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. 2021 Feb;9(2):e001845.
doi: 10.1136/jitc-2020-001845.

Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors

Jian-Guo Zhou #  1   2   3 , Anna-Jasmina Donaubauer #  1   2 , Benjamin Frey  1   2 , Ina Becker  1   2 , Sandra Rutzner  1   2 , Markus Eckstein  2   4 , Roger Sun  5   6 , Hu Ma  3 , Philipp Schubert  1   2 , Claudia Schweizer  1   2 , Rainer Fietkau  1   2 , Eric Deutsch  5   6 , Udo Gaipl #  7   2 , Markus Hecht #  1   2
Affiliations

Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors

Jian-Guo Zhou et al. J Immunother Cancer. 2021 Feb.

Abstract

Background: The predictive power of novel biological markers for treatment response to immune checkpoint inhibitors (ICI) is still not satisfactory for the majority of patients with cancer. One should identify valid predictive markers in the peripheral blood, as this is easily available before and during treatment. The current interim analysis of patients of the ST-ICI cohort therefore focuses on the development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with metastatic cancer to ICI targeting the programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) axis.

Methods: A total of 104 patients were prospectively enrolled. 54 immune cell subsets were prospectively analyzed in patients' peripheral blood by multicolor flow cytometry before treatment with ICI (pre-ICI; n=89), and after the first application of ICI (n=65). Pre-ICI, patients were randomly allocated to a training (n=56) and a validation cohort (n=33). Univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator Cox model were used to create a predictive immune signature, which was also checked after the first ICI, to consider the dynamics of changes in the immune status.

Results: Whole blood samples were provided by 89 patients pre-ICI and by 65 patients after the first ICI. We identified a LIPS which is based on five immune cell subtypes: CD14high monocytes, CD8+/PD-1+ T cells, plasmacytoid dendritic cells, neutrophils, and CD3+/CD56+/CD16+ natural killer (NK)T cells. The signature achieved a high accuracy (C-index 0.74 vs 0.71) for predicting overall survival (OS) benefit in both the training and the validation cohort. In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0.26, 95% CI 0.12 to 0.56, p=0.00025; HR 0.30, 95% CI 0.10 to 0.91, p=0.024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI.

Conclusion: Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood.

Trial registration number: NCT03453892.

Keywords: biomarkers; immunotherapy; programmed cell death 1 receptor; tumor; tumor biomarkers; tumor microenvironment.

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Conflict of interest statement

Competing interests: MH reports conflict of interest with Merck Serono (advisory boards, honoraria for lectures, travel grants, research funding); MSD (advisory boards, travel grants, research funding); AstraZeneca (research funding); Novartis (research funding); BMS (advisory boards, honoraria for lectures); Teva (travel grants). USG received support for presentation activities for Dr Sennewald Medizintechnik GmbH, has received support for investigator initiated clinical studies (IITs) from MSD and AstraZeneca and contributed at Advisory Boards Meetings of AstraZeneca and Bristol-Myers Squibb. JGZ received support from AstraZeneca (travel grants). HM reports conflict of interest with Merck Serono (advisory boards, honoraria for lectures, travel grants); MSD (advisory boards, honoraria for lectures, travel grants); AstraZeneca (advisory boards, honoraria for lectures, travel grants); BMS (advisory boards, honoraria for lectures, travel grants). RS received travel and accommodation expenses from AstraZeneca. ED reports grants and honoraria from ROCHE GENENTECH, grants from SERVIER, grants and honoraria from ASTRAZENECA, MERCK SERONO, BMS and MSD, outside the submitted work. SR conflict of interest with AstraZeneca (research funding); MSD (research funding). ME conflict of interest with Diaceutics (employment, honoraria, advisory role, speakers’ bureau, travel expenses); AstraZeneca (honoraria, advisory role, speakers’ bureau, travel expenses); Roche (honoraria, travel expenses); MSD (honoraria, speakers’ bureau); GenomicHealth (honoraria, advisory role, speakers bureau, travel expenses); Astellas (honoraria, speakers’ bureau); Janssen-Cilag (honoraria, advisory role, research funding, travel expenses); Stratifyer (research funding, patents). RF conflict of interest with MSD (honoraria, advisory role, research funding, travel expenses); Fresenius (honoraria); BrainLab (honoraria); AstraZeneca (honoraria, advisory role, research funding, travel expenses); Merck Serono (advisory role, research funding, travel expenses); Novocure (advisory role, speakers’ bureau, research funding); Sennewald (speakers’ bureau, travel expenses).

Figures

Figure 1
Figure 1
Research design (A) and flowchart (B) for the identification of a predictive signature by immunophenotyping (IPT) for patients with metastatic cancer treated with immune checkpoint inhibitors (ICI). ROC, receiver operating characteristic.
Figure 2
Figure 2
Characteristics of the liquid immune profile-based signature (LIPS) in the training and validation cohorts. (A) The risk score of each patient with metastatic cancer (patient ID) treated with immune checkpoint inhibitors (ICI) in the training cohort. (B) Overall survival and survival status of patients with metastatic cancer in the training cohort. (C) Heat map of immune cell counts of patients with metastatic cancer in the training cohort. (D) The risk score of each patient with metastatic cancer treated with ICI in the validation cohort. (E) Overall survival and survival status of patients with metastatic cancer in the validation cohort. (F) Heat map of immune cell counts of patients with metastatic cancer in the validation cohort. NKT cells, natural killer T cells; pDCs, plasmacytoid dendritic cells; PD-1, programmed cell death protein 1.
Figure 3
Figure 3
The ability of the liquid immune profile-based signature (LIPS) to predict the overall survival (OS) in the training and validation cohorts. (A) The OS in training cohort stratified by the LIPS into high-risk and low-risk with the p value. (B) Time-dependent receiver operating characteristic (ROC) curves of LIPS in the training cohort. (C) The OS in validation cohort stratified by the LIPS into high-risk and low-risk with the p value. (D) Time-dependent ROC curves of LIPS in the validation cohort. AUC, area under the ROC curve.
Figure 4
Figure 4
Liquid immune profile-based signature (LIPS) predicts survival benefit from immune checkpoint inhibitors (ICI) treatment in all patients with metastatic cancer of the ST-ICI cohort. (A) The overall survival (OS) in all patients stratified by the LIPS into high-risk and low-risk with the p value. (B) Time-dependent receiver operating characteristic (ROC) curves of LIPS for OS in all patients. (C) The progression-free survival (PFS) in all patients stratified by the LIPS into high-risk and low-risk with the p value. (D) Time-dependent ROC curves of LIPS for PFS in all patients. AUC, area under the ROC curve.
Figure 5
Figure 5
Subgroup analysis of the liquid immune profile-based signature (LIPS) predict survival benefit of patients treated with ICI by baseline characteristics. Subgroup analysis of the LIPS predict overall survival (OS) benefit (A) and progression-free survival (PFS) benefit (B) of patients treated with ICI. HR and 95% CIs. HNSCC, head and neck squamous cell carcinoma; ICI, immune checkpoint inhibitors; NSCLC, non-small cell lung cancer; TPS, tumor proportion score.
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