scholarly journals 240 Discovery of biomarkers of resistance to immune checkpoint blockade in non-small-cell lung cancer (NSCLC) using high-plex digital spatial profiling

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A258-A258
Author(s):  
Myrto Moutafi ◽  
Sandra Martinez-Morilla ◽  
Prajan Divakar ◽  
Ioannis Vathiotis ◽  
Niki Gavrielatou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Biomarker Discovery ◽  
Univariate Analysis ◽  
Predictive Biomarkers ◽  
Protein Detection ◽  
Statistical Testing ◽  
Multiple Testing Correction ◽  
Stromal Compartment ◽  
Pre Treatment

BackgroundDespite the clinical effectiveness of Immune Checkpoint Inhibitors (ICI) in lung cancer, only around 20% remain disease free at 5 years. Predictive biomarkers for ICIs are neither sensitive nor specific. Here, we used the GeoMx Digital Spatial Profiler (DSP) (NanoString, Inc.) to analyze high-plex protein in a quantitative and spatially resolved manner from single formalin-fixed paraffin embedded tissue sections toward the goal of identification of new biomarkers with better predictive value.MethodsPre-treatment samples from 56 patients with NSCLC treated with ICI were collected, represented in Yale tissue microarray 471 (YTMA471), and analyzed. A panel of 71 photocleavable oligonucleotide-labeled primary antibodies (NanoString Human IO panel) was used for protein detection. Protein expression was measured in 4 molecularly defined tissue compartments, defined by fluorescence co-localization (tumor [panCK+], leukocytes [CD45+/CD68-], macrophages [CD68+] and an aggregate stromal immune cell compartment, defined as the sum of leukocyte and macrophage expression [panCK-/CD45+/CD68+]) generating 284 variables representing potential predictive biomarkers. Promising candidates were orthogonally validated with Quantitative Immunofluorescence (QIF). Pre-treatment samples from 40 patients with NSCLC (YTMA404) that received ICI, and 174 non-ICI treated operable NSCLC patients (YTMA423) were analyzed to provide independent cohort validation. All statistical testing was performed using a two-sided significance level of α=0.05 and multiple testing correction (Benjamini-Hochberg method, FDR < 0.1).ResultsInitial biomarker discovery on 284 protein variables were generated by univariate analysis using continuous log-scaled data. High PD-L1 expression in tumor cells predicted longer survival (PFS; HR 0.67, p=0.017) and validated the training cohort. We found 4 markers associated with PFS, and 3 with OS in the stromal compartment. Of these, expression of CD66b in stromal immune cells predicted significantly shorter OS (HR 1.31, p=0.016) and shorter PFS (HR 1.24, p = 0.04). Tertile analysis using QIF on all three tissue cohorts for CD66b expression, assessed by QIF, showed that CD66b was indicative but not prognostic for survival [discovery cohort, YTMA471 (OS; HR 3.02, p=0.013, PFS; HR 2.38, p=0.023), validation cohort; YTMA404 (OS; HR 2.97, p=0.018, PFS; HR 1.85, p=0.1), non-ICI treated cohort YTMA423 (OS; HR 1.02, p>0.9, PFS; HR 0.72, p=0.4)].ConclusionsUsing the DSP technique, we have discovered that CD66b expressed in the stromal immune [panCK-/CD45+/CD68+] molecular compartment is associated with resistance to ICI therapy in NSCLC. This observation was validated by an orthogonal approach in an independent ICI treated NSCLC cohort. Since CD66b identifies neutrophils, further studies are warranted to characterize the role of neutrophils in ICI resistance.AcknowledgementsDr Moutafi is supported by a scholarship from the Hellenic Society of Medical Oncologists (HESMO)Ethics ApprovalAll tissue samples were collected and used under the approval from the Yale Human Investigation Committee protocol #9505008219 with an assurance filed with and approved by the U.S. Department of Health and Human Services

scholarly journals Prognostic significance of pre-treatment ALBI grade in advanced non-small cell lung cancer receiving immune checkpoint therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryosuke Matsukane ◽  
Hiroyuki Watanabe ◽  
Kojiro Hata ◽  
Kimitaka Suetsugu ◽  
Toshikazu Tsuji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Propensity Score ◽  
Liver Function ◽  
Small Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Hepatic Function ◽  
Small Cell ◽  
Small Cell Lung ◽  
Hepatic Reserve ◽  
Pre Treatment

AbstractThe liver is an essential organ for regulating innate and acquired immunity. We hypothesized that the pre-treatment hepatic function affects the clinical outcome of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). We analyzed 140 patients with NSCLC who received ICIs. We investigated the association between pre-treatment liver function, assessed using the albumin–bilirubin (ALBI) grade, and clinical outcomes in univariate, multivariate, and propensity score matching analyses. Patients were divided into four grades according to pre-treatment liver function. Eighty-eight patients had good hepatic reserve (ALBI grade 1 or 2a), whereas 52 patients had poor hepatic reserve (ALBI grade 2b or 3). In the univariate Kaplan–Meier analysis, the ALBI grade 1, 2a group had a significantly prolonged progression-free survival (PFS, 5.3 versus 2.5 months, p = 0.0019) and overall survival (OS, 19.6 vs. 6.2 months, p = 0.0002). These results were consistent, regardless of whether the analysis was performed in patients with a performance status of 0 or 1 at pre-treatment (N = 124) or in those selected using propensity score matching (N = 76). In the multivariate analysis, pre-treatment ALBI grade was an independent prognostic factor for both PFS (hazard ratio [HR] 0.57, 95% confidence interval [95% CI] 0.38–0.86, p = 0.007) and OS (HR 0.45, 95% CI 0.29–0.72, p = 0.001). Our results suggest that pre-treatment hepatic function assessed by ALBI grade could be an essential biomarker for predicting the efficacy of treatment with ICIs in NSCLC.

