Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer

International Journal of Cancer

Volumn 136, Issue 9, 2015, Pages 2091-2098

  Gyorffy, Balázs ^a,b   Karn, Thomas ^c   Sztupinszki, Zsófia ^a   Weltz, Boglárka ^a   Müller, Volkmar ^d   Pusztai, Lajos ^e  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b SEMMELWEIS UNIVERSITY   (Hungary)

^c GOETHE UNIVERSITY   (Germany)

^d UNIVERSITY MEDICAL CENTER HAMBURG EPPENDORF   (Germany)

^e YALE CANCER CENTER   (United States)

Author keywords

Breast cancer; Gene expression; Survival

Indexed keywords

ACCURACY; ARTICLE; BREAST CANCER; CANCER CLASSIFICATION; CANCER PROGNOSIS; CANCER RISK; CASE SPECIFIC TRAINING COHORT; CASE STUDY; CLASSIFIER; CLINICAL ASSESSMENT; COHORT ANALYSIS; GENE EXPRESSION; GENOME; INTERMETHOD COMPARISON; INTERNET; PREDICTION; PRIORITY JOURNAL; RECURRENCE FREE SURVIVAL; RISK ASSESSMENT; TRIPLE NEGATIVE BREAST CANCER; VALIDATION PROCESS; DISEASE FREE SURVIVAL; FEMALE; GENETICS; HUMAN; KAPLAN MEIER METHOD; MIDDLE AGED; PATHOLOGY; PROGNOSIS;

TRANSCRIPTOME;

DISEASE-FREE SURVIVAL; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; MIDDLE AGED; PROGNOSIS; TRANSCRIPTOME; TRIPLE NEGATIVE BREAST NEOPLASMS;

EID: 84923166135     PISSN: 00207136     EISSN: 10970215     Source Type: Journal    
DOI: 10.1002/ijc.29247     Document Type: Article

References (21)

1. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-Treated, nodenegative breast cancer. N Engl J Med 2004;351: 2817-26.
2. Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009;27: 1160-7.
3. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006;98: 262-72.
4. van't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415: 530-6.
5. Rhodes A, Jasani B, Balaton AJ, et al. Frequency of oestrogen and progesterone receptor positivity by immunohistochemical analysis in 7016 breast carcinomas: Correlation with patient age, assay sensitivity, threshold value, and mammographic screening. J Clin Pathol 2000;53: 688-96.
6. Rhodes A, Jasani B, Balaton AJ, et al. Study of interlaboratory reliability and reproducibility of estrogen and progesterone receptor assays in Europe. Documentation of poor reliability and identification of insufficient microwave antigen retrieval time as a major contributory element of unreliable assays. Am J Clin Pathol 2001;115: 44-58.
7. Layfield LJ, Goldstein N, Perkinson KR, et al. Interlaboratory variation in results from immunohistochemical assessment of estrogen receptor status. Breast J 2003;9: 257-9.
8. Planey CR, Butte AJ. Database integration of 4923 publicly-Available samples of breast cancer molecular and clinical data. AMIA Joint Summits Transl Sci Proc 2013;2013: 138-42.
9. Wray CJ, Ko TC, Tan FK. Secondary use of existing public microarray data to predict outcome for hepatocellular carcinoma. J Surg Res 2014;188: 137-42.
10. Gyorffy B, Benke Z, Lanczky A, et al. RecurrenceOnline: An online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat 2012;132: 1025-34.
11. Gyorffy B, Schafer R. Meta-Analysis of gene expression profiles related to relapse-free survival in 1, 079 breast cancer patients. Breast Cancer Res Treat 2009;118: 433-41.
12. Gautier L, Cope L, Bolstad BM, et al. Affy-Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004;20: 307-15.
13. Gyorffy B, Molnar B, Lage H, et al. Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One 2009;4: E5645.
14. Li Q, Birkbak NJ, Gyorffy B, et al. Jetset: Selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics 2011;12: 474.
15. Gyorffy B, Lanczky A, Eklund AC, et al. An online survival analysis tool to rapidly assess the effect of 22, 277 genes on breast cancer prognosis using microarray data of 1, 809 patients. Breast Cancer Res Treat 2010;123: 725-31.
16. Foody GM. Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing Environ 2009; 113: 5.
17. Steinberg DM, Fine J, Chappell R. Sample size for positive and negative predictive value in diagnostic research using case-control designs. Biostatistics 2009;10: 94-105.
18. Weigelt B, Baehner FL, Reis-Filho JS. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: A retrospective of the last decade. J Pathol 2010; 220: 263-80.
19. Weigelt B, Pusztai L, Ashworth A, et al. Challenges translating breast cancer gene signatures into the clinic. Nat Rev Clin Oncol 2012;9: 58-64.
20. Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2, 000 breast tumours reveals novel subgroups. Nature 2012; 486: 346-52.
21. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009;360: 790-800.