Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.

<h4>Rationale</h4>Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogene...

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Autores principales:	Dung-Tsa Chen, Wenyaw Chan, Zachary J Thompson, Ram Thapa, Amer A Beg, Andreas N Saltos, Alberto A Chiappori, Jhanelle E Gray, Eric B Haura, Trevor A Rose, Ben Creelan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/659848f0970b4b5e8a041e80db5248b8
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spelling	oai:doaj.org-article:659848f0970b4b5e8a041e80db5248b82021-12-02T20:05:15ZUtilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.1932-620310.1371/journal.pone.0252041https://doaj.org/article/659848f0970b4b5e8a041e80db5248b82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252041https://doaj.org/toc/1932-6203<h4>Rationale</h4>Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogeneity defined as diversity of tumor growth profiles in RECIST target lesions. Furthermore, many cancer clinical trials have been activated to evaluate various treatment options for heterogeneity-related subgroups (e.g., 29 trials so far listed in clinicaltrials.gov for cancer patients with oligometastasis). Some of the trials have shown survival benefit by tailored treatment strategies. This evidence presents the unmet need to incorporate lesion heterogeneity to improve RECIST response classification.<h4>Method</h4>An approach for Lesion Heterogeneity Classification (LeHeC) was developed using a contemporary statistical approach to assess target lesion variation, characterize patient treatment response, and translate informative evidence to improving treatment strategy. A mixed effect linear model was used to determine lesion heterogeneity. Further analysis was conducted to classify various types of lesion variation and incorporate with RECIST to enhance response classification. A study cohort of 110 target lesions from 36 lung cancer patients was used for evaluation.<h4>Results</h4>Due to small sample size issue, the result was exploratory in nature. By analyzing RECIST target lesion data, the LeHeC approach detected a high prevalence (n = 21; 58%) of lesion heterogeneity. Subgroup classification revealed several informative distinct subsets in a descending order of lesion heterogeneity: mix of progression and regression (n = 7), mix of progression and stability (n = 9), mix of regression and stability (n = 5), and non-heterogeneity (n = 15). Evaluation for association of lesion heterogeneity and RECIST best response classification showed lesion heterogeneity commonly occurred in each response group (stable disease: 16/27; 59%; partial response: 3/5; 60%; progression disease: 2/4; 50%). Survival analysis showed a differential trend of overall survival between heterogeneity and non-heterogeneity in RECIST response groups.<h4>Conclusion</h4>This is the first study to evaluate lesion heterogeneity, an underappreciated metric, for RECIST application in oncology clinical trials. Results indicated lesion heterogeneity is not an uncommon event. The LeHeC approach could enhance RECIST response classification by utilizing granular lesion level discovery of heterogeneity.Dung-Tsa ChenWenyaw ChanZachary J ThompsonRam ThapaAmer A BegAndreas N SaltosAlberto A ChiapporiJhanelle E GrayEric B HauraTrevor A RoseBen CreelanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0252041 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dung-Tsa Chen Wenyaw Chan Zachary J Thompson Ram Thapa Amer A Beg Andreas N Saltos Alberto A Chiappori Jhanelle E Gray Eric B Haura Trevor A Rose Ben Creelan Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
description	<h4>Rationale</h4>Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogeneity defined as diversity of tumor growth profiles in RECIST target lesions. Furthermore, many cancer clinical trials have been activated to evaluate various treatment options for heterogeneity-related subgroups (e.g., 29 trials so far listed in clinicaltrials.gov for cancer patients with oligometastasis). Some of the trials have shown survival benefit by tailored treatment strategies. This evidence presents the unmet need to incorporate lesion heterogeneity to improve RECIST response classification.<h4>Method</h4>An approach for Lesion Heterogeneity Classification (LeHeC) was developed using a contemporary statistical approach to assess target lesion variation, characterize patient treatment response, and translate informative evidence to improving treatment strategy. A mixed effect linear model was used to determine lesion heterogeneity. Further analysis was conducted to classify various types of lesion variation and incorporate with RECIST to enhance response classification. A study cohort of 110 target lesions from 36 lung cancer patients was used for evaluation.<h4>Results</h4>Due to small sample size issue, the result was exploratory in nature. By analyzing RECIST target lesion data, the LeHeC approach detected a high prevalence (n = 21; 58%) of lesion heterogeneity. Subgroup classification revealed several informative distinct subsets in a descending order of lesion heterogeneity: mix of progression and regression (n = 7), mix of progression and stability (n = 9), mix of regression and stability (n = 5), and non-heterogeneity (n = 15). Evaluation for association of lesion heterogeneity and RECIST best response classification showed lesion heterogeneity commonly occurred in each response group (stable disease: 16/27; 59%; partial response: 3/5; 60%; progression disease: 2/4; 50%). Survival analysis showed a differential trend of overall survival between heterogeneity and non-heterogeneity in RECIST response groups.<h4>Conclusion</h4>This is the first study to evaluate lesion heterogeneity, an underappreciated metric, for RECIST application in oncology clinical trials. Results indicated lesion heterogeneity is not an uncommon event. The LeHeC approach could enhance RECIST response classification by utilizing granular lesion level discovery of heterogeneity.
format	article
author	Dung-Tsa Chen Wenyaw Chan Zachary J Thompson Ram Thapa Amer A Beg Andreas N Saltos Alberto A Chiappori Jhanelle E Gray Eric B Haura Trevor A Rose Ben Creelan
author_facet	Dung-Tsa Chen Wenyaw Chan Zachary J Thompson Ram Thapa Amer A Beg Andreas N Saltos Alberto A Chiappori Jhanelle E Gray Eric B Haura Trevor A Rose Ben Creelan
author_sort	Dung-Tsa Chen
title	Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
title_short	Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
title_full	Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
title_fullStr	Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
title_full_unstemmed	Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
title_sort	utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/659848f0970b4b5e8a041e80db5248b8
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Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer.

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