Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2006

Open Access 01-12-2006 | Research article

The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer

Authors: Anirban P Mitra, Arpit A Almal, Ben George, David W Fry, Peter F Lenehan, Vincenzo Pagliarulo, Richard J Cote, Ram H Datar, William P Worzel

Published in: BMC Cancer | Issue 1/2006

Login to get access

Abstract

Background

Previous studies on bladder cancer have shown nodal involvement to be an independent indicator of prognosis and survival. This study aimed at developing an objective method for detection of nodal metastasis from molecular profiles of primary urothelial carcinoma tissues.

Methods

The study included primary bladder tumor tissues from 60 patients across different stages and 5 control tissues of normal urothelium. The entire cohort was divided into training and validation sets comprised of node positive and node negative subjects. Quantitative expression profiling was performed for a panel of 70 genes using standardized competitive RT-PCR and the expression values of the training set samples were run through an iterative machine learning process called genetic programming that employed an N-fold cross validation technique to generate classifier rules of limited complexity. These were then used in a voting algorithm to classify the validation set samples into those associated with or without nodal metastasis.

Results

The generated classifier rules using 70 genes demonstrated 81% accuracy on the validation set when compared to the pathological nodal status. The rules showed a strong predilection for ICAM1, MAP2K6 and KDR resulting in gene expression motifs that cumulatively suggested a pattern ICAM1>MAP2K6>KDR for node positive cases. Additionally, the motifs showed CDK8 to be lower relative to ICAM1, and ANXA5 to be relatively high by itself in node positive tumors. Rules generated using only ICAM1, MAP2K6 and KDR were comparably robust, with a single representative rule producing an accuracy of 90% when used by itself on the validation set, suggesting a crucial role for these genes in nodal metastasis.

