Microbiota, Inflammatory Environment, Clinical and Radiomic Features as Predictors of Normal Tissue Response in Radiotherapy for Prostate and Head-and-neck Cancer
Conditions: Prostate Cancer, Head and Neck Cancer
Sponsor: Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

 
 
 
 
 
 
MICRObiota, infLammatory Environment, clinicAl and 
Radiomic features as predictors of Normal tissue 
response in radiotherapy for prostatE and head-and-
neck canceR. (MICRO-LEARNER) 
 
 
Version 1.0  
19th December 2016 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Prof. Riccardo Valdagni 
17/11/2016 
 
 
 

BACKGROUND AND STATE OF THE ART  
 
In December 2013 the International Agency for Research on Cancer released its global cancer estimates for 
2012. The worldwide number of cancer survivors within five years of diagnosis was estimated to be ~32.6 
million for 2012. In Italy cancer of the prostate (PCa) has a forecast of ~50000 new cases in 2020, while 
~6400 head-and-neck cancer (HNCa) are foreseen. With increasing life expectancies and improvements in 
diagnosis and treatment, the number of PCa and HNCa patients and survivors is expected to continue to 
rise. As the illness increasingly becomes a chronic disease, patients’ quality-of-life needs to be addressed in 
a systematic manner. 
The European Society for Radiotherapy and Oncology (ESTRO) recently developed its 2020 vision, and it 
stated: “Vision 1.2: The majority of patients will live cancer free with minimal toxicity following the use of 
radical radiation therapy when used as a single curative modality of treatment or when used in 
combination with surgery, systemic chemotherapy and/or systemic targeted therapeutics. In order to 
achieve this vision ESTRO will enable and support the following priorities: […] (c) Innovative research and 
development on the potential future use of novel biological modifiers of tumour and normal tissue 
response. To enable the above improvements in clinical care, ESTRO emphases and supports […]  the 
development of validated predictive models of treatment outcome based on complex databases 
comprising clinical, biologic, genetic, imaging, dosimetric and population data.” 
 
Radiotherapy represents the most effective non-surgical modality in the curative treatment of PCa and 
HNCa. Around a half of survivors underwent radiotherapy as part of their curative care.  
Yet, many patients receiving potentially curative radiotherapy will experience toxicity due to the 
unavoidable irradiation of surrounding healthy tissue. The toxicity varies in severity from minor to severe 
and in duration from weeks to a lifetime. Around 7-10% of people suffer with severe long-term side-effects 
but much more experience mild/moderate chronic toxicity.  Such mild/moderate toxicity can have a 
marked effect on subsequent psychological outcome and the side-effects of radiotherapy have been shown 
to impair quality-of-life in cancer survivors.  
 
Radiotherapy is an important curative treatment for cancer and side-effects in survivors impact on quality-
of-life. 
 
The ability to predict those patients likely to develop toxicity could potentially enable individualization of 
the treatment including therapy strategy, dose prescription, fractionation choice, use of supportive 
therapies, which should improve survival and decrease morbidity.   
 
Radioinduced toxicity is a multifactorial problem, related not only to delivered dose, but also to an intrinsic 
process within tissues responding to cellular injury. Individual biological background and expression 
pattern, premorbid conditions, as well as the cellular microenvironment, could be important factors in the 
development of side-effects, although their exact contributions are unknown [1]. 
In recent years with the increase in data collected, models have been developed to attempt to predict 
before the start of treatment patients at risk of side-effects [2].  
 
Nevertheless these models are still missing some crucial points. The most important is the capability of 
describing the individual patient radio-sensitivity. We know that genetics influence a patient’s risk of 
developing side-effects. A number of assays/approaches have been explored, but, at present, none has 
been proven to be useful in clinical practice. Another issue is related to the lack of understanding normal 

tissue radioinduced modifications at the microstructural level, to overcome the approximation of organs as 
having homogeneous radio-sensitivity. This point is particularly important in the actual scenario of modern 
image-guided intensity modulated radiotherapy, where steep dose gradients are obtained and reduced 
fractions of the normal structures are irradiated. 
 
