Optimising inter-patient image registration for image-based data mining in breast radiotherapy.

Background and purpose: Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM. Materials and methods: Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: i) registering prone/supine cohorts independently and ii) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU). Results: DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found. Conclusions: B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.

The PARP1 selective inhibitor saruparib (AZD5305) elicits potent and durable antitumor activity in patient-derived BRCA1/2-associated cancer models.

BACKGROUND: Poly (ADP-ribose) polymerase 1 and 2 (PARP1/2) inhibitors (PARPi) are targeted therapies approved for homologous recombination repair (HRR)-deficient breast, ovarian, pancreatic, and prostate cancers. Since inhibition of PARP1 is sufficient to cause synthetic lethality in tumors with homologous recombination deficiency (HRD), PARP1 selective inhibitors such as saruparib (AZD5305) are being developed. It is expected that selective PARP1 inhibition leads to a safer profile that facilitates its combination with other DNA damage repair inhibitors. Here, we aimed to characterize the antitumor activity of AZD5305 in patient-derived preclinical models compared to the first-generation PARP1/2 inhibitor olaparib and to identify mechanisms of resistance. METHODS: Thirteen previously characterized patient-derived tumor xenograft (PDX) models from breast, ovarian, and pancreatic cancer patients harboring germline pathogenic alterations in BRCA1, BRCA2, or PALB2 were used to evaluate the efficacy of AZD5305 alone or in combination with carboplatin or an ataxia telangiectasia and Rad3 related (ATR) inhibitor (ceralasertib) and compared it to the first-generation PARPi olaparib. We performed DNA and RNA sequencing as well as protein-based assays to identify mechanisms of acquired resistance to either PARPi. RESULTS: AZD5305 showed superior antitumor activity than the first-generation PARPi in terms of preclinical complete response rate (75% vs. 37%). The median preclinical progression-free survival was significantly longer in the AZD5305-treated group compared to the olaparib-treated group (> 386 days vs. 90 days). Mechanistically, AZD5305 induced more replication stress and genomic instability than the PARP1/2 inhibitor olaparib in PARPi-sensitive tumors. All tumors at progression with either PARPi (39/39) showed increase of HRR functionality by RAD51 foci formation. The most prevalent resistance mechanisms identified were the acquisition of reversion mutations in BRCA1/BRCA2 and the accumulation of hypomorphic BRCA1. AZD5305 did not sensitize PDXs with acquired resistance to olaparib but elicited profound and durable responses when combined with carboplatin or ceralasertib in 3/6 and 5/5 models, respectively. CONCLUSIONS: Collectively, these results show that the novel PARP1 selective inhibitor AZD5305 yields a potent antitumor response in PDX models with HRD and delays PARPi resistance alone or in combination with carboplatin or ceralasertib, which supports its use in the clinic as a new therapeutic option.

Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer.

Importance: RAD51C and RAD51D are involved in DNA repair by homologous recombination. Germline pathogenic variants (PVs) in these genes are associated with an increased risk of ovarian and breast cancer. Understanding the homologous recombination deficiency (HRD) status of tumors from patients with germline PVs in RAD51C/D could guide therapeutic decision-making and improve survival. Objective: To characterize the clinical and tumor characteristics of germline RAD51C/D PV carriers, including the evaluation of HRD status. Design, Setting, and Participants: This retrospective cohort study included 91 index patients plus 90 relatives carrying germline RAD51C/D PV (n = 181) in Spanish hospitals from January 1, 2014, to December 31, 2021. Genomic and functional HRD biomarkers were assessed in untreated breast and ovarian tumor samples (n = 45) from June 2022 to February 2023. Main Outcomes and Measures: Clinical and pathologic characteristics were assessed using descriptive statistics. Genomic HRD by genomic instability scores, functional HRD by RAD51, and gene-specific loss of heterozygosity were analyzed. Associations between HRD status and tumor subtype, age at diagnosis, and gene-specific loss of heterozygosity in RAD51C/D were investigated using logistic regression or the t test. Results: A total of 9507 index patients were reviewed, and 91 patients (1.0%) were found to carry a PV in RAD51C/D; 90 family members with a germline PV in RAD51C/D were also included. A total of 157 of carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, mainly breast cancer or ovarian cancer. The most prevalent PVs were c.1026+5_1026+7del (11 of 56 [19.6%]) and c.709C>T (9 of 56 [16.1%]) in RAD51C and c.694C>T (20 of 35 [57.1%]) in RAD51D. In untreated breast cancer and ovarian cancer, the prevalence of functional and genomic HRD was 55.2% (16 of 29) and 61.1% (11 of 18) for RAD51C, respectively, and 66.7% (6 of 9) and 90.0% (9 of 10) for RAD51D. The concordance between HRD biomarkers was 91%. Tumors with the same PV displayed contrasting HRD status, and age at diagnosis did not correlate with the occurrence of HRD. All breast cancers retaining the wild-type allele were estrogen receptor positive and lacked HRD. Conclusions and Relevance: In this cohort study of germline RAD51C/D breast cancer and ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD were explained by retention of the wild-type allele, which was more frequent among estrogen receptor-positive breast cancers. Understanding which tumors are associated with RAD51C/D and HRD is key to identify patients who can benefit from targeted therapies, such as PARP (poly [adenosine diphosphate-ribose] polymerase) inhibitors.

