Genome-wide association study identifies a region on chromosome 11q14.3 associated with late rectal bleeding following radiation therapy for prostate cancer

A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma

BACKGROUND AND PURPOSE

Radiation-induced brain injury (RBI) is a severe radiotoxicity for nasopharyngeal carcinoma (NPC) patients, greatly affecting their long-term life quality and survival. We aim to establish a comprehensive predictive model including clinical factors and newly developed genetic variants to improve the precision of RBI risk stratification.

MATERIALS AND METHODS

By performing a large registry-based retrospective study with magnetic resonance imaging follow-up on RBI development, we conducted a genome-wide association study and developed a polygenic risk score (PRS) for RBI in 1189 NPC patients who underwent intensity-modulated radiotherapy. We proposed a tolerance dose scheme for temporal lobe radiation based on the risk predicted by PRS. Additionally, we established a nomogram by combining PRS and clinical factors for RBI risk prediction.

RESULTS

The 38-SNP PRS could effectively identify high-risk individuals of RBI (P = 1.42 × 10-34). Based on genetic risk calculation, the recommended tolerance doses of temporal lobes should be 57.6 Gy for individuals in the top 10 % PRS subgroup and 68.1 Gy for individuals in the bottom 50 % PRS. Notably, individuals with high genetic risk (PRS > P50) and receiving high radiation dose in the temporal lobes (D0.5CC > 65 Gy) had an approximate 50-fold risk over individuals with low PRS and receiving low radiation dose (HR = 50.09, 95 %CI = 24.27-103.35), showing an additive joint effect (Pinteraction < 0.001). By combining PRS with clinical factors including age, tumor stage, and radiation dose of temporal lobes, the predictive accuracy was significantly improved with C-index increased from 0.78 to 0.85 (P = 1.63 × 10-2).

CONCLUSIONS

The PRS, together with clinical factors, could improve RBI risk stratification and implies personalized radiotherapy.

Moving the Needle Forward in Genomically-Guided Precision Radiation Treatment

Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient's tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation

Introduction

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation.

Methods

We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs.

Results

SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges.

Discussion

The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Emerging Roles of Circulating Tumor DNA for Increased Precision and Personalization in Radiation Oncology

Recent breakthroughs in circulating tumor DNA (ctDNA) technologies present a compelling opportunity to combine this emerging liquid biopsy approach with the field of radiogenomics, the study of how tumor genomics correlate with radiotherapy response and radiotoxicity. Canonically, ctDNA levels reflect metastatic tumor burden, although newer ultrasensitive technologies can be used after curative-intent radiotherapy of localized disease to assess ctDNA for minimal residual disease (MRD) detection or for post-treatment surveillance. Furthermore, several studies have demonstrated the potential utility of ctDNA analysis across various cancer types managed with radiotherapy or chemoradiotherapy, including sarcoma and cancers of the head and neck, lung, colon, rectum, bladder, and prostate . Additionally, because peripheral blood mononuclear cells are routinely collected alongside ctDNA to filter out mutations associated with clonal hematopoiesis, these cells are also available for single nucleotide polymorphism analysis and could potentially be used to detect patients at high risk for radiotoxicity. Lastly, future ctDNA assays will be utilized to better assess locoregional MRD in order to more precisely guide adjuvant radiotherapy after surgery in cases of localized disease, and guide ablative radiotherapy in cases of oligometastatic disease.

Radiogenomics in lung cancer: Where are we?

Huge technological and biomedical advances have improved the survival and quality of life of lung cancer patients treated with radiotherapy. However, during treatment planning, a probability that the patient will experience adverse effects is assumed. Radiotoxicity is a complex entity that is largely dose-dependent but also has important intrinsic factors. One of the most studied is the genetic variants that may be associated with susceptibility to the development of adverse effects of radiotherapy. This review aims to present the current status of radiogenomics in lung cancer, integrating results obtained in association studies of SNPs (single nucleotide polymorphisms) related to radiotherapy toxicities. We conclude that despite numerous publications in this field, methodologies and endpoints vary greatly, making comparisons between studies difficult. Analyzing SNPs from the candidate gene approach, together with the study in cohorts limited by the sample size, has complicated the possibility of having validated results. All this delays the incorporation of genetic biomarkers in predictive models for clinical application. Thus, from all analysed SNPs, only 12 have great potential as esophagitis genetic risk factors and deserve further exploration. This review highlights the efforts that have been made to date in the radiogenomic study of radiotoxicity in lung cancer.

Low dose ionizing radiation effects on the immune system

Ionizing radiation interacts with the immune system in many ways with a multiplicity that mirrors the complexity of the immune system itself: namely the need to maintain a delicate balance between different compartments, cells and soluble factors that work collectively to protect, maintain, and restore tissue function in the face of severe challenges including radiation damage. The cytotoxic effects of high dose radiation are less relevant after low dose exposure, where subtle quantitative and functional effects predominate that may go unnoticed until late after exposure or after a second challenge reveals or exacerbates the effects. For example, low doses may permanently alter immune fitness and therefore accelerate immune senescence and pave the way for a wide spectrum of possible pathophysiological events, including early-onset of age-related degenerative disorders and cancer. By contrast, the so called low dose radiation therapy displays beneficial, anti-inflammatory and pain relieving properties in chronic inflammatory and degenerative diseases. In this review, epidemiological, clinical and experimental data regarding the effects of low-dose radiation on the homeostasis and functional integrity of immune cells will be discussed, as will be the role of immune-mediated mechanisms in the systemic manifestation of localized exposures such as inflammatory reactions. The central conclusion is that ionizing radiation fundamentally and durably reshapes the immune system. Further, the importance of discovery of immunological pathways for modifying radiation resilience amongst other research directions in this field is implied.