Investigation of approaches to avoid, protect against, ameliorate or treat the long-term side-effects of 
radiotherapy is also a poorly researched area.  
Recent research indicates that microorganisms living in the host have a role in initially driving or controlling 
inflammation from external causes. A dysbiotic milieu may change the metabolism of the host and provoke 
increased inflammation [3]. Dysbiosis affects the metabolism of short-chain fatty acids, which are known to 
have immunomodulatory properties and play a part in radiation-induced toxicity.  
 
The intestinal/salivary bacteria are thus strongly suspected of being very important in mediating the 
response to inflammation and lesions. 
 
The development of next-generation sequencing technologies and metabolic phenotyping makes 
stratification of patients at risk of radiotherapy-induced gastrointestinal toxicity a realistic possibility. 
 
Another possibility to be investigated is the role on the inflammatory environment (as measured by levels 
of inflammatory markers) in modulating the response to radiation. A significant increase in salivary levels of 
IL1β, IL6, and TNFα, in HNCa patients during radiotherapy or radio-chemotherapy was detected, with 
cytokines positively associated with the severity of mucosal toxicity. Higher increase of IL1β and IL6 three 
weeks after treatment initiation was predictive of worse oral mucositis, representing a potential tool for 
the early identification of patients at risk [4]. 
 
In the field of characterization of normal tissue response to radiation, a promising emerging field is the use 
of imaging as source of numeric data describing tissue microstructure that can be analysed by bio-
engineering techniques. These techniques, being completely automatic and reproducible, and able to 
provide quantitative indices of the structural and functional tissue properties, while maintaining 
information about spatial heterogeneity, could have an impact on investigating tissue response to radiation 
at the individual level [5].  
 
Chargari, Cancer Treat Rev. 2016;45:58-67 
Landoni, Phys Med. 2016;32(3):521-32 
Ferreira, Lancet Oncol. 2014;15(3):e139-47 
Bossi, Oncotarget. 2016,in press 
Scalco, Radiother Oncol. 2013;109(3):384-7 
 
 
 
 

PRELIMINARY DATA 
 
(A) Feasibility of collection saliva specimens for microbiome analysis 
Fifteen saliva specimens from HNCa patients were collected (Omnigene-oral collection devices, Oragene). 
Samples were stored at room temperature for 30 days before DNA extraction. Microbial DNA was extracted 
using QiASymphony DSP pathogen midi kit. DNA was quantified by Qubit. The box plot (figure 1a) shows 
the total amount (ng) obtained after microbial DNA extraction: median value = 2900 ng (range: 1250-7250 
ng). Quality of the material was assessed using the oral microbial DNA qPCR Array (Qiagen) for microbial 
identification and profiling on 4 specimens. The array is designed using probes detecting 16S rRNA gene for 
93 different oral bacteria strains. We were able to detect a range from 59 to 73 out of 93 bacterial strains. 
The preliminary study shows that: i) total DNA amount is enough for downstream applications including 
next generation sequencing; ii) the obtained material is suitable for 16S Metagenomics identification and 
quantification. 
 
(B) Feasibility of results on monitoring the expression of inflammatory molecules during radiotherapy. 
Plasma levels of selected inflammatory molecules (IL-1,IL-6,IL-8,TNF,CCL2,PTX3) were measured in prostate 
cancer patients undergoing radiotherapy. Inflammatory molecules kinetics was determined as a function of 
radiation dose. Expression of inflammation markers was correlated with patients’ characteristics and with 
acute radio-induced toxicity. Results are presented in figure 1b. 
 
(C) Feasibility of modelling dose-volume response with interaction among organs 
Modelling of severe oral mucositis (G3muc) on a population of 132 HNCa patients (toxicity rate 30.3%). 
G3muc was associated to small volume of the oral cavity receiving high doses and to the mean dose to 
parotid glands (probability curves in figure 2a). This suggests interaction between the two organs, with a 
reduced salivary flow entailing a minor protection from inflammation. The result was confirmed in a further 
study (78 oropharyngeal cancer patients).  
 
