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Background: The SARS-CoV-2 outbreak significantly affected Gustave Roussy cancer center Here, we report the Gustave Roussy experience on older patients (OP) with cancer during the SARS-CoV-2 outbreak Methods: Cancer pts with suspected SARS-CoV-2 infection were admitted at Gustave Roussy starting March, 12th Screening indications have been adapted over time All the COVID-19 pts positively tested and managed at Gustave Roussy between March 14th and April 15th have been included in a redcap database Pts and underlying oncological and COVID-19 diseases characteristics have been collected Cancer and COVID-19 managements, and outcomes have been assessed The primary endpoint of this analysis was the clinical deterioration, defined as the need for O2 supplementation of 6l/min or more, or death of any cause Results: Among the first 137 cancer pts diagnosed with SARS-CoV-2, 36 patients were aged 70 years old or over (26%) Most of them were female (61%) with a median age of 75 5 years old Most frequent underlying cancers were solid tumors (92%) including GI (19%), lung (17%), GYN (14%) and head and neck (14%) Most OP (36%) were ECOG Performans status 2 versus 24% in younger patients (YP) The diagnosis of SARS-CoV-2 infection was made by RT-PCR or thoracic CT scan alone in 97% and 3% of the cases, respectively in OP and in 92% and 8% in YP Most OP experienced symptoms prior to testing (92%) compared to YP (80%) Symptoms differed according to age with more cough with sputum production in OP (14% versus 5%), dyspnea (39% versus 31%), diarrhea (17% versus 9%), shivers (8% versus 0%), sore throat (8% versus 4%) and no anosmia nor agueusia The majority of OP was hospitalized (81%) compared to 72% of YP and treated with HCQ/AZI (15;52%) compared to 25 (35%) YP with inclusion in the ONCOVID trial (EudraCT: 2020-01250-21) They did not receive any IL-6 inhibitor Only one OP was admitted in the ICU (3%) Clinical deterioration occurred in 10 OP (29%) There was no impact of age on clinical worsening (HR=1 157;95%CI 0 55-2 42;p=0 7) However age was associated with worse overall survival (OS) (HR=2 45 95%CI 1 02-5 92;p=0 0463) Results will be updated at the meeting Conclusions: OP with cancer had a different disease presentation, same rate of clinical worsening but worse OS in SARS-CoV-2 infection Legal entity responsible for the study: The authors Funding: Has not received any funding Disclosure: All authors have declared no conflicts of interest

Background: Outcomes and risk factors associated with COVID-19 worsening among cancer patients have previously been reported. However, the actual impact of SARs-Co-V2 infection on the cancer treatment strategy remains unknown. Here, we report the Gustave Roussy (GR) experience, one year after the onset of the pandemic focusing on the impact of COVID-19 in patients with ongoing management of oncohematological disease. Methods: All patients positively tested for SARS-CoV-2 and managed at GR between Mar 14th 2020 and Feb 15th 2021 (data cut-off) have been included. Patients underlying oncohematological disease and COVID19 characteristics have been collected. Cancer and COVID-19 management and outcomes have been assessed. Primary endpoint was the overall impact of COVID-19 on oncological and hematological treatment strategy assessed at 1, 3, 6 and 12 months. Results: At the time of the analysis, 423 patients (median age: 62 years) were found positive for SARS-CoV-2 and managed at GR with a median follow up of 5.6 months (0-13 months). Among them, 284 (67%) were admitted due to COVID-19. Clinical deterioration occurred in 87 patients (21%), 43 patients (10%) were transferred in intensive care unit and 123 (29%) patients died, among which 47 (11%) died from COVID-19. Overall, 329 (78%) patients were on active treatment for underlying oncohematological disease at time of COVID diagnosis. Impact of COVID-19 on cancer treatment strategy in those patients is presented in the Table. The majority (N=268, 81%) had no change in oncological strategy. For those who experienced a delay, median delay in treatment was 21 days (N=99, [1-77]), 30 days (N=15, [15-56]), 7 days (N=8,[3-35]) for systemic treatment, surgery and radiotherapy respectively. [Formula presented] Conclusions: COVID-19 outbreak is associated with a significant mortality in patients with cancer. However, for patients who did not die from COVID-19, we provide the first report supporting that ongoing treatment was maintained or could be resumed in the majority of cases in a timely manner. Legal entity responsible for the study: Gustave Roussy. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Background: The severe pneumonitis in coronavirus disease 2019 (COVID-19) requires prolonged treatment in intensive care units, leading to overwhelmed hospital facilities Treatment with tocilizumab (Actemra, Roche), a monoclonal antibody targeting interleukin 6 receptor (IL6R), has shown promising efficacy in alleviating the severe pneumonitis However, only around 50% of the treated patients benefit from this intervention It is therefore an unmet medical need to identify biomarkers associated with the severity of disease and theranostic biomarkers to predict and differentiate potential responders from non-responders to the treatment Methods: An unbiased hyper reaction monitoring mass spectrometry (HRM™-MS) approach was used to analyze serum samples from severe COVID-19 cases before and 7 days after treatment with tocilizumab (n = 28), enabling simultaneous identification and quantification of all detectable serum proteins All samples were measured using 1h gradient on a nano-flow LC-MS/MS setup operated in data-independent acquisition (DIA) mode Data was