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Progress on Biomarkers of Cancer Diagnosis and Prognosis William CS CHO Queen Elizabeth Hospital, Hong Kong May 22, 2010 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). Conclusions Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC. 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). 70 Gene Prognosis Profile Supervised analysis Tumor samples 70 significant prognosis genes van´t Veer et al., Nature 415, p. 530-536, 2002 threshold set with 10% false negatives 91 % sensitivity, 73% specificity 70 prognosis genes are involved in all aspects of tumor cell biology proliferation angiogenesis intravasation, survival, extravasation adhesion to extracellular matrix local invasion adhesion to extracellular matrix proliferation angiogenesis Genes of unknown function (25) Independent validation: Buyse et al. (2006) JNCI. 98, 1183-1192. 307 patients High reproducibility of microarray experiments (99%) Reproducibility; repeat of the experiment Glas et al, BMC Genomics 2007. 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 Published literature • Selection of 250 candidate genes Genomic databases DNA array-based experiments • Testing the relationship between the 250 candidate genes and risk of recurrence in a series of 447 pts from three clinical studies 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 Lymph Node Status Tumor Size New tools in the Genomic Era… Oncotype DX® Age Tumor Grade ER/PR HER2 Adjuvant! Computer-based model 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 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC ESTROGEN ER PR Bcl2 SCUBE2 RS = BAG1 GSTM1 INVASION Stromelysin 3 Cathepsin L2 CD68 HER2 GRB7 HER2 + 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 Paik et al. N Engl J Med. - 0.07 x BAG1 2004;351:2817-26. Rate of Distant Recurrence at 10 years Recurrence Score Low RS < 18 Rec. Rate = 6.8% C.I. = 4.0% - 9.6% 40 35 Intermediate RS 18 - 31 Rec. Rate = 14.3% C.I. = 8.3% - 20.3% High RS 31 Rec. Rate = 30.5% C.I. = 23.6% - 37.4% 30 25 20 15 Recurrence Rate 95% C.I. 10 5 0 0 5 10 15 20 25 30 Recurrence Score 35 40 45 50 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. Fire Craig C. Mello Cho WC. MicroRNAs in cancer - from research to therapy. Biochim Biophys Acta - Rev Cancer 2010;1805(2):209-217. C. elegans 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 • • • • Transcription/chromatin structure regulators Translational regulators Protein function modulators RNA/Protein localization regulators snoRNAs Housekeeping RNAs •tRNA •rRNA •snRNA •tmRNA •Rnase P RNA •vRNAs •gRNAs •MRP RNA •SRP RNAs •Telomerase RNA 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 noncancerous lung tissues were analyzed by miRNA microarrays - 43 miRNAs showed statistical differences 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 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 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 miR-29, miR-29b Leukemia, cholangiocarcinoma miR-31 Colorectal cancer miR-34a Pancreatic cancer 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 Diagnosis Prognosis Pancreatic cancer Neuroblastoma microRNAs Tumorigenesis miR-155, has-miR-155 Breast cancer, leukemia, pancreatic cancer miR-181, imR-181a, imR-181b, imR-181c Leukemia, glioblastoma, thyroid carcinoma miR-183 Colorectal cancer miR-184 Neuroblastoma miR-193 Gastric cancer Diagnosis Lung cancer miR-196a-2 Pancreatic cancer miR-221 Glioblastoma, thyroid carcinoma miR-222 Thyroid carcinoma miR-223 Leukemia Pancreatic cancer miR-301 Pancreatic cancer miR-376 Pancreatic cancer let-7, let-7a, let-7a-1, has-let-7a-2, let-7a-3 Prognosis 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. Beyond the genome Same genome Different proteome Characterizing proteins and DNA at the molecular level is the key to understanding their function Functional genomics t-RNA mRNA t-RNA Ribosome t-RNA (....) Protein t-RNA DNA Genomics Post Translational Modifications X (....) X Proteomics Active Protein CHO PO4 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 The emergence of proteomics and its application DNA static genome Transcriptional control RNA message variable: transcriptome Translational control Protein product variable: proteome Post-translational modification Genome Era Post-genome Era Intrinsic factors: physiological & pathological status, … Sample preparation & processing Cho WC, Cheng CH. Oncoproteomics: current trends and future perspectives. Expert Rev Proteomics 2007;4(3):401-410. Extrinsic factors: environment, pathogens, drug, … Functional protein expressed Automation sample application ESI-TOF MS MALDI-TOF MS Low-throughput High-throughput Peptide ions (MS) Peptide fragment ions (MS-MS) Protein chip, e.g. SELDITOF MS ESI: Electrospray ionization MALDI: Matrix-assisted laser desorption ionization Bioinformatics Experimental or clinical results Database interrogation Protein identification SELDI: Surface-enhanced laser desorption ionization TOF: Time of flight Validation and application Surface-enhanced laser desorption/ionization (SELDI) Chemical Surfaces – Protein Expression Profiling: Hydrophobic Cationic Anionic IMAC Normal Phase H50 – C9 chains WCX2 - SAX2 – Chelates metals NP20 – H4 – C16 chains Carboxylate 4O Ammonium (Cu, Ni, Zn, Ga, Mn, …) SiO2 Biological Surfaces – Protein Interaction Assays: PS-10 or PS-20 Protein conjugation Antibody - Antigen Receptor - Ligand DNA - Protein Cho WC. Proteinchip. In: Encyclopedia of Cancer: 2nd Edition. 