Background High response rates (RR) have led to the approval of the CD19 specific CAR T-cell products Axicabtagene-Ciloleucel and Tisagenlecleucel for the treatment of refractory/relapsed B-cell precursor ALL (BCP-ALL) and Diffuse Large B-cell lymphoma (DLBCL). However, only a subgroup of patients achieves long-term remission. Additionally, most patients experience adverse effects such as cytokine release syndrome or neurotoxicity. Therefore, we need to better understand mechanisms of relapse and disease progression or toxicity to improve effectiveness of CD19 CAR T-cell therapy. As the gut microbiota plays an important role in modulating T-cell based immunotherapy, we hypothesize, that its signature also impacts clinical outcomes of CAR T-cell therapy.
Materials and Methods We are currently collecting and 16S rRNA sequencing fecal biospecimen from BCP-ALL and DLBCL patients before, during and after treatment with Axicabtagene-Ciloleucel and Tisagenlecleucel at the University Hospital of the LMU. Microbiota data are integrated into a patient-centered ‘hospitalome’ including onset and type of infection and of immunotoxicity, concomitant anti-infective and immunosuppressive agents as well as response to CAR T-cell therapy.
Results Preliminary data analysis revealed, that 4–14 days after CAR T-cell infusion the alpha diversity of the gut microbiome of each patient decreases drastically, whereas pre-lymphodepletion gut microbiota has a high diversity. Furthermore, the microbiota composition during the course of treatment changes as seen by beta diversity changes. In more detail, after CAR T-cell infusion, we observed instances of gut microbiota mono-domination with Enterococci or the genus Rikinella.
Conclusions The gut microbiome of patients being treated with CAR T cells undergoes large and diverse compositional changes. We currently explore how this microbiome heterogeneity relates to the distinct responses and immunotoxicity of patients after CAR T-cell therapy. Hence, this study will enable microbiome-based stratification of patients, in order to predict and improve patient outcome to this personalized T-cell recruiting immunotherapy approach.
Disclosure Information V. Blumenberg: None. E. Zamir: None. S. Schmidt: None. R. Gaiser: None. N. Cullin: None. V. Bücklein: None. C. Schmidt: None. M. von Bergwelt: None. E. Elinav: None. C.K. Stein-Thoeringer: None. M. Subklewe: None.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.