Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common malignancies. But the molecular mechanism is unknown. In this study, we downloaded the transcriptional profiles and clinical data of 344 STAD and 30 normal samples from The Cancer Genome Atlas (TCGA) database. Stromal and immune scores of STAD were calculated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and association of stromal/immune scores with tumor differentiation/T/N/M stage and survival was investigated. The differentially expressed genes (DEGs) between high and low score groups (based on media) were identified, and prognostic genes over-/underexpressed in both STAD and stromal/immune signature were retrieved. Furthermore, proportions of 22 infiltrating immune cells for the cohort from TCGA were estimated by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm, and association of 22 immune cells with tumor differentiation/T/N/M stage and survival was investigated. Next, coexpression analysis of 22 immune cells and intersection genes over-/underexpressed in both STAD and stromal signature was conducted. We found high stromal and immune scores and macrophage infiltration predicting poor tumor differentiation and severe local invasion, obtained a list of prognostic genes based on stromal-immune signature, and explored the interaction of collagen, chemokines such as CXCL9, CXCL10, and CXCL11, and macrophage through coexpression analysis and may provide novel prognostic biomarkers and immunotherapeutic targets for STAD.

A New Stromal Signature Applicable to Formalin-Fixed Paraffin-Embedded Tissues Identifies Patients at Risk in Prospective Clinical Trials of the German High-Grade Non-Hodgkin Lymphoma Study Group

Diffuse large B-cell-lymphoma (DLBCL) is a heterogeneous disease in its sites of origin, genetic alterations, and clinical behavior. Gene expression profiling (GEP) has led to the identification of two molecular subtypes, GCB-like and ABC-like DLBCL, that follow different molecular circuits and hence, likely represent different diseases. Next to the ABC-like GEP, the rearrangement and/or expression of MYC and TP53 mutations in tumors characterize patient subsets with inferior prognosis. However, the clinical impact of cell of origin (COO) subtyping and the identification of prognostic biomarkers differ between studies. In many clinical trials, patients identified "at risk" experience cure and long-time survival. Important factors modulating the impact of tumor cell-specific factors may be conferred by the host microenvironment, and stromal signatures have been shown to be correlated to survival in DLBCL. The robust analysis of stromal signatures is hampered by the lack of assays applicable to routinely used formalin-fixed paraffin-embedded (FFPE) materials. We have constructed a molecular signature applicable to FFPE, interrogating the quantitative and qualitative composition of the microenvironment in DLBCL. The signature was trained using an algorithm that extracts prognostic information out of the ratios of pairs of genes. The genes that drive prognosis are expressed in T-cells and macrophages and have function in the communication between both cell types. The model was validated using the NanoString assay in a cohort of 466 DLBCL patients enrolled in 7 prospective clinical trials (MInT, MegaCHOEP phase III and observation, RICOVER-60, RICOVER-noRTh, DENSER, SMARTER) of the German High grade non-Hodgkin's lymphoma study group (DSHNHL). Grouping of the patients into quartiles according to the expression of the continuous stromal signature score (ranging from -1.880to 4.441) resulted in three quartiles (Q1-3) with comparable clinical behavior (stromal signature low). Patients from quartile 4 (Q4), however, characterized by high expression of the signature (stromal signature high), showed a clearly inferior outcome in EFS, PFS and OS (Figure 1 a-c). This result could be independently reproduced in the seven clinical trials named above, thus clearly depicting the robustness of the signature. Multivariate analysis revealed that high expression of the stromal signature is a prognostic risk factor independent of the IPI factors in EFS (HR 1.7, 95 % CI 1.2-2.4, p-value =0.001), PFS (HR 1.8, 95 % CI 1.2-2.5, p-value =0.001) and OS (HR1.8, 95 % CI 1.3-2.7, p-value =0.001). Combining stromal signature count with IPI-score led to the identification of a high-risk cohort (Fig. 1 d-f). Of importance, additional multivariate analyses adjusted for the IPI factors performed within selected trials showed that the stromal signature provides prognostic information independent of the COO status, MYC and dual MYC/BCL2 rearrangements, TP53 mutations and the MYC/BCL2 double expresser status. In summary, our data from prospectively randomized trials of the DSHNHL underline the importance of the microenvironment in the prognostic stratification of DLBCL patients and suggest that the composition and quality of the tumor stroma is an independent risk factor in DLBCL. Analysis of stromal features, therefore, may provide a rationale for targeted treatment approaches, e.g. with immunomodulatory substances (IMIDs), in patients at risk. Figure 1. Figure 1. Disclosures Klapper: Regeneron: Honoraria, Research Funding; HTG Molecular Diagnostics, Inc.: Research Funding; F.Hoffman-La Roche: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria, Research Funding. Richter:HTG Molecular Diagnostics, Inc.: Research Funding. Poeschel:Roche: Other: Travel grants; Amgen: Other: Travel grants. Held:BMS: Consultancy, Other: Travel grants, Research Funding; Spectrum: Research Funding; Roche: Consultancy, Other: Travel grants, Research Funding; Amgen: Research Funding; MSD: Consultancy.

Stromal genes discriminate preinvasive from invasive disease, predict outcome, and highlight inflammatory pathways in digestive cancers

The stromal compartment is increasingly recognized to play a role in cancer. However, its role in the transition from preinvasive to invasive disease is unknown. Most gastrointestinal tumors have clearly defined premalignant stages, and Barrett’s esophagus (BE) is an ideal research model. Supervised clustering of gene expression profiles from microdissected stroma identified a gene signature that could distinguish between BE metaplasia, dysplasia, and esophageal adenocarcinoma (EAC). EAC patients overexpressing any of the five genes (TMEPAI, JMY, TSP1, FAPα, and BCL6) identified from this stromal signature had a significantly poorer outcome. Gene ontology analysis identified a strong inflammatory component in BE disease progression, and key pathways included cytokine–cytokine receptor interactions and TGF-β. Increased protein levels of inflammatory-related genes significantly up-regulated in EAC compared with preinvasive stages were confirmed in the stroma of independent samples, and in vitro assays confirmed functional relevance of these genes. Gene set enrichment analysis of external datasets demonstrated that the stromal signature was also relevant in the preinvasive to invasive transition of the stomach, colon, and pancreas. These data implicate inflammatory pathways in the genesis of gastrointestinal tract cancers, which can affect prognosis.