Conclusion

Our study demonstrates the use of standardized quantitative gene expression values from primary bladder tumor tissues as inputs in a genetic programming system to generate classifier rules for determining the nodal status. Our method also suggests the involvement of ICAM1, MAP2K6, KDR, CDK8 and ANXA5 in unique mathematical combinations in the progression towards nodal positivity. Further studies are needed to identify more class-specific signatures and confirm the role of these genes in the evolution of nodal metastasis in bladder cancer.
Appendix
Available only for authorised users
Literature
1.
Tyczynski JE, Parkin DM: Bladder cancer in Europe. ENCR Cancer Fact Sheets. 2003, 3: 1-4.
2.
World Health Organization: The World Health Report 2004 – changing history. Geneva. 2004
3.
4.
Urinary bladder. AJCC Cancer Staging Manual. Edited by: Greene FL, Page DL, Fleming ID, Fritz A, Balch CM, Haller DG, Morrow M. 2002, New York: Springer-Verlag, 367-374. 6
5.
Lotan Y, Gupta A, Shariat SF, Palapattu GS, Vazina A, Karakiewicz PI, Bastian PJ, Rogers CG, Amiel G, Perotte P, Schoenberg MP, Lerner SP, Sagalowsky AI: Lymphovascular invasion is independently associated with overall survival, cause-specific survival, and local and distant recurrence in patients with negative lymph nodes at radical cystectomy. J Clin Oncol. 2005, 23: 6533-6539.PubMed
6.
Leissner J, Hohenfellner R, Thuroff JW, Wolf HK: Lympadenectomy in patients with transitional cell carcinoma of the urinary bladder; significance for staging and prognosis. BJU Int. 2000, 85: 817-823.PubMed
7.
Stein JP, Lieskovsky G, Cote R, Groshen S, Feng AC, Boyd S, Skinner E, Bochner B, Thangathurai D, Mikhail M, Raghavan D, Skinner DG: Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J Clin Oncol. 2001, 19: 666-675.PubMed
8.
Ather MH, Fatima S, Sinanoglu O: Extent of lymphadenectomy in radical cystectomy for bladder cancer. World J Surg Oncol. 2005, 3: 43-PubMedPubMedCentral
9.
Bonassi S, Neri M, Puntoni R: Validation of biomarkers as early predictors of disease. Mutat Res. 2001, 480: 349-58.PubMed
10.
Kawamukai K, Cesario A, Margaritora S, Meacci E, Piraino A, Vita ML, Tessitore A, Cusumano G, Granone P: TNM independent prognostic factors in lung cancer. Rays. 2004, 29: 373-376.PubMed
11.
Willey JC, Crawford EL, Jackson CM, Weaver DA, Hoban JC, Khuder SA, DeMuth JP: Expression measurement of many genes simultaneously by quantitative RT-PCR using standardized mixtures of competitive templates. Am J Respir Cell Mol Biol. 1998, 19: 6-17.PubMed
12.
Crawford EL, Warner KA, Khuder SA, Zahorchak RJ, Willey JC: Multiplex standardized RT-PCR for expression analysis of many genes in small samples. Biochem Biophys Res Commun. 2002, 293: 509-516.PubMed
13.
Koza JR: Genetic Programming: On the Programming of Computers by Means of Natural Selection. 1992, Cambridge: MIT Press
14.
Pagliarulo V, George B, Beil SJ, Groshen S, Laird PW, Cai J, Willey J, Cote RJ, Datar RH: Sensitivity and reproducibility of standardized-competitive RT-PCR for transcript quantification and its comparison with real time RT-PCR. Mol Cancer. 2004, 3: 5-PubMedPubMedCentral
15.
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Plummer WD, Parl FF, Moore JH: Multifactor Dimensionality Reduction Reveals High-Order Interactions among Estrogen Metabolism Genes in Sporadic Breast Cancer. Am J Hum Genet. 2001, 69: 138-147.PubMedPubMedCentral
16.
Deserno WM, Harisinghani MG, Taupitz M, Jager GJ, Witjes JA, Mulders PF, Hulsbergen van de Kaa CA, Kaufmann D, Barentsz JO: Urinary bladder cancer: Preoperative nodal staging with ferumoxtran-10-enhanced MR imaging. Radiology. 2004, 233: 449-456.PubMed
17.
Kurahashi T, Hara I, Oka N, Kamidono S, Eto H, Miyake H: Detection of micrometastases in pelvic lymph nodes in patients undergoing radical cystectomy for focally invasive bladder cancer by real-time reverse transcriptase-PCR for cytokeratin 19 and uroplakin II. Clin Cancer Res. 2005, 11: 3773-3777.PubMed
18.
Hollenbeck BK, Montie JE: Early cystectomy for clinical stage T1 bladder cancer. Nat Clin Pract Urol. 2004, 1: 4-5.PubMed
19.
Crawford EL, Peters GJ, Noordhuis P, Rots MG, Vondracek M, Grafstrom RC, Lieuallen K, Lennon G, Zahorchak RJ, Georgeson MJ, Wali A, Lechner JF, Fan PS, Kahaleh MB, Khuder SA, Warner KA, Weaver DA, Willey JC: Reproducible gene expression measurement among multiple laboratories obtained in a blinded study using standardized RT (StaRT)-PCR. Mol Diagn. 2001, 6: 217-225.PubMed
20.
Zou KH, Hall WJ, Shapiro DE: Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. Stat Med. 1997, 16: 2143-2156.PubMed
21.
Hanley JA, McNeil BJ: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982, 143: 29-36.PubMed
22.
Vapnik VN: The Nature of Statistical Learning Theory. 1995, Berlin: Springer-Verlag
23.
Rissanen J: Modeling by shortest data description. Automatica. 1978, 14: 465-471.
24.