Harnessing genome-wide association studies to minimize adverse radiation-induced side effects

Radiotherapy is used as definitive treatment in approximately two-thirds of all cancers. However, like any treatment, radiation has significant acute and long-term side effects including secondary malignancies. Even when similar radiation parameters are used, 5%–10% of patients will experience adverse radiation side effects. Genomic susceptibility is thought to be responsible for approximately 40% of the clinical variability observed. In the era of precision medicine, the link between genetic susceptibility and radiation-induced side effects is further strengthening. Genome-wide association studies (GWAS) have begun to identify single-nucleotide polymorphisms (SNPs) attributed to overall and tissue-specific toxicity following radiation for treatment of breast cancer, prostate cancer, and other cancers. Here, we review the use of GWAS in identifying polymorphisms that are predictive of acute and long-term radiation-induced side effects with a focus on chest, pelvic, and head-and-neck irradiation. Integration of GWAS with “omic” data, patient characteristics, and clinical correlates into predictive models could decrease radiation-induced side effects while increasing therapeutic efficacy.

The Road so Far in Colorectal Cancer Pharmacogenomics: Are We Closer to Individualised Treatment?

In recent decades, survival rates in colorectal cancer have improved greatly due to pharmacological treatment. However, many patients end up developing adverse drug reactions that can be severe or even life threatening, and that affect their quality of life. These remain a limitation, as they may force dose reduction or treatment discontinuation, diminishing treatment efficacy. From candidate gene approaches to genome-wide analysis, pharmacogenomic knowledge has advanced greatly, yet there is still huge and unexploited potential in the use of novel technologies such as next-generation sequencing strategies. This review summarises the road of colorectal cancer pharmacogenomics so far, presents considerations and directions to be taken for further works and discusses the path towards implementation into clinical practice.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

Genetically-regulated transcriptomics & copy number variation of proctitis points to altered mitochondrial and DNA repair mechanisms in individuals of European ancestry

Background

Proctitis is an inflammation of the rectum and may be induced by radiation treatment for cancer. The genetic heritability of developing radiotoxicity and prior role of genetic variants as being associated with side-effects of radiotherapy necessitates further investigation for underlying molecular mechanisms. In this study, we investigated gene expression regulated by genetic variants, and copy number variation in prostate cancer survivors with radiotoxicity.

Methods

We investigated proctitis as a radiotoxic endpoint in prostate cancer patients who received radiotherapy (n = 222). We analyzed the copy number variation and genetically regulated gene expression profiles of whole-blood and prostate tissue associated with proctitis. The SNP and copy number data were genotyped on Affymetrix® Genome-wide Human SNP Array 6.0. Following QC measures, the genotypes were used to obtain gene expression by leveraging GTEx, a reference dataset for gene expression association based on genotype and RNA-seq information for prostate (n = 132) and whole-blood tissue (n = 369).

Results

In prostate tissue, 62 genes were significantly associated with proctitis, and 98 genes in whole-blood tissue. Six genes - CABLES2, ATP6AP1L, IFIT5, ATRIP, TELO2, and PARD6G were common to both tissues. The copy number analysis identified seven regions associated with proctitis, one of which (ALG1L2) was also associated with proctitis based on transcriptomic profiles in the whole-blood tissue. The genes identified via transcriptomics and copy number variation association were further investigated for enriched pathways and gene ontology. Some of the enriched processes were DNA repair, mitochondrial apoptosis regulation, cell-to-cell signaling interaction processes for renal and urological system, and organismal injury.

Conclusions

We report gene expression changes based on genetic polymorphisms. Integrating gene-network information identified these genes to relate to canonical DNA repair genes and processes. This investigation highlights genes involved in DNA repair processes and mitochondrial malfunction possibly via inflammation. Therefore, it is suggested that larger studies will provide more power to infer the extent of underlying genetic contribution for an individual’s susceptibility to developing radiotoxicity.

Micronuclei Formation upon Radioiodine Therapy for Well-Differentiated Thyroid Cancer: The Influence of DNA Repair Genes Variants

Radioiodine therapy with ¹³¹I remains the mainstay of standard treatment for well-differentiated thyroid cancer (DTC). Prognosis is good but concern exists that ¹³¹I-emitted ionizing radiation may induce double-strand breaks in extra-thyroidal tissues, increasing the risk of secondary malignancies. We, therefore, sought to evaluate the induction and 2-year persistence of micronuclei (MN) in lymphocytes from 26 ¹³¹I-treated DTC patients and the potential impact of nine homologous recombination (HR), non-homologous end-joining (NHEJ), and mismatch repair (MMR) polymorphisms on MN levels. MN frequency was determined by the cytokinesis-blocked micronucleus assay while genotyping was performed through pre-designed TaqMan^® Assays or conventional PCR-restriction fragment length polymorphism (RFLP). MN levels increased significantly one month after therapy and remained persistently higher than baseline for 2 years. A marked reduction in lymphocyte proliferation capacity was also apparent 2 years after therapy. MLH1 rs1799977 was associated with MN frequency (absolute or net variation) one month after therapy, in two independent groups. Significant associations were also observed for MSH3 rs26279, MSH4 rs5745325, NBN rs1805794, and tumor histotype. Overall, our results suggest that ¹³¹I therapy may pose a long-term challenge to cells other than thyrocytes and that the individual genetic profile may influence ¹³¹I sensitivity, hence its risk-benefit ratio. Further studies are warranted to confirm the potential utility of these single nucleotide polymorphisms (SNPs) as radiogenomic biomarkers in the personalization of radioiodine therapy.

