A transcriptomic signature predicting septic outcome in patients undergoing autologous stem cell transplantation

authors

  • Labiad Yasmine
  • Venton Geoffroy
  • Laure Farnault
  • Baier Céline
  • Colle Julien
  • Mercier Cédric
  • Vadim Ivanov
  • Nicolino-Brunet Corinne
  • Béatrice Loriod
  • Fernandez-Nunez Nicolas
  • Torres Magali
  • Mattei Jean-Camille
  • Rihet Pascal
  • Nguyen Catherine
  • Costello Régis

keywords

  • Treatment related mortality TRM
  • Transcriptomic Analysis
  • Sepsis
  • Systemic Inflammatory Response Syndrome SIRS
  • Autologous Hematopoietic Stem Cell Transplantation Auto-HSCT

document type

ART

abstract

Autologous hematopoietic stem cell transplantation (auto-HSCT) is a standard treatment in multiple myeloma and relapsed or refractory lymphomas. After auto-HSCT, hematologic reconstitution and infectious complications are the main two critical issues. Though many patients develop infectious complications after therapeutic intensification, it remains impossible to predict infection for each individual. The goal of this work was to determine and identify a predictive transcriptomic signature of systemic inflammatory response syndrome (SIRS) and/or sepsis in patients receiving auto-HSCT. High throughput transcriptomic and bioinformatics analysis were performed to analyze gene expression modulation in peripheral blood mononuclear cells (PBMCs) in 21 patients undergoing auto-HSCT for hematological malignancies (lymphoma or multiple myeloma [MM]). Transcriptomic analysis of PBMCs samples collected just after conditioning regimen identified an eleven genes signature (CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orf192 and LOC10289230 and XLOC-005643) that was able to early predict (at least 2 to 7 days before its occurrence) the development of SIRS or sepsis. The possibility of SIRS or sepsis occurrence early prediction (2-7 days before occurrence) opens up to new therapeutic strategies based on pre-emptive antibiotic and/or antifungal prophylaxis adapted to the specific risk profile of each patient.

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