Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens

Abstract Purpose To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients. Experimental design Serum samples were obtained from...

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Main Authors: Luhui Shen, Justin R. Brown, Stephen Albert Johnston, Mehmet Altan, Kathryn F. Sykes
Format: Article
Language:English
Published: BMC 2023-05-01
Series:Journal of Translational Medicine
Subjects:
Online Access:https://doi.org/10.1186/s12967-023-04172-w
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author Luhui Shen
Justin R. Brown
Stephen Albert Johnston
Mehmet Altan
Kathryn F. Sykes
author_facet Luhui Shen
Justin R. Brown
Stephen Albert Johnston
Mehmet Altan
Kathryn F. Sykes
author_sort Luhui Shen
collection DOAJ
description Abstract Purpose To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients. Experimental design Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity. Results Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins. Conclusions Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.
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spelling doaj.art-93e22905aa9e4ceaa5ae5045789ad2b52023-05-28T11:26:19ZengBMCJournal of Translational Medicine1479-58762023-05-0121111410.1186/s12967-023-04172-wPredicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigensLuhui Shen0Justin R. Brown1Stephen Albert Johnston2Mehmet Altan3Kathryn F. Sykes4Calviri, IncCalviri, IncCalviri, IncMD Anderson Cancer Center, Department of Thoracic-Head & Neck Medical Oncology, Division of Cancer MedicineCalviri, IncAbstract Purpose To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients. Experimental design Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity. Results Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins. Conclusions Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.https://doi.org/10.1186/s12967-023-04172-wCheckpoint inhibitorsImmune-related adverse eventsFrameshift neoantigensPeptide microarraysPredictive diagnosticLung cancer
spellingShingle Luhui Shen
Justin R. Brown
Stephen Albert Johnston
Mehmet Altan
Kathryn F. Sykes
Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
Journal of Translational Medicine
Checkpoint inhibitors
Immune-related adverse events
Frameshift neoantigens
Peptide microarrays
Predictive diagnostic
Lung cancer
title Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
title_full Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
title_fullStr Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
title_full_unstemmed Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
title_short Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
title_sort predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens
topic Checkpoint inhibitors
Immune-related adverse events
Frameshift neoantigens
Peptide microarrays
Predictive diagnostic
Lung cancer
url https://doi.org/10.1186/s12967-023-04172-w
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