2009. Springer. HTP automation Biomek 2000 (Beckman) Programmed protocols for highly reproducible sample processing Proteinchip System PCS4000 Aquarius (Tecan) Sample fractionation, chip binding and data acquisition in SELDI-TOF MS Serum / lysate sample Disease samples Control samples vs Cho WC, et al. Clin Cancer Res 2004;10:43-52. + Urea / CHAPS / TrisHCl pH 9 Sample anion exchange pre-fractionation Strong anion exchange resin Q HyperD resin Organic eluant Fractionation pH 9/ flow through pH 3 eluant Cho WC, et al. J Cell Biochem 2006;99(1):256-68. pH 7 eluant pH 5 eluant pH 4 eluant Chip binding Cho WC. Chin J Biotech 2006;22(6):871-876. Weak cation exchange (WCX2 / CM10) 100 mM NaAc, pH 4 Cu(II) (IMAC3 / IMAC30) 100 mM phosphate, 0.5 M NaCl, pH 7 Data acquisition T O FM S D etector L a ser Protein Biology System (PBS) IIc SELDI-TOF mass spectrometer 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. Biomarker discovery • Markers can be easily found by comparing protein maps. • SELDI is faster and more reproducible than 2D PAGE. (Normal) (Cancer) • Has been being used to discover protein biomarkers of diseases such as ovarian cancer, breast cancer, prostate and bladder cancers. Cho WC. Contribution of oncoproteomics to cancer biomarker discovery. Mol Cancer 2007;6:25. 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 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) 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 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. 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 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) 10000 11000 12000 13000 14000 15000 Mass data collection for protein identification 1021 1386 854 1524 sample tryptic digestion 2-D gel purification identification 1600 600 mass spectrometry (peptide mapping information) database Protein search Identification of marker by MS/MS +TOF Product (2176.9): 70 MCA scans from spot B 2177.wiff, Smoothed 34/37 ions matched with Serum Amyloid A 931.4332 484 Max. 484.0 counts. 450 400 I n t e n s it y , c o u n t s 350 300 250 200 914.4099 418.2148 1317.6205 150 100 617.2782 315.1216 497.1998 461.2252 50 0 200 400 600 861.3582 661.2993 800 2178.9799 896.3904 1000 1247.5223 1200 m/z, amu 1400 1600 1800 2000 2200 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 % Diff = 206.8% increased 4 p = 4.8E-6 decreased 1.727 +/- 0.781 GC: Biomarker: 7,765 Da Mean +/- SD 0.835 +/- 0.753 3 % Diff = 194.4% increased 6 p = 6.1E-6 decreased Mean +/- SD 2.382 +/- 1.128 5 4 2 1.225 +/- 0.734 3 1 2 0 1 -1 0 Before After Chemotherapy (N=35) EP, Etoposide and Cisplatinum; Before After Chemotherapy (N=29) 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):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 Stage I/II (20) Site 1 (100) Benign (50) Multivariate Model Derivation Discovery 1 Control (30) Cross Comparison Stage I/II (35) Site 2 (176) Candidate Markers Stage III/IV (2) Discovery 2 Multivariate Models Benign (90) Control (49) Stage I/II (35) Site 3 (164) Stage III/IV (103) Benign (26) Independent Validation Control 63 Protein ID Ca (41) Other Ca 1 (20) Site 5 (142) Other Ca 2 (20) Independent Validation by Immunoassay Results: • Descriptive statistics • Two-group t-tests • Performance • ROC curve analysis ROC curve, area=0.94327, std = 0.094973, alpha= 2.791, beta= 0.47154 1 0.9 Other Ca 3 (20) 0.8 0.7 0.6 Sensitivity Site 4 (63) 0.5 0.4 0.3 Control (41) 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 1 - Specificity 0.7 0.8 0.9 1 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 Pioneers in multimarker research Peak A Criteria Peak B Criteria Cancer Normal Peak C Criteria Cancer ID the biomarkers, Link to biology of disease Normal Sensitivity “True Positives” Specificity “True Negatives” Single Marker 65% 35% Biomarker Pattern >90% >90% 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 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, transthyretin identified by SELDI apolipoprotein • CA125 ● Indicate the likelihood of benign or malignant A1, Scientific American Cho WC. Proteomic approaches to cancer target identification. Drug Discov Today: Ther Strategies 2007;4(4):245-250. Targets of Cancer Therapy 1 2 5 4 Plasma Membrane 6 Growth Factor Signaling 3 7 Microtubule Dynamics 7 12 RNA Translation 7 8 Nuclear Membrane 1. Growth factors 2. Growth factor receptors 3. Adaptor proteins 4. Docking proteins/binding proteins 5. Guanine nucleotide exchange factors 9 11 10 Gene Transcription Cell Growth Motility DNA Replication and Repair Survival Proliferation Angiogenesis 6. Phosphatases and phospholipases 7. Signaling kinases 8. Ribosomes 9. Transcription factors 10. Histones 11. DNA 12. Microtubules In colon cancer KRAS mutation determines response to EGFR therapy Mutant KRAS +EGFR -EGFR Wild type KRAS +EGFR -EGFR 51 Amado et al. J Clin Oncol; 26:1626-1634 2008 In colon cancer KRAS mutation determines response to EGFR therapy KRAS mut: 32% PIK3CA mut: 13% BRAF mut: 10% Mutant KRAS +EGFR -EGFR Wild type KRAS +EGFR -EGFR 52 Amado et al. J Clin Oncol; 26:1626-1634 2008 Conventional cancer treatment: Dx Rx Personalized cancer treatment: → Rx Treatment: Diagnosis Stage, Grade, IHC Treatment Chemotherapy Pathway targeted therapy 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 2010 Thank You E-mail: [email protected]