ESTRO 36 Abstract Book

S252

ESTRO 36

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even a day to complete and that it is often not know a-

priori what can be ultimately achieved in the trade-off

between organs-at-risk sparing and target coverage.

Another challenge in treatment planning is that the

quality of a treatment plan is only partly captured by

quantitative metrics such as DVH parameters and that

prediction models of toxicity are not yet integrated in the

process of treatment planning.

In this presentation the above challenges will be

addressed. It will be discussed if (big) radiotherapy data

of plans and dosimetry can make treatment plans better.

Prediction models combined with comparative treatment

planning can be used for decision making and further

personalizing the treatment. Some practical examples will

be presented regarding the selection of patients for

proton therapy. In addition, we will address to what

extent the accuracy of the prediction models impact such

clinical decisions. Finally, the role of treatment-plan

automation will be discussed in improving the quality of

treatment planning and different approaches of

automation will be compared.

SP-0475 Moving Big Data into Clinical Practice – A

positive outlook

S. Vinod

Liverpool Hospital, Cancer Therapy Centre, Liverpool

BC, Australia

The evidence-base underlying treatment of oncology

patients is derived from the 2-3% of patients enrolled in

prospective clinical trials. Outcomes in these highly

selected patients are then applied to the general patient

population. However, adherence to guideline treatment

varies from 44% in lung cancer to 91% in breast cancer due

to clinician uncertainty about the efficacy and toxicity of

evidence-based treatments in individual patients. An

alternative source of evidence is Big Data. This is what we

already collect in routine clinical practice including

clinical data, imaging data and genomic data. The type

and nature of data collected and the platform of

collection varies, however current systems can overcome

this to successfully enable distributed learning. Multi-

institutional data can be used to develop predictive

models relating outcomes to specific patient, tumour and

treatment characteristics. The strength of Big Data is in

the sheer number of patients and hence applicability of

findings to the general clinic population. Moving Big Data

into clinical practice requires translation of model outputs

to decision support systems to enable shared decision

making between clinicians and patients. It also requires

trust of the model by patients and clinicians. There is a

need to demonstrate that model predictions based on

objective parameters are superior to clinician’s subjective

judgement alone. Clinical trials of decision support

systems are necessary to evaluate whether Big Data can

change clinical practice. Only then can we truly deliver

personalised medicine tailored to an individual patient’s

specific parameters.

Symposium: Locally advanced breast cancer

SP-0476 Personalised local and locoregional

radiotherapy in breast cancer

T. Tramm

Aarhus University, Department of Clinical Medicine - The

Department of Pathology, Aarhus, Denmark

Genomic profiling has unveiled the heterogeneity of

breast cancer, and revealed prognostic differences and

prediction on benefit from systemic therapy. Although

the literature on gene expression profiles related to

prediction for response to different systemic treatment

strategies has been substantial, only a limited number of

studies have described molecular signatures associated

with local control and benefit from radiotherapy (RT).

The use of single markers or combinations of

immunohistochemical markers to divide patients

according to risk of LRR is potentially easily applicable in

a daily clinical setting.

Especially, the immunohistochemical approximations of

the intrinsic subtypes (based on

e.g.

ER, PR, HER2 and

Ki67) have attracted attention. Most consistently, the

Luminal A subtype has been associated with a low risk of

loco-regional recurrence (LRR). In a subgroup analysis of

the Canadian hypofractionation trial, it has also been

examined, if different treatment schemes may be more or

less suitable for the various subtypes, but no interaction

between hypofractionation and intrinsic subtypes was

found.

A number of molecular signatures prognostic of LRR have

also been identified, but until recently, the majority of

these signatures have failed to validate in independent

cohorts. Two studies by a Dutch group did not succeed in

identifying a specific gene-set predicting risk of

recurrence after breast conserving therapy (BCT), though

a gene-profile based on the wound response signature was

described as being of independent prognostic value. Later,

the same group developed a 111-gene signature, but it did

not show independent prognostic value in multivariate

analysis, and lost prognostic impact when tested in other

cohorts. A Swedish gene-profile aiming to identify patients

developing LRR despite of RT after BCT has also not been

independently validated.

The ideal setting for identification of a prognostic factor

is in a non-treated study population. Gene profiles

predicting LRR after BCT are, however, not strictly

prognostic, but include an element of prediction of

benefit from RT, since the vast majority of patients

treated with BCT have been treated with RT.

A few prognostic gene-expression profiles predicting risk

of LRR after mastectomy have, however, also been

published. One of these, the 18-gene classifier, was

developed from 135 non-irradiated patients treated with

mastectomy. The 18-gene classifier was found to be an

independent predictor of LRR in multivariate analysis

regardless of ER status and nodal stage. The performance

of the classifier has been tested in 87 patients treated

with BCT, but the index is not yet validated and holds no

predictive information in terms of postmastectomy

radiotherapy (PMRT). The DBCG-RT profile has, however,

been found to hold both prognostic information in terms

of LRR and predictive impact in regard to PMRT. The gene

profile was derived from a training set 191 high-risk breast

cancer patients treated with mastectomy and randomized

to PMRT or not, and independently validated in another

112 patients. Among non-irradiated patients in the

training set, the profile attained prognostic impact by

identifying two groups with a significant 6-fold difference

in LRR risk. Furthermore, the DBCG-RT profile showed a

predictive impact, since PMRT could be seen to reduce the

risk of LRR in the “High LRR risk” patients, whereas the

“Low LRR risk” patients experienced no additional benefit

from PMRT. More recently, a radiation sensitivity

signature has been derived from breast cancer cell lines,

and has been found to accurately identify patients with

LRR among 185 breast cancer patients. The latter two

signatures have been found to be independent of the

intrinsic subtypes.

Finally, exploring the heterogeneity of the

tumormicroenvironment may lead to targets that can

affect radiosensitivity or reverse radioresistance. Hypoxic

areas may leave possibilities for potential therapeutic

targets, and a more profound understanding of the

interaction between the immune system and RT (including

different treatment schemes) may lead to an increased

understanding of non-targeted effects.

The progress towards integrating molecular profiling into

precision radiation oncology is currently in its infancy, but