Evaluating immune response and metabolic related biomarkers pre-allogenic hematopoietic stem cell transplant in acute myeloid leukemia.

Journal Article (Journal Article)

Although hematopoietic stem cell transplantation (HCT) is the only curative treatment for acute myeloid leukemia (AML), it is associated with significant treatment related morbidity and mortality. There is great need for predictive biomarkers associated with overall survival (OS) and clinical outcomes. We hypothesized that circulating metabolic, inflammatory, and immune molecules have potential as predictive biomarkers for AML patients who receive HCT treatment. This retrospective study was designed with an exploratory approach to comprehensively characterize immune, inflammatory, and metabolomic biomarkers. We identified patients with AML who underwent HCT and had existing baseline plasma samples. Using those samples (n = 34), we studied 65 blood based metabolomic and 61 immune/inflammatory related biomarkers, comparing patients with either long-term OS (≥ 3 years) or short-term OS (OS ≤ 1 years). We also compared the immune/inflammatory response and metabolomic biomarkers in younger vs. older AML patients (≤30 years vs. ≥ 55 years old). In addition, the biomarker profiles were analyzed for their association with clinical outcomes, namely OS, chronic graft versus host disease (cGVHD), acute graft versus host disease (aGVHD), infection and relapse. Several baseline biomarkers were elevated in older versus younger patients, and baseline levels were lower for three markers (IL13, SAA, CRP) in patients with OS ≥ 3 years. We also identified immune/inflammatory response markers associated with aGVHD (IL-9, Eotaxin-3), cGVHD (Flt-1), infection (D-dimer), or relapse (IL-17D, bFGF, Eotaxin-3). Evaluation of metabolic markers demonstrated higher baseline levels of medium- and long-chain acylcarnitines (AC) in older patients, association with aGVHD (lactate, long-chain AC), and cGVHD (medium-chain AC). These differentially expressed profiles merit further evaluation as predictive biomarkers.

Full Text

Duke Authors

Cited Authors

  • Siamakpour-Reihani, S; Cao, F; Lyu, J; Ren, Y; Nixon, AB; Xie, J; Bush, AT; Starr, MD; Bain, JR; Muehlbauer, MJ; Ilkayeva, O; Byers Kraus, V; Huebner, JL; Chao, NJ; Sung, AD

Published Date

  • 2022

Published In

Volume / Issue

  • 17 / 6

Start / End Page

  • e0268963 -

PubMed ID

  • 35700185

Pubmed Central ID

  • PMC9197059

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0268963


  • eng

Conference Location

  • United States