Drivers and Brakes of CAR T Cell Efficacy Determined by the Tumor Immune Microenvironment

Introduction

Cancer immunotherapies with chimeric antigen receptor (CAR) T cells have shown dramatic clinical efficacy in patients with B cell neoplasms. Thus, their clinical use is expected to increase considerably in the near future. However, for poorly understood reasons, not all patients with lymphoma benefit from these expensive therapies.

Importance of Patient Stratification

The ability to stratify patients into probable responders vs. non-responders prior to immunotherapy will improve treatment efficacy, limit patient exposure to adverse effects, and mitigate the significant economic costs associated with these therapies.

Understanding the Tumor Immune Microenvironment

We and others have previously demonstrated that effective antitumoral immunity requires complex, spatially coordinated interactions between different cellular elements within the tumor immune microenvironment (TIME).

Characteristics of the TIME

There is evidence that patient response to immunotherapy is attributed to specific characteristics of the TIME, such as:

• The composition of immune cell types
• The spatial arrangement of these cells
• The activation states of immune cell types

Therefore, a better understanding of the TIME, and of how immunotherapies come into effect in live, intact human tissues, is critical for the selection of successful immunotherapies for our patients.

Project Aim

The overarching aim of the CAR-TIME project is to explore and visualize the cellular and molecular mechanisms of CAR T cell efficacy in lymphoma, determined by CAR T cell interactions with the TIME.

Methodology

This shall be achieved by:

1. Creating a high-dimensional map of the TIME of diffuse large B cell lymphoma
2. Performing live tissue cultures treated with immunotherapies
3. Establishing a novel live cell microscopy platform to interrogate intact human lymphoma tissue treated with CAR T cells

Expected Outcomes

Drug perturbations, multidimensional imaging technologies, RNA sequencing, and integrative bioinformatics analysis will illuminate mechanisms of therapy response vs. resistance, reveal novel predictive biomarkers, and inform future combination immunotherapy strategies to improve patient outcomes.