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Background. Given the limited collaborative international studies that evaluated COVID-19 in patients with cancer in comparison to patients without cancer, we aimed to determine the independent risk factors associated with increased 30-day mortality and the impact of novel treatment modalities in a large group of cancer and non-cancer patients with COVID-19 from multiple countries. Methods. We retrospectively collected de-identified data on cancer and non-cancer patients diagnosed with COVID-19 between January and November 2020, at 16 centers in Asia, Australia, Europe, North America, and South America. A logistic regression model was used to identify independent predictors of all-cause mortality within 30 days after COVID-19 diagnosis. Results. Of the total 4015 COVID-19 confirmed patients entered, we analyzed 3966 patients, 1115 cancer and 2851 non-cancer patients. Cancer patients were older than non-cancer patients (median age, 61 vs 50 years;p< 0.0001);more likely to be pancytopenic , had pulmonary disorders, hypertension, diabetes mellitus. In addition, they were more likely to present with higher inflammatory biomarkers (D-dimer, ferritin and procalcitonin), but were less likely to present with clinical symptoms. By multivariable logistic regression analysis, cancer was an independent risk factor for 30-day mortality (OR 1.46;95% CI 1.03 to 2.07;p=0.035). Older age (≥65 years) was the strongest predictor of 30-day mortality in all patients (OR 4.55;95% CI 3.34 to 6.20;p< 0.0001). Remdesivir was the only therapeutic agent independently associated with decreased 30-day mortality (OR 0.58;CI 0.39-0.88;p=0.009). Among patients on lowflow oxygen at admission, patients who received remdesivir had a lower 30-day mortality rate than those who were on high flow oxygen (5.9% vs 17.6%;p=0.03). Patients transfused with convalescent plasma within 1 day of diagnosis had a lower 30-day mortality rate than those transfused later (1% vs 7%, p=0.04). Conclusion. Cancer is an independent risk factor for increased 30-day all-cause mortality from COVID-19. Remdesivir, particularly in patients receiving low-flow oxygen, can reduce 30-day all-cause mortality, as well as convalescent plasma given early after COVID-19 diagnosis.
ABSTRACT
Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner. We leverage the ability to run ensemble MD simulations on GPU clusters, while the data from atomistic MD simulations are streamed continuously to AI/ML approaches to guide the conformational search in a biophysically meaningful manner on a wafer-scale AI accelerator. We demonstrate the efficacy of Stream-AI-MD simulations for two scientific use-cases: (1) folding a small prototypical protein, namely ββα-fold (BBA) FSD-EY and (2) understanding protein-protein interaction (PPI) within the SARS-CoV-2 proteome between two proteins, nsp16 and nsp10. We show that Stream-AI-MD simulations can improve time-to-solution by ~50X for BBA protein folding. Further, we also discuss performance trade-offs involved in implementing AI-coupled HPC workflows on heterogeneous computing architectures. © 2021 ACM.
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Background: Most COVID-19 (C19) vaccine trials excluded patients with active cancer. Here, we report our real-world patient-reported and clinical outcomes of BNT162b2 mRNA C19 vaccine in patients with cancer. Methods: Our institutional Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE) follows a longitudinal observational cohort of pts w cancer getting C19 vaccine. Pts complete a validated PRO tool, MD Anderson Symptom Inventory (MDASI, 13 core, 6 interference plus 17 items of symptoms from prior vaccine trials) pre-dose 1, then daily x 6d, then weekly, then on day of dose 2, then daily x 6d, then weekly x 3w. Demographics, cancer variables, prior immune checkpoint inhibitors (ICI), C19 status pre- & post-vaccine are aggregated via Syntropy platform: Palantir Foundry. Primary outcome is incidence of PRO symptoms bw dose 1 & 2 across AYA 15-39y, mid-age 40-64y & senior 65y+ cohorts. Secondary outcomes include PRO symptom incidence post-dose 2, post-vaccine change in cancer symptoms, post-vaccine symptom severity based on prior ICI, and confirmed C19 > 7 days post-dose 2. First planned 8-wk interim analysis is reported here. Results: 6388 pts w cancer (4973 w mets) received a BNT162b2 vaccine dose (4811 both doses, 1577 received one & await dose 2). Overall, median age 64y (range 16-95y);382 AYAs, 2927 mid-age, 3079 seniors (65-70y n = 1158, 70-79y n = 1521, 80-89y n = 378, 90y+ n = 22). 4099 (64%) are White, 823 (13%) AA, 791 (12%) Hispanic, 441 (7%) Asians. Primary cancers: breast (1397), GU (821), heme (775), thoracic/HN (745), and CRC (385). Prior to dose 1, 1862 had no prior systemic tx while 4526 pts did including 3243 who had only non-IO tx (chemo, targeted tx), 1,283 had immunotherapy including 857 who had ICIs prior to dose 1. Patient-reported symptoms after C19 Vaccine: Of 6388 pts, 4714 (74% response rate, median age 67y, range 16-95y) completed 16485 PRO surveys. After 2 doses, seniors reported lower mean scores vs mid-age or AYAs on 22 of 36 symptoms including injection site pain, palpitations, itch, rash, malaise, fevers/chills, arthralgia, myalgia, headache, pain, fatigue, nausea, disturbed sleep, distress (p < 0.05). Pts w prior ICIs had higher severity of itch, rash (p < 0.05) from baseline after both dose 1 & 2 vs pts without systemic tx. Post dose 1, pts with prior ICI had higher increase in fatigue, malaise, itch, rash, myalgia, anorexia from their baseline vs pts without systemic tx (p < 0.05). C19 Outcomes: Of 6388 pts, 616 had a C19 test at any time post-dose 1: 23 (0.36%) tested positive of whom 20 (0.3%) were between dose 1 & 2;two (0.031%) were within 7 days post-dose 2, and one patient (0.016%) tested positive 16 days after dose 2, requiring admission. Conclusions: This real-world observational cohort demonstrates post-vaccine symptom burden and outcomes in patients with cancer. Second interim analysis is planned at 16 weeks.
