The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy
Background Optimal prognostic and predictive biomarkers for patients with advanced‐stage cancer patients who received immunotherapy (IO) are lacking. Inflammatory markers, such as the neutrophil‐to‐lymphocyte ratio (NLR), the monocyte‐to‐lymphocyte ratio (MLR), and the platelet‐to‐lymphocyte ratio (...
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| Veröffentlicht in: | Cancer 2019-01, Vol.125 (1), p.127-134 |
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| creator | Bilen, Mehmet A. Martini, Dylan J. Liu, Yuan Lewis, Colleen Collins, Hannah H. Shabto, Julie M. Akce, Mehmet Kissick, Haydn T. Carthon, Bradley C. Shaib, Walid L. Alese, Olatunji B. Pillai, Rathi N. Steuer, Conor E. Wu, Christina S. Lawson, David H. Kudchadkar, Ragini R. El‐Rayes, Bassel F. Master, Viraj A. Ramalingam, Suresh S. Owonikoko, Taofeek K. Harvey, R. Donald |
| description | Background
Optimal prognostic and predictive biomarkers for patients with advanced‐stage cancer patients who received immunotherapy (IO) are lacking. Inflammatory markers, such as the neutrophil‐to‐lymphocyte ratio (NLR), the monocyte‐to‐lymphocyte ratio (MLR), and the platelet‐to‐lymphocyte ratio (PLR), are readily available. The authors investigated the association between these markers and clinical outcomes of patients with advanced‐stage cancer who received IO.
Methods
A retrospective review was conducted of 90 patients with advanced cancer who received treatment on phase 1 clinical trials of IO‐based treatment regimens. NLR, MLR, and PLR values were log‐transformed and treated as continuous variables for each patient. Overall survival (OS), progression‐free survival (PFS), and clinical benefit were used to measure clinical outcomes. For univariate associations and multivariable analyses, Cox proportional‐hazards models or logistic regression models were used.
Results
The median patient age was 63 years, and most were men (59%). The most common histologies were melanoma (33%) and gastrointestinal cancers (22%). High baseline NLR, MLR, and PLR values were associated significantly with worse OS and PFS (P < .05) and a lower chance of benefit (NLR and PLR; P < .05). Increased NLR, MLR, and PLR values 6 weeks after baseline were associated with shorter OS and PFS (P ≤ .052).
Conclusions
Baseline and early changes in NLR, MLR, and PLR values were strongly associated with clinical outcomes in patients who received IO‐based treatment regimens on phase 1 trials. Confirmation in a homogenous patient population treated on late‐stage trials or outside of trial settings is warranted. These values may warrant consideration for inclusion when risk stratifying patients enrolled onto phase 1 clinical trials of IO agents.
High baseline and early increases in the neutrophil‐to‐lymphocyte, monocyte‐to‐lymphocyte, and platelet‐to‐lymphocyte ratios are significantly associated with poor outcomes in patients with advanced‐stage cancer who receive immunotherapy. These markers of inflammation may warrant consideration in updated prognostic models for patients enrolled on phase 1 clinical trials. |
