The 2019 mathematical oncology roadmap

Rockne, Russell C.; Hawkins-Daarud, Andrea; Swanson, Kristin R.; Sluka, James P.; Glazier, James A.; Macklin, Paul; Hormuth, David A.; Jarrett, Angela M.; Lima, Ernesto A. B. F.; Oden, J. Tinsley; Biros, George; Yankeelov, Thomas E.; Curtius, Kit; Bakir, Ibrahim Al; Wodarz, Dominik; Komarova, Natalia L.; Aparicio, Luis; Bordyuh, Mykola; Rabadán, Raúl; Finley, Stacey D.; Enderling, Heiko; Caudell, Jimmy J.; Moros, Eduardo G.; Anderson, Alexander R.A.; Gatenby, Robert A.; Kaznatcheev, Artem; Jeavons, Peter; Krishnan, Nikhil; Pelesko, Julia; Wadhwa, Raoul; Yoon, Nara; Nichol, Daniel; Marusyk, Andriy; Hinczewski, Michael; Scott, Jacob G.

doi:10.1088/1478-3975/ab1a09

Cited by 180 publications

(154 citation statements)

References 117 publications

(175 reference statements)

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“…Since we tend to view the change in gene-frequencies as the most important aspect, we tend to treat cancer as a disease of individual aberrant cells. But given that frequency-dependent selection can so drastically transform the mode of evolution, we need to also focus on the aberrant ecology of cells [18,19,34,38,39]. My hope is that a formal algorithmic theory of eco-evolutionary dynamics like my ecologically extended algorithmic Darwinism can help in this effort.…”

Section: Discussionmentioning

confidence: 99%

Evolution is exponentially more powerful with frequency-dependent selection

Kaznatcheev

2020

Preprint

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Valiant [1] proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype's fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment or static fitness landscape. Valiant's model distinguished families of environments that are "adaptable-to" from those that are not. But this model of evolution omits the vital ecological interactions between different evolving agents -it neglects the rich biotic environment that is central to the struggle for existence.In this article, I extend algorithmic Darwinism to include the ecological dynamics of frequencydependent selection as a population-dependent bias to the distribution of challenges that specify an environment. This extended algorithmic Darwinism replaces simple invasion of wild-type by a mutant-type of higher scalar fitness with an evolutionary game between wild-type and mutanttype based on their frequency-dependent fitness function. To analyze this model, I develop a game landscape view of evolution, as a generalization of the classic fitness landscape approach that is popular in biology.I show that this model of eco-evo dynamics on game landscapes can provide an exponential speed-up over the purely evolutionary dynamics of the strict algorithmic Darwinism proposed by Valiant. In particular, I prove that the noisy-Parity environment -which is known to be not adaptable-to under strict algorithmic Darwinism (and conjectured to be not PAC-learnable)is adaptable-to by eco-evo dynamics. Thus, the ecology of frequency-dependent selection does not just increase the tempo of evolution, but fundamentally transforms its mode.The eco-evo dynamic for adapting to the noisy-Parity environment proceeds by two stages: (1) a quick stage of point-mutations that moves the population to one of exponentially many local fitness peaks; followed by (2) a slower stage where each 'step' follows a double-mutation by a point-mutation. This second stage allows the population to hop between local fitness peaks to reach the unique global fitness peak in polynomial time. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, evolutionary games, and long-term evolution.

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Section: Discussionmentioning

confidence: 99%

Evolution is exponentially more powerful with frequency-dependent selection

Kaznatcheev

2020

Preprint

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“…Technology is advancing at an uncontrolled speed and the era of "omics", big data and artificial intelligence has generated enormous amounts of information that led to the successful development of many targeted approaches to cancer treatment [138]. System biology approaches or mathematical oncology feed on these massive data and are of great promise [139]. However, understanding cancer biology is still far from being achieved.…”

Section: The Future-combination Of Precision and Personalized Approachesmentioning

confidence: 99%

Zebrafish Avatars towards Personalized Medicine—A Comparative Review between Avatar Models

