American Society for Radiation Oncology (ASTRO) Survey of Radiation Biology Educators in U.S. and Canadian Radiation Oncology Residency Programs

The goal of this survey was to obtain detailed information on the faculty currently responsible for teaching radiation biology courses to radiation oncology residents in the U.S. and Canada

STROGAR – STrengthening the Reporting Of Genetic Association studies in Radiogenomics

AbstractDespite publication of numerous radiogenomics studies to date, positive single nucleotide polymorphism (SNP) associations have rarely been reproduced in independent validation studies. A major reason for these inconsistencies is a high number of false positive findings because no adjustments were made for multiple comparisons. It is also possible that some validation studies were false negatives due to methodological shortcomings or a failure to reproduce relevant details of the original study. Transparent reporting is needed to ensure these flaws do not hamper progress in radiogenomics. In response to the need for improving the quality of research in the area, the Radiogenomics Consortium produced an 18-item checklist for reporting radiogenomics studies. It is recognised that not all studies will have recorded all of the information included in the checklist. However, authors should report on all checklist items and acknowledge any missing information. Use of STROGAR guidelines will advance the field of radiogenomics by increasing the transparency and completeness of reporting

Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity.

BACKGROUND: Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy. METHODS: The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods. RESULTS: A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity. CONCLUSION: Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens.This work was supported by Cancer Research UK (C1094/A11728 to CMLW and NGB for the RAPPER study, C26900/A8740 to GCB, C5047A17528 to RE), the Royal College of Radiologists (GCB), Prostate Cancer UK (P2012148 to RE), The ELLIPSE Consortium on behalf of the GAME-ON Network, The National Institute for Health Research (GCB), Addenbrooke’s Charitable Trust (GCB), NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, The National Institute for Health Research Cambridge Biomedical Research Centre (NGB), UK Medical Research Council (LD), the Experimental Cancer Medicine Centre (CMLW), the Royal Marsden NHS Foundation Trust (DPD), the United States National Institutes of Health (1R01CA134444 to BSR), the American Cancer Society (RSGT-05-200-01-CCE to BSR), the United States Department of Defense (PC074201 to BSR), Mount Sinai Tisch Cancer Institute Developmental Fund Award (BSR), the Instituto de Salud Carlos III (FIS PI10/00164 and PI13/02030 to AV), Fondo Europeo de Desarrollo Regional (FEDER 2007-2013 to AV), Xunta de Galicia and the European Social Fund (POS-A/2013/034 to LF), and the Alberta Cancer Board Research Initiative Program (103.0393.71760001404 to MP). Laboratory infrastructure for the RAPPER study was funded by Cancer Research UK [C8197/A10123]. DD acknowledges support from the National Institute for Health Research RM/ICR Biomedical Research Centre and all the researchers at the Royal Marsden Hospital and the Institute of Cancer Research. The RAPPER cohort comprises patients and data recruited into the RT01 and CHHiP UK radiotherapy trials. The RT01 trial was supported by the UK Medical Research Council. The CHHiP trial (CRUK/06/016) was supported by the Department of Health and Cancer Research UK (C8262/A7253); trial recruitment was facilitated within centers by the National Institute for Health Research Cancer Research Network.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Nature Publishing Group

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146290/1/mp13136.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146290/2/mp13136_am.pd

Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer.

Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.This work was supported by Cancer Research UK (C1094/A11728 to CMLW and NGB for the RAPPER study, C26900/A8740 to GCB, and C8197/A10865 to AMD), the Royal College of Radiologists (C26900/ A8740 to GCB), the National Institute for Health Research (GCB; no grant number), Addenbrooke's Charitable Trust (GCB; no grant number), Institute of Cancer Research (National Institute for Health Research) Biomedical Research Centre (C46/A2131 to DPD and SG), the National Institute for Health Research Cambridge Biomedical Research Centre (NGB; no grant number), UK Medical Research Council (RG70550 to LD), the Joseph Mitchell Trust (AMD; no grant number), the Experimental Cancer Medicine Centre (CMLW; no grant number), Cancer Research UK Program grant Section of Radiotherapy (C33589/ A19727 to SLG), the United States National Institutes of Health (1R01CA134444 to BSR and HO, 2P30CA014520-34 to SB, and 1K07CA187546-01A1 to SLK), the American Cancer Society (RSGT-05- 200-01-CCE to BSR), the U.S. Department of Defense (PC074201 to BSR and HO), Mount Sinai Tisch Cancer Institute Developmental Fund Award (BSR; no grant number), the Instituto de Salud Carlos III (FIS PI10/00164 and PI13/02030 to AV and PI13/01136 to AC), Fondo Europeo de Desarrollo Regional (FEDER 2007–2013 to AV and AC; no grant number), Instituto de Salud Carlos III (FIS PI10/00164 and PI13/ 02030 to AV and PI13/01136 to AC), Xunta de Galicia and the European Social Fund (POS-A/2013/034 to LF), and the Alberta Cancer Board Research Initiative Program (103.0393.71760001404 to MP). AMD receives support from the REQUITE study, which is funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 601826. Laboratory infrastructure for the RAPPER study was funded by Cancer Research UK [C8197/A10123] and the Manchester Experimental Cancer Medicine Centre. The RAPPER cohort comprises individuals and data recruited into the RT01 and CHHiP UK radiotherapy trials. The RT01 trial was supported by the UK Medical Research Council. The CHHiP trial (CRUK/06/016) was supported by the Department of Health and Cancer Research UK (C8262/A7253); trial recruitment was facilitated within centers by the National Institute for Health Research Cancer Research Network. DPD and SLG acknowledge NHS funding to the NIHR Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and Institute of Cancer Research.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ebiom.2016.07.02

γ-H2AX Kinetics as a Novel Approach to High Content Screening for Small Molecule Radiosensitizers

Persistence of γ-H2AX after ionizing radiation (IR) or drug therapy is a robust reporter of unrepaired DNA double strand breaks in treated cells.DU-145 prostate cancer cells were treated with a chemical library ±IR and assayed for persistence of γ-H2AX using an automated 96-well immunocytochemistry assay at 4 hours after treatment. Hits that resulted in persistence of γ-H2AX foci were tested for effects on cell survival. The molecular targets of hits were validated by molecular, genetic and biochemical assays and in vivo activity was tested in a validated Drosophila cancer model.We identified 2 compounds, MS0019266 and MS0017509, which markedly increased persistence of γ-H2AX, apoptosis and radiosensitization in DU-145 cells. Chemical evaluation demonstrated that both compounds exhibited structurally similar and biochemical assays confirmed that these compounds inhibit ribonucleotide reductase. DNA microarray analysis and immunoblotting demonstrates that MS0019266 significantly decreased polo-like kinase 1 gene and protein expression. MS0019266 demonstrated in vivo antitumor activity without significant whole organism toxicity.MS0019266 and MS0017509 are promising compounds that may be candidates for further development as radiosensitizing compounds as inhibitors of ribonucleotide reductase

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.