Mini-course 1: Meyling Hua-Che Cheok 

Course title: Cancer biology and relevant medical terminology

Description: Cancer is the second leading cause of death worldwide. Interdisciplinary cancer research is a growing field with current interest. This introductory course is designed for non-biologists to develop a basic understanding of cancer biology, diagnosis and treatment. Three main topics will be covered: Cancer biology (etiology, cancer cell, genetics, cancer stem cell), Hallmarks of cancer (tumor growth, metastasis), and Diagnostics and therapy in oncology (imaging, staging, therapy, clinical trials, ethical issues, resistance).

Mini-course 2: Angélique Stéphanou

Course title: Hybrid Multi-scale Modelling in Oncology

Description: The course aims at presenting hybrid multi-scale models in oncology. In an introductory session, the purpose of these models will be presented and will show how the different spatial and temporal scales are represented and integrated, the different model types will be discussed (session 1). The implementation of a model of tumour growth will be described and will first focus on the continuous representations of intracellular kinetic reactions with ODEs and reaction-diffusion phenomena with PDEs (session 2). Then the coupling with discrete representations to treat the individual cells evolutions will be developed using an agent-based model (session 3). Finally some applications of the model to test some therapies (chemotherapies and radiotherapy) will be proposed. Simulations will be realised with the Physicell software (http://physicell.org/) and the results will be confronted to experimental or clinical data (interactive session 4).

References:

1. Caraguel F., A.C. Lesart, F. Estève, B. van der Sanden and A. Stéphanou. Towards the design of a patient-specific virtual tumour. Computational and Mathematical Methods in Medicine (2016), 7851789. doi:10.1155/2016/7851789
2. Stéphanou A., P. Ballet and G. Powathil. Hybrid Data-Based Modelling in Oncology : Successes, Challenges and Hopes, Mathematical Modelling of Natural Phenomena (2020) 15: 21. https://hal.archives-ouvertes.fr/hal-02405782
3. Stéphanou A. and V. Volpert. Hybrid modelling in biology : a classification review. Mathematical Modelling of Natural Phenomena, Hybrid models in biology (2015), 11(1):37-48. doi:10.1051/mmnp/201611103
4. Ghaffarizadeh A, Heiland R, Friedman SH,Mumenthaler SM,Macklin P. PhysiCell: An open source physics based cell simulator for 3-D multicellular systems. PLOS Computational Biology. 14(2):e1005991

Mini-course 3: Florence Hubert

Course title: Transport equations in oncology

Description: In oncology, predicting the evolution of disease and the efficacy of treatments is a challenge in which mathematical modelling can play a key role. We will describe in this course two different issues. The first one concerns the prediction of the metastatic state of the patients, the second one the better understanding of chemotherapeutic drugs that are anti-microtubule agents. For both issues transport models have been used.
Transport equations are used in structured population dynamics to model the temporal evolution of a population described by additional traits, which enable a more detailed description of its behavior (See [4]). The model of metastatic emission uses a transport equation inspired from the MacKendrich-Von Foerster equation (See [2]). Microtubule instabilities can be described thanks to transport equations with growth-fragmentation processes [3, 1]. We will start this course by a presentation of some classical tumor growth models based on Ordinary Differential equations and then present the two families of transport equations through the two examples.

References

1. J. A. Cañizo, P. Gabriel, and H. Yoldaş. Spectral gap for the growth-fragmentation equation via Harris’s theorem. SIAM J. Math. Anal., Vol.53, No.5, 5185–5214,(2021).
2. N. Hartung & al. Mathematical Modeling of tumor growth and metastatics spreading : validation in tumor-bearing mice, Cancer Research 74, 6397–6407, (2014).
3. S. Honoré, F. Hubert, M. Tournus, D. White. A growth-fragmentation approach for modeling microtubule dynamic instability, Bulletin of Mathematical Biology, 81 p. 722–758, (2019).
4. B. Perthame. Transport equations in biology, Springer.

Mini-course 4: Rachid Ouifki

Course title: Mathematical Modelling and Oncolytic Virotherapy in Cancer Treatment Using Delay Differential Equations

Description: Oncolytic Virotherapy, a promising approach in cancer treatment, involves the use of viruses to target and destroy cancer cells. The effectiveness of oncolytic virotherapy relies on a complex interplay of biological factors, making mathematical modelling an essential tool for understanding and optimizing its therapeutic potential. In this course, we explore the application of delay differential equations (DDEs) for modelling oncolytic virotherapy.

The course comprises the following sections:

• A short introduction to DDE theory.
• A brief overview of vital biological processes in oncolytic virotherapy, with emphasis on virus replication, tumor growth, immune response, and associated time delays.
• A concise review of DDE-based mathematical models, highlighting their advantages in capturing complex interactions within the tumor microenvironment.
• Examination of stability properties in some DDE models to uncover their long-term behavior.
• Practical numerical simulations of these DDE models using Matlab, with tutorial sessions to assist participants in getting acquainted.

