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Progress on Biomarkers of Cancer Diagnosis and
Prognosis
William CS CHO
May 22, 2010
Queen Elizabeth Hospital, Hong Kong
ConclusionsOur five-gene signature is closely associated with relapse-free and overall survivalamong patients with NSCLC.
ConclusionsOur five-gene signature is closely associated with relapse-free and overall survivalamong 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).
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).
Kaplan–Meier Estimates of Survival of Patientswith NSCLC According to the Five-Gene Signatures asMeasured 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 cohortwho 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).
Kaplan–Meier Estimates of Survival of Patientswith NSCLC According to the Five-Gene Signatures asMeasured 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 cohortwho 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).
threshold set with 10% false negatives91 % sensitivity, 73% specificity
70 Gene Prognosis ProfileSupervised analysis
van´t Veer et al., Nature 415, p. 530-536, 2002
70 significant prognosis genes
Tu
mor
sam
ple
s
proliferation
angiogenesis
adhesion to extracellular matrix
local invasion
intravasation, survival, extravasation
proliferation
angiogenesis
adhesion to extracellular matrix
Genes of unknown function (25)
70 prognosis genes are involved in all aspects of tumor cell biology
Independent validation:Buyse et al. (2006)
JNCI. 98, 1183-1192.
307 patients
High reproducibility of microarray experiments (99%)
Glas et al, BMC Genomics 2007.
Reproducibility; repeat of the experiment
No Recurrences in the Good Prognosis Group
MammaPrint:
Good Prognosis(N=23)
Poor Prognosis(N=144)
Marieke Straver et al.,Br Cancer Res and Treat.2009
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
16 cancer-related genes + 5 reference genes → Oncotype DX (recurrence score)
Paik et al. NEJM. 2004.
How Do We Assess Risk in Breast Cancer Patients?
Classic Pathological Criteria
Oncotype DX®
New tools in the Genomic Era…
Age
Tumor Size
Lymph Node Status
ER/PRHER2
Tumor Grade Adjuvant!
Computer-based model
RS = + 0.47 x HER2 Group Score - 0.34 x ER Group Score + 1.04 x Proliferation Group Score+ 0.10 x Invasion Group Score + 0.05 x CD68- 0.08 x GSTM1- 0.07 x BAG1
PROLIFERATIONKi-67
STK15Survivin
Cyclin B1MYBL2
ESTROGENERPR
Bcl2SCUBE2
INVASIONStromelysin 3Cathepsin L2
HER2GRB7HER2
BAG1 GSTM1
REFERENCEBeta-actinGAPDHRPLPO
GUSTFRC
CD68
Paik et al. N Engl J Med. 2004;351:2817-26.
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:
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:
Oncotype DX 21-gene recurrence scoreOncotype DX 21-gene recurrence score
Recurrence Score
40
35
30
25
20
15
10
5
00 5 10 15 20 25 30 35 40 45 50
Recurrence Score
Rat
e o
f D
ista
nt
Rec
urr
ence
at
10 y
ears
95% C.I.
Recurrence Rate
LowRS < 18Rec. Rate = 6.8%C.I. = 4.0% - 9.6%
IntermediateRS 18 - 31Rec. Rate = 14.3%C.I. = 8.3% - 20.3%
HighRS 31Rec. Rate = 30.5%C.I. = 23.6% - 37.4%
Paik S. et al. N Engl J Med 2004;351:2817-26
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.
Nobel Prize in Physiology or Medicine 2006
Andrew Z. FireCraig C. MelloAndrew Z. FireCraig C. Mello
C. elegansCho WC. MicroRNAs in cancer - from research to therapy. Biochim Biophys Acta - Rev Cancer 2010;1805(2):209-217. Cho WC. MicroRNAs in cancer - from research to therapy. Biochim Biophys Acta - Rev Cancer 2010;1805(2):209-217.