Vapnik VN, Chervonenkis AY: On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab Apl. 1971, 16: 264-280.
25.
Schaffer C: Selecting a classification method by cross-validation. Machine Learning. 1993, 13: 135-143.
26.
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2001, New York: Springer
27.
Narasimhamurthy A: A framework for the analysis of majority voting. Lectures notes in Computer Science. 2003, 2749: 268-274.
28.
Narasimhamurthy A: Theoretical bounds for majority voting performance for a binary classification problem. IEEE Trans Pattern Anal Mach Intell. 2005, 27: 1988-1995.PubMed
29.
Hubbard AK, Rothlein R: Intercellular adhesion molecule-1 (ICAM-1) expression and cell signaling cascades. Free Radic Biol Med. 2000, 28: 1379-1386.PubMed
30.
Roche Y, Pasquier D, Rambeaud JJ, Seigneurin D, Duperray A: Fibrinogen mediates bladder cancer cell migration in an ICAM-1-dependent pathway. Thromb Haemost. 2003, 89: 1089-1097.PubMed
31.
Ozer G, Altinel M, Kocak B, Balci M, Altan A, Gonenc F: Potential value of soluble intercellular adhesion molecule-1 in the serum of patients with bladder cancer. Urol Int. 2003, 70: 167-171.PubMed
32.
Palumbo JS, Potter JM, Kaplan LS, Talmage K, Jackson DG, Degen JL: Spontaneous hematogenous and lymphatic metastasis, but not primary tumor growth or angiogenesis, is diminished in fibrinogen-deficient mice. Cancer Res. 2002, 62: 6966-6972.PubMed
33.
Rosette C, Roth RB, Oeth P, Braun A, Kammerer S, Ekblom J, Denissenko MF: Role of ICAM1 in invasion of human breast cancer cells. Carcinogenesis. 2005, 26: 943-950.PubMed
34.
Roux PP, Blenis J: ERK and p38 MAPK-activated protein kinases: a family of protein kinases with diverse biological functions. Microbiol Mol Biol Rev. 2004, 68: 320-344.PubMedPubMedCentral
35.
Zarubin T, Han J: Activation and signaling of the p38 MAP kinase pathway. Cell Res. 2005, 15: 11-18.PubMed
36.
Wang Q, Doerschuk CM: The p38 mitogen-activated protein kinase mediates cytoskeletal remodeling in pulmonary microvascular endothelial cells upon intracellular adhesion molecule-1 ligation. J Immunol. 2001, 166: 6877-6884.PubMed
37.
Ott I, Weigand B, Michl R, Seitz I, Sabbari-Erfani N, Neumann FJ, Schomig A: Tissue factor cytoplasmic domain stimulates migration by activation of the GTPase Rac1 and the mitogen-activated protein kinase p38. Circulation. 2005, 111: 349-355.PubMed
38.
Esteva FJ, Sahin AA, Smith TL, Yang Y, Pusztai L, Nahta R, Buchholz TA, Buzdar AU, Hortobagyi GN, Bacus SS: Prognostic significance of phosphorylated P38 mitogen-activated protein kinase and HER-2 expression in lymph node-positive breast carcinoma. Cancer. 2004, 100: 499-506.PubMed
39.
Shin I, Kim S, Song H, Kim HR, Moon A: H-Ras-specific activation of Rac-MKK3/6-p38 pathway: its critical role in invasion and migration of breast epithelial cells. J Biol Chem. 2005, 280: 14675-14683.PubMed
40.
Kim MS, Lee EJ, Kim HR, Moon A: p38 kinase is a key signaling molecule for H-Ras-induced cell motility and invasive phenotype in human breast epithelial cells. Cancer Res. 2003, 63: 5454-5461.PubMed
41.
Behren A, Binder K, Vucelic G, Herberhold S, Hirt B, Loewenheim H, Preyer S, Zenner HP, Simon C: The p38 SAPK pathway is required for Ha-ras induced in vitro invasion of NIH3T3 cells. Exp Cell Res. 2005, 303: 321-330.PubMed
42.
Shibuya M: Vascular endothelial growth factor receptor-2: its unique signaling and specific ligand, VEGF-E. Cancer Sci. 2003, 94: 751-756.PubMed
43.
Gakiopoulou-Givalou H, Nakopoulou L, Panayotopoulou EG, Zervas A, Mavrommatis J, Giannopoulos A: Non-endothelial KDR/flk-1 expression is associated with increased survival of patients with urothelial bladder carcinomas. Histopathology. 2003, 43: 272-279.PubMed
44.
Ferrer FA, Miller LJ, Lindquist R, Kowalczyk P, Laudone VP, Albertsen PC, Kreutzer DL: Expression of vascular endothelial growth factor receptors in human prostate cancer. Urology. 1999, 54: 567-572.PubMed
45.
Tassan JP, Jaquenoud M, Leopold P, Schultz SJ, Nigg EA: Identification of human cyclin-dependent kinase 8, a putative protein kinase partner for cyclin C. Proc Natl Acad Sci USA. 1995, 92: 8871-8875.PubMedPubMedCentral
46.
Liao SM, Zhang J, Jeffery DA, Koleske AJ, Thompson CM, Chao DM, Viljoen M, van Vuuren HJ, Young RA: A kinase-cyclin pair in the RNA polymerase II holoenzyme. Nature. 1995, 374: 193-196.PubMed
47.
Rachez C, Lemon BD, Suldan Z, Bromleigh V, Gamble M, Naar AM, Erdjument-Bromage H, Tempst P, Freedman LP: Ligand-dependent transcription activation by nuclear receptors requires the DRIP complex. Nature. 1999, 398: 824-828.PubMed
48.
Malik S, Gu W, Wu W, Qin J, Roeder RG: The USA-derived transcriptional coactivator PC2 is a submodule of TRAP/SMCC and acts synergistically with other PCs. Mol Cell. 2000, 5: 753-760.PubMed
49.
Wang G, Cantin GT, Stevens JL, Berk AJ: Characterization of mediator complexes from HeLa cell nuclear extract. Mol Cell Biol. 2001, 21: 4604-4613.PubMedPubMedCentral
50.
