scholarly journals Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives

2021 ◽  
Vol 18 (5) ◽  
pp. 6305-6327
Author(s):  
Cassidy K. Buhler ◽  
◽  
Rebecca S. Terry ◽  
Kathryn G. Link ◽  
Frederick R. Adler ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Cancer Treatment ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Allee Effect ◽  
Resource Competition ◽  
Treatment Strategies ◽  
Cell Populations ◽  
Emergence Of Resistance ◽  
Resistant Cells

<abstract><p>When eradication is impossible, cancer treatment aims to delay the emergence of resistance while minimizing cancer burden and treatment. Adaptive therapies may achieve these aims, with success based on three assumptions: resistance is costly, sensitive cells compete with resistant cells, and therapy reduces the population of sensitive cells. We use a range of mathematical models and treatment strategies to investigate the tradeoff between controlling cell populations and delaying the emergence of resistance. These models extend game theoretic and competition models with four additional components: 1) an Allee effect where cell populations grow more slowly at low population sizes, 2) healthy cells that compete with cancer cells, 3) immune cells that suppress cancer cells, and 4) resource competition for a growth factor like androgen. In comparing maximum tolerable dose, intermittent treatment, and adaptive therapy strategies, no therapeutic choice robustly breaks the three-way tradeoff among the three therapeutic aims. Almost all models show a tight tradeoff between time to emergence of resistant cells and cancer cell burden, with intermittent and adaptive therapies following identical curves. For most models, some adaptive therapies delay overall tumor growth more than intermittent therapies, but at the cost of higher cell populations. The Allee effect breaks these relationships, with some adaptive therapies performing poorly due to their failure to treat sufficiently to drive populations below the threshold. When eradication is impossible, no treatment can simultaneously delay emergence of resistance, limit total cancer cell numbers, and minimize treatment. Simple mathematical models can play a role in designing the next generation of therapies that balance these competing objectives.</p></abstract>

scholarly journals Differential Effects of Combined ATR/WEE1 Inhibition in Cancer Cells

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3790
Author(s):  
Gro Elise Rødland ◽  
Sissel Hauge ◽  
Grete Hasvold ◽  
Lilli T. E. Bay ◽  
Tine T. H. Raabe ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Viability ◽  
Cancer Treatment ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Combined Treatment ◽  
Cancer Cell Lines ◽  
Variable Extent ◽  
Synergistic Induction

Inhibitors of WEE1 and ATR kinases are considered promising for cancer treatment, either as monotherapy or in combination with chemo- or radiotherapy. Here, we addressed whether simultaneous inhibition of WEE1 and ATR might be advantageous. Effects of the WEE1 inhibitor MK1775 and ATR inhibitor VE822 were investigated in U2OS osteosarcoma cells and in four lung cancer cell lines, H460, A549, H1975, and SW900, with different sensitivities to the WEE1 inhibitor. Despite the differences in cytotoxic effects, the WEE1 inhibitor reduced the inhibitory phosphorylation of CDK, leading to increased CDK activity accompanied by ATR activation in all cell lines. However, combining ATR inhibition with WEE1 inhibition could not fully compensate for cell resistance to the WEE1 inhibitor and reduced cell viability to a variable extent. The decreased cell viability upon the combined treatment correlated with a synergistic induction of DNA damage in S-phase in U2OS cells but not in the lung cancer cells. Moreover, less synergy was found between ATR and WEE1 inhibitors upon co-treatment with radiation, suggesting that single inhibitors may be preferable together with radiotherapy. Altogether, our results support that combining WEE1 and ATR inhibitors may be beneficial for cancer treatment in some cases, but also highlight that the effects vary between cancer cell lines.

scholarly journals Cancer stem cells and cisplatin‐resistant cells isolated from non‐small‐lung cancer cell lines constitute related cell populations

2014 ◽  
Vol 3 (5) ◽  
pp. 1099-1111 ◽  
Author(s):  
Blanca D. Lopez‐Ayllon ◽  
Veronica Moncho‐Amor ◽  
Ander Abarrategi ◽  
Inmaculada Ibañez Cáceres ◽  
Javier Castro‐Carpeño ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Cell Populations ◽  
Lung Cancer Cell ◽  
Related Cell ◽  
Resistant Cells

scholarly journals Quantifying the influence of mutation detection on tumour subclonal reconstruction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Lydia Y. Liu ◽  
Vinayak Bhandari ◽  
Adriana Salcedo ◽  
Shadrielle M. G. Espiritu ◽  
Quaid D. Morris ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Genome Sequencing ◽  
Somatic Mutations ◽  
Mutation Detection ◽  
Cell Populations ◽  
Whole Genome ◽  
Prostate Cancers

