K. Jeyaseelan

Sci—Thur AM: YIS ‐ 02: Radiogenomic Modeling of Normal Tissue Toxicities in Prostate Cancer Patients Receiving Hypofractionated Radiotherapy

Inter-patient radiation sensitivity variability has recently been shown to have a genetic component. This genetic component may play a key role in explaining the fluctuating rates of radiation-induced toxicities (RITs). Single nucleotide polymorphisms (SNPs) have thus far yielded inconsistent results in delineating RITs while copy number variations (CNVs) have not yet been investigated for such purposes. We explore a radiogenomic modeling approach to investigate the association of CNVs and SNPs, along with clinical and dosimetric variables, in radiation induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients treated with curative hypofractionated irradiation. A cohort of 62 prostate cancer patients who underwent hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 to 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Late toxicity rates for RB grade 2 & 3 and grade 3 alone were 29.0% and 12.9%, respectively. ED toxicity was found to be 62.9%. Radiogenomic model performance was evaluated using receiver operating characteristic area under the curve (AUC) and resampling by cross-validation. Binary variables were evaluated using Chi-squared contingency table analysis and multivariate models by Spearmans rank correlation coefficient (rs). Ten patients were found to have three copies of xrcc1 CNV (RB: χ2=14.6, p<0.001 and ED: χ2=4.88, p=0.0272) and twelve had heterozygous rs25489 SNP (RB: χ2=0.278, p=0.599 and ED: χ2=0.112, p=0.732). Radiogenomic modeling yielded significant, cross-validated NTCP models for RB (AUC=0.665) and ED (AUC=0.754). These results indicate that CNVs may be potential predictive biomarkers of both late ED and RB.

Mesenchymal Stem Cells Adopt Lung Cell Phenotype in Normal and Radiation-induced Lung Injury Conditions.

Lung tissue exposure to ionizing irradiation can invariably occur during the treatment of a variety of cancers leading to increased risk of radiation-induced lung disease (RILD). Mesenchymal stem cells (MSCs) possess the potential to differentiate into epithelial cells. However, cell culture methods of primary type II pneumocytes are slow and cannot provide a sufficient number of cells to regenerate damaged lungs. Moreover, effects of ablative radiation doses on the ability of MSCs to differentiate in vitro into lung cells have not been investigated yet. Therefore, an in vitro coculture system was used, where MSCs were physically separated from dissociated lung tissue obtained from either healthy or high ablative doses of 16 or 20 Gy whole thorax irradiated rats. Around 10±5% and 20±3% of cocultured MSCs demonstrated a change into lung-specific Clara and type II pneumocyte cells when MSCs were cocultured with healthy lung tissue. Interestingly, in cocultures with irradiated lung biopsies, the percentage of MSCs changed into Clara and type II pneumocytes cells increased to 40±7% and 50±6% at 16 Gy irradiation dose and 30±5% and 40±8% at 20 Gy irradiation dose, respectively. These data suggest that MSCs to lung cell differentiation is possible without cell fusion. In addition, 16 and 20 Gy whole thorax irradiation doses that can cause varying levels of RILD, induced different percentages of MSCs to adopt lung cell phenotype compared with healthy lung tissue, providing encouraging outlook for RILD therapeutic intervention for ablative radiotherapy prescriptions.

Investigation of Stem-like Cells Role in Regional Radiosensitivity of the Lung

Objective: Lung is a complex organ with puzzling patterns of radiosensitivity, depending on both the volume and the region of the lung irradiated. In this study, we aimed to investigate stem-like cells distribution in lung lobes and their potential role in regional radiosensitivity and incidences of radiation-induced lung damage (RILD). Methods: Fifteen male Sprague-Dawley rats (8 weeks, 200–250 g) were grouped into two groups: control (sham irradiated, n=6) and treatment (irradiated, n=9). The treatment group received 3 regimens of whole thorax x-ray doses and divided into 3 subgroups: 12 Gy (n=3), 16 Gy (n=3) and 20 Gy (n=3), and monitored for 16 weeks post-radiation. Immunohistochemistry techniques were employed to localize and quantify the distribution of type II pneumocytes, Clara cells and cluster of differentiation (CD) positive stem cells (CD24+, CD44v6+, CD73+), in the upper, middle and lower lobes of the right lung in all rats. Results: The upper lobe was found to harbour more stem-like cells compared to the middle/lower lobes (p < 0.05). The middle and lower lobes contained comparable percentages of different stem-like cells. All stem-like cells tested were distributed unsystematically in the lung tissue with no specific identifiable niches. Conclusion: The upper lobe harbours more population of stem-like cells compared to the lower lobe, this might explain the variation in regional radiosensitivity, with the lower lung lobe being more prone to radiation injury compared to the upper lobe. No specific stem cell niche could be identified in our study. These results may support the development of new-targeted radioprotection strategies to reduce incidences of RILD during radiotherapy.

WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk.

PURPOSE We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. METHODS We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were also included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. RESULTS The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. CONCLUSION We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data. The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21).

