Variation In Radiosensitivity Research Articles

Robustness of carbon-ion radiotherapy against DNA damage repair associated radiosensitivity variation based on a biophysical model.

Interpatient variation of tumor radiosensitivity is rarely considered during the treatment planning process despite its known significance for the therapeutic outcome. To apply our mechanistic biophysical model to investigate the biological robustness of carbon ion radiotherapy (CIRT) against DNA damage repair interference (DDRi) associated patient-to-patient variability in radiosensitivity and its potential clinical advantages against conventional radiotherapy approaches. The "UNIfied and VERSatile bio response Engine" (UNIVERSE) was extended by carbon ions and its predictions were compared to a panel of in vitro and in vivo data including various endpoints and DDRi settings within clinically relevant dose and linear energy transfer (LET) ranges. The implications of UNIVERSE predictions were then assessed in a clinical patient scenario considering DDRi variance. UNIVERSE tests well against the applied benchmarks. While in vitro survival curves were predicted with an R2>0.92, deviations from in vivo RBE data were less than 5.6% The conducted paradigmatic patient plan study implies a markedly reduced significance of DDRi based radiosensitivity variability in CIRT (13% change of in target) compared to conventional radiotherapy (62%) and that boosting the LET within the target further amplifies this robustness of CIRT (8%). In the case of heightened tumor radiosensitivity, a dose de-escalation strategy for photons allows a reduction of the maximum effective dose within the normal tissue (NT) from a of 2.65 to 1.64Gy, which lies below the level found for CIRT ( =2.41Gy) for the analyzed plan and parameters. However, even after de-escalation, the integral effective dose in the NT is found to be substantially higher for conventional radiotherapy in comparison to CIRT ( of 0.75, 0.46, and 0.24Gy for the conventional plan, its de-escalation and CIRT, respectively). The framework offers adequate predictions of in vitro and in vivo radiation effects of CIRT while allowing the consideration of DRRi based solely on parameters derived from photon data. The results of the patient planning study underline the potential of CIRT to minimize important sources of interpatient divergence in therapy outcome, especially when combined with techniques that allow to maximize the LET within the tumor. Despite the potential of de-escalation strategies for conventional radiotherapy to reduce the maximum effective dose in the NT, CIRT appears to remain a more favorable option due to its ability to reduce the integral effective dose within the NT.

Equivalent uniform aerobic dose in radiotherapy for hypoxic tumors

Objective. Equivalent uniform aerobic dose (EUAD) is proposed for comparison of integrated cell survival in tumors with different distributions of hypoxia and radiation dose. Approach. The EUAD assumes that for any non-uniform distributions of radiation dose and oxygen enhancement ratio (OER) within a tumor, there is a uniform distribution of radiation dose under hypothetical aerobic conditions with OER = 1 that produces equal integrated survival of clonogenic cells. This definition of EUAD has several advantages. First, the EUAD allows one to compare survival of clonogenic cells in tumors with intra-tumor and inter-tumor variation of radio sensitivity due to hypoxia because the cell survival is recomputed under the same benchmark oxygen level (OER = 1). Second, the EUAD for homogeneously oxygenated tumors is equal to the concept of equivalent uniform dose. Main results. We computed the EUAD using radiotherapy dose and the OER derived from the 18F-Fluoromisonidazole PET (18F-FMISO PET) images of hypoxia in patients with glioblastoma, the most common and aggressive type of primary malignant brain tumor. The 18F-FMISO PET images include a distribution of SUV (Standardized Uptake Value); therefore, the SUV is converted to partial oxygen pressure (pO2) and then to the OER. The prognostic value of EUAD in radiotherapy for hypoxic tumors is demonstrated using correlation between EUAD and overall survival (OS) in radiotherapy for glioblastoma. The correction to the EUAD for the absolute hypoxic volume that traceable to the tumor control probability improves the correlation with OS. Significance. While the analysis proposed in this research is based on the 18F-FMISO PET images for glioblastoma, the EUAD is a universal radiobiological concept and is not associated with any specific cancer or any specific PET or MRI biomarker of hypoxia. Therefore, this research can be generalized to other cancers, for example stage III lung cancer, and to other hypoxia biomarkers.

Validating Radiosensitivity with Pre-Exposure Differential Gene Expression in Peripheral Blood Predicting Survival and Non-Survival in a Second Irradiated Rhesus Macaque Cohort.