| doi_str_mv | 10.1002/cncr.31778 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2121496108</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2159492258</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3938-86ceba762334577b3ed3aa16fda1b4d95d7ff48039107061878af3ff034a4b323</originalsourceid><addsrcrecordid>eNp9kcuKFDEUhoMoTs_oxgeQgBsZqDG3qqSW0niDQUFGcFecymUqY1WlTFLT9M6dW5_RJzFtjy5cuAp_-M7HSX6EnlByQQlhL_Ss4wWnUqp7aENJKytCBbuPNoQQVdWCfz5BpyndlChZzR-iE044a6lQG_T9arB4ieF6Dil7jWE2JVrjdfa3FvtpAZ1xcNjPboRpghziHvc-TBC_2JjKPV4gezvnhHdDwAOUMTC3MGtrfn77kTJcW6wPMeIcLWRr8M7nobindQ55sBGW_SP0wMGY7OO78wx9ev3qavu2uvzw5t325WWlectVpRpte5AN41zUUvbcGg5AG2eA9sK0tZHOCUV4S4kkDVVSgePOES5A9JzxM_T86C1v_rralLvJJ23HEWYb1tQxyqhoG0pUQZ_9g96ENc5lu0LVrWgZqw_U-ZHSMaQUreuW6Mvn7DtKukM93aGe7nc9BX56p1z7yZq_6J8-CkCPwM6Pdv8fVbd9v_14lP4CrH-dsA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2159492258</pqid></control><display><type>article</type><title>The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy</title><source>Wiley Free Content</source><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><creator>Bilen, Mehmet A. ; Martini, Dylan J. ; Liu, Yuan ; Lewis, Colleen ; Collins, Hannah H. ; Shabto, Julie M. ; Akce, Mehmet ; Kissick, Haydn T. ; Carthon, Bradley C. ; Shaib, Walid L. ; Alese, Olatunji B. ; Pillai, Rathi N. ; Steuer, Conor E. ; Wu, Christina S. ; Lawson, David H. ; Kudchadkar, Ragini R. ; El‐Rayes, Bassel F. ; Master, Viraj A. ; Ramalingam, Suresh S. ; Owonikoko, Taofeek K. ; Harvey, R. Donald</creator><creatorcontrib>Bilen, Mehmet A. ; Martini, Dylan J. ; Liu, Yuan ; Lewis, Colleen ; Collins, Hannah H. ; Shabto, Julie M. ; Akce, Mehmet ; Kissick, Haydn T. ; Carthon, Bradley C. ; Shaib, Walid L. ; Alese, Olatunji B. ; Pillai, Rathi N. ; Steuer, Conor E. ; Wu, Christina S. ; Lawson, David H. ; Kudchadkar, Ragini R. ; El‐Rayes, Bassel F. ; Master, Viraj A. ; Ramalingam, Suresh S. ; Owonikoko, Taofeek K. ; Harvey, R. Donald</creatorcontrib><description>Background
Optimal prognostic and predictive biomarkers for patients with advanced‐stage cancer patients who received immunotherapy (IO) are lacking. Inflammatory markers, such as the neutrophil‐to‐lymphocyte ratio (NLR), the monocyte‐to‐lymphocyte ratio (MLR), and the platelet‐to‐lymphocyte ratio (PLR), are readily available. The authors investigated the association between these markers and clinical outcomes of patients with advanced‐stage cancer who received IO.
Methods
A retrospective review was conducted of 90 patients with advanced cancer who received treatment on phase 1 clinical trials of IO‐based treatment regimens. NLR, MLR, and PLR values were log‐transformed and treated as continuous variables for each patient. Overall survival (OS), progression‐free survival (PFS), and clinical benefit were used to measure clinical outcomes. For univariate associations and multivariable analyses, Cox proportional‐hazards models or logistic regression models were used.
Results
The median patient age was 63 years, and most were men (59%). The most common histologies were melanoma (33%) and gastrointestinal cancers (22%). High baseline NLR, MLR, and PLR values were associated significantly with worse OS and PFS (P < .05) and a lower chance of benefit (NLR and PLR; P < .05). Increased NLR, MLR, and PLR values 6 weeks after baseline were associated with shorter OS and PFS (P ≤ .052).
Conclusions
Baseline and early changes in NLR, MLR, and PLR values were strongly associated with clinical outcomes in patients who received IO‐based treatment regimens on phase 1 trials. Confirmation in a homogenous patient population treated on late‐stage trials or outside of trial settings is warranted. These values may warrant consideration for inclusion when risk stratifying patients enrolled onto phase 1 clinical trials of IO agents.
