Radiotherapy Response Research Articles

Radiotherapy Response Assessment of Multiple Myeloma: A Dual-Energy CT Approach With Virtual Non-Calcium Images.

BackgroundLife expectancy of patients with multiple myeloma (MM) has increased over the past decades, underlining the importance of local tumor control and avoidance of dose-dependent side effects of palliative radiotherapy (RT). Virtual noncalcium (VNCa) imaging from dual-energy computed tomography (DECT) has been suggested to estimate cellularity and metabolic activity of lytic bone lesions (LBLs) in MM.ObjectiveTo explore the feasibility of RT response monitoring with DECT-derived VNCa attenuation measurements in MM.MethodsThirty-three patients with 85 LBLs that had been irradiated and 85 paired non-irradiated LBLs from the same patients were included in this retrospective study. Irradiated and non-irradiated LBLs were measured by circular regions of interest (ROIs) on conventional and VNCa images in a total of 216 follow-up measurements (48 before and 168 after RT). Follow-ups were rated as therapy response, stable disease, or local progression according to the MD Anderson criteria. Receiver operating characteristic (ROC) analysis was performed to discriminate irradiated vs. non-irradiated and locally progressive vs. stable/responsive LBLs using absolute attenuation post-irradiation and percentage attenuation change for patients with pre-irradiation DECT, if available.ResultsAttenuation of LBLs decreased after RT depending on the time that had passed after irradiation [absolute thresholds for identification of irradiated LBLs 30.5–70.0 HU [best area under the curve [AUC] 0.75 (0.59–0.91)] and -77.0 to -22.5 HU [best AUC 0.85 (0.65–1.00)]/-50% and -117% to -167% proportional change of attenuation on conventional and VNCa images, respectively]. VNCa CT was significantly superior for identification of RT effects in LBLs with higher calcium content [best VNCa AUC 0.96 (0.91–1.00), best conventional CT AUC 0.64 (0.45–0.83)]. Thresholds for early identification of local irradiation failure were >20.5 HU on conventional CT [AUC 0.78 (0.68–0.88)] and >-27 HU on VNCa CT [AUC 0.83 (0.70–0.96)].ConclusionTherapy response of LBLs after RT can be monitored by VNCa imaging based on regular myeloma scans, which yields potential for optimizing the lesion-specific radiation dose for local tumor control. Decreasing attenuation indicates RT response, while above threshold attenuation of LBLs precedes local irradiation failure.

A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer.

Purpose Radiotherapy resistance is now recognized as the major obstacle to the effective therapeutic management of non-small-cell lung cancer (NSCLC). As a single biomarker has limited effect in stratifying NSCLC patients, this research aimed to identify long non-coding RNAs (lncRNAs) correlated with radiotherapy response to ameliorate forecast of NSCLC prognosis. Methods In a cohort of NSCLC patients with radiotherapy history (n = 96) from TCGA, genetic data of lncRNA expression profiling were performed. To identify radioresponse-related lncRNA sets which dysregulated significantly between radiosensitive (RS) and radioresistant (RR) groups, differential expression analysis was carried out. Cox relative regression was implemented to set up a radioresponse-related risk model. Moreover, we adopted survival analysis to measure the predictive potentiality of the prognosis model. Results Four radioresponse-related lncRNAs (CASC19, LINC01977, LINC02471, and MAGI2-AS3) were screened to create a prognostic signature. Then, we described a lncRNA signature-based regulatory network and explored the correlation of the immune microenvironment and the signature. Additionally, in vitro assays uncovered inhibition of LINC01977 weakened radioresistance of NSCLC cells. Conclusion We provided a novel radioresponse-related lncRNAs signature with excellent clinical potency for an effective prognostic forecast of patients.