Sun X, Zhang Y, Cho H, Rickert P, Lees E, Lane W, Reinberg D: NAT, a human complex containing Srb polypeptides that functions as a negative regulator of activated transcription. Mol Cell. 1998, 2: 213-222.PubMed
51.
Akoulitchev S, Chuikov S, Reinberg D: TFIIH is negatively regulated by cdk8-containing mediator complexes. Nature. 2000, 407: 102-106.PubMed
52.
Hayes MJ, Rescher U, Gerke V, Moss SE: Annexin-actin interactions. Traffic. 2004, 5: 571-576.PubMed
53.
Hayes MJ, Moss SE: Annexins and Disease. Biochem Biophys Res Comm. 2004, 322: 1166-1170.PubMed
54.
Cookson BT, Engelhardt S, Smith C, Bamford HA, Prochazka M, Tait JF: Organization of the human annexin V (ANX5) gene. Genomics. 1994, 20: 463-467.PubMed
55.
Tzima E, Trotter PJ, Orchard MA, Walker JH: Annexin V relocates to the platelet cytoskeleton upon activation and binds to a specific isoform of actin. Eur J Biochem. 2000, 267: 4720-4730.PubMed
56.
Belhocine T, Steinmetz N, Hustinx R, Bartsch P, Jerusalem G, Seidel L, Rigo P, Green A: Increased uptake of the apoptosis-imaging agent (99 m)Tc recombinant human Annexin V in human tumors after one course of chemotherapy as a predictor of tumor response and patient prognosis. Clin Cancer Res. 2002, 8: 2766-2774.PubMed
57.
Hawkins TE, Das D, Young B, Moss SE: DT40 cells lacking the Ca2+ -binding protein annexin 5 are resistant to Ca2+ -dependent apoptosis. Proc Natl Acad Sci USA. 2002, 8054-8059.
58.
Townson JL, Naumov GN, Chambers AF: The role of apoptosis in tumor progression and metastasis. Curr Mol Med. 2003, 3: 631-642.PubMed
59.
Dyrskjot L, Thykjaer T, Kruhoffer M, Jensen JL, Marcussen N, Hamilton-Dutoit S, Wolf H, Orntoft TF: Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet. 2003, 33: 90-96.PubMed
60.
Daida JM: What Makes a Problem GP-Hard? A Look at How Structure Affects Content. Genetic Programming Theory and Practice. Edited by: Riolo RL, Worzel B. 2003, Boston: Kluwer Academic, 99-118.
61.
Brameier M, Haan J, Krings A, MacCallum RM: Automatic discovery of cross-family sequence features associated with protein function. BMC Bioinformatics. 2006, 7: 16-PubMedPubMedCentral
62.
Driscoll JA, Worzel B, MacLean D: Classification of gene expression data with genetic programming. Genetic Programming Theory and Practice. Edited by: Riolo RL, Worzel B. 2003, Boston: Kluwer Academic, 25-42.
63.
Hong JH, Cho SB: Cancer prediction using diversity-based ensemble genetic programming. Lecture Notes in Computer Science. 2005, 3558: 294-304.
64.
Langdon WB, Buxton BF: Genetic programming for mining DNA chip data from cancer patients. Genetic Programming and Evolvable Machines. 2004, 5: 251-257.
65.
Moore JH, Parker JS, Hahn LW: Symbolic discriminant analysis for mining gene expression patterns. Lecture Notes in Computer Science. 2001, 2167: 372-
66.
Eggermont J, Kok JN, Kosters WA: Genetic Programming for Data Classification:Partitioning the Search Space. Proceedings of the 2004 ACM symposium on Applied computing: 14–17 March 2004; Nicosia. Edited by: Haddad HM, Omicini A, Wainwright RL, Liebrock LM. 2004, New York: ACM Press, 1001-1005.
67.
Bellman R: Adaptive Control Processes: A Guided Tour. 1961, Princeton: Princeton University Press
68.
Andre D, Koza JR: A parallel implementation of genetic programming that achieves super-linear performance. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications: 9–11 August 1996; Sunnyvale. Edited by: Arabnia HR. 1996, Las Vegas: CSREA Press, 1163-1174.
69.
Mitra AP, Datar RH, Cote RJ: Molecular staging of bladder cancer. BJU Int. 2005, 96: 7-12.PubMed
Metadata
Title
The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer
Authors
Anirban P Mitra
Arpit A Almal
Ben George
David W Fry
Peter F Lenehan
Vincenzo Pagliarulo
Richard J Cote
Ram H Datar
William P Worzel
Publication date
01-12-2006
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2006
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-6-159

Other articles of this Issue 1/2006

BMC Cancer 1/2006 Go to the issue

Research article

Combined immunohistochemistry of β-catenin, cytokeratin 7, and cytokeratin 20 is useful in discriminating primary lung adenocarcinomas from metastatic colorectal cancer

Research article

Serial detection of circulating tumour cells by reverse transcriptase-polymerase chain reaction assays is a marker for poor outcome in patients with malignant melanoma

Research article

Quantification of Epstein-Barr virus DNA load, interleukin-6, interleukin-10, transforming growth factor-β1 and stem cell factor in plasma of patients with nasopharyngeal carcinoma

Research article

Genistein inhibits radiation-induced activation of NF-κB in prostate cancer cells promoting apoptosis and G2/M cell cycle arrest

Research article

Fibroblast-derived MT1-MMP promotes tumor progression in vitro and in vivo

Research article

PPARγ-independent induction of growth arrest and apoptosis in prostate and bladder carcinoma
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits