3520 - Single-cell RNA Sequencing of Triple Negative Breast Cancer Patient-Derived Xenograft Reveals Distinct Cellular Populations Spatially Mapped to Histological Sections

Background

Breast cancer is a heterogeneous disease consisting of distinct subtypes, including estrogen receptor positive, HER2 receptor positive and triple negative breast cancers (TNBC). The TNBC subtype lacks comprehensive targeted therapies and only 20-30% of patients have a complete response to neoadjuvant chemotherapy. TNBC present extensive genomic variation at the time of diagnosis and distinctive selective pressures exerted by treatment can promote the outgrowth of tumor subclones with the greatest survival characteristics. The overall aim of this project is to spatially map single-cell RNA-sequencing (scRNAseq) populations in primary and patient-derived xenograft (PDX) tumor sections, and further isolate and mechanistically study tumor subclones correlated with poor patient outcome.

Methods

We established a patient-derived xenograft (PDX) from a patient’s primary TNBC tumor. Single cells from the TNBC PDX tumor were obtained by enzymatic dissociation. Single-cell libraries were generated using the GemCode Single-Cell Instrument and Single Cell 3' Library & Gel Bead Kit v2 and Chip Kit (10x Genomics) according to the manufacturer’s protocol. The R package Seurat (v2.1) was used to analyze the single-cell RNA-seq data. Multiplex immunofluorescence (IF) was performed on primary and PDX tumors sections.

Results

We identified seven different biological clusters as principal components in the scRNAseq analysis. For each cluster we selected specific genes that could exclusively represent each single population and exclude the others. We were able to successfully map all seven populations and identify a high correlation in the spatial cluster distribution between primary and PDX samples.

Conclusions

Using scRNAseq and multiplex IF we spatially superimposed single cell populations to human and PDX histological samples. This novel methodology represents a clinically relevant tool to spatially and temporally map specific cellular populations through disease progression and along treatment.

Clinical trial identification

Legal entity responsible for the study

Morag Park.

Funding

Stand Up To Cancer.

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.