Skip to main content

Advertisement

Springer Nature Link
Log in

Algorithm for in vitro diagnostic multivariate index assay

  • Conference Paper
  • Presidential symposium: From standardization to personalization in breast cancer treatment
  • Published:
Breast Cancer Aims and scope Submit manuscript

Abstract

Recently emerging diagnostic tools such as MammaPrint and oncotype-DX are beginning to have impact on clinical practice of breast cancer. They are based on gene expression profiling, i.e., gene expression analysis of a large number of genes. Their unique characteristic is the use of a score calculated from expression values of a number of genes, for which the Food and Drug Administration (FDA) created a new diagnostic category entitled “in vitro diagnostic multivariate index assay (IVDMIA).” In contrast to conventional biomarkers, IVDMIA requires an algorithm to calculate the diagnostic score. The linear classifier is the preferred algorithm. When the number of diagnostic genes is n, each tumor is represented by a point in an n-dimensional space made from gene expression values. Diagnostic algorithms (linear classifier) make an (n−1)-dimensional plane in the n-dimensional space to separate two patient groups. Calculation of the diagnostic score is achieved by dimension reduction. Currently, IVDMIA is restricted to gene expression profiling, and will also be applied to malignancies other than breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. van‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–6.

    Article  Google Scholar 

  2. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009.

    Article  PubMed  Google Scholar 

  3. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817–26.

    Article  PubMed  CAS  Google Scholar 

  4. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99:147–57.

    Article  PubMed  Google Scholar 

  5. Kato K, Ishii S. Statistical analysis of gene expression profiles. Tanpakushitsu Kakusan Koso. 2003;48:2300–9.

    PubMed  CAS  Google Scholar 

  6. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–7.

    Article  PubMed  CAS  Google Scholar 

  7. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning; data mining, inference and prediction. New York: Springer; 2001.

    Google Scholar 

  8. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9.

    Article  PubMed  CAS  Google Scholar 

  9. Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359:572–7.

    Article  PubMed  CAS  Google Scholar 

  10. Service RF. Proteomics. Will biomarkers take off at last? Science. 2008;321:1760.

    Article  PubMed  Google Scholar 

  11. Service RF. Proteomics. Proteomics ponders prime time. Science. 2008;321:1758–61.

    Article  PubMed  CAS  Google Scholar 

  12. Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R, et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet. 2003;362:362–9.

    Article  PubMed  CAS  Google Scholar 

  13. Iwao-Koizumi K, Matoba R, Ueno N, Kim SJ, Ando A, Miyoshi Y, et al. Prediction of docetaxel response in human breast cancer by gene expression profiling. J Clin Oncol. 2005;23:422–31.

    Article  PubMed  CAS  Google Scholar 

  14. Hess KR, Anderson K, Symmans WF, Valero V, Ibrahim N, Mejia JA, et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol. 2006;24:4236–44.

    Article  PubMed  CAS  Google Scholar 

  15. Shirahata M, Iwao-Koizumi K, Saito S, Ueno N, Oda M, Hashimoto N, et al. Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis. Clin Cancer Res. 2007;13:7341–56.

    Article  PubMed  CAS  Google Scholar 

  16. Shirahata M, Oba S, Iwao-Koizumi K, Saito S, Ueno N, Oda M, Hashimoto N, et al. Using gene expression profiling to identify a prognostic molecular spectrum in gliomas. Cancer Sci. 2009;100:165–72.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases, 1-3-3 Nakamichi, Higashinari-ku, Osaka, 537-8511, Japan

    Kikuya Kato

Authors
  1. Kikuya Kato
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kikuya Kato.

Additional information

This article is based on a presentation delivered at Presidential Symposium, “From standardization to personalization in breast cancer treatment,” held on 26 September 2008 at the 16th Annual Meeting of the Japanese Breast Cancer Society in Osaka.

About this article

Cite this article

Kato, K. Algorithm for in vitro diagnostic multivariate index assay. Breast Cancer 16, 248–251 (2009). https://doi.org/10.1007/s12282-009-0141-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12282-009-0141-9

Keywords