References:

1. JJ Davis and B Fang. Oncolytic virotherapy for cancer treatment: challenges and solutions. The Journal of Gene Medicine: A cross-disciplinary journal for research on the science of gene transfer and its clinical applications, 7(11):1380–1389, 2005.
2. H Fukuhara, Y Ino, and T Todo. Oncolytic virus therapy: a new era of cancer treatment at dawn. Cancer science, 107(10):1373–1379, 2016.
3. D Wodarz. Viruses as antitumor weapons: defining conditions for tumor remission. Cancer research, 61(8):3501–3507, 2001.
4. H Smith. An introduction to delay differential equations with applications to the life sciences. springer; 2011.
5. LF Shampine, S Thompson. Numerical solution of delay differential equations. Delay Differential Equations: Recent Advances and New Directions. 2009:1–27.

Mini-course 5: Michelle Carey and Sophie Dabo-Niang

Course title: Statistical methods in oncology - part 1 

Description: This course introduces essential statistical concepts and techniques specifically tailored to the field of oncology. Participants will learn how to analyze cancer-related data, make informed decisions, and contribute to advancements in cancer research and treatment. This course structure provides a comprehensive overview of statistical methods in oncology, catering to participants from diverse backgrounds and levels of expertise. It equips them with the skills needed to contribute meaningfully to cancer research and clinical practice.

The course comprises the following modules:

Module 1: Introduction to Oncology and Cancer Data

• Overview of cancer epidemiology and oncology research
• Types of cancer data (clinical, genomic, imaging)
• Data collection, storage, and management in oncology

Module 2: Descriptive Statistics for Cancer Data

• Summary statistics and data visualization
• Exploring cancer data distributions
• Introduction to survival analysis and time-to-event data

Module 3: Hypothesis Testing and Inference in Oncology

• Hypothesis testing for cancer studies
• Confidence intervals and effect size estimation
•  Multiple comparisons and adjustments in oncology research

Module 4: Regression Analysis in Oncology

• Linear regression for cancer data
• Logistic regression for binary outcomes (e.g., cancer diagnosis)
• Survival regression (Cox proportional hazards model)

Module 5: Survival Analysis in Depth

• Kaplan-Meier survival curves
• Log-rank test and other survival comparisons
• Prognostic factors and Cox regression modeling

Module 6: Clinical Trial Design and Analysis

• Principles of clinical trial design in oncology
• Randomization, blinding, and control groups
• Analyzing clinical trial data: endpoints and sample size calculation

Mini-course 6: Nada Abdelatif

Course title: Statistical methods in oncology - part 2

Description: This course introduces essential statistical concepts and techniques specifically tailored to the field of oncology. Participants will learn how to analyze cancer-related data, make informed decisions, and contribute to advancements in cancer research and treatment. This course structure provides a comprehensive overview of statistical methods in oncology, catering to participants from diverse backgrounds and levels of expertise. It equips them with the skills needed to contribute meaningfully to cancer research and clinical practice.

Module 7: Analysis of Genomic Data

• Introduction to genomic data types (gene expression, DNA sequencing)
• Differential gene expression analysis
• Introduction to bioinformatics tools for cancer genomics

Module 8: Imaging Data Analysis

• Medical imaging modalities in oncology (MRI, CT, PET)
• Image processing and feature extraction
• Radiomics and machine learning in cancer imaging

Module 9: Advanced Topics

• Bayesian statistics in oncology
• Longitudinal data analysis (repeated measures)
• Handling missing data in cancer studies

Module 10: Case Studies and Projects

• Real-world case studies in oncology research
• Group projects applying statistical methods to cancer datasets
• Presentation of findings and interpretation

Module 11: Ethical and Regulatory Considerations

• Ethical issues in cancer research
• Regulatory guidelines and compliance (e.g., HIPAA)
• Reporting and publication standards in oncology studies

Module 12: Future Trends and Emerging Technologies

• Emerging technologies in oncology research (e.g., liquid biopsy, AI)
• Challenges and opportunities in the era of precision medicine
• Preparing for the future of cancer data analysis

Mini-course 7: Shawn Liebenberg

Course title: Data Mining with Python
Description: The aim of the course is to present the main algorithms useful for data science, in particular data mining and prediction from examples. The course provides an introduction to the issues involved, in comparison with statistics. It presents the general approach and methodology for implementing, tuning, evaluating and comparing these algorithms. It then considers two main classes of methods.

• Segmentation or unsupervised classification. We study the main algorithms, namely k-means, hierarchical clustering, the Expectation-Maximization (EM) algorithm, a density-based algorithm (DBSCAN). The aim is to learn about these methods and how to compare them, so as to be able to make the right choice for each case.
• Supervised classification. We present decision tree algorithms and linear separation algorithms, in particular, support vector machines (or wide margin machines). Practical exercises will be carried out in Python, using the algorithms presented in class on different datasets. The emphasis will be on evaluation and comparison the right algorithm for a given problem.

Brief program:

• General principles of segmentation and k-means algorithms, hierarchical clustering
• General principles of supervised classification and decision tree and linear separation algorithms - Performance evaluation of algorithms and elements of method comparison

Skills acquired:

• Use of segmentation algorithms: k-means and hierarchical classification
• Use of decision tree algorithms and understanding of possible choices: pruning, missing values, choice of gain discrete attributes (or categorical variables)
• Use of support vector machines and understanding of the choice of regularization parameter and choice of kernel
• Use of Python libraries, manipulation of Python datasets (Numpy, Pandas, Scikit-Learn)
• Comparison of algorithms, comparison of results obtained by applying algorithms to various datasets