Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy
Abstract Background There are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12967-019-1865-8 |
| _version_ | 1828847265214627840 |
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| author | Matteo Pallocca Davide Angeli Fabio Palombo Francesca Sperati Michele Milella Frauke Goeman Francesca De Nicola Maurizio Fanciulli Paola Nisticò Concetta Quintarelli Gennaro Ciliberto |
| author_facet | Matteo Pallocca Davide Angeli Fabio Palombo Francesca Sperati Michele Milella Frauke Goeman Francesca De Nicola Maurizio Fanciulli Paola Nisticò Concetta Quintarelli Gennaro Ciliberto |
| author_sort | Matteo Pallocca |
| collection | DOAJ |
| description | Abstract Background There are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either leave out many possible responders, as in the case of PD-L1 testing for first-line metastatic lung cancer, or apply to a small subset of patients, such as the recently approved treatment for microsatellite instability-high or mismatch repair deficiency tumors. In this study, we conducted a survey of the available data on ICI trials with matched genomic or transcriptomic datasets in order to cross-validate the proposed biomarkers, to assess whether their prediction power was confirmed and, mainly, to investigate if their combination was able to generate a better predictive tool. Methods We extracted clinical information and sequencing data details from publicly available datasets, along with a list of possible biomarkers obtained from the recent literature. After an operation of data harmonization, we validated the performance of all the biomarkers taken individually. Furthermore, we tested two strategies to combine the best performing biomarkers in order to improve their predictive value. Results When considered individually, some of the biomarkers, such as the ImmunoPhenoScore, and the IFN-γ signature, did not confirm their originally proposed predictive power. The best absolute scoring biomarkers are TIDE, one of the ICB resistance signatures and CTLA4 with a mean AUC > 0.66. Among the combinations tested, generalized linear models showed the best performance with an AUC of 0.78. Conclusions We confirmed that the available biomarkers, taken individually, fail to provide a satisfactory predictive value. Unfortunately, also combination of some of them only provides marginal improvements. Hence, in order to generate a more robust way to predict ICI efficacy it is necessary to analyze and combine additional biomarkers and interrogate a wider set of clinical data. |
| first_indexed | 2024-12-12T22:06:12Z |
| format | Article |
| id | doaj.art-b489c74a3a514dcdba84c262daf9eed8 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-12T22:06:12Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-b489c74a3a514dcdba84c262daf9eed82022-12-22T00:10:23ZengBMCJournal of Translational Medicine1479-58762019-04-011711810.1186/s12967-019-1865-8Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracyMatteo Pallocca0Davide Angeli1Fabio Palombo2Francesca Sperati3Michele Milella4Frauke Goeman5Francesca De Nicola6Maurizio Fanciulli7Paola Nisticò8Concetta Quintarelli9Gennaro Ciliberto10SAFU Unit, IRCCS Regina Elena National Cancer InstituteUnit of Biostatistics and Clinical Trials, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCSTakis srlUOS Biostatistics, IRCCS Regina Elena National Cancer InstituteMedical Oncology 1, IRCCS Regina Elena National Cancer InstituteUOSD Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer InstituteSAFU Unit, IRCCS Regina Elena National Cancer InstituteSAFU Unit, IRCCS Regina Elena National Cancer InstituteUOSD Immunology and Immunotherapy Unit, IRCCS Regina Elena National Cancer InstituteDepartment of Paediatric Haematology, IRCCS Ospedale Pediatrico Bambino GesùIRCCS Regina Elena National Cancer InstituteAbstract Background There are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either leave out many possible responders, as in the case of PD-L1 testing for first-line metastatic lung cancer, or apply to a small subset of patients, such as the recently approved treatment for microsatellite instability-high or mismatch repair deficiency tumors. In this study, we conducted a survey of the available data on ICI trials with matched genomic or transcriptomic datasets in order to cross-validate the proposed biomarkers, to assess whether their prediction power was confirmed and, mainly, to investigate if their combination was able to generate a better predictive tool. Methods We extracted clinical information and sequencing data details from publicly available datasets, along with a list of possible biomarkers obtained from the recent literature. After an operation of data harmonization, we validated the performance of all the biomarkers taken individually. Furthermore, we tested two strategies to combine the best performing biomarkers in order to improve their predictive value. Results When considered individually, some of the biomarkers, such as the ImmunoPhenoScore, and the IFN-γ signature, did not confirm their originally proposed predictive power. The best absolute scoring biomarkers are TIDE, one of the ICB resistance signatures and CTLA4 with a mean AUC > 0.66. Among the combinations tested, generalized linear models showed the best performance with an AUC of 0.78. Conclusions We confirmed that the available biomarkers, taken individually, fail to provide a satisfactory predictive value. Unfortunately, also combination of some of them only provides marginal improvements. Hence, in order to generate a more robust way to predict ICI efficacy it is necessary to analyze and combine additional biomarkers and interrogate a wider set of clinical data.http://link.springer.com/article/10.1186/s12967-019-1865-8Immuno-checkpoint inhibitors biomarkersGenomicsImmunotherapyImmunoPhenoScoreTIDERNA-seq |
| spellingShingle | Matteo Pallocca Davide Angeli Fabio Palombo Francesca Sperati Michele Milella Frauke Goeman Francesca De Nicola Maurizio Fanciulli Paola Nisticò Concetta Quintarelli Gennaro Ciliberto Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy Journal of Translational Medicine Immuno-checkpoint inhibitors biomarkers Genomics Immunotherapy ImmunoPhenoScore TIDE RNA-seq |
| title | Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| title_full | Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| title_fullStr | Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| title_full_unstemmed | Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| title_short | Combinations of immuno-checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| title_sort | combinations of immuno checkpoint inhibitors predictive biomarkers only marginally improve their individual accuracy |
| topic | Immuno-checkpoint inhibitors biomarkers Genomics Immunotherapy ImmunoPhenoScore TIDE RNA-seq |
| url | http://link.springer.com/article/10.1186/s12967-019-1865-8 |
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