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Tumor marker-encoding genes: a bunch of mysterious diamonds in the pile of evolutionary compost A.V. Baranova, George Mason University.

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Presentation on theme: "Tumor marker-encoding genes: a bunch of mysterious diamonds in the pile of evolutionary compost A.V. Baranova, George Mason University."— Presentation transcript:

1 Tumor marker-encoding genes: a bunch of mysterious diamonds in the pile of evolutionary compost A.V. Baranova, George Mason University

2 TUMOR MARKERS Tumor markers are proteins or mRNAs that are detected in higher-than-normal amounts in the blood, urine or body tissues of some patients with certain types of cancer. Most tumor markers are produced by the tumor itself, but not by normal tissue Tumor markers could be immunogenic; they could serve as targets for anti-tumor immune response

3 Changes in tumor marker concentration during the course of disease: No response to treatment Good response Remission Relapse No treatemnt Second remission

4 Known examples of tumor markers PSA and PAP Prostate Specific Antigen (PSA) and Prostatic Acid Phosphatase (PAP) Prostatic carcinoma, but also in prostatitis and benign prostatic hyperplasia (BPH) Carcinoembryonic Antigen (CEA) Used for monitoring of colorectal and lung cancer Alpha-fetoprotein (AFP) AFP is normally produced by a developing fetus. An elevated level of AFP strongly suggests the presence of either primary liver cancer or germ cell cancer CA125 Ovarian carcinoma. Very good for monitoring of response and recurrence

5 Healthy Benign inflammatory conditions “Grey diagnostic area” CANCER ? Level of single tumor marker often is not definitive enough

6 Combining of known tumor markers into marker panels will help to increase sensitivity and specificity of diagnostics MULTIPLE TUMOR MARKERS NEED TO BE OBTAINED FOR EACH COMMON TYPE OF CANCER preferably by high-throughput methods

7 Protein based methods of tumor marker discovery Occasional findings in course of other research Various modifications of SEREX (serological expression of cDNA expression libraries Proteomics–based methods, including: 2D –PAGE analysis SELDI-TOF mass-spectroscopy analysis Reverse proteomics arrays

8 Remove background True screening Aim: to reveal the repertoire of antigens eliciting an antibody response in cancer patients. Cancer Immunome Database [www2.licr.org/CancerImmunomeDB] Near 100 of good TMs are revealed by this method

9 From: Wellcome Trust website Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is a technique for separating complex mixtures of proteins by their mass and/or charge.

10 From: Wellcome Trust website APPLY WOLTAGE TOP TO BOTTOM

11 From: Wellcome Trust website APPLY WOLTAGE SIDE TO SIDE

12 2D –PAGE analysis Normal serumTumor serum Potential tumor marker

13 Example: from University of Kent website MORE TYPICAL 2D-PAGE GEL

14 Surface-enhanced laser desorption/ionization (SELDI-TOF) mass-spectrometry proteins of interest are selectively absorbed to a chemically modified surface. Mass/charge

15 From Virginia Prostate Center website TUMOR Hyperplasia Normal

16 DNA-based Differential Display A common term for group of methods for a search of the transcripts which expression in the sample of interest differs from that in so-called “control sample”. High throughput methodsLow throughput methods High budget: SAGE, microarrays Low budget: Computational approaches

17 It’s time for computational genomics In UniGene, individual human ESTs are already clustered by homology. Each EST is derived from described tissue source including tumor and normal samples. GenBank contains 5,120,207 of human ESTs on May 30, 2005

18 Example of ESTs that belong to the same gene/ UNIGENE cluster

19 Manual verification of cDNA libraries sources cDNA libraries in GeneBank TUMOR NORMAL UNDEFINED A sources for cross-verification: UNIGENE, CGAP, TIGR, STRATAGENE, PubMed Figure of year 2001

20 HSAnalyst and ClustOut software HSAnalyst and ClustOut software developed in Functional Genomics Group, IGG RAS Software is able to sort EST clusters according to their size, tissue origin or other categories describing individual ESTs

21 First round of work (year 2000). Cluster-sorting software HSAnalyst To list all human clusters containing 100% of ESTs from tumor-derived cDNA libraries with more than 16 ESTs in the cluster - 21 cluster To list all human clusters containing less than 10% of ESTs from normal cDNA libraries with more than 16 ESTs in the cluster – 83 clusters

