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Identifying predictive biomarkers of CIMAvaxEGF success in non-small cell lung cancer patients.

ABSTRACT: BACKGROUND:Immunosenescence biomarkers and peripheral blood parameters are evaluated separately as possible predictive markers of immunotherapy. Here, we illustrate the use of a causal inference model to identify predictive biomarkers of CIMAvaxEGF success in the treatment of Non-Small Cell Lung Cancer Patients. METHODS:Data from a controlled clinical trial evaluating the effect of CIMAvax-EGF were analyzed retrospectively, following a causal inference approach. Pre-treatment potential predictive biomarkers included basal serum EGF concentration, peripheral blood parameters and immunosenescence biomarkers. The proportion of CD8?+?CD28- T cells, CD4+ and CD8+ T cells, CD4/CD8 ratio and CD19+ B cells. The 33 patients with complete information were included. The predictive causal information (PCI) was calculated for all possible models. The model with a minimum number of predictors, but with high prediction accuracy (PCI?>?0.7) was selected. Good, rare and poor responder patients were identified using the predictive probability of treatment success. RESULTS:The mean of PCI increased from 0.486, when only one predictor is considered, to 0.98 using the multivariate approach with all predictors. The model considering the proportion of CD4+ T cell, basal Epidermal Growth Factor (EGF) concentration, neutrophil to lymphocyte ratio, Monocytes, and Neutrophils as predictors were selected (PCI?>?0.74). Patients predicted as good responders according to the pre-treatment biomarkers values treated with CIMAvax-EGF had a significant higher observed survival compared with the control group (p?=?0.03). No difference was observed for bad responders. CONCLUSIONS:Peripheral blood parameters and immunosenescence biomarkers together with basal EGF concentration in serum resulted in good predictors of the CIMAvax-EGF success in advanced NSCLC. Future research should explore molecular and genetic profile as biomarkers for CIMAvax-EGF and it combination with immune-checkpoint inhibitors. The study illustrates the application of a new methodology, based on causal inference, to evaluate multivariate pre-treatment predictors. The multivariate approach allows realistic predictions of the clinical benefit of patients and should be introduced in daily clinical practice.

SUBMITTER: Lorenzo-Luaces P

PROVIDER: S-EPMC7433036 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identifying predictive biomarkers of CIMAvaxEGF success in non-small cell lung cancer patients.

Lorenzo-Luaces Patricia P Sanchez Lizet L Saavedra Danay D Crombet Tania T Van der Elst Wim W Alonso Ariel A Molenberghs Geert G Lage Agustin A

BMC cancer 20200817 1

<h4>Background</h4>Immunosenescence biomarkers and peripheral blood parameters are evaluated separately as possible predictive markers of immunotherapy. Here, we illustrate the use of a causal inference model to identify predictive biomarkers of CIMAvaxEGF success in the treatment of Non-Small Cell Lung Cancer Patients.<h4>Methods</h4>Data from a controlled clinical trial evaluating the effect of CIMAvax-EGF were analyzed retrospectively, following a causal inference approach. Pre-treatment pote ...[more]

PMID: 32807114

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Project description:Background: Despite remarkable success of immunotherapies with checkpoint blockade antibodies targeting programmed cell death protein 1 (PD-1), the majority of patients with non-small-cell lung cancer (NSCLC) have yet to receive durable benefits. We used the metabolomic profiling of early on-treatment serum to explore predictors of clinical outcomes of anti-PD-1 treatment in patients with advanced NSCLC. Methods: We recruited 74 Chinese patients who had stage IIIB/IV NSCLC-proven tumor progression and were treated with PD-1 inhibitor. The study was comprised of a discovery cohort of patients treated with nivolumab and two validation cohorts of patients receiving tislelizumab or nivolumab. Serum samples were collected 2-3 weeks after the first infusion of PD-1 inhibitor. Metabolomic profiling of serum was performed using ultrahigh performance lipid chromatograph-mass spectrometry. The serum metabolite biomarkers were identified using an integral workflow of nontargeted metabolomic data analysis. Results: A serum metabolite panel consisting of hypoxanthine and histidine was identified and validated as a predictor of response to PD-1 blockade treatment in patients with advanced NSCLC. High levels of both hypoxanthine and histidine in early on-treatment serum were associated with improved progression-free survival [hazard ratio (HR) = 0.078, 95% confidence interval (CI), 0.027-0.221, p < 0.001] and overall survival (HR = 0.124, 95% CI, 0.039-0.397, p < 0.001) in the discovery cohort. The serum metabolite panel showed a high sensitivity and specificity in distinguishing responders and non-responders in the validation cohorts 1 and 2, with an area under the receiver-operating characteristic curve of 0.933 and 1.000, respectively. High levels of serum hypoxanthine and histidine were correlated with improved progression-free survival in the validation cohort 1 (HR = 0.137, 95% CI, 0.040-0.467, p = 0.001) and in the validation cohort 2 (HR = 0.084, 95% CI, 0.009-0.762, p = 0.028). Conclusion: Our results revealed that hypoxanthine and histidine in early on-treatment serum are predictive biomarkers of response to PD-1 blockade therapy in patients with advanced NSCLC. The serum biomarker panel would enable early identification of NSCLC patients who may benefit from PD-1 blockade therapy.

Dataset Information

Identifying predictive biomarkers of CIMAvaxEGF success in non-small cell lung cancer patients.

Publications

Identifying predictive biomarkers of CIMAvaxEGF success in non-small cell lung cancer patients.

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