Citations

ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020 and 2022 and used Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest calculated introduction into Minnesota was to Hennepin County from a domestic source around 22 January 2020; 6 weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota and, eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International Airport) totaling 45 (out of 107) over the 2-year period. Southern Minnesota counties were most common for domestic introductions, with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois, which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations ( n = 772) over the 2-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases nor the major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have an impact on virus diversity across each individual county. IMPORTANCE We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission “sinks.” In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Abstract Background: Ovarian endometrioid carcinoma (OEC) is an uncommon, understudied histotype that is distinguished from other histotypes by unique genomic features and high hormone receptor expression. There is growing interest in the biological relevance of subtyping using immunohistochemistry (IHC). Methods: Women with primary invasive OEC diagnosed from 1989 to 2010 were retrospectively identified from the population-based Alberta Cancer Registry, Canada. Pathology slide review and IHC staining for Napsin A, TP53, and WT1 were used to reduce histologic misclassification, resulting in N=166 participants (low-stage N=138, high-stage N=28), excluding those who received neo-adjuvant chemotherapy. The following markers were stained and scored on tissue microarrays: TP53 (normal/abnormal), PMS2/MSH6 (mismatch repair proficient [MMRp]/deficient [MMRd]), PGR (loss/retained), and CTNNB1 (nuclear/cytoplasmic). Using a hierarchical decision tree by stage group (low/high), we categorized tumors as TP53 abnormal, MMRd, PGR loss, nuclear CTNNB1, and no specific immunohistochemical profile (NSIP). Distributions and five-year disease specific survival (DSS) was estimated for each group; hazard ratios (HRs) adjusted for age were estimated using Cox regression with NSIP as referent group. Results: Among low-stage cases, five-year survival was as follows: 80% for TP53 abnormal (N=10, 7%), 87% for MMRd (N=15, 11%), 80% for PGR loss (N=10, 7%), 98% for nuclear CTNNB1 (N=63, 46%), and 95% for NSIP group (N=40, 29%). Overall, IHC group was associated with DSS among low stage cases (LRT p-value=0.006), and the group with nuclear CTNNB1 had a small but statistically significant DSS advantage (HR=0.089, 95% CI 0.011-0.742, p=0.025). TP53 and PGR loss suggested shorter DSS than the referent NSIP group, but not statistically significantly so (HR=1.26 and 3.08, respectively). Among high-stage cases, five-year DSS was as follows: 57% for TP53 abnormal (N=7, 25%), 75% for MMRd (N=8, 29%), 33% for nuclear CTNNB1 (N=3, 11%), and 17% for NSIP (N=10, 36%). There were no high-stage cases in the PGR-loss group. Nuclear CTNNB1 group had a shorter DSS among high-stage cases, but this was not statistically significantly so (HR=1.16, p=0.85). Conclusion: Pending replication in additional datasets, results suggest that OEC cases in nuclear CTNNB1 or NSIP IHC groups may have a favorable prognosis in which de-escalation of therapy can be considered. In addition, abnormal TP53 and loss of PGR may identify a high-risk subset of low-stage OEC. Citation Format: Brooke Jorgensen, Stacey Winham, Julie Cunningham, Sebastian Armasu, Chen Wang, Hunter Atkinson, Bryan McCauley, Linda Kelemen, Martin Koebel, Ellen Goode. Towards a combined immunohistochemistry-based subtyping of ovarian endometrioid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6499.

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change > 1.5 and p value < 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.

Abstract Background: Better understanding of prognostic factors in tubo-ovarian high-grade serous carcinoma (HGSC) is critical, as diagnosis confers an aggressive disease course. Variation in tumor DNA methylation shows promise predicting outcome, yet prior studies were largely platform-specific and unable to evaluate multiple molecular features. Methods: We analyzed genome-wide DNA methylation in 1,040 frozen HGSC, including 325 previously reported upon, seeking a multi-platform quantitative methylation signature that we evaluated in relation to clinical features, tumor characteristics, time to recurrence/death, extent of CD8+ tumor-infiltrating lymphocytes (TIL), gene expression molecular subtypes, and gene expression of the ATP-binding cassette transporter TAP1. Results: Methylation signature was associated with shorter time to recurrence, independent of clinical factors (N = 715 new set, hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.10–2.46; P = 0.015; N = 325 published set HR, 2.87; 95% CI, 2.17–3.81; P = 2.2 × 10−13) and remained prognostic after adjustment for gene expression molecular subtype and TAP1 expression (N = 599; HR, 2.22; 95% CI, 1.66–2.95; P = 4.1 × 10−8). Methylation signature was inversely related to CD8+ TIL levels (P = 2.4 × 10−7) and TAP1 expression (P = 0.0011) and was associated with gene expression molecular subtype (P = 5.9 × 10−4) in covariate-adjusted analysis. Conclusions: Multi-center analysis identified a novel quantitative tumor methylation signature of HGSC applicable to numerous commercially available platforms indicative of shorter time to recurrence/death, adjusting for other factors. Along with immune cell composition analysis, these results suggest a role for DNA methylation in the immunosuppressive microenvironment. Impact: This work aids in identification of targetable epigenome processes and stratification of patients for whom tailored treatment may be most beneficial.

AbstractBackgroundCyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo‐ovarian high‐grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large‐scale, histotype‐specific validation has been performed. The hypothesis was that high‐level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC.MethodsWithin the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated.ResultsHigh‐level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high‐level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08‐1.47, p = .034, and HR, 1.18; 95% CI, 1.05‐1.32, p = .015, respectively). This was also true for cases with combined high‐level amplification/overexpression (HR, 1.26; 95% CI, 1.09‐1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1‐SD increase; 95% CI, 0.94‐1.06; p = .58). CCNE1 high‐level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss.ConclusionThis study provides large‐scale validation that CCNE1 high‐level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC.

Abstract Background Patients taking chronic opioids are at high risk of opioid induced adrenal insufficiency (OIAI). Previously, we showed that the prevalence of OIAI was 9% based on low morning cortisol, corticotropin (ACTH), and dehydroepiandrosterone sulfate (DHEAS). Data on the overnight metyrapone test to diagnose OIAI are sparse. We aimed to determine the prevalence of OIAI using overnight metyrapone test and identify changes in the steroid metabolome of patients treated with opioids. Methods Cross-sectional study of adult patients treated with opioid therapy of at least 20 morphine milligram equivalents (MME)/day for at least 3 months. Measurements included ACTH, cortisol, and DHEAS. Overnight metyrapone test was administered in all patients, and 24h urine steroid profiling was optional (25 steroids measured by liquid chromatography mass spectrometry1). All patients completed the quality-of-life survey (AddiQoL). Prevalence of OIAI was defined as sum of post-metyrapone 11-deoxycortisol and cortisol of <15 mcg/dL. Result: In 22 patients (median age 59 years [range, 38-74], 9, 41% women), median MME was 33 (20-161) mg, and median cumulative opioid exposure was 245 (10-2550) mg*years. Following the overnight metyrapone test, 7 (32%) patients were diagnosed with OIAI. Patients with OIAI had lower DHEAS concentrations (median of 21 mcg/dL[ 5-113] vs 54 [22-173], p =0. 03), but not cortisol (median 10[5.3-16] mcg/dL vs 13[5.3-22], p=0. 06) or ACTH (median 27[8.1-51] pg/mL vs 26[5.9-55], p=0.8). Patients with OIAI were treated with higher daily MME (100 mg[30-150] vs 30[20-161], p=0. 03) and had a higher cumulative exposure to opioids (median, 660 mg*years [250-2250] vs 150 [10-1288], p =0. 01). AddiQol scores were similar between groups (median of 68[56-84] vs 73[48-83], p= 0.97). Urine steroid profiling was performed in 18 patients. Patients with OIAI demonstrated 4 times lower total urine glucocorticoids (16334 mcg/24h vs 4377 mcg/24h, p=0. 003), all <10% of the reference range, and 3 times lower total urinary androgens (median of 805 mcg/24h vs 2320 mcg/24h, p=0.14). Conclusion Prevalence of OIAI using overnight metyrapone test is high at 32%. Patients diagnosed with OIAI were treated with higher doses of MME/day and had a higher cumulative exposure to opioids when compared to those without OIAI. Applying 24h urine steroid profiling we showed that total glucocorticoids are 4 times lower in those with OIAI (and all <10% of reference ranges) as opposed to those without OIAI. We showed that baseline measurements of ACTH, DHEAS, and cortisol may incorrectly exclude OIAI, and a dynamic test, such as overnight metyrapone test is needed when suspicion for OIAI is high. | 1. Hines JM, Bancos I, Bancos C, et al. High-Resolution, Accurate-Mass (HRAM) Mass Spectrometry Urine Steroid Profiling in the Diagnosis of Adrenal Disorders. Clin Chem. 2017;63(12): 1824-1835. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

Abstract Purpose: To identify molecular subclasses of clear cell ovarian carcinoma (CCOC) and assess their impact on clinical presentation and outcomes. Experimental Design: We profiled 421 primary CCOCs that passed quality control using a targeted deep sequencing panel of 163 putative CCOC driver genes and whole transcriptome sequencing of 211 of these tumors. Molecularly defined subgroups were identified and tested for association with clinical characteristics and overall survival. Results: We detected a putative somatic driver mutation in at least one candidate gene in 95% (401/421) of CCOC tumors including ARID1A (in 49% of tumors), PIK3CA (49%), TERT (20%), and TP53 (16%). Clustering of cancer driver mutations and RNA expression converged upon two distinct subclasses of CCOC. The first was dominated by ARID1A-mutated tumors with enriched expression of canonical CCOC genes and markers of platinum resistance; the second was largely comprised of tumors with TP53 mutations and enriched for the expression of genes involved in extracellular matrix organization and mesenchymal differentiation. Compared with the ARID1A-mutated group, women with TP53-mutated tumors were more likely to have advanced-stage disease, no antecedent history of endometriosis, and poorer survival, driven by their advanced stage at presentation. In women with ARID1A-mutated tumors, there was a trend toward a lower rate of response to first-line platinum-based therapy. Conclusions: Our study suggests that CCOC consists of two distinct molecular subclasses with distinct clinical presentation and outcomes, with potential relevance to both traditional and experimental therapy responsiveness. See related commentary by Lheureux, p. 4838