22 Statistics FEBS Letters, 508 (2001), 143-148

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25 RT-PCR of Brachiury gene in human tumor and normal tissues

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27 Cytokine production and cytotoxic activity of CTLs specific for three Brachyury-derived peptides. A, CD8+ Tcells generated from PBMC of a healthy donor against peptidesT-p2 and T-p3 were stimulated for 24 h in the presence of Brachyury (T)-specific peptides or irrelevant peptide-pulsed autologous DCs. IFN-g was evaluated in the supernatants by ELISA. B. Cytotoxic activity (6-h assay) of CTLs generated with peptideT-p2 against peptide- pulsed C1R-A2 targets. Two effector-to-target ratios (E:T) were used as indicated.

28 Cytotoxic activity of Brachyury-specific CTLs against tumor targets. CTLs established from the blood of (C) a colorectal cancer patient (patient1) and (D) an ovarian cancer patient (patient 2) were used after three IVS for cytotoxic killing of H441 and AsPC-1 tumor cells.

29 Tumor marker properties of Brachiury were predicted computationally It was the first study to show that (a) a T-box transcription factor and (b) a molecule implicated in mesodermal development, i.e., EMT, can be a potential target for human T-cell–mediated cancer immunotherapy. up-regulation of Brachyury occurs in certain tumor tissues and cancer cell lines Brachyury-specific CTLs can be generated from the blood of cancer patients and normal donors, which, in turn, can effectively lyse Brachyury-expressing tumor cells.

30 Recent round of work (year 2006). Cluster-sorting software ClustOut Clusters containing 100% ESTs from human libraries derived from tumor samples - 328 clusters Clusters containing less than 10% ESTs from human libraries derived from normal samples - 501 clusters

31 Tumor prevalent clusters 52 are genes with Gene IDs (10%) 501 10 known tumor markers Insulin 268/3113 Parathyroid hormone 1/83 Unknown human genes with unstudied ORFs 13 Non-coding mRNAs 466 (87%)

32 Known tumor markers in cancer- specific subtraction GeneFunctionNormal/cancer MYEOV myeloma overexpressed gene 2/113 MAGEA6 melanoma antigens, family A 8/103 MAGEA3 7/76 MAGEA12 0/27 MAGEA1 1/13 MAGEA9 1/13 CEACAM8carcinoembryonic antigen-related cell adhesion molecule 8 2/30 CTAG2Cancer/testis antigen 2 2/20 TRAG3taxol resistance associated gene 3 0/10 APOBEC1apolipoprotein B mRNA editing enzyme, catalytic polypeptide 0/59 10/501

33 Prospective marker proteins in cancer-specific subtraction GeneFunctionNorm/cancer ASCL1 achaete-scute complex-like 1 (Drosophila) 33/394 WNT7B wingless-type MMTV integration site family, member 7B 22/285 DLL3 delta-like 3 (Drosophila) 18/193 NOX1NADPH oxidase 14/76 KREMEN2 kringle containing transmembrane protein 2 5/74 SPRR2A small proline-rich protein 2A 1/69 MS4A3 membrane-spanning 4-domains, subfamily A, member 3 5/65 GPR35 G protein-coupled receptor 35 3/46 SPRR1A small proline-rich protein 1A 4/46 OTPorthopedia homolog (Drosophila) 1/41

34 Known human genes represented by only tumor-derived EST Published in FEBS Letters, Nov 2001

35 501 clusters with less than 10% of ESTs from normal tussues Reverse subtraction: genes prevalent in normal samples Normal prevalent clusters : 1048 clusters with less than 10% of ESTs from tumor tussues 560 with 100% Tumor prevalent clusters: 328 with 100%

36 Normal prevalent clusters 612 are genes with Gene IDs (56,6%) 1048 10 crystallins 7 keratins Tear prealbumin, lens intrinsic membrane protein 2 Unknown human genes with unstudied ORFs 37 Non-coding mRNAs 399 (36,9%) 16 (!) different solute carrier proteins (2,6%)

37 Non-coding RNAs in tumor-specific and normal–specific subtractions In normal – prevalent clusters: 399 out of 1048 (36,9%) In tumor prevalent clusters: 466 out of 501 (87%)