(D) Multi-parametric characterization of penile bulb using MRI images 
A preliminary investigation about the variations induced on the penile bulb after radiotherapy was carried 
out on six patients with prostate cancer treated with RT using multi-parametric MRI images acquired before 
treatment’s start and at the end of RT.  
We found that ADC value in penile bulb increased and its increment correlated with higher mean doses to 
(R2=0.9, p<0.01, Figure 2b). This behaviour could be explained by the inflammatory status that normally 
follows RT, which is not visible on anatomical images. 
 
(E) Variations on obturator muscles assessed by anatomical MRI 
A preliminary investigation about the variations induced on the obturator muscles after radiotherapy was 
carried out on 13 patients with prostate cancer treated with RT using T2w-MRI and T1w-MRI after contrast 
injection, acquired before treatment start and at 12 month follow-up. It was found a significant increase in 
the mean values on both T1w-MRI and T2w-MRI, together with an increase in the number of higher values 
in the histogram, well visible by the different histogram shape (Figure 2d). This was related to a more 
enhanced area after RT in the region near the prostate (Figure 2c). A possible explanation of this 
enhancement can be the inflammatory status of this muscle region, which received the highest dose.  
 
(E) Role of HPV status in toxicity prediction for HNCa  

A study on 148 HNCa patients, on relationship between patient-reported late dysphagia and 
clinical/molecular features, highlighted association with gender, HPV status.  
The possible role of HPV status in determining tissue response to radiation is also suggested by analysis of 
MRI features. 23 HNCa cases were considered: tumour ADC mean values were significantly lower for HPV-
positive (859 x 10-6 mm2/sec vs 1099 x 10-6 mm2/sec for HPV-negative, p=0.02). This can likely reflect 
different tumour cellularity and microenvironment among the two patient groups.  
 

HYPOTHESIS  
 
The development of radioinduced toxicity is not solely related to the dose and to the way radiotherapy is 
delivered. There are some individual characteristics that make some people more susceptible to develop 
acute and chronic side effects, this is often called the 'consequential effect'. 
An important driver of the 'consequential effect' is increasingly believed to be the microbiota.        
Microbiota are a complex ecosystem of up to 1,000 bacterial species in any one individual. The species vary 
greatly between individuals but, within each individual, their composition remains stable for the majority of 
the species over time. The diversity of the microbiota is high in healthy people and low in people with side 
effects after radiotherapy, a process which very closely parallels findings in people with other (not 
radioinduced) inflammatory conditions. 
 
The specific hypothesis investigated by this research proposal are: 
(A) Gut and salivary microbiota play a role in determining the single patient susceptibility to radiation. 
Different rates of side-effects can be detected in patients exhibiting differences in the composition of their 
microbiota. In the longer term, the ultimate hypothesis of the proposed research programme will be to use 
information on microbiota to find ways of changing the make-up of gut/salivary bacteria to benefit 
patients. 
 
(B) Information on baseline inflammatory markers (plasma/salivary levels) and on their kinetics in the first 
two weeks of radiotherapy can be used as indicators of enhanced radiosensitivity. This information can be 
used for effective adaptive planning to reduce dose in selected radiosensitive patients. 
 
(C) Information on radioinduced microstructural changes in normal tissues, as measured by medical 
imaging, can help in gaining insight into tissue response. Specifically, local damage can be revealed by both 
anatomical and functional imaging, measured in an objective way and associated to three-dimensional dose 
distributions. The role of different tissues/organs and their possible interaction in determining specific side-
effects can be investigated. This information (radiomic features) can lead to a more effective planning dose 
optimization, which is of peculiar importance in the era of intensity modulated image-guided radiotherapy: 
steep dose gradients can be obtained, provide it is known where optimization should be performed. 
 
(D) Information coming from microbiome measurement, inflammatory marker levels and radiomic features 
can be included in models for the prediction of radioinduced toxicity, with significant improvement of their 
performance and capability of identifying patients at high/low risk of side-effects. This will constitute a step 
forward in radiation oncology, allowing for patient-tailored treatment to modulate toxicity and producing 
the basic structure of a possible queryable therapeutic algorithm. 
 
