Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital.

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.

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Online Public Interest in Urological Cancers During the COVID-19 Pandemic: What Can "Dr. Google" Teach Us?

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has greatly affected health care priorities. OBJECTIVE: To explore and analyse trends in public online search for urological cancers. DESIGN SETTING AND PARTICIPANTS: We performed a retrospective analysis using the Google Health Trends online tool. Data related to urological cancer terms ("prostate cancer", "kidney cancer", and "bladder cancer") were extracted. We analysed trends for the whole world and for five countries: Italy, the UK, France, Sweden, and the USA. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A join-point regression model was used to define significant changes in trends over time. Week percentage changes (WPCs) were estimated to summarise linear trends. The Mann-Whitney test was used to compare the search volume during the COVID-19 pandemic period (from January 2020 to April 2021) and the equivalent period of 2018 and 2019. RESULTS AND LIMITATIONS: During COVID-19, worldwide online interest decreased significantly for all urological cancers, especially prostate cancer (WPC: -13.9%, p = 0.004; WPC: -5.4%, p < 0.001; and WPC: -4.3%, p < 0.001, for prostate, kidney, and bladder cancers, respectively). The most important decline was observed in the USA. The interest for all cancers was significantly less during the COVID-19 pandemic than in the same periods of 2018 and 2019. CONCLUSIONS: Online interest in urological cancers decreased significantly during the COVID-19 pandemic. Future studies will tell us whether this will translate into worse oncological outcomes. PATIENT SUMMARY: Patients are increasingly searching the Internet to get information on cancer. We explored Google queries during the COVID-19 pandemic and found that online interest decreased significantly for all urological cancers, especially prostate cancer. We do not know yet whether this will translate into worse prognosis for patients.

Efficacy, safety and ancillary analyses of pembrolizumab in combination with nintedanib for the treatment of patients with relapsed advanced mesothelioma

BackgroundWe report the results from the advanced malignant mesothelioma (aMM) expansion cohort of the PEMBIB Phase Ib trial (NCT02856425) evaluating the safety, efficacy & biomarkers of an antiangiogenic tyrosine kinase inhibitor (nintedanib) with an anti-PD1 immunotherapy (pembrolizumab).MethodsPatients with aMM relapsing after at least one line of platinum doublet chemotherapy and not previously pre-exposed to IO were treated with a combination of oral nintedanib (150mg BID) & IV pembrolizumab (200mg Q3W) with a 7 days nintedanib lead-in preceding pembrolizumab initiation. Baseline and on-treatment (cycle D2, day 1 [C2D1]) fresh tumor & blood samples were prospectively phenotyped by flow cytometry (FC). RNAseq was run on tumor samples. Immune factors were titrated on tumor secretome and plasma.Results30 aMM patients were treated and 29 evaluable for response. Median age was 68 years old (38–85) and 86% of aMM were epithelioid. The most frequent adverse events (AE) (grades 1–3) related to the combination were liver enzymes increase, fatigue, nausea, and diarrhea. 4 (13.3%) patients developed grade 3–5 immune- related AE. Patients died of cancer progression (n=14, 46.7%), myocarditis with thrombo-embolic event (n=1, 3.3%) and COVID-19 (n=1, 3.3%). Median follow-up was 14.8 months (95%CI [9.70–18.2]). Best Overall Response Rates (BORR) per RECISTv1.1 were Partial Response (PR, n=7/29;24.1%), Stable Disease (SD, n=17/29;58.6%) and Progressive Disease (n=5/29;17.2%). Disease Control Rate (DCR) (defined as PR + SD) was 46.6% at 6 months. Patients with DCR at 6 months had significantly higher percentage of PDL1 expression on tumor cells (by Immuno-Histo-Chemistry, antibody clone SP263) and higher CD8+ T cells infiltrate in tumor biopsies (by FC) at screening. Upon treatment, soluble plasma rate of CXCL9 and CXCL13 increased in all patients, as well as tumor immune infiltrates estimated by deconvolution of tumor biopsies RNA-seq. But deconvoluted estimates of NK cells, T cells and myeloid dendritic cells infiltrates on baseline tumors and C2D1 biopsies were higher in patients with DCR at 6 months. Pre & on-treatment IL6 and IL8 rates in tumor secretome & plasma were higher in patients without DCR. Gene Set Enrichment Analyses on RNA-seq from screening biopsies highlighted an enrichment in E2F, MYC and KRAS gene pathways and lower expression of type 1 interferon signature in patients without DCR than those with DCR at 6 months.ConclusionsWith a BORR of 24% and a DCR of 47% at 6 months, pembrolizumab and nintedanib combination provided valuable therapeutic benefits for patients with aMM.Trial RegistrationClinicalTrialsgov, NCT02856425. Registered August 4, 2016 — Prospectively registered,https://clinicaltrials.gov/ct2/show/NCT02856425?term=PEMBIB&draw=2&rank=1.Ethics ApprovalThe protocol was first approved by the Agence Nationale de Sécurité du Médicament (ANSM) on June 24th 2016 (Ref #160371A-12). The protocol was also approved by the Ethical Committee (Comité de Protection des Personnes Ile de France 1) on Jul 12th 2016 (Ref #2016-mai-14236ND).

