Abstract
Despite the progress in cancer diagnosis the timely detection of many cancer types is still a grand challenge. For various human cancer types including lung cancer, prostate cancer, and breast cancer, several groups recently demonstrated that autoantibody profiling might be a promising approach towards earlier and more accurate cancer diagnosis.
In this paper, we confirm the ability of autoantibody profiling as a diagnostic test by providing evidence that not only cancer sera can be distinguished well from normal controls, but also from sera of patients with noncancerous diseases. Altogether, we screened blood sera of 191 cancer patients, 60 physiologically unaffected controls, and 177 sera of patients with noncancerous diseases for more than 1800 immunogenic clones. The measured autoantibody fingerprints were evaluated using a novel image analysis pipeline.
For 13 antigens, statistically significant (p<0.05) and at least two-fold elevated immuno-reactivity in cancer sera compared to normal sera could be observed. Nine of these antigens also showed increased reactivity compared to sera of patients with other diseases, including the tumor marker vimentin. Supervised discrimination between cancer and normal sera by using linear Support Vector Machines was possible with an accuracy of 94.04%, a specificity of 83.38%, and a sensitivity of 97.44%. Here, our so-called MIMM (minimally invasive multiple marker) approach showed no significant difference in the classification accuracy between low and higher tumor grades. The classification in healthy and diseased sera showed an even higher accuracy of 96.12% while the discrimination in cancer sera and diseased controls revealed an accuracy of 69.58%.
These results demonstrate that autoantibody profiling offers the possibility of cancer screening for a variety of different cancer types as well as inflammatory diseases at an early disease stage.
Similar content being viewed by others
References
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Statist Soc B 57:289–300
Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19(2):185–193
Brichory FM, Misek DE, Yim AM, Krause MC, Giordano TJ, Beer DG, Hanash SM (2001) An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer. Proc Natl Acad Sci USA 98:9824–9829
Chapman CJ, Murray A, McElveen JE, Sahin U, Luxemburger U, Türeci O, Wiewrodt R, Barnes AC, Robertson JF (2007) Autoantibodies in Lung Cancer – possibilities for early detection and subsequent cure. Thorax 63:228–233
Chatterjee M, Mohapatra S, Ionan A, Bawa G, Ali-Fehmi R, Wang X, Nowak J, Ye B, Nahhas FA, Lu K, Witkin SS, Fishman D, Munkarah A, Morris R, Levin NK, Shirley NN, Tromp G, Abrams J, Draghici S, Tainsky MA (2006) Diagnostic markers of ovarian cancer by high-throughput antigen cloning and detection on arrays. Cancer Res 66:1181–1190
Comtesse N, Zippel A, Walle S, Monz D, Backes C, Fischer U, Mayer J, Ludwig N, Keller A, Hildebrandt A, Steudel W I, Lenhof HP, Meese E (2005) Complex humoral immune response against a benign tumor: Frequent antibody response against specific antigens as diagnostic targets. Proc Natl Acad Sci USA 102:9601–9606
Erkanli A, Taylor DD, Dean D, Eksir F, Egger D, Geyer J, Nelson BH, Stone B, Fritsche HA, Roden RB (2006) Application of Bayesian modeling of autologous antibody responses against ovarian tumor-associated antigens to cancer detection. Cancer Res 66:1792–1798
Heubeck B, Wendler O, Bumm K, Schäfer R, Müller-Vogt U, Häusler M, Meese E, Iro H, Steinhart H (2006) Tumor-associated antigenic pattern in squamous cell carcinomas of the head and neck – Analysed by SEREX. Eur J Cancer, Epub
Keller A, Comtesse N, Ludwig N, Meese E, Lenhof HP (2007) SePaCS – a web-based application for classi?cation of seroreactivity pro?les. Nucleic Acids Res 35:W683–W687
Keller A, Ludwig N, Comtesse N, Hildebrandt A, Meese E, Lenhof HP (2006) A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors. BMC Bioinform 7:539
Leidinger P, Keller A, Ludwig N, Rheinheimer S, Hamacher J, Huwer H, Stehle I, Lenhof HP, Meese E (2008) Toward an early diagnosis of lung cancer: an autoantibody signature for squamous cell lung carcinoma. Int J Cancer 123(7):1631–1636
Ludwig N, Keller A, Comtesse N, Rheinheimer S, Pallasch C, Fischer U, Fassbender K, Steudel WI, Lenhof HP, Meese E (2008) Pattern of serum autoantibodies allows accurate distinction between a tumor and pathologies of the same organ. Clin Cancer Res 14(15):4767–74
Mann H, Wilcoxon F (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Mat Stat 18:50–60
Soille P (2003) Morphological image analysis. Springer, Berlin New York
Vapnik V (1995) The Nature of Statistical Learning Theory, Chap. 5.4. Springer, New York
Vaughan HA, St Clair F, Scanlan MJ, Chen YT, Maraskovsky E, Sizeland A, Old LJ, Cebon J (2003) The humoral immune response to head and neck cancer antigens as defined by the serological analysis of tumor antigens by recombinant cDNA expression cloning. Cancer Immun 4:5
Wang X, Yu J, Sreekumar A, Varambally S, Shen R, Giacherio D, Mehra R, Montie JE, Pienta KJ, Sanda MG, Kantoff PW, Rubin MA, Wei JT, Ghosh D, Chinnaiyan AM (2005) Autoantibody signatures in prostate cancer. N Engl J Med 353:1224–1235
Wilcoxon F (1945) Individual comparison by ranking methods. Biometric Bull 1:80–83
Zhong L, Coe SP, Stromberg AJ, Khattar NH, Jett JR, Hirschowitz EA (2006) Profiling tumor-associated antibodies for early detection of non-small cell lung cancer. J Thorac Oncol 1:513–519
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Keller, A., Ludwig, N., Heisel, S. et al. Large-scale antibody profiling of human blood sera: The future of molecular diagnosis. Informatik Spektrum 32, 332–338 (2009). https://doi.org/10.1007/s00287-009-0354-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00287-009-0354-5