extracted using Spectronaut™ (Biognosys) Univariate and multivariate statistical analyses were conducted to identify biomarker candidates Pathway analysis was used to identify dysregulated biological functions and signaling pathways Results: Over 450 proteins were quantified across all samples by HRM-MS Univariate statistical analysis identified significantly changing proteins across conditions (mortality day 30, pre-post treatment, responder/non-responder, q-value > 0 05 and fold change >1 5) Multivariate analysis (PLS-DA) was also used to classify proteins based on their abundance across condition Proteomic data was further integrated with clinical outcome data to identify a panel of protein biomarker candidates potentially useful in predicting tocilizumab treatment efficiency and the COVID-19 disease severity Conclusions: Unbiased proteomic profiling of COVID-19 patient serum identified a panel of candidate protein biomarkers that associate with tocilizumab treatment response as well as the ensuing course of the disease Further validation of these biomarker candidates opens the way for a personalized medicine approach in treating COVID-19 Legal entity responsible for the study: Biognosys AG Funding: Biognosys AG Disclosure: J-M Michot;F-X Danlos;F Pommeret;A Marabelle: Full/Part-time employment: Institut Gustave Roussy V Dozio;E Kishazi;C Escher;K Kakalacheva - Beeler: Full/Part-time employment: Biognosys AG M Vasse;J Rohmere;M Roumier: Full/Part-time employment: Hopital Foch

Background The GINECO previously developed the GVS to identify geriatric vulnerability in oOC pts (Falandry, 2013). The derivation cohort was EWOT-3 database of 111 oOC pts treated with first line carboplatin. The GVS combines albumin (≥ or Methods Pts ≥70 yrs diagnosed with FIGO stage III/IV epithelial OC and no organ failure were screened for GVS. Those with GVS≥3 were proposed EWOC-1 randomized trial, evaluating 3 treatment regimens in the vulnerable pts. Other pts’ data were collected in the “EWOC-1 registry”. External validation of GVS was performed in the whole population (V1), in the EWOC-1 registry subgroup (V2), and in the subgroup of pts treated with carboplatin paclitaxel regimens (V3). Cross-validation analyses (calibration, discrimination, and performance analysis) were performed according current recommendations (Steyerber, 2014). Results From 12/2013 to 11/2018, 447 elderly patients were included, 120 (27%) in EWOC-1 trial and 327 in EWOC-1 registry (V1: n = 447, V2: n = 327, V3: n = 324). Patients’ cancer characteristics were similar in the validation cohorts compared to the derivation one. Median follow up was 22 mo; missing values were limited ( Conclusions GVS provides a reproducible and robust prediction model of vulnerability in oOC pts, independent of geographic and historic effect (V1), as well as treatment patterns (V3), validated on an international population. Clinical trial identification PROTOCOL EWOC-1 - ENGOT OV-23 - 2013-000266-11. Legal entity responsible for the study Centre Hospitalier Lyon Sud. Funding Hospital Clinical Research Program (PHRC). Disclosure D. Lorusso: Honoraria (self), Honoraria (institution), Advisory / Consultancy: MERCK; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): Clovis; Advisory / Consultancy: Immunogen; Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses, Shareholder / Stockholder / Stock options, Licensing / Royalties, Full / Part-time employment: PharmaMar; Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses, Shareholder / Stockholder / Stock options, Licensing / Royalties, Full / Part-time employment: Roche; Advisory / Consultancy, Travel / Accommodation / Expenses, Shareholder / Stockholder / Stock options, Licensing / Royalties, Full / Part-time employment: Tesaro; Officer / Board of Directors, Spouse / Financial dependant, Non-remunerated activity/ies: GCIG. A. Floquet: Advisory / Consultancy: Clovis; Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Advisory / Consultancy, Travel / Accommodation / Expenses: Tesaro; Travel / Accommodation / Expenses: Roche. O. Tredan: Honoraria (self): Roche; Honoraria (self): AstraZeneca; Honoraria (self): Pfizer; Honoraria (self): Novartis; Honoraria (self): Lilly; Honoraria (self): BMS; Honoraria (self): MSD. E. Pujade-Lauraine: Honoraria (self), Honoraria (institution), Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self), Research grant / Funding (institution), Travel / Accommodation / Expenses, Shareholder / Stockholder /: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self), Research grant / Funding (institution), Travel / Accommodation / Expenses, Shareholder / Stockholder / Stock options, Licensing / Royalties, Full: Roche; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): Clovis; Honoraria (self), Honoraria (institution), Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (institution), Travel / Accommodation / Expenses, Shareholder / Stockholder / Stock options, Full / Part-time e: Tesaro; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): Genmab; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): Incyte; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): MSD; Advisory / Consultancy, Speaker Bureau / Expert testimony, Leadership role, Research grant / Funding (self): Pfizer. C. Falandry: Honoraria (self): Leo Pharma; Honoraria (self), Honoraria (institution): Pfizer; Honoraria (self): MSD oncology; Honoraria (self): Teva; Honoraria (self): AstraZeneca; Honoraria (self): Baxter; Honoraria (self): Eisai; Honoraria (self): Janssen; Honoraria (self): Novartis; Honoraria (institution): Chugai Pharma; Honoraria (institution): Pierre Fabre; Honoraria (institution): Astellas Pharma. All other authors have declared no conflicts of interest.