Non-coding RNA: the NA formerly known as “junk”
•tRNA•rRNA•snRNA•tmRNA•Rnase P RNA•vRNAs•gRNAs•MRP RNA•SRP RNAs•Telomerase RNA
•Transcription/chromatin structure regulators•Translational regulators•Protein function modulators•RNA/Protein localization regulators
RNA Transcripts
Regulatory RNAmiRNAsiRNApiRNA
Anti-sense RNA
Protein-coding mRNA Non-coding RNA Transcripts
snoRNAsHousekeeping RNAs
NC-RNAs compose majority of transcription in complex genomes
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
• 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
Unique MicroRNA Profile in Lung Cancer Diagnosis and Prognosis
Yanaihara N, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9:189-198.Yanaihara N, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9:189-198.
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, 120:1046-1054.
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):209-217.
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, 64:3753-3756.
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, 297:1901-1908.
microRNAs Tumorigenesis Diagnosis Prognosis
miR-9 Neuroblastoma
miR-10b Breast cancer
miR-15, miR-15a Leukemia, pituitary adenoma
miR-16, miR-16-1 Leukemia, pituitary adenoma
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 Pancreatic cancer Neuroblastoma
miR-96 Colorectal cancer
miR-98 Head & neck cancer
miR-103 Pancreatic cancer
miR-107 Leukemia, pancreatic cancer
miR-125a, miR-125b Neuroblastoma, breast cancer
miR-128 Glioblastoma
miR-133b Colorectal cancer
miR-135b Colorectal cancer
miR-143 Colon cancer
miR-145 Breast cancer, colorectal cancer
miR-146 Thyroid carcinoma
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 Pancreatic cancer
miR-222 Thyroid carcinoma
miR-223 Leukemia
miR-301 Pancreatic cancer
miR-376 Pancreatic cancer
let-7, let-7a, let-7a-1, has-let-7a-2, let-7a-3 Lung cancer, colon cancer Lung cancer
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.
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.
Same genomeDifferent proteome
Beyond the genome
Characterizing proteins and DNA at the molecular level is the key to understanding their function
DNA
mRNA
t-RNA
t-RNA
t-RNA t-RNA
Ribosome
(....)
Protein
CHOPO4
(....)
Post TranslationalModifications
X
X
Active Protein
Genomics
Functional genomics
Proteomics
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.
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):401-410.
Traditional vs High-throughput approach
Transcriptional control
Translational control
Post-translational modification
Automation sample application
Intrinsic factors: physiological &
pathological status, …
Validation and application
Protein identification
Database interrogation
Peptide fragment ions (MS-MS)
Peptide ions (MS)
High-throughputLow-throughput
DNAstatic genome
RNAmessage variable: transcriptome
Proteinproduct variable: proteome
Functional protein expressed
ESI-TOF MS MALDI-TOF MS
Extrinsic factors:environment, pathogens, drug, …
Samplepreparation
& processing
Bioinformatics
Experimental orclinical results
Genome Era
Post-genome Era
Protein chip, e.g. SELDI-TOF MS
The emergence of proteomics and its application
ESI: Electrospray ionization
MALDI: Matrix-assisted laser desorption ionization
SELDI: Surface-enhanced laser desorption ionization
TOF: Time of flight
Cho WC, Cheng CH. Oncoproteomics: current trends and future perspectives. Expert Rev Proteomics 2007;4(3):401-410.
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 conjugationAntibody - Antigen Receptor - Ligand DNA - Protein
Biological Surfaces – Protein Interaction Assays:
Surface-enhanced laser desorption/ionization (SELDI)
Cho WC. Proteinchip. In: Encyclopedia of Cancer: 2nd Edition. 2009. Springer.
HTP automation
Programmed protocols for highly reproducible sample processing
Proteinchip System PCS4000
Aquarius (Tecan)
Biomek 2000 (Beckman)
vsDisease samples Control samples
+ Urea / CHAPS / TrisHCl pH 9
Strong anion exchange resinQ HyperD resin
Sample anion exchangepre-fractionation
pH 9/flow through
Organiceluant
pH 7eluant
pH 5eluant
pH 4eluant
pH 3eluant
Weak cation exchange (WCX2 / CM10)100 mM NaAc, pH 4
Cu(II) (IMAC3 / IMAC30)100 mM phosphate, 0.5 M NaCl, pH 7
Detec
tor
Detec
tor
La ser
T O F-M S
Protein Biology System (PBS) IIc SELDI-TOF mass spectrometer
Serum / lysate sample
Chip binding
Data acquisition
Fractionation
Cho WC, et al. Clin Cancer Res 2004;10:43-52.