AbstractWhole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

scholarly journals Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

2020 ◽  
Vol 117 (32) ◽  
pp. 19455-19464 ◽  
Author(s):  
Helen K. Alexander ◽  
R. Craig MacLean
Keyword(s):  
De Novo ◽  
Single Cells ◽  
Treatment Strategies ◽  
Bacterial Populations ◽  
Resistance Plasmids ◽  
Evolution Of Resistance ◽  
Resistant Strains ◽  
Low Probability ◽  
Emergence Of Resistance ◽  
Resistant Cells

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using twoPseudomonas aeruginosastrains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

scholarly journals Two-Step Targeted Drug Delivery Via Proteinaceous Barnase-Barstar Interface and PLGA-Based Nano-Carrier

2021 ◽  
Author(s):  
Victoria O. Shipunova ◽  
Elena N. Komedchikova ◽  
Anna S. Sogomonyan ◽  
Polina A. Kotelnikova ◽  
Maxim P. Nikitin ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Cancer Treatment ◽  
Cancer Cells ◽  
Drug Delivery System ◽  
Delivery System ◽  
Nile Blue ◽  
Treatment Strategies ◽  
Systemic Toxicity ◽  
Blue Dye ◽  
Two Stage

Abstract The conventional methods of treating cancer with chemo- and radiotherapy present plenty of serious problems, such as low therapeutic index and high systemic toxicity. The advanced cancer treatment strategies utilize nanoformulations of drugs that can enter a tumor due to the enhanced permeability and retention (EPR) effect. However, EPR fails in the treatment of several human diseases. Mainstream biomedical studies are focused on creating the drugs that would enter the tumor with higher effectiveness and require smaller doses for administration. A two-stage drug delivery system is an encouraging alternative solution. At first, the primary, non-toxic targeting module is delivered to the tumor cells, followed by injection of the second complementary targeting module at a considerably lower dose, thus decreasing systemic toxicity. To meet the challenge, we have developed a two-stage drug delivery system (DDS), mediated by the high-affinity binding of the Barnase*Barstar protein pair. Barnase and Barstar act as lego bricks linking the first and the second modules on the surface of the cancer cell. Barnase (12 kDa) is a natural ribonuclease from Bacillus amyloliquefaciens, while Barstar (10 kDa) is its natural inhibitor. The Barnase*Barstar is one of the strongest known protein*protein complexes with Kaff = 1014 M−1 exhibiting extraordinarily stability in severe conditions. Artificial scaffold polypeptide DARPin9_29 genetically fused with Barstar served is a first module of the developed two-step DDS. DARPin9_29 (14 kDa) specifically recognizes the tumor marker HER2 overexpressed on human breast cancer cells. As a second module, a therapeutic nano-cargo was developed based on fluorescent polymer PLGA nanoparticles loaded with diagnostic Nile Blue dye and the chemotherapeutic drug doxorubicin. This nano-PLGA structure was covalently coupled to Barnase. We showed two-stage efficient labeling of HER2-overexpressing cancer cells using the first non-toxic module DARPin9_29-Barstar and the second toxic nano-module PLGA-Barnase. We demonstrated the doxorubicin-induced cytotoxicity of this two-step DDS based on polymer nanoparticles and proteinaceous Barnase-Barstar interface and showed more than 10-fold therapeutic dose reduction versus free doxorubicin. We believe that the developed two-step DDS based on PLGA nano-cargo and protein interface will promote the creation of new-generation cancer treatment strategies.

scholarly journals Quantifying the Influence of Mutation Detection on Tumour Subclonal Reconstruction

2018 ◽  
Author(s):  
Lydia Y. Liu ◽  
Vinayak Bhandari ◽  
Adriana Salcedo ◽  
Shadrielle M. G. Espiritu ◽  
Quaid D. Morris ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Genome Sequencing ◽  
Somatic Mutations ◽  
Mutation Detection ◽  
Cell Populations ◽  
Whole Genome ◽  
Prostate Cancers

