Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODS: Using transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTS: We developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONS: When applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

Epithelial TGFß engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features.

The pro-tumourigenic role of epithelial TGFß signalling in colorectal cancer (CRC) is controversial. Here, we identify a cohort of born to be bad early-stage (T1) colorectal tumours, with aggressive features and a propensity to disseminate early, that are characterised by high epithelial cell-intrinsic TGFß signalling. In the presence of concurrent Apc and Kras mutations, activation of epithelial TGFß signalling rampantly accelerates tumourigenesis and share transcriptional signatures with those of the born to be bad T1 human tumours and predicts recurrence in stage II CRC. Mechanistically, epithelial TGFß signalling induces a growth-promoting EGFR-signalling module that synergises with mutant APC and KRAS to drive MAPK signalling that re-sensitise tumour cells to MEK and/or EGFR inhibitors. Together, we identify epithelial TGFß signalling both as a determinant of early dissemination and a potential therapeutic vulnerability of CRC's with born to be bad traits.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

PURPOSE: Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN: 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 RNA sequencing 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 tumor 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. RESULTS: 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 tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS: 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.

Activation of innate-adaptive immune machinery by poly(I:C) exposes a therapeutic vulnerability to prevent relapse in stroma-rich colon cancer.

OBJECTIVE: Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy. DESIGN: To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours. RESULTS: By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002). CONCLUSION: This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC.

Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods.

AIMS: Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software. METHODS AND RESULTS: TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores. CONCLUSIONS: We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.

The Potential of Digital Image Analysis to Determine Tumor Cell Content in Biobanked Formalin-Fixed, Paraffin-Embedded Tissue Samples.

Introduction: Best practices dictate that biobanks ensure accurate determination of tumor content before supplying formalin-fixed, paraffin-embedded (FFPE) tissue samples to researchers for nucleic acid extraction and downstream molecular testing. It is advisable that trained and competent individuals, who understand the requirements of the downstream molecular tests, perform the microscopic morphological examination. However, the special skills, time, and costs associated with these assessments can be prohibitive, especially in large case cohorts requiring extensive pathological review. Determination of tumor content reliably by digital image analysis (DIA) could represent a significant advantage if validated, utilized, and deployed by biobanks. Materials and Methods: Whole slide digital scanned images of colorectal, lung, and breast cancer specimens were created. The scanned images were imported into the DIA software QuPath and digital annotations were completed by biobank technicians, under the direction of trained histopathology senior scientists. Automated cell detection was conducted and tumor epithelial cells were classified and quantified. Results: DIA scores were highly concordant with the manual assessment for 376 of 435 samples (86%). A detailed review of discordant cases indicated digital scores had a higher accuracy than the manual estimation. Conclusion: Automated digital quantification has the potential to replace visual estimations with reduced subjectivity and increased reliability compared with manual tumor estimations. We recommend the use of DIA by biobanks involved in provision of FFPE tissue samples, especially in large research studies requiring high volumes of cases to be analyzed.

The adaptive immune and immune checkpoint landscape of neoadjuvant treated esophageal adenocarcinoma using digital pathology quantitation.

BACKGROUND: Limited studies examine the immune landscape in Esophageal Adenocarcinoma (EAC). We aim to identify novel associations, which may inform immunotherapy treatment stratification. METHODS: Three hundred twenty-nine EAC cases were available in Tissue Microarrays (TMA) format. A discovery cohort of 166 EAC cases were stained immunohistochemically for range of adaptive immune (CD3, CD4, CD8 and CD45RO) and immune checkpoint biomarkers (ICOS, IDO-1, PD-L1, PD-1). A validation cohort of 163 EAC cases was also accessed. A digital pathology analysis approach was used to quantify biomarker density. RESULTS: CD3, CD4, CD8, CD45RO, ICOS and PD-1 were individually predictive of better overall survival (OS) (Log rank p = < 0.001; p = 0.014; p = 0.001; p = < 0.001; p = 0.008 and p = 0.026 respectively). Correlation and multivariate analysis identified high CD45RO/ICOS patients with significantly improved OS which was independently prognostic (HR = 0.445, (0.223-0.886), p = 0.021). Assessment of CD45RO and ICOS high cases in the validation cohort revealed an associated with improved OS (HR = 0.601 (0.363-0.996), p = 0.048). Multiplex IHC identified cellular co-expression of high CD45RO/ICOS. High CD45RO/ICOS patients have significantly improved OS. CONCLUSIONS: Multiplexing identifies true cellular co-expression. These data demonstrate that co-expression of immune biomarkers are associated with better outcome in EAC and may provide evidence for immunotherapy treatment stratification.