Aristeidis Sionakidis

Thesis title: Molecular Dynamics of the Response to Breast Cancer Therapies


I am a final-year PhD student in the MRC DTP in Precision Medicine. My research focuses primarily on the application of modern bioinformatics and statistical tools on the analysis of high-dimensional, clinical breast cancer datasets in an effort to predict response to neoadjuvant treatment and achieve better outcomes.

I completed my undergraduate studies in Pharmacy (Aristotle University of Thessaloniki) in 2018. I have also completed my Master's degree in Precision Medicine and Pharmacological Innovation (University of Glasgow) in 2020. My undergraduate research project's aim was to successfully clone the coding sequence of the human IL2 gene (interleukin-2) and use the purified product for research on T-cells. My Master's thesis involved the analysis of hypertension-related pharmacogenomic data and visualisation of results. My supervisor was Professor Sandosh Padmanabhan (University of Glasgow).


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2013 - 2018: BSc in Pharmacy | Aristotle University of Thessaloniki

2019 - 2020: MSc in Precision Medicine and Pharmacological Innovation | University of Glasgow

Research summary


  • My project's aim is to study the diverse presentation of breast tumours in depth, focussing on molecular, histological and non-biological factors. Through this  comprehensive approach, we aspire to collectively analyse data on different kinds of molecular markers and other non-molecular variables (e.g. demographic data), using a variety of statistical and machine learning methods - both traditional and modern ones. Currently, the focus has been set on gene expression profile analysis.

Other projects:

  • I am doing research on a comprehensive gene expression analysis on clinical PDAC samples, using published gene expression data, with a focus on miRNA involvement in tumour pathology and the discovery of biomarkers that could serve as early diagnostic tools.
  • I am also investigating markers of recurrence in Oral Squamous Cell Carcinoma (OSCC) in the tongue.
  • I am collaborating with Professor Sandosh Padmanabhan and his lab on cardiovascular disease (particularly hypertension-related) and cancer-related projects and the applications of machine learning and pharmacogenomics approaches on this field.

Current research interests

Breast Cancer, Pancreatic Cancer, Hypertension, Gene Expression Profiling, Data Analysis, Machine Learning

Past research interests

Cloning of the human IL2 gene for CAR T-cell strategies. Pharmacogenomics of hypertension and association with cancer.

Affiliated research centres

Project activity

PhD Project: "Molecular Dynamics of the Response to Breast Cancer Therapies"

Other Projects:

  • Pancreatic Ductal Adenocarcinoma (PDAC) and Gene Expression Analysis with a focus on miRNA biomarkers and early diagnosis tools
  • Markers of recurrence in patients with Oral Squamous Cell Carcinoma (OSCC, site: tongue) 
  • Machine Learning and Data Analysis applications in hypertension


Conference details

  • 2021 EBCSS: Edinburgh Breast Cancer Society Symposium
  • 2021 EBCSS: Chemotherapy Training Day
  • 2022 AACR Annual Meeting, New Orleans (04/2022): Poster presentation on my PhD project (AACR Scholar-In-Training Award recipient)
  • 2022 EACR Annual Congress, Seville (06/2022): Poster presentation on a project focused on Pancreatic Ductal Adenocarcinoma (PDAC)
  • 2022 MEG-UK Annual Meeting, Edinburgh (11/2022): Poster presentation (PDAC)
  • 2023 EBCSS, Edinburgh Breast Cancer Society Symposium (02/2023): Poster presentation (Breast Cancer project)
  • Sionakidis, A., McCallum, L., & Padmanabhan, S. (2021). Unravelling the tangled web of hypertension and cancer. Clinical science (London, England : 1979), 135(13), 1609–1625.
  • du Toit, C., Tran, T. Q. B., Deo, N., Aryal, S., Lip, S., Sykes, R., Manandhar, I., Sionakidis, A., Stevenson, L., Pattnaik, H., Alsanosi, S., Kassi, M., Le, N., Rostron, M., Nichol, S., Aman, A., Nawaz, F., Mehta, D., Tummala, R., McCallum, L., … Padmanabhan, S. (2023). Survey and Evaluation of Hypertension Machine Learning Research. Journal of the American Heart Association, e027896. Advance online publication.
  • Sionakidis, A, Lalagkas, PN, Malousi, A, Vizirianakis, IS. Identification of diagnostic markers of pancreatic ductal adenocarcinoma using transcriptomic tumour and blood sample data. Clin Transl Disc. 2023; 3:e248.