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

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

KDM3B Single-Nucleotide Polymorphisms Impact Radiation Therapy Toxicity Through Circular RNA-Mediated KDM3B Expression and Inflammatory Responses

PURPOSE

Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) associated with radiation therapy (RT) toxicities in patients with prostate cancer. SNP rs17599026 in intron 21 of KDM3B is significantly associated with the development of late urinary toxicity, specifically in the increase in urinary frequency 2 years after RT compared with pretreatment conditions. The present study aimed to provide mechanistic insights for this association.

METHODS AND MATERIALS

Using human tissues and cell lines, we examined the protein expression of KDM3B and molecular mechanisms underlying the SNP modulation by variants of KDM3B SNP alleles. In animals with normal and heterozygous expressions of Kdm3b, we examined the relationship between Kdm3b expression and radiation toxicity.

RESULTS

KDM3B rs17599026 lies in a motif important for circular RNA expression that is responsible for sponging miRNAs to regulate KDM3B expression. Using a murine model with heterozygous deletion of the Kdm3b gene, we found that lower Kdm3b expression is associated with altered pattern of urination after bladder irradiation, which is related to differential degrees of tissue inflammation as measured by analyses of gene expression, lymphocyte infiltration, and noninvasive ultrasound imaging.

CONCLUSIONS

KDM3B SNPs can impact its expression through regulating noncoding RNA expression. Differential KDM3B expression underlies radiation toxicity through tissue inflammation at the molecular and physiological level. Our study outcome offers a foundation for mechanism-based mitigation for radiation toxicity for prostate cancer survivors.

Incorporating Genetic Risk Into Prostate Cancer Care: Implications for Early Detection and Precision Oncology

The availability and cost of germline and somatic genetic testing have dramatically improved over the past two decades, enabling precision medicine approaches in oncology, with significant implications for prostate cancer (PCa) care. Roughly 12% of individuals with advanced disease are carriers of rare pathogenic germline variants that predispose to particularly aggressive and earlier-onset disease. Several of these variants are already established as clinically actionable by modern precision oncology therapeutics, while others may come to aid the selection of active surveillance, definitive local therapies, and systemic therapies. Concurrently, the number of common variants (ie, incorporated into polygenic risk scores) associated with PCa risk has continued to grow, but with several important considerations both at the intersection of race and ancestry and for early detection of aggressive disease. Family history has historically been used as a proxy for this inherited genetic risk of PCa, but recently emerging evidence examining this relation has shifted our understanding of how best to leverage this tool in PCa care. This review seeks to clarify and contextualize the existing and emerging precision oncology paradigms that use inherited genetic risk in PCa care, for both early detection and localized disease management.

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.

Large-scale meta-genome-wide association study reveals common genetic factors linked to radiation-induced acute toxicities across cancer types

BACKGROUND

This study was designed to identify common genetic susceptibility and shared genetic variants associated with acute radiation-induced toxicity across 4 cancer types (prostate, head and neck, breast, and lung).

METHODS

A genome-wide association study meta-analysis was performed using 19 cohorts totaling 12 042 patients. Acute standardized total average toxicity (STATacute) was modelled using a generalized linear regression model for additive effect of genetic variants, adjusted for demographic and clinical covariates (rSTATacute). Linkage disequilibrium score regression estimated shared single-nucleotide variation (SNV-formerly SNP)-based heritability of rSTATacute in all patients and for each cancer type.

RESULTS

Shared SNV-based heritability of STATacute among all cancer types was estimated at 10% (SE = 0.02) and was higher for prostate (17%, SE = 0.07), head and neck (27%, SE = 0.09), and breast (16%, SE = 0.09) cancers. We identified 130 suggestive associated SNVs with rSTATacute (5.0 × 10‒8 < P < 1.0 × 10‒5) across 25 genomic regions. rs142667902 showed the strongest association (effect allele A; effect size ‒0.17; P = 1.7 × 10‒7), which is located near DPPA4, encoding a protein involved in pluripotency in stem cells, which are essential for repair of radiation-induced tissue injury. Gene-set enrichment analysis identified 'RNA splicing via endonucleolytic cleavage and ligation' (P = 5.1 × 10‒6, P = .079 corrected) as the top gene set associated with rSTATacute among all patients. In silico gene expression analysis showed that the genes associated with rSTATacute were statistically significantly up-regulated in skin (not sun exposed P = .004 corrected; sun exposed P = .026 corrected).

CONCLUSIONS

There is shared SNV-based heritability for acute radiation-induced toxicity across and within individual cancer sites. Future meta-genome-wide association studies among large radiation therapy patient cohorts are worthwhile to identify the common causal variants for acute radiotoxicity across cancer types.

Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility

BACKGROUND

Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility.

METHODS

We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis.

RESULTS

Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62).

CONCLUSIONS

We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect.

IMPACT

PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.

Normal Tissue Toxicity Prediction: Clinical Translation on the Horizon

Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.

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.

Effect of Race and Ethnicity on Risk of Radiotherapy Toxicity and Implications for Radiogenomics

AIMS

Patient factors affect the risk of radiotherapy toxicity, but many are poorly defined. Studies have shown that race affects cancer incidence, survival, drug response, molecular pathways and epigenetics. Effects on radiosensitivity and radiotherapy toxicity are not well studied. The aim of the present study was to identify the effects of race and ethnicity on the risk of radiotherapy toxicity.

MATERIALS AND METHODS

A systematic review was carried out of PubMed, Ovid Medline and Ovid Embase with no year limit. PRISMA 2020 guidelines were followed. Two independent assessors reviewed papers.

RESULTS

Of 607 papers screened, 46 fulfilled the inclusion criteria. Papers were published between 1996 and 2021 and involved 30-28,354 individuals (median 433). Most involved patients with prostate (33%), breast (26%) and lung (9%) cancer. Both early and late toxicities were studied. Some studies reported a higher risk of toxicity in White men with prostate cancer compared with other races and ethnicities. For breast cancer patients, some reported an increased risk of toxicity in White women compared with other race and ethnic groups. In general, it was difficult to draw conclusions due to insufficient reporting and analysis of race and ethnicity in published literature.

CONCLUSIONS

Reporting of race and ethnicity in radiotherapy studies must be harmonised and improved and frameworks are needed to improve the quality of reporting. Further research is needed to understand how ancestral heritage might affect radiosensitivity and risk of radiotherapy toxicity.

Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model

The use of a 10 gene transcriptional signature as part of the GARD model has been shown to be predictive of radiotherapy benefit for a range of cancers, with the potential to determine an optimal overall dose per patient. We used publicly available RNA-seq transcriptomics data from a luminal B breast cancer patient and from 14 prostate cancer patients to explore the radiosensitivity indices (RSI) and so GARD estimates of both tumour and proximal normal biopsies from each individual. Clear differences of clinical relevance in derived radiobiological properties between tumour and proximal normal tissues were evident for the breast cancer patient, whilst such differences across the prostate cancer cohort were more equivocal. Using the prostate cancer cohort's median tumour predicted GARD value as a threshold for high therapeutic effect for radiotherapy, we found evidence that a higher overall prescribed dose than the widely used 72 Gy/36fx could benefit half of these patients. This exploratory study demonstrates the potential combining the GARD model with sequencing based transcriptomics could have in informing personalised radiotherapeutic practise for both breast and prostate cancer patients.

Genetic predictors of radiation-induced morbidity in prostate cancer patients

Cancer survivors often face adverse effects of treatment, which have a significant impact on morbidity and mortality. Normal-tissue side effects following radiotherapy (RT), as one of therapeutic modalities, are common and may seriously affect quality of life which is especially important in long-term prostate cancer (PC) survivors. Upgrading in our knowledge in radiation biology have led to the better understanding that genetics plays a significant role in determining a patient's predisposition to developing late RT toxicity, leading to the new field of research called "radiogenomics". With the evolution of DNA sequencing technologies and genomic analysis, radiogenomics made an appearance as a state-of-the-art science in the field of personalized medicine with the goal of detection the genetic determinants RT adverse reactions. A single-nucleotide polymorphism (SNPs) - based assay could be used to predict the risk of RT side effects along with clinical features and treatment factors. Several SNPs have been identified that are associated with late radiation-induced morbidity in PC patients. Most importantly, these SNPs make up genes expressed in the tissues that are likely at the root of these symptoms, including the bladder, rectum, and small intestine, which are most exposed in PC RT. Furthermore, genome-wide association studies are likely to lead to an increasing number of genetic polymorphisms that can predict long-term RT complications. Finally, radiogenomics attempts to predict which PC patients will show radiosensitivity, so that radiation oncologists, as well as surgeons, can change treatment accordingly in order to reduce side effects or increase the RT effectiveness.

Biological Pathways Associated With the Development of Pulmonary Toxicities in Mesothelioma Patients Treated With Radical Hemithoracic Radiation Therapy: A Preliminary Study

Radical hemithoracic radiotherapy (RHR), after lung-sparing surgery, has recently become a concrete therapeutic option for malignant pleural mesothelioma (MPM), an asbestos-related, highly aggressive tumor with increasing incidence and poor prognosis. Although the toxicity associated to this treatment has been reduced, it is still not negligible and must be considered when treating patients. Genetic factors appear to play a role determining radiotherapy toxicity. The aim of this study is the identification of biological pathways, retrieved through whole exome sequencing (WES), possibly associated to the development of lung adverse effects in MPM patients treated with RHR. The study included individuals with MPM, treated with lung-sparing surgery and chemotherapy, followed by RHR with curative intent, and followed up prospectively for development of pulmonary toxicity. Due to the strong impact of grade 3 pulmonary toxicities on the quality of life, compared with less serious adverse events, for genetic analyses, patients were divided into a none or tolerable pulmonary toxicity (NoSTox) group (grade ≤2) and a severe pulmonary toxicity (STox) group (grade = 3). Variant enrichment analysis allowed us to identify different pathway signatures characterizing NoSTox and Stox patients, allowing to formulate hypotheses on the protection from side effects derived from radiotherapy as well as factors predisposing to a worst response to the treatment. Our findings, being aware of the small number of patients analyzed, could be considered a starting point for the definition of a panel of pathways, possibly helpful in the management of MPM patients.

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Radiation-induced lung injury (RILI) is one of the main dose-limiting side effects in patients with thoracic cancer during radiotherapy. No reliable predictors or accurate risk models are currently available in clinical practice. Severe radiation pneumonitis (RP) or pulmonary fibrosis (PF) will reduce the quality of life, even when the anti-tumor treatment is effective for patients. Thus, precise prediction and early diagnosis of lung toxicity are critical to overcome this longstanding problem. This review summarizes the primary mechanisms and preclinical animal models of RILI reported in recent decades, and analyzes the most promising biomarkers for the early detection of lung complications. In general, ideal integrated models considering individual genetic susceptibility, clinical background parameters, and biological variations are encouraged to be built up, and more prospective investigations are still required to disclose the molecular mechanisms of RILI as well as to discover valuable intervention strategies.

Physical and Biological Characteristics of Particle Therapy for Oncologists

Particle therapy is a promising and evolving modality of radiotherapy that can be used to treat tumors that are radioresistant to conventional photon beam radiotherapy. It has unique biological and physical advantages compared with conventional radiotherapy. The characteristic feature of particle therapy is the "Bragg peak," a steep and localized peak of dose, that enables precise delivery of the radiation dose to the tumor while effectively sparing normal organs. Especially, the charged particles (e.g., proton, helium, carbon) cause a high rate of energy loss along the track, thereby leading to high biological effectiveness, which makes particle therapy attractive. Using this property, the particle beam induces more severe DNA double-strand breaks than the photon beam, which is less influenced by the oxygen level. This review describes the general biological and physical aspects of particle therapy for oncologists, including non-radiation oncologists and beginners in the field.

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.

Telomere Length Dynamics and Chromosomal Instability for Predicting Individual Radiosensitivity and Risk via Machine Learning

The ability to predict a cancer patient's response to radiotherapy and risk of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can result in dysfunctional telomere pathologies that coincidentally overlap with many radiation-induced late effects, ranging from degenerative conditions like fibrosis and cardiovascular disease to proliferative pathologies like cancer. Here, telomere length was longitudinally assessed in a cohort of fifteen prostate cancer patients undergoing Intensity Modulated Radiation Therapy (IMRT) utilizing Telomere Fluorescence in situ Hybridization (Telo-FISH). To evaluate genome instability and enhance predictions for individual patient risk of secondary malignancy, chromosome aberrations were assessed utilizing directional Genomic Hybridization (dGH) for high-resolution inversion detection. We present the first implementation of individual telomere length data in a machine learning model, XGBoost, trained on pre-radiotherapy (baseline) and in vitro exposed (4 Gy γ-rays) telomere length measurements, to predict post radiotherapy telomeric outcomes, which together with chromosomal instability provide insight into individual radiosensitivity and risk for radiation-induced late effects.

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.

Radiation-induced damage in the lower gastrointestinal tract: Clinical presentation, diagnostic tests and treatment options

Radiation therapy is an important ally when treating malignant lesions in the pelvic area, but it is not exempt of adverse events. There are some measures that can be taken to reduce the possibility of these effects, but some are non-modifiable factors related to previous treatments, location of the lesions or comorbidities. There is a wide variety of clinical presentations that can be of an acute or chronic onset that go from mild to severe forms or that can have a great impact in the quality of life. Medical available therapies as metronidazole, sucralfate, mesalizine or probiotics, can be of aid although some lack of solid evidence of efficacy. Endoscopic treatment can be performed with argon plasma coagulation, bipolar cautery, radiofrequency, laser therapy or dilation. Hyperbaric therapy can be applied in refractory cases and surgery must be reserved to selected patients due to its high morbidity and mortality.

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.

Multi-ethnic transcriptome-wide association study of prostate cancer

The genetic risk for prostate cancer has been governed by a few rare variants with high penetrance and over 150 commonly occurring variants with lower impact on risk; however, most of these variants have been identified in studies containing exclusively European individuals. People of non-European ancestries make up less than 15% of prostate cancer GWAS subjects. Across the globe, incidence of prostate cancer varies with population due to environmental and genetic factors. The discrepancy between disease incidence and representation in genetics highlights the need for more studies of the genetic risk for prostate cancer across diverse populations. To better understand the genetic risk for prostate cancer across diverse populations, we performed PrediXcan and GWAS in a case-control study of 4,769 self-identified African American (2,463 cases and 2,306 controls), 2,199 Japanese American (1,106 cases and 1,093 controls), and 2,147 Latin American (1,081 cases and 1,066 controls) individuals from the Multiethnic Genome-wide Scan of Prostate Cancer. We used prediction models from 46 tissues in GTEx version 8 and five models from monocyte transcriptomes in the Multi-Ethnic Study of Atherosclerosis. Across the three populations, we predicted 19 gene-tissue pairs, including five unique genes, to be significantly (lfsr < 0.05) associated with prostate cancer. One of these genes, NKX3-1, replicated in a larger European study. At the SNP level, 110 SNPs met genome-wide significance in the African American study while 123 SNPs met significance in the Japanese American study. Fine mapping revealed three significant independent loci in the African American study and two significant independent loci in the Japanese American study. These identified loci confirm findings from previous GWAS of prostate cancer in diverse populations while PrediXcan-identified genes suggest potential new directions for prostate cancer research in populations across the globe.

Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network

Background/Aim: Accurate prediction of radiotherapy results is indispensable for the individualized selection of treatment modalities of cancer. We examined the application of the artificial neural network (ANN) model in predicting radiotherapy results using clinical factors and immunohistochemical staining of Ku70 as inputs. Patients and Methods: We analyzed 79 prostate cancer patients with localized adenocarcinoma treated with radiotherapy between August 2001 and October 2010. We also analyzed 46 hypopharyngeal cancer patients with squamous cell carcinoma treated with radiotherapy between March 2002 and December 2009. The properly trained ANN analysis using a standard feedforward, back-propagation neural network was used to predict the radiotherapy treatment results. Results: The areas under the receiver-operating characteristic curve (AUC) were 0.939 for patients treated with intensity modulated radiotherapy (IMRT)+androgen deprivation therapy (ADT), 0.803 for IMRT alone, and 0.960 for 3D-conformal radiotherapy (CRT) alone in prostate cancer. Sensitivity and specificity were 85.7% and 90.4% for IMRT+ADT, 75.0% and 88.5% for IMRT alone, and 92.3% and 100% for 3D-CRT alone. The AUC was 0.901 for hypopharyngeal cancer. Sensitivity and specificity were 66.7% and 88.2%, respectively. Conclusion: We demonstrated a possibility to predict the radiotherapy treatment results in prostate and hypopharyngeal cancer using ANN in combination with Ku70 expression and clinical factors as inputs.

