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

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dc.contributor.author Puranik, Ameya D.
dc.contributor.author Dromain, Clarisse
dc.contributor.author Fleshner, Neil
dc.contributor.author Sathekge, Mike Machaba
dc.contributor.author Pavel, Marianne
dc.contributor.author Eberhardt, Nina
dc.contributor.author Zengerling, Friedemann
dc.contributor.author Marienfeld, Ralf
dc.contributor.author Grunert, Michael
dc.contributor.author Prasad, Vikas
dc.date.accessioned 2021-09-13T07:32:14Z
dc.date.available 2021-09-13T07:32:14Z
dc.date.issued 2021
dc.description.abstract Tumor 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.department Nuclear Medicine en_ZA
dc.description.librarian pm2021 en_ZA
dc.description.uri http://www.mdpi.com/journal/cancers en_ZA
dc.identifier.citation Puranik, 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.issn 2072-6694 (online)
dc.identifier.other 10.3390/ cancers13143607
dc.identifier.uri http://hdl.handle.net/2263/81768
dc.language.iso en en_ZA
dc.publisher MDPI en_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.subject Heterogeneity en_ZA
dc.subject Prostate cancer en_ZA
dc.subject Neuroendocrine tumor en_ZA
dc.subject Target heterogeneity en_ZA
dc.subject Tumor en_ZA
dc.title Target heterogeneity in oncology : the best predictor for differential response to radioligand therapy in neuroendocrine tumors and prostate cancer en_ZA
dc.type Article en_ZA


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