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
Designing and interpreting 4D tumour spheroid experiments
Ryan J. Murphy, Alexander P. Browning, Gency Gunasingh, Nikolas K. Haass, Matthew J. SimpsonA 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. YankeelovClassical 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 KatherCacatoo: building, exploring, and sharing spatially structured models of biological systems
Bram van DijkSuppressing evolution of antibiotic resistance through environmental switching
Bryce Morsky, Dervis Can VuralCirculating Tumour DNA in Melanoma—Clinic Ready?
Ann Tivey, Fiona Britton, Julie-Ann Scott, Dominic Rothwell, Paul Lorigan, Rebecca LeeCooperation in alternating interactions with memory constraints
Peter S. Park, Martin A. Nowak, Christian HilbeBiallelic mutations in cancer genomes reveal local mutational determinants
Jonas Demeulemeester, Stefan C. Dentro, Moritz Gerstung, Peter Van Loo
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. MaclarenA 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 QuarantaCan 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 Katherp53 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 JonesSpatialCorr: 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 KendziorskiPopulation Calibration using Likelihood-Free Bayesian Inference
Christopher Drovandi, Brodie Lawson, Adrianne L Jenner, Alexander P Browning
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)
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
Current subscriber count: >1.1k