Competitive release in tumors

Ma, Yan; West, Jeffrey; Pk, Newton

doi:10.1101/263335

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…After a few rounds of cytotoxic chemotherapy, most of the drug-sensitive clones within the tumour are either eliminated or substantially reduced, consequently providing a more favourable environment for the expansion of the surviving drug-resistance subclones because they are then released from the previous suppressive competition with drug-sensitive subclones for metabolic resources and space. This "competitive release" principle of co-evolutionary ecology and population dynamics appears to be the main mechanism by which genetically heterogeneous cancers develop resistance to chemotherapeutic agents [49,56,57].…”

Section: Intratumour Heterogeneity and Resistance To Cancer Chemotherapymentioning

confidence: 99%

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Feller

Khammissa

Ballyram

et al. 2023

IJERPH

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Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.

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Section: Intratumour Heterogeneity and Resistance To Cancer Chemotherapymentioning

confidence: 99%

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Feller

Khammissa

Ballyram

et al. 2023

IJERPH

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“…As we can see in Fig 3A, the long-term response does not correlate with the initial one (Pearson correlation equals -0.45). It leads to the hypothesis that even if the patient has an excellent initial response, they might have short OS as a result of a competitive release of resistant cells [20]. Indeed, the mean fraction of resistant cells after treatment for patients with short OS and the good excellent initial response equals approximately 1.…”

Section: Initial Response To Platinum Doublet Does Not Correlate Withmentioning

confidence: 99%

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

et al. 2020

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We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

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Competitive release in tumors

Cited by 2 publications

References 19 publications

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

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