Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies

Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metasta...
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Gespeichert in:

Autor*in:	Maria Silvia Cona [verfasserIn] Mara Lecchi [verfasserIn] Sara Cresta [verfasserIn] Silvia Damian [verfasserIn] Michele Del Vecchio [verfasserIn] Andrea Necchi [verfasserIn] Marta Maria Poggi [verfasserIn] Daniele Raggi [verfasserIn] Giovanni Randon [verfasserIn] Raffaele Ratta [verfasserIn] Diego Signorelli [verfasserIn] Claudio Vernieri [verfasserIn] Filippo de Braud [verfasserIn] Paolo Verderio [verfasserIn] Massimo Di Nicola [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	immune-checkpoint inhibitors LDH biomarkers

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 11(2019), 2, p 223
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:2, p 223

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers11020223

Katalog-ID:	DOAJ049591355

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allfields	10.3390/cancers11020223 doi (DE-627)DOAJ049591355 (DE-599)DOAJce6335c32b1a417681f71c1c7e6a01d1 DE-627 ger DE-627 rakwb eng RC254-282 Maria Silvia Cona verfasserin aut Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients. immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mara Lecchi verfasserin aut Sara Cresta verfasserin aut Silvia Damian verfasserin aut Michele Del Vecchio verfasserin aut Andrea Necchi verfasserin aut Marta Maria Poggi verfasserin aut Daniele Raggi verfasserin aut Giovanni Randon verfasserin aut Raffaele Ratta verfasserin aut Diego Signorelli verfasserin aut Claudio Vernieri verfasserin aut Filippo de Braud verfasserin aut Paolo Verderio verfasserin aut Massimo Di Nicola verfasserin aut In Cancers MDPI AG, 2010 11(2019), 2, p 223 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:11 year:2019 number:2, p 223 https://doi.org/10.3390/cancers11020223 kostenfrei https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 kostenfrei https://www.mdpi.com/2072-6694/11/2/223 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 223
spelling	10.3390/cancers11020223 doi (DE-627)DOAJ049591355 (DE-599)DOAJce6335c32b1a417681f71c1c7e6a01d1 DE-627 ger DE-627 rakwb eng RC254-282 Maria Silvia Cona verfasserin aut Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients. immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mara Lecchi verfasserin aut Sara Cresta verfasserin aut Silvia Damian verfasserin aut Michele Del Vecchio verfasserin aut Andrea Necchi verfasserin aut Marta Maria Poggi verfasserin aut Daniele Raggi verfasserin aut Giovanni Randon verfasserin aut Raffaele Ratta verfasserin aut Diego Signorelli verfasserin aut Claudio Vernieri verfasserin aut Filippo de Braud verfasserin aut Paolo Verderio verfasserin aut Massimo Di Nicola verfasserin aut In Cancers MDPI AG, 2010 11(2019), 2, p 223 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:11 year:2019 number:2, p 223 https://doi.org/10.3390/cancers11020223 kostenfrei https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 kostenfrei https://www.mdpi.com/2072-6694/11/2/223 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 223
allfields_unstemmed	10.3390/cancers11020223 doi (DE-627)DOAJ049591355 (DE-599)DOAJce6335c32b1a417681f71c1c7e6a01d1 DE-627 ger DE-627 rakwb eng RC254-282 Maria Silvia Cona verfasserin aut Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients. immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mara Lecchi verfasserin aut Sara Cresta verfasserin aut Silvia Damian verfasserin aut Michele Del Vecchio verfasserin aut Andrea Necchi verfasserin aut Marta Maria Poggi verfasserin aut Daniele Raggi verfasserin aut Giovanni Randon verfasserin aut Raffaele Ratta verfasserin aut Diego Signorelli verfasserin aut Claudio Vernieri verfasserin aut Filippo de Braud verfasserin aut Paolo Verderio verfasserin aut Massimo Di Nicola verfasserin aut In Cancers MDPI AG, 2010 11(2019), 2, p 223 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:11 year:2019 number:2, p 223 https://doi.org/10.3390/cancers11020223 kostenfrei https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 kostenfrei https://www.mdpi.com/2072-6694/11/2/223 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 223
allfieldsGer	10.3390/cancers11020223 doi (DE-627)DOAJ049591355 (DE-599)DOAJce6335c32b1a417681f71c1c7e6a01d1 DE-627 ger DE-627 rakwb eng RC254-282 Maria Silvia Cona verfasserin aut Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients. immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mara Lecchi verfasserin aut Sara Cresta verfasserin aut Silvia Damian verfasserin aut Michele Del Vecchio verfasserin aut Andrea Necchi verfasserin aut Marta Maria Poggi verfasserin aut Daniele Raggi verfasserin aut Giovanni Randon verfasserin aut Raffaele Ratta verfasserin aut Diego Signorelli verfasserin aut Claudio Vernieri verfasserin aut Filippo de Braud verfasserin aut Paolo Verderio verfasserin aut Massimo Di Nicola verfasserin aut In Cancers MDPI AG, 2010 11(2019), 2, p 223 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:11 year:2019 number:2, p 223 https://doi.org/10.3390/cancers11020223 kostenfrei https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 kostenfrei https://www.mdpi.com/2072-6694/11/2/223 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 223