 
 
 

AIMS  
 
1. To perform a clinical trial (discovering population), including PCa and HNCa patients, allowing 
prospective evaluation of gut/salivary microbiota before radiotherapy and at the end of treatment. Perform 
measurements of changes in microbioma composition and determine the association between acute/mid-
term toxicity and baseline (changes during treatment) microbiota. Perform validation of findings on an 
independent population, enrolled with same criteria. 
 
2. 
To 
develop 
predictive 
models 
for 
radioinduced 
(acute/mid-term) 
toxicity 
including 
dosimetric/clinical/treatment information with the addition of features suggestive of individual sensitivity 
(microbioma characteristics, inflammatory marker levels and kinetics, tissue features as extracted by 
radiomic studies on imaging). Evaluation of the performance and clinical utility of these extended models. 
 
3. To develop a complex database of PCa and HNCa patients, comprising clinical, biologic, genetic, imaging, 
dosimetric and population data. This database will be of value for future studies and for 
benchmarking/validation of models and results obtained by other research groups. The database will 
include biobanking of salivary and blood sample for future (genetic) studies. 
 
 
 
 

 EXPERIMENTAL DESIGN 
 
Prospective Clinical Trial: discovering population 
 
130 PCa and 130 HNCa consecutive patients will be enrolled in 15-18 months. All patients will receive 
radiotherapy at radical curative doses at the National Cancer Institute in Milan, and they will receive follow-
up visits at the same centre. 
 
Detailed pre-treatment evaluation will include: recording of demographic features, clinical history, 
comorbidities and habits, evaluation of normal tissue functioning by the health practitioner (CTCAE scoring 
system), evaluation of normal tissue functioning through validated patient reported outcome (PROs) 
questionnaires, evaluation of quality of life through validated tools, evaluation of organ functioning by 
instrumental measures (i.e. baseline swallowing screening with flexible endoscopic evaluation of 
swallowing – FEES – and unstimulated salivary flow for HNCa patients), biochemical examinations, 
gut/salivary microbiome measurement, determination of baseline level of plasma/salivary inflammatory 
markers, baseline multi-parametric magnetic resonance imaging (MRI). Additional salivary and blood 
samples will be collected and stored for future studies. 
 
Patients will receive radiotherapy and possible adjuvant (hormone or chemo) therapies as foreseen by 
institutional guidelines. In this aspect the here proposed trial is an observational study, no modification to 
standard regimens is considered. 
Specifically, PCa patients are treated to 78 Gy, 2Gy/fraction, in exclusive setting and to 70 Gy, 2Gr/fraction 
in the post-prostatectomy setting. Lymph node irradiation is performed (50 Gy, 2Gy/fraction when 
indicated by risk class). In definitive setting, HNCa patients are treated at a total dose of 70 Gy ,59.4 Gy and 
56 Gy in 33 fractions to high risk Planning Target Volume (PTV), intermediate risk PTV and low risk PTV, 
respectively. In high risk post-operative setting they are treated to 66 Gy (according to histopathological 
features), 60 and 56.1 to high risk PTV, intermediate risk PTV and low risk PTV, respectively; while, in 
intermediate risk post-operative case, intermediate risk PTV and low risk PTV irradiated to 60 Gy and  56 
Gy, respectively.   
Radiotherapy is performed with volumetric arc modulation, with k-voltage cone-beam CT (kCBCT) or 
Calypso transponder image guidance. 
 
Evaluation during treatment will include weekly assessment of toxicity, as measured by the clinician 
(CTCAE) and by PROs and biochemical measurements.  
Evaluation of inflammatory markers will be performed after a dose of 20 Gy. 
For a subpopulation of 60 oropharyngeal cancer patients treated with definitive radiotherapy +/- 
chemotherapy an additional MRI study during the second week of treatment is foreseen. 
 