High baseline and early increases in the neutrophil‐to‐lymphocyte, monocyte‐to‐lymphocyte, and platelet‐to‐lymphocyte ratios are significantly associated with poor outcomes in patients with advanced‐stage cancer who receive immunotherapy. These markers of inflammation may warrant consideration in updated prognostic models for patients enrolled on phase 1 clinical trials.</description><identifier>ISSN: 0008-543X</identifier><identifier>EISSN: 1097-0142</identifier><identifier>DOI: 10.1002/cncr.31778</identifier><identifier>PMID: 30329148</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Biomarkers ; Biomarkers, Tumor - immunology ; Cancer ; Clinical outcomes ; Clinical trials ; Clinical Trials, Phase I as Topic ; Female ; Hazards ; Humans ; Immunotherapy ; Immunotherapy - methods ; Impact prediction ; Inflammation ; Leukocyte Count ; Logistic Models ; Lymphocyte Count ; Lymphocytes ; Male ; Medical prognosis ; Medical research ; Melanoma ; Middle Aged ; Monocytes ; Neoplasm Staging ; Neoplasms - blood ; Neoplasms - drug therapy ; Neoplasms - immunology ; Neoplasms - pathology ; Oncology ; Patients ; phase 1 clinical trials ; Platelet Count ; Prognosis ; Regression analysis ; Regression models ; Retrospective Studies ; Survival ; Survival Analysis ; Treatment Outcome</subject><ispartof>Cancer, 2019-01, Vol.125 (1), p.127-134</ispartof><rights>2018 American Cancer Society</rights><rights>2018 American Cancer Society.</rights><rights>2019 American Cancer Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3938-86ceba762334577b3ed3aa16fda1b4d95d7ff48039107061878af3ff034a4b323</citedby><cites>FETCH-LOGICAL-c3938-86ceba762334577b3ed3aa16fda1b4d95d7ff48039107061878af3ff034a4b323</cites><orcidid>0000-0003-4003-1103 ; 0000-0003-4747-7722 ; 0000-0002-2661-5746 ; 0000-0002-0757-3106</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcncr.31778$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcncr.31778$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30329148$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bilen, Mehmet A.</creatorcontrib><creatorcontrib>Martini, Dylan J.</creatorcontrib><creatorcontrib>Liu, Yuan</creatorcontrib><creatorcontrib>Lewis, Colleen</creatorcontrib><creatorcontrib>Collins, Hannah H.</creatorcontrib><creatorcontrib>Shabto, Julie M.</creatorcontrib><creatorcontrib>Akce, Mehmet</creatorcontrib><creatorcontrib>Kissick, Haydn T.</creatorcontrib><creatorcontrib>Carthon, Bradley C.</creatorcontrib><creatorcontrib>Shaib, Walid L.</creatorcontrib><creatorcontrib>Alese, Olatunji B.</creatorcontrib><creatorcontrib>Pillai, Rathi N.</creatorcontrib><creatorcontrib>Steuer, Conor E.</creatorcontrib><creatorcontrib>Wu, Christina S.</creatorcontrib><creatorcontrib>Lawson, David H.</creatorcontrib><creatorcontrib>Kudchadkar, Ragini R.</creatorcontrib><creatorcontrib>El‐Rayes, Bassel F.</creatorcontrib><creatorcontrib>Master, Viraj A.</creatorcontrib><creatorcontrib>Ramalingam, Suresh S.</creatorcontrib><creatorcontrib>Owonikoko, Taofeek K.</creatorcontrib><creatorcontrib>Harvey, R. Donald</creatorcontrib><title>The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy</title><title>Cancer</title><addtitle>Cancer</addtitle><description>Background
Optimal prognostic and predictive biomarkers for patients with advanced‐stage cancer patients who received immunotherapy (IO) are lacking. Inflammatory markers, such as the neutrophil‐to‐lymphocyte ratio (NLR), the monocyte‐to‐lymphocyte ratio (MLR), and the platelet‐to‐lymphocyte ratio (PLR), are readily available. The authors investigated the association between these markers and clinical outcomes of patients with advanced‐stage cancer who received IO.
Methods
A retrospective review was conducted of 90 patients with advanced cancer who received treatment on phase 1 clinical trials of IO‐based treatment regimens. NLR, MLR, and PLR values were log‐transformed and treated as continuous variables for each patient. Overall survival (OS), progression‐free survival (PFS), and clinical benefit were used to measure clinical outcomes. For univariate associations and multivariable analyses, Cox proportional‐hazards models or logistic regression models were used.