EFFECTS OF CRYOTHERAPY ON PAIN, PRIOR TO THE INTRAVENOUS ADMINISTRATION OF CHEMOTHERAPEUTIC AGENTS AMONG CANCER PATIENTS

Background and objectives: Cancer is a disease of the cell in which the normal mechanisms of the control of growth and proliferation have been altered. Multiple treatment modalities including chemotherapy, radiation therapy, surgery and biologic response modifier therapy may be used at various times. Pain is one of the most common symptoms in cancer patients. Pain can be caused by cancer, cancer treatment, or a combination of factors. The study was done to identify the effects of cryotherapy on pain prior to the intravenous administration of chemotherapeutic agents among cancer patients in a selected hospital at Coimbatore. Materials and methods: Quantitative research approach was used to assess the effects of cryotherapy on pain. Purposive sampling technique was used to select 30 samples. The research design used for this study was pre-experimental one group pre-test― post-test design. Cryotherapy was given for the duration of 5 -10 minutes prior to the intravenous administration of chemotherapeutic agents to cancer patients. Numerical rating scale was used to assess the pain before and after the intervention. A structured questionnaire was used to collect the data from 30 samples. Results: The analysis revealed that the pre-test mean score of pain was 6.00 with standard deviation of 1.74 and in post-test the mean score was 2.43 with standard deviation of 1.85. The paired ‘t’ test value of 17.690 was very highly significant at p<0.001 level. Conclusion: Results indicated that cryotherapy is effective on pain, prior to the intravenous administration of chemotherapeutic agents among patients with cancer. However, the study can be conducted by using large populations to generalise the findings.

Identification of Autophagy Related circRNA-miRNA-mRNA-Subtypes Network With Radiotherapy Responses and Tumor Immune Microenvironment in Non-small Cell Lung Cancer.

Lung cancer (LC) is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide, and most LCs are non-small cell lung cancer (NSCLC). Radiotherapy is one of the most effective treatments for patients with lung cancer, either alone or in combination with other treatment methods. However, radiotherapy responses vary considerably among NSCLC patients. The efficacy of radiotherapy is influenced by several factors, among which autophagy is of importance. Autophagy is induced by radiotherapy and also influences cell responses to radiation. We explored the clinical significance of autophagy-related genes (ARGs) and gene sets (ARGSs) and the underlying mechanism in NSCLC patients treated with radiotherapy. First, differentially expressed ARGs (SNCA, SESN3, DAPL1, and ELAPOR1) and miRNAs (miR-205-5p, miR-26a-1-3p, miR-6510-3p, miR-194-3p, miR-215-5p, and miR-375-3p) were identified between radiotherapy-resistant and radiotherapy-sensitive groups. An autophagy-related radiosensitivity risk signature (ARRS) by nine ARmRNAs/miRNAs and an autophagy-related overall survival risk signature (AROS) by three ARmRNAs were then constructed with estimated AUCs of 0.8854 (95% CI: 0.8131–0.9576) and 0.7901 (95% CI: 0.7168–0.8685), respectively. The correlations between ARGSs or prognostic signatures and clinicopathological factors, short-term radiotherapy responses (radiotherapy sensitivity), long-term radiotherapy responses (overall survival), and immune characteristics were analyzed. Both ARGSs and prognostic signatures were related to immune checkpoint inhibitors (ICIs), infiltration of tumor-infiltrating immune cells (TIICs), and the activity of the cancer immune cycle. Finally, after target prediction and correlation analysis, circRNA (hsa_circ_0019709, hsa_circ_0081983, hsa_circ_0112354, hsa_circ_0040569, hsa_circ_0135500, and hsa_circ_0098966)-regulated miRNA/ARmRNA axes (miR-194-3p/SESN3, miR-205-5p/ELAPOR1, and miR-26a-1-3p/SNCA) were considered potential modulatory mechanisms by influencing the regulation of autophagy, macroautophagy, and chaperone-mediated autophagy.

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer.

Background Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases. Methods The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature. Results WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT. Conclusion We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.

Hypoxia in Solid Tumors: How Low Oxygenation Impacts the "Six Rs" of Radiotherapy.

