Love, Tanzy

Background/Objective: Although most melanocytic lesions are diagnosed as benign or malignant by histopathologic evaluation, with or without the aid of immunohistochemistry, diagnosis may remain uncertain in a minority of cases. Assessment of copy number abnormalities (CNAs) may provide sufficient additional evidence to favor either a benign or malignant diagnosis in both pediatric and adult cases and in melanocytic lesions of various subtypes, including Spitzoid, mucosal, and acral. CNAs are common in melanomas, while they are rare, with few exceptions, in benign lesions. Detection of CNAs by fluorescence in situ hybridization (FISH) and chromosomal microarray (CMA) has been well established for melanocytic lesions, with advantages and disadvantages for each. The objective of this meta-analysis was to evaluate the utility of CNA testing for the diagnosis of melanoma, across subtypes, when a lesion remains ambiguous after histopathologic and immunohistochemical assessment. In addition, the utility of CNAs to determine prognosis in established diagnoses of melanoma was also evaluated. Methods: The Cancer Genomics Consortium Working Group for Melanocytic Lesions reviewed published data from January 1998 through September 2022 of CNAs in melanocytic lesions detected by either FISH or CMA and conducted a meta-analysis of the findings. Results: Specific abnormalities common in primary cutaneous melanomas of various subtypes and uveal melanomas were enumerated. Differences in CNAs found in primary versus metastatic lesions were also determined, and published evidence for prognosis was summarized. Conclusions: The working group established evidence-based recommendations for the use of CNA testing for evaluation of ambiguous melanocytic lesions.

This study aimed to evaluate and compare the performance of three anti-S and one anti-N assays that were available to the project in detecting antibody levels after three commonly used SARS-CoV-2 vaccines (Pfizer, Moderna, and Johnson & Johnson). It also aimed to assess the association of age, sex, race, ethnicity, vaccine timing, and vaccine side effects on antibody levels in a cohort of 827 individuals. In September 2021, 698 vaccinated individuals donated blood samples as part of the Association for Diagnostics & Laboratory Medicine (ADLM) COVID-19 Immunity Study. These individuals also participated in a comprehensive survey covering demographic information, vaccination status, and associated side effects. Additionally, 305 age- and gender-matched samples were obtained from the ADLM 2015 sample bank as pre-COVID-19-negative samples. All these samples underwent antibody level analysis using three anti-S assays, namely Beckman Access SARS-CoV-2 IgG (Beckman assay), Ortho Clinical Diagnostics VITROS Anti-SARS-CoV-2 IgG (Ortho assay), Siemens ADVIA Centaur SARS-CoV-2 IgG (Siemens assay), and one anti-N antibody assay: Bio-Rad Platelia SARS-CoV-2 Total Ab assay (BioRad assay). A total of 827 samples (580 COVID-19 samples and 247 pre-COVID-19 samples) received results for all four assays and underwent further analysis. Beckman, Ortho, and Siemens anti-S assays showed an overall sensitivity of 99.5%, 97.6%, and 96.9%, and specificity of 90%, 100%, and 99.6%, respectively. All three assays indicated 100% sensitivity for individuals who received the Moderna vaccine and boosters, and over 99% sensitivity for the Pfizer vaccine. Sensitivities varied from 70.4% (Siemens), 81.5% (Ortho), and 96.3% (Beckman) for individuals who received the Johnson & Johnson vaccine. BioRad anti-N assays demonstrated 46.2% sensitivity and 99.25% specificity based on results from individuals with self-reported infection. The highest median anti-S antibody levels were measured in individuals who received the Moderna vaccine, followed by Pfizer and then Johnson & Johnson vaccines. Higher anti-S antibody levels were significantly associated with younger age and closer proximity to the last vaccine dose but were not associated with gender, race, or ethnicity. Participants with higher anti-S levels experienced significantly more side effects as well as more severe side effects (e.g., muscle pain, chills, fever, and moderate limitations) (p < 0.05). Anti-N antibody levels only indicated a significant correlation with headache. This study indicated performance variations among different anti-S assays, both among themselves and when analyzing individuals with different SARS-CoV-2 vaccines. Caution should be exercised when conducting large-scale studies to ensure that the same platform and/or assays are used for the most effective interpretation of the data.

Objectives: The objective of this prospective study was to investigate the role of adaptive immunity in response to SARS-CoV-2 vaccines. Design and Methods: A cohort of 677 vaccinated individuals participated in a comprehensive survey of their vaccination status and associated side effects, and donated blood to evaluate their adaptive immune responses by neutralizing antibody (NAb) and T cell responses. The cohort then completed a follow-up survey to investigate the occurrence of breakthrough infections. Results: NAb levels were the highest in participants vaccinated with Moderna, followed by Pfizer and Johnson & Johnson. NAb levels decreased with time after vaccination with Pfizer and Johnson & Johnson. T cell responses showed no significant difference among the different vaccines and remained stable up to 10 months after the study period for all vaccine types. In multivariate analyses, NAb responses (<95 U/mL) predicted breakthrough infection, whereas previous infection, the type of vaccine, and T cell responses did not. T cell responses to viral epitopes (<0.120 IU/mL) showed a significant association with the self-reported severity of COVID-19 disease. Conclusion: This study provides evidence that NAb responses to SARS-CoV-2 vaccination correlate with protection against infection, whereas the T cell memory responses may contribute to protection against severe disease but not against infection.

Introduction: Lymphoma is the most common cancer among adolescents and young adults (AYAs). We examined changes in health-related quality of life (HRQoL) and its predictors in AYA patients (pts). Patients and Methods: We identified AYA pts (aged 18-39) enrolled 2002-2015 in a prospective cohort of pts with newly diagnosed lymphoma from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, part of the Lymphoma Epidemiology of Outcomes cohort. Enrollment could occur prior to or after initiation of treatment. We measured HRQoL using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at baseline, 12, and 24 months. FACT-G yields five HRQoL domain scores: emotional well-being (EWB), functional WB (FWB), physical WB (PWB), social/family WB (SFWB), and total FACT-G score (a sum of the domains). Pts completing &lt;80% of the FACT-G questions were excluded. Linear mixed models with random subject intercepts estimated changes in FACT-G scores from baseline. The covariates in multivariate analysis were lymphoma subtype, stage, and treatment. Interaction effects between treatment (chemotherapy and/or radiation) and subtype were added to the model. We calculated effect sizes (ES) for the magnitude of mean change scores: 0.2, 0.5, and 0.8 were considered small, medium, and large ESs, respectively. Only ESs for mean score differences with p&lt;0.05 are reported. Results: We identified 467 pts; median age at diagnosis was 30 years, median follow up was 5.9 years. 53% of pts completed the baseline FACT-G assessment pre-treatment, 47% completed after treatment began. Pts assessed after treatment initiation had lower baseline total FACT-G (ES -0.25), FWB (ES -0.27), and PWB scores (-0.46); but baseline EWB was higher in pts assessed prior to treatment (ES 0.20). There was no association between HRQoL scores at baseline or over time and lymphoma subtype, stage, or treatment type, or interactions. Total FACT-G scores modestly improved over time, ES 0.32 at 1 year and ES 0.45 at 2 years after enrollment. EWB, FWB, and PWB also improved over time (ES 0.36, 0.44, 0.30 at 1 year; and 0.49, 0.56, 0.38 at 2 years, respectively). SFWB scores slightly worsened over time (ES -0.24 at 1 year and -0.12 at 2 years). Conclusions: AYA pts with lymphoma had higher baseline total FACT-G scores, FWB and PWB prior to therapy initiation compared to after initiation. HRQoL improved from diagnosis through the first 2 years after diagnosis, except for SFWB. Neither stage, lymphoma subtype, nor treatment type affected change in HRQoL. The lack of improvement in SFWB suggests social interventions and future studies should examine factors impacting SFWB in AYA pts. Disclosures Cohen: Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding; Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Bayer: Consultancy. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.