12–17 Jul 2026
University of Graz
Europe/Vienna timezone

Memory T Cells as Predictors of Progression-Free Survival in High-Grade Serous Ovarian Cancer: A Mathematical Modelling Approach

15 Jul 2026, 08:50
20m
15.06 - HS (University of Graz)

15.06 - HS

University of Graz

92
Contributed Talk Mathematical Oncology Contributed Talks

Speaker

Iman Al Buwaiqi (School of Mathematics and Statistics, University of Sydney, Australia)

Description

High-grade serous ovarian cancer (HGSC) almost always relapses after first-line treatment, yet how long patients remain progression-free varies enormously. One plausible explanation is that the immune state at the end of chemotherapy — in particular, how many memory T cells survive treatment — shapes the pace of tumour regrowth. Whether this differs between patients with and without homologous recombination deficiency (HRD), a DNA repair defect that increases sensitivity to platinum-based chemotherapy, remains open.
We address this using an ODE model of tumour-immune dynamics in HGSC, built around two cancer cell populations: immune-recognisable cells that drive T cell responses, and immune-invisible cells that accumulate through irreversible escape mutations. A sensitivity analysis shows that T cell proliferative capacity — not per-cell killing rate — determines whether immune control is maintained.
Adding platinum-based chemotherapy clears much of the tumour but leaves memory T cells relatively intact: through antigen-driven division, slower natural turnover, and lower chemotherapy sensitivity, memory cells outnumber effector T cells by over 600-fold at end of treatment. We then ask whether this memory reservoir delays relapse, and whether the effect differs in HRD-positive patients — providing a mechanistic rationale for memory T cell levels as a prognostic marker in HGSC.

Authors

Iman Al Buwaiqi (School of Mathematics and Statistics, University of Sydney, Australia) Kirstie McLoughlin (The Daffodil Centre, University of Sydney, Australia) Melissa Merritt (The Daffodil Centre, University of Sydney, Australia) Peter Kim (School of Mathematics and Statistics, University of Sydney, Australia)

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