Abstract 6327: Profiling T-cell subpopulations based on spatial proteome in mantle cell lymphoma
Authors: Lokhande Lavanya , Rodrigues Joana de Matos , Gerdtsson Anna Sandstrom , Porwit Anna , Jerkeman Mats , Ek Sara 
Published: June 2022

Background: In precision medicine era, we need to develop tools that better predict treatment response with, for e.g. immuno-oncology drugs. One hypothesis is that diverse treatment response is governed by differences in the tumor microenvironment (TME) both within the tumor and surrounding areas. Thus, novel ways to measure the heterogeneity within defined immune-cell populations is the key and now possible by using spatially-resolved multiplexed platforms. Here, we have investigated the phenotype of four T-cell subtypes both within and excluded from the tumor-rich regions in a large cohort of primary mantle cell lymphoma (MCL) patients. We propose that profiling different cell populations could potentially provide insight into patient prognosis and identify clinically actionable targets, but also in understanding whether tumor cells locally re-educate T-cells through cell-to-cell interaction or by soluble factors

Method: We analyzed archival FFPE presented as tissue microarrays of 196 cores from 104 MCL patients, collected from the Biobank of Lymphoma of Southern Sweden, by using the, GeoMX-digital spatial profiler (DSP, Nanostring Inc.). Cellular regions of interest were selected by immunofluorescense staining of CD8, CD3 and CD57 to identify four subpopulations of T cells - early cytotoxic (CD3+ CD8+ CD57-), early non-cytotoxic (CD3+ CD8- CD57-), late cytotoxic (CD3+ CD8+ CD57+) and late non-cytotoxic (CD3+ CD8- CD57+). 601 regions distributed among the four T-cell subtypes were collected from both within the tumor-rich and tumor deficient zones and probed for 69 proteins for sub-characterization.

Result: Our results show that the distribution and expression profile of all subpopulations differ considerably based on their position relative to the tumor. Using linear mixed effects model to account for effect of multiple sampling per patient, we observed higher cytolytic activity (upregulation of granzyme A and B) (FDR cut-off &lt; 0.05) in early cytotoxic and late non-cytotoxic cells, within the tumor-rich zone compared to the tumor-deficient area. In contrast, proteins such as STING, VISTA, neurofibromin and fibronectin were consecutively upregulated in all subtypes in tumor-deficient zones. We also observe differential expression profiles for proteins involved in for example cell death regulation and apoptotic signaling pathways. Our analysis provide insight into effector functions important for response to treatment in MCL.

Conclusion: This study reveals the phenotypic differences of T-cell subsets in MCL with a higher resolution compared to previous studies. The spatially-resolved analysis shows that the proximity to tumor cells highly affect the profile of different T-cell subsets and that further analysis of re-education mechanisms should be pursued. This study advance the understanding of the MCL tissue microenvironment and for the basis for connecting such differences to clinical outcome.

Citation Format: Lavanya Lokhande, Joana de Matos Rodrigues, Anna Sandstrom Gerdtsson, Anna Porwit, Mats Jerkeman, Sara Ek. Profiling T-cell subpopulations based on spatial proteome in mantle cell lymphoma [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 6327.