Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple item page
dc.contributor.authorMutuku, S.M.
dc.contributor.authorSpotbeen, X.
dc.contributor.authorTrim, P.J.
dc.contributor.authorSnel, M.F.
dc.contributor.authorButler, L.M.
dc.contributor.authorSwinnen, J.V.
dc.date.issued2022
dc.descriptionPublished: 27 March 2022
dc.description.abstractDue to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.
dc.description.statementofresponsibilityShadrack M. Mutuku, Xander Spotbeen, Paul J. Trim, Marten F. Snel, Lisa M. Butler and Johannes V. Swinnen
dc.identifier.citationCancers, 2022; 14(7):1-16
dc.identifier.doi10.3390/cancers14071702
dc.identifier.issn2072-6694
dc.identifier.issn2072-6694
dc.identifier.orcidMutuku, S.M. [0000-0002-6285-9895]
dc.identifier.orcidTrim, P.J. [0000-0001-8734-3433]
dc.identifier.orcidSnel, M.F. [0000-0002-8502-7274]
dc.identifier.orcidButler, L.M. [0000-0003-2698-3220]
dc.identifier.urihttps://hdl.handle.net/2440/136968
dc.language.isoen
dc.publisherMDPI AG
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dc.source.urihttps://doi.org/10.3390/cancers14071702
dc.subjectprostate cancer; lipids; biomarkers; mass spectrometry imaging; lipidomics; metabolomics; MALDI
dc.titleUnravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_136968.pdf
Size:
722.72 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Medicine publications
South Australian Immunogenomics Cancer Institute (SAIGENCI) publications