The genetic landscapes of urological cancers and their clinical implications in the era of high-throughput genome analysis

OBJECTIVE

With the advent of high-throughput genome analysis, we are increasingly able to sequence and hence understand the pathogenic processes underlying individual cancers. Recently, consortiums such as The Cancer Genome Atlas (TCGA) have performed large-scale projects to this end, providing significant amounts of information regarding the genetic landscapes of several cancers.

PATIENTS AND METHODS

We performed a narrative review of studies from the TCGA and other major studies. We aimed to summarise data exploring the clinical implications of specific genetic alterations, both prognostically and therapeutically, in four major urological cancers. These were renal cell carcinoma, muscle-invasive bladder cancer/carcinoma, prostate cancer, and testicular germ cell tumours.

RESULTS

With these four urological cancers, great strides have been made in the molecular characterisation of tumours. In particular, recent studies have focussed on identifying molecular subtypes of tumours with characteristic genetic alterations and differing prognoses. Other prognostic alterations have also recently been identified, including those pertaining to epigenetics and microRNAs. In regard to treatment, numerous options are emerging for patients with these cancers such as including immune checkpoint inhibition, epigenetic-based treatments, and agents targeting MAPK, PI3K, and DNA repair pathways. There are a multitude of trials underway investigating the effects of these novel agents, the results of which are eagerly awaited.

CONCLUSIONS

As medicine chases the era of personalised care, it is becoming increasingly important to provide individualised prognoses for patients. Understanding how specific genetic alterations affects prognosis is key for this. It will also be crucial to provide highly targeted treatments against the specific genetics of a patient's tumour. With work performed by the TCGA and other large consortiums, these aims are gradually being achieved. Our review provides a succinct overview of this exciting field that may underpin personalised medicine in urological oncology.

Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy

BACKGROUND

A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic studies.

METHODS

We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10-8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were two-sided.

RESULTS

Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10-10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10-10), and rs11122573 with hematuria (Pmeta = 1.8 × 10-8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10-6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese cohorts.

CONCLUSIONS

This study increases the understanding of the architecture of common genetic variants affecting radiotoxicity, points to novel radio-pathogenic mechanisms, and develops risk models for testing in clinical studies. Further multinational radiogenomics studies in larger cohorts are worthwhile.

Genome-Wide Association Study of Susceptibility Loci for Radiation-Induced Brain Injury

BACKGROUND

Radiation-induced brain injury is a nonnegligible issue in the management of cancer patients treated by partial or whole brain irradiation. In particular, temporal lobe injury (TLI), a deleterious late complication in nasopharyngeal carcinoma, greatly affects the long-term life quality of these patients. Although genome-wide association studies (GWASs) have successfully identified single nucleotide polymorphisms (SNPs) associated with radiation toxicity, genetic variants contributing to the radiation-induced brain injury have not yet been assessed.

METHODS

We recruited and performed follow-up for a prospective observational cohort, Genetic Architecture of Radiotherapy Toxicity and Prognosis, using magnetic resonance imaging for TLI diagnosis. We conducted genome-wide association analysis in 1082 patients and validated the top associations in two independent cohorts of 1119 and 741 patients, respectively. All statistical tests were two-sided.

RESULTS

We identified a promoter variant rs17111237 (A > G, minor allele frequency [MAF] = 0.14) in CEP128 associated with TLI risk (hazard ratio = 1.45, 95% confidence interval = 1.26 to 1.66, Pcombined=3.18 × 10-7) which is in moderate linkage disequilibrium (LD) with rs162171 (MAF = 0.18, R2 = 0.69), the top signal in CEP128 (hazard ratio = 1.46, 95% confidence interval = 1.29-1.66, Pcombined= 6.17 × 10-9). Combining the clinical variables with the top SNP, we divided the patients into different subgroups with varying risk with 5-year TLI-free rates ranging from 33.7% to 95.5%. CEP128, a key component of mother centriole, tightly interacts with multiple radiation-resistant genes and plays an important role in maintaining the functional cilia, which otherwise will lead to a malfunction of the neural network. We found that A > G alteration at rs17111237 impaired the promoter activity of CEP128 and knockdown of CEP128 decreased the clonogenic cell survival of U87 cells under radiation. Noteworthy, 12.7% (27/212) of the GWAS-based associated genes (P < .001) were enriched in the neurogenesis pathway.

CONCLUSIONS

This three-stage study is the first GWAS of radiation-induced brain injury that implicates the genetic susceptibility gene CEP128 involved in TLI development and provides the novel insight into the underlying mechanisms of radiation-induced brain injury.

Precision Radiotherapy and Radiation Risk Assessment: How Do We Overcome Radiogenomic Diversity?

Precision medicine is a rapidly developing area that aims to deliver targeted therapies based on individual patient characteristics. However, current radiation treatment is not yet personalized; consequently, there is a critical need for specific patient characteristics of both tumor and normal tissues to be fully incorporated into dose prescription. Furthermore, current risk assessment following environmental, occupational, or accidental exposures to radiation is based on population effects, and does not account for individual diversity underpinning radiosensitivity. The lack of personalized approaches in both radiotherapy and radiation risk assessment resulted in the current situation where a population-based model, effective dose, is being used. In this review article, to stimulate scientific discussion for precision medicine in both radiotherapy and radiation risk assessment, we propose a novel radiological concept and metric - the personalized dose and the personalized risk index - that incorporate individual physiological, lifestyle-related and genomic variations and radiosensitivity, outlining the potential clinical application for precision medicine. We also review on recent progress in both genomics and biobanking research, which is promising for providing novel insights into individual radiosensitivity, and for creating a novel conceptual framework of precision radiotherapy and radiation risk assessment.

