Target heterogeneity in oncology : the best predictor for differential response to radioligand therapy in neuroendocrine tumors and prostate cancer

dc.contributor.authorPuranik, Ameya D.
dc.contributor.authorDromain, Clarisse
dc.contributor.authorFleshner, Neil
dc.contributor.authorSathekge, Mike Machaba
dc.contributor.authorPavel, Marianne
dc.contributor.authorEberhardt, Nina
dc.contributor.authorZengerling, Friedemann
dc.contributor.authorMarienfeld, Ralf
dc.contributor.authorGrunert, Michael
dc.contributor.authorPrasad, Vikas
dc.date.accessioned2021-09-13T07:32:14Z
dc.date.available2021-09-13T07:32:14Z
dc.date.issued2021
dc.description.abstractTumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.en_ZA
dc.description.departmentNuclear Medicineen_ZA
dc.description.librarianpm2021en_ZA
dc.description.urihttp://www.mdpi.com/journal/cancersen_ZA
dc.identifier.citationPuranik, A.D.; Dromain, C.; Fleshner, N.; Sathekge, M.; Pavel, M.; Eberhardt, N.; Zengerling, F.; Marienfeld, R.; Grunert, M.; Prasad, V. Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer. Cancers 2021, 13, 3607. https://doi.org/10.3390/cancers13143607.en_ZA
dc.identifier.issn2072-6694 (online)
dc.identifier.other10.3390/ cancers13143607
dc.identifier.urihttp://hdl.handle.net/2263/81768
dc.language.isoenen_ZA
dc.publisherMDPIen_ZA
dc.rights© 2021 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.en_ZA
dc.subjectHeterogeneityen_ZA
dc.subjectProstate canceren_ZA
dc.subjectNeuroendocrine tumoren_ZA
dc.subjectTarget heterogeneityen_ZA
dc.subjectTumoren_ZA
dc.titleTarget heterogeneity in oncology : the best predictor for differential response to radioligand therapy in neuroendocrine tumors and prostate canceren_ZA
dc.typeArticleen_ZA

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