Background: Despite mitigation and treatment strategies, COVID-19 continues to negatively impact patients (pts) with cancer. Identifying factors that remain consistently associated with morbidity and mortality is critical for risk identification and care delivery. Methods: Using CCC19 registry data through 12/31/2021 we report clinical outcomes (30-day case fatality rate [CFR], mechanical ventilation use (MV), intensive care unit admission (ICU), and hospitalization) in adult pts with cancer and laboratory confirmed SARS-CoV-2, stratified by patient, cancer, and treatment-related factors. Results: In this cohort of 11,417 pts (with 4% reported vaccination prior to COVID-19), 55% required hospitalization, 15% ICU, 9% MV, and 12% died. Overall outcome rates remained similar for 2020 and 2021 (Table). Hydroxychloroquine was utilized in 11% and other anti-COVID-19 drugs (remdesivir, tocilizumab, convalescent plasma, and/or steroids) in 30%. Higher CFRs were observed in older age, males, Black race, smoking (14%), comorbidities (pulmonary [17%], diabetes mellitus [16%], cardiovascular [19%], renal [21%]), ECOG performance status 2+ (31%), co-infection (25%), especially fungal (35%), and initial presentation with severe COVID-19 (48%). Pts with hematologic malignancy, active/progressing cancer status, or receiving systemic anti-cancer therapy within 1-3 months prior to COVID-19 also had worse CFRs. CFRs were similar across anti-cancer modalities. Other outcomes (ICU, MV, hospitalization) followed similar distributions by pt characteristics. Conclusions: Unfavorable outcome rates continue to remain high over 2 years, despite fewer case reports in 2021 owing to multiple factors (e.g., pandemic dynamics, respondent fatigue, overwhelmed healthcare systems). Pts with specific socio-demographics, performance status, comorbidities, type and status of cancer, immunosuppressive therapies, and COVID-19 severity at presentation experienced worse COVID-19 severity; and these factors should be further examined through multivariable modeling. Understanding epidemiological features, patient and cancer-related factors, and impact of anti-COVID-19 interventions can help inform risk stratification and interpretation of results from clinical trials.