A New Set of in Silico Tools to Support the Interpretation of ATM Missense Variants Using Graphical Analysis.

Establishing the pathogenic nature of variants in ATM, a gene associated with breast cancer and other hereditary cancers, is crucial for providing patients with adequate care. Unfortunately, achieving good variant classification is still difficult. To address this challenge, we extended the range of in silico tools with a series of graphical tools devised for the analysis of computational evidence by health care professionals. We propose a family of fast and easy-to-use graphical representations in which the impact of a variant is considered relative to other pathogenic and benign variants. To illustrate their value, the representations are applied to three problems in variant interpretation. The assessment of computational pathogenicity predictions showed that the graphics provide an intuitive view of prediction reliability, complementing and extending conventional numerical reliability indexes. When applied to variant of unknown significance populations, the representations shed light on the nature of these variants and can be used to prioritize variants of unknown significance for further studies. In a third application, the graphics were used to compare the two versions of the ATM-adapted American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines, obtaining valuable information on their relative virtues and weaknesses. Finally, a server [ATMision (ATM missense in silico interpretation online)] was generated for users to apply these representations in their variant interpretation problems, to check the ATM-adapted guidelines' criteria for computational evidence on their variant(s) and access different sources of information.

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.

Comparison of prone and supine positioning for breast cancer radiotherapy using REQUITE data: dosimetry, acute and two years physician and patient-reported outcomes.

OBJECTIVE: Most patients receive whole breast radiotherapy in a supine position. However, two randomised trials showed lower acute toxicity in prone position. Furthermore, in most patients, prone positioning reduced doses to the organs at risk. To confirm these findings, we compared toxicity outcomes, photographic assessment, and dosimetry between both positions using REQUITE data. METHODS: REQUITE is an international multi-centre prospective observational study that recruited 2069 breast cancer patients receiving radiotherapy. Data on toxicity, health-related quality of life (HRQoL), and dosimetry were collected, as well as a photographic assessment. A matched case control analysis compared patients treated prone (n = 268) versus supine (n = 493). Exact matching was performed for the use of intensity-modulated radiotherapy, boost, lymph node irradiation, chemotherapy and fractionation, and the nearest neighbour for breast volume. Primary endpoints were dermatitis at the end of radiotherapy, and atrophy and cosmetic outcome by photographic assessment at two years. RESULTS: At the last treatment fraction, there was no significant difference in dermatitis (p = .28) or any HRQoL domain, but prone positioning increased the risk of breast oedema (p < .001). At 2 years, patients treated in prone position had less atrophy (p = .01), and higher body image (p < .001), and social functioning (p < .001) scores. The photographic assessment showed no difference in cosmesis at 2 years (p = .22). In prone position, mean heart dose (MHD) was significantly lower for left-sided patients (1.29 Gy vs 2.10 Gy, p < .001) and ipsilateral mean lung dose (MLD) was significantly lower for all patients (2.77 Gy vs 5.89 Gy, p < .001). CONCLUSIONS: Prone radiotherapy showed lower MLD and MHD compared to supine position, although the risk of developing breast oedema during radiotherapy was higher. At 2 years the photographic assessment showed no difference in the cosmetic outcome, but less atrophy was seen in prone-treated patients and this seems to have a positive influence on the HRQoL domain of body image.

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.

(Pre)treatment risk factors for late fatigue and fatigue trajectories following radiotherapy for breast cancer.

Fatigue is common in breast-cancer survivors. Our study assessed fatigue longitudinally in breast cancer patients receiving adjuvant radiotherapy (RT) and aimed to identify risk factors associated with long-term fatigue and underlying fatigue trajectories. Fatigue was measured in a prospective multicenter cohort (REQUITE) using the Multidimensional Fatigue Inventory (MFI-20) and analyzed using mixed models. Multivariable logistic models identified factors associated with fatigue dimensions at 2 years post-RT and latent class growth analysis identified individual fatigue trajectories. A total of 1443, 1302, 1203 and 1098 patients completed the MFI-20 at baseline, end of RT, after 1 and 2 years. Overall, levels of fatigue significantly increased from baseline to end of RT for all fatigue dimensions (P < .05) and returned to baseline levels after 2 years. A quarter of patients were assigned to latent trajectory high (23.7%) and moderate (24.8%) fatigue classes, while 46.3% and 5.2% to the low and decreasing fatigue classes, respectively. Factors associated with multiple fatigue dimensions at 2 years include age, BMI, global health status, insomnia, pain, dyspnea and depression. Fatigue present at baseline was consistently associated with all five MFI-20 fatigue dimensions (ORGeneralFatigue = 3.81, P < .001). From latent trajectory analysis, patients with a combination of factors such as pain, insomnia, depression, younger age and endocrine therapy had a particularly high risk of developing early and persistent high fatigue years after treatment. Our results confirmed the multidimensional nature of fatigue and will help clinicians identify breast cancer patients at higher risk of having persistent/late fatigue so that tailored interventions can be delivered.

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.

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.

Cell Senescence-Related Pathways Are Enriched in Breast Cancer Patients With Late Toxicity After Radiotherapy and Low Radiation-Induced Lymphocyte Apoptosis.

Background: Radiation-induced late effects are a common cause of morbidity among cancer survivors. The biomarker with the best evidence as a predictive test of late reactions is the radiation-induced lymphocyte apoptosis (RILA) assay. We aimed to investigate the molecular basis underlying the distinctive RILA levels by using gene expression analysis in patients with and without late effects and in whom we had also first identified differences in RILA levels. Patients and Methods: Peripheral blood mononuclear cells of 10 patients with late severe skin complications and 10 patients without symptoms, selected from those receiving radiotherapy from 1993 to 2007, were mock-irradiated or irradiated with 8 Gy. The 48-h response was analyzed in parallel by RILA assay and gene expression profiling with Affymetrix microarrays. Irradiated and non-irradiated gene expression profiles were compared between both groups. Gene set enrichment analysis was performed to identify differentially expressed biological processes. Results: Although differentially expressed mRNAs did not reach a significant adjusted p-value between patients suffering and not suffering clinical toxicity, the enriched pathways indicated significant differences between the two groups, either in irradiated or non-irradiated cells. In basal conditions, the main differentially expressed pathways between the toxicity and non-toxicity groups were the transport of small molecules, interferon signaling, and transcription. After 8 Gy, the differences lay in pathways highly related to cell senescence like cell cycle/NF-κB, G-protein-coupled receptors, and interferon signaling. Conclusion: Patients at risk of developing late toxicity have a distinctive pathway signature driven by deregulation of immune and cell cycle pathways related to senescence, which in turn may underlie their low RILA phenotype.

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

Preclinical In Vivo Validation of the RAD51 Test for Identification of Homologous Recombination-Deficient Tumors and Patient Stratification.

