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S276
ESTRO 35 2016
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SP-0581
Integrative data analysis for PRO
M.A. Gambacorta
1
Gambacorta Maria Antonietta, Roma, Italy
1
Personalized Radiation Oncology (PRO) integrating omics
technology is a rapidly developing concept that will have an
enormous impact on oncologic treatments and specifically
radiation therapy in the near future. Tumor behaviour and
outcomes related to oncologic treatments are related to
several factors of which connections are nowdays poorly
known. Different branches of medicine have developed their
own lines of research which are sometimes difficult to be
interpreted, difficult to be integrated with classical clinical
factors and for these reasons, difficult to be applied in
clinical practice. In clinical prediction and decision making
process, results provided by omics are rarely used, whereas
clinicians usually use clinical and imaging data for
understanding tumor behaviour, predicting patients'
outcomes and for choosing the the most suitable treatment.
The clinical decision is usually based on general guidelines
which extrapolate information from randomized clinical trial.
Moreover independent factors derived from several RCT are
used by the Radiation Oncologist to make his prevision on
tumor behaviour and consequently to choose the „right
treatment“ for a specific patient. Randomized clinical trials
enclose patients with characteristics chosen beforehand and
usually omics informations are rarely or never included. This
lead to a potential missing of several information that could
refine prediction and thus promote personalized treatments
and to an erroneous outcomes prediction that can lead to un-
appropriate treatment decision for a specific patient.
Integrative data analysis has the potential to correlate data
of different origins (genetic, radiology, clinic...) with
patient’s outcomes and to create a consistent dataset useful
to obtain a trustful analysis for the Decision Support System.
The DSS can easily be applied in clinical practice helping the
Radiation Oncologist to utilize several information that
otherwise would be excluded in the process of decision
making. The possibility to predict the outcome for a certain
patient in combination with a specific treatment with more
accuracy, will lead to better identification of risk groups and
thus better treatment decisions in individual patients, but it
will also stimulate research focused on specific risk groups
which try to find new treatment options or other
combinations of treatment options for these subgroups.
These treatments will be more personalized, which will not
only save patients from unnecessary toxicity and
inconvenience, but will also facilitate the choice of the most
appropriate treatment . The resulting predictive models,
based on patient features, enable a more patient specific
selection from the treatment options menu and a possibility
to share decisions with patients based on an objective
evaluation of risks and benefits. Finally, considering the
important role that predictive models could play in the
clinical practice, clinicians must be aware of the limits of
these prediction models. They need to be internally validated
taking into account the quality of the collected data. An
external validation of models is also essential to support
general applicability of the prediction model. Therefore
structural collaboration between different groups is crucial to
generate enough anonymized large databases from patients
included or not in clinical trials.
OC-0582
Gene signatures predict loco-regional control after
postoperative radiochemotherapy in HNSCC
S. Schmidt
1
OncoRay – National Center for Radiation Research in
Oncology, Faculty of Medicine and University Hospital Carl
Gustav Carus- Technische Universität Dresden, Dresden,
Germany
1,2,3,4
, A. Linge
1,2,4,5
, F. Lohaus
1,2,5
, V. Gudziol
6
, A.
Nowak
7
, I. Tinhofer
8,9
, V. Budach
8,9
, A. Sak
10,11
, M.
Stuschke
10,11
, P. Balermpas
12
, C. Rödel
13,14
, M. Avlar
15,16
, A.L.
Grosu
15,17
, A. Abdollahi
18,19,20,21,22
, J. Debus
18,20,21,22,23
, C.
Belka
24,25
, S. Pigorsch
24,26
, S.E. Combs
24,27
, D. Mönnich
28,29
, D.
Zips
28,29
, G.B. Baretton
2,30,31
, F. Buchholz
2,32
, M. Baumann
1,2,3,5
,
M. Krause
1,2,3,5
, S. Löck
1,2,3,5
2
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Dresden,
Dresden, Germany
3
Helmholtz-Zentrum Dresden – Rossendorf, Institute of
Radiooncology, Dresden, Germany
5
Faculty of Medicine and University Hospital Carl Gustav
Carus- Technische Universität Dresden, Department of
Radiation Oncology, Dresden, Germany
6
Faculty of Medicine and University Hospital Carl Gustav
Carus- Technische Universität Dresden, Department of
Otorhinolaryngology, Dresden, Germany
7
Faculty of Medicine and University Hospital Carl Gustav
Carus- Technische Universität Dresden, Department of Oral
and Maxillofacial Surgery, Dresden, Germany
8
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Berlin, Berlin,
Germany
9
Charité University Hospital, Department of Radiooncology
and Radiotherapy, Berlin, Germany
10
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Essen, Essen,
Germany
11
Medical Faculty- University of Duisburg-Essen, Department
of Radiotherapy, Essen, Germany
12
Goethe-University Frankfurt, Department of Radiotherapy
and Oncology, Frankfurt am Main, Germany
13
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Frankfurt,
Frankfurt am Main, Germany
14
Department of Radiotherapy and Oncology, Goethe-
University Frankfurt, Frankfurt am Main, Germany
15
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Freiburg,
Freiburg, Germany
16
University of Freiburg- Germany, Department of Radiation
Oncology- Clinical Study Section, Freiburg, Germany
17
University of Freiburg, Department of Radiation Oncology,
Freiburg, Germany
18
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Heidelberg,
Heidelberg, Germany
19
University of Heidelberg Medical School and German Cancer
Research Center DKFZ, Translational Radiation Oncology,
Heidelberg, Germany
20
University of Heidelberg Medical School and German Cancer
Research Center DKFZ, National Center for Tumor Diseases
NCT, Heidelberg, Germany
21
University of Heidelberg Medical School- Heidelberg Ion
Therapy Center HIT, Department of Radiation Oncology,
Heidelberg, Germany
22
University of Heidelberg Medical School and German Cancer
Research Center DKFZ, Heidelberg Institute of Radiation
Oncology HIRO- National Center for Radiation Research in
Oncology NCRO, Heidelberg, Germany
23
University of Heidelberg Medical School and German Cancer
Research Center DKFZ, Clinical Cooperation Unit Radiation
Oncology, Heidelberg, Germany
24
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Munich,
München, Germany
25
Ludwig-Maximilians-Universität,
Department
of
Radiotherapy and Radiation Oncology, München, Germany
26
Technische Universität München, Department of Radiation
Oncology, München, Germany
27
Department of Radiation Oncology, Technische Universität
München, München, Germany
28
German Cancer Research Center DKFZ, Heidelberg and
German Cancer Consortium DKTK partner site Tübingen,
Tübingen, Germany
29
Faculty of Medicine and University Hospital Tübingen-
Eberhard Karls Universität Tübingen, Department of
Radiation Oncology, Tübingen, Germany
30
University Hospital Carl Gustav Carus- Technische
Universität Dresden, Tumour- and Normal Tissue Bank-
University Cancer Centre UCC, Dresden, Germany
31
Faculty of Medicine and University Hospital Carl Gustav
Carus- Technische Universität Dresden, Institute of
Pathology, Dresden, Germany