A prognostic model of all-cause mortality at 30 days in patients with cancer and COVID-19

Background: Patients with cancer are at higher risk of dying of COVID-19. Known risk factors for 30-day all-cause mortality (ACM-30) in patients with cancer are older age, sex, smoking status, performance status, obesity, and co-morbidities. We hypothesized that common clinical and laboratory parameters would be predictive of a higher risk of 30-day ACM, and that a machine learning approach (random forest) could produce high accuracy. Methods: In this multi-institutional COVID-19 and Cancer Consortium (CCC19) registry study, 12,661 patients enrolled between March 17, 2020 and December 31, 2021 were utilized to develop and validate a model of ACM-30. ACM-30 was defined as death from any cause within 30 days of COVID-19 diagnosis. Pre-specified variables were: age, sex, race, smoking status, ECOG performance status (PS), timing of cancer treatment relative to COVID19 diagnosis, severity of COVID19, type of cancer, and other laboratory measurements. Missing variables were imputed using random forest proximity. Random forest was utilized to model ACM-30. The area under the curve (AUC) was computed as a measure of predictive accuracy with out-of-bag prediction. One hundred bootstrapped samples were used to obtain the standard error of the AUC. Results: The median age at COVID-19 diagnosis was 65 years, 53% were female, 18% were Hispanic, and 16.7% were Black. Over half were never smokers and the median body mass index was 28.2. Random forest with under sampling selected 20 factors prognostic of ACM-30. The AUC was 88.9 (95% CI 88.5-89.2). Highly informative parameters included: COVID-19 severity at presentation, cancer status, age, troponin level, ECOG PS and body mass index. Conclusions: This prognostic model based on readily available clinical and laboratory values can be used to estimate individual survival probability within 30-days for COVID-19. In addition, this model can be used to select or classify patients with cancer and COVID-19 into risk groups based on validated cut points, for treatment selection, prophylaxis prioritization, and/or enrollment in clinical trials. Future work includes external validation using other large datasets of patients with COVID-19 and cancer

Safety and efficacy of immune checkpoint inhibitors in advanced urological cancers with pre-existing autoimmune disorders: a retrospective international multicenter study

BACKGROUND: There is limited experience regarding the safety and efficacy of checkpoint inhibitors (CPI) in patients with autoimmune disorders (AD) and advanced urological cancers as they are generally excluded from clinical trials due to risk of exacerbations. METHODS: This multicenter retrospective cohort analysis of patients with advanced renal cell cancer (RCC) and urothelial cancer (UC) with pre-existing AD treated with CPI catalogued the incidence of AD exacerbations, new immune-related adverse events (irAEs) and clinical outcomes. Competing risk models estimated cumulative incidences of exacerbations and new irAEs at 3 and 6 months. RESULTS: Of 106 patients with AD (58 RCC, 48 UC) from 10 centers, 35 (33%) had grade 1/2 clinically active AD of whom 10 (9%) required corticosteroids or immunomodulators at baseline. Exacerbations of pre-existing AD occurred in 38 (36%) patients with 17 (45%) requiring corticosteroids and 6 (16%) discontinuing CPI. New onset irAEs occurred in 40 (38%) patients with 22 (55%) requiring corticosteroids and 8 (20%) discontinuing CPI. Grade 3/4 events occurred in 6 (16%) of exacerbations and 13 (33%) of new irAEs. No treatment-related deaths occurred. Median follow-up was 15 months. For RCC, objective response rate (ORR) was 31% (95% CI 20% to 45%), median time to treatment failure (TTF) was 7 months (95% CI 4 to 10) and 12-month overall survival (OS) was 78% (95% CI 63% to 87%). For UC, ORR was 40% (95% CI 26% to 55%), median TTF was 5.0 months (95% CI 2.3 to 9.0) and 12-month OS was 63% (95% CI 47% to 76%). CONCLUSIONS: Patients with RCC and UC with well-controlled AD can benefit from CPI with manageable toxicities that are consistent with what is expected of a non-AD population. Prospective study is warranted to comprehensively evaluate the benefits and safety of CPI in patients with AD.status: publishe

Management of Diseases Caused by Pectobacterium and Dickeya Species

Management of soft rot Pectobacteriaceae (SRP) is a challenge as there are no control agents available and no effective resistance present in commercial cultivars. In addition, many species of SRP have a broad host range and spread via rotten plant material takes place readily. In this chapter, the possibilities for disease management are outlined. Management is mainly based on seed certification to limit the risks of using infected planting material, and on hygiene and cultivation practices that reduce cross-contamination within and between seed lots. Balanced nutrition also supports the suppressiveness of crops against SRP. Experimental data show that inoculum in seed tubers can be reduced by thermotherapy and the use of biocides. Under controlled conditions, application of seed potatoes with biocontrol agents has showed promising results but few data are present on the efficacy of biocontrol in the field. Resistance in wild Solanum species against SRP has been found but to date no genes have been transferred to cultivars. However, new breeding technologies, such as CRISPR/CAS 9 and the use of true potato seed (TPS), will give us new perspectives on the generation of resistant cultivars