Tumor Microenvironment in Gene Signatures: Critical Biology or Confounding Noise?

Adaptive Regularized Tri-Factor Non-Negative Matrix Factorization for Cell Type Deconvolution

Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type mRNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an Adaptive Regularized Tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art semi-reference-based and reference-free methods. In a real-world application, our method accurately estimates cell proportions, as evidenced by the nearly perfect Pearson's correlation between ARTdeConv estimates and flow cytometry measurements in a dataset from a trivalent influenza vaccine study. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners.

Immune-Related Genes can Serve as Potential Biomarkers for Predicting Severe Acute Pancreatitis

We aimed to investigate immune-related candidate genes for predicting the severity of acute pancreatitis (AP). RNA sequencing profile GSE194331 was downloaded, and differentially expressed genes (DEGs) were investigated. Meanwhile, the infiltration of immune cells in AP were assessed using CIBERSORT. Genes related with the infiltration of immune cells were investigated using weighted gene co-expression network analysis (WGCNA). Furthermore, immune subtypes, micro-environment, and DEGs between immune subtypes were explored. Immune-related genes, protein-protein interaction (PPI) network, and functional enrichment analysis were further performed. Overall, 2533 DEGs between AP and healthy controls were obtained. After trend cluster analysis, 411 upregulated and 604 downregulated genes were identified. Genes involved in two modules were significantly positively related to neutrophils and negatively associated with T cells CD4 memory resting, with correlation coefficient more than 0.7. Then, 39 common immune-related genes were obtained, and 56 GO BP were enriched these genes, including inflammatory response, immune response, and innate immune response; 10 KEGG pathways were enriched, including cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and IL-17 signaling pathway. Genes, including S100A12, MMP9, IL18, S100A8, HCK, S100A9, RETN, OSM, FGR, CAMP, were selected as genes with top 10 degree in PPI, and the expression levels of these genes increased gradually in subjects of healthy, mild, moderately severe, and severe AP. Our findings indicate a central role of immune-related genes in predicting the severity of AP, and the hub genes involved in PPI represent logical candidates for further study.

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.

Stromal Galectin-1 Promotes Colorectal Cancer Cancer-Initiating Cell Features and Disease Dissemination Through SOX9 and β-Catenin: Development of Niche-Based Biomarkers

Over 90% of colorectal cancer (CRC) patients have mutations in the Wnt/β-catenin pathway, making the development of biomarkers difficult based on this critical oncogenic pathway. Recent studies demonstrate that CRC tumor niche-stromal cells can activate β-catenin in cancer-initiating cells (CICs), leading to disease progression. We therefore sought to elucidate the molecular interactions between stromal and CRC cells for the development of prognostically relevant biomarkers. Assessment of CIC induction and β-catenin activation in CRC cells with two human fibroblast cell-conditioned medium (CM) was performed with subsequent mass spectrometry (MS) analysis to identify the potential paracrine factors. In vitro assessment with the identified factor and in vivo validation using two mouse models of disease dissemination and metastasis was performed. Prediction of additional molecular players with Ingenuity pathway analysis was performed, with subsequent in vitro and translational validation using human CRC tissue microarray and multiple transcriptome databases for analysis. We found that fibroblast-CM significantly enhanced multiple CIC properties including sphere formation, β-catenin activation, and drug resistance in CRC cells. MS identified galectin-1 (Gal-1) to be the secreted factor and Gal-1 alone was sufficient to induce multiple CIC properties in vitro and disease progression in both mouse models. IPA predicted SOX9 to be involved in the Gal-1/β-catenin interactions, which was validated in vitro, with Gal-1 and/or SOX9—particularly Gal-1^high/SOX9^high samples—significantly correlating with multiple aspects of clinical disease progression. Stromal-secreted Gal-1 promotes CIC-features and disease dissemination in CRC through SOX9 and β-catenin, with Gal-1 and SOX9 having a strong clinical prognostic value.

A stroma-corrected ZEB1 transcriptional signature is inversely associated with antitumor immune activity in breast cancer

The epithelial-to-mesenchymal transition (EMT) is an essential developmental process which can be hijacked by cancer cells, leading to enhanced metastasis and chemoresistance in experimental models. Recent studies have linked gene expression of EMT-associated gene signatures to increased inflammatory immune response in multiple cancer types. However, these studies did not account for the potential confounding effects of gene expression by tumor-infiltrating mesenchymal stromal cells. In this study, we comprehensively dissect the associations between multiple EMT transcription factors and EMT markers with stromal and immune tumor infiltration. We find that EMT-related genes are highly correlated with intratumoral stromal cell abundance and identify a specific relationship between stroma-corrected ZEB1 expression and decreased immune activity in multiple cancer types. We derive a stroma-corrected ZEB1-activated transcriptional signature and demonstrate that this signature includes several known inhibitors of inflammation, including BMPR2. Finally, multivariate survival analysis reveals that ZEB1 and its expression signature are significantly associated with reduced overall survival in breast cancer patients. In conclusion, this study identifies a novel association between stroma-adjusted ZEB1 expression and tumor immune activity and addresses the critical issue of confounding between EMT-associated genes and tumor stromal content.

