Collagen-related gene expression level predicts the prognosis and immune therapy response

The extracellular matrix protein EMILIN-1 impacts on the microenvironment by hampering gastric cancer development and progression

BACKGROUND

The contribution of the tumor microenvironment and extracellular matrix to the aggressive biology of Gastric Cancer (GC) has been recently characterized; however, the role of EMILIN-1 in this context is unknown. EMILIN-1 is an essential structural element for the maintenance of lymphatic vessel (LV) integrity and displays anti-proliferative properties as demonstrated in skin and colon cancer. Given the key role of LVs in GC progression, the aim of this study was to investigate the role of EMILIN-1 in GC mouse models.

METHODS

We used the syngeneic YTN16 cells which were injected subcutaneously and intraperitoneally in genetically modified EMILIN-1 mice. In alternative, carcinogenesis was induced using N-Methyl-N-nitrosourea (MNU). Mouse-derived samples and human biopsies were analyzed by IHC and IF to the possible correlation between EMILIN-1 expression and LV pattern.

RESULTS

Transgenic mice developed tumors earlier compared to WT animals. 20 days post-injection tumors developed in EMILIN-1 mutant mice were larger and displayed a significant increase of lymphangiogenesis. Treatment of transgenic mice with MNU associated with an increased number of tumors, exacerbated aggressive lesions and higher levels of LV abnormalities. A significant correlation between the levels of EMILIN-1 and podoplanin was detected also in human samples, confirming the results obtained with the pre-clinical models.

CONCLUSIONS

This study demonstrates for the first time that loss of EMILIN-1 in GC leads to lymphatic dysfunction and proliferative advantages that sustain tumorigenesis, and assess the use of our animal model as a valuable tool to verify the fate of GC upon loss of EMILIN-1.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update

Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term "predictive factors, immunotherapy, gastric cancer" to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy's predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.

Understanding the matrix: collagen modifications in tumors and their implications for immunotherapy

Tumors are highly complex and heterogenous ecosystems where malignant cells interact with healthy cells and the surrounding extracellular matrix (ECM). Solid tumors contain large ECM deposits that can constitute up to 60% of the tumor mass. This supports the survival and growth of cancerous cells and plays a critical role in the response to immune therapy. There is untapped potential in targeting the ECM and cell-ECM interactions to improve existing immune therapy and explore novel therapeutic strategies. The most abundant proteins in the ECM are the collagen family. There are 28 different collagen subtypes that can undergo several post-translational modifications (PTMs), which alter both their structure and functionality. Here, we review current knowledge on tumor collagen composition and the consequences of collagen PTMs affecting receptor binding, cell migration and tumor stiffness. Furthermore, we discuss how these alterations impact tumor immune responses and how collagen could be targeted to treat cancer.

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images

BACKGROUND

Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC).

METHODS

DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS.

RESULTS

Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression.

CONCLUSION

DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms.