Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy

BACKGROUND

Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis.

MATERIALS AND METHODS

280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression.

RESULTS

There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (-3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from "skin" contours showed higher agreement with observed events.

CONCLUSION

Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

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.

The Effect of Age on Health-Related Quality of Life in Patients Treated for Early Stage, Estrogen Receptor Positive Breast Cancer

PURPOSE/OBJECTIVE(S)

Prospective studies on Health-related quality of life (HRQoL) evaluating the effect of age on outcomes in breast cancer (BC) is not well studied. This study aims to examine the physical and mental health perceptions in BC patients enrolled in an international, multi-center REQUITE study.

MATERIALS/METHODS

This study includes 2,057 patients with ER+ early-stage BC treated with breast conservation surgery, radiation (RT), and endocrine therapy (ET) across Europe and North America between April 2014 and March 2017. The prospectively collected HRQoL dataset includes EORTC30 and Multidimensional Fatigue Inventory (MFI-20) at baseline, post-RT, 1, 2, and 3 years of follow up. Patients were stratified by age: 851 aged <70 (younger) and 201 aged ≥ 70 (older). The median age (range) for the younger and older cohort is 57 years (30-69 years) and 75 years (70-86 years), respectively. Analysis includes descriptive statistics and univariable logistic regression.

RESULTS

Older patients had a greater burden of comorbid conditions including increased BMI (27.3 vs 26.5; P = 0.006), history of diabetes (10.9% vs 6.6%; P = 0.0336), heart disease (15.4% vs 6.1%; P<0.0001), rheumatoid arthritis (6.5% vs 3.2%; P = 0.0280), hypertension (54.2% vs 25.0%; P<0.0001), and polypharmacy (37.8% vs 16.7%; P<0.0001) compared to younger patients. Higher utilization of aromatase inhibitor (67.7% vs 42.4%; P<0.0001) in older patients, and tamoxifen (63.7% vs 36.5%; P<0.0001) in younger patients. The T-stage distribution in younger and older patients is T1 = 82.4% vs 68.7%, T2 = 9.8% vs 26.4%, T3 = 0.1% vs 1%, respectively. The 3-year relapse-free survival was similar in both groups (P = 0.183). Significant worsening in fatigue (P<0.0001, P<0.0001), pain (P = 0.0274, P<0.0001), cognitive functioning (P = 0.0291, P<0.0001), and global health status (P = 0.0064, P<0.0001) was observed during follow up from baseline in both groups (older patients, younger patients). Compared to older patients where significant deterioration persisted, younger patients showed improvement in most HRQoL measures over the duration of follow up years. Older patients had poorer global health status (OR = 1.19 vs 0.8, P = 0.0214) than younger patients from baseline at 2 years. On the MFI-20 measure, both age groups showed worsening fatigue from baseline at post-RT, but eventual recovery noted in the 1-to-3-year follow up period.

CONCLUSION

Older patients, present with greater comorbidities, polypharmacy, and later stages in BC, reported worsening fatigue, pain, cognitive functioning, and global health status during 3 years follow up. One limitation of this study is that patients were predominately White, potentially limiting the generalizability of these observations. Further studies supplementing biomarkers and prognostic signatures with functional measures such as HRQoL may provide a useful tool to guide risk-tailored treatment in older patients with breast cancer.

Managing RT Schedules of Early-Stage Breast Cancer Patients with a Genetic-Dosimetric Validated Model for Late Fibrosis

PURPOSE/OBJECTIVE(S)

Define a multifactorial risk prediction model for RT-induced fibrosis and investigate the benefit of a personalized approach for breast cancer (BC) patients (pts) treated with whole breast RT.

MATERIALS/METHODS

In a previous study, we confirmed the predictive role of 30 SNPs from the literature and built an interaction aware Polygenic Risk Score (PRS, following the methods from Franco RO 2021) for Late Fibrosis (FG2+) on a cohort of 1500 pts from the REQUITE EU/USA prospective observational study. The PRS weights the radiosensitive (RS) and radioresistant (RR) genetic components and can be included in NTCP models. In a subgroup from the same cohort (390 pts), we have also confirmed an NTCP model based on biologically Equivalent Uniform Dose (BEUD) from PTV DVHs for pts treated at 40-50 Gy and no RT boost. Here, we combine PRS and BEUD into a sigmoid model allowing PRS to modulate BEUD50 (BEUD leading to 50% FG2+), i.e., we permitted a personalized BEUD50. We can also consider this as translating the PRS into a personalized equivalent BEUD, which is added/subtracted to the treatment BEUD. We evaluated model performances through ROC-AUC, calibration plot and Precision-Recall AUC.

RESULTS

A total of 381 pts had complete dosimetric/genetic data, prescribed dose 40-50 Gy, and no fibrotic alteration at RT start. We scored FG2+ in 87 pts (23%). PRS ranged from -13 (more RR pts) to 7 (more RS), and a unit in PRS corresponds to 5.3 Gy BEUD or 3 Gy in EQ EUD2 Gy. Table 1 summarizes model performances, with details for subgroups below/above the quartiles I/III of the BEUD distribution. The PRS-only model correctly describes the toxicity rates in the whole population (calibration slope/offset = 0/1). Still, it overestimates/underestimates the absolute risks in the low/high dose ranges. The integrated model improves AUC-ROC and AUC-PRC by 5% and 10% and guarantees a better calibration in pts receiving low/high BEUD to the PTV.

CONCLUSION

We developed a multifactorial model for FG2+ based on two previously validated models and reported the improvement against single-factor models. The BEUD+PRS model is suitable for assisting clinicians in managing early-stage BC pts. The number of fractions or the daily dose could be reduced for RS pts. The integrated model resulted in a possible quantitative tool for driving the planning decision process. Also, it showed a better performance in the high BEUD region, suggesting the potential value of its extension toward RT including boost or ultra hypofractionation. We are testing this extension in the whole REQUITE cohort.

Genome-wide association study of treatment-related toxicity two years following radiotherapy for breast cancer

BACKGROUND AND PURPOSE

Up to a quarter of breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity. If patients at high risk of adverse effects could be identified at diagnosis, their treatment could be tailored accordingly. This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity two years following whole breast radiotherapy.

MATERIALS AND METHODS

A genome-wide association study (GWAS) was performed in 1,640 breast cancer patients with complete SNP, clinical, treatment and toxicity data, recruited across 18 European and US centres into the prospective REQUITE cohort study. Toxicity data (CTCAE v4.0) were collected at baseline, end of radiotherapy, and annual follow-up. A total of 7,097,340 SNPs were tested for association with the residuals of toxicity endpoints, adjusted for clinical, treatment co-variates and population substructure.

RESULTS

Quantile-quantile plots showed more associations with toxicity above the p < 5 × 10-5 level than expected by chance. Eight SNPs reached genome-wide significance. Nipple retraction grade ≥ 2 was associated with the rs188287402 variant (p = 2.80 × 10-8), breast oedema grade ≥ 2 with rs12657177 (p = 1.12 × 10-10), rs75912034 (p = 1.12 × 10-10), rs145328458 (p = 1.06 × 10-9) and rs61966612 (p = 1.23 × 10-9), induration grade ≥ 2 with rs77311050 (p = 2.54 × 10-8) and rs34063419 (p = 1.21 × 10-8), and arm lymphoedema grade ≥ 1 with rs643644 (p = 3.54 × 10-8). Heritability estimates across significant endpoints ranged from 25% to 39%. Our study did not replicate previously reported SNPs associated with breast radiation toxicity at the pre-specified significance level.

CONCLUSIONS

This GWAS for long-term breast radiation toxicity provides further evidence for significant association of common SNPs with distinct toxicity endpoints.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Evaluating Approaches for Constructing Polygenic Risk Scores for Prostate Cancer in Men of African and European Ancestry

Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS 269 ). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS 269 . Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS 269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS 269 constructed with fine-mapping.

Comparing symptom reporting by prostate cancer patients and healthcare professionals in the international multicentre REQUITE study

INTRODUCTION

Previous studies showed that healthcare professionals and patients had only moderate to low agreement on their assessment of treatment-related symptoms. We aimed to determine the levels of agreement in a large cohort of prostate cancer patients.

METHODS

Analyses were made of data from 1,756 prostate cancer patients treated with external beam radiotherapy (RT) and/or brachytherapy in Europe and the USA and recruited into the prospective multicentre observational REQUITE study. Eleven pelvic symptoms at the end of RT were compared after translating patient-reported outcomes (PROs) into CTCAE-based healthcare professional ratings. Gwet's AC2 agreement coefficient and 95% confidence intervals were calculated for each symptom. To compare severity of grading between patients and healthcare professionals, percent agreement and deviations for each symptom were graphically depicted. Stratified and sensitivity analyses were conducted to identify potential influencing factors and to assess heterogeneity and robustness of results.

RESULTS

The agreement for the 11 pelvic symptoms varied from very good (AC2 > 0.8: haematuria, rectal bleeding, management of sphincter control) to poor agreement (AC2 ≤ 0.2: proctitis and urinary urgency). Fatigue had a negative impact on the agreement. Patients tended to grade symptoms more severely than healthcare professionals. Information on sexual dysfunction was missing more frequently in healthcare professional assessment than PROs.

CONCLUSION

Agreement was better for observable than subjective symptoms, with patients usually grading symptoms more severely than healthcare professionals. Our findings emphasize that PROs should complement symptom assessment by healthcare professionals and be taken into consideration for clinical decision-making to incorporate the patient perspective.

The correlation between pre-treatment symptoms, acute and late toxicity and patient-reported health-related quality of life in non-small cell lung cancer patients: Results of the REQUITE study

BACKGROUND AND PURPOSE

To investigate the association between clinician-scored toxicities and patient-reported health-related quality of life (HRQoL), in early-stage (ES-) and locally-advanced (LA-) non-small cell lung cancer (NSCLC) patients receiving loco-regional radiotherapy, included in the international real-world REQUITE study.

MATERIALS AND METHODS

Clinicians scored eleven radiotherapy-related toxicities (and baseline symptoms) with the Common Terminology Criteria for Adverse Events version 4. HRQoL was assessed with the European Organization for Research and Treatment of Cancer core HRQoL questionnaire (EORTC-QLQ-C30). Statistical analyses used the mixed-model method; statistical significance was set at p = 0.01. Analyses were performed for baseline and subsequent time points up to 2 years after radiotherapy and per treatment modality, radiotherapy technique and disease stage.

RESULTS

Data of 435 patients were analysed. Pre-treatment, overall symptoms, dyspnea, chest wall pain, dysphagia and cough impacted overall HRQoL and specific domains. At subsequent time points, cough and dysphagia were overtaken by pericarditis in affecting HRQoL. Toxicities during concurrent chemo-radiotherapy and 3-dimensional radiotherapy had the most impact on HRQoL. Conversely, toxicities in sequential chemo-radiotherapy and SBRT had limited impact on patients' HRQoL. Stage impacts the correlations: LA-NSCLC patients are more adversely affected by toxicity than ES-NSCLC patients, mimicking the results of radiotherapy technique and treatment modality.

CONCLUSION

Pre-treatment symptoms and acute/late toxicities variously impact HRQoL of ES- and LA-NSCLC patients undergoing different treatment approaches and radiotherapy techniques. Throughout the disease, dyspnea seems crucial in this association, highlighting the additional effect of co-existing comorbidities. Our data call for optimized radiotherapy limiting toxicities that may affect patients' HRQoL.

No Association Between Polygenic Risk Scores for Cancer and Development of Radiation Therapy Toxicity

PURPOSE

Our aim was to test whether updated polygenic risk scores (PRS) for susceptibility to cancer affect risk of radiation therapy toxicity.

METHODS AND MATERIALS

Analyses included 9,717 patients with breast (n=3,078), prostate (n=5,748) or lung (n=891) cancer from Radiogenomics and REQUITE Consortia cohorts. Patients underwent potentially curative radiation therapy and were assessed prospectively for toxicity. Germline genotyping involved genome-wide single nucleotide polymorphism (SNP) arrays with nontyped SNPs imputed. PRS for each cancer were generated by summing literature-identified cancer susceptibility risk alleles: 352 breast, 136 prostate, and 24 lung. Weighted PRS were generated using log odds ratio (ORs) for cancer susceptibility. Standardized total average toxicity (STAT) scores at 2 and 5 years (breast, prostate) or 6 to 12 months (lung) quantified toxicity. Primary analysis tested late STAT, secondary analyses investigated acute STAT, and individual endpoints and SNPs using multivariable regression.

RESULTS

Increasing PRS did not increase risk of late toxicity in patients with breast (OR, 1.000; 95% confidence interval [CI], 0.997-1.002), prostate (OR, 0.99; 95% CI, 0.98-1.00; weighted PRS OR, 0.93; 95% CI, 0.83-1.03), or lung (OR, 0.93; 95% CI, 0.87-1.00; weighted PRS OR, 0.68; 95% CI, 0.45-1.03) cancer. Similar results were seen for acute toxicity. Secondary analyses identified rs138944387 associated with breast pain (OR, 3.05; 95% CI, 1.86-5.01; P = 1.09 × 10-5) and rs17513613 with breast edema (OR, 0.94; 95% CI, 0.92-0.97; P = 1.08 × 10-5).

CONCLUSIONS

Patients with increased polygenic predisposition to breast, prostate, or lung cancer can safely undergo radiation therapy with no anticipated excess toxicity risk. Some individual SNPs increase the likelihood of a specific toxicity endpoint, warranting validation in independent cohorts and functional studies to elucidate biologic mechanisms.

Treatment time and circadian genotype interact to influence radiotherapy side-effects. A prospective European validation study using the REQUITE cohort

BACKGROUND

Circadian rhythm impacts broad biological processes, including response to cancer treatment. Evidence conflicts on whether treatment time affects risk of radiotherapy side-effects, likely because of differing time analyses and target tissues. We previously showed interactive effects of time and genotypes of circadian genes on late toxicity after breast radiotherapy and aimed to validate those results in a multi-centre cohort.

METHODS

Clinical and genotype data from 1690 REQUITE breast cancer patients were used with erythema (acute; n=340) and breast atrophy (two years post-radiotherapy; n=514) as primary endpoints. Local datetimes per fraction were converted into solar times as predictors. Genetic chronotype markers were included in logistic regressions to identify primary endpoint predictors.

FINDINGS

Significant predictors for erythema included BMI, radiation dose and PER3 genotype (OR 1.27(95%CI 1.03-1.56); P < 0.03). Effect of treatment time effect on acute toxicity was inconclusive, with no interaction between time and genotype. For late toxicity (breast atrophy), predictors included BMI, radiation dose, surgery type, treatment time and SNPs in CLOCK (OR 0.62 (95%CI 0.4-0.9); P < 0.01), PER3 (OR 0.65 (95%CI 0.44-0.97); P < 0.04) and RASD1 (OR 0.56 (95%CI 0.35-0.89); P < 0.02). There was a statistically significant interaction between time and genotypes of circadian rhythm genes (CLOCK OR 1.13 (95%CI 1.03-1.23), P < 0.01; PER3 OR 1.1 (95%CI 1.01-1.2), P < 0.04; RASD1 OR 1.15 (95%CI 1.04-1.28), P < 0.008), with peak time for toxicity determined by genotype.

INTERPRETATION

Late atrophy can be mitigated by selecting optimal treatment time according to circadian genotypes (e.g. treat PER3 rs2087947C/C genotypes in mornings; T/T in afternoons). We predict triple-homozygous patients (14%) reduce chance of atrophy from 70% to 33% by treating in mornings as opposed to mid-afternoon. Future clinical trials could stratify patients treated at optimal times compared to those scheduled normally.

FUNDING

EU-FP7.

Ocular complications with the use of radium-223: a case series

Background

Radium-223 is used for the treatment of osseous metastases in castrate-resistant prostate cancer, and has been shown to increase time to the first skeletal-related event, reduce the rate of hospitalization, and improve quality of life. It is well tolerated, with hematologic toxicity as the main adverse event. Thus far, no ocular complication has been reported in the literature after initial administration of radium-223 with a single case reported of ocular complications after a patient’s second course of radium-223.

Case presentations

We present three cases of ocular complications after the use of radium-223 in patients with metastatic prostatic adenocarcinoma. Ocular complications presented as blurry vision, and formal diagnosis included uveitis and hyphema.

Conclusions

Documentation of adverse events is exceedingly important due to the high incidence of metastatic prostate cancer and increasing interest for the use of radium-223 in other osteoblastic disease. The authors postulate that these ocular complications may be a result of radiation’s potential effect on neovascularization, polypharmacy, or the biomolecular effects of radium-223 on integral signaling proteins, potentially coupled with poor underlying ocular health.

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

BACKGROUND

Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.

METHODS

In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.

RESULTS

The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.

CONCLUSIONS

We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Use of angiotensin converting enzyme inhibitors is associated with reduced risk of late bladder toxicity following radiotherapy for prostate cancer

BACKGROUND AND PURPOSE

Genome-wide association studies (GWAS) of late hematuria following prostate cancer radiotherapy identified single nucleotide polymorphisms (SNPs) near AGT, encoding angiotensinogen. We tested the hypothesis that patients taking angiotensin converting enzyme inhibitors (ACEi) have a reduced risk of late hematuria. We additionally tested genetically-defined hypertension.

MATERIALS AND METHODS

Prostate cancer patients undergoing potentially-curative radiotherapy were enrolled onto two multi-center observational studies, URWCI (N = 256) and REQUITE (N = 1,437). Patients were assessed pre-radiotherapy and followed prospectively for development of toxicity for up to four years. The cumulative probability of hematuria was estimated by the Kaplan-Meier method. Multivariable grouped relative risk models assessed the effect of ACEi on time to hematuria adjusting for clinical factors and stratified by enrollment site. A polygenic risk score (PRS) for blood pressure was tested for association with hematuria in REQUITE and our Radiogenomics Consortium GWAS.

RESULTS

Patients taking ACEi during radiotherapy had a reduced risk of hematuria (HR 0.51, 95%CI 0.28 to 0.94, p = 0.030) after adjusting for prior transurethral prostate and/or bladder resection, heart disease, pelvic node radiotherapy, and bladder volume receiving 70 Gy, which are associated with hematuria. A blood pressure PRS was associated with hypertension (odds ratio per standard deviation 1.38, 95%CI 1.31 to 1.46, n = 5,288, p < 0.001) but not hematuria (HR per standard deviation 0.96, 95%CI 0.87 to 1.06, n = 5,126, p = 0.41).

CONCLUSIONS

Our study is the first to show a radioprotective effect of ACEi on bladder in an international, multi-site study of patients receiving pelvic radiotherapy. Mechanistic studies are needed to understand how targeting the angiotensin pathway protects the bladder.

Development and optimisation of a machine-learning prediction model for acute desquamation following breast radiotherapy in the multi-centre REQUITE cohort

Purpose

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.

Methods and Materials

Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation.

Results

One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort.

Conclusions

ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

AIM

To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi).

MATERIALS AND METHODS

Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC).

RESULTS

Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints.

CONCLUSIONS

Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

BACKGROUND

Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

MATERIALS AND METHOD

180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS

166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

CONCLUSIONS

Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

ATM Variants in Breast Cancer: Implications for Breast Radiation Therapy Treatment Recommendations

PURPOSE

Advances in germline genetic testing have led to a surge in identification of ataxia-telangiectasia mutated (ATM) variant carriers among breast cancer patients, raising numerous questions regarding use of breast radiation therapy (RT) in this population.

METHODS

A literature search using PubMed identified articles assessing association(s) between the germline ATM variant status and the risk of toxicity after breast RT. An expert panel of breast radiation oncologists, genetic counselors, and basic scientists convened to review the association between ATM variants and radiation-induced toxicity or secondary malignancy risk and to determine any impact on breast RT recommendations.

RESULTS

Carriers of pathogenic variants in ATM have a 2- to 4-fold increased risk for developing breast cancer. ATM variants do not consistently increase risks of toxicities after RT, except possibly among patients with the single nucleotide variant c5557G>A (rs1801516), in whom a small increased risk for the development of both acute and late radiation effects has been identified. In most breast cancer patients with ATM variants, the excess 5-year absolute risk of developing a secondary contralateral breast cancer (CBC) after radiation is extremely low. The exception is in women younger than 45 years old with deleterious rare ATM missense variants, who may be at higher risk for developing a radiation-induced CBC over time.

CONCLUSIONS

Adjuvant radiation is safe for most breast cancer patients who harbor ATM variants. The possible exceptions are patients with the variant c5557G>A (rs1801516) and patients younger than 45 years old with certain rare deleterious ATM variants, who may be at higher risk for developing CBC. These latter patients should be counseled regarding this potential risk, and every effort should be made to minimize the contralateral breast dose. However, the inconsistency of published data limits precise recommendations, magnifying the need for further prospective studies and the development of a centralized database cataloging RT outcomes and genetic status.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort

Background: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study.

Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057).

Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort.

Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.

The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer Risk and Carriers Share a Common Ancestor

The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case-control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1-3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families.

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.

Survey of Radiation Oncologists to Assess Interest and Potential Use of a Genetic Test Predicting Susceptibility for the Development of Toxicities After Prostate Cancer Radiation Therapy

PURPOSE

A genetic test predicting susceptibility for the development of toxicities after prostate cancer radiation therapy is in development. This test intends to help physicians with treatment decision making.

METHODS AND MATERIALS

Radiation oncologists were surveyed using a web-based questionnaire to gauge their interest in using a genetic test predictive of increased risk of radiation therapy toxicities as an aid in determining therapy for men with prostate cancer. Responses were summarized using frequencies, and a χ2 test compared responses among participants. Multivariable ordinal regression identified factors associated with anticipated adoption or nonadoption of such a genetic test by radiation oncologists.

RESULTS

Among 204 radiation oncologists (64% from the United States, 36% from other countries), 86.3% would order a genetic test and 80.2% said the test would be useful for treatment discussions. There was wide acceptance (76.7%) to offer a genetic test to all patients considering radiation therapy for prostate cancer. Additionally, 98.1% indicated that patients would be receptive to the test information. There were no significant differences in the likelihood of ordering a genetic test based on practice setting, familiarity with scientific literature, time spent on research, or geographic location (all P > .05).

CONCLUSIONS

Radiation oncologists who treat prostate cancer are interested in and willing to order a genetic test predictive of susceptibility to radiation therapy toxicity to aid their treatment decision making.

I-125 or Pd-103 for brachytherapy boost in men with high-risk prostate cancer: A comparison of survival and morbidity outcomes

PURPOSE

Brachytherapy boost improves biochemical recurrence rates in men with high-risk prostate cancer (HRPC). Few data are available on whether one isotope is superior to another. We compared the oncologic and morbidity outcomes of I-125 and Pd-103 in men with HRPC receiving brachytherapy.