Radiotherapy is an important component of cancer treatment, with approximately 50% of all cancer patients receiving radiation therapy during their course of illness. Nevertheless, solid tumors frequently exhibit hypoxic areas, which can hinder therapies efficacy, especially radiotherapy one. Indeed, hypoxia impacts the six parameters governing the radiotherapy response, called the « six Rs of radiation biology » (for Radiosensitivity, Repair, Repopulation, Redistribution, Reoxygenation, and Reactivation of anti-tumor immune response), by inducing pleiotropic cellular adaptions, such as cell metabolism rewiring, epigenetic landscape remodeling, and cell death weakening, with significant clinical repercussions. In this review, according to the six Rs, we detail how hypoxia, and associated mechanisms and pathways, impact the radiotherapy response of solid tumors and the resulting clinical implications. We finally illustrate it in hypoxic endocrine cancers through a focus on anaplastic thyroid carcinomas.

PD42-06 BOOSTING THE COMBINATION OF RADIATION AND ANTI-PD-L1 WITH STING AGONIST IN A PRECLINICAL MUSCLE INVASIVE BLADDER CANCER MURINE MODEL

You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology II (PD42)1 Sep 2021PD42-06 BOOSTING THE COMBINATION OF RADIATION AND ANTI-PD-L1 WITH STING AGONIST IN A PRECLINICAL MUSCLE INVASIVE BLADDER CANCER MURINE MODEL Gautier Marcq, Jiamin Huang, Côme Tholomier, Surashri Shinde-Jadhav, Ronald Kool, Rodrigo Skowronski, Fadi Brimo, Jose Joao Mansure, and Wassim Kassouf Gautier MarcqGautier Marcq More articles by this author , Jiamin HuangJiamin Huang More articles by this author , Côme TholomierCôme Tholomier More articles by this author , Surashri Shinde-JadhavSurashri Shinde-Jadhav More articles by this author , Ronald KoolRonald Kool More articles by this author , Rodrigo SkowronskiRodrigo Skowronski More articles by this author , Fadi BrimoFadi Brimo More articles by this author , Jose Joao MansureJose Joao Mansure More articles by this author , and Wassim KassoufWassim Kassouf More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002056.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To evaluate the efficacy of a STING agonist in the combination of radiation therapy (RT) with anti-PDL-1 in preclinical muscle invasive bladder cancer (MIBC) murine model. METHODS: A syngeneic bladder cancer model was used. MB49 murine MIBC cell line was injected (5.105 cells/200 uL) subcutaneously (s.c.) in both flanks of C57BL/6. When tumors reached 0.15 cm3, mice were randomly assigned to each of the following 8 groups (10 mice⁄group): (1) Control; (2) STING agonist alone; (3) RT alone (2 fractions of 6.25Gy in the right flank); (4) RT + STING agonist; (5) anti-PD-L1 alone; (6) RT + anti-PD-L1; (7) STING agonist + anti-PD-L1; (8) RT + STING agonist + anti-PD-L1. Tumor volume of 1.5 cm3 was used as the primary endpoint. Mice were monitored daily for 4 weeks. ANOVA for repeated measures was used to assess differences in tumor growth. Tumor microenvironment (TME) was analyzed by flow cytometry. RESULTS: The STING agonist alone showed significantly better anti-tumor activity than the control group (p<0.001). The addition of STING agonist to RT improved response to treatment (p=0.0007). Finally, the addition of STING agonist to combined RT with anti-PD-L1also improved the response to treatment (p<0.0001). Concerning the non-irradiated abscopal tumor site, STING agonist enhanced the anti-tumor response of both RT and RT + anti-PD-L1 (p<0.001) treatments with maximal response seen in the triple combination (RT, STING agonist, and anti-PD-L1, p<0.001). TME analysis revealed higher CD8 infiltration in the RT arms including a STING agonist and a better immunosuppressive profile (Figure). CONCLUSIONS: Activation of STING pathway boosts the efficacy of combined RT and anti-PD-L1 within the irradiated and the abscopal sites. Source of Funding: Canadian Institutes of Health Research PD42-06 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e726-e726 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gautier Marcq More articles by this author Jiamin Huang More articles by this author Côme Tholomier More articles by this author Surashri Shinde-Jadhav More articles by this author Ronald Kool More articles by this author Rodrigo Skowronski More articles by this author Fadi Brimo More articles by this author Jose Joao Mansure More articles by this author Wassim Kassouf More articles by this author Expand All Advertisement Loading ...