Genomics models in radiotherapy: From mechanistic to machine learning

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

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.

Molecular Profiling for Predictors of Radiosensitivity in Patients with Breast or Head-and-Neck Cancer

Nearly half of all cancers are treated with radiotherapy alone or in combination with other treatments, where damage to normal tissues is a limiting factor for the treatment. Radiotherapy-induced adverse health effects, mostly of importance for cancer patients with long-term survival, may appear during or long time after finishing radiotherapy and depend on the patient’s radiosensitivity. Currently, there is no assay available that can reliably predict the individual’s response to radiotherapy. We profiled two study sets from breast (n = 29) and head-and-neck cancer patients (n = 74) that included radiosensitive patients and matched radioresistant controls.. We studied 55 single nucleotide polymorphisms (SNPs) in 33 genes by DNA genotyping and 130 circulating proteins by affinity-based plasma proteomics. In both study sets, we discovered several plasma proteins with the predictive power to find radiosensitive patients (adjusted p < 0.05) and validated the two most predictive proteins (THPO and STIM1) by sandwich immunoassays. By integrating genotypic and proteomic data into an analysis model, it was found that the proteins CHIT1, PDGFB, PNKD, RP2, SERPINC1, SLC4A, STIM1, and THPO, as well as the VEGFA gene variant rs69947, predicted radiosensitivity of our breast cancer (AUC = 0.76) and head-and-neck cancer (AUC = 0.89) patients. In conclusion, circulating proteins and a SNP variant of VEGFA suggest that processes such as vascular growth capacity, immune response, DNA repair and oxidative stress/hypoxia may be involved in an individual’s risk of experiencing radiation-induced toxicity.

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.

Disparities in prostate cancer incidence and mortality rates: Solvable or not?

Prostate cancer (PCa) is recognized as a disease possessing not only great variation in its geographic and racial distribution but also tremendous variation in its potential to cause morbidity and death and it, therefore, ought not to be considered a homogenous disease entity. Morbidity and death from PCa are disproportionately higher in men of African ancestry (MAA) who are generally observed to have more aggressive disease and worse outcomes following treatment compared to men of European ancestry (MEA). The higher rates of PCa among MAA relative to MEA appear to be multifactorial and related to inherent differences in biological aggressiveness; a continued lack of awareness of the disease and methods of prevention; a lower prevalence of screen-detected PCa; comparatively lower access to quality healthcare as well as systemic and institutionalized disparities in the administration of optimal care to MAA in developed countries such as the United States of America where high-quality care is available. Even when access to quality healthcare is assured in equal access settings, it appears that MAA still have worse outcomes after PCa treatment stage-for-stage and grade-for-grade compared to MEA, suggesting that, inherent racial, ethnic and biological differences are paramount in predicting poor outcomes. This review has explored the different contributing factors to the current disparities in PCa incidence and mortality rates with emphasis on the incongruence in how research has been conducted in understanding the disease towards developing therapies.

A bird's-eye view of Italian genomic variation through whole-genome sequencing

The genomic variation of the Italian peninsula populations is currently under characterised: the only Italian whole-genome reference is represented by the Tuscans from the 1000 Genome Project. To address this issue, we sequenced a total of 947 Italian samples from three different geographical areas. First, we defined a new Italian Genome Reference Panel (IGRP1.0) for imputation, which improved imputation accuracy, especially for rare variants, and we tested it by GWAS analysis on red blood traits. Furthermore, we extended the catalogue of genetic variation investigating the level of population structure, the pattern of natural selection, the distribution of deleterious variants and occurrence of human knockouts (HKOs). Overall the results demonstrate a high level of genomic differentiation between cohorts, different signatures of natural selection and a distinctive distribution of deleterious variants and HKOs, confirming the necessity of distinct genome references for the Italian population.