Cho WC. Chin J Biotech 2006;22(6):871-876.
Cho WC, et al. J Cell Biochem 2006;99(1):256-68.
Cho WC, et al. Dis Markers 2006;22(3):153-66.
Cho WC, et al. J Ethnopharmacol 2006;108(2):272-9.
Cho WC, et al. Clin Chem 2007;53(2):241-250.
Sample fractionation, chip binding and data acquisition in SELDI-TOF MS
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)
Cho WC. Contribution of oncoproteomics to cancer biomarker discovery. Mol Cancer 2007;6:25.
Proteins as biomarkers
• 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
The protein composition may be associated with disease processes in the organism and thus have potential utility as diagnostic markers.
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.
Nasopharyngeal cancer (NPC)
• 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)
Normal nasopharynx
Nasopharynx with tumor
Tumor on the right eustachian cushion
Cho WC. Most common cancers in Asia-Pacific region: nasopharyngeal carcinoma. In: Cancer report of Asian-Pacific region 2010. 284-289.Cho WC. Most common cancers in Asia-Pacific region: nasopharyngeal carcinoma. In: Cancer report of Asian-Pacific region 2010. 284-289.
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
10000 11000 12000 13000 14000 15000
10000 11000 12000 13000 14000 15000
GC10 A3 (P1F6)GC10 A5 (P1F6)GC17 A3 (P1F6)GC29 A3 (P1F6)GC6 A1 (P1F6)GC1 A7 (P2F6)GC25 A5 (P2F6)GC37 A7 (P2F6)GC3 A7 (P3F6)GC24 A1 (P3F6)GC23 BT1 (P3F6)GC20 BT1 (P3F6)GC11 A4 (P3F6)GC11 A9 (P3F6)GC9 BT1 (P4F6)GC13 BT1 (P5F6)GC8 A3 (P5F6)GC15 BT1 (P5F6)GC14 BT1 (P5F6)GC21 A3 (P6F6)GC18 A3 (P6F6)GC12 A4 (P6F6)GC26 A6 (P7F6)GC22 BT1 (P7F6)GC27 A3 (P8F6)GC27 A10 (P8F6)GC28 A7 (P8F6)GC36 A8 (P9F6)GC35 A3 (P9F6)GC32 BT1 (P10F6)GC31 A13 (P10F6)GC34 A3 (P11V6)PS165 A8 (P3F6)PS165 A15 (P3F6)PS178 A8 (P3F6)PS178 A15 (P3F6)PS192 A8 (P4F6)PS192 A15 (P4F6)PS205 A8 (P4F6)PS205 A15 (P4F6)PS213 A8 (P2F6)PS213 A15(P2F6)PS217 A8 (P5F6)PS217 A15 (P5F6)PS223 A8 (P6F6)PS223 A15 (P6F6)PS250 A8 (P4F6)PS250 A15 (P4F6)PS253 A8 (P2F6)PS253 A15 (P2F6)PS260 A9 (P4F6)PS260 A16 (P4F6)PS279 A8 (P5F6)PS279 A15 (P5F6)
1021 1386
1524854
600 1600tryptic digestionmass spectrometry
(peptide mapping information)2-D gel purification
sample
Protein search Protein search databaseidentificationidentification
Mass data collection for protein identification
+TOF Product (2176.9): 70 MCA scans from spot B 2177.wiff, Smoothed Max. 484.0 counts.
200 400 600 800 1000 1200 1400 1600 1800 2000 2200m/z, amu
0
50
100
150
200
250
300
350
400
450
484
Inte
ns
ity, c
ou
nts
931.4332
1317.6205914.4099418.2148
617.2782861.3582 2178.9799
497.1998461.2252315.1216 661.2993 1247.5223896.3904
Identification of marker by MS/MS
34/37 ions matched with Serum Amyloid A
Longitudinal follow up of biomarker, 11,695 Da in 3 relapsed NPC patients & 11 remission patients
Cho WC, et al. Clin Cancer Res 2004, 10(1):43-52
Serum biomarkers with changes before and after chemotherapy in relapsed NPC patients
EP: Biomarker: 7,659 Da GC: Biomarker: 7,765 Da
EP, Etoposide and Cisplatinum; GC, Gemcitabine and Cisplatinum.