Radiosensitivity differs in humans and possibly in closely related nonhuman primates. The reasons for variation in radiosensitivity are not well known. In an earlier study, we examined gene expression (GE) pre-radiation in peripheral blood among male (n = 62) and female (n = 60) rhesus macaques (n = 122), which did or did not survive (up to 60 days) after whole-body exposure of 7.0 Gy (LD66/60). Eight genes (CHD5, CHI3L1, DYSF, EPX, IGF2BP1, LCN2, MBOAT4, SLC22A4) revealed significant associations with survival. Access to a second rhesus macaque cohort (males = 40, females = 23, total n = 63) irradiated with 5.8-7.2 Gy (LD29-50/60) and some treated with gamma-tocotrienol (GT3, a radiation countermeasure) allowed us to validate these gene expression changes independently. Total RNA was isolated from whole blood samples and examined by quantitative RT-PCR on a 96-well format. cycle threshold (Ct)-values normalized to 18S rRNA were analyzed for their association with survival. Regardless of the species-specific TaqMan assay, similar results were obtained. Two genes (CHD5 and CHI3L1) out of eight revealed a significant association with survival in the second cohort, while only CHD5 (involved in DNA damage response and proliferation control) showed mean gene expression changes in the same direction for both cohorts. No expected association of CHD5 GE with dose, treatment, or sex could be established. Instead, we observed significant associations for those comparisons comprising pre-exposure samples with CHD5 Ct values ≤ 11 (total n = 17). CHD5 Ct values ≤ 11 in these comparisons were mainly associated with increased frequencies (61-100%) of non-survivors, a trend which depending on the sample numbers, reached significance (P = 0.03) in males and, accordingly, in females. This was also reflected by a logistic regression model including all available samples from both cohorts comprising CHD5 measurements (n = 104, odds ratio 1.38, 95% CI 1.07-1.79, P = 0.01). However, this association was driven by males (odds ratio 1.62, 95% CI 1.10-2.38, P = 0.01) and CHD5 Ct values ≤ 11 since removing low CHD5 Ct values from this model, converted to insignificance (P = 0.19). A second male subcohort comprising high CHD5 Ct values ≥ 14.4 in both cohorts (n = 5) appeared associated with survival. Removing these high CHD5 Ct values converted the model borderline significant (P = 0.051). Based on the probability function of the receiver operating characteristics (ROC) curves, 8 (12.3%) and 5 (7.7%) from 65 pre-exposure RNA measurements in males, death and survival could be predicted with a negative and positive predictive value ranging between 85-100%. An associated odds ratio reflected a 62% elevated risk for dying or surviving per unit change (Ct-value) in gene expression, considering the before-mentioned CHD5 thresholds in RNA copy numbers. In conclusion, we identified two subsets of male animals characterized by increased (Ct values ≤ 11) and decreased (Ct values ≥ 14.4) CHD5 GE copy numbers before radiation exposure, which independently of the cohort, radiation exposure or treatment appeared to predict the death or survival in males.

Theranostics and artificial intelligence: new frontiers in personalized medicine.

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose. In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC. Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer.

Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment. We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model. The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952. A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.

The Impact of Prenatal Exposure to Chernobyl Fallout in Finland

I estimate the impact of low doses of radiation on school performance and income levels by exploiting regional and temporal variation in levels of radioactivity combined with variation in radiosensitivity during the foetal period. I observe asignificantly lower school performance among students exposed to higher levels of prenatal irradiation as well as a lower level of educational attainment. I also estimate the impact on income at ages 26–31. My findings align with the recent literature and confirm the findings presented in Almond et al. (2009).

Increased relative biological effectiveness and periventricular radiosensitivity in proton therapy of glioma patients

