Our publications related to cancer biomarkers

Found 11 results
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2017
Menyhart O, Budczies J, Munkácsy G, Esteva FJ, Szabó A, Miquel TP, Győrffy B.  2017.  DUSP4 is associated with increased resistance against anti-HER2 therapy in breast cancer. Oncotarget. 8(44):77207-77218.
2016
Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L, Győrffy B.  2016.  miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 160(3):446.
2015
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.
Pongor L, Kormos M, Hatzis C, Pusztai L, Szabó A, Győrffy B.  2015.  A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients.. Genome Med. 7(1):104.
2014
Győrffy B, Bottai G, Lehmann-Che J, Kéri G, Orfi L, Iwamoto T, Desmedt C, Bianchini G, Turner NC, de Thè H et al..  2014.  TP53 mutation-correlated genes predict the risk of tumor relapse and identify MPS1 as a potential therapeutic kinase in TP53-mutated breast cancers.. Mol Oncol. 8(3):508-19.
2013
Mihály Z, Kormos M, Lánczky A, Dank M, Budczies J, Szász MA, Győrffy B.  2013.  A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer.. Breast Cancer Res Treat. 140(2):219-32.
2012
Gyorffy B., Benke Z., Lanczky A., Balazs B., Szallasi Z., Timar J., Schafer R..  2012.  RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat. 132:1025-34.
2011
Li Q., Birkbak N.J, Gyorffy B., Szallasi Z., Eklund A.C.  2011.  Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics. 12:474.
2010
Gyorffy B., Lanczky A., Eklund A.C, Denkert C., Budczies J., Li Q., Szallasi Z..  2010.  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. 123:725-31.
2009
Gyorffy B., Molnar B., Lage H., Szallasi Z., Eklund A.C.  2009.  Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One. 4:e5645.

Pages

How to cite the RS calculator

RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data, Breast Cancer Res Treat (2012). Read it here.

How to cite the dynamic re-training algorithm

Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer, Int J Cancer (2015). Read it here.

Techshow

On the Hungarian Innovation TechShow RecurrenceOnline has been selected as one of the 21 most innovative ideas shaping our future.

Semmelweis Journal

RecurrenceOnline has been described in the Semmelweis Journal. Read the pdf version here (only in Hungarian).

Projects

RecurrenceOnline was featured in Projects Magazine, Europe's leading science and technology research magazine. Download the pdf here.