Epithelial-to-mesenchymal transition signature assessment in colorectal cancer quantifies tumour stromal content rather than true transition

The process of epithelial‐to‐mesenchymal transition (EMT) in cancer is a well‐described process whereby epithelial tumour cells undergo molecular/phenotypic changes and transition to a mesenchymal biology. To aid in the transcriptional characterisation of this process, gene expression signatures have been developed that attribute a relative EMT score to samples in a given cohort. We demonstrate how such EMT signatures can identify epithelial cell line models with high levels of transition but also highlight that, unsurprisingly, fibroblast cell lines, which are inherently mesenchymal, have a higher EMT score relative to any epithelial cell line studied. In line with these data, we demonstrate how increased tumour stromal composition, and reduced epithelial cellularity, significantly correlates with increasing EMT signature score, which is evident using either in silico subtyping analysis (p < 0.00001) or in situ histopathological characterisation (p < 0.001). Considered together, these results reinforce the importance not only of interdisciplinary research to correctly define the nature of EMT biology but also the requirement for a cadre of multidisciplinary researchers who can analyse and interpret the underlying pathological, bioinformatic and molecular data that are essential for advancing our understanding of the malignant process. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Transcriptional subtyping and CD8 immunohistochemistry identifies poor prognosis stage II/III colorectal cancer patients who benefit from adjuvant chemotherapy

Purpose

Transcriptomic profiling of colorectal cancer (CRC) has led to the identification of four consensus molecular subtypes (CMS1 to 4) that have prognostic value in stage II and III disease. More recently, the Colorectal Cancer Intrinsic Subtypes (CRIS) classification system has helped to define the biology specific to the epithelial component of colorectal tumors; however, the clinical value of these classification systems in the prediction of response to standard-of-care adjuvant chemotherapy remains unknown.

Patients and Methods

Using samples from four European sites, we assembled a novel cohort of patients with stage II and III CRC (n = 156 samples) and performed transcriptomic profiling and targeted sequencing and generated a tissue microarray to enable integrated multiomics analyses. We also accessed data from two published cohorts of patients with stage II and III CRC: GSE39582 and GSE14333 (n = 479 and n = 185 samples, respectively).

Results

The epithelial-rich CMS2 subtype of CRC benefitted significantly from treatment with adjuvant chemotherapy in both stage II and III disease (P = .02 and P < .001, respectively), whereas the CMS3 subtype significantly benefitted in stage III only (P = .001). After CRIS substratification of CMS2, we observed that only the CRIS-C subtype significantly benefitted from treatment with adjuvant chemotherapy in stage II and III disease (P = .0081 and P < .001, respectively), whereas the CRIS-D subtype significantly benefitted in stage III only (P = .0034). We also observed that CRIS-C patients with low levels of CD8⁺ tumor-infiltrating lymphocytes were most at risk for relapse in both stage II and III disease (log-rank P = .0031; hazard ratio, 12.18 [95% CI, 1.51 to 98.58]).

Conclusion

Patient stratification using a combination of transcriptional subtyping and CD8 immunohistochemistry analyses is capable of identifying patients with poor prognostic stage II and III disease who benefit from adjuvant standard-of-care chemotherapy. These findings are particularly relevant for patients with stage II disease, where the overall benefit of adjuvant chemotherapy is marginal.

KRAS Mutation and Consensus Molecular Subtypes 2 and 3 Are Independently Associated with Reduced Immune Infiltration and Reactivity in Colorectal Cancer

Purpose:KRAS mutation is a common canonical mutation in colorectal cancer, found at differing frequencies in all consensus molecular subtypes (CMS). The independent immunobiological impacts of RAS mutation and CMS are unknown. Thus, we explored the immunobiological effects of KRAS mutation across the CMS spectrum.Experimental Design: Expression analysis of immune genes/signatures was performed using The Cancer Genome Atlas (TCGA) RNA-seq and the KFSYSCC microarray datasets. Multivariate analysis included KRAS status, CMS, tumor location, MSI status, and neoantigen load. Protein expression of STAT1, HLA-class II, and CXCL10 was analyzed by digital IHC.Results: The Th1-centric co-ordinate immune response cluster (CIRC) was significantly, albeit modestly, reduced in KRAS-mutant colorectal cancer in both datasets. Cytotoxic T cells, neutrophils, and the IFNγ pathway were suppressed in KRAS-mutant samples. The expressions of STAT1 and CXCL10 were reduced at the mRNA and protein levels. In multivariate analysis, KRAS mutation, CMS2, and CMS3 were independently predictive of reduced CIRC expression. Immune response was heterogeneous across KRAS-mutant colorectal cancer: KRAS-mutant CMS2 samples have the lowest CIRC expression, reduced expression of the IFNγ pathway, STAT1 and CXCL10, and reduced infiltration of cytotoxic cells and neutrophils relative to CMS1 and CMS4 and to KRAS wild-type CMS2 samples in the TCGA. These trends held in the KFSYSCC dataset.Conclusions:KRAS mutation is associated with suppressed Th1/cytotoxic immunity in colorectal cancer, the extent of the effect being modulated by CMS subtype. These results add a novel immunobiological dimension to the biological heterogeneity of colorectal cancer. Clin Cancer Res; 24(1); 224-33. ©2017 AACR.

CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models

Colorectal cancers (CRCs) can be divided into four gene expression-based biologically distinct consensus molecular subtypes (CMS). This classification provides a potential framework for stratified treatment, but to identify novel CMS-drug associations, translation of the subtypes to pre-clinical models is essential. The currently available classifier is dependent on gene expression signals from the immune and stromal compartments of tumors and fails to identify the poor-prognostic CMS4-mesenchymal group in immortalized cell lines, patient-derived organoids and xenografts. To address this, we present a novel CMS classifier based on a filtered set of cancer cell-intrinsic, subtype-enriched gene expression markers. This new classifier, referred to as CMScaller, recapitulated the subtypes in both in vitro and in vivo models (551 in total). Importantly, by analyzing public drug response data from patient-derived xenografts and cell lines, we show that the subtypes are predictive of response to standard CRC drugs. CMScaller is available as an R package.

Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH.

Tumour expression profiling is currently used for prognostic and predictive purposes without taking into account the intra patient heterogeneity. Here the authors show that cancer cell specific signatures overcome the tumour heterogeneity effect and result in better classification of colorectal cancer patients.