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Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics
PURPOSE: Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561994/ https://www.ncbi.nlm.nih.gov/pubmed/31020352 http://dx.doi.org/10.1007/s00280-019-03840-3 |
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author | Mistry, Hitesh B. Helmlinger, Gabriel Al-Huniti, Nidal Vishwanathan, Karthick Yates, James |
author_facet | Mistry, Hitesh B. Helmlinger, Gabriel Al-Huniti, Nidal Vishwanathan, Karthick Yates, James |
author_sort | Mistry, Hitesh B. |
collection | PubMed |
description | PURPOSE: Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data. METHODS: Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data. RESULTS: Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response. CONCLUSIONS: This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00280-019-03840-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6561994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-65619942019-06-28 Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics Mistry, Hitesh B. Helmlinger, Gabriel Al-Huniti, Nidal Vishwanathan, Karthick Yates, James Cancer Chemother Pharmacol Original Article PURPOSE: Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data. METHODS: Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data. RESULTS: Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response. CONCLUSIONS: This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00280-019-03840-3) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2019-04-24 2019 /pmc/articles/PMC6561994/ /pubmed/31020352 http://dx.doi.org/10.1007/s00280-019-03840-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Mistry, Hitesh B. Helmlinger, Gabriel Al-Huniti, Nidal Vishwanathan, Karthick Yates, James Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title | Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title_full | Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title_fullStr | Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title_full_unstemmed | Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title_short | Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
title_sort | resistance models to egfr inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561994/ https://www.ncbi.nlm.nih.gov/pubmed/31020352 http://dx.doi.org/10.1007/s00280-019-03840-3 |
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