The Implications of Genetic Testing on Radiation Therapy Decisions: A Guide for Radiation Oncologists

The advent of affordable and rapid next-generation DNA sequencing technology, along with the US Supreme Court ruling invalidating gene patents, has led to a deluge of germline and tumor genetic variant tests that are being rapidly incorporated into clinical cancer decision-making. A major concern for clinicians is whether the presence of germline mutations may increase the risk of radiation toxicity or secondary malignancies. Because scarce clinical data exist to inform decisions at this time, the American Society for Radiation Oncology convened a group of radiation science experts and clinicians to summarize potential issues, review relevant data, and provide guidance for adult patients and their care teams regarding the impact, if any, that genetic testing should have on radiation therapy recommendations. During the American Society for Radiation Oncology workshop, several main points emerged, which are discussed in this manuscript: (1) variants of uncertain significance should be considered nondeleterious until functional genomic data emerge to demonstrate otherwise; (2) possession of germline alterations in a single copy of a gene critical for radiation damage responses does not necessarily equate to increased risk of radiation-induced toxicity; (3) deleterious ataxia-telangiesctasia gene mutations may modestly increase second cancer risk after radiation therapy, and thus follow-up for these patients after indicated radiation therapy should include second cancer screening; (4) conveying to patients the difference between relative and absolute risk is critical to decision-making; and (5) more work is needed to assess the impact of tumor somatic alterations on the probability of response to radiation therapy and the potential for individualization of radiation doses. Data on radiosensitivity related to specific genetic mutations is also briefly discussed.

Clinical and epidemiological observations on individual radiation sensitivity and susceptibility

Purpose: To summarize existing knowledge and to understand individual response to radiation exposure, the MELODI Association together with CONCERT European Joint Programme has organized a workshop in March 2018 on radiation sensitivity and susceptibility.Methods: The workshop reviewed the current evidence on this matter, to inform the MELODI Strategic Research Agenda (SRA), to determine social and scientific needs and to come up with recommendations for suitable and feasible future research initiatives to be taken for the benefit of an improved medical diagnosis and treatment as well as for radiation protection.Results: The present paper gives an overview of the current evidence in this field, including potential effect modifiers such as age, gender, genetic profile, and health status of the exposed population, based on clinical and epidemiological observations.Conclusion: The authors conclude with the following recommendations for the way forward in radiation research: (a) there is need for large (prospective) cohort studies; (b) build upon existing radiation research cohorts; (c) use data from well-defined cohorts with good exposure assessment and biological material already collected; (d) focus on study quality with standardized data collection and reporting; (e) improve statistical analysis; (f) cooperation between radiobiology and epidemiology; and (g) take consequences of radiosensitivity and radiosusceptibility into account.

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

PURPOSE

REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data.

METHODS

An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated.

RESULTS

The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician- (47,025 forms) and patient- (54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade ≥2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung).

CONCLUSION

The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers.

PATIENT SUMMARY

Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short- and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity.

Potential screening assays for individual radiation sensitivity and susceptibility and their current validation state

Purpose: The workshop on 'Individual Radiosensitivity and Radiosusceptibility' organized by MELODI and CONCERT on Malta in 2018, evaluated the current state of assays to identify sensitive and susceptible subgroups. The authors provide an overview on potential screening assays detecting individuals showing moderate to severe early and late radiation reactions or are at increased risk to develop cancer upon radiation exposure.Conclusion: It is necessary to separate clearly between tissue reactions and stochastic effects such as cancer when comparing the existing literature to validate various test systems. Requirements for the assays are set up. The literature is reviewed for assays that are reliable and robust. Sensitivity and specificity of the assays are regarded and scrutinized for modifying factors. Accuracy of an assay system is required to be more than 90% to balance risks of adverse reactions against risk to fail to cure the cancer. No assay/biomarker is in routine use. Assays that have shown predictive potential for radiosensitivity include SNPs, the RILA assay, and the pATM assay. A tree of risk guideline for radiologists is provided to assist medical treatment decisions. Recommendations for effective research include the setup of common retrospective and prospective cohorts/biobanks to validate current and future tests.