PARP inhibitors (PARPi) are approved drugs for platinum-sensitive, high-grade serous ovarian cancer (HGSOC) and for breast, prostate, and pancreatic cancers (PaC) harboring genetic alterations impairing homologous recombination repair (HRR). Detection of nuclear RAD51 foci in tumor cells is a marker of HRR functionality, and we previously established a test to detect RAD51 nuclear foci. Here, we aimed to validate the RAD51 score cut off and compare the performance of this test to other HRR deficiency (HRD) detection methods. Laboratory models from BRCA1/BRCA2-associated breast cancer, HGSOC, and PaC were developed and evaluated for their response to PARPi and cisplatin. HRD in these models and patient samples was evaluated by DNA sequencing of HRR genes, genomic HRD tests, and RAD51 foci detection. We established patient-derived xenograft models from breast cancer (n = 103), HGSOC (n = 4), and PaC (n = 2) that recapitulated patient HRD status and treatment response. The RAD51 test showed higher accuracy than HRR gene mutations and genomic HRD analysis for predicting PARPi response (95%, 67%, and 71%, respectively). RAD51 detection captured dynamic changes in HRR status upon acquisition of PARPi resistance. The accuracy of the RAD51 test was similar to HRR gene mutations for predicting platinum response. The predefined RAD51 score cut off was validated, and the high predictive value of the RAD51 test in preclinical models was confirmed. These results collectively support pursuing clinical assessment of the RAD51 test in patient samples from randomized trials testing PARPi or platinum-based therapies. SIGNIFICANCE: This work demonstrates the high accuracy of a histopathology-based test based on the detection of RAD51 nuclear foci in predicting response to PARPi and cisplatin.

Overview of health-related quality of life and toxicity of non-small cell lung cancer patients receiving curative-intent radiotherapy in a real-life setting (the REQUITE study).

OBJECTIVES: Radiotherapy-induced toxicity may negatively impact health-related quality of life (HRQoL). This report investigates the impact of curative-intent radiotherapy on HRQoL and toxicity in early stage and locally-advanced non-small cell lung cancer patients treated with radiotherapy or chemo-radiotherapy enrolled in the observational prospective REQUITE study. MATERIALS AND METHODS: HRQoL was assessed using the European Organisation for Research and Treatment of Cancer QLQ-C30 questionnaire up to 2 years post radiotherapy. Eleven toxicities were scored by clinicians using the Common Terminology Criteria for Adverse Events (CTCAE) version 4. Toxicity scores were calculated by subtracting baseline values. Mixed model analyses were applied to determine statistical significance (p ≤ 0.01). Meaningful clinical important differences (MCID) were determined for changes in HRQoL. Analysis was performed on the overall data, different radiotherapy techniques, multimodality treatments and disease stages. RESULTS: Data of 510 patients were analysed. There was no significant change in HRQoL or its domains, except for deterioration in cognitive functioning (p = 0.01). Radiotherapy technique had no significant impact on HRQoL. The addition of chemotherapy was significantly associated with HRQoL over time (p <.001). Overall toxicity did not significantly change over time. Acute toxicities of radiation-dermatitis (p =.003), dysphagia (p =.002) and esophagitis (p <.001) peaked at 3 months and decreased thereafter. Pneumonitis initially deteriorated but improved significantly after 12 months (p =.011). A proportion of patients experienced meaningful clinically important improvements and deteriorations in overall HRQoL and its domains. In some patients, pre-treatment symptoms improved gradually. CONCLUSIONS: While overall HRQoL and toxicity did not change over time, some patients improved, whereas others experienced acute radiotherapy-induced toxicities and deteriorated HRQoL, especially physical and cognitive functioning. Patient characteristics, more so than radiotherapy technique and treatment modality, impact post-radiotherapy toxicity and HRQoL outcomes. This stresses the importance of considering the potential impact of radiotherapy on individuals' HRQoL, symptoms and toxicity in treatment decision-making.

Clinical consequences of BRCA2 hypomorphism.

The tumor suppressor FANCD1/BRCA2 is crucial for DNA homologous recombination repair (HRR). BRCA2 biallelic pathogenic variants result in a severe form of Fanconi anemia (FA) syndrome, whereas monoallelic pathogenic variants cause mainly hereditary breast and ovarian cancer predisposition. For decades, the co-occurrence in trans with a clearly pathogenic variant led to assume that the other allele was benign. However, here we show a patient with biallelic BRCA2 (c.1813dup and c.7796 A > G) diagnosed at age 33 with FA after a hypertoxic reaction to chemotherapy during breast cancer treatment. After DNA damage, patient cells displayed intermediate chromosome fragility, reduced survival, cell cycle defects, and significantly decreased RAD51 foci formation. With a newly developed cell-based flow cytometric assay, we measured single BRCA2 allele contributions to HRR, and found that expression of the missense allele in a BRCA2 KO cellular background partially recovered HRR activity. Our data suggest that a hypomorphic BRCA2 allele retaining 37-54% of normal HRR function can prevent FA clinical phenotype, but not the early onset of breast cancer and severe hypersensitivity to chemotherapy.

