Predicting tumour response

Overview
RPrecision radiotherapy depends on understanding the biological differences that determine why individual tumours respond differently to treatment. Our research develops biological and computational approaches to predict tumour response by integrating molecular biomarkers, tumour sex chromosome biology and mathematical modelling. By identifying previously overlooked sources of biological variation, we aim to build more accurate prediction models and support truly personalised radiotherapy.

Focus Areas

Tumour sex chromosome biology

Understanding how sex chromosome state contributes to tumour heterogeneity, biomarker performance and radiation response.

Predictive modelling

Biologically informed mathematical models of tumour response and clinical decision support.

Biomarkers

Development and validation of molecular biomarkers and prognostic tools for precision radiotherapy.

Translation & implementation

Evaluating how biological discoveries can improve clinical practice, guidelines and equitable treatment delivery.

Flagship Publications (Selected)

• Sex‑Linked Biology

  • Radiotherapy cancer patient: female inclusive, but male dominated(Int J Radiat Biol, 2020) — Landmark analysis revealing systemic under‑interrogation of sex biology in RT.

  • SBRT kidney sex representation review (cTRO, 2025) — Evidence base and equity critique in disease‑specific RT research.

  • In silico analysis of survival with YBX1 in male and female solid tumours (Sci Rep, 2024) — Demonstrated sex‑contingent biomarker associations.

  • Evaluation of sex‑linked genes and treatment response in lung cancer (Radiother Oncol, 2025) — Establishes sex‑linked genes as quantifiable drivers of treatment response.

• Predictive Modeling & Vision

  • Predicting individual tumor response dynamics in LA‑NSCLC RT: mathematical modelling study(IJROBP, 2024) — Next‑gen response models linking biology to dose dynamics.

  • A joint physics & radiobiology DREAM team vision(Radiother Oncol, 2024) — International roadmap for better response prediction.

  • Research trends in RBE: bibliometric study (Radiat Res, 2024) — Landscape analysis informing modeling priorities.

• Biomarkers, Nomograms & Personalization

  • YB‑1: The key to personalised prostate cancer management? (Cancer Lett, 2020).

  • Nomograms are key decision‑making tools in prostate cancer RT (Urol Oncol, 2018).

  • Personalisation of RT prescriptions in LA‑NSCLC (Radiother Oncol, 2018).

• Practice, Implementation & Inclusion

  • Investigating the needs and concerns of LGBTQ+ cancer patients (J Homosex, 2024/2025).

  • Practical recommendations for radiodermatitis management (Radiat Oncol, ESTRO RTT, 2025).

  • Prehabilitation for patients undergoing RT: scoping review (Clin Oncol, 2024).

Computational platforms

• Pan-cancer genomics

• Mathematical modelling

• Biomarker development

• Clinical outcome modelling

• AI-assisted analytics

Scientific contributions

• Demonstrated that tumour sex chromosome biology influences biomarker performance and clinical outcome.

• Developed biologically informed models of tumour response.

• Advanced precision radiotherapy through integration of molecular biology, modelling and clinical datasets.

• Contributed to international discussions on biologically informed and inclusive radiotherapy.

Looking ahead

Current prediction models remain incomplete because they capture only part of tumour biology. Our current research investigates tumour sex chromosome state as a previously overlooked biological determinant of radiation response and explores how this information can transform prediction, patient stratification and precision radiotherapy.

Get Involved

• We collaborate on sex‑aware analytics, modeling‑driven trials, and equity‑focused guideline development. Partners from data science, translational labs, and clinical oncology are welcome.