IMPORTANCEAndrogen deprivation therapy (ADT) has been theorized to decrease the severity of SARS-CoV-2 infection in patients with prostate cancer owing to a potential decrease in the tissuebased expression of the SARS-CoV-2 coreceptor transmembrane protease, serine 2 (TMPRSS2). OBJECTIVE To examine whether ADT is associated with a decreased rate of 30-day mortality from SARS-CoV-2 infection among patients with prostate cancer. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed patient data recorded in the COVID-19 and Cancer Consortium registry between March 17, 2020, and February 11, 2021. The consortium maintains a centralized multi-institution registry of patients with a current or past diagnosis of cancer who developed COVID-19. Data were collected and managed using REDCap software hosted at Vanderbilt University Medical Center in Nashville, Tennessee. Initially, 1228patients aged 18 years or older with prostate cancer listed as their primary malignant neoplasm were included; 122 patients with a second malignant neoplasm, insufficient follow-up, or low-quality data were excluded. Propensity matching was performed using the nearest-neighbor method with a 1:3 ratio of treated units to control units, adjusted for age, body mass index, race and ethnicity, Eastern Cooperative Oncology Group performance status score, smoking status, comorbidities (cardiovascular, pulmonary, kidney disease, and diabetes), cancer status, baseline steroid use, COVID-19 treatment, and presence of metastatic disease. EXPOSURES Androgen deprivation therapy use was defined as prior bilateral orchiectomy or pharmacologic ADT administered within the prior 3 months of presentation with COVID-19. MAIN OUTCOMES AND MEASURESThe primary outcome was the rate of all-cause 30-day mortality after COVID-19 diagnosis for patients receiving ADT compared with patients not receiving ADT after propensity matching. RESULTSAfter exclusions, 1106 patients with prostate cancer (before propensity score matching: median age, 73 years [IQR, 65-79 years]; 561 (51%) self-identified as non-Hispanic White) were included for analysis. Of these patients, 477 were included for propensity score matching (169 who received ADT and 308 who did not receive ADT). After propensity matching, there was no significant difference in the primary end point of the rate of all-cause 30-day mortality (OR, 0.77; 95% CI, 0.42-1.42).
ImportanceCytokine storm due to COVID-19 can cause high morbidity and mortality and may be more common in patients with cancer treated with immunotherapy (IO) due to immune system activation.ObjectiveTo determine the association of baseline immunosuppression and/or IO-based therapies with COVID-19 severity and cytokine storm in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included 12 046 patients reported to the COVID-19 and Cancer Consortium (CCC19) registry from March 2020 to May 2022. The CCC19 registry is a centralized international multi-institutional registry of patients with COVID-19 with a current or past diagnosis of cancer. Records analyzed included patients with active or previous cancer who had a laboratory-confirmed infection with SARS-CoV-2 by polymerase chain reaction and/or serologic findings.ExposuresImmunosuppression due to therapy; systemic anticancer therapy (IO or non-IO).Main Outcomes and MeasuresThe primary outcome was a 5-level ordinal scale of COVID-19 severity: no complications; hospitalized without requiring oxygen; hospitalized and required oxygen; intensive care unit admission and/or mechanical ventilation; death. The secondary outcome was the occurrence of cytokine storm.ResultsThe median age of the entire cohort was 65 years (interquartile range [IQR], 54-74) years and 6359 patients were female (52.8%) and 6598 (54.8%) were non-Hispanic White. A total of 599 (5.0%) patients received IO, whereas 4327 (35.9%) received non-IO systemic anticancer therapies, and 7120 (59.1%) did not receive any antineoplastic regimen within 3 months prior to COVID-19 diagnosis. Although no difference in COVID-19 severity and cytokine storm was found in the IO group compared with the untreated group in the total cohort (adjusted odds ratio [aOR], 0.80; 95% CI, 0.56-1.13, and aOR, 0.89; 95% CI, 0.41-1.93, respectively), patients with baseline immunosuppression treated with IO (vs untreated) had worse COVID-19 severity and cytokine storm (aOR, 3.33; 95% CI, 1.38-8.01, and aOR, 4.41; 95% CI, 1.71-11.38, respectively). Patients with immunosuppression receiving non-IO therapies (vs untreated) also had worse COVID-19 severity (aOR, 1.79; 95% CI, 1.36-2.35) and cytokine storm (aOR, 2.32; 95% CI, 1.42-3.79).Conclusions and RelevanceThis cohort study found that in patients with cancer and COVID-19, administration of systemic anticancer therapies, especially IO, in the context of baseline immunosuppression was associated with severe clinical outcomes and the development of cytokine storm.Trial RegistrationClinicalTrials.gov Identifier: NCT04354701
556 Background: Bladder cancer has one of the highest rates of human epidermal growth factor receptor 2 (HER2) alteration. Novel HER2-directed agents are being investigated in metastatic BC. We sought to define the incidence and clinical characteristics of HER2-altered BC across disease states. Methods: We retrospectively analyzed our single-institution, clinically annotated cohort of urothelial BC pts with available genomic profiling data (MSK-IMPACT). We quantified the prevalence of HER2 alterations, defined as driver mutation (based on OncoKB), and/or amplification, across BC disease states. We examined the association between HER2 alteration and disease progression and survival. The Kaplan-Meier method was used for time-to-event analyses. Results: A total of 1073 BC pts underwent MSK IMPACT profiling of tumor tissue derived from the following disease states: 36% (n = 380) non-muscle invasive (NMI)BC, 41% (n = 443) muscle invasive (MI)BC, and 23% (n = 250) (met)BC. At initial diagnosis, the median age was 67 years, 77% (n = 822) were male, 86% (n = 928) were white, and 66% (n = 710) were smokers. Overall, 16% (n = 177) of pts had any oncogenic HER2 alteration (Table), including 11% with a HER2 driver mutation and 7% with HER2 amplification The most frequent mutations were S310F (40%, n = 53) and S310Y (14%, n = 19). The rate of HER2 amplification was different among the three groups (p = 0.002), 9% in MIBC and metBC compared to 3% in NMIBC. Among 514 pts with NMIBC, the median time to progression (TTP) to MIBC or metBC was 111.6 months (95% Cl: 85.7-NR). Among NMIBC pts, TTP was significantly shorter for HER2-amplified (n = 17) vs. non-amplified (n = 497) (HR = 1.99, 95%CI: 1.05-3.76, p = 0.034, median 26 vs. 114 months). Among pts with metBC receiving frontline platinum-based chemotherapy (n = 143), the median overall survival (OS) was 25.3 months (95%CI: 18.5-33.9). OS was numerically higher in pts with any oncogenic HER2 alteration (n = 26) compared to wild-type (n = 117) (HR = 0.59, 95% Cl: 0.33-1.07, p = 0.082), though this difference was not statistically significant. The median OS for platinum-refractory metBC pts receiving 2nd line immunotherapy (n = 63) was 10.3 months (95%CI: 7.2-31.6), and the association between OS and HER2 alteration was not significant (HR = 0.57, 95%CI: 0.24-1.37, p = 0.2). Conclusions: HER2 amplification is more frequent in MIBC and metBC than in NMIBC. In NMIBC, HER2 amplification is associated with shorter TTP to MIBC or metBC. HER2 alteration in metBC is associated with a non-significant trend towards improved OS in frontline platinum-treated pts, though this analysis is limited by small sample size.[Table: see text]
e18755 Background: The 2016 21st Century Cures Act supports the use of Real-World Data (RWD) for regulatory decision/approval. Due to technological advances, a vast amount of health-related data are now available, but most are not standardized nor readily useable for research. Also, currently available standardized RWD models are not applicable across cancer types or oncology specialties (surgery, medical oncology, radiation oncology, pathology, radiology, etc.). To address these deficiencies Memorial Sloan Kettering Cancer Center (MSKCC) built a comprehensive, pan-cancer, pan-specialty RWD model. Methods: The Core Clinical Data Element (CCDE) data model incorporates aspects of existing academic and biopharma data models, including PRISSMM framework, ASCO’s mCODE, and NAACCR tumor registry model. The data model encompasses 11 domains that are critical to the understanding of the patient’s cancer journey, including: demographic, comorbidities, diagnosis, pathology, imaging, genomics, cancer surgeries, radiation oncology treatments, medical oncology treatments, cancer status/progression, and additional health information. To align with current standards, we are using ICD-10, ICDO3, CTACE V5.0, HL7, SNOMED and LOINC code sets. Further, this adaptable model allows for 5-10 disease specific elements to accommodate for disease heterogenicity and capture the differences among cancer types. Results: The CCDE database includes 1,126 of total data elements. MSKCC has 52,704 patients with MSK-IMPACT (Next-Generation sequencing platform with 505 genes panel) testing of which, we have identified 1,132 bladder cancer patients with at-least one year of cancer care follow-up for the initial curation cohort. Patients were identified as having an OncoTree bladder tumor type code that is assigned by a pathologist who attests the diagnosis by reviewing results from clinical tests on tumor specimens. To the date, 641 patients including 46,415 curated forms have been curated (Table). Conclusions: The comprehensive MSKCC’s CCDE data model standardizes the common and critical pan-cancer and pan-specialty elements for RWD. The dataset resulting from this curation efforts will provide robust structured and unified genomic and phenomic data across tumor types for future research enabling greater collaboration across various cancer types as well as oncology specialties.[Table: see text]
e18775 Background: The production of high-quality real-world data requires comprehensive and meticulous data quality assurance (QA) methods to guarantee that adequate standards of accuracy, completeness, and consistency are met. Memorial Sloan Kettering Cancer Center (MSKCC) synthesizes manually curated Electronic Health Record (EHR) data to collect and harmonize the fundamental data elements across all cancer types. Centralized real-time analysis of curated data quality can allow for rigorous review to identify areas of strength and opportunities for improvement in the curation process. Methods: MSKCC built the Core Clinical Data Element (CCDE) data model, which encompasses aspects of PRISSMM, ASCO’s mCODE, and NAACCR tumor registry frameworks, to capture standardized real-world, pan-cancer, pan-specialty data across 11 modules, including cancer genomics, imaging, pathology, surgery, and radiation. A key component within the QA process is source data verification (SDV), the comparison of curated data against source documents to identify inconsistencies. Any discrepancies detected are classified into major and minor violations. Major violations are errors or omissions on core data elements that would impact time interval calculations, such as an incorrect procedure date. Minor violations are errors or omissions on less critical data elements, such as a missing radiation therapy dose. Identifying these inconsistences allows the QA team to recognize patterns in curation errors and distinguish areas for curator retraining. Results: With limited functionality in basic standard data quality checks that exist across various data storage platforms, an interactive application was developed using the R Shiny package to access data as cases are recorded and summarize findings from SDV in real time. The app has two panels, each stratified by CCDE module. The first panel details the total number of forms curated and percentage of forms that underwent SDV, with each form representing one of the 11 modules. The other panel consists of a set of tables that summarize specific major and minor violations based on user selection of a denominator of either patients (e.g. how many patients had a violation on at least one imaging report) or forms (e.g. how many imaging reports had a violation). We will demonstrate the utility of the app and discuss benefits of real time evaluation in large-scale, real-world EHR curation efforts. Conclusions: We recommend automated, user-friendly tools to assess data quality of such efforts. With real-time analysis, the tool allows for ongoing and regular data checks, enabling clarification of directives and retraining of curators as necessary early in the curation process. As the data curation efforts expand to more cancer cohorts, the app examines data quality of each cohort to ensure consistent evaluation. This offers transparency of data quality to ensure usability in real-world data for rigorous research.
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