Toward Personalized Medicine in Radiotherapy of Hepatocellular Carcinoma: Emerging Radiomic Biomarker Candidates of Response and Toxicity.

Radiology and radiotherapy are currently undergoing radical transformation with use of biomarkers and digital technologies such as artificial intelligence. These current and upcoming changes in radiology speak of an overarching new vision for personalized medicine. This is particularly evident in the case of radiotherapy of cancers, and of liver cancer in particular. The development of modern radiotherapy with stereotactic body radiotherapy allows targeted treatments to be delivered to the tumor site, limiting the dose to surrounding healthy organs, thus becoming a new therapeutic alternative for hepatocellular carcinoma and other liver tumors. However, not all patients have the same response to radiotherapy or display the same side-effect profile. Biomarkers of response and toxicity in liver radiotherapy would facilitate the vision and practice of personalized medicine. This expert review examines the available molecular, radiomic, and radiogenomic biomarker candidates for acute liver toxicity with potential use for prediction of radiotherapy-induced liver toxicity. To this end, I highlight for oncologists and life scientists that radiomics allows diagnostic images to be analyzed using computer algorithms to extract information imperceptible to the human eye and of relevance to forecasting clinical outcomes. This article underscores particularly (1) the microRNA-based biomarker candidates as among the most promising predictors of radiation-induced liver toxicity and (2) the texture features in radiomic analyses for response prediction. Radiotherapy of hepatocellular carcinoma is edging toward personalized medicine with emerging radiomic biomarker candidates. Future large-scale biomarker studies are called for to enable personalized medicine in liver cancers.

Combined metformin-salicylate treatment provides improved anti-tumor activity and enhanced radiotherapy response in prostate cancer; drug synergy at clinically relevant doses

BackgroundThere is need for well-tolerated therapies for prostate cancer (PrCa) secondary prevention and to improve response to radiotherapy (RT). The anti-diabetic agent metformin (MET) and the aspirin metabolite salicylate (SAL) are shown to activate AMP-activated protein kinase (AMPK), suppress de novo lipogenesis (DNL), the mammalian target of rapamycin (mTOR) pathway and reduce PrCa proliferation in-vitro. The purpose of this study was to examine whether combined MET+SAL treatment could provide enhanced PrCa tumor suppression and improve response to RT. MethodsAndrogen-sensitive (22RV1) and resistant (PC3, DU-145) PrCa cells and PC3 xenografts were used to examine whether combined treatment with MET+SAL can provide improved anti-tumor activity compared to each agent alone in non-irradiated and irradiated PrCa cells and tumors. Mechanisms of action were investigated with analysis of signaling events, mitochondria respiration and DNL activity assays. ResultsWe observed that PrCa cells are resistant to clinically relevant doses of MET. Combined MET + SAL treatment provides synergistic anti-proliferative activity at clinically relevant doses and enhances the anti-proliferative effects of RT. This was associated with suppression of oxygen consumption rate (OCR), activation of AMPK, suppression of acetyl-CoA carboxylase (ACC)-DNL and mTOR-p70s6k/4EBP1 and HIF1α pathways. MET + SAL reduced tumor growth in non-irradiated tumors and enhanced the effects of RT. ConclusionMET+SAL treatment suppresses PrCa cell proliferation and tumor growth and enhances responses to RT at clinically relevant doses. Since MET and SAL are safe, widely-used and inexpensive agents, these data support the investigation of MET+SAL in PrCa clinical trials alone and in combination with RT.

Somatic copy number alterations are predictive of progression-free survival in patients with lung adenocarcinoma undergoing radiotherapy.

