This week in MathOnco 296

Tumor-ECM, adaptive therapy, immune kinetics, optimization, and more

“This week in Mathematical Oncology” — May 23, 2024

> mathematical-oncology.org

From the editor:

Today’s edition incluldes papers on tumor-ECM, adaptive therapy, immune kinetics, optimization, and more. The adaptive therapy paper was led by our very own Maxi Strobl & Sandy Anderson, and I think you’ll enjoy it! Maxi has also contributed this week’s cover artwork, too.

By the way, did you know that there is a “Seminars” page on our website? For example, we’ve updated the Moffitt IMO Department seminars page with ~5 years worth of math oncology recorded talks, here.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy
   Maximilian A.R. Strobl, Alexandra L. Martin, Jeffrey West, Jill Gallaher, Mark Robertson-Tessi, Robert Gatenby, Robert Wenham, Philip K. Maini, Mehdi Damaghi, Alexander R.A. Anderson

2. Decoding immune kinetics: unveiling secrets using custom-built mathematical models
   Amber M. Smith

3. A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework
   Dimitrios Katsaounis, Nicholas Harbour, Thomas Williams, Mark AJ Chaplain & Nikolaos Sfakianakis

4. Spatial heterogeneity in tumor adhesion qualifies collective cell invasion
   C. Venkata Sai Prasanna, Mohit Kumar Jolly, Ramray Bhat

5. Computational modeling reveals key factors driving treatment-free remission in chronic myeloid leukemia patients
   Xiulan Lai, Xiaopei Jiao, Haojian Zhang, Jinzhi Lei

6. A guide to artificial intelligence for cancer researchers
   Raquel Perez-Lopez, Narmin Ghaffari Laleh, Faisal Mahmood & Jakob Nikolas Kather

7. Optimizing Clinical Translation of Bispecific T-cell Engagers through Context Unification with a Quantitative Systems Pharmacology Model
   Xiaozhi Liao, Timothy Qi, Jiawei Zhou, Can Liu, Yanguang Cao

8. Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy
   Oyku Yagmur Basar, Sawsan Mohammed, M. Walid Qoronfleh, Ahmet Acar

1. Distinct evolutionary patterns of tumor immune escape and elimination determined by ECM architectures
   Yijia Fan, Jason T. George

2. Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness
   Eli N. Weinstein, Alan N. Amin, Jonathan Frazer, Debora S. Marks

1. Computational Resources for Cancer Research Portal
   ”Introducing the Computational Resources for Cancer Research portal! This is a central hub for exploring—and contributing to—open-access resources, and meeting potential research collaborators in the Computational Cancer Community. Presented by the National Cancer Institute (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) and the Cancer Data Science Initiatives group at the Frederick National Laboratory for Cancer Research (FNLCR), the portal is designed to help increase understanding, access, and adoption of computational resources for cancer researchers and to foster new collaborative research projects. It includes resources developed by the NCI-Department of Energy (DOE) Collaboration to accelerate cancer research using advanced scientific computing. Additional resources contributed by the cancer research community will be added as they become available.”

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:

Artist: Maximilian Strobl (@StroblMAR)

Caption: This week, I thought I would do a little behind the scenes about what I enjoy doing outside of the newsletter. In my work, I think a lot about how to calibrate and test mathematical models of cancer treatment scheduling. To truly understand the dynamical processes driving tumor cell kinetics, we need measurements at multiple time points. But these can be hard to come by in practice. This is because measurement can be a destructive process (e.g. tissue staining) and/or because if we want measurements every 4h that means someone has to collect these every 4h. Automated, time-series microscopy, in which a microscope is integrated with an incubator, so that the cells can be cultured and imaged automatically, is a simple yet powerful technique to get high resolution spatio-temporal data – and art! Shown here is a collage of bright field images I have been analyzing in recent weeks. More specifically, these are ovarian cancer cells treated with carboplatin and paclitaxel chemotherapy, and segmented with ilastik. For recent other examples of how to use such data to drive mathematical models, check out issues 290, 239, or 209.

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.

1. Jobs

2. Conferences / Meetings

3. Special issues

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