TY  - JOUR
A1  - Baur, Johannes
A1  - Büntemeyer, Tjark-Ole
A1  - Megerle, Felix
A1  - Deutschbein, Timo
A1  - Spitzweg, Christine
A1  - Quinkler, Marcus
A1  - Nawroth, Peter
A1  - Kroiss, Matthias
A1  - Germer, Christoph-Thomas
A1  - Fassnacht, Martin
A1  - Steger, Ulrich
T1  - Outcome after resection of Adrenocortical Carcinoma liver metastases: a retrospective study
JF  - BMC Cancer
N2  - Background:
Metastatic Adrenocortical Carcinoma (ACC) is a rare malignancy with a poor 5-year-survival rate (<15%). A surgical approach is recommended in selected patients if complete resection of distant metastasis can be achieved. To date there are only limited data on the outcome after surgical resection of hepatic metastases of ACC.

Methods:
A retrospective analysis of the German Adrenocortical Carcinoma Registry was conducted. Patients with liver metastases of ACC but without extrahepatic metastases or incomplete tumour resection were included.

Results:
Seventy-seven patients fulfilled these criteria. Forty-three patients underwent resection of liver metastases of ACC. Complete tumour resection (R0) could be achieved in 30 (69.8%). Median overall survival after liver resection was 76.1 months in comparison to 10.1 months in the 34 remaining patients with unresected liver metastases (p < 0.001). However, disease free survival after liver resection was only 9.1 months. Neither resection status (R0/R1) nor extent of liver resection were significant predictive factors for overall survival. Patients with a time interval to the first metastasis/recurrence (TTFR) of greater than 12 months or solitary liver metastases showed significantly prolonged survival.

Conclusions:
Liver resection in the case of ACC liver metastases can achieve long term survival with a median overall survival of more than 5 years, but disease free survival is short despite metastasectomy. Time to recurrence and single versus multiple metastases are predictive factors for the outcome.
KW  - Adrenocortical Carcinoma
KW  - liver resection
KW  - retrospective study
KW  - prognosis
KW  - survival analysis
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-159409
VL  - 17
IS  - 522
ER  - 
TY  - JOUR
A1  - Vey, Johannes
A1  - Kapsner, Lorenz A.
A1  - Fuchs, Maximilian
A1  - Unberath, Philipp
A1  - Veronesi, Giulia
A1  - Kunz, Meik
T1  - A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning
JF  - Cancers
N2  - The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
KW  - bioinformatics tool
KW  - R package
KW  - machine learning
KW  - meta-analysis
KW  - biomarker signature
KW  - gene expression analysis
KW  - survival analysis
KW  - functional analysis
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193240
SN  - 2072-6694
VL  - 11
IS  - 10
ER  -