Objective:Lung cancer is the most common cause of cancer-related deaths worldwide. Somatic copy number alterations (SCNAs) have been used to predict responses to therapies in many cancers, including lung cancer. However, little is known about whether they are predictive of radiotherapy outcomes. We aimed to understand the prognostic value and biological functions of SCNAs.Methods:We analyzed the correlation between SCNAs and clinical outcomes in The Cancer Genome Atlas data for 486 patients with non-small cell lung cancer who received radiotherapy. Gene set enrichment analyses were performed to investigate the potential mechanisms underlying the roles of SCNAs in the radiotherapy response. Our results were validated in 20 patients with lung adenocarcinoma (LUAD) receiving radiotherapy.Results:SCNAs were a better predictor of progression-free survival (PFS) in LUAD (P = 0.024) than in lung squamous carcinoma (P = 0.18) in patients treated with radiotherapy. Univariate and multivariate regression analyses revealed the superiority of SCNAs in predicting PFS in patients with LUAD. Patients with stage I cancer and low SCNA levels had longer PFS than those with high SCNA levels (P = 0.022). Our prognostic nomogram also showed that combining SCNAs and tumor/node/metastasis provided a better model for predicting long-term PFS. Additionally, high SCNA may activate the cell cycle pathway and induce tumorigenesis.Conclusions:SCNAs may be used to predict PFS in patients with early-stage LUAD with radiotherapy, in combination with TNM, with the aim of predicting long-term PFS. Therefore, SCNAs are a novel predictive biomarker for radiotherapy in patients with LUAD.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer.

Simple SummaryThere are currently no effective biomarkers to select chemotherapy, immunotherapy, and radiotherapy for treating lung cancer patients. This study identified genetic networks containing major immune-checkpoint inhibitors CD27, PD1, and PDL1, and their associated prognostic genes and proliferation genes in lung cancer tumors. A 5-gene prognostic model was developed and validated in extensive cohorts to select patients at a high risk for developing metastasis. CRISPR-Cas9 and RNA interference screening data were used in the selection of proliferation genes. These genes were associated with chemoresponse and radiotherapy response in lung cancer cell lines and patient tumors. This immune-omics network led to the discovery of repositioning drugs for improving lung cancer treatment.To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan–Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis

PurposeThe value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.Materials and MethodsPatients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian network model to predict radiotherapy response.ResultsAltogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure–free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83.ConclusionDurable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

A Novel Approach for the Discovery of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy (RT) is an important treatment modality for the local control of breast cancer (BC). Unfortunately, not all patients that receive RT will obtain a therapeutic benefit, as cancer cells that either possess intrinsic radioresistance or develop resistance during treatment can reduce its efficacy. For RT treatment regimens to become personalised, there is a need to identify biomarkers that can predict and/or monitor a tumour’s response to radiation. Here we describe a novel method to identify such biomarkers. Liquid chromatography-mass spectrometry (LC-MS) was used on conditioned media (CM) samples from a radiosensitive oestrogen receptor positive (ER+) BC cell line (MCF-7) to identify cancer-secreted biomarkers which reflected a response to radiation. A total of 33 radiation-induced secreted proteins that had higher (up to 12-fold) secretion levels at 24 h post-2 Gy radiation were identified. Secretomic results were combined with whole-transcriptome gene expression experiments, using both radiosensitive and radioresistant cells, to identify a signature related to intrinsic radiosensitivity. Gene expression analysis assessing the levels of the 33 proteins showed that 5 (YBX3, EIF4EBP2, DKK1, GNPNAT1 and TK1) had higher expression levels in the radiosensitive cells compared to their radioresistant derivatives; 3 of these proteins (DKK1, GNPNAT1 and TK1) underwent in-lab and initial clinical validation. Western blot analysis using CM samples from cell lines confirmed a significant increase in the release of each candidate biomarker from radiosensitive cells 24 h after treatment with a 2 Gy dose of radiation; no significant increase in secretion was observed in the radioresistant cells after radiation. Immunohistochemistry showed that higher intracellular protein levels of the biomarkers were associated with greater radiosensitivity. Intracellular levels were further assessed in pre-treatment biopsy tissues from patients diagnosed with ER+ BC that were subsequently treated with breast-conserving surgery and RT. High DKK1 and GNPNAT1 intracellular levels were associated with significantly increased recurrence-free survival times, indicating that these two candidate biomarkers have the potential to predict sensitivity to RT. We suggest that the methods highlighted in this study could be utilised for the identification of biomarkers that may have a potential clinical role in personalising and optimising RT dosing regimens, whilst limiting the administration of RT to patients who are unlikely to benefit.

