ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial
Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification. Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before su...
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creator | Audinot, B. Drubay, D. Gaspar, N. Mohr, A. Cordero, C. Marec-Bérard, P. Lervat, C. Piperno-Neumann, S. Jimenez, M. Mansuy, L. Castex, M.-P. Revon-Riviere, G. Marie-Cardine, A. Berger, C. Piguet, C. Massau, K. Job, B. Moquin-Beaudry, G. Le Deley, M.-C. Tabone, M.-D. Berlanga, P. Brugières, L. Crompton, B.D. Marchais, A. Abbou, S. |
description | Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.
Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients’ individual risk of event.
ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.
The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.
•ctDNA detection is a non-invasive procedure.•The use of lpWGS is cost-effective.•This procedure represents a considerable step in reducing therapeutic burden and trauma for patients with cancer.•PRONOS is a tool that estimates relapse risk using ctDNA at diagnosis and known clinical risk factor in osteosarcoma. |
doi_str_mv | 10.1016/j.annonc.2023.12.006 |
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Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients’ individual risk of event.
ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.
The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.
•ctDNA detection is a non-invasive procedure.•The use of lpWGS is cost-effective.•This procedure represents a considerable step in reducing therapeutic burden and trauma for patients with cancer.•PRONOS is a tool that estimates relapse risk using ctDNA at diagnosis and known clinical risk factor in osteosarcoma.</description><identifier>ISSN: 0923-7534</identifier><identifier>EISSN: 1569-8041</identifier><identifier>DOI: 10.1016/j.annonc.2023.12.006</identifier><identifier>PMID: 38142939</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Cancer ; ctDNA ; Humanities and Social Sciences ; Life Sciences ; Methodology ; Methods and statistics ; osteosarcoma ; Other Statistics ; prognostic score ; Statistics ; stratification</subject><ispartof>Annals of oncology, 2024-06, Vol.35 (6), p.559-568</ispartof><rights>2023 European Society for Medical Oncology</rights><rights>Copyright © 2023 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c314t-eaead15a8810cd0c9eaa9171ab0f22fedd8142a668eaa728ffde6aa8635ca6623</citedby><cites>FETCH-LOGICAL-c314t-eaead15a8810cd0c9eaa9171ab0f22fedd8142a668eaa728ffde6aa8635ca6623</cites><orcidid>0000-0003-0954-8294 ; 0000-0002-7159-8869 ; 0000-0002-9997-9727 ; 0000-0002-2720-7664 ; 0000-0002-7772-5208</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38142939$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://inserm.hal.science/inserm-04516533$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Audinot, B.</creatorcontrib><creatorcontrib>Drubay, D.</creatorcontrib><creatorcontrib>Gaspar, N.</creatorcontrib><creatorcontrib>Mohr, A.</creatorcontrib><creatorcontrib>Cordero, C.</creatorcontrib><creatorcontrib>Marec-Bérard, P.</creatorcontrib><creatorcontrib>Lervat, C.</creatorcontrib><creatorcontrib>Piperno-Neumann, S.</creatorcontrib><creatorcontrib>Jimenez, M.</creatorcontrib><creatorcontrib>Mansuy, L.</creatorcontrib><creatorcontrib>Castex, M.-P.</creatorcontrib><creatorcontrib>Revon-Riviere, G.</creatorcontrib><creatorcontrib>Marie-Cardine, A.</creatorcontrib><creatorcontrib>Berger, C.</creatorcontrib><creatorcontrib>Piguet, C.</creatorcontrib><creatorcontrib>Massau, K.</creatorcontrib><creatorcontrib>Job, B.</creatorcontrib><creatorcontrib>Moquin-Beaudry, G.</creatorcontrib><creatorcontrib>Le Deley, M.-C.</creatorcontrib><creatorcontrib>Tabone, M.-D.</creatorcontrib><creatorcontrib>Berlanga, P.</creatorcontrib><creatorcontrib>Brugières, L.</creatorcontrib><creatorcontrib>Crompton, B.D.</creatorcontrib><creatorcontrib>Marchais, A.</creatorcontrib><creatorcontrib>Abbou, S.</creatorcontrib><title>ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial</title><title>Annals of oncology</title><addtitle>Ann Oncol</addtitle><description>Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.
Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients’ individual risk of event.
ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.
The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.
•ctDNA detection is a non-invasive procedure.•The use of lpWGS is cost-effective.•This procedure represents a considerable step in reducing therapeutic burden and trauma for patients with cancer.•PRONOS is a tool that estimates relapse risk using ctDNA at diagnosis and known clinical risk factor in osteosarcoma.</description><subject>Cancer</subject><subject>ctDNA</subject><subject>Humanities and Social Sciences</subject><subject>Life Sciences</subject><subject>Methodology</subject><subject>Methods and statistics</subject><subject>osteosarcoma</subject><subject>Other Statistics</subject><subject>prognostic score</subject><subject>Statistics</subject><subject>stratification</subject><issn>0923-7534</issn><issn>1569-8041</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1DAUhS1ERYfCGyDkJYsm9U-SSVggjUqhSCO6ANbWrX3TeJTYU9sZ1FfgqeshpUtWlo6_e-x7DiHvOCs5483FrgTnvNOlYEKWXJSMNS_IitdNV7Ss4i_JinVCFutaVqfkdYw7lolOdK_IqWx5JTrZrcgfnT5_39D7GVyyvdWQrHfUTvvgDxgpxmSnRfM99XPSfsqydXSfVXQp0t82DXSwd0NxF8Ag9TGhjxAyCR8p0BTAxfGvB4w0ptk80D74iaYB6c0PkT-VGQvjG3LSwxjx7dN5Rn59ufp5eV1sb75-u9xsCy15lQoEBMNraFvOtGG6Q4COrzncsl6IHo05LgdN0-aLtWj73mAD0Day1lkV8oycL74DjGof8n7hQXmw6nqzVdZFDJNiVc2bWsoDz_iHBc-R3M85EDXZqHEcwaGfoxIdq9etFOLoXC2oDj7GgP2zPWfqWJraqaU0dSxNcaHy9nns_dML8-2E5nnoX0sZ-LQAmGM5WAwq6hy-RmMD6qSMt_9_4RE-gqzp</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Audinot, B.</creator><creator>Drubay, D.</creator><creator>Gaspar, N.</creator><creator>Mohr, A.</creator><creator>Cordero, C.</creator><creator>Marec-Bérard, P.</creator><creator>Lervat, C.</creator><creator>Piperno-Neumann, S.</creator><creator>Jimenez, M.</creator><creator>Mansuy, L.</creator><creator>Castex, M.-P.</creator><creator>Revon-Riviere, G.</creator><creator>Marie-Cardine, A.</creator><creator>Berger, C.</creator><creator>Piguet, C.</creator><creator>Massau, K.</creator><creator>Job, B.</creator><creator>Moquin-Beaudry, G.</creator><creator>Le Deley, M.-C.</creator><creator>Tabone, M.-D.</creator><creator>Berlanga, P.</creator><creator>Brugières, L.</creator><creator>Crompton, B.D.</creator><creator>Marchais, A.</creator><creator>Abbou, S.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>BXJBU</scope><scope>IHQJB</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-0954-8294</orcidid><orcidid>https://orcid.org/0000-0002-7159-8869</orcidid><orcidid>https://orcid.org/0000-0002-9997-9727</orcidid><orcidid>https://orcid.org/0000-0002-2720-7664</orcidid><orcidid>https://orcid.org/0000-0002-7772-5208</orcidid></search><sort><creationdate>20240601</creationdate><title>ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial</title><author>Audinot, B. ; Drubay, D. ; Gaspar, N. ; Mohr, A. ; Cordero, C. ; Marec-Bérard, P. ; Lervat, C. ; Piperno-Neumann, S. ; Jimenez, M. ; Mansuy, L. ; Castex, M.-P. ; Revon-Riviere, G. ; Marie-Cardine, A. ; Berger, C. ; Piguet, C. ; Massau, K. ; Job, B. ; Moquin-Beaudry, G. ; Le Deley, M.-C. ; Tabone, M.-D. ; Berlanga, P. ; Brugières, L. ; Crompton, B.D. ; Marchais, A. ; Abbou, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c314t-eaead15a8810cd0c9eaa9171ab0f22fedd8142a668eaa728ffde6aa8635ca6623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cancer</topic><topic>ctDNA</topic><topic>Humanities and Social Sciences</topic><topic>Life Sciences</topic><topic>Methodology</topic><topic>Methods and statistics</topic><topic>osteosarcoma</topic><topic>Other Statistics</topic><topic>prognostic score</topic><topic>Statistics</topic><topic>stratification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Audinot, B.