Assessment at the end of radiotherapy will include evaluation of toxicity by the health practitioner and by 
PROs, evaluation of quality of life through validated tools, FEES and unstimulated salivary flow for HNCa 
patients, biochemical examinations, gut/salivary microbiome measurement, determination of level of 
plasma/salivary inflammatory markers. 
 
Evaluation at 3, 6 and 12 months will include: evaluation of toxicity by the health practitioner  and by PROs, 
evaluation of quality of life through validated tools, FEES and unstimulated salivary flow for HNCa patients 
and biochemical examinations. 

Minimum follow-up is set to 12 months for the specific purpose of evaluation of acute and mid-term 
toxicity, which are the endpoints considered in this project. Nevertheless, follow-up will continue until 3 
year after the end of radiotherapy (beyond the end of the project) in order to allow evaluation of incidence, 
prevalence and patterns of late side-effects. After 12 months, follow-up will be performed every 6 months 
and limited to assessment of toxicity by by the health practitioner and by PROs, 
 
Follow-up MRI studies will be performed at 3, 12 and 24 months for HNCa patients and at 12 months for 
PCa patients. 
 
Prospective Clinical Trial: validation population 
 
70 PCa and 70 HNCa consecutive patients will be enrolled in 12 months, starting immediately after the end 
of enrolment of the discovering population. 
The specific aim of this second phase is validation of results on microbiota, specifically on association 
between selected baseline microbiota profiles and the risk of acute toxicity (for the purpose of this project) 
and for mid-term/late toxicity (beyond this projects). This result should be the more significant from the 
clinical point of view, allowing development of a therapeutic algorithm to be used before treatment and 
permitting introduction ways of changing the make-up of gut/salivary bacteria in patients at high risk of 
toxicity. 
 
Baseline assessment, treatment and follow-up evaluation of toxicity by the clinician and by PROs will follow 
the scheme described for the developing population. Microbioma measurement will be only performed at 
baseline. Imaging and assessment of inflammatory marker levels will not be accomplished in the validation 
population. 
 
Analysis of gut and oral microbiota 
 
Saliva and stool will be collected using oral- gut-, respectively, OMNIgene devices (Oragene) following 
manufacture’s recommendations. Samples will be stored in a stabilization buffer at room temperature until 
DNA extraction. 
 
DNA extraction will be carried out using QiASymphony DSP pathogen midi kit starting from 1 ml of saliva 
and following manufacturer’s instructions. DNA extraction for stool specimens will be carried out using the 
QIAamp DNA Stool Mini Kit (Qiagen) starting from 200 mg of feces after additional incubation at 95 °C for 
10 min of the stool sample with the lysis buffer to improve the bacterial cell rupture as detailed in [6]. The 
concentration will be estimated by Qubit 3.0 Fluorometer (Invitrogen). 
 
Methods of Microbiome detection 
the 16S regions will be amplified with 16S Ion Metagenomics Kit ™ (Life Technologies) using 2 separate PCR 
oligo pools covering V2, V4, V8 and V3, V6-7, V9 hypervariable bacterial 16S rRNA regions. Equal volumes of 
both amplification reactions per each sample will be combined. Staring from 50 nanograms of combined 
amplicons DNA libraries will be prepared using Ion Plus Fragment Library Kit and Ion Xpress Barcodes 
Adapters, 1–16 (Life Technologies). After adapter-ligation and nick-repair, DNA will be amplified as follow: 1 
cycle of 95°C for 5 min; 5 cycles of 95°C for 15sec, 58°C for 15 sec, 70°C for 1 min. The final library will be 
purified using 1.4 volumes of Agencourt AMPure beads (Beckman Coulter) and eluted in Tris-EDTA buffer. 
Quality check and quantification will be assessed by DNA high sensitivity kit on 2100 Bioanalyzer (Agilent 

Technologies). Each sample will be adjusted to 50 pM concentration. Library preparation will be performed 
by ION Chef System (Life Technologies) according to the manufacturer’s instructions. Sequencing of the 
retrieved templates spheres was carried out on Ion 318™ v2 chip using Ion Torrent PGM™ system and 
employing the Ion Sequencing 400 kit (Life Technologies). Base calling and run demultiplexing will be 
performed by Torrent Suite (Life Technologies, Grand Island, NY) with default parameters.  
 