BRCA1 and BRCA2 whole cDNA analysis in unsolved hereditary breast/ovarian cancer patients.

Germline pathogenic variants in BRCA1 and BRCA2 genes (BRCA1/2) explain an important fraction of hereditary breast/ovarian cancer (HBOC) cases. Genetic testing generally involves examining coding regions and exon/intron boundaries, thus the frequency of deleterious variants in non-coding regions is unknown. Here we analysed BRCA1/2 whole cDNA in a large cohort of 320 unsolved high-risk HBOC cases in order to identify potential splicing alterations explained by variants in BRCA1/2 deep intronic regions. Whole RNA splicing profiles were analysed by RT-PCR using Sanger sequencing or high-resolution electrophoresis in a QIAxcel instrument. Known predominant BRCA1/2 alternative splicing events were detected, together with two novel events BRCA1 ▼21 and BRCA2 Δ18q_27p. BRCA2 exon 3 skipping was detected in one patient (male) affected with breast cancer, caused by a known Portuguese founder mutation (c.156_157insAluYa5). An altered BRCA2 splicing pattern was detected in three patients, consisting in the up-regulation of ▼20A, Δ22 and ▼20A+Δ22 transcripts. In silico analysis and semi-quantitative data identified the polymorphism BRCA2 c.8755-66T>C as a potential modifier of Δ22 levels. Our findings suggest that mRNA alterations in BRCA1/2 caused by deep intronic variants are rare in Spanish population. However, RNA analysis complements DNA-based strategies allowing the identification of alterations that could go undetected by conventional testing.

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.

Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes.

The contribution of deep intronic splice-altering variants to hereditary breast and ovarian cancer (HBOC) is unknown. Current computational in silico tools to predict spliceogenic variants leading to pseudoexons have limited efficiency. We assessed the performance of the SpliceAI tool combined with ESRseq scores to identify spliceogenic deep intronic variants by affecting cryptic sites or splicing regulatory elements (SREs) using literature and experimental datasets. Our results with 233 published deep intronic variants showed that SpliceAI, with a 0.05 threshold, predicts spliceogenic deep intronic variants affecting cryptic splice sites, but is less effective in detecting those affecting SREs. Next, we characterized the SRE profiles using ESRseq, showing that pseudoexons are significantly enriched in SRE-enhancers compared to adjacent intronic regions. Although the combination of SpliceAI with ESRseq scores (considering ∆ESRseq and SRE landscape) showed higher sensitivity, the global performance did not improve because of the higher number of false positives. The combination of both tools was tested in a tumor RNA dataset with 207 intronic variants disrupting splicing, showing a sensitivity of 86%. Following the pipeline, five spliceogenic deep intronic variants were experimentally identified from 33 variants in HBOC genes. Overall, our results provide a framework to detect deep intronic variants disrupting splicing.

Evaluation of the association of heterozygous germline variants in NTHL1 with breast cancer predisposition: an international multi-center study of 47,180 subjects.

Bi-allelic loss-of-function (LoF) variants in the base excision repair (BER) gene NTHL1 cause a high-risk hereditary multi-tumor syndrome that includes breast cancer, but the contribution of heterozygous variants to hereditary breast cancer is unknown. An analysis of 4985 women with breast cancer, enriched for familial features, and 4786 cancer-free women revealed significant enrichment for NTHL1 LoF variants. Immunohistochemistry confirmed reduced NTHL1 expression in tumors from heterozygous carriers but the NTHL1 bi-allelic loss characteristic mutational signature (SBS 30) was not present. The analysis was extended to 27,421 breast cancer cases and 19,759 controls from 10 international studies revealing 138 cases and 93 controls with a heterozygous LoF variant (OR 1.06, 95% CI: 0.82-1.39) and 316 cases and 179 controls with a missense variant (OR 1.31, 95% CI: 1.09-1.57). Missense variants selected for deleterious features by a number of in silico bioinformatic prediction tools or located within the endonuclease III functional domain showed a stronger association with breast cancer. Somatic sequencing of breast cancers from carriers indicated that the risk associated with NTHL1 appears to operate through haploinsufficiency, consistent with other described low-penetrance breast cancer genes. Data from this very large international multicenter study suggests that heterozygous pathogenic germline coding variants in NTHL1 may be associated with low- to moderate- increased risk of breast cancer.