Humoral Responses to Repetitive Doses of COVID-19 mRNA Vaccines in Patients with CAR-T-Cell Therapy.

Background: Due to B-cell aplasia following CAR-T-cell therapy, patients are at risk of severe SARS-CoV-2 course. Methods: COVID-19 vaccines were assessed by IgG antibody tests against SARS-CoV-2 spike protein (anti-S1/S2). Vaccination procedures: group (1): CAR-T-cells followed by two to four vaccine doses; group (2): Two vaccine doses prior to CAR-T-cells, followed by doses 3 or 4. Results: In group 1 (n = 32), 7/30 patients (23.2%) had positive antibody tests after a second dose, 9/23 (39.1%) after a third dose, and 3/3 patients after a fourth dose. A third dose led to seroconversion in 5 of 21 patients (23.8%) with available data, while a fourth dose did so in 2/3 patients. Higher B-cells (AUC: 96.2%, CI: 89-100, p = 0.0006) and lower CAR-T-cell copies (AUC: 77.3%, CI: 57-97, p = 0.0438) were predictive of positive humoral vaccine response. In group 2 (n = 14), 6/14 patients (42.9%) had a positive antibody test after a second dose, 3/8 patients (37.5%) after a third dose, and 3/4 patients after a fourth dose. A third dose led to seroconversion in 1/8 patients (12.5%), while a fourth dose did so in 3/4 patients. Conclusion: Additional vaccine doses increased seroconversion rates whilst high B-cell counts and low CAR-T-cell copy numbers were associated with positive antibody response.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

BACKGROUND: Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. METHODS: We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay. RESULTS: Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. CONCLUSIONS: IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.