This week in MathOnco: 141
Multi-drug resistance, life-history traits & cancer, inferring evolutionary parameters, learning ODEs from ABMs
Dec. 3, 2020
From the editor:
Dear readers,
This week’s edition might look a little different. I’ve decided to move “This week in MathOnco” to a new hosting service (substack.com). I hope the experience will be more enjoyable and user-friendly. You can browse the back-catelogue of all previous issues
here
.
This week’s edition includes articles on multi-drug resistance, life history traits & cancer, inferring evolutionary parameters, learning ODEs from ABMs and more. Enjoy!
-Jeffrey West
Learning differential equation models from stochastic agent-based model simulations
John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. FloresCompetition delays multi-drug resistance evolution during combination therapy
Ernesto Berríos-Caro, Danna R. Gifford, Tobias GallaHarnessing adaptive novelty for automated generation of cancer treatments
Igor Balaz, Tara Petrić. Marina Kovacevic, Michail-Antisthenis Tsompanas, Namid StillmandEstablishing patient-tailored variability-based paradigms for anti-cancer therapy: using the inherent trajectories which underlie cancer for overcoming drug resistance
Yaron Ilan, Zachary SpigelmanLifetime cancer prevalence and life history traits in mammals
Amy Boddy, Lisa Abegglen, Allan Pessier, Athena Aktipis, Joshua Schiffman, Carlo Maley, Carmel WitteTowards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer
Angela M. Jarrett, David A. Hormuth, Vikram Adhikarla, Prativa Sahoo, Daniel Abler, Lusine Tumyan, Daniel Schmolze, Joanne Mortimer, Russell C. Rockne & Thomas E. YankeelovWhy Tumor Genetic Heterogeneity May Require Rethinking Cancer Genesis and Treatment
Bruce Gottlieb, Mark Trifiro, Gerald Batist
Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic‐Pharmacodynamic Modeling Approaches
Anwesha Chaudhury, Xu Zhu, Lulu Chu, Ardeshir Goliaei, Carl H. June, Jeffrey D. Kearns, Andrew M. SteinSoftware must be recognised as an important output of scholarly research
Caroline Jay, Robert Haines, Daniel S. KatzInferring parameters of cancer evolution from sequencing and clinical data
Nathan Lee, Ivana BozicParadox resolved: The allometric scaling of cancer risk across species
Christopher P. Kempes, Geoffrey B. West, John W. Pepper
Evolutionary Therapy - Request for Application (RFA)
Anticancer Fund: Independent research fund focusing on cancer treatments
Knowledge about tumour evolutionary dynamics has been growing rapidly. However, there has been a limited translation of that knowledge into therapeutic trials. The most clinically advanced strategy is adaptive therapy. Adaptive therapy is a treatment strategy attempting to prolong response to treatment by delaying the emergence of resistance. The goal of adaptive therapy is to maintain a controllable stable tumour burden by allowing a significant population of treatment-sensitive cells to survive. The main principle of the intervention is to control the tumour and prolong survival by allowing on/off treatment periods based on a valid marker.
This RFA will accept clinical trials on adaptive therapy and any other evolutionarily informed strategy, as long as they meet all criteria (see eligibility criteria here).
What does heterogeneity mean for cancer treatment models?
Morgan Craig: The Mathematical Oncology Blog
Somatic evolution in cancer implies that cancer growth is decidedly heterogenous: from one cell of origin, the most aggressive clones emerge, and multiple scales of genetic heterogeneity are encompassed within each cell. Those of us in math oncology (and others, of course) will recognize the contributions of population geneticists to our field. To understand the growth of genetically-distinct clones in the context of neoplasms, we frequently deploy Moran, branching, or Wright-Fisher models as ways to study genetic drift and selection. Intratumour spatial and temporal heterogeneity are also particularly important in the context of cancer treatment, given that sensitive clones will be eradicated by effective therapies leaving only resistant ones in their wake, which therefore changes the overall tumour composition both structurally and dynamically. Given this multifactorial genetic, spatial, phenotypic, and temporal complexity, the design of effective and durable anti-cancer treatments depends on our ability to assess and predict the impact of intrinsic and environmental variability on outcomes.
The Cancer Code: A Revolutionary New Understanding of a Medical Mystery
Jason Fung: "In The Cancer Code, Dr. Jason Fung offers a revolutionary new understanding of this invasive, often fatal disease—what it is, how it manifests, and why it is so challenging to treat. In this rousing narrative, Dr. Fung identifies the medical community’s many missteps in cancer research—in particular, its focus on genetics, or what he terms the “seed” of cancer, at the expense of examining the “soil,” or the conditions under which cancer flourishes. Dr. Fung—whose groundbreaking work in the treatment of obesity and diabetes has won him international acclaim—suggests that the primary disease pathway of cancer is caused by the dysregulation of insulin. In fact, obesity and type 2 diabetes significantly increase an individual’s risk of cancer."
"Mathematical Models of Cellular Immunotherapies in Cancer"
Guest Editors: V. Pérez-García, L. de Pillis, P. Altrock, R. RockneFrom Ecology to Cancer Biology and Back Again
Guest Editors: Fred Adler, Sarah Amend, Chris WhelanFrontiers in quantitative cancer modeling
Guest Editors: Mohit Kumar Jolly, Heiko Enderling
NEW: PhD on Modelling Cell Life and Death (Dan Tennant/Fabian Spill)
Mathematical Modeling Expert in Oncology Translational Science (Boehringer Ingelheim)
Research Associate - Biostatistician (University of Manchester)
Research Fellow in Computational Systems Biology Cancer Research (Simon Mitchell)
Research Fellow in Laboratory and Computational Systems Biology Cancer Research (Simon Mitchell)
Postdoctoral Fellow in Cancer Resistance Modeling, Pfizer (Blerta Shtylla)
Principal Scientist – Oncology PK/PD Modelling (Boehringer Ingelheim)
Postdoctoral Research Position in Computational Immunology (Sylvain Cussat-Blanc)
Postdoc Position - TKI treatments in lung cancer (David Basanta)
Systems Biology Modeler Positions in Biopharma Consulting Company (Helen Moore)
Computational Approaches to Breast Cancer Evolution - Postdoc (Marc Ryser)
Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)
Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)
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