AbstractWhole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluated sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

Changes in Lung Cancer Cell Line Affected by Cytotoxicity of Lagenaria Siceraria Plant Extract

2021 ◽  
Vol 15 (5) ◽  
pp. 1282-1284
Author(s):  
Moein Shaneh
Keyword(s):  
Lung Cancer ◽  
Side Effects ◽  
Cancer Treatment ◽  
Cancer Cells ◽  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Lung Cancer Cell Line ◽  
Lagenaria Siceraria ◽  
Lung Cancer Cell

Chemotherapy is a type of cancer treatment in which the lack of selective cytotoxicity often leads to intolerable side effects. Today, the use of medicinal plants is essential in treating cancer due to their fewer side effects. Lagenaria siceraria Standl is critical for cytotoxicity studies due to its polyphenolic, cucurbitacins, pectin, flavonoids, and saponin compounds. In this study, the cytotoxic effects of plant fruit extract were investigated on lung cancer cell lines. To this end, the hydroalcoholic extract of the plant fruit was initially prepared by the percolation method. Then, the effects of solutions containing samples with different concentrations (5000, 500, 1000, 100, 100, 250, 10, 1, 0.1μg.ml-1) were investigated by MTT assay on lung cancer cell line (A549). Cisplatin was considered as a positive control. Statistical calculations were carried out using Prism V.3 software to compare IC50, and the data were analyzed by analysis of variance (ANOVA) and t-test. The results indicated that the IC50 level of cisplatin anti-cancer drug, as a common drug in the market, is significantly lower than Lagenaria siceraria extract. However, the extract of this plant revealed a significant growth inhibitory effect on lung cancer cells. The results also showed that Lagenaria siceraria extract is an effective cytotoxic compound on lung cancer cells. More extensive studies are needed to find effective plant extracts compounds to find and design new and effective cancer treatment drugs. Keywords: Lagenaria siceraria, Cell line, Lung cancer, IC50, MTTassay

scholarly journals Initial cell density encodes proliferative potential in cancer cell populations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chiara Enrico Bena ◽  
Marco Del Giudice ◽  
Alice Grob ◽  
Thomas Gueudré ◽  
Mattia Miotto ◽  
...  
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Cancer Cells ◽  
Cell Density ◽  
Cancer Cell ◽  
Initial Density ◽  
Logistic Growth ◽  
Cell Populations ◽  
Initial Cell ◽  
Initial Cell Density

AbstractIndividual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

scholarly journals Analisis dan Strategi Pengendalian Model Matematika Interaksi Sel Kanker Leukemia Mielositik Kronis dan Sel Imunitas

2020 ◽  
Vol 2 (2) ◽  
pp. 89
Author(s):  
Nanda Amalia Rahma ◽  
Cicik Alfiniyah ◽  
Windarto Windarto
Keyword(s):  
Equilibrium Point ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Immune Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Populations ◽  
Asymptotically Stable ◽  
Chronic Myelocytic Leukemia ◽  
Myelocytic Leukemia

Leukemia is a disease in the classification of cancer in the blood that is characterized by abnormal growth of blood cells in the bone marrow or lymphoid tissue, and generally occurs in leukocytes or white blood cells. White blood cells that look for types of pathogenic diseases that harm the human body and then damage it are the task of the immune system. This thesis analyzes the mathematical model of chronic myelocytic leukemia cancer cell interactions and immune cells to determine the rate of increase in the population of chronic myelocytic leukemia cancer cells to the effect of immune cells. Based on the analysis of the model obtained two equilibrium points namely the equilibrium point of the extinction of chronic myelocytic leukemia cancer cells (E0) and the equilibrium point of the coexistence of chronic myelocytic leukemia cancer cells (E1). The equilibrium point of extinction will be asymptotically stable, whereas the equilibrium point of coexistence tends to be asymptotically stable using phase fields with the help of MATLAB software. Numerical simulation results show that there is an increase in the number of chronic myelocytic leukemia cancer cell populations and a decrease in the number of vulnerable blood cell populations. When immune cells increase in population, chronic myelocytic leukemia in cancer cells decreases in population but is not significant.

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