INTRODUCTION

RESULTS

CONCLUSIONS

IFN- γNetwork

MC38 aPD-1, aCD4MC38 aPD-1

DEEP PROTEOMIC PROFILING OF CD4+ AND CD8+ T CELL-DEPLETED TUMOR MODELS IN PD-1 TREATMENT

Jan Muntel1, Ying Jin2, Haochen Yu1, Nicholas Dupuis1, Yongli Shan2, Annie X. An2, Wubin Qian2, Davy Ouyang2, Kristina Beeler1, Roland Bruderer1

1) Biognosys AG, Wagistrasse 21, 8952 Schlieren (Zurich), Switzerland 2) Crown Bioscience Inc., San Diego (CA), USA

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100015002000250030003500

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Tum

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e (m

m³)

Study Days

MC38 PBSMC38 aPD-1MC38 aPD-1, aCD8MC38 aPD-1, aCD4

Granzyme B Perforin ZAP70 IDO1

PD-L1 LAG3 VISTA NOS2

Relative abundance (z-score)

PBSaPD-1aPD-1, aCD8aPD-1, aCD4

Hepa1-6MC38

TUMOR MODEL

TREATMENT

Immunotherapies targeting the PD-1/PD-L1 axis have been shown to be effective in only ~20% of cancer patients and the mechanism of action (MOA) underlying the differences between responders and non-responders remains poorly understood. Immunophenotyping of patient samples play important roles in understanding MOAs, however a clear understanding of the

heterogeneous responses remains elusive due to the intrinsic complexity of tumor immunity. It is therefore critical to understand the roles of different lineages of immune cells in mediating PD-1 response, while gaining an overall understanding of the tumor micro-environment (TME) . To address this, tumor-bearing syngenic mice were treated with an anti-PD-1 antibody in combination

Figure 1. Schematic Representation of the Mass Spectrometric WorkflowProteins are extracted from 1mg of FFPE tissue for each sample and processed into tryptic peptides. Quantification was performed with DIA-MS/HRM HighD workflow. Data analysis was conducted using SpectronautTM software.

Figure 3. Functional Analysis Identify IFN-γ Up-regulation Associated with CD4 Depletion in MC38 (A) In MC 38, PLS-DA analysis distinguishes between groups treated with anti-PD1 versus anti-PD1 and anti-CD4 (B) 12 out of the 25 top candidate proteins identified from the PLS-DA analysis are involved in IFN-γ signaling cascade (C) Upstream regulator analysis shows that the proteomic data is mostly consistent with the

Figure 2. Proteomics Data Recapitulates Efficacy Data (A) 9’248 proteins are quantified. Unsupervised hierarchical clustering clearly distinguishes two mouse strains but not different treatment groups(B) In MC38 immuno-oncology (IO) model, key protein markers demonstrate treatment responses consistent with tumor suppression efficacy.

Jan Muntel, PhDSenior Scientist

jan.muntel@biognosys.comwww.biognosys.com

with targeted CD4+ or CD8+ T cell depletion with the purpose of understanding how these lineages impacted anti-PD-1 efficacy. Tumor tissues were subsequently investigated with an unbiased proteomics workflow based on data-independent acquisition (DIA) mass spectrometry to provide insights into TME in different treatment contexts.

A B

C

D

BA

• Syngenic mouse model show differential response to PD1 blockade and CD4+, CD8+ T cell depletion

• Hyper Reaction Monitoring Mass Spectrometry (HRMTM-MS) discovery proteomics quantified over 9’000 Proteins

• Functional analysis suggests that CD4 depletion boosts tumor suppression under PD1 blockade through up-regulation of IFN-γ signaling cascade

predicted regulations of proteins in when IFN-γ is unregulated (D) The proteomic data on key IO markers correlate with the RNASeq data from the same tumor samples.

MC38 PBS MC38 aPD-1 MC38 aPD-1, aCD8 MC38 aPD-1, aCD4