Potential of serum microRNAs as biomarkers of radiation injury and tools for individualization of radiotherapy

Due to tremendous technological advances, radiation oncologists are now capable of personalized treatment plans and deliver the dose in a highly precise manner. However, a crucial challenge is how to escalate radiation doses to cancer cells while reducing damage to surrounding healthy tissues. This determines the probability of achieving therapeutic success whilst safeguarding patients from complications. The current dose constraints rely on observational data. Therefore, incidental toxicity observed in a minority of patients limits the admissible dose thresholds for the whole population, theoretically narrowing down the curative potential of radiotherapy. Future tools for measurements of individual's radiosensitivity before and during treatment would allow proper treatment personalization. Variation in tissue tolerance is at least partially genetically-determined and recent progress in the field of molecular biology raises the possibility that novel assays will allow to predict the response to ionizing radiation. Recently, microRNAs have garnered interest as stable biomarkers of tumor radiation response and normal-tissue toxicity. Preclinical studies in mice and nonhuman primates have shown that serum circulating microRNAs can be used to accurately distinguish pre- and postirradiation states and predict the biological impact of high-dose irradiation. First reports from human studies are also encouraging, however biology-driven precision radiation oncology, which tailors treatment to individual patient's needs, still remains to be translated into clinical studies. In this review, we summarize current knowledge about the potential of serum microRNAs as biodosimeters and biomarkers for radiation injury to lung and hematopoietic cells.

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT

BACKGROUND

Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity after 27% in liver RT and 50% in head-and-neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms.

EXPERIMENTAL DESIGN

We employed a database of 125 liver stereotactic body RT (SBRT) cases with follow-up data to train deep learning-based toxicity predictor. Convolutional neural networks (CNNs) were applied to discover the consistent patterns in 3D dose plans associated with toxicities. To enhance the predicting power, we first pretrain the CNNs with transfer learning from 3D CT images of 2644 human organs. CNNs were then trained on liver SBRT cases. Furthermore, nondosimetric pretreatment features, such as patients' demographics, underlying liver diseases, liver-directed therapies, were inputted into the fully connected neural network for more comprehensive prediction. The saliency maps of CNNs were used to estimate the toxicity risks associated with irradiation of anatomical regions of specific OARs. In addition, we applied machine learning solutions to map numerical pretreatment features with hepatobiliary toxicity manifestation.

RESULTS

Among 125 liver SBRT patients, 58 were treated for liver metastases, 36 for hepatocellular carcinoma, 27 for cholangiocarcinoma, and 4 for other histologies. We observed that CNN we able to achieve accurate hepatobiliary toxicity prediction with the AUC of 0.79, whereas combining CNN for 3D dose plan analysis and fully connected neural networks for numerical feature analysis resulted in AUC of 0.85. Deep learning produces almost two times fewer false-positive toxicity predictions in comparison to DVH-based predictions, when the number of false negatives, i.e., missed toxicities, was minimized. The CNN saliency maps automatically estimated the toxicity risks for portal vein (PV) regions. We discovered that irradiation of the proximal portal vein is associated with two times higher toxicity risks (risk score: 0.66) that irradiation of the left portal vein (risk score: 0.31).

CONCLUSIONS

The framework offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during SBRT.

A Review of Prostate Cancer Genome-Wide Association Studies (GWAS)

Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845-57. ©2018 AACR.

Immunogenetics of prostate cancer: a still unexplored field of study

The immune system is a double-edged sword with regard to the prostate cancer (PCa) battle. Immunogenetics, the study of the potential role of immune-related polymorphisms, is taking its first steps in the treatment of this malignancy. This review summarizes the most recent papers addressing the potential of immunogenetics in PCa, reporting immune-related polymorphisms associated with tumor aggressiveness, treatment toxicity and patients' prognosis. With some peculiarities, RNASEL, IL-6, IL-10, IL-1β and MMP7 have arisen as the most significant biomarkers in PCa treatment and management, having a potential clinical role. Validation prospective clinical studies are required to translate immunogenetics into precision treatment of PCa.

TNFSF10/TRAIL regulates human T4 effector memory lymphocyte radiosensitivity and predicts radiation-induced acute and subacute dermatitis

Sensitivity of T4 effector-memory (T4EM) lymphocytes to radiation-induced apoptosis shows heritability compatible with a Mendelian mode of transmission. Using gene expression studies and flow cytometry, we show a higher TNF-Related Apoptosis Inducing Ligand (TRAIL/TNFSF10) mRNA level and a higher level of membrane bound TRAIL (mTRAIL) on radiosensitive compared to radioresistant T4EM lymphocytes. Functionally, we show that mTRAIL mediates a pro-apoptotic autocrine signaling after irradiation of T4EM lymphocytes linking mTRAIL expression to T4EM radiosensitivity. Using single marker and multimarker Family-Based Association Testing, we identified 3 SNPs in the TRAIL gene that are significantly associated with T4EM lymphocytes radiosensitivity. Among these 3 SNPs, two are also associated with acute and subacute dermatitis after radiotherapy in breast cancer indicating that T4EM lymphocytes radiosensitivity may be used to predict response to radiotherapy. Altogether, these results show that mTRAIL level regulates the response of T4EM lymphocytes to ionizing radiation and suggest that TRAIL/TNFSF10 genetic variants hold promise as markers of individual radiosensitivity.