Results
The median patient age was 63 years, and most were men (59%). The most common histologies were melanoma (33%) and gastrointestinal cancers (22%). High baseline NLR, MLR, and PLR values were associated significantly with worse OS and PFS (P < .05) and a lower chance of benefit (NLR and PLR; P < .05). Increased NLR, MLR, and PLR values 6 weeks after baseline were associated with shorter OS and PFS (P ≤ .052).
Conclusions
Baseline and early changes in NLR, MLR, and PLR values were strongly associated with clinical outcomes in patients who received IO‐based treatment regimens on phase 1 trials. Confirmation in a homogenous patient population treated on late‐stage trials or outside of trial settings is warranted. These values may warrant consideration for inclusion when risk stratifying patients enrolled onto phase 1 clinical trials of IO agents.
High baseline and early increases in the neutrophil‐to‐lymphocyte, monocyte‐to‐lymphocyte, and platelet‐to‐lymphocyte ratios are significantly associated with poor outcomes in patients with advanced‐stage cancer who receive immunotherapy. These markers of inflammation may warrant consideration in updated prognostic models for patients enrolled on phase 1 clinical trials.</description><subject>Biomarkers</subject><subject>Biomarkers, Tumor - immunology</subject><subject>Cancer</subject><subject>Clinical outcomes</subject><subject>Clinical trials</subject><subject>Clinical Trials, Phase I as Topic</subject><subject>Female</subject><subject>Hazards</subject><subject>Humans</subject><subject>Immunotherapy</subject><subject>Immunotherapy - methods</subject><subject>Impact prediction</subject><subject>Inflammation</subject><subject>Leukocyte Count</subject><subject>Logistic Models</subject><subject>Lymphocyte Count</subject><subject>Lymphocytes</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Melanoma</subject><subject>Middle Aged</subject><subject>Monocytes</subject><subject>Neoplasm Staging</subject><subject>Neoplasms - blood</subject><subject>Neoplasms - drug therapy</subject><subject>Neoplasms - immunology</subject><subject>Neoplasms - pathology</subject><subject>Oncology</subject><subject>Patients</subject><subject>phase 1 clinical trials</subject><subject>Platelet Count</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Retrospective Studies</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Treatment Outcome</subject><issn>0008-543X</issn><issn>1097-0142</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcuKFDEUhoMoTs_oxgeQgBsZqDG3qqSW0niDQUFGcFecymUqY1WlTFLT9M6dW5_RJzFtjy5cuAp_-M7HSX6EnlByQQlhL_Ss4wWnUqp7aENJKytCBbuPNoQQVdWCfz5BpyndlChZzR-iE044a6lQG_T9arB4ieF6Dil7jWE2JVrjdfa3FvtpAZ1xcNjPboRpghziHvc-TBC_2JjKPV4gezvnhHdDwAOUMTC3MGtrfn77kTJcW6wPMeIcLWRr8M7nobindQ55sBGW_SP0wMGY7OO78wx9ev3qavu2uvzw5t325WWlectVpRpte5AN41zUUvbcGg5AG2eA9sK0tZHOCUV4S4kkDVVSgePOES5A9JzxM_T86C1v_rralLvJJ23HEWYb1tQxyqhoG0pUQZ_9g96ENc5lu0LVrWgZqw_U-ZHSMaQUreuW6Mvn7DtKukM93aGe7nc9BX56p1z7yZq_6J8-CkCPwM6Pdv8fVbd9v_14lP4CrH-dsA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Bilen, Mehmet A.</creator><creator>Martini, Dylan J.</creator><creator>Liu, Yuan</creator><creator>Lewis, Colleen</creator><creator>Collins, Hannah H.</creator><creator>Shabto, Julie M.</creator><creator>Akce, Mehmet</creator><creator>Kissick, Haydn T.</creator><creator>Carthon, Bradley C.</creator><creator>Shaib, Walid L.</creator><creator>Alese, Olatunji B.</creator><creator>Pillai, Rathi N.</creator><creator>Steuer, Conor E.</creator><creator>Wu, Christina S.</creator><creator>Lawson, David H.</creator><creator>Kudchadkar, Ragini R.</creator><creator>El‐Rayes, Bassel F.</creator><creator>Master, Viraj A.</creator><creator>Ramalingam, Suresh S.</creator><creator>Owonikoko, Taofeek K.