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Tumor-infiltrating immune cells play an important role against cancer and are critical to control tumor growth and spread. Immunotherapy and immune checkpoint blockade, which aim to reinvigorate exhausted T cells have revolutionized cancer therapy. Despite inducing long-term response in many cancer types, these therapies remain ineffective for a majority of the patients. A better understanding of the tumor microenvironment, and in particular the characterization of immune cells and other stromal cells, along with their abundance and distribution, may help to better stratify patients and understand mechanisms of resistance to immunotherapy. Brightplex® is a chromogenic multiplex technology allowing the detection of several biomarkers on a single FFPE slide to identify and quantify complex cell populations such as T-cells expressing immune checkpoints, M1 and M2 macrophages, Treg cells and myeloid-derived suppressor cells. However, the exquisite characterization of some cell types within the tumor microenvironment or their activation status may require the identification of soluble proteins that cannot be detected by IHC. For example, the detection of secreted proteins such as cytokines or activation factors would allow one to determine the activation status of immune cells or cancer-associated fibroblasts (CAF). In that case, the detection of RNA transcripts corresponding to soluble biomarkers by In-Situ hybridization can be used as a substitute to protein detection.Here, we propose a new multiplex platform combining In Situ Hybridization (ISH) and immunohistochemistry (IHC) using Brightplex® technology. This platform allows the detection of several biomarkers that can be either RNA transcripts or proteins on a single FFPE tissue section. Thanks to that technique it is possible to: 1) detect and quantify TGFb in cancer associated fibroblasts, or IL-10 in immunosuppressive cells, 2) Quantify the level of RNA expression by individual cells, 3) assess the spatial distribution of activated cells based on complex phenotypes within the tumor or other regions of interest. This new workflow combining RNA transcript detection and proteins detection using Brightplex® is automated on Bond RX platform. Following staining and slide digitization, images are fused to create a virtual multi-channel image where cells of interest are detected by digital pathology (DP). Following cell detection, their densities and spatial distribution are obtained using R-studio software.Integrated into an Immunogram, an analytics platform which integrates multi-omics datasets from Veracyte Biopharma Atlas, this new tool could be a powerful solution to understand the tumor landscape and predict response to immunotherapy and patient outcome. Citation Format: Alex Trinh, Marion Olive, Aurélie Collignon, Maité Chamourin, Georgia Culley, Clemence Jaume, Vanina Leca, Assil Benchaaben, Giovanni Bussotti, Alboukadel Kassambara, Jerome Galon, Jacques Fieschi. Deciphering the tumor microenvironment at single-cell resolution using a workflow combining RNA transcript and protein detection with Brightplex®, a sequential chromogenic multiplex assay. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4624.

Background: Resistance to PD1/L1 immune checkpoint inhibitors (ICIs) in advanced NSCLC patients is observed in about 80% of individuals with no robust predictive biomarker yet. The PIONeeR trial (NCT03493581) aims to predict such resistances through a comprehensive multiparametric biomarkers analysis. Methodology: Among the >300 advanced NSCLC patients (pts) recruited in PIONeeR, we focused on the first 137 ≥2nd line ECOG PS0-1 pts treated with single-agent nivolumab, pembrolizumab or atezolizumab. Tumor tissue was collected at baseline and pts were re-biopsied at 6 weeks, and blood-sampled every cycle throughout the 24 weeks post C1D1. Response to PD1/L1 ICIs was assessed by RECIST 1.1 every 6 weeks. Immune contexture was characterized in tumor & blood of each pt through FACS for circulating immune cell subtypes quantification and endothelial activation, blood soluble factors dosage, dual- & multiplex IHC/digital pathology to quantify immune cells infiltrating the tumor, WES for TMB & ICI plasma dosage, leading to 331 measured biomarkers in addition to routine clinical parameters. Multivariable (MV) logistic regression was used to examine the association of each biomarker (controlled by sex, age, smoking status, histological type & PDL1+ Tumor Cells) with the risk of Early Progression (EP), i.e. within 3.5 months of treatment. Multivariable Cox regression analysis was conducted for association with PFS and OS. Results: Overall, the 137 pts were mainly male (64%), smokers (92%) and 1% PDL1+ TC in 36% of the cases, and 21% of pts were still on treatment at data cut-off. Archived samples were available for 80% of pts at inclusion and re-biopsy was available in 52.9% of