PurposeCurrently, there is an intense debate on variations in intra-cerebral radiosensitivity and relative biological effectiveness (RBE) in proton therapy of primary brain tumours. Here, both effects were retrospectively investigated using late radiation-induced brain injuries (RIBI) observed in follow-up after proton therapy of patients with diagnosed glioma. MethodsIn total, 42 WHO grade 2–3 glioma patients out of a consecutive patient cohort having received (adjuvant) proton radio(chemo)therapy between 2014 and 2017 were eligible for analysis. RIBI lesions (symptomatic or clinically asymptomatic) were diagnosed and delineated on contrast-enhanced T1-weighted magnetic resonance imaging scans obtained in the first two years of follow-up. Correlation of RIBI location and occurrence with dose (D), proton dose-averaged linear energy transfer (LET) and variable RBE dose parameters were tested in voxel- and in patient-wise logistic regression analyses. Additionally, anatomical and clinical parameters were considered. Model performance was estimated through cross-validated area-under-the-curve (AUC) values. ResultsIn total, 64 RIBI lesions were diagnosed in 21 patients. The median time between start of proton radio(chemo)therapy and RIBI appearance was 10.2 months. Median distances of the RIBI volume centres to the cerebral ventricles and to the clinical target volume border were 2.1 mm and 1.3 mm, respectively. In voxel-wise regression, the multivariable model with D, D × LET and periventricular region (PVR) revealed the highest AUC of 0.90 (95 % confidence interval: 0.89–0.91) while the corresponding model without D × LET revealed a value of 0.84 (0.83–0.86). In patient-level analysis, the equivalent uniform dose (EUD11, a = 11) in the PVR using a variable RBE was the most prominent predictor for RIBI with an AUC of 0.63 (0.32–0.90). ConclusionsIn this glioma cohort, an increased radiosensitivity within the PVR was observed as well as a spatial correlation of RIBI with an increased RBE. Both need to be considered when delivering radio(chemo)therapy using proton beams.

Evaluation of a Sumoylation Inhibitor as an Intrinsic and Extrinsic Radiosensitizer

<h3>Purpose/Objective(s)</h3> Radiation therapy (RT) reduces tumor burden through both intrinsic initiation of tumor cell death and enhancement of anti-tumor immunity. However, radiation treatments are not universally efficacious due to variation in radiosensitivity and anti-tumor immunity. Low proliferative rate, hypoxia, and T cell effector dysfunction can significantly degrade response to RT. Here we evaluate a rational combination therapy pairing radiosensitization and immune activation in conjunction with standard of care RT. Recent studies demonstrated the sumoylation inhibitor, TAK-981, has intrinsic capacity to reduce the proliferation of tumor cells <i>in vitro</i> as a monotherapy and decrease tumor volumes in syngeneic murine colon carcinoma models (CT26 and MC38) in combination with PD-1 or CTLA-4 checkpoint blockade. In these models a dendritic cell dependent type I interferon pathway was implicated to mediate the anti-tumor immune response and enhanced in vivo tumor control in TAK-981 and checkpoint blockade combination therapy, similar to previously elucidated mechanisms of immune activation following hypofractionated radiotherapy. Additionally, multiple intrinsic and extrinsic protein mediators (Myc, IRF3, p21) of tumor cell response to radiation are regulated by the post-translational modification of sumoylation. Consequently, we evaluated whether combining RT with TAK-981 resulted in enhanced tumor radiosensitivity in vitro and improved local tumor control in vivo, consistent with enhanced anti-tumor immune response in murine tumor models. <h3>Materials/Methods</h3> We utilized a longitudinal real time microscopic proliferation assay to evaluate RT response. Titrations of TAK-981 were added to established in vitro tumor cell cultures 4 hours prior to increasing doses of single fraction photon RT. Impacts of RT, TAK-981 or the combination treatment were assessed based upon cell proliferation for 72 hours. We assessed efficacy of this novel combination in vivo using the MC38 colorectal carcinoma model. C57BL6 mice with established tumors (> 50mm2) received TAK981 (15mg/kg) alone three times weekly or in combination with single fraction 8 Gy RT and compared to RT alone to evaluate local tumor control and impacts on overall survival. <h3>Results</h3> Our data demonstrates a synergistic and dose dependent reduction in MC38 colon carcinoma in vitro proliferation in response to combination RT and TAK-981. In vivo MC38 tumor models demonstrate no effect of TAK-981 alone compared to vehicle treated mice while co-administration with a single 8 Gy fraction of radiotherapy synergistically enhanced tumor control and overall survival compared to RT alone. <h3>Conclusion</h3> Our preliminary data support potential dual synergistic roles for the sumoylation inhibitor TAK-981 with radiotherapy, functioning as both an intrinsic radiosensitizer and immunomodulator.

Genomic Profiling Reveals Novel Predictive Biomarkers for Chemo-Radiotherapy Efficacy and Thoracic Toxicity in Non-Small-Cell Lung Cancer.

