“This week in Mathematical Oncology” — February 08, 2023
> mathematical-oncology.org
From the editor:
Next week I’ll be giving a talk in which I’m attempting to describe what exactly is the field of mathematical oncology. A tough task, but I was reminded of this 2021 paper, which described the role of theory in biology as either Figure 1 or Figure 7. What do you think?
Don’t miss the new job posting below, for a postdoc in City of Hope! The jobs board lists quite a few other positions, as well .
Enjoy,
Jeffrey West
jeffrey.west@moffitt.org
State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia
David E. Frankhouser, Russell C. Rockne, Lisa Uechi, Dandan Zhao, …, Yu-Hsuan Fu, Ya-Huei Kuo, Bin Zhang & Guido MarcucciPredicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling
Alexander P. Browning, Thomas D. Lewin, Ruth E. Baker, Philip K. Maini, Eduardo G. Moros, Jimmy Chaudell, Helen M. Byrne & Heiko EnderlingModeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution
Anuraag BukkuriViruses, cancers, and evolutionary biology in the clinic: a commentary on Leeks et al. 2023
J. Arvid Ågren, Jacob G. ScottOnco-Breastomics: An Eco-Evo-Devo Holistic Approach
Anca-Narcisa Neagu, Danielle Whitham, Pathea Bruno, Aneeta Arshad, Logan Seymour, Hailey Morrissiey, Angiolina I. Hukovic, Costel C. DarieRadiation necrosis after radiation therapy treatment of brain metastases: A computational approach
Beatriz Ocaña-Tienda, Odelaisy León-Triana, Julián Pérez-Beteta, Juan Jiménez-Sánchez, Víctor M. Pérez-GarcíaMapping cancer biology in space: applications and perspectives on spatial omics for oncology
Sumin Lee, Gyeongjun Kim, JinYoung Lee, Amos C. Lee & Sunghoon KwonDeep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Yeman Brhane Hagos, Catherine S.Y. Lecat, Dominic Patel, Anna Mikolajczak, …, Lydia S.H. Lee, Manuel Rodriguez-Justo, Kwee L. Yong, Yinyin YuanComputational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc
Alice Antonello, Riccardo Bergamin, Nicola Calonaci, Jacob Househam, …, Vasavi Sundaram, Alona Sosinsky, William C. H. Cross & Giulio Caravagna
A review of mechanistic learning in mathematical oncology
John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn-Luque, Sarah C. BrüningkPancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations
Daniel R Plaugher, David R Murrugarra
The Eye of the Needle in Oncolytic Virotherapy
The Mathematical Oncology Blog
Thomas Hillen: “What a cool idea: simply infect all cancer cells with a deadly virus, which then kills all cancer cells and problem solved! Wow. How did somebody come up with this thrilling idea? Well, it was observed in cancer patients who caught a viral infection that, sometimes, the tumor became smaller as a result of the infection. Since these observations, the hunt for the proper virus has started. Researchers look at herpes virus, measles virus, vaccina virus, vesicular stomatitis virus, adenoviruses and reoviruses, and probably more. Do they work? Well, two of these have already been approved by governmental health agencies, one is the adenovirus H101 for the treatment of head and neck cancer and another one is indeed a herpes virus (T-VEC) for the treatment of advanced melanoma.“
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: Rethinking the Immunotherapy Numbers Game published in the Journal for ImmunoTherapy of Cancer
Artist: Rebecca Bekker (@rebecca_bekker)
Caption: Traditionally, therapies have been combined with immunotherapy without much consideration of how each component part impact the immune system. For example, the figure depicts the tumour-immune state of a hypothetical patient in the top left corner (red – cancer cells, blue – immune cells) and the impact of therapy thereon. Treatment with either radiation, chemotherapy or targeted agents results in a small tumour and suppressed immune system (leftwards, down arrow). Subsequent administration of immune-checkpoint-inhibitors may change the underlying immune properties (leftward arrow), but not in a manner that is sufficient to push the patient into the region of tumour control (green shaded region). Despite potentially having treatment options available, the toxicity profile of the patient may preclude their use. In this paper we argue that mathematical modelling is a useful tool to visualize the effects of various therapies on the tumour-immune state; and conceptualize how to rationally combine therapies, to best play the so-called immunotherapy numbers game.
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