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

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Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. / Győrffy, Balázs; Karn, Thomas; Sztupinszki, Zsófia; Weltz, Boglárka; Müller, Volkmar; Pusztai, Lajos.

In: INT J CANCER, Vol. 136, No. 9, 01.05.2015, p. 2091-8.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Győrffy, B, Karn, T, Sztupinszki, Z, Weltz, B, Müller, V & Pusztai, L 2015, 'Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer', INT J CANCER, vol. 136, no. 9, pp. 2091-8. https://doi.org/10.1002/ijc.29247

APA

Győrffy, B., Karn, T., Sztupinszki, Z., Weltz, B., Müller, V., & Pusztai, L. (2015). Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. INT J CANCER, 136(9), 2091-8. https://doi.org/10.1002/ijc.29247

Vancouver

Győrffy B, Karn T, Sztupinszki Z, Weltz B, Müller V, Pusztai L. Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. INT J CANCER. 2015 May 1;136(9):2091-8. https://doi.org/10.1002/ijc.29247

Bibtex

@article{01749e66ab6f4c4e9699b8459b3354af,
title = "Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer",
abstract = "The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers.",
author = "Bal{\'a}zs Gy{\H o}rffy and Thomas Karn and Zs{\'o}fia Sztupinszki and Bogl{\'a}rka Weltz and Volkmar M{\"u}ller and Lajos Pusztai",
note = "{\textcopyright} 2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC.",
year = "2015",
month = may,
day = "1",
doi = "10.1002/ijc.29247",
language = "English",
volume = "136",
pages = "2091--8",
journal = "INT J CANCER",
issn = "0020-7136",
publisher = "Wiley-Liss Inc.",
number = "9",

}

RIS

TY - JOUR

T1 - Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer

AU - Győrffy, Balázs

AU - Karn, Thomas

AU - Sztupinszki, Zsófia

AU - Weltz, Boglárka

AU - Müller, Volkmar

AU - Pusztai, Lajos

N1 - © 2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC.

PY - 2015/5/1

Y1 - 2015/5/1

N2 - The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers.

AB - The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers.

U2 - 10.1002/ijc.29247

DO - 10.1002/ijc.29247

M3 - SCORING: Journal article

C2 - 25274406

VL - 136

SP - 2091

EP - 2098

JO - INT J CANCER

JF - INT J CANCER

SN - 0020-7136

IS - 9

ER -