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Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models
Cancer heterogeneity represents the main issue for defining an effective treatment in clinical practice, and the scientific community is progressively moving towards the development of more personalized therapeutic regimens. Radiotherapy (RT) remains a fundamental therapeutic treatment used for many...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712665/ https://www.ncbi.nlm.nih.gov/pubmed/33080870 http://dx.doi.org/10.3390/jpm10040177 |
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author | Savoca, Gaetano Calvaruso, Marco Minafra, Luigi Bravatà, Valentina Cammarata, Francesco Paolo Iacoviello, Giuseppina Abbate, Boris Evangelista, Giovanna Spada, Massimiliano Forte, Giusi Irma Russo, Giorgio |
author_facet | Savoca, Gaetano Calvaruso, Marco Minafra, Luigi Bravatà, Valentina Cammarata, Francesco Paolo Iacoviello, Giuseppina Abbate, Boris Evangelista, Giovanna Spada, Massimiliano Forte, Giusi Irma Russo, Giorgio |
author_sort | Savoca, Gaetano |
collection | PubMed |
description | Cancer heterogeneity represents the main issue for defining an effective treatment in clinical practice, and the scientific community is progressively moving towards the development of more personalized therapeutic regimens. Radiotherapy (RT) remains a fundamental therapeutic treatment used for many neoplastic diseases, including breast cancer (BC), where high variability at the clinical and molecular level is known. The aim of this work is to apply the generalized linear quadratic (LQ) model to customize the radiant treatment plan for BC, by extracting some characteristic parameters of intrinsic radiosensitivity that are not generic, but may be exclusive for each cell type. We tested the validity of the generalized LQ model and analyzed the local disease-free survival rate (LSR) for breast RT treatment by using four BC cell cultures (both primary and immortalized), irradiated with clinical X-ray beams. BC cells were chosen on the basis of their receptor profiles, in order to simulate a differential response to RT between triple negative breast and luminal adenocarcinomas. The MCF10A breast epithelial cell line was utilized as a healthy control. We show that an RT plan setup based only on α and β values could be limiting and misleading. Indeed, two other parameters, the doubling time and the clonogens number, are important to finely predict the tumor response to treatment. Our findings could be tested at a preclinical level to confirm their application as a variant of the classical LQ model, to create a more personalized approach for RT planning. |
format | Online Article Text |
id | pubmed-7712665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77126652020-12-04 Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models Savoca, Gaetano Calvaruso, Marco Minafra, Luigi Bravatà, Valentina Cammarata, Francesco Paolo Iacoviello, Giuseppina Abbate, Boris Evangelista, Giovanna Spada, Massimiliano Forte, Giusi Irma Russo, Giorgio J Pers Med Article Cancer heterogeneity represents the main issue for defining an effective treatment in clinical practice, and the scientific community is progressively moving towards the development of more personalized therapeutic regimens. Radiotherapy (RT) remains a fundamental therapeutic treatment used for many neoplastic diseases, including breast cancer (BC), where high variability at the clinical and molecular level is known. The aim of this work is to apply the generalized linear quadratic (LQ) model to customize the radiant treatment plan for BC, by extracting some characteristic parameters of intrinsic radiosensitivity that are not generic, but may be exclusive for each cell type. We tested the validity of the generalized LQ model and analyzed the local disease-free survival rate (LSR) for breast RT treatment by using four BC cell cultures (both primary and immortalized), irradiated with clinical X-ray beams. BC cells were chosen on the basis of their receptor profiles, in order to simulate a differential response to RT between triple negative breast and luminal adenocarcinomas. The MCF10A breast epithelial cell line was utilized as a healthy control. We show that an RT plan setup based only on α and β values could be limiting and misleading. Indeed, two other parameters, the doubling time and the clonogens number, are important to finely predict the tumor response to treatment. Our findings could be tested at a preclinical level to confirm their application as a variant of the classical LQ model, to create a more personalized approach for RT planning. MDPI 2020-10-17 /pmc/articles/PMC7712665/ /pubmed/33080870 http://dx.doi.org/10.3390/jpm10040177 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Savoca, Gaetano Calvaruso, Marco Minafra, Luigi Bravatà, Valentina Cammarata, Francesco Paolo Iacoviello, Giuseppina Abbate, Boris Evangelista, Giovanna Spada, Massimiliano Forte, Giusi Irma Russo, Giorgio Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title | Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title_full | Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title_fullStr | Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title_full_unstemmed | Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title_short | Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models |
title_sort | local disease-free survival rate (lsr) application to personalize radiation therapy treatments in breast cancer models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712665/ https://www.ncbi.nlm.nih.gov/pubmed/33080870 http://dx.doi.org/10.3390/jpm10040177 |
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