Costa

Estrada

Mendes

et al. 2020

Cells

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Cancer frequency and prevalence have been increasing in the past decades, with devastating impacts on patients and their families. Despite the great advances in targeted approaches, there is still a lack of methods to predict individual patient responses, and therefore treatments are tailored according to average response rates. "Omics" approaches are used for patient stratification and choice of therapeutic options towards a more precise medicine. These methods, however, do not consider all genetic and non-genetic dynamic interactions that occur upon drug treatment. Therefore, the need to directly challenge patient cells in a personalized manner remains. The present review addresses the state of the art of patient-derived in vitro and in vivo models, from organoids to mouse and zebrafish Avatars. The predictive power of each model based on the retrospective correlation with the patient clinical outcome will be considered. Finally, the review is focused on the emerging zebrafish Avatars and their unique characteristics allowing a fast analysis of local and systemic effects of drug treatments at the single-cell level. We also address the technical challenges that the field has yet to overcome.The arrival of precision medicine and immunotherapy are giving hope to finally manage cancer treatment. Many advances were made possible due to the discovery of molecular pathways in tumor cells and on their interaction with the surrounding tumor microenvironment (TME) [1], leading to the development of many new therapies. The latest successes in HER2-targeted therapy and the discovery of immune checkpoint inhibitors are great examples of this [2]. However, not all patients respond and many are not eligible for these "new therapies". Thus, nowadays, the majority of patients are still treated with traditional chemo/radiotherapy and surgery according to clinical guidelines, which are developed and approved based on average efficacy rates.As a result of this "one-size-fits-all" approach, treatments may prove to be efficient for some patients but not for others. For example, in the international therapeutic guidelines (NCCN and ESMO) for advanced colorectal cancer (CRC) there are two main chemotherapeutic arms (FOLFOX and FOLFIRI), which show very similar response rates of~50% [3,4]. In other words,~50% of patients respond to treatment while~50% do not. Clinicians do not know in which group patients will fall, as there is no predictive screening test to provide this information. Thus, patients that start with FOLFOX and do not respond change to FOLFIRI, and vice-versa. This applies for CRC and many other cancers, being especially relevant in the metastatic scenario when oncology therapy guidelines reach branch

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“…In the above simulations, tumor relapse dynamics in different mice can be fitted by varying the parameters Z[ and % for the heterogenous responses in individual mice. This raise a possibility of predicting the outcome of CAR-T treatment by identifying the personalized parameters through an estimation of parameter values based on a short-term observation after CAR-T infusion (48). Clinically, it is crucial to predict, according to the responses at early stage after CAR-T infusion, whether the patient would be cured with tumor cells free, or, if otherwise, the day of tumor relapse.…”

Section: Predictability Of the Computational Modelmentioning

confidence: 99%

Individual cell-based modeling of tumor cell plasticity-induced immune escape after CAR-T therapy

Zhang

Shao

Jiao

et al. 2020

Preprint

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Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B cell acute lymphoblastic leukemia (B-ALL). The majority of patients achieve a complete response following a single infusion of CD19-targeted CAR-modified T cells (CAR-19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell based computational model for tumor cell plasticity and the heterogeneous responses to the CAR-T treatment. Model simulations reproduced the process of tumor relapse, and predicted that CAR-T stress-induced cell plasticity can lead to tumor relapse in B-ALL. Model predictions were verified by applying the second-generation CAR-T cells to mice injected with NALM-6-GL leukemic cells, in which 60% of the mice relapse within 3 months, and relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with CD34 transcription. These findings lead to a mechanism of tumor replace by which CAR-T treatment induced tumor cells to transition to hematopoietic stem-like cells (HSLCs) and myeloid-like cells and hence escape of CAR-T targeting. The computational model framework was successfully developed to recapitulate the individual evolutionary dynamics, which could predict clinical survival outcomes in B-ALL patients after CAR-T therapy. ([CD34], [CD123]) = 1 H1 + H [CD34] % J K L J H1 + H [CD123] B J K M J .

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The 2019 mathematical oncology roadmap

Cited by 180 publications

References 117 publications

Evolution is exponentially more powerful with frequency-dependent selection

Evolution is exponentially more powerful with frequency-dependent selection

Zebrafish Avatars towards Personalized Medicine—A Comparative Review between Avatar Models

Individual cell-based modeling of tumor cell plasticity-induced immune escape after CAR-T therapy

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