% Diff = 206.8% p = 4.8E-6
1.727 0.835
+/- 0.781 +/- 0.753
-1
0
1
2
3
4
Before AfterChemotherapy (N=35)
increased decreased Mean +/- SD
% Diff = 194.4% p = 6.1E-6
2.382
1.225
+/- 1.128
+/- 0.734
0
1
2
3
4
5
6
Before AfterChemotherapy (N=29)
increased decreased Mean +/- SD
Cho WC et al. ProteinChip array profiling for identification of disease- and chemotherapy-associated biomarkers of nasopharyngeal carcinoma. Clin Chem. 2007;53(2):241-50.Cho WC et al. ProteinChip array profiling for identification of disease- and chemotherapy-associated biomarkers of nasopharyngeal carcinoma. Clin Chem. 2007;53(2):241-50.
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
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)
Stage I/II (35)
Ca (41)
Other Ca 2 (20)
Control (41)
Other Ca 1 (20)
Other Ca 3 (20)
IndependentValidation
CrossComparison
CandidateMarkers
Site 2 (176)
Site 3 (164)
Site 4 (63)
Site 5 (142)
MultivariateModels
Protein ID
Independent Validation by Immunoassay
Results:• Descriptive statistics• Two-group t-tests• Performance• ROC curve analysis
MultivariateModel
Derivation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1ROC curve, area=0.94327, std = 0.094973, alpha= 2.791, beta= 0.47154
1 - Specificity
Sen
sitiv
ity
Discovery 1
Discovery 2
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
ID the biomarkers, Link to biology of disease
Pioneers in multimarker research
Sensitivity“True
Positives”
Specificity “True
Negatives”
Single Marker 65% 35%
Biomarker Pattern >90% >90%
Peak A Criteria
Peak B Criteria
Peak C Criteria
Cancer CancerNormal Normal
FDA Cleared the OVA1 Teston 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
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
Scientific American
Cho WC. Proteomic approaches to cancer target identification. Drug Discov Today: Ther Strategies 2007;4(4):245-250.
Targets of Cancer Therapy
Cell Growth Motility Survival Proliferation Angiogenesis
P
P
P
P
PDK1,2Growth Factor
Signaling
Gene Transcription DNA Replication and Repair
1
6 3
5
8
9
10
11
2Plasma Membrane
Nuclear Membrane
127
4
7
7 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
Microtubule Dynamics
RNA Translation
52
In colon cancer KRAS mutation determines response to EGFR therapy
Mutant KRAS +EGFR -EGFR
Wild type KRAS +EGFR -EGFR
Amado et al. J Clin Oncol; 26:1626-1634 2008
53
In colon cancer KRAS mutation determines response to EGFR therapy
Mutant KRAS +EGFR -EGFR
Wild type KRAS +EGFR -EGFR
Amado et al. J Clin Oncol; 26:1626-1634 2008
KRAS mut: 32% PIK3CA mut: 13%
BRAF mut: 10%
Conventional cancer treatment:
RxRx
Treatment
Chemotherapy
Treatment
Chemotherapy
DxDx
Diagnosis
Stage, Grade, IHC
Diagnosis
Stage, Grade, IHC
RxRx
Treatment:
Pathway targeted therapy
Treatment:
Pathway targeted therapy
Personalized cancer treatment:
→→
A 159-gene signature of activated PI3K pathway in colon cancer
Pathway & network analysis
Cho WC. Proteomics technologies and challenges. Genomics Proteomics Bioinformatics 2007;5(2):77-85.
Cho WC (ed): An omics perspective on cancer research. New York, NY: Springer 2010Cho WC (ed): An omics perspective on cancer research. New York, NY: Springer 2010