these cases. The median follow up was 19.8 months, 22.5% of pts did not progress at data cut-off while 62% presented EP. Tumor Cytotoxic T-cells density, especially PD1+ were lower in EP (MV OR=0.45, p=0.022); conversely, higher proportions of circulating cytotoxic T-cells and activated T-cells (HLA-DR+) were observed in EP (MV OR=3.8, p65% inter-pt variability was observed in plasma exposures for all ICIs, with 8-10% of pts displaying trough levels below the target engagement threshold. Data will be presented through unsupervised clustering algorithms & multi-modal supervised learning methods. Changes after 6 weeks of treatment will be analyzed to further investigate drugs mechanisms of action. Conclusion: The PIONeeR trial provides with the 1st comprehensive biomarkers’ analysis to establish predictive models of resistance in advanced NSCLC pts treated with PD1/L1 ICIs and highlights how tumor and circulating biomarkers are complementary. Citation Format: Laurent Greillier, Florence Monville, Vanina Leca, Frédéric Vely, Stephane Garcia, Joseph Ciccolini, Florence Sabatier, Gilbert Ferrani, Nawel Boudai, Lamia Ghezali, Marcellin Landri, Clémence Marin, Mourad Hamimed, Laurent Arnaud, Melanie Karlsen, Kevin Atsou, Sivan Bokobza, Pauline Fleury, Arnaud Boyer, Clarisse Audigier-Valette, Stéphanie Martinez, Hervé Pegliasco, Patrice Ray, Lionel Falchero, Antoine Serre, Nicolas Cloarec, Louisiane Lebas, Stephane Hominal, Patricia Barre, Sarah Zahi, Ahmed Frikha, Pierre Bory, Maryannick Le Ray, Lilian Laborde, Virginie Martin, Richard Malkoun, Marie Roumieux, Julien Mazieres, Maurice Perol, Eric Vivier, Sebastien Benzekry, Jacques Fieschi, Fabrice Barlesi. Comprehensive biomarkers analysis to explain resistances to PD1-L1 ICIs: The precision immuno-oncology for advanced non-small cell lung cancer (PIONeeR) trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB120.

BackgroundPD1/L1 Immune Checkpoint Inhibitors (ICI) have significantly improved long-term outcome in about 20% of advanced Non Small Cells Lung Cancer (NSCLC) patients (pts), but 80% present primary or secondary resistance. The PIONeeR project (NCT03493581) aims to predict the response/resistance to PD1/L1 ICIs in advanced NSCLC pts through a comprehensive agnostic multiparametric and longitudinal biomarkers assessment. Data presented here are a focus on the quantification of tumor infiltration by lymphocytes, their activation as potential markers of the resistance to treatment by ICI.MethodsAdvanced NSCLC pts with available archived tumor tissue at screening visit (VS), treated with standard PD1/L1 ICIs (nivolumab, pembrolizumab or atezolizumab), alone (2nd line or more) or combined with chemotherapy (1st line), were re-biopsied at 6 weeks (V2) of treatment. PD1/L1 ICIs overall response rate (ORR) was assessed by RECIST 1.1 every 6 weeks. The multiplex IHC test ”Immunoscore® CR T Cells Exhaustion” (IS TCE) quantifies cytotoxic lymphocytes expressing three checkpoints: PD1, LAG3, TIM3, extrapolating their exhaustion status, both in the stroma and parenchyma. The unsupervised neural-network-based machine learning algorithm SOM (Self-Organizing Maps) was used to classify samples based on the 27 IS TCE variables. Statistical significance of survival differences between groups was evaluated using the log-rank test.ResultsAmong the first 100 pts, (male (64%), smokers (91,8%), ConclusionsIS TCE test may help stratifying and predicting responders to anti PD1/L1 therapy through checkpoint expressing lymphocytes quantification and spatial distribution. Additional tests performed on the PIONeeR cohort to explore other aspects of the immune response to cancer should complete these results.AcknowledgementsThis work is supported by French National Research Agency (ANR-17-RHUS-0007), a partnership of AMU, APHM, AstraZeneca, Centre Léon Bérard, CNRS, HalioDx, ImCheck Therapeutics, Innate Pharma, Inserm, Institut Paoli Calmettes and sponsored by AP HM. Drug supply is funded by AstraZeneca. Special thanks to patients and families.Trial RegistrationNCT03493581Ethics ApprovalThe study is conducted in accordance with Good Clinical Practice and the French applicable regulatory requirements (Public Health Code, article L.1121-1/La loi n° 2012–300 du 5 mars 2012 relative aux recherches impliquant la personne humaine (dite loi Jardé), the applicable subject privacy requirements, and the ethical principles that are outlined in the Declaration of Helsinski. The study was approved by the French Ethic Committee, CPP Ouest II - Angers, ref. CPP: 2028/08, Ref ANSM (French competent authority) 2018020500208, 2018072600120, 2019083000148. Freely given written informed consent was signed and obtained from each individual participating in the study, before any study specific procedure was undertaken and after the provision of information about the study by the investigator during a physician-patient consultation and sufficient time for reflection.