Discovery and application of immune biomarkers for hematological malignancies

Zafeiris, D, Vadakekolathu, J ORCID: https://orcid.org/0000-0002-2671-4285, Wagner, S ORCID: https://orcid.org/0000-0002-5221-9851, Pockley, AG ORCID: https://orcid.org/0000-0001-9593-6431, Ball, GR ORCID: https://orcid.org/0000-0001-5828-7129 and Rutella, S ORCID: https://orcid.org/0000-0003-1970-7375, 2017. Discovery and application of immune biomarkers for hematological malignancies. Expert Review of Molecular Diagnostics, 17 (11), pp. 983-1000. ISSN 1473-7159

Preview	Text PubSub9262_560a_Rutella_.pdf - Post-print Download (426kB) | Preview
Preview	Text PubSub9262_560a_Rutella_Figure 1.pdf - Supplemental Material Download (901kB) | Preview
Preview	Text PubSub9262_560a_Rutella_Figure 2.pdf - Supplemental Material Download (64kB) | Preview
	Presentation PubSub9262_560a_Rutella_Figure 3.pptx - Supplemental Material Download (1MB)
	Presentation PubSub9262_560a_Rutella_Figure 4.pptx - Supplemental Material Download (2MB)

Abstract

Introduction: Haematological malignancies originate and progress in primary and secondary lymphoid organs, where they establish a uniquely immune-suppressive tumour microenvironment. Although high-throughput transcriptomic and proteomic approaches are being employed to interrogate immune surveillance and escape mechanisms in patients with solid tumours, and to identify actionable targets for immunotherapy, our knowledge of the immunological landscape of haematological malignancies, as well as our understanding of the molecular circuits that underpin the establishment of immune tolerance, is not comprehensive.
Areas covered: This article will discuss how multiplexed immunohistochemistry, flow cytometry/mass cytometry, proteomic and genomic techniques can be used to dynamically capture the complexity of tumour-immune interactions. Moreover, the analysis of multi-dimensional, clinically annotated data sets obtained from public repositories such as Array Express, TCGA and GEO is crucial to identify immune biomarkers, to inform the rational design of immune therapies and to predict clinical benefit in individual patients. We will also highlight how artificial neural network models and alternative methodologies integrating other algorithms can support the identification of key molecular drivers of immune dysfunction.
Expert comment: High-dimensional technologies have the potential to enhance our understanding of immune-cancer interactions and will support clinical decision making and the prediction of therapeutic benefit from immune-based interventions.

Item Type:	Journal article
Alternative Title:	Discovery and application of immune biomarkers for haematological malignancies
Publication Title:	Expert Review of Molecular Diagnostics
Creators:	Zafeiris, D., Vadakekolathu, J., Wagner, S., Pockley, A.G., Ball, G.R. and Rutella, S.
Publisher:	Taylor & Francis
Date:	2017
Volume:	17
Number:	11
ISSN:	1473-7159
Identifiers:	Number Type 10.1080/14737159.2017.1381560 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	10 Oct 2017 10:31
Last Modified:	04 Feb 2022 16:59
URI:	https://irep.ntu.ac.uk/id/eprint/31807

Actions (login required)

Edit View

Statistics

Views

Downloads