ONCO-03 Contributed Talks

Veronika Hofmann

Technical University of Munich

"Spectral Spatial Analysis of Cancer Biopsies: Validation through in-silico data and extension to logistic growth models"

MD Anderson's Enderling lab recently invented a spectral spatial analysis method for estimating tumor cell diffusivity and proliferation rate from single-point-in-time biopsies of breast cancer. In combination with clinical data from the patients these parameters could help identify a new biomarker for radiotherapy. In their first study, they investigate the relationship between the power spectral density (PSD) of the three-dimensional reaction-diffusion (RD) equation with exponential growth (as model of spreading cancer cells) and the two-point correlation function of the cell distribution in the biopsy (a spatial statistic). Their results make the approach seem promising, and this work aims to validate and extend their findings. Firstly, we develop a model to generate in-silico data to validate the parameter estimation method. This is done by solving the RD equation for different growth terms (exponential and logistic), adding Gaussian noise and 'translating' its continuous results into spatial point patterns which are interpreted as cell nuclei in the 'biopsy', and then applying the method to see if the original parameters can be retrieved. This model contains several features: dimensionality can be switched between 2D and 3D, cell size can be adjusted, cuts can be added to the point pattern, and in the 3D case, biopsy thickness is variable and the plane where the slice through the 'tumor' is made can be freely chosen. And secondly, the spectral analysis method is altered by proposing a numerical solution to the PSD of the RD equation with logistic growth (valid for arbitrary dimensions). Logistic growth is assumed to be the more realistic model, however, it is harder to handle as no analytical solution is available for the equation, and hence neither for the PSD. The validation results from the in-silico data are assessed and their meaning for the application to real patient data is discussed under consideration of the different types of cell growth.

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Nicholas Lai

University of Oxford

"Mathematical Modelling of Tertiary Lymphoid Structures in Cancer"

Tertiary lymphoid structures (TLSs) are organised aggregates of immune cells that form at sites of inflammation in chronic diseases, such as cancer. It is hypothesised that, in cancer, TLSs act as local hubs for the generation and regulation of a tumour-specific immune response from inside the tumour microenvironment (TME). TLSs initially form as well-mixed aggregates of T- and B-cells and mature into organised structures consisting of an inner B-cell zone surrounded by an outer T-cell zone. The presence of TLSs correlates with positive patient outcomes in several cancer types, but the mechanisms governing their formation, maturation, and role in the antitumour response remain poorly understood. Motivated by analysis of spatial transcriptomics images of TLSs in colorectal cancer, we develop an agent-based model to investigate TLS formation, maturation, and function in cancer. We model T-cells and B-cells as discrete agents which are attracted to diffusible chemokines (CXCL13 and CCL19) produced by resident stromal cells in the TME. These interactions lead to the formation of a well-mixed lymphoid aggregate that later matures into distinct T- and B-cell zones due to the segregated expression of these chemokines. Our results identify key parameters governing TLS development and suggest conditions under which TLSs are able to control tumour growth. This framework provides a qualitative basis for understanding TLS dynamics and their potential role in cancer immunotherapy.

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Chenxu Zhu

Institute for Computational Biomedicine - Disease Modeling

"Machine learning-assisted mechanistic modeling to predict disease progression in acute myeloid leukemia patients"

Blood cell formation is a complex process which is driven by hematopoietic stem cells (HSCs). HSCs give rise to progenitors and precursors which eventually produce mature blood cells, such as white blood cells, red blood cells, and platelets. Acute myeloid leukemia (AML) is an aggressive blood cancer which originates from leukemic stem cells (LSCs) and is characterized by the accumulation of aberrant immature cells, referred to as leukemic blasts. Due to the impairment of healthy blood cell formation, many AML patients suffer from life-threatening complications, such as bleeding or infection. Although treated with high-dose chemotherapy, many patients relapse and need salvage therapy. To reveal the mechanisms of disease progression and relapse, we proposed a mathematical model that accounts for competition of HSCs and LSCs in the stem cell niche and physiological feedback regulations before, during, and after chemotherapy. We fit the model to data of 7 individual patients and simulate variations of the treatment protocol. Our simulation results can recapitulate the non-monotonic recovery of HSCs observed in relapsing patients. The model suggests using the decline of HSC counts during remission as an indication for salvage therapy in patients lacking minimal residual disease markers. To bring our model closer to clinical applications, we propose a machine learning assisted mechanistic model that ensuring adherence to biological principles while learning from a larger clinical AML dataset. By embedding mechanistic constraints into machine learning, we aim to identify patient-specific predictors of relapse while preserving biological interpretability.

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