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Background and Purpose: We investigate discrepancies in the assessment of treatment-related symptoms in lung cancer between healthcare professionals and patients, and factors contributing to these discrepancies., Materials and Methods: Data from 515 participants in the REQUITE study were analysed. Five symptoms (cough, dyspnoea, bronchopulmonary haemorrhage, chest wall pain, dysphagia) were evaluated both before and after radiotherapy. Agreement between healthcare professionals and people with lung cancer was quantified using Gwet's-AC 2 coefficient. The influence of clinical variables, comorbidities, and quality-of-life outcomes on agreement was examined through stratified analyses., Results: We found varying levels of agreement between healthcare professionals and people with lung cancer. Bronchopulmonary haemorrhage and dysphagia exhibited very good agreement (meanAC 2  > 0.81), while cough and chest wall pain showed substantial agreement (meanAC 2  = 0.64 and 0.76, respectively). Dyspnoea had the lowest agreement (meanAC 2  = 0.59), with prior chemotherapy significantly reducing agreement levels. Chronic obstructive pulmonary disease (COPD) and early cancer stages also contributed to discrepancies in dyspnoea assessments. Regarding quality-of-life, the most relevant factor was fatigue, which reduced agreement in the assessment of dyspnoea (AC 2  = 0.55 vs 0.70), dysphagia (AC 2  = 0.48 vs 0.69), cough (AC 2  = 0.58 vs 0.82), and chest wall pain (AC 2  = 0.77 vs 0.91)., Conclusions: Our findings indicate strong alignment between healthcare professionals' and people with lung cancer evaluations of observable treatment-related symptoms, but less consistency for subjective symptoms such as dyspnoea. Factors such as prior chemotherapy, COPD, and cancer stage should be considered when interpreting symptom assessments. Furthermore, our study underscores the importance of integrating quality-of-life considerations, particularly fatigue, into symptom evaluations to mitigate potential biases in symptom perception., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dirk De Ruysscher reports relationships with AstraZeneca, BMS, BeiGene, Philips, and Olink that include research grants/support and advisory board positions, with institutional financial interests (no personal financial interests). Dirk De Ruysscher reports a relationship with Eli Lilly and Company that includes advisory board positions with institutional financial interests (no personal financial interests). Yolande Lievens reports a relationship with the European Society for Radiotherapy and Oncology (ESTRO) HERO-VBHC that includes funding grants. Liv Veldeman reports relationships with the Foundation Against Cancer, Founds voor Innovatie en Klinisch Onderzoek UZ Gent, the Clinical Mandate of the Foundation Against Cancer, and Stand Up To Cancer that include funding grants. Liv Veldeman holds a patent for Radiotherapy Board and Couch, issued as US20170028218A1 and EP3122256A1. Co-author Yolande Lievens has been an ESTRO Scientific Committee member, ESTRO-HERO Co-chair, a member of the Belgian College of Oncology, and EORTC-ESTRO E2-RADIATE Project PI, with no associated payments. Other author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.]., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS)., Methods: We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan., Results: Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10 -9 ). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62)., Conclusions: This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.