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PURPOSE Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET). METHODS Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort. RESULTS The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation. CONCLUSION CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

Advances in research have transformed the management of melanoma in the past decade. In parallel, patient advocacy has gained traction, and funders are increasingly prioritizing patient and public involvement. Here we discuss the ways in which patients and the public can be engaged in different stages of the research process, from developing, prioritizing and refining the research question to preclinical studies and clinical trials, then finally to ongoing research in the clinic. We discuss the challenges and opportunities that exist at each stage in order to ensure that a representative population of patients and the public contribute to melanoma research both now and in the future.

approximately 5% to 10% of VUS have been reported in the study of germline mutations of BRCA 1/2 genes. The lack of evidence on the interpretation and management of patients with VUS in BRCA 1/2 increases the difficulty of correct genetic counselling. The aim of this study is to implement the reclassification of VUS obtained in genetic studies of BRCA 1/2 mutation carriers to offer a better understanding and improvement of healthcare quality in GCU.Methods: A descriptive study of germline mutations in BRCA 1/2 observed in 125 families was conducted in the GCU of the Medical Oncology Department of the University Healthcare Complex of Salamanca between 2001 and 2020. The patients were reclassified through a clinical review using the kConFab, ClinVar and Varsome databases. The reclassification of VUS was performed using the criteria of the International Agency for Research on Cancer (IARC).Results: A total of 111 VUS were found in BRCA 1/2 genes. Thirty-nine (35.1%) VUS belonged to the BRCA1 mutation, and 72 (64.9%) corresponded to the BRCA2 mutation. In 54 mutations (48.6%), reclassification was possible according to IARC criteria; 28 (25.2%) were reinterpreted as benign mutations, three (2.7%) as pathogenic, 16 (14.4%) as probably benign and seven (6.3%) as probably pathogenic. Seventy-five percent (84/111) of the reclassified VUS were missense. The most frequent associated tumour was breast cancer in 58.6%, followed by ovarian cancer in 16.2%.Conclusions: According to our results, the analysis and reclassification of VUS allows a better approach to the genetic counselling of patients and their families. Of the total reported VUS, the most common reclassification was to the benign subgroup with a probability of pathogenicity of less than 0.1%. These data suggest that most VUS are polymorphisms. These results allow a better clinical interpretation and contribute to the good management of clinical decisions.Legal entity responsible for the study: The authors.