Validation of genetic predictors of late radiation-induced morbidity in prostate cancer patients

INTRODUCTION

Normal tissue morbidity sets the dose limit for radiotherapy (RT) in cancer treatment and has importance for quality of life for cancer survivors. A previous study of prostate cancer patients treated with RT generated clinical data for radiation-induced morbidity measured by anorectal physiological methods and validated questionnaires. Other studies have identified genetic predictors associated with late radiation-induced morbidity outcome. We have expanded biobank material aiming to validate single nucleotide polymorphisms (SNPs) and a gene expression classifier with endpoints on patient-reported outcomes and biomechanical properties of the anorectum from our cohort matching originally published endpoints.

MATERIALS AND METHODS

The present cohort of prostate cancer patients was treated with RT curative intent in 1999-2007. Nine SNPs associated with late radiation-induced morbidity were tested in 96 patients (rs2788612, rs1800629, rs264663, rs2682585, rs2268363, rs1801516, rs13035033, rs7120482 and rs17779457). A validated gene expression profile predictive of resistance to radiation-induced skin fibrosis was tested in 42 patients. An RT-induced anorectal dysfunction score (RT-ARD) served as a fibrosis-surrogate and a measure of overall radiation-induced morbidity.

RESULTS

The lowest p-value found in the genotype analyses was for SNP rs2682585 minor allele (A) in the FSHR gene and the RT-ARD score with odds ratios (OR) = 1.76; 95% CI (0.98-3.17) p = .06, which was out of concordance with original data showing a protective effect of the minor allele. The gene expression profile in patients classified as fibrosis-resistant was associated with high RT-ARD scores OR 4.18; 95% CI (1.1-16.6), p = .04 conflicting with the hypothesis that fibrosis-resistant patients would experience lower RT-ARD scores.

CONCLUSIONS

We aimed to validate nine SNPs and a gene expression classifier in a cohort of prostate cancer patients with unique scoring of radiation-induced morbidity. One significant association was found, pointing to the opposite direction of originally published data. We conclude that the material was not able to validate previously published genetic predictors of radiation-induced morbidity.

Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity

The overall goal of radiogenomics is the identification of genomic markers that are predictive for the development of adverse effects resulting from cancer treatment with radiation. The principal rationale for a focus on toxicity in radiogenomics is that for many patients treated with radiation, especially individuals diagnosed with early-stage cancers, the survival rates are high, and therefore a substantial number of people will live for a significant period of time beyond treatment. However, many of these patients could suffer from debilitating complications resulting from radiotherapy. Work in radiogenomics has greatly benefited from creation of the Radiogenomics Consortium (RGC) that includes investigators at multiple institutions located in a variety of countries. The common goal of the RGC membership is to share biospecimens and data so as to achieve large-scale studies with increased statistical power to enable identification of relevant genomic markers. A major aim of research in radiogenomics is the development of a predictive instrument to enable identification of people who are at greatest risk for adverse effects resulting from cancer treatment using radiation. It is anticipated that creation of a predictive assay characterized by a high level of sensitivity and specificity will improve precision radiotherapy and assist patients and their physicians to select the optimal treatment for each individual.

Local tumor control and DNA-PK activity of peripheral blood lymphocytes in prostate cancer patients receiving radiotherapy

Repair of DNA damage is critical for genomic stability, and DNA-dependent protein kinase (DNA-PK) has an important role in repairing double-strand breaks. We examined whether the DNA-PK activity of peripheral blood lymphocytes (PBLs) was related to biochemical (prostate-specific antigen: PSA) relapse and radiation toxicity in prostate cancer patients who have received radiotherapy. A total of 69 patients with localized adenocarcinoma of the prostate participated in this study. Peripheral blood was collected 2 years or later after radiotherapy and centrifuged, then DNA-PK activity was measured by a filter binding assay. The high DNA-PK activity group had a significantly higher PSA relapse-free survival rate than the low DNA-PK activity group. The 10-year PSA relapse-free survival was 87.0% in the high DNA-PK activity group, whereas it was 52.7% in the low DNA-PK activity group. Multivariate analysis showed the Gleason score and the level of DNA-PK activity were significant predictors of PSA relapse after radiotherapy. In addition, the low DNA-PK activity group tended to have a higher incidence of Grade 1-2 urinary toxicity than the high DNA-PK activity group. Prostate cancer patients with low DNA-PK activity had a higher rate of PSA relapse and a higher incidence of urinary toxicity. DNA-PK activity in PBLs might be a useful marker for predicting PSA relapse and urinary toxicity, possibly contributing to personalized treatment of prostate cancer.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Clinical and dosimetric predictors of late rectal bleeding of prostate cancer after TomoTherapy intensity modulated radiation therapy

INTRODUCTION

Rectal bleeding after radiotherapy impacts the quality of life of long-term surviving prostate cancer patients. We sought to identify factors associated with late rectal bleeding following intensity modulated radiation therapy (IMRT) using TomoTherapy for prostate cancer.

METHODS

We retrospectively analysed 82 patients with localised prostate cancer treated with TomoTherapy. Most patients (95.1%) received neoadjuvant and concurrent hormone therapy. Forty-two patients (51.2%) graded as high risk using D'Amico's classification underwent radiotherapy involving the pelvic nodal area. Late bleeding complications were quantified using the Common Terminology Criteria for Adverse Events v4.0. Multiple clinical and dosimetric factors were considered with reference to rectal bleeding.

RESULTS

The median follow-up period was 538 (range, 128-904) days. Grades 1, 2 and 3 rectal bleeding were observed in 14 (17.1%), four (4.9%) and one (1.2%) patient respectively. In multivariate analysis, the following factors were significantly associated with Grade ≥1 late rectal bleeding: volume, mean dose (P = 0.012) and rectal V30 (P = 0.025), V40 (P = 0.011), V50 (P = 0.017) and V60 (P = 0.036). When exclusively considering Grade 2-3 rectal bleeding, significant associations were observed with the use of anticoagulants or antiaggregates (P = 0.007), rectal V30 (P = 0.021) and V40 (P = 0.041) in univariate analysis.