Chemo-radiotherapy (CRT) remains the main treatment modality for non-small-cell lung cancer (NSCLC). However, its clinical efficacy is largely limited by individual variations in radio-sensitivity and radiotherapy-associated toxicity. There is an urgent need to identify genetic determinants that can explain patients’ likelihood to develop recurrence and radiotherapy-associated toxicity following CRT. In this study, we performed comprehensive genomic profiling, using a 474-cancer- and radiotherapy-related gene panel, on pretreatment biopsy samples from patients with unresectable stage III NSCLCs who underwent definitive CRT. Patients’ baseline clinical characteristics and genomic features, including tumor genetic, genomic and molecular pathway alterations, as well as single nucleotide polymorphisms (SNPs), were correlated with progression-free survival (PFS), overall survival (OS), and radiotherapy-associated pneumonitis and/or esophagitis development after CRT. A total of 122 patients were enrolled between 2014 and 2019, with 84 (69%) squamous cell carcinomas and 38 (31%) adenocarcinomas. Genetic analysis confirmed the association between the KEAP1-NRF2 pathway gene alterations and unfavorable survival outcome, and revealed alterations in FGFR family genes, MET, PTEN, and NOTCH2 as potential novel and independent risk factors of poor post-CRT survival. Combined analysis of such alterations led to improved stratification of the risk populations. In addition, patients with EGFR activating mutations or any oncogenic driver mutations exhibited improved OS. On the other hand, we also identified genetic markers in relation to radiotherapy-associated thoracic toxicity. SNPs in the DNA repair-associated XRCC5 (rs3835) and XRCC1 (rs25487) were associated with an increased risk of high-grade esophagitis and pneumonitis respectively. MTHFR (rs1801133) and NQO1 (rs1800566) were additional risk alleles related to higher susceptibility to pneumonitis and esophagitis overall. Moreover, through their roles in genome integrity and replicative fidelity, somatic alterations in ZNF217 and POLD1 might also serve as risk predictors of high-grade pneumonitis and esophagitis. Taken together, leveraging targeted next-generating sequencing, we identified a set of novel clinically applicable biomarkers that might enable prediction of survival outcomes and risk of radiotherapy-associated thoracic toxicities. Our findings highlight the value of pre-treatment genetic testing to better inform CRT outcomes and clinical actions in stage III unresectable NSCLCs.

TCP post-radioembolization and TCP post-EBRT in HCC are similar and can be predicted using the in vitro radiosensitivity

BackgroundTumor equivalent uniform dose (EUD) is proposed as a predictor of patient outcome after liver radioembolization (RE) of hepatocellular carcinoma (HCC) and can be evaluated with 90Y-TOF-PET. The aim is to evaluate the correlation between PET-based tumors EUD and the clinical response evaluated with dual molecular tracer (11C-acetate and 18F-FDG) PET/CT post-RE.Methods34 HCC tumors in 22 patients were prospectively evaluated. The metabolic response was characterized by the total lesion metabolism variation (ΔTLM) between baseline and follow-up. This response allowed to compute a tumor control probability (TCP) as a function of the tumor EUD.ResultsThe absorbed dose response correlation was highly significant (R = 0.72, P < 0.001). With an absorbed dose threshold of 40 Gy, the metabolic response was strongly different in both groups (median response 35% versus 100%, P < 0.001). Post-RE TCP as a function of the EUD was very similar to that observed in external beam radiation therapy (EBRT), with TCP values equal to 0.5 and 0.95 for a EUD of 51 Gy and 100 Gy, respectively. The TCP was perfectly predicted by the Poisson model assuming an inter tumor radiosensitivity variation of 30% around the HCC cell in vitro value.ConclusionsEUD-based 90Y TOF-PET/CT predicts the metabolic response post-RE in HCC assessed using dual molecular PET tracers and provides a similar TCP curve to that observed in EBRT. In vivo and in vitro HCC radiosensitivities are similar. Both TCPs show that a EUD of 100 Gy is needed to control HCC for the three devices (resin spheres, glass spheres, EBRT). Observed absorbed doses achieving this 100 Gy-EUD ranged from 190 to 1800 Gy!

Isolated Clones of a Human Colorectal Carcinoma Cell Line Display Variation in Radiosensitivity Following Gamma Irradiation.

