ABSTRACT
Introduction: The COVID-19 pandemic continues to disproportionately impact people with cancer. Mortality estimates among cancer patients vary and are influenced by numerous factors including cancer type, treatment, disease stage, and patient demographics. To date, attempts to explore these associations have been limited by small cohorts. Methods: Here, we present a meta-analysis of data available through the Reboot: COVID-Cancer Project, a living and freely available resource that includes published clinical studies that report outcomes for cancer patients with COVID-19. Studies were identified using targeted search queries in PubMed, MedRxiv, BioRxiv, and the SSRN eLibrary, followed by rule-based approaches and extensive manual validation and data extraction. The data is updated monthly and can be explored through an interactive dashboard as well as downloaded. Case fatality rates (CFR;the number of deaths per 100 confirmed cases during the study period) were calculated using a random-effects model. Study heterogeneity and sample size bias was assessed using the Egger regression test. Results: As of December 18, 2020, the resource contained 225 publications comprising 21,839 cancer patients with COVID-19. Of these, there was sufficient sample size to quantify CFRs for 22 cancer types across 19,147 patients from 109 publications. The pooled CFR among all cancer patients was 27% (95% CI: 25-30%). For solid tumors and hematological malignancies, the CFRs were 23% (95% CI: 20-25%) and 30% (95% CI: 27-33%), respectively. Within solid tumors, patients with lung (CFR: 32%, 95% CI: 27-36%), prostate (CFR: 30%, 95% CI: 17-43%), and central nervous system (CFR: 27%, 95% CI: 18-36%) malignancies had relatively high CFRs, whereas patients with breast (CFR: 10%, 95% CI: 7-14%) and thyroid (CFR: 5%, 95% CI: 1-11%) malignancies had relatively low CFRs. Among patients with hematological malignancies, CFRs ranged from 10% (95% CI: 3-18%) in patients with chronic myelogenous leukemia to 39% (95% CI: 20-57%) in patients with acute myeloid leukemia. Discussion: We observed significant heterogeneity of COVID-19 CFRs between cancer subtypes. This may in part reflect differences in patient demographics, treatment history, or disease state. Subtype-specific analysis can stratify cancer patients by risk for COVID-19 mortality and advise management strategies. The Reboot: COVID-Cancer Project provides an accessible means to evaluate subtype-specific COVID-19 fatality rates on a by-publication basis.
ABSTRACT
Introduction: More than 200 treatments have been tested for COVID-19 in over 7000 clinical trials. Most of these treatments are repurposed generic drugs, many of which have been studied extensively for the treatment of cancer. As cancer patients are particularly vulnerable, there is a need to understand how COVID-19 treatments might affect a patient's cancer. As part of the Reboot: COVID-Cancer Project, a living and freely available resource of clinical studies that report outcomes for cancer patients, we have developed a semi-automated pipeline to identify all relevant published clinical studies and registered clinical trials where COVID-19 drugs were tested for the treatment of cancer. Methods: Published clinical studies were assembled using targeted search queries in PubMed, rule-based approaches, and machine learning models. Machine learning models applied to natural language processing tasks were used to predict the drug, cancer type, study type, and therapeutic association. We used domain-specific rules and post-processing steps to further refine results, including determining whether a drug was used alone or in combination. Registered clinical trials were compiled from clinicaltrials.gov using targeted search queries, automated mapping, and rule-based screening. We extracted key information about each trial, such as the drug, cancer type, phase, location, trial status, age, gender, and availability of results. We applied our pipeline to a curated set of 202 drugs being tested for the treatment of COVID-19 in at least two interventional clinical trials worldwide, of which 27 are FDA-approved drugs that are standard of care for cancer, and 115 are FDA-approved drugs primarily used for non-cancer indications. Results: We found 28,138 published clinical studies and 9,118 registered clinical trials where the 202 drugs were tested for cancer. The published clinical studies include 5,286 case studies, 2,559 randomized controlled trials (RCTs), and 20,294 non-RCT clinical trials or observational studies. In 37% of the cases, the drug was used alone and not in combination. Lymphoid cancers were the most commonly tested, comprising 30% of studies. Possible benefit of the drug was found in 64% of publications. Of the 115 FDA-approved non-cancer drugs being tested for COVID-19, there is at least one published clinical study for 84 (73%) drugs. An additional 12 FDA-approved non-cancer drugs have been tested for the treatment of cancer in clinical trials, but have no results reported. Of the registered clinical trials, 39% are currently active, 66% are Phase 2 or later, and lymphoid cancers are again the most common, representing 29% of the trials. Discussion: Given the interconnection between COVID-19 and cancer, it is essential to understand how drugs used for COVID-19 might impact a patient's cancer. We have created a living resource for rapid review of information. The datasets are updated monthly and are freely available via an interactive dashboard.