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Blinatumomab is a first-in-class immunotherapy based on the bispecific T-cell engager (BiTE®) immune-oncology platform, which redirects CD3+ T cells to kill CD19+ target cells. The objective of this analysis was to describe the correlation between B- and T-cell kinetics and response to blinatumomab in patients with relapsed or refractory (r/r) non-Hodgkin lymphoma (NHL). The clinical efficacy of treatment with blinatumomab in patients with r/r NHL was recently investigated in a phase 1 dose-escalation and expansion trial (NCT00274742) wherein 76 patients received blinatumomab by continuous intravenous infusion at various doses (0.5–90 μg/m2/day). B-Cell depletion and expansion of CD3+, CD4+, and CD8+ T cells was analyzed in patients stratified per clinical response (complete response [CR], n = 16; partial response [PR], stable disease [SD], or progressive disease [PD], n = 54) for at least 4 weeks (additional 4 weeks after clinical benefit) from the date of administration of blinatumomab until dose-limiting toxicity or PD. B-cell depletion kinetics were faster in patients who had a CR than in patients who did not have a complete response (PR, SD, or PD). T-cell expansion (T-cell counts exceeding the baseline level on day 22) was more pronounced in patients with CR than in patients without CR. T-cell expansion in patients with CR correlated with increased T-cell counts of both CD4+ and CD8+ T cells compared with patients without CR. Patients with r/r NHL who achieved a CR had faster B-cell depletion and increased expansion of CD3+, CD4+, and CD8+ T cells than patients who did not achieve a CR.
(1) Background: molecular tumor boards (MTBs) are crucial instruments for discussing and allocating targeted therapies to suitable cancer patients based on genetic findings. Currently, limited evidence is available regarding the regional impact and the outreach component of MTBs; (2) Methods: we analyzed MTB patient data from four neighboring Bavarian tertiary care oncology centers in Würzburg, Erlangen, Regensburg, and Augsburg, together constituting the WERA Alliance. Absolute patient numbers and regional distribution across the WERA-wide catchment area were weighted with local population densities; (3) Results: the highest MTB patient numbers were found close to the four cancer centers. However, peaks in absolute patient numbers were also detected in more distant and rural areas. Moreover, weighting absolute numbers with local population density allowed for identifying so-called white spots—regions within our catchment that were relatively underrepresented in WERA MTBs; (4) Conclusions: investigating patient data from four neighboring cancer centers, we comprehensively assessed the regional impact of our MTBs. The results confirmed the success of existing collaborative structures with our regional partners. Additionally, our results help identifying potential white spots in providing precision oncology and help establishing a joint WERA-wide outreach strategy.