Research Interests

My primary research interests are digital health, evolutionary models, and cancer informatics for improving interventions. My work spans various dimensions of cancer research, including tumor imaging, survival prediction, and eco-evolutionary models.

Sensor Data as a Measure of Cancer Evolution

Whilst mutational processes underly evolution and selection in cancer these can be difficult to measure and observe. During disease progression, patients experience phenotypic and symptomatic changes. Using digital health measures of activity, sleep, neurological, and physical function may allow us to understand disease progression and patient experience from a new perspective. Charting symptom and functional evolution with cheap wearable devices could complement expensive intermittent imaging. Ideally, we can leverage continuous monitoring to improve care, intervention timing, and cancer control.

Feature Engineering for Digital Twins

Sensor-derived datasets comprise high-frequency time series. Feature engineering allows us to derive different summary statistics from raw or processed data. Topological data analysis (TDA) provides suites of new tools that can be leveraged to extract features and is highly effective across multiple types of clinical data including within cancer informatics. Using TDA-derived features as inputs for data-driven patient models may allow us to identify new emergent patterns in clinical images, evolutionary trajectories, and patient motion data

Modeling and Game theory

Human diseases are typically bolstered by ecology and interactions. This adds combinatorial complexity to a system's dynamics. I am interested in how these effects play a role in some of our cancer models and how the composition of a patient's cancer can affect symptoms, treatment efficacy and disease severity.

Topology is cool

Key Papers

  1. Montfort*, Anne and Barker-Clarke*, Rowan and Piskorz, Anna M and Supernat, Anna and Moore, Luiza and Al-Khalidi, Sarwah and B{\"o}hm, Steffen and Pharoah, Paul and McDermott, Jacqueline and Balkwill, Frances R and others, Combining measures of immune infiltration shows additive effect on survival prediction in high-grade serous ovarian carcinoma, British Journal of Cancer, 122(12), pages 1803-1810, year 2020, publisher Nature Publishing Group UK

  2. Somasundaram, Eashwar and Litzler, Adam and Wadhwa, Raoul and Barker-Clarke, Rowan and Scott, Jacob, Persistent homology of tumor CT scans is associated with survival in lung cancer, Medical physics, 48(11), pages 7043-7051, year 2021

  3. King, Eshan S and Pelesko, Julia and Maltas, Jeff A and Barker-Clarke, Rowan J and Dolson, Emily and Scott, Jacob G, Fitness seascapes facilitate the prediction of therapy resistance under time-varying selection, bioRxiv, pages 2022-06, year 2022, publisher Cold Spring Harbor Laboratory

  4. Barker-Clarke, Rowan J and Gray, Jason and Tadele, Dagim and Hinczewski, Michael and Scott, Jacob G, Masking, maintenance and mimicry: the interplay of cell-intrinsic and cell-extrinsic effects in evolutionary games, bioRxiv, pages 2023-03, year 2023, publisher Cold Spring Harbor Laboratory

  5. Somasundaram, Eashwar and Wadhwa, Raoul R and Litzler, Adam and Barker-Clarke, Rowan and Qi, Peng and Videtic, Gregory and Stephans, Kevin and Pennell, Nathan A and Raymond, Daniel and Yang, Kailin and others, Clinical Nomogram Using Novel Computed Tomography--Based Radiomics Predicts Survival in Patients With Non--Small-Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy, JCO Clinical Cancer Informatics, 7, pages e2200173, year 2023, publisher Wolters Kluwer Health

  6. Barker-Clarke, R and Weaver, DT and Scott, JG, Graph 'texture' features as novel metrics that can summarize complex biological graphs, Physics in Medicine & Biology, 68(17), pages 174001, year 2023, publisher IOP Publishing

My long-term research interests lie in the development of applications of topology to biological systems.

About Rowan

Rowan Barker-Clarke is a dedicated researcher in the field of computational biology and cancer informatics. With a strong background in data analysis and modeling, Rowan has made significant contributions to the understanding of cancer and its treatment. His work spans various dimensions of cancer research, including tumor imaging, survival prediction, and evolutionary modeling.

Links

Toolset

A tiny tebe.ch project