This week in MathOnco 292
Project Optimus, radiotherapy, mutation accumulation, and deep learning / AI
“This week in Mathematical Oncology” — Apr 18, 2024
> mathematical-oncology.org
From the editor:
Welcome! This week’s edition has some papers on Project Optimus, radiotherapy, mutation accumulation, and deep learning / AI. If you’re curious, I recently wrote a thread of my thoughts on traveling & hosting invited talks.
Enjoy,
Jeffrey West
jeffrey.west@moffitt.org
Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development
Wei Gao, Jiang Liu, Blerta Shtylla, Karthik Venkatakrishnan, Donghua Yin, Mirat Shah, Timothy Nicholas, Yanguang CaoPatient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data
Guillermo Lorenzo, Syed Rakin Ahmed, David A. Hormuth II, Brenna Vaughn, Jayashree Kalpathy-Cramer, Luis Solorio, Thomas E. Yankeelov, Hector GomezA resource-based mechanistic framework for castration-resistant prostate cancer (CRPC)
B. Vibishan, Harshavardhan B.V., Sutirth DeyLymphoDose: a lymphocyte dose estimation framework - application to brain radiotherapy
François de Kermenguy, Nathan Benzazon, Pauline Maury, Rémi Vauclin, …, Céline Clémenson, Michele Mondini, Eric Deutsch and Charlotte RobertHow mutation accumulation depends on the structure of the cell lineage tree
Imre Derényi, Márton C. Demeter, Mario Pérez-Jiménez, Dániel Grajzel, and Gergely J. Szöllősi
Punctuational evolution is pervasive in distal site metastatic colonization
George Butler, Sarah R. Amend, Robert Axelrod, Chris Venditti, Kenneth J. PientaCINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution
Khanh N. Dinh, Ignacio Vázquez-García, Andrew Chan, Rhea Malhotra, Adam Weiner, Andrew W. McPherson, Simon TavaréInference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data
Zijin Xiang, Zhihan Liu, Khanh N DinhHybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy
Joseph Butner, Prashant Dogra, Caroline Chung, Eugene Koay, James Welsh, David Hong, Vittorio Cristini, Zhihui Wang
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: Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties published in npj Systems Biology and Applications
Artist: Maisa van Genderen (@Maisaavg), Federica Eduati (@FEduati)
Caption: Androgen deprivation therapy (ADT) for prostate cancer targets the androgen signaling pathway within tumor cells to impede cancer cell proliferation. However, recent findings indicate that the androgen pathway plays crucial roles in other cells within the tumor microenvironment, making them susceptible to ADT effects. To study this, we have developed an agent-based model that integrates current knowledge of ADT's impact on tumor cells (depicted in red in the simulation), cancer-associated fibroblasts (green), and both M1 (dark blue) and M2 type (light blue) macrophages. By simulating the presence (upper row) or absence (bottom row) of androgen, we uncover how ADT influences the dynamics of these cellular players in the tumor microenvironment, shedding light on tumor response and resistance mechanisms. Our model effectively mirrors established effects of ADT, including relevant spatial cellular patters, and predicts its potential to induce immunomodulatory responses in macrophages, which can cause enhanced patient survival. See tweet thread on the paper.
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