Effects of palliative radiotherapy and bisphosphonate usage on bone turnover marker levels in cancer patients with osteolytic bone metastases.

Objectives In this study, we aimed to investigate the bone turnover marker levels according to bisphosphonate usage and radiotherapy (RT) in cancer patients with metastases in osteolytic pattern.Patients and methods A total of 52 patients (13 males, 39 females; median age: 52 years; range, 37 to 78 years) treated with RT for osteolytic bone metastases between April 2005 and April 2006 were retrospectively analyzed. Bone-specific alkaline phosphatase (BAP), amino-terminal cross-linked telopeptide of type I collagen (NTX-I), amino-terminal propeptide of type I procollagen (PINP), osteocalcin (OC), deoxypyridinoline (DPD), pyridinoline (PYD), alkaline phosphatase (ALP), creatinine, calcium (Ca), phosphate (P), magnesium (Mg), and 24-h urine Ca levels were measured in blood and urine before the initiation of RT, six weeks and six months after RT.Results A decrease in BAP, PINP, and creatinine levels was observed after RT (Week 6 p=0.006, Month 6 p=0.008). Sixteen patients who already used bisphosphonate before RT were excluded from statistical calculation. The remaining 36 patients who were treated with bisphosphonate after the first blood test were evaluated separately. In this group of patients, BAP, PINP, NTX, creatinine, and Ca levels significantly increased at six weeks after RT. The PINP and creatinine values significantly decreased at six months after RT. The variation between two different RT arms was assessed with repeated measures variance analysis. There was a statistically significant difference for NTX, OC, and creatinine levels between the first and second measurements.Conclusion Radiotherapy is an effective method in the treatment of osteolytic bone metastases. Bone turnover markers can provide an objective evaluation on RT response and parallel to imaging modalities criteria for evaluation. Bisphosphonates may alter the levels of these indicators. However, in this study, there were no statistically significant differences between the levels of markers for two different RT schedules.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Tumor Blood Flow Is a Predictor of Radiotherapy Response in Patients With Nasopharyngeal Carcinoma.

PurposeThe aim of this study was to evaluate tumor blood flow (TBF) as a predictor of radiotherapy response for nasopharyngeal carcinoma (NPC).Materials and MethodA total of 134 patients were divided into two groups, the complete response (CR) group and the partial response (PR) group based on RECIST 1.1 recommendations. The statistical difference was evaluated for pre- and mid- or post-treatment TBF and changes of TBF for tumors and metastatic lymph nodes between CR and PR, respectively. The receiver operation characteristic (ROC) curve was utilized to evaluate the accuracy of TBF in predicting the response of radiation therapy. The association between TBF and SUVmax was also investigated.ResultsThe reduction of TBF in CR was significantly lower than that in PR for primary tumors (P <0.001) and metastatic lymph nodes (P <0.001). The multivariate logistic regression analysis indicated that the reduction of TBF is an independent predictor of the response of radiation therapy for primary tumors (P <0.001) and metastatic lymph nodes (P <0.001). The accuracy of TBF reduction in predicting the response of radiation therapy was 0.817 in primary tumors and 0.924 in metastatic lymph nodes, respectively. No significant correlation was observed between the TBF values and SUVmax of primary tumors (r = -0.008, P = 0.954) and metastasis lymph nodes (r = -0.061, P = 0.652).ConclusionThis study suggests that the reduction of TBF is a promising parameter for evaluating the response of radiation therapy.

Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.

Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6weekly CBCTs). The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83±0.09, 0.82±0.08, and 0.81±0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81±0.06 and 0.85±0.02. We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.

SP-0347 Organoid model system to study response in radiotherapy

SP-0347 Organoid model system to study response in radiotherapy

SP-0019 Targeting tumour metabolism for enhancing radiotherapy response

SP-0019 Targeting tumour metabolism for enhancing radiotherapy response