scholarly journals Identifying predictive biomarkers of CIMAvaxEGF success in Non–Small Cell Lung Cancer Patients

2019 ◽  
Author(s):  
Patricia Lorenzo-Luaces ◽  
Lizet Sanchez ◽  
Danay Saavedra ◽  
Tania Crombet ◽  
Wim Van der Elst ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Causal Inference ◽  
Peripheral Blood ◽  
Predictive Biomarkers ◽  
Blood Parameters ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Pre Treatment

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 clinical trial evaluating the effect on survival time of CIMAvax-EGF versus best supportive care were analyzed retrospectively following the 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 EGF concentration, NLR, 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. The study illustrates the application of a new methodology, based on causal inference, to evaluate multivariate pre-treatment predictors.

scholarly journals Identifying predictive biomarkers of CIMAvaxEGF success in Non–Small Cell Lung Cancer Patients.

2020 ◽  
Author(s):  
Patricia Lorenzo-Luaces ◽  
Lizet Sanchez ◽  
Danay Saavedra ◽  
Tania Crombet ◽  
Wim Van der Elst ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Causal Inference ◽  
Peripheral Blood ◽  
Predictive Biomarkers ◽  
Blood Parameters ◽  
Small Cell ◽  
Multivariate Approach ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Pre Treatment

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.

scholarly journals Intratumoral versus Circulating Lymphoid Cells as Predictive Biomarkers in Lung Cancer Patients Treated with Immune Checkpoint Inhibitors: Is the Easiest Path the Best One?

Cells ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1525
Author(s):  
Marta Gascón ◽  
Dolores Isla ◽  
Mara Cruellas ◽  
Eva M. Gálvez ◽  
Rodrigo Lastra ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Nk Cell ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Short Review ◽  
Lymphoid Cells ◽  
Homogeneous Sample ◽  
Lung Cancer Patients ◽  
Fast Prediction

The molecular and cell determinants that modulate immune checkpoint (ICI) efficacy in lung cancer are still not well understood. However, there is a necessity to select those patients that will most benefit from these new treatments. Recent studies suggest the presence and/or the relative balance of specific lymphoid cells in the tumor microenvironment (TEM) including the T cell (activated, memory, and regulatory) and NK cell (CD56dim/bright) subsets, and correlate with a better response to ICI. The analyses of these cell subsets in peripheral blood, as a more accessible and homogeneous sample, might facilitate clinical decisions concerning fast prediction of ICI efficacy. Despite recent studies suggesting that lymphoid circulating cells might correlate with ICI efficacy and toxicity, more analyses and investigation are required to confirm if circulating lymphoid cells are a relevant picture of the lung TME and could be instrumental as ICI response biomarkers. This short review is aimed to discuss the recent advances in this fast-growing field.

Predictive biomarkers of response for immune checkpoint inhibitors in non–small-cell lung cancer

2019 ◽  
Vol 106 ◽  
pp. 144-159 ◽  
Author(s):  
Arsela Prelaj ◽  
Rebecca Tay ◽  
Roberto Ferrara ◽  
Nathalie Chaput ◽  
Benjamin Besse ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals Digital Quantification of Tumor PD-L1 Predicts Outcome of PD-1-Based Immune Checkpoint Therapy in Metastatic Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Jan-Malte Placke ◽  
Camille Soun ◽  
Jenny Bottek ◽  
Rudolf Herbst ◽  
Patrick Terheyden ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Progression Free Survival ◽  
Immune Checkpoint Blockade ◽  
Therapy Outcome ◽  
Tissue Samples ◽  
Pre Treatment ◽  
Digital Quantification

BackgroundPD-1-based immune checkpoint blockade (ICB) is a highly effective therapy in metastatic melanoma. However, 40-60% of patients are primarily resistant, with valid predictive biomarkers currently missing. This study investigated the digitally quantified tumor PD-L1 expression for ICB therapy outcome prediction.Patients and MethodsTumor tissues taken prior to PD-1-based ICB for unresectable metastatic disease were collected within the prospective multicenter Tissue Registry in Melanoma (TRIM). PD-L1 expression (clone 28-8; cut-off=5%) was determined by digital and physician quantification, and correlated with therapy outcome (best overall response, BOR; progression-free survival, PFS; overall survival, OS).ResultsTissue samples from 156 patients were analyzed (anti-PD-1, n=115; anti-CTLA-4+anti-PD-1, n=41). Patients with PD-L1-positive tumors showed an improved response compared to patients with PD-L1-negative tumors, by digital (BOR 50.5% versus 32.2%; p=0.026) and physician (BOR 54.2% versus 36.6%; p=0.032) quantification. Tumor PD-L1 positivity was associated with a prolonged PFS and OS by either digital (PFS, 9.9 versus 4.6 months, p=0.021; OS, not reached versus 13.0 months, p=0.001) or physician (PFS, 10.6 versus 5.6 months, p=0.051; OS, not reached versus 15.6 months, p=0.011) quantification. Multivariable Cox regression revealed digital (PFS, HR=0.57, p=0.007; OS, HR=0.44, p=0.001) and physician (OS, HR=0.54, p=0.016) PD-L1 quantification as independent predictors of survival upon PD-1-based ICB. The combination of both methods identified a patient subgroup with particularly favorable therapy outcome (PFS, HR=0.53, p=0.011; OS, HR=0.47, p=0.008).ConclusionPre-treatment tumor PD-L1 positivity predicted a favorable outcome of PD-1-based ICB in melanoma. Herein, digital quantification was not inferior to physician quantification, and should be further validated for clinical use.

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