Data processing 
BAM files directly obtained from the Ion Torrent PGM output will be processed using Ion Reporter Software 
(Life Technologies) and the following filtering parameters will be applied: (i) each read will be trimmed 
tolerating until three errors at primers’ sequence, (ii) read with length lower than 165 bp will be discarded, 
and (iii) a hash table will be created to identify unique sequences and their abundance. The alignment will 
be performed using MicroSEQ ID and Green Genes databases in order to obtain the taxonomic 
identification of sequences. To further investigate bacterial biodiversity, operational taxonomic units 
(OTUs) cluster analysis will be performed based on hypervariable region V4. Sequencing reads will be 
extracted from the total dataset using cutadapt [7]. Reads will be analyzed using the micca pipeline (version 
0.1, http://compmetagen.github.io/micca/). De novo sequence clustering, chimera filtering, and taxonomy 
assignment were performed by micca-otu-denovo (parameters -s 0.97 -c): operational taxonomic units will 
be assigned by clustering the sequences with a threshold of 97 % pair-wise identity, and their 
representative sequences will be classified using the RDP [8] software version 2.8. Finally, a phylogenetic 
tree will be inferred using FastTree [9] software embedded in micca-phylogeny (parameters: -a template-
template-min-perc75).  
 
Assessment of inflammatory marker levels 
 
Plasma and saliva samples (obtained from PCa and HNCa patients, respectively) collected at baseline, 
during radiotherapy (20 Gy) and at the end of treatment, will be analyzed to prospectively determine 
baseline levels and changes of selected inflammatory markers. Specifically, for PCa, CCL-2, TGF-β, TNFα, 
TNFR-1 and PDGF will be considered, while, for HNCa, IL-6, TNFα and IL-1b will be measured. 
Evaluation will be carried out using commercially available ELISA kits, according to manufacturer’s 
protocols. 
 
Analysis of images and extraction of radiomic features  
 
Image analysis for the characterization and evaluation of radiation-induced effects on organs at risk will be 
mainly carried out on multi-parametric MR images (consisting in T2w-MRI, T1w-MRI with and without 
contrast injection, DW-MRI and DCE-MRI) acquired before treatment’s start and during the follow-up 
period; in the case of head-and-neck cancer treatment, a further MRI acquisition will be available at mid-
treatment. Planning CT and daily/weekly CBCT will be eventually considered for anatomical evaluations and 
for dose deformation from CT to the multi-parametric MRI. The organs at risk considered in the 
morphological and functional analyses will be the penile bulb and the pelvic floor muscles in the pelvic 
district, the parotid glands, oral mucosa, larynx and constrictor muscles in the head-and-neck district.  
 
Spatial registration 
The first step of the multi-parametric analysis is the spatial registration of all the acquired images to a 
common reference (generally, the initial T2w-MRI); a non-rigid image registration method, already 
optimized and validated on a subset of subjects [10], will be applied, 1) to realign DCE-MRI and DW-MRI to 

their correspondent T2w-MRI, 2) to deform mp-MRI acquired at different time-points to the reference T2w-
MRI, 3) to estimate the deformation between T2w-MRI and planning CT, which will be applied on the 
corresponding dose map. In this way, it is possible to perform a quantitative image analysis on the same 
considered volume, in order to extract multiple features from a single region, in terms of both ROI-based 
and voxel-based analysis. Moreover, using the estimated deformation field, it is possible to quantify in first 
instance the volumetric modifications of the structures and to propagate organs contours delineate on the 
first T2w-MRI on the other images, without the need of new manual segmentations. 
 
