Prediction of Outcome After Chemoradiotherapy for Head and Neck Cancer Using Functional Imaging and Tumor Biology. A Prospective, Non-commercial and Mono-centric Study.
1. BACKGROUND AND SETTING
Concurrent (chemo-) radiotherapy (CRT) is the current standard of care for patients
with locally advanced head and neck squamous cell carcinoma (HNSCC). The proximity of
important functional structures with the tumour makes treatment however highly complex.
While locoregional control rates have improved over the last decade, treatment related
toxicity can be severe with xerostomia and dysphagia gravely complicating the patient's
quality of life.
Furthermore, it becomes increasingly clear that while these tumours can be identical in
location and basic histology, their response to treatment differs greatly. This implies
that for a subgroup of patients, equal locoregional control rates could be achieved
using a less intense and consequently less toxic treatment schedule. This in contrast
to the group of patients who develop a locoregional recurrence during follow up, for
whom current treatment is apparently insufficient. These patients might benefit from a
more intense treatment schedule, i.e. a higher dose of radiation.
These differences in sensitivity to treatment can be explained by variations in
biological, molecular and genetic factors. Clinical parameters alone are insufficient
for response prediction. Identifying the different molecular and genetic factors, would
help us increase the accuracy of response prediction and based on these factors tailor
the treatment more individually to each patient. Therefore we want to develop a
prognostic and predictive model incorporating well-defined molecular and imaging
parameters which show great promise in response prediction after ionizing radiation.
1.2. Genetic and molecular tumour characteristics
The first parameter we want to investigate further is tumour hypoxia. Hypoxia has been
identified as a factor that increases tumour aggressiveness and decreases
radiosensitivity. In the past, several techniques have been applied to detect
biologically relevant tumour hypoxia, but none of them are used today in routine
clinical practice. Recently, a polymerase chain reaction (PCR) -based hypoxia
classifier gene signature was published by Toustrup et al. This classifier allows us to
study biologically relevant tumour hypoxia and consequently tumour aggressiveness and
resistance to ionizing irradiation. This analysis will be performed on biopsies
obtained prior to treatment. Part of this biopsy material will also be stored into our
biobank. This will facilitate future research on promising molecular and genetics
parameters (such as HPV correlated overexpression of p16 for example) on a well-defined
and structured patient database.
1.3. Imaging parameters
Aside from these important molecular and genetic tumour characteristics, several
functional imaging parameters will also be included in our model. In contrast to
anatomical imaging, functional imaging modalities provide us with a deeper insight in
the tumour's underlying biological activity and microstructure.
Diffusion weighted magnetic resonance imaging (DWI) can characterize tissues based on
the random displacement of water protons. This displacement can be quantified using the
apparent diffusion coefficient (ADC). Preliminary studies in HNSCC have demonstrated
the prognostic and predictive potential of repetitive DWI early during and after
Dynamic contrast enhanced magnetic resonance imaging(DCE-MRI) is a second technique,
which shows great promise as an early indicator of response to ionizing radiation. Many
malignant tumors manifest neovascularity or angiogenesis. These processes are however
flawed and as a result these newly synthesized vessels manifest a high permeability,
tortuosity and generally a poor functionality. This might result in poor oxygen supply
to the tumour cells, which may compromise the effectiveness of radiation treatment of
the tumour. Therefore the vascular properties of a tumour, assessed with DCE-MRI, could
prove a prognostic indicator of its aggressiveness.
2. STUDY OBJECTIVES
2.1. Primary objectives
The main objective of this study is to correlate clinical, molecular and radiological
predictors with outcome, as defined by locoregional control and disease free survival.
In this way we will develop a prognostic/predictive model. The predictive model will be
instrumental in the individualization of treatment ensuring optimized treatment and
avoiding under- and overtreatment.
2.2. Secondary objectives
- We want to get new insights in the tumor biology and microstructure by correlating
imaging and molecular/genetic markers in a well-defined patient population.
- We want to validate the different imaging techniques.
- We want to make a correlation between hypoxia and other predictive biomarkers in
the future, by storing the tissue obtained in this study in our biobank.
3. STUDY DESIGN
We propose to set up a study where we will prospectively include patients with locally
advanced head and neck cancer who will be treated with concurrent chemoradiotherapy as
decided after multidisciplinary consultation. An outline of the trial is presented in
the figure below. All patients recognized eligible (non-metastatic locally advanced
head and neck squamous cell carcinoma, treated with chemoradiotherapy, karnofsky
performance status ≥ 70% and ≥ 18 years old) will be included after written informed
consent. Staging with a laryngoscopy, CT of the neck, MRI of the neck and a PET-CT will
be routinely performed.
The CRT treatment consists of radiotherapy up to 72 Gy using an accelerated
hyperfractionated schedule. Day 1 and day 22 of the treatment Cisplatin at 100mg/m²
will be administered.
Tumour biopsies will be obtained a few days prior to treatment. The hypoxia gene
expression profile will be derived from the tumour material (15 genes on PCR that can
be analyzed individually or as a group through one binary variable).
The tissue used for RNA and DNA extraction needs to be flanked by H&E staining
confirming tumor presence. From the biopsy the two ends will be cut off and fixed in
paraffin. This tissue will be stored in the biobank. The middle part of the tissue will
be stored into RNA-later, and will be used in part to extract DNA and RNA. RNA will be
used to synthesize cDNA to perform the qPCR analysis for the hypoxic classifier.
Patients will also undergo a DWI MRI and DCE imaging before and 3 weeks into CRT.
Lesions will be quantitatively assessed by manual delineation of regions of interest
(ROI) over the tumour on the native DWI and DCE-MRI images. ADC values and (semi-)
quantitative DCE parameters will be calculated respectively.
All the above described data will be integrated into the prognostic model together with
the available clinical data.
4. DEFINITION OF ENDPOINTS The main endpoint of this study is to validate the above
described prognostic model. Using this prognostic model, we can predict response to
treatment. This will help us to tailor the treatment more individually to each patient.
Intervention Model: Single Group Assignment, Masking: Open Label
To correlate clinical, molecular and radiological predictors with outcome, as defined by locoregional control and disease free survival. In this way we will develop a prognostic/predictive model.
The predictive model will be instrumental in the individualization of treatment ensuring optimized treatment and avoiding under- and overtreatment.
4 year time period
Sandra Nuyts, PhD, MD
Universitaire Ziekenhuizen Leuven
Belgium: Federal Agency for Medicines and Health Products, FAMHP