</creatorcontrib><creatorcontrib>Drubay, D.</creatorcontrib><creatorcontrib>Gaspar, N.</creatorcontrib><creatorcontrib>Mohr, A.</creatorcontrib><creatorcontrib>Cordero, C.</creatorcontrib><creatorcontrib>Marec-Bérard, P.</creatorcontrib><creatorcontrib>Lervat, C.</creatorcontrib><creatorcontrib>Piperno-Neumann, S.</creatorcontrib><creatorcontrib>Jimenez, M.</creatorcontrib><creatorcontrib>Mansuy, L.</creatorcontrib><creatorcontrib>Castex, M.-P.</creatorcontrib><creatorcontrib>Revon-Riviere, G.</creatorcontrib><creatorcontrib>Marie-Cardine, A.</creatorcontrib><creatorcontrib>Berger, C.</creatorcontrib><creatorcontrib>Piguet, C.</creatorcontrib><creatorcontrib>Massau, K.</creatorcontrib><creatorcontrib>Job, B.</creatorcontrib><creatorcontrib>Moquin-Beaudry, G.</creatorcontrib><creatorcontrib>Le Deley, M.-C.</creatorcontrib><creatorcontrib>Tabone, M.-D.</creatorcontrib><creatorcontrib>Berlanga, P.</creatorcontrib><creatorcontrib>Brugières, L.</creatorcontrib><creatorcontrib>Crompton, B.D.</creatorcontrib><creatorcontrib>Marchais, A.</creatorcontrib><creatorcontrib>Abbou, S.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Annals of oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Audinot, B.</au><au>Drubay, D.</au><au>Gaspar, N.</au><au>Mohr, A.</au><au>Cordero, C.</au><au>Marec-Bérard, P.</au><au>Lervat, C.</au><au>Piperno-Neumann, S.</au><au>Jimenez, M.</au><au>Mansuy, L.</au><au>Castex, M.-P.</au><au>Revon-Riviere, G.</au><au>Marie-Cardine, A.</au><au>Berger, C.</au><au>Piguet, C.</au><au>Massau, K.</au><au>Job, B.</au><au>Moquin-Beaudry, G.</au><au>Le Deley, M.-C.</au><au>Tabone, M.-D.</au><au>Berlanga, P.</au><au>Brugières, L.</au><au>Crompton, B.D.</au><au>Marchais, A.</au><au>Abbou, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial</atitle><jtitle>Annals of oncology</jtitle><addtitle>Ann Oncol</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>35</volume><issue>6</issue><spage>559</spage><epage>568</epage><pages>559-568</pages><issn>0923-7534</issn><eissn>1569-8041</eissn><abstract>Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.
Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients’ individual risk of event.
ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.
The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.
•ctDNA detection is a non-invasive procedure.•The use of lpWGS is cost-effective.•This procedure represents a considerable step in reducing therapeutic burden and trauma for patients with cancer.•PRONOS is a tool that estimates relapse risk using ctDNA at diagnosis and known clinical risk factor in osteosarcoma.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38142939</pmid><doi>10.1016/j.annonc.2023.12.006</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-0954-8294</orcidid><orcidid>https://orcid.org/0000-0002-7159-8869</orcidid><orcidid>https://orcid.org/0000-0002-9997-9727</orcidid><orcidid>https://orcid.org/0000-0002-2720-7664</orcidid><orcidid>https://orcid.org/0000-0002-7772-5208</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cancer ctDNA Humanities and Social Sciences Life Sciences Methodology Methods and statistics osteosarcoma Other Statistics prognostic score Statistics stratification |
title | ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial |
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