Analysis and feature extraction 
Regarding the image analysis methods, when anatomic MRI (T2w and T1w) are considered, some pre-
processing steps are needed. First, T2w-MRI images have to be corrected for magnetic field 
inhomogeneities by using the non-parametric non-uniform intensity normalization (N3) algorithm [11]. 
Second, a normalization step has to be performed between T2w-MRI1 and T2w-MRI2 for each patient, in 
order to reduce possible errors due to the non-quantitative value of signal intensity, using the histogram 
matching method [12]. 
After MR signal intensity normalization, advanced quantitative analysis will be performed by extracting 
different features from anatomic MRI. In particular, textural and fractal features will be estimated to 
characterize the structural organization of organs tissue. Textural features (e.g. first and second order 
statistical parameters) describe the histogram distribution and the relationships of gray-level values in the 
image, related to gray-level frequency and spatial distribution within the region of interest [13]. Fractal 
analysis can also synthetically describe the spatial heterogeneity of a region of interest by a geometric 
measure, as the fractal dimension [14]. The same analysis can be applied on CT images, to assess possible 
correlations between the two imaging modalities. 
Regarding the functional images, the diffusion properties will be analyzed by extracting the apparent 
diffusion coefficient (ADC) [15] following both (1) a ROI-based and (2) a voxel-wise approach. In detail, ADC 
will be estimated by least-square fitting the mono-exponential model, using different b-values. Finally, the 
perfusion analysis will be carried out by computing semi-quantitative parameters based on mathematical 
modelling of the DCE time series (wash-in and wash-out rates, the Integral Area Under the Curve IAUC, 
signal peak intensity, onset time and time-to-peak) [16], as well as quantitative indices following the 
extended Tofts model [17] (the volume transfer constant, the volume of extravascular extracellular space 
EES per unit volume of tissue, and the flux rate constant between EES and plasma), which are inferred using 
an optimization curve fitting approach. Moreover, texture analysis can be also performed on the 
parametric maps estimated from functional MRI, thus giving more value to the advanced image analysis 
protocol. In case dose maps will be present and deformed on MRI images, voxel-by-voxel correlations 
between dose and parametric maps will be estimated. 
Lastly, the best combination of features will be selected to better describe radiation-induced variations in 
organs of interest and to correlate them with the prescribed dose and toxicity outcomes. 
 
Creation of a large standardized database 
 
Thousands of different types of data will be generated, collected and reported during the experimental 
process. They include structured data, like PROs or toxicity scoring by the clinician, but also semi-structured 
and unstructured  data, such as dose distributions of radiotherapy treatment plans or CT/MRI images or 
results of microbial sequencing. A pre-processing and harmonization of data coming from different sources 
is necessary before proceeding with the analysis. All clinical and radiotherapy treatment data will be 
uploaded on the VODCA system software (Visualization and Organization of Data for Cancer Analysis, Vodca 

Inc) that allows to manage multiple sources of data and is tailored to meet the needs of radiotherapy trials. 
It allows the extraction of large datasheets that will be employed for the statistical analysis and model 
building. 
 
Development of advanced numerical tools for data investigation 
 
Advanced numerical algorithms, including supervised or unsupervised learning techniques, will be 
established and developed for the analysis. 
An innovative method based on in-silico experimentation and aimed at identifying the best predictors of a 
binary endpoint will be applied for identification of leading robust predictors of toxicity and minimization of 
the influence of noise. The well-known logistic regression is the core of the method, whose results are 
simpler to interpret, with respect to more recent and complex methods. In this approach, logistic 
regression is enhanced with upstream and downstream data processing to find stable predictors, even 
when the number of available cases is limited or when the endpoint is not balanced, i.e. when there are 
many more negative than positive cases, as it usually happens in clinical research. For upstream and 
downstream data processing, the idea is borrowed by business analytics, like market basket analysis, as 
well as social network analysis. This method was tested with satisfactory results in previous work [18]. 
 
 
 
 

METHODOLOGIES AND STATISTICAL ANALYSIS  
 
R statistical computing programming language [www.r-project.org] and KNIME data mining environment 
[KNIME GmbH, Germany] will be used for statistical analyses. 
 