WE-D-BRE-02: BEST IN PHYSICS (THERAPY) – Radiogenomic Modeling of Normal Tissue Toxicities in Prostate Cancer Patients Receiving Hypofractionated Radiotherapy

PURPOSE It has been realized that inter-patient radiation sensitivity variability is a multifactorial process involving dosimetric, clinical, and genetic factors. Therefore, we explore a new framework to integrate physical, clinical, and biological data denoted as radiogenomic modeling. In demonstrating the feasibility of this work, we investigate the association of genetic variants (copy number variations [CNVs] and single nucleotide polymorphisms [SNPs]) with radiation induced rectal bleeding (RB) and erectile dysfunction (ED) while taking into account dosimetric and clinical variables in prostate cancer patients treated with curative irradiation. METHODS A cohort of 62 prostate cancer patients who underwent hypofractionated radiotherapy (66 Gy in 22 fractions) was retrospectively genotyped for CNV and SNP rs25489 in the xrcc1 DNA repair gene. Dosevolume metrics were extracted from treatment plans of 54 patients who had complete dosimetric profiles. Treatment outcomes were considered to be a RESULT OF FUNCTIONAL MAPPING OF RADIOGENOMIC INPUT VARIABLES ACCORDING TO A LOGIT TRANSFORMATION. MODEL ORDERS WERE ESTIMATED USING RESAMPLING BY LEAVE-ONE OUT CROSS-VALIDATION (LOO-CV). RADIOGENOMIC MODEL PERFORMANCE WAS EVALUATED USING AREA UNDER THE ROC CURVE (AUC) AND LOO-CV. FOR CONTINUOUS UNIVARIATE DOSIMETRIC AND CLINICAL VARIABLES, SPEARMANS RANK COEFFICIENTS WERE CALCULATED AND P-VALUES REPORTED ACCORDINGLY. IN THE CASE OF BINARY VARIABLES, CHI-SQUARED STATISTICS AND CONTINGENCY TABLE CALCULATIONS WERE USED. RESULTS Ten patients were found to have three copies of xrcc1 CNV (RB: χ2=14.6 [p<0.001] and ED: χ2=4.88[p=0.0272]) and twelve had heterozygous rs25489 SNP (RB: χ2=0.278[p=0.599] and ED: χ2=0.112[p=0.732]). LOO-CV identified penile bulb D60 as the only significant QUANTEC predictor (rs=0.312 [p=0.0145]) for ED. Radiogenomic modeling yielded statistically significant, cross-validated NTCP models for RB (rs=0.243[p=0.0443], AUC=0.665) and ED (rs=0.276[p=0.0217], AUC=0.754). CONCLUSION The radiogenomic modeling approach presented herein has been shown to identify NTCP models which have increased predictive power. Furthermore, CNVs appears to be useful genetic variants when added to dosimetric NTCP models. This work was partially supported by CIHR grant MOP-114910.

TU‐G‐108‐05: Assessment of Different Machine Learning Techniques for Multivariate Radiation Pneumonitis Modeling

PURPOSE We intended to perform an independent verification of different machine learning methods for inferring radiation pneumonitis (RP) risk from patient-specific biological and dosimetric informationMethods: 29 NSCLC patients who received chemoradiation were recruited from two institutions (22 from WUSTL, 7 from McGill). Blood samples were collected from each patient before and during radiotherapy (RT). From each sample the concentration of the five following candidate biomarkers were measured: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dimensionality of the raw biomarker data was reduced by a semi-supervised variable selection scheme. The reduced biomarker variables along with three known dosimetric RP predictors (mean lung dose, V20, tumor position in superior-inferior direction of the lung) were used as features for classifying high RP risk (CTCAE grade 2 or higher) patients. Four different machine learning methods (Bayesian Network, Logistic Regression, Naive Bayes, Support Vector Machine) were used for training classifiers on the WUSTL subset and were tested with respect to classification performance on the McGill subset. RESULTS The following 4 biomarker variables were chosen by the variable filtering: (1) pre-RT concentration level of α2M, (2) ratio of pre-to intra-RT levels of α2M, (3) intra-RT ACE level, (4) intra-RT TGFβ level. The best performance was achieved by Support Vector Machine in terms of classification accuracy (85.71%) and the area under the ROC curve (AUC) (0.9167). Bayesian Network recorded the same accuracy and slightly lower AUC (0.8750). Less accurate models were Naive Bayes and logistic regression in which overfitting was predominant with the largest difference in classification performance between the training and the testing dataset. CONCLUSION Preliminary results from this ongoing study suggests the need of a machine learning approach capable of modeling inter-relation between physical and biological variables where commonly used multivariate logistic regression would be inappropriate.

SU‐E‐T‐276: Treatment Planning Strategies for Lung Injury Studies in Rat Models in 6 MV Delivery

PURPOSE To study planning strategies that can be used in small animal radiation-induced lung toxicity experiments using 6 MV accelerator with high density MLC. METHODS Three different types of plans were designed on CT images of a Sprague Dawley rat model to irradiate 50% of the total lung volume (lung divided into apex and base) with a prescription dose of 24 Gy to the partial lung. Two VMAT arc therapy plans were optimized to cover to the prescription dose, either the apex or base of the lung. Two AP- PA plans were designed to completely block either lung apex or base while irradiating the remaining 50% of the lung. Finally, two AP-PA plans were designed to cover, to the prescription dose, the apex or base of the lung. The plans were designed and optimized using the Eclipse AAA algorithm and recalculated using the MMCTP/EGS/Beam Monte Carlo system. RESULTS When completely blocking the lung base, the apex will be underdosed by up to 30%; when completely covering the apex by the prescribed dose, the base will receive overdosing (V50%=73%). The VMAT plan leads to a more conformal dose distribution and spares unnecessary skin exposure when compared to AP-PA MV or kV delivery. Despite the small size of rat model, the 6 MV VMAT delivery is superior in terms of dose conformality and sparing of the heart and the non-irradiated 50% of the lung compared to the standard, simpler, AP-PA delivery. MC dosimetry in lung shows that the delivered dose is 10% higher than predicted by AAA because of the predominance of small fields in the delivery. CONCLUSIONS Clinical state-of- the-art planning and delivery techniques can be scaled down accurately to rats. The use of these techniques is essential in small animal studies to render conclusions of radiation response investigations translatable to human studies.