METHODS AND MATERIALS

Of 797 patients with HRPC, 190 (23.8%) received I-125 or 607 received Pd-103 with a median of 45 Gy of external beam irradiation. Freedom from biochemical failure (FFBF), freedom from metastases (FFMs), cause-specific survival (CSS), and morbidity were compared for the two isotopes by the ANOVA and the χ² test with survival determined by the Kaplan-Meier method and Cox regression.

RESULTS

Men treated with I-125 had a higher stage (p < 0.001), biological equivalent dose (BED) (p < 0.001), and longer hormone therapy (neoadjuvant hormone therapy, p < 0.001), where men treated with Pd-103 had a higher Gleason score (GS, p < 0.001) and longer followup (median 8.3 vs. 5.3 years, p < 0.001). Ten-year FFBF, FFM, and CSS for I-125 vs. Pd-103 were 77.5 vs. 80.2% (p = 0.897), 94.7 vs. 91.9% (p = 0.017), and 95.4 vs. 91.8% (p = 0.346), respectively. Men with T3 had superior CSS (94.1 vs. 79.5%, p = 0.001) with I-125. Significant covariates by Cox regression for FFBF were prostate specific antigen (PSA), the GS, and the BED (p < 0.001), for FFM PSA (p < 0.001) and GS (p = 0.029), and for CSS PSA, the GS (p < 0.001) and the BED (p = 0.022). Prostate cancer mortality was 7/62 (15.6%) for BED ≤ 150 Gy, 18/229 (7.9%) for BED >150-200 Gy, and 20/470 (5.9%) for BED >200 Gy (p = 0.029). Long-term morbidity was not different for the two isotopes.

CONCLUSIONS

Brachytherapy boost with I-125 and Pd-103 appears equally effective yielding 10-year CSS of over 90%. I-125 may have an advantage in T3 disease. Higher doses yield the most favorable survival.

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.

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.

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.

Publisher Correction: Shared heritability and functional enrichment across six solid cancers

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Author Correction: Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10-8), breast and ovarian cancer (rg = 0.24, p = 7 × 10-5), breast and lung cancer (rg = 0.18, p =1.5 × 10-6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study

BACKGROUND

Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer-metabolite associations using two-sample Mendelian randomization (MR).

METHODS

The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.

RESULTS

Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.

CONCLUSIONS

We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.

IMPACT

The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

Radiogenomic Predictors of Adverse Effects following Charged Particle Therapy

Radiogenomics is the study of genomic factors that are associated with response to radiation therapy. In recent years, progress has been made toward identifying genetic risk factors linked with late radiation-induced adverse effects. These advances have been underpinned by the establishment of an international Radiogenomics Consortium with collaborative studies that expand cohort sizes to increase statistical power and efforts to improve methodologic approaches for radiogenomic research. Published studies have predominantly reported the results of research involving patients treated with photons using external beam radiation therapy. These studies demonstrate our ability to pool international cohorts to identify common single nucleotide polymorphisms associated with risk for developing normal tissue toxicities. Progress has also been achieved toward the discovery of genetic variants associated with radiation therapy-related subsequent malignancies. With the increasing use of charged particle therapy (CPT), there is a need to establish cohorts for patients treated with these advanced technology forms of radiation therapy and to create biorepositories with linked clinical data. While some genetic variants are likely to impact toxicity and second malignancy risks for both photons and charged particles, it is plausible that others may be specific to the radiation modality due to differences in their biological effects, including the complexity of DNA damage produced. In recognition that the formation of patient cohorts treated with CPT for radiogenomic studies is a high priority, efforts are underway to establish collaborations involving institutions treating cancer patients with protons and/or carbon ions as well as consortia, including the Proton Collaborative Group, the Particle Therapy Cooperative Group, and the Pediatric Proton Consortium Registry. These important radiogenomic CPT initiatives need to be expanded internationally to build on experience gained from the Radiogenomics Consortium and epidemiologists investigating normal tissue toxicities and second cancer risk.

Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10-8) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10-9; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10-9; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa1.

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

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.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.