This week in MathOnco 294

Science communication, agent-based models, ecological scales, metabolism, and more.

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May 02, 2024

`> mathematical-oncology.org`

`From the editor:`

In any field, science communication is important. How might we do this in math oncology? Perhaps by defending the usefulness of math modeling in oncology, clearly defining terms, & providing historical success stories. I enjoyed those examples on Twitter this week, and I thought you might too.

Headed to the UK for #CancerEvoEco24? Be sure to say hello to our MathOnco Art Director

Maximilian Strobl

to discuss how you can be featured in the newsletter.

Thanks,

Jeffrey West jeffrey.west@moffitt.org

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation
Nicolò Cogno, Cristian Axenie, Roman Bauer, Vasileios Vavourakis
3D genomic mapping reveals multifocality of human pancreatic precancers
Alicia M. Braxton, Ashley L. Kiemen, Mia P. Grahn, André Forjaz, J…, Ralph H. Hruban, Pei-Hsun Wu, Denis Wirtz & Laura D. Wood
Evolvability of cancer-associated genes under APOBEC3A/B selection
Joon-Hyun Song, Liliana M. Dávalos, Thomas MacCarthy, Mehdi Damaghi

Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters
Awino Maureiq E. Ojwang’, Sarah Bazargan, Joseph O. Johnson, Shari Pilon-Thomas, Katarzyna A. Rejniak
Modeling tumors as species-rich ecological communities
Guim Aguadé-Gorgorió, Alexander R.A. Anderson, Ricard Solé
Computational flow cytometry immunophenotyping at diagnosis is unable to predict relapse in childhood B-cell Acute Lymphoblastic Leukemia
Álvaro Martínez-Rubio, Salvador Chulián, Ana Niño-López, Rocío Picón-González, …, Cristina Blázquez Goñi, Víctor M. Pérez-García, María Rosa
P53 Orchestrates Cancer Metabolism: Unveiling Strategies to Reverse the Warburg Effect
Roba Abukwaik, Elias Vera-Siguenza, Daniel Tennant, Fabian Spill

Tumours form without genetic mutations
Nature News & Views: “Researchers find that brief and reversible inhibition of a gene-silencing mechanism leads to irreversible tumour formation in fruit flies, challenging the idea that cancer is caused only by permanent changes to DNA.”

The newsletter now has a dedicated homepage where we post the cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the paper: A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model published in Cancer Research Communications.

Artist: Guillermo Lorenzo (@guillelorenzogz)

Caption: In our recent work, we carried out a pilot project on personalized computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomathematical model. We describe the development of the disease as a combination of tumor cell mobility and proliferation. Longitudinal multiparametric magnetic resonance imaging data collected during standard-of-care active surveillance enable the construction of a virtual 3D representation of the patient’s prostate and provide estimates of tumor morphology and tumor cell density. Our results show that our technology can reproduce and predict spatiotemporal growth of prostate cancer during clinically relevant times in active surveillance. Additionally, we calculated a panel of metrics from the personalized tumor predictions and used them as features in a logistic classifier of clinical progression to higher risk disease, which requires moving the patient from active surveillance to radical treatment (e.g., surgery, radiotherapy). The global proliferation activity of the tumor was found to be a significant biomarker and together with the total tumor index, yielded a classifier with AUC=0.83, operating at 75% optimal sensitivity and specificity, and capable of predicting clinical progression by more than one year earlier than standard clinical practice. Thus, although further development and validation over larger datasets are required, we believe that our predictive technology is a promising approach to guide clinical decision-making and design personalized monitoring plans for each individual prostate cancer patient. See the full publication in Cancer Research Communications here.

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

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