Biological misinterpretation of transcriptional signatures in tumour samples can unknowingly undermine mechanistic understanding and faithful alignment with preclinical data

Abstract

Precise mechanism-based gene expression signatures (GESs) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signalling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumour samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumour stroma percentage can directly influence GESs, undermining the intended mechanistic signalling.Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single cell RNAseq and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely-used GESs to be influenced, or confounded, by stromal content in tumour tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets.Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumour models not fully recapitulating the stromal and immune microenvironment. As such, efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

Spatially Resolved Transcriptomics Deconvolutes Histological Prognostic Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases

“…However, in the current study TSP assessment conferred no prognostic influence when present in the metastatic setting. An affiliate group recently demonstrated that extensive stroma in the primary tumour confounded bulk transcriptomic analysis approaches (17).…”

“…It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 22, 2022. ; https://doi.org/10.1101/2022.09. 21.508569 doi: bioRxiv preprint transcriptomics are apparent with 'stromal noise' potentially concealing biological pathway discovery and intra-tumoral heterogeneity characterisation (16,17). While single-cell RNA sequencing has led to important discoveries (18,19), crucially, topographic cellular orientatio n is lost along with vital biological insights relating to tissue morphology, cellular interactio ns and TME location-specific cellular expression.…”

“…Four Consensus Molecular Subtypes (CMS) of primary CRC have been defined through bulk transcriptomic profiling generating a roadmap for translational CRC biology (14) but have failed to project into the metastatic setting (15). Increasingly, constraints of bulk transcriptomics are apparent with ‘stromal noise’ potentially concealing biological pathway discovery and intra-tumoral heterogeneity characterisation (16, 17). While single-cell RNA sequencing has led to important discoveries (18, 19), crucially, topographic cellular orientation is lost along with vital biological insights relating to tissue morphology, cellular interact ions and TME location-specific cellular expression.…”

Pathway level subtyping identifies a slow-cycling and transcriptionally lethargic biological phenotype associated with poor clinical outcomes in colon cancer independent of genetics

“…354 patients with matched PDS call and Ki67 + assessment. The total tumour area was also enumerated and assessed based on the digital histology scoring via HALO platform (Indica Labs, Albuquerque, NM, USA) on the Hematoxylin and Eosin (H&E) whole slides images from FOCUS (n = 356), as previously described 53 .…”