Using RNA sequencing to investigate therapeutic strategies in paediatric cancer
Using RNA sequencing to investigate therapeutic strategies in paediatric cancer
Neuroblastoma and rhabdomyosarcoma are two paediatric cancers characterised by poor prognosis and high relapse rates in high-risk cases. There is a need to understand treatment resistance/relapse and find more effective therapeutic options for these patients. Neuroblastoma arises from neural crest cells of the sympathetic nervous system that show impaired differentiation, this may involve promoting differentiation in minimal residual disease to reduce the risk of relapse. Two single agent treatments have been shown to promote neuroblastoma differentiation; EZH2, a histone methyltransferase that controls essential cellular processes, and retinoic acid, currently used in standard of care treatment for neuroblastoma. Whether the combination of both treatments could enhance differentiation has not yet been explored. This research aimed to investigate mechanisms of actions of the combination of EZH2 inhibitors with retinoic acid through the analysis of bulk RNA sequencing data to determine whether the combination therapy might further promote neuroblastoma differentiation, reduce minimal residual disease and risk of relapse. Rhabdomyosarcoma is the most common soft tissue sarcoma in children. While most tumours initially respond to treatment, relapse and acquired resistance are common, leading to poor survival outcomes. Understanding why this resistance to therapy occurs may help to predict patient response, identify targets in resistant cells and ultimately prevent relapse. This research aimed to identify and validate a molecular signature of therapy resistance for resistant rhabdomyosarcoma. This was attempted through the analysis of RNA sequencing data from models of intrinsic and acquired chemotherapy resistance, as well as using a machine learning approach. Ultimately, these signatures may be applied to patient samples to predict treatment response, to identify tumours at high risk of recurrence, and to select effective treatments for these patients.
University of Southampton
Putnam, Christina Maria
d912e215-d52a-4db1-8582-25fa0322b3c1
February 2026
Putnam, Christina Maria
d912e215-d52a-4db1-8582-25fa0322b3c1
Walters, Zoë
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Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Putnam, Christina Maria
(2026)
Using RNA sequencing to investigate therapeutic strategies in paediatric cancer.
University of Southampton, Doctoral Thesis, 308pp.
Record type:
Thesis
(Doctoral)
Abstract
Neuroblastoma and rhabdomyosarcoma are two paediatric cancers characterised by poor prognosis and high relapse rates in high-risk cases. There is a need to understand treatment resistance/relapse and find more effective therapeutic options for these patients. Neuroblastoma arises from neural crest cells of the sympathetic nervous system that show impaired differentiation, this may involve promoting differentiation in minimal residual disease to reduce the risk of relapse. Two single agent treatments have been shown to promote neuroblastoma differentiation; EZH2, a histone methyltransferase that controls essential cellular processes, and retinoic acid, currently used in standard of care treatment for neuroblastoma. Whether the combination of both treatments could enhance differentiation has not yet been explored. This research aimed to investigate mechanisms of actions of the combination of EZH2 inhibitors with retinoic acid through the analysis of bulk RNA sequencing data to determine whether the combination therapy might further promote neuroblastoma differentiation, reduce minimal residual disease and risk of relapse. Rhabdomyosarcoma is the most common soft tissue sarcoma in children. While most tumours initially respond to treatment, relapse and acquired resistance are common, leading to poor survival outcomes. Understanding why this resistance to therapy occurs may help to predict patient response, identify targets in resistant cells and ultimately prevent relapse. This research aimed to identify and validate a molecular signature of therapy resistance for resistant rhabdomyosarcoma. This was attempted through the analysis of RNA sequencing data from models of intrinsic and acquired chemotherapy resistance, as well as using a machine learning approach. Ultimately, these signatures may be applied to patient samples to predict treatment response, to identify tumours at high risk of recurrence, and to select effective treatments for these patients.
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Published date: February 2026
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Local EPrints ID: 509296
URI: http://eprints.soton.ac.uk/id/eprint/509296
PURE UUID: f6e37072-b541-43c3-8a4e-5889e7e73f1a
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Date deposited: 18 Feb 2026 17:39
Last modified: 19 Feb 2026 02:52
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Christina Maria Putnam
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