Monitoring Radiotherapeutic Response in Prostate Cancer Patients Using High Throughput FTIR Spectroscopy of Liquid Biopsies

Radiation therapy (RT) is used to treat approximately 50% of all cancer patients. However, RT causes a wide range of adverse late effects that can affect a patient's quality of life. There are currently no predictive assays in clinical use to identify patients at risk of normal tissue radiation toxicity. This study aimed to investigate the potential of Fourier transform infrared (FTIR) spectroscopy for monitoring radiotherapeutic response. Blood plasma was acquired from 53 prostate cancer patients at five different time points: prior to treatment, after hormone treatment, at the end of radiotherapy, two months post radiotherapy and eight months post radiotherapy. FTIR spectra were recorded from plasma samples at all time points and the data was analysed using MATLAB software. Discrimination was observed between spectra recorded at baseline versus follow up time points, as well as between spectra from patients showing minimal and severe acute and late toxicity using principal component analysis. A partial least squares discriminant analysis model achieved sensitivity and specificity rates ranging from 80% to 99%. This technology may have potential to monitor radiotherapeutic response in prostate cancer patients using non-invasive blood plasma samples and could lead to individualised patient radiotherapy.

Radiogenomics in the Era of Advanced Radiotherapy

Most radiogenomics studies investigate how genetic variation can help to explain the differences in early and late radiotherapy toxicity between individuals. The field of radiogenomics in photon beam therapy has grown rapidly in recent years, carving out a unique translational discipline, which has progressed from candidate gene studies to larger scale genome-wide association studies, meta-analyses and now prospective validation studies. Genotyping is increasingly sophisticated and affordable, and whole-genome sequencing may soon become readily available as a diagnostic tool in the clinic. The ultimate aim of radiogenomics research is to tailor treatment to the individual with a test based on a combination of treatment, clinical and genetic factors. This personalisation would allow the greatest tumour control while minimising acute and long-term toxicity. Here we discuss the evolution of the field of radiogenomics with reference to the most recent developments and challenges.

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.

microRNA expression pattern as an ancillary prognostic signature for radiotherapy

Background

In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy.

Methods

Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data.

Results

We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan–Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405.

Conclusions

The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1711-4) contains supplementary material, which is available to authorized users.

Dicentric Dose Estimates for Patients Undergoing Radiotherapy in the RTGene Study to Assess Blood Dosimetric Models and the New Bayesian Method for Gradient Exposure

The RTGene study was focused on the development and validation of new transcriptional biomarkers for prediction of individual radiotherapy patient responses to ionizing radiation. In parallel, for validation purposes, this study incorporated conventional biomarkers of radiation exposure, including the dicentric assay. Peripheral blood samples were taken with ethical approval and informed consent from a total of 20 patients undergoing external beam radiotherapy for breast, lung, gastrointestinal or genitourinary tumors. For the dicentric assay, two samples were taken from each patient: prior to radiotherapy and before the final fraction. Blood samples were set up using standard methods for the dicentric assay. All the baseline samples had dicentric frequencies consistent with the expected background for the normal population. For blood taken before the final fraction, all the samples displayed distributions of aberrations, which are indicative of partial-body exposures. Whole-body and partial-body cytogenetic doses were calculated with reference to a 250-kVp X-ray calibration curve and then compared to the dose to blood derived using two newly developed blood dosimetric models. Initial comparisons indicated that the relationship between these measures of dose appear very promising, with a correlation of 0.88 (P = 0.001). A new Bayesian zero-inflated Poisson finite mixture method was applied to the dicentric data, and partial-body dose estimates showed no significant difference (P > 0.999) from those calculated by the contaminated Poisson technique. The next step will be further development and validation in a larger patient group.

