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

William Cho

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

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

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

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

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

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

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

Biomarkers with Clinical Application

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)

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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 = + 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 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC Paik et al. N Engl J Med. 2004;351:2817-26.

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

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

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, 120:1046-1054. 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. William Cho

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, 64:3753-3756. William Cho

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. William Cho

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

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.

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

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):401-410. 38

Traditional vs High-throughput approach William Cho 39

The emergence of proteomics and its application Cho WC, Cheng CH. Oncoproteomics: current trends and future perspectives. Expert Rev Proteomics 2007;4(3):401-410. William Cho ESI: Electrospray ionization MALDI: Matrix-assisted laser desorption ionization SELDI: Surface-enhanced laser desorption ionization TOF: Time of flight 40

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

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

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

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

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

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