title: Functional pathway analysis in acute myeloid leukemia using single cell network profiling assay: effect of specimen source (bone marrow or peripheral blood) on assay readouts.
abstract: BACKGROUND
  Single cell network profiling (SCNP) is used to simultaneously measure the effects of modulators on signaling networks at the single cell level. SCNP-based biomarker assays predictive of response to induction therapy and relapse risk in acute myeloid leukemia (AML) patients are being developed. Such assays have typically used bone marrow (BM) as the sample source of blasts. Because circulating peripheral blasts are detectable in ∼65% of AML patients and peripheral blood (PB) sampling is less invasive than BM sampling, this study was performed to assess the effect of sample source on AML blasts signaling as measured in SCNP assay.
  
  
  METHODS
  SCNP using multiparametric flow cytometry was used to evaluate the activation state of intracellular signaling molecules in leukemic blasts under basal conditions and after treatment with modulators in 46 pairs of BM mononuclear cells/PB mononuclear cells. The relationship between readouts of modulated intracellular proteins ("nodes") was measured using linear regression, Bland-Altman method, and Lin's concordance correlation coefficient.
  
  
  RESULTS
  The majority (156/161) of signaling nodes show strong correlations between paired PB and BM samples independently from the statistical method used. Notable exceptions were two PB samples with almost undetectable levels of circulating blasts compared with paired BM samples.
  
  
  CONCLUSIONS
  Our results demonstrate that specimen source (BM or PB) does not significantly affect proteomic signaling in patients with AML and circulating blasts. The ability to use PB as a sample source will facilitate the monitoring of cellular signaling effects following administration of targeted therapies and at time points when BM aspirates are not clinically justifiable.
authors.affiliation: Clinical Affairs, Nodality Inc., South San Francisco, California 94080, USA. alessandra.cesano@nodality.com
authors.forename: Alessandra
authors.initials: A
authors.lastname: Cesano
authors.forename: David B
authors.initials: DB
authors.lastname: Rosen
authors.forename: Pat
authors.initials: P
authors.lastname: O'Meara
authors.forename: Santosh
authors.initials: S
authors.lastname: Putta
authors.forename: Urte
authors.initials: U
authors.lastname: Gayko
authors.forename: David C
authors.initials: DC
authors.lastname: Spellmeyer
authors.forename: Larry D
authors.initials: LD
authors.lastname: Cripe
authors.forename: Zhuoxin
authors.initials: Z
authors.lastname: Sun
authors.forename: Hajime
authors.initials: H
authors.lastname: Uno
authors.forename: Mark R
authors.initials: MR
authors.lastname: Litzow
authors.forename: Martin S
authors.initials: MS
authors.lastname: Tallman
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Paietta
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000032:analysis; D001773:Blood Cells / Q000378:metabolism*; D001854:Bone Marrow Cells / Q000378:metabolism*; D002460:Cell Line; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000378:metabolism*; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy / Q000378:metabolism*; D008297:Male; D008565:Membrane Proteins / Q000378:metabolism; D008875:Middle Aged; D040901:Proteomics; D015398:Signal Transduction; D059010:Single-Cell Analysis
chemical_list: D015415:Biomarkers; D047908:Intracellular Signaling Peptides and Proteins; D008565:Membrane Proteins