Literature DB >> 32402283

A Biomarker for Predicting Responsiveness to Stem Cell Therapy Based on Mechanism-of-Action: Evidence from Cerebral Injury.

Richard E Hartman1, Neal H Nathan2, Nirmalya Ghosh3, Cameron D Pernia2, Janessa Law4, Ruslan Nuryyev2, Amy Plaia3, Alena Yusof3, Beatriz Tone3, Melissa Dulcich1, Dustin R Wakeman5, Nejmi Dilmac5, Walter D Niles5, Richard L Sidman6, Andre Obenaus7, Evan Y Snyder8, Stephen Ashwal9.   

Abstract

To date, no stem cell therapy has been directed to specific recipients-and, conversely, withheld from others-based on a clinical or molecular profile congruent with that cell's therapeutic mechanism-of-action (MOA) for that condition. We address this challenge preclinically with a prototypical scenario: human neural stem cells (hNSCs) against perinatal/neonatal cerebral hypoxic-ischemic injury (HII). We demonstrate that a clinically translatable magnetic resonance imaging (MRI) algorithm, hierarchical region splitting, provides a rigorous, expeditious, prospective, noninvasive "biomarker" for identifying subjects with lesions bearing a molecular profile indicative of responsiveness to hNSCs' neuroprotective MOA. Implanted hNSCs improve lesional, motor, and/or cognitive outcomes only when there is an MRI-measurable penumbra that can be forestalled from evolving into necrotic core; the core never improves. Unlike the core, a penumbra is characterized by a molecular profile associated with salvageability. Hence, only lesions characterized by penumbral > core volumes should be treated with cells, making such measurements arguably a regenerative medicine selection biomarker.
Copyright © 2020. Published by Elsevier Inc.

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Keywords:  MRI; cerebral palsy; hypoxic-ischemic injury; neuroprotection; patient stratification; penumbra; recovery-of-function; regenerative medicine; stroke; transplantation

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Year:  2020        PMID: 32402283     DOI: 10.1016/j.celrep.2020.107622

Source DB:  PubMed          Journal:  Cell Rep            Impact factor:   9.423


  1 in total

1.  Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease.

Authors:  Christophe Lenglos; Sue-Jin Lin; Yashar Zeighami; Tobias R Baumeister; Felix Carbonell; Yasser Iturria-Medina
Journal:  Sci Rep       Date:  2022-03-31       Impact factor: 4.996

  1 in total

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