Role of Germline Genetics in Identifying Survivors at Risk for Adverse Effects of Cancer Treatment

The growing population of cancer survivors often faces adverse effects of treatment, which have a substantial impact on morbidity and mortality. Although certain adverse effects are thought to have a significant heritable component, much work remains to be done to understand the role of germline genetic factors in the development of treatment-related toxicities. In this article, we review current understanding of genetic susceptibility to a range of adverse outcomes among cancer survivors (e.g., fibrosis, urinary and rectal toxicities, ototoxicity, chemotherapy-induced peripheral neuropathy, subsequent malignancies). Most previous research has been narrowly focused, investigating variation in candidate genes and pathways such as drug metabolism, DNA damage and repair, and inflammation. Few of the findings from these earlier candidate gene studies have been replicated in independent populations. Advances in understanding of the genome, improvements in technology, and reduction in laboratory costs have led to recent genome-wide studies, which agnostically interrogate common and/or rare variants across the entire genome. Larger cohorts of patients with homogeneous treatment exposures and systematic ascertainment of well-defined outcomes as well as replication in independent study populations are essential aspects of the study design and are increasingly leading to the discovery of variants associated with each of the adverse outcomes considered in this review. In the long-term, validated germline genetic associations hold tremendous promise for more precisely identifying patients at highest risk for developing adverse treatment effects, with implications for frontline therapy decision-making, personalization of long-term follow-up guidelines, and potential identification of targets for prevention or treatment of the toxicity.

Radiation biology and oncology in the genomic era

Radiobiology research is building the foundation for applying genomics in precision radiation oncology. Advances in high-throughput approaches will underpin increased understanding of radiosensitivity and the development of future predictive assays for clinical application. There is an established contribution of genetics as a risk factor for radiotherapy side effects. An individual's radiosensitivity is an inherited polygenic trait with an architecture that includes rare mutations in a few genes that confer large effects and common variants in many genes with small effects. Current thinking is that some will be tissue specific, and future tests will be tailored to the normal tissues at risk. The relationship between normal and tumor cell radiosensitivity is poorly understood. Data are emerging suggesting interplay between germline genetic variation and epigenetic modification with growing evidence that changes in DNA methylation regulate the radiosensitivity of cancer cells and histone acetyltransferase inhibitors have radiosensitizing effects. Changes in histone methylation can also impair DNA damage response signaling and alter radiosensitivity. An important effort to advance radiobiology in the genomic era was establishment of the Radiogenomics Consortium to enable the creation of the large radiotherapy cohorts required to exploit advances in genomics. To address challenges in harmonizing data from multiple cohorts, the consortium established the REQUITE project to collect standardized data and genotyping for ~5,000 patients. The collection of detailed dosimetric data is important to produce validated multivariable models. Continued efforts will identify new genes that impact on radiosensitivity to generate new knowledge on toxicity pathogenesis and tests to incorporate into the clinical decision-making process.

Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer

INTRODUCTION

Previous studies revealed that dose escalated radiotherapy for prostate cancer patients leads to higher tumor control probabilities (TCP) but also to higher rectal toxicities. An isotoxic model was developed to maximize the given dose while controlling the toxicity level. This was applied to analyze the effect of an implantable rectum spacer (IRS) and extended with a genetic test of normal tissue radio-sensitivity. A virtual IRS (V-IRS) was tested using this method. We hypothesized that the patients with increased risk of toxicity would benefit more from an IRS.

MATERIAL AND METHODS

Sixteen localized prostate cancer patients implanted with an IRS were included in the study. Treatment planning was performed on computed tomography (CT) images before and after the placement of the IRS and with a V-IRS. The normal tissue complication probability (NTCP) was calculated using a QUANTEC reviewed model for Grade > =2 late rectal bleeding and the number of fractions of the plans were adjusted until the NTCP value was under 5%. The resulting treatment plans were used to calculate the TCP before and after placement of an IRS. This was extended by adding the effect of two published genetic single nucleotide polymorphisms (SNP's) for late rectal bleeding.

RESULTS

The median TCP resulting from the optimized plans in patients before the IRS was 75.1% [32.6-90.5%]. With IRS, the median TCP is significantly higher: 98.9% [80.8-99.9%] (p < .01). The difference in TCP between the V-IRS and the real IRS was 1.8% [0.0-18.0%]. Placing an IRS in the patients with SNP's improved the TCP from 49.0% [16.1-80.8%] and 48.9% [16.0-72.8%] to 96.3% [67.0-99.5%] and 90.1% [49.0-99.5%] (p < .01) respectively for either SNP.

CONCLUSION

This study was a proof-of-concept for an isotoxic model with genetic biomarkers with a V-IRS as a multifactorial decision support system for the decision of a placement of an IRS.