Impact of COVID-19 on healthcare organisation and cancer outcomes.

BACKGROUND: Changes in the management of patients with cancer and delays in treatment delivery during the COVID-19 pandemic may impact the use of hospital resources and cancer mortality. PATIENTS AND METHODS: Patient flows, patient pathways and use of hospital resources during the pandemic were simulated using a discrete event simulation model and patient-level data from a large French comprehensive cancer centre's discharge database, considering two scenarios of delays: massive return of patients from November 2020 (early-return) or March 2021 (late-return). Expected additional cancer deaths at 5 years and mortality rate were estimated using individual hazard ratios based on literature. RESULTS: The number of patients requiring hospital care during the simulation period was 13,000. In both scenarios, 6-8% of patients were estimated to present a delay of >2 months. The overall additional cancer deaths at 5 years were estimated at 88 in early-return and 145 in late-return scenario, with increased additional deaths estimated for sarcomas, gynaecological, liver, head and neck, breast cancer and acute leukaemia. This represents a relative additional cancer mortality rate at 5 years of 4.4 and 6.8% for patients expected in year 2020, 0.5 and 1.3% in 2021 and 0.5 and 0.5% in 2022 for each scenario, respectively. CONCLUSIONS: Pandemic-related diagnostic and treatment delays in patients with cancer are expected to impact patient survival. In the perspective of recurrent pandemics or alternative events requiring an intensive use of limited hospital resources, patients should be informed not to postpone care, and medical resources for patients with cancer should be sanctuarised.

Impact of COVID-19 Pandemic on Cancer Research.

The COVID-19 pandemic is profoundly changing cancer researchers and cancer research. Leaders from different fields and at different career stages share their perspectives.

Coronavirus disease (COVID-19) outbreak and phase 1 trials: should we consider a specific patient management?

The outbreak of the Coronavirus disease (COVID-19) pandemic has deeply challenged healthcare systems and care of patients with cancer. Phase 1 studies are among the most complicated clinical trials and require thorough patient selection, as well as intensive patient monitoring. In this perspective, we discuss the key factors that should be considered for the conduct of phase 1 trials and management of COVID-19-positive patients with cancer enrolled in such trials. We notably present the risks and challenges raised by COVID-19-infected phase 1 patients, in terms of safety, toxicity causality assessment, drug efficacy evaluation and clinical research priorities. We finally propose some guidelines for the conduct of phase 1 trials and management of COVID-19-infected patients in a pandemic time.

Cancer Treatment and Research During the COVID-19 Pandemic: Experience of the First 6 Months.

The coronavirus disease-2019 (COVID-19) pandemic has had a significant impact on patients with underlying malignancy. In this article, we summarize emerging data related to patients with cancer and COVID-19. Among patients with COVID-19, a higher proportion have an underlying diagnosis of cancer than seen in the general population. Also, patients with malignancy are likely to be more vulnerable than the general population to contracting COVID-19. Mortality is significantly higher in patients with both cancer and COVID-19 compared with the overall COVID-19-positive population. The early months of the pandemic saw a decrease in cancer screening and diagnosis, as well as postponement of standard treatments, which could lead to excess deaths from cancer in the future.