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A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases

SIMPLE SUMMARY: It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentratio...

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Autores principales: Anaya, Daniel A., Dogra, Prashant, Wang, Zhihui, Haider, Mintallah, Ehab, Jasmina, Jeong, Daniel K., Ghayouri, Masoumeh, Lauwers, Gregory Y., Thomas, Kerry, Kim, Richard, Butner, Joseph D., Nizzero, Sara, Ramírez, Javier Ruiz, Plodinec, Marija, Sidman, Richard L., Cavenee, Webster K., Pasqualini, Renata, Arap, Wadih, Fleming, Jason B., Cristini, Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866038/
https://www.ncbi.nlm.nih.gov/pubmed/33503971
http://dx.doi.org/10.3390/cancers13030444
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author Anaya, Daniel A.
Dogra, Prashant
Wang, Zhihui
Haider, Mintallah
Ehab, Jasmina
Jeong, Daniel K.
Ghayouri, Masoumeh
Lauwers, Gregory Y.
Thomas, Kerry
Kim, Richard
Butner, Joseph D.
Nizzero, Sara
Ramírez, Javier Ruiz
Plodinec, Marija
Sidman, Richard L.
Cavenee, Webster K.
Pasqualini, Renata
Arap, Wadih
Fleming, Jason B.
Cristini, Vittorio
author_facet Anaya, Daniel A.
Dogra, Prashant
Wang, Zhihui
Haider, Mintallah
Ehab, Jasmina
Jeong, Daniel K.
Ghayouri, Masoumeh
Lauwers, Gregory Y.
Thomas, Kerry
Kim, Richard
Butner, Joseph D.
Nizzero, Sara
Ramírez, Javier Ruiz
Plodinec, Marija
Sidman, Richard L.
Cavenee, Webster K.
Pasqualini, Renata
Arap, Wadih
Fleming, Jason B.
Cristini, Vittorio
author_sort Anaya, Daniel A.
collection PubMed
description SIMPLE SUMMARY: It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome. ABSTRACT: Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.
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spelling pubmed-78660382021-02-07 A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases Anaya, Daniel A. Dogra, Prashant Wang, Zhihui Haider, Mintallah Ehab, Jasmina Jeong, Daniel K. Ghayouri, Masoumeh Lauwers, Gregory Y. Thomas, Kerry Kim, Richard Butner, Joseph D. Nizzero, Sara Ramírez, Javier Ruiz Plodinec, Marija Sidman, Richard L. Cavenee, Webster K. Pasqualini, Renata Arap, Wadih Fleming, Jason B. Cristini, Vittorio Cancers (Basel) Article SIMPLE SUMMARY: It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome. ABSTRACT: Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases. MDPI 2021-01-25 /pmc/articles/PMC7866038/ /pubmed/33503971 http://dx.doi.org/10.3390/cancers13030444 Text en © 2021 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
Anaya, Daniel A.
Dogra, Prashant
Wang, Zhihui
Haider, Mintallah
Ehab, Jasmina
Jeong, Daniel K.
Ghayouri, Masoumeh
Lauwers, Gregory Y.
Thomas, Kerry
Kim, Richard
Butner, Joseph D.
Nizzero, Sara
Ramírez, Javier Ruiz
Plodinec, Marija
Sidman, Richard L.
Cavenee, Webster K.
Pasqualini, Renata
Arap, Wadih
Fleming, Jason B.
Cristini, Vittorio
A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title_full A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title_fullStr A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title_full_unstemmed A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title_short A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases
title_sort mathematical model to estimate chemotherapy concentration at the tumor-site and predict therapy response in colorectal cancer patients with liver metastases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866038/
https://www.ncbi.nlm.nih.gov/pubmed/33503971
http://dx.doi.org/10.3390/cancers13030444
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