CONCLUSIONS

Our results suggested that the intermediate rectal dose-volume (V30-V60) was a significant predictor for mild to severe late rectal bleeding (Grade ≥1). Rectal dose-volumes >V70, which represented the volume of the highest doses, were not predictive in this study.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Individual patient data meta-analysis shows a significant association between the ATM rs1801516 SNP and toxicity after radiotherapy in 5456 breast and prostate cancer patients

PURPOSE

Several small studies have indicated that the ATM rs1801516 SNP is associated with risk of normal tissue toxicity after radiotherapy. However, the findings have not been consistent. In order to test this SNP in a well-powered study, an individual patient data meta-analysis was carried out by the International Radiogenomics Consortium.

MATERIALS AND METHODS

The analysis included 5456 patients from 17 different cohorts. 2759 patients were given radiotherapy for breast cancer and 2697 for prostate cancer. Eight toxicity scores (overall toxicity, acute toxicity, late toxicity, acute skin toxicity, acute rectal toxicity, telangiectasia, fibrosis and late rectal toxicity) were analyzed. Adjustments were made for treatment and patient related factors with potential impact on the risk of toxicity.

RESULTS

For all endpoints except late rectal toxicity, a significantly increased risk of toxicity was found for carriers of the minor (Asn) allele with odds ratios of approximately 1.5 for acute toxicity and 1.2 for late toxicity. The results were consistent with a co-dominant pattern of inheritance.

CONCLUSION

This study convincingly showed a significant association between the ATM rs1801516 Asn allele and increased risk of radiation-induced normal tissue toxicity.

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

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.

Common variation in BRCA1 may have a role in progression to lethal prostate cancer after radiation treatment

BACKGROUND

To evaluate whether single-nucleotide polymorphisms (SNPs) reflecting common variation in the tumor suppressor BRCA1 affect prostate cancer outcomes. Because radiation therapy (RT) induces DNA damage, we hypothesized that common variation in BRCA1 has a role in progression to lethal prostate cancer, particularly in patients receiving RT.

METHODS

We followed 802 men diagnosed with localized prostate cancer (cT1-T3/N0/M0) who were treated with RT in the US Health Professionals Follow-up Study (HPFS) and Physicians' Health Study (PHS), for progression to lethal prostate cancer. Six SNPs (rs3737559, rs1799950, rs799923, rs915945, rs4474733 and rs8176305) were genotyped in HPFS to capture common variation across BRCA1. rs4474733 and rs8176305 were also evaluated in the PHS cohort. Cox proportional hazards models were used to estimate per-allele hazard ratios (HR) and 95% confidence intervals (CI) stratified by primary treatment.

RESULTS

In the RT group (n=802), 71 men progressed to lethal disease during a mean follow-up of 12 years. We found that two SNPs, rs4473733 (HR: 0.65; 95% CI 0.42-0.99) and rs8176305 (HR: 2.03; 95% CI 1.33-3.10), were associated with lethal prostate cancer in men receiving RT.

CONCLUSIONS

Common variation in BRCA1 may influence clinical outcomes in patients receiving RT for localized prostate cancer by modifying the response to RT. Our findings merit further follow-up studies to validate these SNPs and better understand their functional and biological significance.

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.

Outcome modeling techniques for prostate cancer radiotherapy: Data, models, and validation

Prostate cancer is a frequently diagnosed malignancy worldwide and radiation therapy is a first-line approach in treating localized as well as locally advanced cases. The limiting factor in modern radiotherapy regimens is dose to normal structures, an excess of which can lead to aberrant radiation-induced toxicities. Conversely, dose reduction to spare adjacent normal structures risks underdosing target volumes and compromising local control. As a result, efforts aimed at predicting the effects of radiotherapy could invaluably optimize patient treatments by mitigating such toxicities and simultaneously maximizing biochemical control. In this work, we review the types of data, frameworks and techniques used for prostate radiotherapy outcome modeling. Consideration is given to clinical and dose-volume metrics, such as those amassed by the QUANTEC initiative, and also to newer methods for the integration of biological and genetic factors to improve prediction performance. We furthermore highlight trends in machine learning that may help to elucidate the complex pathophysiological mechanisms of tumor control and radiation-induced normal tissue side effects.

ERCC2 polymorphisms and radiation-induced adverse effects on normal tissue: systematic review with meta-analysis and trial sequential analysis

Background

The relationship between ERCC2 polymorphisms and the risk of radiotoxicity remains inconclusive. The aim of our study is to systematically evaluate the association between ERCC2 polymorphisms and the risk of radiotoxicity.

Methods

Publications were identified through a search of the PubMed and Web of Science databases up to August 15, 2015. The pooled odds ratios (ORs) with corresponding 95 % confidence intervals (CIs) were calculated to evaluate the association between ERCC2 polymorphisms and radiotoxicity. Trial sequential analysis (TSA) and power calculation were performed to evaluate the type 1 and type 2 errors.

Results

Eleven studies involving 2584 patients were ultimately included in this meta-analysis. Conventional meta-analysis identified a significant association between ERCC2 rs13181 polymorphism and radiotoxicity (OR = 0.71, 95 % CI: 0.55-0.93, P = 0.01), but this association failed to get the confirmation of TSA.

