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Conclusion
Our preliminary results suggest that the use of FFF beams
does not influence cancer cell survival rate when
compared with standard flattened
beams. The effects of
higher dose per fraction have to be further investigated.
PO-0973 Dimensionality reduction of clonogenic
survival data to identify candidates for
radiosensitization
N. Brix
1
, R. Hennel
1
, C. Belka
1
, K. Lauber
1
1
LMU University Hospital Grosshadern, Department of
Radiation Oncology, Munich, Germany
Purpose or Objective
With approximately 70,000 new cases per year in
Germany, breast cancer is the most common malignancy
in women. Together with surgery and chemotherapy, the
majority of patients is undergoing radiotherapy. While
stratification by clinicopathological parameters – such as
hormone receptor and Her2 expression – is part of the
clinical routine, biomarkers for tumor radioresistance and
targets for radiosensitization are currently not available.
The colony formation assay represents a versatile tool to
analyze cellular radiosensitivity
in vitro
making it
indispensable for the identification of factors involved in
tumor cell radioresistance. As an alternative to the linear-
quadratic model, we propose a novel approach of
dimensionality reduction to fully exploit the information
obtained from clonogenic survival assays which allows, for
instance, correlation with gene expression data.
Material and Methods
Clonogenic survival of 13 breast cancer cell lines and
normal human mammary epithelial cells upon irradiation
with 0-8 Gy was analyzed in colony formation assays. The
data derived thereof were subjected to linear-quadratic
fitting and principal component analysis (PCA) to extract
scores of radioresistance for each cell line.
Next, mRNA expression levels of more than 40 DNA
damage response (DDR) regulators were measured by qRT-
PCR. In order to identify predictors of radioresistance and
potential targets for radiosensitization, mRNA expression
levels were correlated with the PCA-derived
radioresistance scores.
Results
Among the 14 cell lines analyzed, strong differences in
clonogenic survival were observed. Using the linear-
quadratic model, very high goodness-of-fit levels were
obtained (R
2
≥0.98). However, obvious differences in
radiosensitivity between several cell lines were not
revealed by the respective α/β values which failed to
reflect the overall steepness of survival curves.
Data reduction by PCA allowed the extraction of
radioresistance scores. Notably, more than 70% of the
variance in the dataset was covered by the first PC.
Correlation of radioresistance scores with mRNA
expression levels of DDR regulators identified potential
predictors of radioresistance. Target validation using RNA
interference and selection of suitable pharmacological
inhibitors are ongoing.
Conclusion
Dimensionality reduction by PCA is a suitable method to
extract scores of radioresistance from clonogenic survival
datasets which can be correlated with other types of data,
such as mRNA expression levels. This approach facilitates
the identification of DDR regulators which may be further
validated as potential biomarkers of radioresistance
and/or targets for radiosensitization.
PO-0974 Biomarkers of radiosensitivity for patient
stratification and personalized radiotherapy treatment
E. Palumbo
1
, C. Piotto
1
, L. Baggio
1
, E. Groff
1
, E. Calura
2
,
F. Busato
1
, B. El Khouzai
1
, E. Fasanaro
1
, M. Rigo
1
, L.
Loreggian
1
, C. Romualdi
2
, A. Russo
3
, M. Mognato
2
, D.
Zafiropoulos
4
, L. Corti
1
1
Istituto Oncologico Veneto IOV-IRCCS, UOC of
Radiotherapy, Padua, Italy
2
University of Padua, Department of Biology, Padova,
Italy
3
University of Padua, Department of Molecular Medicine,
Padova, Italy
4
National Laboratories of Legnaro- Italian Institute of
Nuclear Physics, LNL-INFN, Padua, Italy
Purpose or Objective
The personalization of radiotherapy (RT) represents the
goal of future clinical radiation trials. A screening tool
able to classify each patient according to his/her own
sensitivity to ionizing radiation (IR) before the
administration of RT would be essential to set
personalized dosing schedules, effective in improving RT
outcomes and in reducing side effects. Genetic variation
is a likely source for the normal tissue radiosensitivity
variation observed among individuals. Mutations in key
genes of the DNA-Damage Response (DDR) pathway, or the
individual modulation of DDR gene expression after IR-
exposure, may underlie these differences. This study aims
at defining a genetic signature useful to discriminate
patients undergoing RT as radiosensitive, normal and
radioresistant and to predict the likelihood of a late IR-
toxicity. In this frame, gene expression data concerning
DDR pathway, obtained from blood samples of breast and
head-neck cancer patients, are overlaid with the
individual
in vitro
radiosensitivity index and the
in vivo
tissue radiosensitivity detected during the follow-up. We
expect to identify a 5-10 gene network determining the
individual radiophenotype.
Material and Methods
1. Criteria for patient enrolling: breast or head-neck
cancer diagnosis; exclusion of congenital syndromes
predisposing to radiosensitivity; patients not previously
treated with chemo-radiotherapy; age > 18 years; patient
agreement to undergo follow-up; informed consent. 2. G2-
assay for the prediction of radiosensitivity: an individual
radiosensitivity index (IRS) is calculated according to the
G2-chromosomal radiosensitivity and the G2 checkpoint
efficiency. Details of the protocol are in
1
. 3. Gene
expression analysis: Gene expression analysis is performed
by quantitative real-time PCR (qRT-PCR) on total RNA