</creator><creator>Harvey, R. Donald</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TO</scope><scope>7U7</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4003-1103</orcidid><orcidid>https://orcid.org/0000-0003-4747-7722</orcidid><orcidid>https://orcid.org/0000-0002-2661-5746</orcidid><orcidid>https://orcid.org/0000-0002-0757-3106</orcidid></search><sort><creationdate>20190101</creationdate><title>The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy</title><author>Bilen, Mehmet A. ; Martini, Dylan J. ; Liu, Yuan ; Lewis, Colleen ; Collins, Hannah H. ; Shabto, Julie M. ; Akce, Mehmet ; Kissick, Haydn T. ; Carthon, Bradley C. ; Shaib, Walid L. ; Alese, Olatunji B. ; Pillai, Rathi N. ; Steuer, Conor E. ; Wu, Christina S. ; Lawson, David H. ; Kudchadkar, Ragini R. ; El‐Rayes, Bassel F. ; Master, Viraj A. ; Ramalingam, Suresh S. ; Owonikoko, Taofeek K. ; Harvey, R. Donald</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3938-86ceba762334577b3ed3aa16fda1b4d95d7ff48039107061878af3ff034a4b323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biomarkers</topic><topic>Biomarkers, Tumor - immunology</topic><topic>Cancer</topic><topic>Clinical outcomes</topic><topic>Clinical trials</topic><topic>Clinical Trials, Phase I as Topic</topic><topic>Female</topic><topic>Hazards</topic><topic>Humans</topic><topic>Immunotherapy</topic><topic>Immunotherapy - methods</topic><topic>Impact prediction</topic><topic>Inflammation</topic><topic>Leukocyte Count</topic><topic>Logistic Models</topic><topic>Lymphocyte Count</topic><topic>Lymphocytes</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Medical research</topic><topic>Melanoma</topic><topic>Middle Aged</topic><topic>Monocytes</topic><topic>Neoplasm Staging</topic><topic>Neoplasms - blood</topic><topic>Neoplasms - drug therapy</topic><topic>Neoplasms - immunology</topic><topic>Neoplasms - pathology</topic><topic>Oncology</topic><topic>Patients</topic><topic>phase 1 clinical trials</topic><topic>Platelet Count</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Retrospective Studies</topic><topic>Survival</topic><topic>Survival Analysis</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bilen, Mehmet A.</creatorcontrib><creatorcontrib>Martini, Dylan J.</creatorcontrib><creatorcontrib>Liu, Yuan</creatorcontrib><creatorcontrib>Lewis, Colleen</creatorcontrib><creatorcontrib>Collins, Hannah H.</creatorcontrib><creatorcontrib>Shabto, Julie M.</creatorcontrib><creatorcontrib>Akce, Mehmet</creatorcontrib><creatorcontrib>Kissick, Haydn T.</creatorcontrib><creatorcontrib>Carthon, Bradley C.</creatorcontrib><creatorcontrib>Shaib, Walid L.</creatorcontrib><creatorcontrib>Alese, Olatunji B.</creatorcontrib><creatorcontrib>Pillai, Rathi N.</creatorcontrib><creatorcontrib>Steuer, Conor E.</creatorcontrib><creatorcontrib>Wu, Christina S.</creatorcontrib><creatorcontrib>Lawson, David H.</creatorcontrib><creatorcontrib>Kudchadkar, Ragini R.</creatorcontrib><creatorcontrib>El‐Rayes, Bassel F.</creatorcontrib><creatorcontrib>Master, Viraj A.</creatorcontrib><creatorcontrib>Ramalingam, Suresh S.</creatorcontrib><creatorcontrib>Owonikoko, Taofeek K.</creatorcontrib><creatorcontrib>Harvey, R. Donald</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bilen, Mehmet A.</au><au>Martini, Dylan J.</au><au>Liu, Yuan</au><au>Lewis, Colleen</au><au>Collins, Hannah H.</au><au>Shabto, Julie M.</au><au>Akce, Mehmet</au><au>Kissick, Haydn T.</au><au>Carthon, Bradley C.</au><au>Shaib, Walid L.</au><au>Alese, Olatunji B.</au><au>Pillai, Rathi N.</au><au>Steuer, Conor E.</au><au>Wu, Christina S.</au><au>Lawson, David H.</au><au>Kudchadkar, Ragini R.</au><au>El‐Rayes, Bassel F.</au><au>Master, Viraj A.</au><au>Ramalingam, Suresh S.</au><au>Owonikoko, Taofeek K.</au><au>Harvey, R. Donald</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy</atitle><jtitle>Cancer</jtitle><addtitle>Cancer</addtitle><date>2019-01-01</date><risdate>2019</risdate><volume>125</volume><issue>1</issue><spage>127</spage><epage>134</epage><pages>127-134</pages><issn>0008-543X</issn><eissn>1097-0142</eissn><abstract>Background
