of nasopharyngeal carcinoma. Clin Chem. 2007;53(2): 42

43. Basic statistics of ovarian cancer
Prevalence 40/100,000 (1 in 2500)
23,000 new cases diagnosed annually
14,000 deaths annually
Overall 5 year survival 20-30%
75% of cases are diagnosed in late stage (stage III/IV)
90% cure rate in stage I/IIa
Therefore, detection in earlier stages critical in improving overall survival
William Cho 43

44. Study design for biomarker discovery
Site 1 (100)
Benign (50)
Control (30)
Benign (90)
Control (49)
Stage III/IV (2)
Stage I/II (35)
Benign (26)
Control 63
Stage III/IV (103)
Stage I/II (20)
Ca (41)
Other Ca 2 (20)
Control (41)
Other Ca 1 (20)
Other Ca 3 (20)
Independent
Validation
Cross
Comparison
Candidate
Markers
Site 2 (176)
Site 3 (164)
Site 4 (63)
Site 5 (142)
Multivariate
Models
Protein ID
Independent Validation by Immunoassay
Results:
Descriptive statistics
Two-group t-tests
Performance
ROC curve analysis
Model Derivation
Discovery 1
Discovery 2
William Cho 44

45. Summary of performance
Markers for Stage I/II ovarian cancer discovered using ProteinChip system
503 samples from 5 institutions
Rigorous cross-validation and independent validation study design
Fixed specificity (97%)
3 marker panel (Apolipoprotein A1, inter alpha trypsin inhibitor IV and Transthyretin) : 74% sensitivity
CA125: 65% sensitivity
Fixed sensitivity (83%)
3 marker panel: 94% specificity
CA125: 54% specificity
William Cho 45

46. Pioneers in multimarker research
Peak A Criteria
Peak B Criteria
Peak C Criteria
Cancer
Normal
ID the biomarkers,
Link to biology of disease
Sensitivity “True Positives”
Specificity “True Negatives”
Single Marker
65%
35%
Biomarker Pattern
>90%
William Cho 46

47. FDA Cleared the OVA1 Test on Sep 11, 2009
Translating biomarker discovery from lab to clinic
Based on a prospective double-blind clinical trial involved 516 patients from 27 institutions
269 patients were evaluated by pre-surgical information alone
247 patients were evaluated by pre-surgical information with OVA1 results
OVA1 identified additional patients with potential malignancies
Help to guide surgical decisions
William Cho

48. OVA1
First FDA-cleared protein-based in vitro diagnostic multivariate index assay
First FDA-cleared prognostic test for ovarian cancer in the pre- and post-surgical setting
Test 5 proteins in blood sample
β2-microglobulin, transferrin, apolipoprotein A1, transthyretin identified by SELDI
CA125
Indicate the likelihood of benign or malignant
William Cho

49. Scientific American William Cho
Cho WC. Proteomic approaches to cancer target identification. Drug Discov Today: Ther Strategies 2007;4(4): 49

50. Targets of Cancer Therapy1 P 2 5 4
Plasma Membrane 3 6 7
Microtubule Dynamics
1. Growth factors
2. Growth factor receptors
3. Adaptor proteins
4. Docking proteins/binding proteins
5. Guanine nucleotide exchange factors
6. Phosphatases and phospholipases
7. Signaling kinases
8. Ribosomes
9. Transcription factors
10. Histones
11. DNA
12. Microtubules
PDK1,2 7
Growth Factor Signaling 12
RNA Translation 7 8
Nuclear Membrane 9 11 10
Gene Transcription
DNA Replication and Repair
Cell Growth Motility Survival Proliferation Angiogenesis 50

51. In colon cancer KRAS mutation determines response to EGFR therapy
Mutant KRAS
+EGFR
-EGFR
Wild type KRAS
Amado et al. J Clin Oncol; 26:
William Cho 51 51

52. In colon cancer KRAS mutation determines response to EGFR therapy
PIK3CA mut: 13%
Mutant KRAS
+EGFR
-EGFR
Wild type KRAS
Amado et al. J Clin Oncol; 26:
BRAF mut: 10%
William Cho 52 52

53. William Cho

54. Conventional cancer treatment:
Personalized cancer treatment: Rx
Treatment:
Pathway targeted therapy Rx → Dx
William Cho
Diagnosis
Stage, Grade, IHC
Treatment
Chemotherapy

55. A 159-gene signature of activated PI3K pathway in colon cancer
William Cho 55

56. Pathway & network analysis
William Cho
Cho WC. Proteomics technologies and challenges. Genomics Proteomics Bioinformatics 2007;5(2):77-85. 56

57. William Cho
Cho WC (ed): An omics perspective on cancer research. New York, NY: Springer 2010 57

58. E-mail: chocs@ha.org.hk
Thank You
William Cho