38 Possible explanation: Tumors express more non-coding mRNA than normal tissue due to: -- overall weakness of gene expression control (probably due to demethylation of genome) Awakening of dormant promoters Expression of sequences adjacent to dormant promoters with splicing according to cryptic splice sites

39 Experimental study of Tumor Specificity: Whether software based predictions are true or not??? Kozlov et al., 2003 Krukovskaja et al., 2004

40 Hs.133107 - Non-coding mRNA - Located on Chromosome 12p13 - Represented by 20 EST from the 5 tumor- derived libraries from Brain Tumors, Lung Carcinomas, Ovarian Carcinomas 20/20

41 Amplification of Hs.133107 Fragment on Human Tumor MTC Panel (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 344 bp 1 – 100bp DNA ladder 2 – Breast Carcinoma 3 – Lung Carcinoma 4 – Colon Adenocarcinoma 5 – Lung Carcinoma 6 – Prostatic Adenocarcinoma 7 – Colon Adenocarcinoma 8 – Ovarian Carcinoma 9 – Pancreatic Adenocarcinoma PC - Human DNA

42 Amplification of Hs.133107 Fragment on Human NORMAL MTC Panel 1 (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 344 bp 1 – 100 bp DNA ladder 2 – Brain 3 – Heart 4 – Kidney 5 - Liver 6 – Lung 7 – Pancreas 8 – Placenta 9 – Skeletal Muscle PC - Human DNA

43 Amplification of Hs.133107 Fragment on Human NORMAL MTC Panel 2 (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 344 bp 1 – 100bp DNA ladder 2 – Colon 3 – Ovary 4 – Peripheral Blood Leukocyte 5 - Prostate 6 – Small Intestine 7 – Spleen 8 – Testis 9 – Thymus PC - Human DNA

44 Amplification of Hs.133107 Fragment on Human Immune System MTC Panel, (Clontech) 1 2 3 4 5 6 7 8 NC PC 344 bp 1 – 100 bp DNA ladder 2 – Bone Marrow 3 – Fetal Liver 4 – Lymph Node 5 – Peripheral Blood Leukocyte 6 – Spleen 7 – Thymus 8 – Tonsil PC - Human DNA

45 Amplification of Hs.133107 Fragment on Human Fetal MTC Panel (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 344 bp 1 – 100 bp DNA ladder 2 – Brain 3 – Heart 4 – Kidney 5 - Liver 6 – Lung 7 – Skeletal Muscle 8 – Spleen 9 – Thymus PC - Human DNA

46 Conclusion: Hs.133107 is a Tumor-Specific Sequence

47 Hs.154173 - Non coding mRNA - Located in the intergenic spacer region within rRNA encoding unit - characteristic for lung carcinoma, testicular teratocarcinoma 23/23

48 Amplification of Hs.154173 Fragment on Human Tumor MTC Panel (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 443 bp 1 – 100bp DNA ladder 2 – Breast Carcinoma 3 – Lung Carcinoma 4 – Colon Adenocarcinoma 5 – Lung Carcinoma 6 – Prostatic Adenocarcinoma 7 – Colon Adenocarcinoma 8 – Ovarian Carcinoma 9 – Pancreatic Adenocarcinoma PC - Human DNA

49 Amplification of Hs.154173 Fragment on Human Normal MTC Panel 1 (Clontech) 1 2 3 4 5 6 7 8 9 NC PC 443 bp 1 – 100 bp DNA ladder 2 – Brain 3 – Heart 4 – Kidney 5 - Liver 6 – Lung 7 – Pancreas 8 – Placenta 9 – Skeletal Muscle PC - Human DNA

50 Three other normal panels looks same as previous; Conclusion: Hs.154173 is a Tumor-Specific Sequence

51 Hs.133294 -Protein encoding mRNA -Located on Chromosome 1q21 IQGAP3 gene -Weakly similar to IQGAP (human RAS GTPase-activating- like protein IQGAP1) -Represented in: prostate tumor, HNSCC, breast carcinoma, oligodendroglioma, colon carcinoma, CML, lung carcinoma, ovarian carcinoma, uterus carcinoma, adrenal adenoma -Minor occurrences in normal testis and germinal B-cells 58/62

