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Single-Cell Characterization of Multiple Myeloma (MM) Immune Microenvironment Identifies CD27-negative T cells as Potential Tumor-Reactive Lymphocytes

2019; Elsevier BV; Volume: 19; Issue: 10 Linguagem: Inglês

10.1016/j.clml.2019.09.581

2152-2650

Cirino Botta, Cristina Pérez Ruiz, Ibai Goicoechea, Noemí Puig, María‐Teresa Cedena, Lourdes Cordón, Aintzane Zabaleta, Leire Burgos, Catarina Maia, Sara Rodríguez, Diego Alignani, Idoia Rodríguez, Sarai Sarvide, Amaia Vilas‐Zornoza, Erika Lorenzo-Vivas, Laura Rosiñol, Albert Oriol, María Jesús Blanchard, Rafael Ríos Tamayo, Anna Sureda, Rafael Martínez, Jesús Martín, Joan Bargay, Javier de la Rubia, Marco Rossi, Pierosandro Tagliaferri, Pierfrancesco Tassone, Massimo Gentile, Juana Merino, Felipe Prósper, Alberto Órfão, María‐Victoria Mateos, Juan José Lahuerta, Joan Bladé, Jesús F. San Miguel, Bruno Paiva,

Immune Cell Function and Interaction

The increasing use of immunotherapies urge the optimization of immune monitoring to help tailoring treatment based on better prediction of patients' response according to their immune status. Thus, we characterized the MM immune microenvironment at the single-cell level to identify clinically relevant subsets for effective immune monitoring. We used a semi-automated pipeline to unveil full cellular diversity based on unbiased clustering, in a next-generation flow (NGF) cytometry immune monitoring dataset developed from diagnosis to VRD induction, autologous transplant and VRD consolidation (n=231 MM patients enrolled in the GEM2012MENOS65 trial). Deep characterization of T cells was performed using 17-color flow and combined single-cell (sc) RNA/TCR sequencing. Simultaneous analysis of the entire dataset unbiasedly identified 25 cell clusters (including 9 myeloid and 13 lymphocytes subsets) in the MM immune microenvironment. Up to 120 immune parameters derived from the cellular abundance of each cluster and different cell ratios were determined at all time points. Overall, we observed that a prognostic score including the CD27-/CD27+ T cell ratio (HR:0.21, p=0.013) and ISS (HR:1.41, p=0.015) outperformed each parameter alone (HR:0.06, p=0.007). To gain further insight into the biological significance of the CD27-/CD27+ T cell ratio, we performed scRNA/TCRseq in 44,969 lymphocytes from 9 MM patients. Downstream analysis unveiled that CD27- T cells were mostly CD8 and included senescent, effector and exhausted clusters. By contrast, CD27+ T cells were mainly CD4 and the remaining CD8 T cells had a predominant immune suppressive phenotype. Such T cell clustering was validated by 17-color flow that confirmed the cellular distribution identified by scRNAseq, as well as higher reactivity for PD1, TIGIT, BTLA and TIM3 in CD27+ vs CD27- T cells. Simultaneous scTCRseq revealed a median of 12 clonotypes per patient. Interestingly, most clonotypes where found in CD27- (74/90) as opposed to CD27+ T cells and, using the VDJB database, the CDR3 sequences of clonotypic effector/exhausted CD27- T cells were predicted to recognize known MM-related epitopes such as MLANA, HM1.24 (CD319), or IMP2. In selected patients, we performed exome- and RNA-sequencing of tumor cells and analyzed their HLA profile. Using the T Cell Epitopes – MHC Binding Prediction tool from the IEDB Analysis Resource, we found expression of mutated genes (e.g. UBXN1, UPF2, GNB1L) predicted to bind MHC class I molecules on tumor cells and potentially recognized by autologous clonotypic CD27- T cells. In conclusion, we show for the first time that potential MM-reactive T cells are CD27-negative and that their abundance in the immune-microenvironment of newly-diagnosed MM patients is prognostic, possibly due to their reactivation after treatment with IMiDs and autologous transplant. Because NGF is broadly used, these results are readily applicable for effective T cell immune monitoring.