Conclusions

The minor allele of rs13181 polymorphism may confer a protect effect against radiotoxicity. To confirm this correlation at the level of OR = 0.71, an overall information size of approximate 2800 patients were needed.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-015-0558-6) contains supplementary material, which is available to authorized users.

Incorporating Genetic Biomarkers into Predictive Models of Normal Tissue Toxicity

There is considerable variation in the level of toxicity patients experience for a given dose of radiotherapy, which is associated with differences in underlying individual normal tissue radiosensitivity. A number of syndromes have a large effect on clinical radiosensitivity, but these are rare. Among non-syndromic patients, variation is less extreme, but equivalent to a ±20% variation in dose. Thus, if individual normal tissue radiosensitivity could be measured, it should be possible to optimise schedules for individual patients. Early investigations of in vitro cellular radiosensitivity supported a link with tissue response, but individual studies were equivocal. A lymphocyte apoptosis assay has potential, and is currently under prospective validation. The investigation of underlying genetic variation also has potential. Although early candidate gene studies were inconclusive, more recent genome-wide association studies are revealing definite associations between genotype and toxicity and highlighting the potential for future genetic testing. Genetic testing and individualised dose prescriptions could reduce toxicity in radiosensitive patients, and permit isotoxic dose escalation to increase local control in radioresistant individuals. The approach could improve outcomes for half the patients requiring radical radiotherapy. As a number of patient- and treatment-related factors also affect the risk of toxicity for a given dose, genetic testing data will need to be incorporated into models that combine patient, treatment and genetic data.

Exploiting biological and physical determinants of radiotherapy toxicity to individualize treatment

The recent advances in radiation delivery can improve tumour control probability (TCP) and reduce treatment-related toxicity. The use of intensity-modulated radiotherapy (IMRT) in particular can reduce normal tissue toxicity, an objective in its own right, and can allow safe dose escalation in selected cases. Ideally, IMRT should be combined with image guidance to verify the position of the target, since patients, target and organs at risk can move day to day. Daily image guidance scans can be used to identify the position of normal tissue structures and potentially to compute the daily delivered dose. Fundamentally, it is still the tolerance of the normal tissues that limits radiotherapy (RT) dose and therefore tumour control. However, the dose-response relationships for both tumour and normal tissues are relatively steep, meaning that small dose differences can translate into clinically relevant improvements. Differences exist between individuals in the severity of toxicity experienced for a given dose of RT. Some of this difference may be the result of differences between the planned dose and the accumulated dose (DA). However, some may be owing to intrinsic differences in radiosensitivity of the normal tissues between individuals. This field has been developing rapidly, with the demonstration of definite associations between genetic polymorphisms and variation in toxicity recently described. It might be possible to identify more resistant patients who would be suitable for dose escalation, as well as more sensitive patients for whom toxicity could be reduced or avoided. Daily differences in delivered dose have been investigated within the VoxTox research programme, using the rectum as an example organ at risk. In patients with prostate cancer receiving curative RT, considerable daily variation in rectal position and dose can be demonstrated, although the median position matches the planning scan well. Overall, in 10 patients, the mean difference between planned and accumulated rectal equivalent uniform doses was -2.7 Gy (5%), and a dose reduction was seen in 7 of the 10 cases. If dose escalation was performed to take rectal dose back to the planned level, this should increase the mean TCP (as biochemical progression-free survival) by 5%. Combining radiogenomics with individual estimates of DA might identify almost half of patients undergoing radical RT who might benefit from either dose escalation, suggesting improved tumour cure or reduced toxicity or both.

Association between Single Nucleotide Polymorphisms in XRCC3 and Radiation-Induced Adverse Effects on Normal Tissue: A Meta-Analysis

The X-ray repair cross-complementing group 3 (XRCC3) protein plays an important role in the repair of DNA double-strand breaks. The relationship between XRCC3 polymorphisms and the risk of radiation-induced adverse effects on normal tissue remains inconclusive. Thus, we performed a meta-analysis to elucidate the association between XRCC3 polymorphisms and radiation-induced adverse effects on normal tissue. All eligible studies up to December 2014 were identified through a search of the PubMed, Embase and Web of Science databases. Seventeen studies involving 656 cases and 2193 controls were ultimately included in this meta-analysis. The pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the association between XRCC3 polymorphisms and the risk of radiation-induced normal tissue adverse effects. We found that the XRCC3 p.Thr241Met (rs861539) polymorphism was significantly associated with early adverse effects induced by radiotherapy (OR = 1.99, 95%CI: 1.31-3.01, P = 0.001). A positive association lacking statistical significance with late adverse effects was also identified (OR = 1.28, 95%CI: 0.97-1.68, P = 0.08). In addition, the rs861539 polymorphism was significantly correlated with a higher risk of adverse effects induced by head and neck area irradiation (OR = 2.41, 95%CI: 1.49-3.89, p = 0.0003) and breast irradiation (OR = 1.41, 95%CI: 1.02-1.95, p = 0.04), whereas the correlation was not significant for lung irradiation or pelvic irradiation. Furthermore, XRCC3 rs1799794 polymorphism may have a protective effect against late adverse effects induced by radiotherapy (OR = 0.47, 95%CI: 0.26-0.86, P = 0.01). Well-designed large-scale clinical studies are required to further validate our results.

Ataxia telangiectasia-mutated gene polymorphisms and acute normal tissue injuries in cancer patients after radiation therapy: a systematic review and meta-analysis

PURPOSE

Studies of the association between ataxia telangiectasia-mutated (ATM) gene polymorphisms and acute radiation injuries are often small in sample size, and the results are inconsistent. We conducted the first meta-analysis to provide a systematic review of published findings.