52 5` polyA 5380-5407 ATG 76 TGA 4971 3` ATG 5014 IQGAP coding region 5` polyA 6052-6079 ATG 76 TGA 4971 5235 GUAGGA 5905 GAUUCU 3` intron ATG 5014ATG 5242 IQGAP coding region 5336 5905 mRNA isoforms of IQGAP3 gene 4924 412 bp 3 4924 1084bp PCR products from spliced and unspliced RNA forms of Hs.133294 Only 3’ underspliced mRNA variant of IQGAP3 gene is tumor – specific; (this part of the gene is non-coding either)

53 Conclusion: One of the mRNA isoforms of the IQGAP3 gene represented by Hs.133294 is a Tumor-Specific Sequence

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55 Back to fundamental aspect of the tumor-specific mRNAs What is a degree of conservativeness between human tumor-specific genes and mouse genes? Do they have orthologs in the mouse genome? WHAT IS THEIR FUNCTION ???

56 Closer look into well-established tumor-markers 1) For most of the known tumor markers no clear function in the adult human cells is shown; 2) Looks like these genes not expressed in normal tissues just because they not needed there; 3) Looks like these genes are expressed in tumors just because tumors don’t care !!! Prediction: no clear function  low level of conservation

57 VISTA – special visualization tool for distant cross-species comparison http://www-gsd.lbl.gov/vista/ Inna Dubchak and Eugene Rubin, 2002 Lawrence Berkeley National Laboratory (LBNL) This tool allow graphical representation of the alignment plots

58 How normal gene looks in VISTA Cclbr Alignment 1 Seqs: human/mouse Criteria: 75%, 50 bp Regions: 31 X-axis: human Resolution: 15 Window size: 50 bp 75%

59 How tumor marker gene looks in VISTA (MAGE A4) MGEA4 tumor marker meningioma expressed antigen 75%

60 You don’t believe me? Here is MAGE A9 – another one. 75%

61 More detailed VISTA view NRAS - oncogene FUGU and CHICKEN ARE PRESENT !!!!

62 More detailed VISTA view CEACAM6 Nothing here

63 Some genes are right in the middle: some exons are conservative, some are not (non-coding ones) RFP2 Here it is

64 Graphical alignments NRAS RFP2 CEACAM6

65 What about our own flock of predicted tumor markers ???

66 170 cancer strictly cancer specific EST clusters 74 Clusters corresponding to mouse genes >80% - nt >60% -aa 83 Clusters not corresponding to mouse genes (evolutionary new) < 70% nt <40% - aa 13 borderline clusters “SIMPLE” BLAST analysis

67 Evolution often goes by duplications What is the genomic source of these “evolutionary new” tumor markers????

68 22 of 170 cancer specific gene clusters are recently (only in primates) duplicated in human genome !!! Including known tumor marker genes: MAGE A4 MAGE A9 CTAG2 (cancer/testis specific antigen 2)

69 IDEA: In course of evolution new sequences become ready to be expressed as they got joined to dormant promoter But unnecessary promotors are thoroughly suppressed in well-differentiated adult cells In tumors all these “yet-to-be” genes got their chance

70 TUMOR and NORMAL TUMOR ONLY Same is true for “parts of the genes” – separate exons, especially non-coding exons cancer specific alternative splicing

71 Something to think about: 1. Can we use these non-coding tumor-specific mRNA as a tumor markers and targets for immunotherapy? (protein-coding genes could be used more readily) Yes. They always encode short ORFs linked with AUGs. Such short ORFs are TRUE non-self antigens.

72 2. Why known tumor markers have carcino-embryonic or carcino-testicular profiles of expression? -- embryos contain lots of less differentiated cells with relaxed expression control; -- testes are covered by blood-testes barrier, so immune system is not watching for an expression of non-self ORFs It explains broadest pattern of expression observed in tested Something to think about:

73 George Mason University, Fairfax, VA, USA A.Baranova, M.Sikaroodi, G,Manyam, P. Gillevet

74 The Biomedical Center, St.Petersburg, Russia A.P. Kozlov, L.L.Krukovskaja, D.Polev, I. Duhovlinov, Yu.Galachjants, N.Samusik

75 Vavilov Institute of General Genetics, Moscow, Russia T.Tyazhelova NCI, NIH C.Palena, J.Schlom S.O’Brien

76 Experimental example: DLEU2 gene in human and mouse (evol new gene) Red = mouse-specific exons only; Green = human specific exons only

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