This week in MathOnco 197

Tumor spheroids, fractionated radiation, response dynamics for immunotherapy / chemotherapy, parameter estimation, and more.

“This week in Mathematical Oncology” — Feb. 17, 2022

> mathematical-oncology.org

From the editor:

Welcome to another edition of “This week in MathOnco” — which features articles on tumor spheroids, fractionated radiation, response dynamics for immunotherapy / chemotherapy, parameter estimation, and more.

Jeffrey West
jeffrey.west@moffitt.org

1. Designing and interpreting 4D tumour spheroid experiments
   Ryan J. Murphy, Alexander P. Browning, Gency Gunasingh, Nikolas K. Haass, Matthew J. Simpson

2. A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data
   Junyan Liu, David A. Hormuth II, Jianchen Yang, Thomas E. Yankeelov

3. Classical mathematical models for prediction of response to chemotherapy and immunotherapy
   Narmin Ghaffari Laleh, Chiara Maria Lavinia Loeffler, Julia Grajek, Kateřina Staňková, …, Christian Trautwein, Heiko Enderling, Jan Poleszczuk, Jakob Nikolas Kather

4. Cacatoo: building, exploring, and sharing spatially structured models of biological systems
   Bram van Dijk

5. Suppressing evolution of antibiotic resistance through environmental switching
   Bryce Morsky, Dervis Can Vural

6. Circulating Tumour DNA in Melanoma—Clinic Ready?
   Ann Tivey, Fiona Britton, Julie-Ann Scott, Dominic Rothwell, Paul Lorigan, Rebecca Lee

7. Cooperation in alternating interactions with memory constraints
   Peter S. Park, Martin A. Nowak, Christian Hilbe

8. Biallelic mutations in cancer genomes reveal local mutational determinants
   Jonas Demeulemeester, Stefan C. Dentro, Moritz Gerstung, Peter Van Loo

1. Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models
   Matthew J. Simpson, Ruth E. Baker, Pascal R. Buenzli, Ruanui Nicholson, Oliver J. Maclaren

2. A heterogeneous drug tolerant persister state in BRAF-mutant melanoma is characterized by ion channel dysregulation and susceptibility to ferroptosis
   Corey E. Hayford, Philip E. Stauffer, Blake Baleami, B. Bishal Paudel, …, Kaitlyn E. Johnson, Leonard A. Harris, Amy Brock, Vito Quaranta

3. Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?
   Mohammad El Wajeh, Falco Jung, Dominik Bongartz, Chrysoula Dimitra Kappatou, Narmin Ghaffari Laleh, Alexander Mitsos, Jakob Nikolas Kather

4. p53 mutation in normal esophagus promotes multiple stages of carcinogenesis but is constrained by clonal competition
   Kasumi Murai, Stefan Dentro, Swee Hoe Ong, Roshan Sood, David Fernandez-Antoran, Albert Herms, Vasiliki Kostiou, Benjamin A Hall, Moritz Gerstung, Philip H Jones

5. SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure
   Matthew N. Bernstein, Zijian Ni, Aman Prasad, Jared Brown, Chitrasen Mohanty, Ron Stewart, Michael A. Newton, Christina Kendziorski

6. Population Calibration using Likelihood-Free Bayesian Inference
   Christopher Drovandi, Brodie Lawson, Adrianne L Jenner, Alexander P Browning

1. Data science can help us run better cancer clinical trials
   The Mathematical Oncology Blog
   Deborah Plana: “Our recent work in Nature Communications aims to modernize the study and interpretation of clinical trials by curating, releasing, and re-analyzing data reconstructed from ~150 clinical trials in breast, colorectal, lung, and prostate cancer.” See cancertrials.io for more info.

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.

Caption: This artwork covers our recently published work in Communications Biology entitled Designing and interpreting 4D tumour spheroid experiments. The three experimental images of spheroids equatorial planes are representative of the three key phases of avascular tumour spheroid growth: phase (i) all cells able to proliferate; phase (ii) an outer region of proliferative cells and an inner region of proliferation-inhibited cells; and phase (iii) an outer region of proliferative cells, an intermediate region of proliferation-inhibited cells, and a necrotic core. Colours in the experimental images represent cell cycle status: G1 (magenta) and S/G2/M phases (green); and cell death (grey). To interpret these tumour spheroid experiments we measure outer, inhibited, and necrotic radii and use the seminal Greenspan mathematical model and statistical identifiability analysis (key equations shown in grey). Our approach allows us to determine maximum likelihood estimates and approximate 95% confidence intervals for parameters of Greenspan’s model across a range of experimental designs. We then identify experimental design choices that lead to reliable biological insights. Results are presented for human melanoma cell lines and our framework can be generalised to spheroids grown with different cell types and in different conditions.

Created by: Ryan J. Murphy (@RyanMurphy42)

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