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Background: The oncology community is embracing social media (SM) platforms like Twitter to gain exposure to research, to network, and to engage in real-time discussions. The emergence of SM activity around the ASCO annual meetings has dramatically increased over the past 5 years, with factors such as the COVID-19 pandemic further accelerating use of digital platforms. This growth in SM engagement within the oncology community has previously been presented by totaling the quantity of tweets within a given time frame. Here, we explore the impact of specific trends through impression data. Methods: To evaluate activity trends among certain oncology stakeholders, we utilized an SM analytics platform, Symplur, to conduct a content analysis around ASCO conferences (2016-2020) using hashtags (#ASCOyy) as the search criterion. We focused our analysis on trends in impressions, defined by the theoretical maximum number of Twitter users a given tweet could have directly reached in a follower's timeline. We gathered impressions data to quantitatively assess overall ASCO engagement and evaluate topics of interest, and to discover common ASCO themes and reach within specific stakeholder groups. Results: Our results show the largest increase in impressions was during #ASCO20, despite a plateauing effect seen in the actual number of tweets (Table). The cumulative number of impressions for #ASCO16 was 468.2 million compared with approximately 1.12 billion for #ASCO20. Differentiating this result from the number of tweets related to ASCO, there was stabilization in the absolute number from #ASCO17 onward. When compiling impressions by doctors and by patient advocates, a similar trend emerged, with the most impressions captured during #ASCO20 (Table). Conclusions:As SM use continues to expand in the oncology community, stakeholders have turned to their digital voice to express views and opinions. The impact of impressions versus absolute number of tweets will continue to grow with a stakeholder's follower count, thus building on the digital presence in oncology.
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Background: Sarcoma pts often receive aggressive, highly immunosuppressive therapy and may be at high risk for severe COVID-19. Demographics, outcomes and risk factors for pts with sarcoma and COVID-19 are unknown. We aimed to describe the course of COVID-19 in sarcoma pts and to identify factors associated with adverse outcomes. Methods: The COVID-19 and Cancer Consortium (NCT04354701) is an international registry of pts with cancer and COVID-19. Adult pts (≥18 years old) with a diagnosis of sarcoma and laboratory confirmed SARS-CoV-2 were included from 50 participating institutions. Data including demographics, sarcoma diagnosis and treatment, and course of COVID-19 infection were analyzed. Primary outcome was the composite rate of hospitalization or death at 30 days from COVID-19 diagnosis. Secondary outcomes were 30 day all-cause mortality, rate of hospitalization, O2 need, and ICU admission. Descriptive statistics and univariate Fisher tests are reported. Results: From March 17, 2020 to February 6, 2021, N=204 pts were included. Median follow up was 42 days. Median age was 58 years (IQR 43-67). 97 (48%) were male. 30 (15%) had ECOG performance status ≥2. 104 (51%) received cancer treatment, including surgery or radiation, within 3 months of COVID-19 diagnosis. 153 (75%) had active cancer, of whom 34 (22%) had lung metastases. 100 (49%) pts met the composite primary endpoint;96 (47%) were hospitalized and 18 (9%) died within 30 days from COVID-19 diagnosis. 64 (31%) required oxygen, and 16 (8%) required ICU admission. Primary endpoint rates were similar for pts who received cytotoxic chemotherapy (38/58, 66%) or targeted therapy (16/28, 57%). Pts with higher rates of the primary endpoint included patients ≥60 years old (59% vs 40%, OR 2.04, 95% CI 1.12-3.74, p=0.016), pts with ECOG PS ≥2 vs 0-1 (90% vs 41%, OR 12.2, 95% CI 3.44-66.8, p<0.001), pts receiving any systemic therapy within 3 months of COVID-19 diagnosis (62% vs 39%, OR 2.65, 95% CI 1.43-4.97, p=0.001), and pts with lung metastases (68% vs 42%, OR 2.77, 95% CI 1.19- 6.79, p=0.013). Primary endpoint rates were similar across sarcoma subtypes (Table). Conclusions: This is the largest cohort study of pts with sarcoma and COVID-19 to date. Sarcoma pts have high rates of complications from COVID-19. Older patients, those with poor performance status, those recently receiving systemic cancer therapy, and those with lung metastases appear to have worse outcomes.