Mathematical Oncology

2021 recap

Year in Review

Written by David Basanta - December 31, 2021

Encouraged by Jeff West (my partner in crime in this enterprise), I am going to use what I am pretty certain is the last post of 2021 to write a quick summary of what went on this blog in 2021.

This has been an interesting year. For many of us, and thanks to a global effort to create, test and deploy covid19 vaccines, things that were not possible (or advisable) in 2020 became easier this year. In fact, I am typing these lines in the kitchen at my in-law's house in London, a fact that is only possible thanks to the vaccination effort that has made international travel possible and for small groups of people to meet. Many (many!) PCR and antigen tests were also involved in making this trip possible, of course. But while better than 2020 (admittedly, the bar is low), this year has not seen the return to normalcy that many of us hoped for, in part by the two facts that the vaccines developed last year were not accepted by a significant part of the world with widespread access to them and are not available in parts of the world where people would be happy to use them.

All this seems to have little to do with either mathonco or componco, but it helps explain why (by my own reckoning) many of us have not attended meetings and conferences in person this year. This means that less conventional means to exchange scientific ideas, like this, become even more important as ways for researchers in this community to exchange ideas and research results.

Most posts this year described newly published research. For instance, Anuraag Bukkuri and Fred Adler wrote this post about an interesting perspective on how corruption (in society) and cancer share a few things regarding the interpretation of signals between individuals. Noemi Andor, also a colleague and friend at Moffit wrote this one about her computational approach to study how high ploidy in cancer is shaped by two different and opposing selective forces. Daniel Bergman used an agent-based model to study the interactions between the immune system and cancer during the EMT (Epithelial to Messenchymal Transition). Following the theme of immune-tumor interactions, Michael Raatz described how treatment-tumor interactions shape the tumor’s heterogeneity. Treatment is also the focus of Adrianne Jenner’s post where she highlights the importance of in silico trials to advance cancer research using an example with oncolytic therapies. Nathan Farrokhian’s post highlights this further by stating the need of, not just experimental but also clinical data. Anum Kazerouni and Caleb Philips review the integration of experimental data with mathonco models. While data is important, collaborations are more than the exchange of data as you can read in Elana Fertig’s field guide to cultivate computational biology.

Some of the research we got submissions for addressed more basic biology or mathematical aspects. One example can be seen in the post about superlinear growth, which can be used to characterize the allee effect in cancer by Youness Azimzade. Adam McLean describing work with Megan Franke, described a new mathematical framework to combine gene-regulatory networks with cell-cell communication at the single-cell level. Saskia Haupt wrote about modeling pathways to carcenogenesis using the Kronecker structure. Later on Saskia and Aysel Ahadova use their work on bowel cancer and Lynch syndrome to illustrate, not only their mathematical modeling approach specifically but the role of mathematical biology in general.

A great example of more abstract posts, here is the one by Gregory Kimmel on traveling waves, game theory and spatial public goods. Also on the topic of game theory, Peter Bayer uses this tool to explore how coordination games, games where players receive a higher payoff when they use the same strategy, could be useful in cancer research. Together with the previously mentioned post by Nathan Farrokhian show the growing importance of game theoretical approaches in mathematical models of cancer evolution.

Together with recent research, perspectives and views, one of our traditional types of post is that of new tools. This year we have also seen amazing tools such as the one described here by Sandhya Prabhakaran where she presents Mistic which allows for t-SNE visualization of multiplexed images.

Our own Jeff West is often busy with the incredibly successful mathonco newsletter which reached 1K subscribers in July. In terms of new ideas, he also gave us a different take on adaptive therapies through adaptive dose personalization. Adaptive therapies are also the basis in which Fred Adler wonders about the usefulness of adding mechanisms to our mathematical models. Playing with different models that incorporate the biology of cancer growth and treatment required to model adaptive therapies with different degrees of resolution, Fred and colleagues shown how more realistic models are often necessary but how simple models can be useful in delineating important general principles.

I hope this year’s collection of posts shows the strength as well as the diversity of approaches, ideas and interests of our community. We had contributions from mathematical and computational oncologists, from researchers working on general mathematical frameworks and those asking precise questions with immediate clinical applications, we had perspectives and tools, what will we get next year? If we are lucky, all of this and maybe some cool posts from people attending meetings in person again? We could talk some other time about how meetings will look in a post-covid world, but being able to meet friends and colleagues again would be a great sign that things are looking up.
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