2024 has come to and gone and it is time to take a look at what the year meant for this blog. As was the case last year, you, the mathonco community, have used this blog to explore a wide range of topics. To make things easier, I have divided the posts in three themes: posts describing a singular piece of mathonco research, posts describing a perspective or a big idea and, finally, posts describing a new resource or tool.

Deep Dives: Exploring Cutting-Edge Research: Our community has published quite a few papers this year and you were kind enough to explain and summarize some of them for us:

• Nathaniel Mon Pere explored the fascinating dynamics of "Clonal Competition" in aging human hematopoiesis, providing a framework for understanding how different cell populations compete and evolve over time.
• Jesse Kreger explored the origins of clonal hematopoiesis in "Developmental Hematopoietic Stem Cell Fate", shedding light on the early stages of clonal evolution and how these changes can contribute to disease.
• Kit Gallaher also explored how we can use learning to improve treatment with "Therapy Schedules: Reinforcement Learning". His post explores using deep reinforcement learning to optimize adaptive therapy schedules and personalize treatment strategies.
• We also explored the role of the microenvironment in "Tumor Microenvironment and Response to Treatment". This post by Youness Azimzade discusses the complex interplay between the tumor microenvironment and response to treatment, highlighting how microenvironmental factors influence therapeutic efficacy.
• "Eye of the Needle: Oncolytic Virotherapy" where Thomas Hillen discussed oncolytic virotherapy as a cancer treatment approach.

Thinking Big: Conceptual Frameworks and New Perspectives (Big Ideas) Sometimes, it’s good to step back and look at the bigger picture. This year, this blog explored some key ideas shaping the future of mathematical oncology:

• Ever wonder what happens when a model doesn’t work? In "Learning from Failed Model Predictions" "Learning from Failed Model Predictions", Sara Hamis argued that these “failures” are actually goldmines of information. By dissecting why models fall short, we can refine our approaches and gain a deeper understanding of the system.
• How can we build better, more insightful models? "Mechanistic Learning" where Jeffrey West made the case for combining the strengths of mechanistic modeling and machine learning. It’s bringing together the best of both worlds – incorporating biological knowledge into data-driven methods for a more holistic understanding.
• And speaking of new approaches, "Bridging Artificial Life and Cancer Biology" Sadegh Marzban, also with Jeff West, explored the potential of applying concepts from artificial life, particularly the Lenia framework, to model cancer growth and the complex dynamics of the tumor microenvironment. It’s an innovative approach that opens up exciting new avenues for research.
• Evolution of Phenotypic Plasticity "Evolution of Phenotypic Plasticity" where Simon Syga explored how cancer cells use phenotypic plasticity to adapt to changing environments and therapies.

Resources for the Community: Sharing is Caring! (Tools) Finally, I wanted to highlight some unique and valuable resources you shared with the community this year:

• Visualizing tumor evolution can be a real challenge, but "Navigating Tumor Evolution" by Anjun Hu, introduced us to a powerful new tool, LinG3D, that allows researchers to track the development of tumor clones in both space and time.
• In "Bib File to Rule" Franco Pradelli released a comprehensive .bib file containing over 1500 references in mathematical oncology. It’s a treasure trove of information for anyone working in the field!
• Math Oncology Interviews Thomas Hillen highlighted interviews with leading researchers in the field.

As always, we are grateful to all the people that contributed, some of which I named already in this post, for sharing their ideas and passion. Your posts are the reason for this blog.