Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).Copyright © 2021 Rando et al.

CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies

Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through these articles faster and document the crucial takeaways from each of these articles? In this work, we propose CCS Explorer, an end-to-end system for relevance prediction of sentences, extractive summarization, and patient, outcome, and intervention entity detection from CCS. CCS Explorer is packaged in a web-based graphical user interface where the user can provide any disease name. CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end. For each task, CCS Explorer fine-tunes pre-trained language representation models based on transformers with additional layers. The models are evaluated using two publicly available datasets. CCS Explorer obtains a recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus, CCS Explorer can reliably extract relevant information to summarize articles, saving time by ~660×. © 2022 IEEE.

Sars-Cov-2 Omicron (B11529) Variant Infection Leads to High Morbidity and Mortality in Unvaccinated Patients with Cancer

Background: The Omicron (B.1.1.529) SARS-CoV-2 variant is highly transmissible and escapes vaccinal immunity. Evidence is lacking as to the impact of Omicron in oncological patients. Method(s): Capitalizing on OnCovid study data (NCT04393974), we analysed COVID-19 morbidity and case fatality rate at 28 days (CFR28) of unvaccinated patients across 3 phases defined following the evolution of the pandemic in Europe, according to date of COVID-19 diagnosis: "Pre-vaccination" phase (27/02/2020-30/11/2020), "Alpha- Delta variant" phase (01/12/2020-14/12/2021), "Omicron variant" phase (15/12/2021-31/01/2022). Finding(s): By the data lock of 04/02/2022, 3820 patients from 37 institutions across 6 countries were entered. Out of 3473 eligible patients, 2033 (58.6%), 1075 (30.9%) and 365 (10.5%) were diagnosed during the Pre-vaccination, Alpha-Delta and Omicron phases. In total 659 (61.3%) and 42 (11.5%) were unvaccinated in the Alpha-Delta and Omicron. Unvaccinated patients across the Omicron, Alpha-Delta and Pre-vaccination phases experienced similar CFR28 (27.5%, 28%, 29%, respectively). Following propensity score matching, 42 unvaccinated Omicron patients were matched with 122 and 121 patients from the Pre-vaccination and Alpha-Delta phases respectively, based on country of origin, sex, age, comorbidity burden, primary tumour, cancer stage and status, and the receipt of systemic anticancer therapy at COVID-19. Unvaccinated Omicron patients experienced improved COVID-19 outcomes in comparison to patients diagnosed during the Prevaccination phase. Morbidity and mortality were comparable to those of unvaccinated patients diagnosed during the Alpha-Delta phase. Interpretation(s): Despite time-dependent improvements in outcomes reported in the Omicron phase, patients with cancer remain highly vulnerable to SARS-CoV-2 in absence of vaccinal protection. This study provides unequivocal evidence in support of universal vaccination of patients with cancer as a protective measure against morbidity and mortality from COVID-19.

Clinical effectiveness of SARS-CoV-2 vaccines and booster doses in patients with cancer: An analysis from the European OnCovid registry

Background: Immunogenicity and safety of SARS-CoV-2 vaccines have been widely investigated in patients (pts) with cancer. However, their effectiveness against Coronavirus disease 2019 (COVID-19) and the additional protective effect of a booster dose in this population are yet to be defined. Methods: Using OnCovid study data (NCT04393974), a European registry enrolling consecutive pts with cancer and COVID-19, we evaluated morbidity and 14 days case fatality rates (CFR14) from COVID-19 in pts who were unvaccinated, vaccinated (either partially/full vaccinated but not boosted) and those who had received a third dose. Analyses were restricted to pts diagnosed between 17/11/2021 (first breakthrough infection in a boosted pt) and the 31/01/2022. Pts with unknown vaccination status were excluded. Results: By the data lock of 22/02/2022, out of 3820 consecutive pts from 36 institutions, 415 pts from 3 countries (UK, Spain, Italy) were eligible for analysis. Among them, 51 (12.3%) were unvaccinated, 178 (42.9%) were vaccinated and 186 (44.8%) were boosted. Among vaccinated pts, 26 (14.6%) were partially vaccinated (1 dose). Pts with haematological malignancies had more likely received a booster dose prior to infection (25.4% vs 13.6% and 11.8%, p = 0.02). We found no other associations between vaccination status and pts' characteristics including sex, age, comorbidities, smoking history, tumour stage, tumour status and receipt of systemic anticancer therapy. Compared to unvaccinated pts, boosted and vaccinated pts achieved improved CFR14 (6.8% and 7.0% vs 22.4%, p = 0.01), COVID-19-related hospitalization rates (26.1% and 20.6% vs 41.2%, p = 0.01) and COVID-19-related complications rates (14.5% and 15.7% vs 31.4%). Using multivariable Inverse Probability of Treatment Weighting (IPTW) models adjusted for sex, comorbidities, tumour status and country of origin we confirmed that boosted (OR 0.21, 95%CI: 0.05-0.89) and vaccinated pts (OR 0.19, 95%CI: 0.04-0.81) achieved improved CFR14 compared to unvaccinated pts, whilst a significantly reduced risk of COVID-19 complications (OR 0.26, 95%CI: 0.07-0.93) was reported for vaccinated pts only. Conclusions: SARS-CoV-2 vaccines protect from COVID-19 morbidity and mortality in pts with cancer. Accounting for the enrichment of haematologic pts in the boosted group, the observation of comparable mortality outcomes between boosted and vaccinated pts is reassuring and suggests boosting to be associated with reduced mortality in more vulnerable subjects, despite evidence of adverse features in this group.

Clinical assessment of the Omicron outbreak in Europe and trends in morbidity and mortality from COVID-19 and cancer

Background: Whilst patients (pts) with cancer are at increased risk of adverse outcome from Coronavirus disease 2019 (COVID-19), no evidence exists as to the natural history of the SARS-CoV-2 B.1.1.529 (Omicron) variant in this population. Methods: Capitalizing on OnCovid study data (NCT04393974), a European registry that collects data on consecutive patients with cancer and COVID-19, we analysed COVID-19 morbidity and case fatality rates at 14 days (CFR14) across 3 phases defined following the evolution of the pandemic in Europe, according to date of COVID-19 diagnosis: “Pre-vaccination” phase (27/02/2020-30/ 11/2020), “Alpha-Delta variant” phase (01/12/2020-14/12/2021), “Omicron variant” phase (15/12/2020-31/01/2022). Results: By the data lock of 04/02/2022, 3820 consecutive pts were enrolled, 3473 of whom were eligible for this analysis. Among them, 2033 (58.6%), 1075 (30.9%) and 365 (10.5%) were diagnosed during the Pre-vaccination, Alpha-Delta and Omicron phases. Pts diagnosed in the Omicron phase were more likely aged < 65 years (48.6% vs 42.5%, 39.4% p = 0.01), had < 2 comorbidities (61.9% vs 55.6%, 52.1% p = 0.01). They had more advanced-stage tumours (62.1% vs 53.3%, 49.0%, p < 0.01) and were more likely receiving systemic anticancer therapy (SACT) at COVID-19 diagnosis (54.9% vs 43.9%, 39.6%, p < 0.01). Proportions of fully vaccinated/boosted pts were higher in the Omicron phase (33.9%-48.1%) compared to the Alpha-Delta phase (16.6%-2.3%, p < 0.01). Pts diagnosed in the Omicron phase had improved CFR14 (9.0% vs 13.9%, 23.1%, p < 0.01) lower hospitalization rates due to COVID-19 (24.4% vs 41.4%, 56.6%, p < 0.01), lower complications rates (15.3% vs 33.6%, 39.4%, p < 0.01) and reduced need for COVID-19 specific therapy (22.4% vs 43.0%, 65.7% p < 0.01) compared to the Alpha-Delta and pre-vaccinal phase. After adjusting for country of origin, sex, age, comorbidities, tumour stage, status and receipt of SACT at COVID-19, patients diagnosed in the Omicron phase displayed the lowest risk of death at 14 days compared to earlier phases. Similarly, rates of hospitalization and complicated COVID-19 were lowest for Omicron phase. Conclusions: This is the first study to portray the evolution of the SARS-CoV-2 Omicron outbreak in Europe, documenting an improvement in all COVID-19 outcomes compared to earlier phases of the pandemic. Enhanced healthcare capacity, improved disease management, immunization campaigns alongside differential virulence of viral strains are likely contributing to improved outcomes across phases.

Identification and Development of Therapeutics for COVID-19

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

The acute phase response identifies cancer patients with adverse outcomes from SARS-CoV-2 infection as quantified by the OnCovid Inflammatory Score

We sought to determine parameters of the acute phase response, a feature of innate immunity activated by infectious noxae and cancer, deranged by Covid-19 and establish oncological indices' prognostic potential for patients with concomitant cancer and Covid-19. Between 27/02 and 23/06/2020, OnCovid retrospectively accrued 1,318 consecutive referrals of patients with cancer and Covid-19 aged 18 from the U.K., Spain, Italy, Belgium, and Germany. Patients with myeloma, leukemia, or insufficient data were excluded. The neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), prognostic nutritional index (PNI), modified Glasgow prognostic score (mGPS), and prognostic index (PI) were evaluated for their prognostic potential, with the NLR, PLR, and PNI risk stratifications dichotomized around median values and the pre-established risk categorizations from literature utilized for the mGPS and PI. 1,071 eligible patients were randomly assorted into a training set (TS, n=529) and validation set (VS, n=542) matched for age (67.9±13.3 TS, 68.5±13.5 VS), presence of 1 comorbidity (52.1% TS, 49.8% VS), development of 1 Covid-19 complication (27% TS, 25.9% VS), and active malignancy at Covid-19 diagnosis (66.7% TS, 61.6% VS). Among all 1,071 patients, deceased patients tended to categorize into poor risk groups for the NLR, PNI, mGPS, and PI (P<0.0001) with a return to pre-Covid-19 diagnosis NLR, PNI, and mGPS categorizations following recovery (P<0.01). In the TS, higher mortality rates were associated with NLR>6 (44.6% vs 28%, P<0.0001), PNI<40 (46.6% vs 20.9%, P<0.0001), mGPS (50.6% for mGPS2 vs 30.4% and 11.4% for mGPS1 and 0, P<0.0001), and PI (50% for PI2 vs 40% for PI1 and 9.1% for PI0, P<0.0001). Findings were confirmed in the VS (P<0.001 for all comparisons). Patients in poor risk categories had shorter median overall survival [OS], (NLR>6 30 days 95%CI 1-63, PNI<40 23 days 95%CI 10-35, mGPS2 20 days 95%CI 8-32, PI2 23 days 95%CI 1-56) compared to patients in good risk categories, for whom median OS was not reached (P<0.001 for all comparisons). The PLR was not associated with survival. Analyses of survival in the VS confirmed the NLR (P<0.0001), PNI (P<0.0001), PI (P<0.01), and mGPS (P<0.001) as predictors of survival. In a multivariable Cox regression model including all inflammatory indices and pre-established prognostic factors for severe Covid-19 including sex, age, comorbid burden, malignancy status, and receipt of anti-cancer therapy at Covid-19 diagnosis, the PNI was the only factor to emerge with a significant hazard ratio [HR] in both TS and VS analysis (TS HR 1.97, 95%CI 1.19-3.26, P=0.008;VS HR 2.48, 95%CI 1.47- 4.20, P=0.001). We conclude that systemic inflammation drives mortality from Covid-19 through hypoalbuminemia and lymphocytopenia as measured by the PNI and propose the PNI as the OnCovid Inflammatory Score (OIS) in this context.

Systemic anti-cancer therapy and metastatic cancer are independent mortality risk factors during two UK waves of the COVID-19 pandemic at University College London Hospital

Background: Data from the first wave of COVID-19 infection demonstrated that a history of cancer and SACT was associated with poorer outcomes. Our study compares outcomes for cancer patients matched to non-cancer patients between the two waves in order to explore further how cancer and its treatment may impact COVID-19 mortality. Methods: Data was collected for patients with positive PCR and history of cancer between 1 Mar to 20 May 2020 and 1 Dec to 8 Feb 2021 for wave 1 and 2, respectively. A contemporaneous cohort of patients without cancer were age- and sex-matched for comparison. Results: The total number of patients presenting with COVID-19 was higher in wave two (1135 vs 626). 207 of these patients had cancer, and were matched to 452 patients without cancer from both waves. There was a significantly improved chance of mortality in wave 2 (HR 0.41, p < 0.0001). When adjusting for age, sex and co-morbidities, cancer was an independent risk factor for mortality amongst patients hospitalised with COVID-19 in wave 1 (HR 1.62, p = 0.02), but not in wave 2. There was a trend towards improved survival for hospitalised patients in wave 2 receiving COVID-19 specific treatment including dexamethasone, remdesivir, tocilizumab (HR 0.75, p = 0.086). For the combined cancer cohort, SACT was an independent predictor of mortality, as was metastatic disease. [Formula presented] Conclusions: The mortality for both cancer and non-cancer patients improved between waves of the pandemic. Advances in detection, prevention and treatment may account for this. Cancer was no longer a risk factor for mortality in the second wave, however SACT and metastatic cancer remained risk factors for mortality within the cancer cohort. This emphasises the need for ongoing protection of patients with advanced cancer and those on SACT, including through their prioritisation for COVID-19 vaccination globally. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: H. Shaw: Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Novartis;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: BMS;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: MSD;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Immunocore;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Idera;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Iovance;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Sanofi Genzyme/Regeneron;Financial Interests, Personal, Invited Speaker, Advisory/Consultancy: Macrogenics;Financi Interests, Personal, Invited Speaker, Advisory/Consultancy: Roche. R. Roylance: Financial Interests, Personal, Other, Personal Fees: Novartis;Financial Interests, Personal, Other, Personal Fees & None-financial support: Daiichi Sankyo;Financial Interests, Personal, Other, Personal Fees: Eli-Lilly;Financial Interests, Personal, Other, Personal Fees: Pfizer;Financial Interests, Personal, Other, Personal Fees & None-financial support: G1 Therapeutics;Non-Financial Interests, Personal, Other, None-financial support: Roche;Non-Financial Interests, Personal, Other, None-financial support: AstraZeneca. All other authors have declared no conflicts of interest.

Exploring the Impact of COVID-19 on Aviation Industry: A Text Mining Approach

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic. © 2020 IEEE.

COVID-19 mortality in patients receiving anti-cancer therapy in a UK national cancer centre

Background: The COVID-19 (C19) pandemic has prompted alterations to systemic anti-cancer therapy (SACT) due to concerns of immunosuppression and healthcare exposure. However, the effects of SACT on mortality in patients who acquire C19 are not well understood. As a national cancer centre within a major C19 hotspot, we seek to address these risks at scale. Method(s): Patients with a history of solid cancers and laboratory confirmed C19 (1 Mar to 31 May 2020) were included. Haematological malignancies were excluded. The primary outcome was time from C19 diagnosis to death. The last follow-up date was 22 Jun 2020. Result(s): We identified 94 cancer patients;62 males (median age 73, BMI 24.9), and 32 females (median age 68.5, BMI 25.7). Genitourinary (n = 24) cancers were the most common, followed by gastrointestinal (n = 23), thoracic (n = 15), and gynaecological (n = 9) cancers. 25 patients received SACT: chemotherapy (n = 15), endocrine therapy (n = 8), immunotherapy (n = 4), and targeted anti-cancer therapy (n = 2). 16 patients received SACT with palliative intent. Patients on SACT had a greater incidence of metastatic disease (48.0% vs 10.6%, p 0.001) and were younger (median age 62.5 vs 73.0, p = 0.01). They were also more likely to have renal impairment (p = 0.02), lymphopaenia (p = 0.01) and anaemia (p = 0.04) compared to those not on SACT. The univariate analysis showed age and co-morbidities were associated with mortality (Table). Adjusting for age, ethnicity, co-morbidities and the presence of metastatic cancer, SACT was an independent risk factor for C19 mortality (HR 2.46, 1.09 - 5.5, p = 0.03). Age, South Asian ethnicity, hypertension and cerebrovascular disease were also independent risk factors for C19 mortality. [Formula presented] Conclusion(s): C19 infection poses a substantial risk to cancer patients and our data suggests that SACT is an independent risk factor for mortality in C19 infection. These findings call for a nuanced approach to C19 risk, focusing on established risk factors such as age and co-morbidities to guide treatment decisions. Legal entity responsible for the study: University College London Hospital. Funding(s): Has not received any funding. Disclosure: All authors have declared no conflicts of interest. Copyright © 2020

Do cancer patients really do worse? A study in a UK tertiary hospital within a COVID-19 epicentre

Background: The COVID-19 pandemic remains of pressing concern for patients with cancer. Mortality from COVID-19 is predicted by age and co-morbidities, but the relative contribution of cancer is poorly understood. As a tertiary academic hospital serving a large general and cancer population in a COVID-19 epicentre, we are uniquely placed to investigate this. We report data from our study, comparing cancer patients to an age- and sex-matched non-cancer cohort. Methods: Patients with laboratory confirmed COVID-19 from 1 March to 31 May 2020 were included. Patients with a history of solid cancer were compared to an age- and sex-matched non-cancer cohort. Patients with haematological malignancies were excluded. Results: We identified 94 patients with cancer and 226 patients without cancer. In univariate analysis, age, South Asian ethnicity and co-morbidities predicted mortality (see table). More in the cancer cohort had died compared to the non-cancer cohort (43.6% vs 34.1%). The higher mortality among cancer patients was statistically significant among those aged 70 years and above (OR 2.28, 1.14-4.50, p = 0.02). After adjusting for age, ethnicity and co-morbidities, a history of cancer was an independent predictor of mortality following COVID-19 (HR 1.57, 95% CI:1.04-2.4, p = 0.03). Patients with active malignancy also had similarly increased adjusted mortality (HR 1.64, 95% CI: 1.03 – 2.6, p = 0.04). Increasing age (HR 1.49 every 10 years, 95% CI:1.25-1.8, p <0.001), South Asian ethnicity (HR 2.92, 95% CI:1.73-4.9, p <0.001) and cerebrovascular disease (HR 1.93, 95% CI:1.18-3.2, p = 0.008) were also confirmed as independent predictors of mortality. [Formula presented] Conclusions: Along with known risk factors, cancer confers an independent risk for mortality in COVID-19. Taken together, our findings support the need to continue ‘shielding’ patients with cancer from exposure to COVID-19 infection. Increasing age and co-morbidity should take precedence when weighing up risk factors for severe COVID-19 infection in cancer patients. Legal entity responsible for the study: University College London Hospitals NHS Foundation Trust. Funding: Has not received any funding. Disclosure: H.M. Shaw: Advisory/Consultancy, Speaker Bureau/Expert testimony: Novartis, BMS, MSD;Advisory/Consultancy: Immunocore, Idera, Iovance, Genmab, Sanofi Genzyme/Regeneron, Macrogenics, Roche;Speaker Bureau/Expert testimony: Sanofi Genzyme. All other authors have declared no conflicts of interest.

UK Coronavirus Cancer Monitoring Project (UKCCMP): A national reporting network for real time data of the COVID-19 pandemic

Background: The COVID-19 pandemic required a rapid response and need for real-world data in cancer patients. The nationwide, real-time coordinated UKCCMP reporting network provided an immediate solution. Methods: The ability to set up an interdisciplinary multi-organisational team quickly, covering expert knowledge from clinical, legal, statistical, and computer science was essential. The technical infra-structure allows clinician-led anonymised data entry and rapid dissemination of results with a clinical (RedCap) database as core. However the development of a national cancer reporting network was crucial for the viability of the project. From its inception in March 2020 the reporting network was established via 4 iterative phases. Results: Within the first 4 weeks, >50 centres were involved with coverage throughout the UK. Expansion has continued with >70 centres within 6 weeks reporting over 1200 COVID positive cancer patients. This was achieved through a 4-phase approach: phase 1 - Outline: This involved project protocol development where key data and timelines were confirmed by a small project team followed by whole-team sign-off. phase 2 - Engagement: This involved identification and engagement of existing groups to establish an initial network. Professional body endorsement led to increased recognition and utilisation of their membership networks. Finally regional leads were identified. phase 3 - Invitation: The third phase involved the distribution of a formal invite letter via identified networks. Project specific email and standard mailing lists were created to enhance network identity and communication. phase 4 - Consolidation: Early development of an interactive project website and focus on communication via social media with varied content consolidated interest and led to further extension. Conclusions: Real-time reporting of real world data can be achieved with clearly defined project phases, standardised documentation and an iterative recruitment process. The COVID-19 pandemic necessitated a rapid response, proving that similar reporting networks can be set up quickly and robustly to react to the evidence-based needs of the oncology community in the drive for implementation of change. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: A.C. Olsson-Brown: Honoraria (self): Roche;Honoraria (institution): Roche;Honoraria (self): Bristol Myers Squibb;Research grant/Funding (institution): Bristol Myers Squibb;Research grant/Funding (institution): USB Pharma;Research grant/Funding (institution): Eli Lily;Research grant/Funding (institution): Novartis. D.J. Hughes: Honoraria (self): Novartis;Research grant/Funding (self): NanoMab Technology LtD. S. Sivakumar: Research grant/Funding (self): Celgene. All other authors have declared no conflicts of interest.