2301P - Combining cancer patient spatial transcriptomics and bulk RNA-Seq data to drive insights into NSCLC

Background

Combining cancer patient spatial transcriptomics (ST) and bulk RNA-Seq allows for a more nuanced understanding of the gene expression (GE) profiles. While large-scale sequencing projects have revealed some NSCLC cancer drivers, they cannot fully address the complexity of the heterogeneous tissue composition present in patients. ST enables the measurement of GE in specific regions within a tissue sample, complementing and refining insights gained from bulk RNA-Seq.

Methods

Fresh-frozen, NSCLC tumor and normal samples were profiled using the 10x Genomics Visium Spatial Gene Expression platform. Spatial differential expression and pathway analysis were performed using Space Ranger and David Functional Annotation respectively. Bulk RNA was prepared using Qiagen AllPrep Universal and TruSeq Stranded Total mRNA kits and sequenced on a NovaSeq6000. Integrated data analysis was undertaken using R (Seurat). Images of spatial GE were generated using the Loupe Browser.

Results

Bulk RNA-Seq and ST showed strong overall agreement between expression profiles, facilitating refinement of disease specific alterations observed in large NSCLC cohorts through ST data. Patient-specific GE in specific tumor regions was identified and could guide future precision medicine approaches: for example tumor-related genes EPCAM or PDCD6 were higher expressed in the tumor center, whereas HTRA3 which suppresses tumor cell invasiveness shows higher expression in the microenvironment. Furthermore, the transcription factor KLF5 is higher expressed in tumor regions but shows no strong regulation in bulk data. This highlights how spatially resolved data can provide insights into the identification and assignment of cancer-relevant genes that primarily show only minor regulation in bulk RNA-Seq data.

Conclusions

A more refined in situ understanding of GE profiles in the NSCLC microenvironment, combined with large cohort data will help guide therapeutic target selection. The combination not only refines target expression cell types (E.g. primary tumor or immune system) but also provide a strong indicator for target GE variability within patients' tumors for improved precision medicine approaches.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Indivumed GmbH.

Disclosure

J. Bischof, J. Woodsmith: Financial Interests, Institutional, Full or part-time Employment: Indivumed Therapeutics GmbH. M. Schoeppler, B. Motamedi-Baniassad, N. Kerstedt, K. David: Financial Interests, Institutional, Full or part-time Employment: Indivumed Services GmbH.