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.
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
Purpose The cell cycle mediators Aurora A and B are targets of drugs currently in clinical development. As with other targeted therapies in breast cancer, response to therapy might be associated with target expression in tumors. We therefore assessed expression of Aurora A and B in breast tumors and studied associations with clinical/pathologic variables. Experimental Design Tissue microarrays containing primary specimens from 638 patients with 15-year follow-up were employed to assess expression of Aurora A and B using our automated quantitative analysis method; we used cytokeratin to define pixels as breast cancer (tumor mask) within the array spot and measured Aurora A and B expression within the mask using Cy5-conjugated antibodies. Results Aurora A and B expression was variable in primary breast tumors. High Aurora A expression was strongly associated with decreased survival (P = 0.0005). On multivariable analysis, it remained an independent prognostic marker. High Aurora A expression was associated with high nuclear grade and high HER-2/neu and progesterone receptor expression. Aurora B expression was not associated with survival. Conclusions Aurora A expression defines a population of patients with decreased survival, whereas Aurora B expression does not, suggesting that Aurora A might be the preferred drug target in breast cancer. Aurora A expression in early-stage breast cancer may identify a subset of patients requiring more aggressive or pathway-targeted treatment. Prospective studies are needed to confirm the prognostic role of Aurora A as well as the predictive role of Aurora A expression in patients treated with Aurora A inhibitors.
. These BacMam-baculovirus-transduced mammalian cells expressing Pgp or ABCG2 were used for assessing the functional activity of these transporters. Crude membranes isolated from these cells were further used to study the activity of these transporters by biochemical techniques such as photocross-linking with transport substrate and adenosine triphosphatase assays. In addition, we show that the BacMam expression system can be exploited to coexpress both Pgp and ABCG2 in mammalian cells to determine their contribution to the transport of a common anticancer drug substrate. Collectively, these data demonstrate that the BacMam-baculovirus-based expression system can be used to simultaneously study the transport function and biochemical properties of ABC transporters.
When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments.
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