@article{MTMT:3115066,
	title = {Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer},
	url = {https://m2.mtmt.hu/api/publication/3115066},
	author = {Santarpia, L and Bottai, G and Kelly, CM and Győrffy, Balázs and Székely, Borbála and Pusztai, L},
	doi = {10.1634/theoncologist.2015-0369},
	journal-iso = {ONCOLOGIST},
	journal = {ONCOLOGIST},
	volume = {21},
	unique-id = {3115066},
	issn = {1083-7159},
	abstract = {: Advances in DNA and RNA sequencing revealed substantially greater genomic complexity in breast cancer than simple models of a few driver mutations would suggest. Only very few, recurrent mutations or copy-number variations in cancer-causing genes have been identified. The two most common alterations in breast cancer are TP53 (affecting the majority of triple-negative breast cancers) and PIK3CA (affecting almost half of estrogen receptor-positive cancers) mutations, followed by a long tail of individually rare mutations affecting <1%-20% of cases. Each cancer harbors from a few dozen to a few hundred potentially high-functional impact somatic variants, along with a much larger number of potentially high-functional impact germline variants. It is likely that it is the combined effect of all genomic variations that drives the clinical behavior of a given cancer. Furthermore, entirely new classes of oncogenic events are being discovered in the noncoding areas of the genome and in noncoding RNA species driven by errors in RNA editing. In light of this complexity, it is not unexpected that, with the exception of HER2 amplification, no robust molecular predictors of benefit from targeted therapies have been identified. In this review, we summarize the current genomic portrait of breast cancer, focusing on genetic aberrations that are actively being targeted with investigational drugs. IMPLICATIONS FOR PRACTICE: Next-generation sequencing is now widely available in the clinic, but interpretation of the results is challenging, and its impact on treatment selection is often limited. This work provides an overview of frequently encountered molecular abnormalities in breast cancer and discusses their potential therapeutic implications. This review emphasizes the importance of administering investigational targeted therapies, or off-label use of approved targeted drugs, in the context of a formal clinical trial or registry programs to facilitate learning about the clinical utility of tumor target profiling.},
	year = {2016},
	eissn = {1549-490X},
	pages = {1063-1078},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766}
}

@article{MTMT:2512278,
	title = {Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer},
	url = {https://m2.mtmt.hu/api/publication/2512278},
	author = {Győrffy, Balázs and Surowiak, P and Budczies, J and Lánczky, András},
	doi = {10.1371/journal.pone.0082241},
	journal-iso = {PLOS ONE},
	journal = {PLOS ONE},
	volume = {8},
	unique-id = {2512278},
	year = {2013},
	eissn = {1932-6203},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766}
}

@article{MTMT:1938795,
	title = {Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data of 1287 patients},
	url = {https://m2.mtmt.hu/api/publication/1938795},
	author = {Győrffy, Balázs and Lánczky, András and Szállási, Zoltán},
	doi = {10.1530/ERC-11-0329},
	journal-iso = {ENDOCR-RELAT CANCER},
	journal = {ENDOCRINE-RELATED CANCER},
	volume = {19},
	unique-id = {1938795},
	issn = {1351-0088},
	year = {2012},
	eissn = {1479-6821},
	pages = {197-208},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766; Szállási, Zoltán/0000-0001-5395-7509}
}

@article{MTMT:1436956,
	title = {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},
	url = {https://m2.mtmt.hu/api/publication/1436956},
	author = {Győrffy, Balázs and Lánczky, András and Eklund, AC and Denkert, C and Budczies, J and Li, Q and Szállási, Zoltán},
	doi = {10.1007/s10549-009-0674-9},
	journal-iso = {BREAST CANCER RES TR},
	journal = {BREAST CANCER RESEARCH AND TREATMENT},
	volume = {123},
	unique-id = {1436956},
	issn = {0167-6806},
	abstract = {Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.},
	keywords = {Aged; Adult; Female; Middle Aged; Humans; PREDICTIVE VALUE; GENETICS; ARTICLE; MORTALITY; methodology; human; Chemistry; TIME; Lymphatic Metastasis; Predictive Value of Tests; Prognosis; Neoplasm Staging; Tumor Markers, Biological; Genetic Markers; gene expression regulation; comparative study; genetic marker; Time Factors; pathology; Breast Neoplasms; *Genetic Markers; Oligonucleotide Array Sequence Analysis; Gene Expression Regulation, Neoplastic; Gene Expression Profiling; Reproducibility of Results; reproducibility; Internet; online system; breast tumor; disease free survival; Disease-Free Survival; cancer staging; computer graphics; tumor marker; Online Systems; Kaplan-Meier Estimate; Gene Expression Profiling/*methods; *Oligonucleotide Array Sequence Analysis; Receptors, Estrogen; DNA microarray; Tumor Markers, Biological/analysis/*genetics; Receptors, Estrogen/analysis; *Online Systems; *Gene Expression Regulation, Neoplastic; Breast Neoplasms/chemistry/*genetics/*mortality/pathology/therapy; Estrogen receptor; Kaplan Meier method; Lymph node metastasis},
	year = {2010},
	eissn = {1573-7217},
	pages = {725-731},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766; Szállási, Zoltán/0000-0001-5395-7509}
}

@article{MTMT:1880701,
	title = {A "multiple testing" problémája és a genomiális kísérletekre alkalmazott megoldások},
	url = {https://m2.mtmt.hu/api/publication/1880701},
	author = {Győrffy, Balázs and Gyorffy, A and Tulassay, Zsolt},
	journal-iso = {ORV HETIL},
	journal = {ORVOSI HETILAP},
	volume = {146},
	unique-id = {1880701},
	issn = {0030-6002},
	abstract = {The problem of multiple testing and its solutions for genome-wide studies. Even if there is no real change, the traditional p = 0.05 can cause 5% of the investigated tests being reported significant. Multiple testing corrections have been developed to solve this problem. Here the authors describe the one-step (Bonferroni), multi-step (step-down and step-up) and graphical methods. However, sometimes a correction for multiple testing creates more problems, than it solves: the universal null hypothesis is of little interest, the exact number of investigations to be adjusted for can not determined and the probability of type II error increases. For these reasons the authors suggest not to perform multiple testing corrections routinely. The calculation of the false discovery rate is a new method for genome-wide studies. Here the p value is substituted by the q value, which also shows the level of significance. The q value belonging to a measurement is the proportion of false positive measurements when we accept it as significant. The authors propose using the q value instead of the p value in genome-wide studies.},
	keywords = {Humans; *Data Interpretation, Statistical; In Situ Hybridization; Confidence Intervals; False Positive Reactions; *Models, Statistical; Research Design/*standards; *Genomics},
	year = {2005},
	eissn = {1788-6120},
	pages = {559-563},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766; Tulassay, Zsolt/0000-0003-2452-6640}
}