BackgroundThe incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.MethodsWe executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.ResultsWe discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.ConclusionA multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.

Purpose: Immunotherapy with checkpoint inhibitors is improving the outcomes of several cancers. However, only a subset of patients respond. Therefore, predictive biomarkers are critically needed to guide treatment decisions and develop approaches to the treatment of therapeutic resistance. Experimental Design: We compared bioenergetics of circulating immune cells and metabolomic profiles of plasma obtained at baseline from patients with melanoma treated with anti–PD-1 therapy. We also performed single-cell RNA sequencing (scRNAseq) to correlate transcriptional changes associated with metabolic changes observed in peripheral blood mononuclear cells (PBMC) and patient plasma. Results: Pretreatment PBMC from responders had a higher reserve respiratory capacity and higher basal glycolytic activity compared with nonresponders. Metabolomic analysis revealed that responder and nonresponder patient samples cluster differently, suggesting differences in metabolic signatures at baseline. Differential levels of specific lipid, amino acid, and glycolytic pathway metabolites were observed by response. Further, scRNAseq analysis revealed upregulation of T-cell genes regulating glycolysis. Our analysis showed that SLC2A14 (Glut-14; a glucose transporter) was the most significant gene upregulated in responder patients' T-cell population. Flow cytometry analysis confirmed significantly elevated cell surface expression of the Glut-14 in CD3+, CD8+, and CD4+ circulating populations in responder patients. Moreover, LDHC was also upregulated in the responder population. Conclusions: Our results suggest a glycolytic signature characterizes checkpoint inhibitor responders; consistently, both ECAR and lactate-to-pyruvate ratio were significantly associated with overall survival. Together, these findings support the use of blood bioenergetics and metabolomics as predictive biomarkers of patient response to immune checkpoint inhibitor therapy.

1540 Background: Post-acute sequelae of SARS-CoV-2 or long COVID, is characterized by persistence of symptoms and/or emergence of new symptoms post COVID-19 infection. As evidence accumulates and national initiatives arise to address this increasingly prevalent syndrome, characterization of specific patient groups is still lacking including patients with cancer. Using a nationally representative sample of over 4.3M COVID-19 patients from the National COVID Cohort Collaborative (N3C), we aim to describe characteristics of patients with cancer and long COVID. Methods: We employed two approaches to identify long COVID patients within N3C: i) patients presenting to a long COVID clinic at four N3C sites and ii) patients diagnosed using the recently introduced ICD-10 code: U09.9 Post COVID-19 condition, unspecified. We included patients with at least one positive COVID-19 diagnosis between 1/1/2020 and 2/3/2022. Patients had to survive at least 90 days from the date of their COVID-19 diagnosis. Analyses were performed in the N3C Data Enclave on the Palantir platform. Results: A total of 1700 adult patients with long COVID were identified from the N3C cohort; 634 (37.3%) were cancer patients and 1066 were non-cancer controls. The most common represented cancers were skin (21.9%), breast (17.7%), prostate (8.3%), lymphoma (8.0%) and leukemia (5.7%). Median age of long-COVID cancer patients was 64 years (Interquartile Range: 54-72), 48.6% were 65 years or older, 60.4% females, 76.8% non-Hispanic White, 12.3% were Black, and 3% Hispanic. A total of 41.1% were current or former smokers, 27.7% had an adjusted Charlson Comorbidity Index score of 0, 18.6% score of 1 and 11.2% score of 2. A total of 57.2% were hospitalized for their initial COVID-19 infection, the average length of stay in the hospital was 9.6 days (SD: 16.7 days), 9.1% required invasive ventilation, and 13% had acute kidney injury during hospitalization. The most common diagnosis among the non-cancer long COVID patients was asthma (26%), diabetes (17%), chronic kidney disease (12%), heart failure (9.4%), and chronic obstructive pulmonary disease (7.8%). Among long COVID patients, compared to non-cancer controls, cancer patients were more likely to be older (OR = 2.4, 95%CI: 1.1-5.4, p = 0.03), have comorbidities (OR = 4.3, 95%CI: 2.9-6.2, p < 0.0001), and to be hospitalized for COVID-19 (OR = 1.3, 95%CI: 1.0-1.7, p = 0.05), adjusting for sex, race/ethnicity, body mass index and smoking history. Conclusions: In a nationally representative sample of long COVID patients, there was a relative overrepresentation of patients with cancer. Compared to non-cancer controls, cancer patients were older, more likely to have more comorbidities and to be hospitalized for COVID-19 warranting further investigation to identify risk factors for long COVID in patients with cancer.

In estrogen receptor (ER)-positive breast cancer, changes in biomarker expression after neoadjuvant therapy indicate the therapeutic response and are prognostic. However, there is limited information about the biomarker alteration caused by neoadjuvant endocrine therapy in ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. We recruited ER-positive/HER2-negative breast cancer patients who received neoadjuvant chemotherapy (NCT), neoadjuvant endocrine therapy (NET), or sequential neoadjuvant endocrine-chemotherapy (NECT) at Peking University Cancer Hospital from 2015 to 2021. A total of 579 patients had paired immunohistochemistry information in both diagnostic biopsy samples and post-neoadjuvant therapy surgical samples. Through a paired comparison of the immunohistochemical information in pre-treatment and post-treatment samples, we found that progesterone receptor (PR) expression reductions were more frequent than ER expression reductions (70.8% vs. 35.2%) after neoadjuvant therapy. The percentage of patients who had a decreased Ki-67 index in the post-operative samples was similar in the three groups (79.8% vs. 79.7% vs. 78.4%). Moreover, PR losses caused by NET were related to low baseline PR expression (p = 0.001), while we did not find a significant association between PR losses and Ki-67 reductions (p = 0.428) or ER losses (p = 0.274). All three types of neoadjuvant therapies caused a reduction in ER, PR, and Ki-67 expression. In conclusion, we found that PR loss after NET was only significantly related to low baseline PR expression, and there is no significant difference in the extent of prognostic factor change including Ki-67 and ER between the PR loss and non-loss groups.

e18672 Background: Comprehensive real-world evidence of the virulence of COVID-19 Omicron, Delta, and Alpha variants as well as the effectiveness of booster vaccinations in patients with cancer are lacking. We aimed to fill in these gaps for cancer patients and provide essential insights on the management of the fast-evolving pandemic by leveraging the nationally-representative electronic medical records from the National COVID Cohort Collaborative (N3C) registry. Methods: The virulence of COVID-19 variants was examined according to severe outcomes of infected patients with cancer, compared with non-cancer patients, using the N3C data between 12/01/2020 and 02/03/2022. Variants were inferred according to the time periods of variant dominance at > 95% accuracy. The Cox proportional hazards model was employed to evaluate the effects of COVID-19 variants, adjusting for age, gender, race/ethnicity, geographic regions, vaccination status, cancer types, smoking status, cancer treatments, and adjusted Charlson Comorbidity Index (CCI). Results: Our study cohort included 114,195 COVID-19 patients with cancer and 160,493 without cancer as control. Among them, 52,539 (21%) were infected by Omicron, 82,579 (33%) by Delta, and 115,200 (46%) by Alpha variants. Prior to the COVID-19 breakthrough infection, 7%, 22%, 3%, and 69% were vaccinated with 1 dose, 2 doses, a booster, or unvaccinated respectively. The proportions of hospitalization and death among patients with vs without cancer were 40% and 7% vs 18% and 0.4%, respectively. Characteristics of the cancer subcohort are summarized in the Table. Our analysis showed dramatically lower risks of severe outcomes for patients who were infected by Omicron (HR 0.42, 95%CI: 0.38 – 0.46) and slightly lower risks for Delta (HR 0.93, 95%CI: 0.89 – 0.98) compared with those infected by Alpha, after adjusting for other demographic clinical risk factors, and vaccination status. This trend remained similar in subgroups of patients with solid tumors, hematologic malignancies, or without cancer. Similar associations were observed when virulence was evaluated in association with mortality. The effectiveness of booster vaccinations varied across sub-cohorts stratified by variants and cancer types. Booster shots reduced the risk of severe outcomes for patients with solid tumors infected by Omicron variant or hematologic malignancies infected by Delta variants. Conclusions: Our work provides up-to-date and comprehensive real-world evidence of the virulence of COVID-19 variants in patients with cancer. Omicron variant showed significantly reduced virulence for different cancer types.[Table: see text]

8008 Background: Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems due to multiple factors that may increase the risk of severe COVID-19. The NCATS’ National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of ̃12M COVID-19 cases and controls nationwide. In this study, we aim to analyze risk factors associated with COVID-19 severity and death in MM patients using the N3C database. Methods: Our cohort included MM patients within the N3C registry diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10–CM. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter, and clinical indicators of severity (hospitalization/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation/ECMO). Results: As of 09/10/2021, the N3C registry included 690371 cancer patients, out of which 17791 were MM patients (4707 were COVID-19+). The mean age at diagnosis was 65.9yrs, 57.6% were >65yo, 46.4% were females, and 21.8% were Blacks. 25.6% had a Charlson Comorbidity Index (CCI) score of ≥2. 55.6% required an inpatient or ED visit, and 3.65% required invasive ventilation. 11.4% developed acute kidney injury during hospitalization. Multivariate logistic regression analysis showed histories of pulmonary disease (OR 2.2; 95%CI: 1.7-2.8), renal disease (OR 1.8; 95%CI: 1.4-2.4), and black race (p<0.001) were associated with higher risk of severity. Interestingly, smoking status was significantly associated with a lower risk of severity (OR 0.7; 95%CI: 0.5-0.9). Further, protective association was also observed between COVID-19 severity and blood or marrow transplant (BMT) (OR 0.52; 95%CI: 0.4-0.7), daratumumab therapy (OR 0.64; 95%CI: 0.42-0.99) and COVID-19 vaccination (OR 0.28; 95%CI: 0.18-0.44). IMiDs were associated increase in the risk of COVID-19 severity (OR 2.1; 95%CI: 1.6-2.7). 2.3% of N3C-myeloma COVID-19+ patients died within the first 10 days, while 4.95% died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.5% across the course of the study. Multivariate cox proportional hazard model showed that CCI score ≥2 (HR 4.4; 95%CI: 2.2-8.8), hypertension (HR 1.6; 95%CI: 1.02-2.4), IMiD (HR 2.6; 95%CI: 1.8-3.8) and proteasome inhibitor (HR 1.6; 95%CI: 1.1-2.5) therapy were associated with worse survival. COVID-19 vaccination (HR 0.195; 95%CI: 0.09-0.45) and BMT (HR 0.65; 95%CI: 0.4-0.995) were associated with lower risk of death. Conclusions: We have identified previously unpublished potential risk factors for COVID-19 severity and death in MM as well as validated some published ones. To the best of our knowledge, this is the largest nationwide study on multiple myeloma patients with COVID-19.

Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying to elucidate the molecular correlation between AR and ER. Our results showed that AR negativity was associated with different clinicopathological characteristics and molecular features in ER-positive and ER-negative breast cancer. Moreover, AR-positive breast cancer has better clinicopathological features than AR-negative breast cancer, especially in the ER-negative subtype. These results suggest that the role of AR in ER-negative breast cancer is distinctive from that in ER-positive breast cancer.

This research was carried out to quantify the duration from symptom onset to recovery/death (SOR/SOD) during the first four waves and the Alpha/Delta period of the epidemic in Khyber Pakhtunkhwa, Pakistan, and identify the associated factors. A total of 173,894 COVID-19 cases were admitted between 16 March 2020 and 30 November 2021, including 458 intensive care unit (ICU) cases. The results showed that the case fatality rate (CFR) increased with age, and females had a higher CFR. The median SOR of ICU cases was longer than that of non-ICU cases (27.6 vs. 17.0 days), while the median SOD was much shorter (6.9 vs. 8.4 days). The SOR and SOD in the Delta period were slightly shortened than the Alpha period. Age, cardiovascular diseases, chronic lung disease, diabetes, fever, breathing issues, and ICU admission were risk factors that were significantly associated with SOD (p < 0.001). A control measure, in-home quarantine, was found to be significantly associated with longer SOD (odds ratio = 9.49, p < 0.001). Infected vaccinated individuals had longer SOD than unvaccinated individuals, especially for cases that had received two vaccine doses (p < 0.001). Finally, an advice on getting full-dose vaccination is given specifically to individuals aged 20–59 years.