Abstract PO2-28-08: A spatial transcriptomic study of a triple-negative breast cancer (TNBC) patient-derived xenograft (PDX) model of residual disease refractory to conventional chemotherapy

Abstract

Abstract TNBC is one of the most aggressive subtypes of breast cancer. Combating chemotherapy resistance is critical to improving quality of care and reducing fatality among TNBC patients. In order to understand the mechanisms of chemoresistance in TNBC tumors, it has been proposed that the spatial interactions between cell types in the tumor microenvironment (TME) could offer insights into the differential response to therapy among tumors, and metastatic potential of certain tumors over others. Thus, this study utilizes spatially resolved transcriptomic (SRT) technology to profile the spatial interactions in the TNBC TME. With the goal of better understanding chemoresistance in TNBC and providing a proof-of-concept for new technology, we have launched a longitudinal SRT study of a TNBC PDX of residual disease before, during, and after adriamycin and cyclophosphamide (AC) treatment (Tx). SRT by 10X Genomics Visium was performed on vehicle, AC-treated residual tumor (21 days post-Tx), and AC-treated regrown tumor (50 days post-Tx when tumors regrew to starting tumor volume). This study is divided into two parts: 1) development of a computational pipeline; 2) spatial colocalization analysis of residual and regrown tumors to understand chemoresistance. PART 1: Due to the lack of specialized tools for processing and sorting xenograft reads from SRT data, we developed the Xenomake pipeline, which combines a xenograft sorting algorithm (Xengsort) and spatial barcode demultiplexing pipeline to assign reads into the host and graft organisms for each spatial spot. RESULTS: Xenomake permits clustering the spatial spots into stroma-rich (enriched for mouse mRNAs), and epithelial-rich (enriched for human mRNAs) regions. We show that Xenomake can find differential cytokine production in the stroma and epithelium. Since PDX data separate the tumor into stroma and epithelium by organism, the pipeline enables fine-tuned downstream analysis such as stromal-stromal and stromal-epithelial interactions. Xenomake is thus generally applicable for SRT involving PDX samples. PART 2: Previously we detailed patterns of tumor regression into a residual tumor state, followed by uncontrolled regrowth in the absence of treatment in multiple PDX models of TNBC. Although using PDX models necessitates immune-compromised mice, several types of stromal populations are present and analyzable in these models. Thus, using organism-assigned reads processed by Xenomake, we computationally inferred the localization pattern of stromal cell types in Visium spots including cancer associated fibroblasts (CAF), macrophages (MP), endothelial cells (ENDO), monocytes, and perivascular-like cells. We compared the spatial localization profiles (SLP) of stromal cell types across samples. With human reads, we computed the spatial pathway activity map (SPAM) for the ~30 HALLMARK pathways across samples. We correlated the patterns of SPAM with stroma cell-type SLP to survey the stroma-epithelial interactions across samples. RESULTS: Stroma cells are generally distributed in the tumor periphery in vehicle and AC50, while in AC21 there is a notable increase in stroma abundance and stroma infiltration within tumor mass. SLP of MP (Cd68+ and Csf1r+) is correlated with ENDO (Pecam1+), and with CAF (Acta2+ and Pdgfrb+). Additionally, all 3 cell types are correlated with Vim and Cd44 expression. Although all samples show enrichment of MP, ENDO, and CAF, they interact differently with pathway activities of adjacent epithelial cells. In vehicle and AC50, the 3 cell types are colocalized with OXPHOS, MYC targets, E2F pathway, while in AC21, a switch to colocalization with EMT is observed. Furthermore, hypoxia response and glycolysis display anti-correlation with stroma cell types, meaning that these pathway activities are further away from stroma and occupy distinct territories. The results suggest stroma-tumor metabolic crosstalk and ways of targeting residual disease. Citation Format: Benjamin Strope, Katherine Pendleton, William Bowie, Gloria Echeverria, Qian Zhu. A spatial transcriptomic study of a triple-negative breast cancer (TNBC) patient-derived xenograft (PDX) model of residual disease refractory to conventional chemotherapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-08.