For analysis of the association between presence/absence of acute/mid-term toxicity and microbiome 
profiles/inflammatory marker levels, non-parametric tests (Wilcoxon, Kruskal Wallis and Mann-Whitney 
test) will be performed. The first step will be comparison of microbiome profiles/inflammatory marker 
levels at baseline and during/after radiotherapy (paired sample analysis), thus identifying the effect of 
radiation modulation. Then comparison of microbiome profiles/inflammatory marker levels in different 
groups of patients (exhibiting/not exhibiting toxicity) will be completed, for the identification of profiles 
potentially predictive of the individual response to radiation, both before and (during) after treatment. 
 
Another important issue when developing predictive models in this type of observational studies, is the 
possible number of predictors that can be safely included into multivariate analysis. In these cases, being 
the number of predictors unknown a priori, an important rule of thumb to be considered is that the events 
per variables to assess each predictor is around 10 [18]. Then, seeing the estimated number of 
moderate/severe acute toxicity events, for PCa, with a sample size of 130 patients and 15-20% toxicity rate, 
2-3 variable can be included into model, while for HNCa models with inclusion of at least 4-6 variables are 
expected to be developed (toxicity rates even more than 30%).  
 
The starting point for predictive model development will be classical multivariable logistic analysis (MVA), 
followed 
by 
nomogram 
translation 
of 
MVA 
results. 
In 
a 
first 
step 
information 
on 
clinical/dosimetric/radiomic features will be included into models. 
Data will be then analyzed in the frame of radiobiological normal tissue complication probability (NTCP) 
models, with inclusion of clinical risk factors.  
An innovative method based on in-silico experimentation and aimed at identifying the best predictors of a 
binary endpoint will be applied for identification of leading robust predictors and minimization of the 
influence of noise. The well-known logistic regression is the core of the method, whose results are simpler 
to interpret, with respect to more recent and complex methods. In this approach, logistic regression is 
enhanced with upstream and downstream data processing to find stable predictors, even when the 
number of available cases is limited or when the endpoint is not balanced, i.e. when there are many more 
negative than positive cases, as it usually happens in clinical research. For upstream and downstream data 
processing, the idea is borrowed by business analytics, like market basket analysis, as well as social network 
analysis. This method was tested with satisfactory results in previous work [20].  
 
An imperative point will be the need to develop suitable model visualization tools in order to apply 
modeling techniques in the medical domain and to make the results available as user-friendly tools to both 
the clinicians and the patients. 
 
Information on microbiome profiles/inflammatory marker levels will be added to the significant variables 
obtained at the previous step in order to verify if the predictive ability of the models is improved.  
Several performance measures will be considered to evaluate improved predictive ability: Brier score, 
likelihood, calibration, reclassification table, net reclassification improvement and clinical usefulness.  
 
 
 

Power calculation  
 
In order to analyze the association between presence/absence of acute/mid-term toxicity and microbiome 
profiles/inflammatory marker levels, we are going to use non-parametric tests (Mann-Whitney, Wilcoxon 
matched pairs and Kruskal Wallis). These tests will be used because we are not sure that microbiome 
profiles/inflammatory marker data come from a Gaussian distribution. On the contrary, we foresee that 
radiosensitive/radioresistant patients have anomalous microbiome profiles/inflammatory marker levels 
(placed in the tails of distributions). We can estimate that a sample of 130 patients will allow to detect a 
large effect size, which would be clinically relevant for identification of radiosensitive/radioresistant 
patients (OR≈1.8-2.5, Cohen’s d=0.8), with statistical power 0.8 and significant level p=0.01 and medium 
effect size (OR≈1.3-1.5, Cohen’s d=0.5) with statistical power 0.8 and significant level p=0.05 (two-tailed 
hypotheses in all cases). 
 
When considering development of models, a sample size of 130 patients will have the power to detect a 
clinically relevant odds-ratio equal to ≈2 for a proportion of toxicity endpoint in the range 15-20%, which 
can be foreseen for acute toxicity after radiotherapy for PCa (with an alpha error=0.05 and beta error=0.2). 
For HNCa treatment higher toxicity rates can be forecast (even more than 30%) thus allowing detection 
even of less clinically relevant odds-ratios (≈1.7).  
 
 
 
REFERENCES 
 
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