Background Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.Methods In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1•84, 95% CI 1•53-2•21), male sex (1•63, 1•07-2•48), smoking status (former smoker vs never smoked: 1•60, 1•03-2•47), number of comorbidities (two vs none: 4•50, 1•33-15•28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3•89, 2•11-7•18), active cancer (progressing vs remission: 5•20, 2•77-9•77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2•93, 1•79-4•79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0•24, 0•07-0•84) or the US-Midwest (0•50, 0•28-0•90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. Interpretation Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

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Tyrosine kinase inhibitors were found to be clinically effective for treatment of patients with certain subsets of cancers carrying somatic mutations in receptor tyrosine kinases. However, the duration of clinical response is often limited, and patients ultimately develop drug resistance. Here, we use single-cell RNA sequencing to demonstrate the existence of multiple cancer cell subpopulations within cell lines, xenograft tumors and patient tumors. These subpopulations exhibit epigenetic changes and differential therapeutic sensitivity. Recurrently overrepresented ontologies in genes that are differentially expressed between drug tolerant cell populations and drug sensitive cells include epithelial-to-mesenchymal transition, epithelium development, vesicle mediated transport, drug metabolism and cholesterol homeostasis. We show analysis of identified markers using the LINCS database to predict and functionally validate small molecules that target selected drug tolerant cell populations. In combination with EGFR inhibitors, crizotinib inhibits the emergence of a defined subset of EGFR inhibitor-tolerant clones. In this study, we describe the spectrum of changes associated with drug tolerance and inhibition of specific tolerant cell subpopulations with combination agents.

IMPORTANCE COVID-19 is a life-threatening illness for many patients. Prior studies have established hematologic cancers as a risk factor associated with particularly poor outcomes from COVID-19. To our knowledge, no studies have established a beneficial role for anti-COVID-19 interventions in this at-risk population. Convalescent plasma therapy may benefit immunocompromised individuals with COVID-19, including those with hematologic cancers.OBJECTIVE To evaluate the association of convalescent plasma treatment with 30-day mortality in hospitalized adults with hematologic cancers and COVID-19 from a multi-institutional cohort. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study using data from the COVID-19 and Cancer Consortium registry with propensity score matching evaluated patients with hematologic cancers who were hospitalized for COVID-19. Data were collected between

Introduction: Disparities exist in lung cancer outcomes between African American and white people. The current United States Preventive Services Task Force (USPSTF) lung cancer screening eligibility criteria, which is based solely on age and smoking history, may exacerbate racial disparities. We evaluated whether the PLCOm2012 risk prediction model more effectively selects African American eversmokers for screening. Methods: Lung cancer cases diagnosed between 2010 and 2019 at an urban medical center serving a racially and ethnically diverse population were retrospectively reviewed for lung cancer screening eligibility based on the USPSTF criteria versus the PLCOm2012 model.Results: This cohort of 883 ever-smokers comprised the following racial and ethnic makeup: 258 white (29.2%), 497 African American (56.3%), 69 Hispanic (7.8%), 24 Asian (2.7%), and 35 other (4.0%). Compared with the USPSTF criteria, the PLCOm2012 model increased the sensitivity for the African American cohort at lung cancer risk thresholds of 1.51%, 1.70%, and 2.00% per 6 years (p < 0.0001). For example, at the 1.70% risk threshold, the PLCOm2012 model identified 71.3% African American cases, whereas the USPSTF criteria only identified 50.3% (p < 0.0001). In contrast, in case of whites there was no difference (66.0% versus 62.4%, respectively [p ¼ 0.203]). Of the African American ever-smokers who were PLCO1.7%-positive and USPSTF-negative, the criteria missed from the USPSTF were those with pack-years less than 30 (67.7%), quit time of greater than 15 years (22.5%), and age less than 55 years (13.0%). Conclusions:The PLCOm2012 model was found to be preferable over the USPSTF criteria at identifying African American ever-smokers for lung cancer screening. The broader use of this model in racially diverse populations may help overcome disparities in lung cancer screening and outcomes.