METHODS AND MATERIALS

Publications were identified by searching PubMed up to April 25, 2014. Primary meta-analysis was performed for all acute radiation injuries, and subgroup meta-analyses were based on clinical endpoint. The influence of sample size and radiation injury incidence on genetic effects was estimated in sensitivity analyses. Power calculations were also conducted.

RESULTS

The meta-analysis was conducted on the ATM polymorphism rs1801516, including 5 studies with 1588 participants. For all studies, the cut-off for differentiating cases from controls was grade 2 acute radiation injuries. The primary meta-analysis showed a significant association with overall acute radiation injuries (allelic model: odds ratio = 1.33, 95% confidence interval: 1.04-1.71). Subgroup analyses detected an association between the rs1801516 polymorphism and a significant increase in urinary and lower gastrointestinal injuries and an increase in skin injury that was not statistically significant. There was no between-study heterogeneity in any meta-analyses. In the sensitivity analyses, small studies did not show larger effects than large studies. In addition, studies with high incidence of acute radiation injuries showed larger effects than studies with low incidence. Power calculations revealed that the statistical power of the primary meta-analysis was borderline, whereas there was adequate power for the subgroup analysis of studies with high incidence of acute radiation injuries.

CONCLUSIONS

Our meta-analysis showed a consistency of the results from the overall and subgroup analyses. We also showed that the genetic effect of the rs1801516 polymorphism on acute radiation injuries was dependent on the incidence of the injury. These support the evidence of an association between the rs1801516 polymorphism and acute radiation injuries, encouraging further research of this topic.

The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism-Based Models: A Step Toward Prevention

Radiotherapy is a mainstay of cancer treatment, used in either a curative or palliative manner to treat approximately 50% of patients with cancer. Normal tissue toxicity limits the doses used in standard radiation therapy protocols and impedes improvements in radiotherapy efficacy. Damage to surrounding normal tissues can produce reactions ranging from bothersome symptoms that negatively affect quality of life to severe life-threatening complications. Improved ways of predicting, before treatment, the risk for development of normal tissue toxicity may allow for more personalized treatment and reduce the incidence and severity of late effects. There is increasing recognition that the cause of normal tissue toxicity is multifactorial and includes genetic factors in addition to radiation dose and volume of exposure, underlying comorbidities, age, concomitant chemotherapy or hormonal therapy, and use of other medications. An understanding of the specific genetic risk factors for normal tissue response to radiation has the potential to enhance our ability to predict adverse outcomes at the treatment-planning stage. Therefore, the field of radiogenomics has focused upon the identification of genetic variants associated with normal tissue toxicity resulting from radiotherapy. Innovative analytic methods are being applied to the discovery of risk variants and development of integrative predictive models that build on traditional normal tissue complication probability models by incorporating genetic information. Results from initial studies provide promising evidence that genetic-based risk models could play an important role in the implementation of precision medicine for radiation oncology through enhancing the ability to predict normal tissue reactions and thereby improve cancer treatment.

Relation between Ku80 and microRNA-99a expression and late rectal bleeding after radiotherapy for prostate cancer

BACKGROUND AND PURPOSE

Late rectal bleeding is one of the severe adverse events after radiotherapy for prostate cancer. New biomarkers are needed to allow a personalized treatment.

MATERIALS AND METHODS

Four patients each with grade 0-1 or grade 2-3 rectal bleeding were randomly selected for miRNA array to examine miRNA expression in peripheral blood lymphocytes (PBLs). Based on results of miRNA array, 1 of 348 miRNAs was selected for microRNA assays. Then, expression of DNA-dependent protein kinase mRNA and miR-99a was analyzed in the PBLs of 97 patients. PBLs were exposed to 4Gy of X-ray ex-vivo.

RESULTS

In the discovery cohort, grade 2-3 rectal bleeding was significantly higher in the Ku80 <1.09 expression group compared with ⩾1.09 group (P=0.011). In radiation-induced expression of miR-99a, grade 2-3 rectal bleeding was significantly higher in the miR-99a IR(+)/IR(-) >0.93 group compared with ⩽0.93 group (P=0.013). Most patients with grade 2-3 rectal bleeding were in the group with low Ku80 and high miR-99a expression. In the validation cohort, similar results were obtained.

CONCLUSION

A combination of low Ku80 expression and highly-induced miR-99a expression could be a promising marker for predicting rectal bleeding after radiotherapy.

The current progress and future prospects of personalized radiogenomic cancer study

During the last twenty years, mounting studies have supported the hypothesis that there is a genetic component that plays an important role in clinically observed variability in individual tissue/organ toxicity after radiotherapy. We propose the term “Personalized Radiogenomics” for the translational study of individual genetic variations that may associate with or contribute to the responses of tissues to radiation therapy used in the treatment of all types of cancer. The missions of personalized radiogenomic research are 1) to reveal the related genes, proteins, and biological pathways responsible for non-tumor or tumor tissue toxicity resulting from radiotherapy that could be targeted with radio-sensitizing and/or radio-protective agents, and 2) to identify specific genetic markers that can be used in risk prediction and evaluation models before and after clinical cancer surgery. For the members of the Terry Fox Cancer Research Lab in China Medical University and Hospital, the long-term goal is to develop SNP-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. Worldwide, the field has evolved over the last two decades in parallel with rapid advances in genetic and genomic technology, moving step by step from narrowly focused candidate gene studies to large-scale, collaborative genome-wide association studies. This article will summarize the candidate gene association studies published so far from the Terry Fox Cancer Research Lab as well as worldwide on the risk of radiation-related cancers and highlight some wholegenome association studies showing feasibility in fulfilling the dream of personalized radiogenomic cancer therapy.