Optimal prognostic and predictive biomarkers for patients with advanced‐stage cancer patients who received immunotherapy (IO) are lacking. Inflammatory markers, such as the neutrophil‐to‐lymphocyte ratio (NLR), the monocyte‐to‐lymphocyte ratio (MLR), and the platelet‐to‐lymphocyte ratio (PLR), are readily available. The authors investigated the association between these markers and clinical outcomes of patients with advanced‐stage cancer who received IO.
Methods
A retrospective review was conducted of 90 patients with advanced cancer who received treatment on phase 1 clinical trials of IO‐based treatment regimens. NLR, MLR, and PLR values were log‐transformed and treated as continuous variables for each patient. Overall survival (OS), progression‐free survival (PFS), and clinical benefit were used to measure clinical outcomes. For univariate associations and multivariable analyses, Cox proportional‐hazards models or logistic regression models were used.
Results
The median patient age was 63 years, and most were men (59%). The most common histologies were melanoma (33%) and gastrointestinal cancers (22%). High baseline NLR, MLR, and PLR values were associated significantly with worse OS and PFS (P < .05) and a lower chance of benefit (NLR and PLR; P < .05). Increased NLR, MLR, and PLR values 6 weeks after baseline were associated with shorter OS and PFS (P ≤ .052).
Conclusions
Baseline and early changes in NLR, MLR, and PLR values were strongly associated with clinical outcomes in patients who received IO‐based treatment regimens on phase 1 trials. Confirmation in a homogenous patient population treated on late‐stage trials or outside of trial settings is warranted. These values may warrant consideration for inclusion when risk stratifying patients enrolled onto phase 1 clinical trials of IO agents.
High baseline and early increases in the neutrophil‐to‐lymphocyte, monocyte‐to‐lymphocyte, and platelet‐to‐lymphocyte ratios are significantly associated with poor outcomes in patients with advanced‐stage cancer who receive immunotherapy. These markers of inflammation may warrant consideration in updated prognostic models for patients enrolled on phase 1 clinical trials.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>30329148</pmid><doi>10.1002/cncr.31778</doi><tpages>0</tpages><orcidid>https://orcid.org/0000-0003-4003-1103</orcidid><orcidid>https://orcid.org/0000-0003-4747-7722</orcidid><orcidid>https://orcid.org/0000-0002-2661-5746</orcidid><orcidid>https://orcid.org/0000-0002-0757-3106</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Cancer, 2019-01, Vol.125 (1), p.127-134 |
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| source | Wiley Free Content; MEDLINE; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Biomarkers Biomarkers, Tumor - immunology Cancer Clinical outcomes Clinical trials Clinical Trials, Phase I as Topic Female Hazards Humans Immunotherapy Immunotherapy - methods Impact prediction Inflammation Leukocyte Count Logistic Models Lymphocyte Count Lymphocytes Male Medical prognosis Medical research Melanoma Middle Aged Monocytes Neoplasm Staging Neoplasms - blood Neoplasms - drug therapy Neoplasms - immunology Neoplasms - pathology Oncology Patients phase 1 clinical trials Platelet Count Prognosis Regression analysis Regression models Retrospective Studies Survival Survival Analysis Treatment Outcome |
| title | The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy |
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