ObjectiveTo determine whether the width of the shoulder and the size of the bystander effect are correlated using clonal lineages derived from a cultured cell line.MethodsHCT 116 (p53 wildtype) cells were grown at cloning density and individual viable colonies were picked off and grown to establish a series of cell lines from both unirradiated and irradiated progenitors. These cell lines were then irradiated to generate full survival curves. Highly variant clones were then tested to determine the level of the bystander effect using a medium transfer protocol.ResultsThe multi-target model gave the best fit in these experiments and size of the shoulder n is assessed in terms of radiosensitivity. The parent cell line has an n value of 1.1 while the most variant clones have n values of 0.88 (Clone G) and 5.5 (Clone A). Clonal lines subject to irradiation prior to isolation differed in bystander signal strength in comparison to clonal lines which were not initially irradiated (P = .055).ConclusionsBased on these experiments we suggest there may be a link between shoulder size of a mammalian cell line and the strength of a bystander effect produced in vitro. This may have implications for radiotherapy related to out-of-field effects.

Quantification of chemotherapy contributions to chemoradiotherapy

Chemoradiotherapy (CRT) treatments are those which use chemotherapy drugs to improve the tumor control obtainable from radiotherapy schedules. Such additions are known to be beneficial and are widely prescribed. However, the methodologies employed for defining chemotherapy and radiation doses are entirely different, meaning that quantitative elucidation of the chemotherapy contribution to a CRT schedule remains difficult. One solution is to express the chemotherapy effect in terms of its radiation-equivalent and several authors have examined ways of doing this. This article outlines an approach which uses radiation-alone dose-response data as a platform against which to derive estimates of the drug contribution to CRT treatments. Such a methodology is potentially advantageous as radiation-alone parameters derived from observations of tumor response over a range of doses provide a realistic representation of whole-population behaviour and do not require prior assumptions to be made concerning radiosensitivity variations, etc. Although essentially a theoretical investigation. this approach provides some insight into how derived CRT dose-response curves can vary according to the possible modes of drug action, four of which are considered here, i.e. independent cell kill, dose sensitisation, reduced repopulation or combinations of each. It is shown that, for the moderate drug-induced TCP improvements usually observed in practice, the chemotherapy dose-equivalents derived from individual observations are a reasonable predictor of those operating at other levels of response, irrespective of the underlying drug-radiation synergy.

GSTP1 Ile105Val and GPX1 Pro198Leu Polymorphisms and Their Association with Response to Radiotherapy in Nasopharyngeal Carcinoma Patients

Objectives: Radio-resistance poses a major challenge in nasopharyngeal carcinoma (NPC) treatment. Due to individual variations in radio-sensitivity, biomarkers are needed to tailor radiation treatment. Within this frame, the identification of series of genetic signatures mainly SNPs for NPC patients treated with radiotherapy may help to predict treatment outcome and deliver personalized therapy. The aim of this study was to evaluate the possible association between the GSTP1 Ile105Val and GPX1 Pro198Leu polymorphisms and response to radiotherapy in NPC patients. Methods: From September 2016 to October 2018, a total of 101 patients with confirmed NPC, recruited at Mohammed IV Center for Treatment of Cancer of Casablanca, underwent radiotherapy. DNA was extracted from peripheral blood. Genotyping of the GPX1 Pro198Leu and GPX1 Val105Leu polymorphisms was carried out by PCR amplification and DNA sequencing. SPSS was used to analyse the association of GSTP1 and GPX1 genotypes with clinico-pathological features and response to radiotherapy. Results: The genotyping data revealed the presence of only two genotypes namely Pro/Pro (57.4%) and Pro/Leu (40.6%) for GPX1 gene. The allelic frequencies of C and T alleles were 78.7% and 21.3% respectively. For GSTP gene, the homozygous genotypes Val/Val and Leu/Leu were detected in 35.6% and 12.9% of patients respectively. The heterozygous genotype Val/Leu prevailed (51.5%). Allelic frequencies showed the presence of the two alleles A and G in 57.1% and 42.9% patients respectively. Statistical analysis failed to find any significant association between GSTP Val105Ile and GPX1 Pro198Leu genetic polymorphisms and socio-demographic and clinico-pathological features as well as response to radiotherapy (p>0.05). Conclusion: Further research is warranted on the potential role of SNPs within antioxidant defines genes in radiotherapy response and to identify reliable predictive and non-invasive biomarkers for radio-resistance among NPC patients for personalised therapies. Keywords: Nasopharygeal carcinoma, genetic polymorphisms, GSTP1, GPX1, radio-resistance, prognosis