allfieldsSound	10.3390/cancers11020223 doi (DE-627)DOAJ049591355 (DE-599)DOAJce6335c32b1a417681f71c1c7e6a01d1 DE-627 ger DE-627 rakwb eng RC254-282 Maria Silvia Cona verfasserin aut Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients. immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mara Lecchi verfasserin aut Sara Cresta verfasserin aut Silvia Damian verfasserin aut Michele Del Vecchio verfasserin aut Andrea Necchi verfasserin aut Marta Maria Poggi verfasserin aut Daniele Raggi verfasserin aut Giovanni Randon verfasserin aut Raffaele Ratta verfasserin aut Diego Signorelli verfasserin aut Claudio Vernieri verfasserin aut Filippo de Braud verfasserin aut Paolo Verderio verfasserin aut Massimo Di Nicola verfasserin aut In Cancers MDPI AG, 2010 11(2019), 2, p 223 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:11 year:2019 number:2, p 223 https://doi.org/10.3390/cancers11020223 kostenfrei https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 kostenfrei https://www.mdpi.com/2072-6694/11/2/223 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 2, p 223
language	English
source	In Cancers 11(2019), 2, p 223 volume:11 year:2019 number:2, p 223
sourceStr	In Cancers 11(2019), 2, p 223 volume:11 year:2019 number:2, p 223
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	immune-checkpoint inhibitors LDH biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Cancers
authorswithroles_txt_mv	Maria Silvia Cona @@aut@@ Mara Lecchi @@aut@@ Sara Cresta @@aut@@ Silvia Damian @@aut@@ Michele Del Vecchio @@aut@@ Andrea Necchi @@aut@@ Marta Maria Poggi @@aut@@ Daniele Raggi @@aut@@ Giovanni Randon @@aut@@ Raffaele Ratta @@aut@@ Diego Signorelli @@aut@@ Claudio Vernieri @@aut@@ Filippo de Braud @@aut@@ Paolo Verderio @@aut@@ Massimo Di Nicola @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	614095670
id	DOAJ049591355
language_de	englisch
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callnumber-first	R - Medicine
author	Maria Silvia Cona
spellingShingle	Maria Silvia Cona misc RC254-282 misc immune-checkpoint inhibitors misc LDH misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies
authorStr	Maria Silvia Cona
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topic_title	RC254-282 Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies immune-checkpoint inhibitors LDH biomarkers
topic	misc RC254-282 misc immune-checkpoint inhibitors misc LDH misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc immune-checkpoint inhibitors misc LDH misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc immune-checkpoint inhibitors misc LDH misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies
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title_full	Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies
author_sort	Maria Silvia Cona
journal	Cancers
journalStr	Cancers
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author_browse	Maria Silvia Cona Mara Lecchi Sara Cresta Silvia Damian Michele Del Vecchio Andrea Necchi Marta Maria Poggi Daniele Raggi Giovanni Randon Raffaele Ratta Diego Signorelli Claudio Vernieri Filippo de Braud Paolo Verderio Massimo Di Nicola
container_volume	11
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author-letter	Maria Silvia Cona
doi_str_mv	10.3390/cancers11020223
author2-role	verfasserin
title_sort	combination of baseline ldh, performance status and age as integrated algorithm to identify solid tumor patients with higher probability of response to anti pd-1 and pd-l1 monoclonal antibodies
callnumber	RC254-282
title_auth	Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies
abstract	Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients.
abstractGer	Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients.
abstract_unstemmed	Predictive biomarkers of response to immune-checkpoint inhibitors (ICIs) are an urgent clinical need. The aim of this study is to identify manageable parameters to use in clinical practice to select patients with higher probability of response to ICIs. Two-hundred-and-seventy-one consecutive metastatic solid tumor patients, treated from 2013 until 2017 with anti- Programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) ICIs, were evaluated for baseline lactate dehydrogenase (LDH) serum level, performance status (PS), age, neutrophil-lymphocyte ratio, type of immunotherapy, number of metastatic sites, histology, and sex. A training and validation set were used to build and test models, respectively. The variables’ effects were assessed through odds ratio estimates (OR) and area under the receive operating characteristic curves (AUC), from univariate and multivariate logistic regression models. A final multivariate model with LDH, age and PS showed significant ORs and an AUC of 0.771. Results were statistically validated and used to devise an Excel algorithm to calculate the patient’s response probabilities. We implemented an interactive Excel algorithm based on three variables (baseline LDH serum level, age and PS) which is able to provide a higher performance in response prediction to ICIs compared with LDH alone. This tool could be used in a real-life setting to identify ICIs in responding patients.
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container_issue	2, p 223
title_short	Combination of Baseline LDH, Performance Status and Age as Integrated Algorithm to Identify Solid Tumor Patients with Higher Probability of Response to Anti PD-1 and PD-L1 Monoclonal Antibodies
url	https://doi.org/10.3390/cancers11020223 https://doaj.org/article/ce6335c32b1a417681f71c1c7e6a01d1 https://www.mdpi.com/2072-6694/11/2/223 https://doaj.org/toc/2072-6694
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author2	Mara Lecchi Sara Cresta Silvia Damian Michele Del Vecchio Andrea Necchi Marta Maria Poggi Daniele Raggi Giovanni Randon Raffaele Ratta Diego Signorelli Claudio Vernieri Filippo de Braud Paolo Verderio Massimo Di Nicola
author2Str	Mara Lecchi Sara Cresta Silvia Damian Michele Del Vecchio Andrea Necchi Marta Maria Poggi Daniele Raggi Giovanni Randon Raffaele Ratta Diego Signorelli Claudio Vernieri Filippo de Braud Paolo Verderio Massimo Di Nicola
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doi_str	10.3390/cancers11020223
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up_date	2024-07-04T00:00:43.691Z
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