Deep Learning for Radiotherapy Outcome Prediction Using Dose Data – A Review

Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. We review and summarise studies that applied deep learning to radiotherapy dose data, in particular studies that utilised full three-dimensional dose distributions. Ten papers have reported on deep learning models for outcome prediction utilising spatial dose information, whereas four studies used reduced dimensionality (dose volume histogram) information for prediction. Many of these studies suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications. They demonstrate, however, how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are a number of issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example translation of model developments into treatment plan optimisation, which will require further combined effort from the radiation oncology and artificial intelligence communities.

The Role of Patient- and Treatment-Related Factors and Early Functional Imaging in Late Radiation-Induced Xerostomia in Oropharyngeal Cancer Patients.

Simple SummaryIn the present prospective study, we assessed the role of various Magnetic Resonance Imaging biomarkers combined with self-assessed xerostomia questionnaires and patient- and treatment-related factors, in predicting xerostomia at 12 months after chemoradiotherapy for oropharyngeal squamous cell carcinoma. We hypothesized that the integration of pre-treatment imaging biomarkers, which addresses the tissue heterogeneity and individual variations among patients, could improve the accuracy of conventional prediction models that are based only on dose information, ultimately providing a better understanding of the pathophysiological mechanisms underlying radiation induced salivary dysfunction. The implementation of multifactorial models, driven by machine learning algorithms, may improve prediction accuracy of radiation-induced toxicity and tailor individual treatment options for patients.The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER12) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER12, respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5–85.4%). The model based on V65 (%) and XQ-Intmid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71–88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER12 with a fair to good accuracy.

Genomic Profiling Reveals Novel Predictive Biomarkers for Chemo-Radiotherapy Toxicity and Efficacy in Non-Small-Cell Lung Cancer

<h3>Purpose/Objective(s)</h3> Chemo-radiotherapy (CRT) remains the main treatment modality for non-small-cell lung cancer (NSCLC). However, its clinical efficacy is largely limited by individual variations in radio-sensitivity and risk of radiation-induced thoracic toxicity (RITT). There is an urgent need to identify genetic determinants that can explain patients' likelihood to develop recurrence and RITT following CRT. <h3>Materials/Methods</h3> We performed comprehensive genomic profiling, using a 474-cancer and radiotherapy-related gene panel, on pretreatment biopsy samples from patients with unresectable stage III NSCLCs who underwent definitive CRT. Patients' clinical and genomic features were correlated with progression-free survival (PFS), overall survival (OS), and high-grade RITT development, after CRT. <h3>Results</h3> A total of 122 patients were enrolled (median age, 62 years; 87% males). Of these, the majority (n = 84, 69%) had squamous cell carcinoma (SCC) and the remaining 38 (31%) were adenocarcinoma (ADC). Median PFS and OS of the cohort were 11.4 and 34.6 months, respectively. No clear survival difference was seen between the histological subtypes or other clinical features, other than an association between smoking history and an unfavorable outcome (PFS HR = 1.89 [95% CI, 1.11-3.21], <i>P</i> = 0.02; OS HR = 1.59 [0.80-3.16], <i>P</i> = 0.19). Genetic association analysis identified alterations in NOTCH2, KEAP1, FGFR1/3 alterations as potential risk factors of CRT resistance, with reduced PFS and OS compared with patients carrying the respective wild-type genes. The rates of grade 2 or higher (high-grade) radiation-induced pneumonitis and esophagitis were 32% and 13%, respectively. Polymorphisms in the DNA repair-associated XRCC5 (rs3835) and XRCC1 (rs25487) were associated with an increased risk of high-grade esophagitis and pneumonitis, respectively. Moreover, XRCC5 (rs3835) was also associated with an earlier occurrence of high-grade esophagitis. MTHFR (rs1801133) and NQO1 (rs1800566) were additional risk alleles associated with an increased susceptibility to RITT overall. <h3>Conclusion</h3> Our results provide a set of clinically usable predictive biomarkers for chemo-radiotherapy toxicity and efficacy. These findings suggest that pre-treatment testing for a combination of genetic variants, which likely act together to confer resistance or toxicity after CRT, might be clinically useful.

Genomic Profiling Identified Prognostic Biomarkers of Definitive Chemo-Radiotherapy Response in Esophageal Carcinoma

Esophageal cancer (EC), especially esophageal squamous cell carcinoma is one of the most aggressive tumors in China with a five-year survival rate of approximately 40%. While definitive chemo-radiotherapy (CRT) remains the main treatment modality for EC, its clinical efficacy is largely limited by variations in individuals' radio-sensitivity. The ability to accurately and reliably identify poor CRT responders might be of great value for timely regimen adjustments.In this retrospective study, targeted next-generation sequencing using a 474-gene panel was performed on baseline tumors of 190 inoperable EC patients treated with CRT. Baseline clinical characteristics, genomic alterations and canonical oncogenic pathways were investigated for their associations with clinical outcomes.In this cohort, alterations in TP53 (94.2%), CDKN2A (51.1%), CCND1 (45.3%), MYC (34.2%), FGF19 (34.2%), CDKN1C (33.2%), NOTCH1 (30.5%), NFE2L2 (30.0%), FAT1 (25.8%) and PIK3CA (24.2%) were frequently observed. We revealed distinct genomic profiles between patients who achieved complete or partial response (CR/PR) and those with stable or progressive disease (SD/PD). Specifically, deletion of CDKN2B [29.59% vs. 16.48%, fisher's exact P = 0.039] and amplification of CDK6 [10.2% vs. 0%, fisher's exact P = 0.002] were significantly enriched in the SD/PD group comparing to CR/PR. Pathway analyses demonstrated that genetic alterations in the cell cycle regulation pathway were also more commonly identified in SD/PD than CR/PR group [22.22% vs. 17.83%, fisher's exact P = 0.025]. At baseline, clinical stage, the best response and level of reported radiation toxicity were significantly associated with PFS following CRT. Additionally, mutations in CHEK2 [PFS HR 3.81 (1.89-7.67), P < 0.0001; OS HR 2.44 (1.06-5.64), P = 0.031] and deletion of CDKN1C [PFS HR 1.63 (1.12-2.36), P = 0.009; OS HR 1.57 (0.984-2.5), P = 0.057], among others, could predict poor PFS and OS following CRT and remained meaningful after adjusting for clinical factors.We demonstrated here crucial prognostic factors and abnormal oncogenic pathways that were associated lesser clinical benefit of CRT-treated EC patients. Results from comprehensive pre-treatment genomic testing could provide guidance for future clinical management for inoperable EC patients.

Cell lines of the same anatomic site and histologic type show large variability in intrinsic radiosensitivity and relative biological effectiveness to protons and carbon ions

To show that intrinsic radiosensitivity varies greatly for protons and carbon (C) ions in addition to photons, and that DNA repair capacity remains important in governing this variability. We measured or obtained from the literature clonogenic survival data for a number of human cancer cell lines exposed to photons, protons (9.9keV/μm), and C-ions (13.3-77.1keV/μm). We characterized their intrinsic radiosensitivity by the dose for 10% or 50% survival (D10% or D50% ), and quantified the variability at each radiation quality by the coefficient of variation (COV) in D10% and D50% . We also treated cells with DNA repair inhibitors prior to irradiation to assess how DNA repair capacity affects their variability. We found no statistically significant differences in the COVs of D10% or D50% between any of the radiation qualities investigated. The same was true regardless of whether the cells were treated with DNA repair inhibitors, or whether they were stratified into histologic subsets. Even within histologic subsets, we found remarkable differences in radiosensitivity for high LET C-ions that were often greater than the variations in RBE, with brain cancer cells varying in D10% (D50% ) up to 100% (131%) for 77.1keV/μm C-ions, and non-small cell lung cancer and pancreatic cancer cell lines varying up to 55% (76%) and 51% (78%), respectively, for 60.5keV/μm C-ions. The cell lines with modulated DNA repair capacity had greater variability in intrinsic radiosensitivity across all radiation qualities. Even for cell lines of the same histologic type, there are remarkable variations in intrinsic radiosensitivity, and these variations do not differ significantly between photon, proton or C-ion radiation. The importance of DNA repair capacity in governing the variability in intrinsic radiosensitivity is not significantly diminished for higher LET radiation.