Histologic-Based Tumor-Associated Immune Cells Status in Clear Cell Renal Cell Carcinoma Correlates with Gene Signatures Related to Cancer Immunity and Clinical Outcomes

Defining Melanoma Immune Biomarkers-Desert, Excluded, and Inflamed Subtypes-Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns

The spatial distribution of tumor infiltrating lymphocytes (TILs) defines several histologically and clinically distinct immune subtypes-desert (no TILs), excluded (TILs in stroma), and inflamed (TILs in tumor parenchyma). To date, robust classification of immune subtypes still requires deeper experimental evidence across various cancer types. Here, we aimed to investigate, define, and validate the immune subtypes in melanoma by coupling transcriptional and histological assessments of the lymphocyte distribution in tumor parenchyma and stroma. We used the transcriptomic data from The Cancer Genome Atlas melanoma dataset to screen for the desert, excluded, and inflamed immune subtypes. We defined subtype-specific genes and used them to construct a subtype assignment algorithm. We validated the two-step algorithm in the qPCR data of real-world melanoma tumors with histologically defined immune subtypes. The accuracy of a classifier encompassing expression data of seven genes (immune response-related: CD2, CD53, IRF1, and CD8B; and stroma-related: COL5A2, TNFAIP6, and INHBA) in a validation cohort reached 79%. Our findings suggest that melanoma tumors can be classified into transcriptionally and histologically distinct desert, excluded, and inflamed subtypes. Gene expression-based algorithms can assist physicians and pathologists as biomarkers in the rapid assessment of a tumor immune microenvironment while serving as a tool for clinical decision making.

Hypoxia-inducible factor 2α protein and mRNA expression correlate with histomorphological features in clear cell renal cell carcinoma

Hypoxia-inducible factor 2α (HIF2α) has been identified as a potential biomarker and novel target for systemic therapy in clear cell renal cell carcinoma (ccRCC). The present study aims to evaluate the association of HIF2α protein and HIF2A mRNA expression with clinicopathological factors and histomorphological features related to vasculature and inflammation of ccRCC using a localized ccRCC cohort (n = 428) and The Cancer Genome Atlas (TCGA)-KIRC cohort (n = 433). HIF2α protein expression was immunohistochemically assessed using tissue microarrays and HIF2A mRNA expression was assessed using the TCGA RNA-sequencing data. Positive HIF2α protein and high HIF2A mRNA expression were observed in 145 (33.9 %) and 142 (32.8 %) patients, respectively. Positive nuclear HIF2α protein expression was significantly associated with the clear histological phenotype and architectural patterns related to rich vascular networks (p < 0.001), and no tumor-associated immune cells status (p < 0.05) in addition to favorable prognostic factors such as lower TNM stage, lower WHO/ISUP grade, or the absence of necrosis (p < 0.001). The HIF2A mRNA expression profile by the TCGA cohort showed similar trends as the HIF2α protein profile. In addition, positive HIF2α protein and high HIF2A mRNA expression were associated with higher recurrence-free survival and overall survival, respectively (both p < 0.001). In conclusion, we comprehensively demonstrated the association of HIF2α profiles with clinicopathological factors and histomorphological features related to vasculature and inflammation at both protein and mRNA levels. Histomorphological features expressing HIF2α may provide information on HIF2α targeted therapeutic response as well as prognosis in ccRCC patients.

Spatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is an immunologically and histologically diverse tumor. However, how the structural heterogeneity of tumor microenvironment (TME) affects cancer progression and treatment response remains unclear. Hence, we characterized the TME architectures of ccRCC tissues using imaging mass cytometry (IMC) and explored their associations with clinical outcome and therapeutic response.

METHODS

Using IMC, we profiled the TME landscape of ccRCC and paracancerous tissue by measuring 17 markers involved in tissue architecture, immune cell and immune activation. In the ccRCC tissue, we identified distinct immune architectures of ccRCC tissue based on the mix score and performed cellular neighborhood (CN) analysis to subdivide TME phenotypes. Moreover, we assessed the relationship between the different TME phenotypes and ccRCC patient survival, clinical features and treatment response.

RESULTS

We found that ccRCC tissues had higher levels of CD8⁺ T cells, CD163^- macrophages, Treg cells, endothelial cells, and fibroblasts than paracancerous tissues. Immune infiltrates in ccRCC tissues distinctly showed clustered and scattered patterns. Within the clustered pattern, we identified two subtypes with different clinical outcomes based on CN analysis. The TLS-like phenotype had cell communities resembling tertiary lymphoid structures, characterized by cell-cell interactions of CD8⁺ T cells-B cells and GZMB⁺CD8⁺ T cells-B cells, which exhibited anti-tumor features and favorable outcomes, while the Macrophage/T-clustered phenotype with macrophage- or T cell-dominated cell communities had a poor prognosis. Patients with scattered immune architecture could be further divided into scattered-CN-hot and scattered-CN-cold phenotypes based on the presence or absence of immune CNs, but both had a better prognosis than the macrophage/T-clustered phenotype. We further analyzed the relationship between the TME phenotypes and treatment response in five metastatic ccRCC patients treated with sunitinib, and found that all three responders were scattered-CN-hot phenotype while both non-responders were macrophage/T-clustered phenotype.

CONCLUSION

Our study revealed the structural heterogeneity of TME in ccRCC and its impact on clinical outcome and personalized treatment. These findings highlight the potential of IMC and CN analysis for characterizing TME structural units in cancer research.

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.

Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer

Purpose

Cervical cancer (CC) is one of the most common gynecologic neoplasms. Hypoxia is an essential trigger for activating immunosuppressive activity and initiating malignant tumors. However, the determination of the role of immunity and hypoxia on the clinical outcome of CC patients remains unclear.

Methods

The CC independent cohort were collected from TCGA database. Consensus cluster analysis was employed to determine a molecular subtype based on immune and hypoxia gene sets. Cox relevant analyses were utilized to set up a risk classifier for prognosis assessment. The underlying pathways of classifier genes were detected by GSEA. Moreover, we conducted CIBERSORT algorithm to mirror the immune status of CC samples.

Results

We observed two cluster related to immune and hypoxia status and found the significant difference in outcome of patients between the two clusters. A total of 251 candidate genes were extracted from the two clusters and enrolled into Cox relevant analyses. Then, seven hub genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI, and VEGFA) were selected to create an immune and hypoxia-based risk classifier (IHBRC). The IHBRC can precisely distinguish patient risk and estimate clinical outcomes. In addition, IHBRC was closely bound up with tumor associated pathways such as hypoxia, P53 signaling and TGF β signaling. IHBRC was also tightly associated with numerous types of immunocytes.

Conclusion

This academic research revealed that IHBRC can be served as predictor for prognosis assessment and cancer treatment estimation in CC.

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data.

METHODS

Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues.

RESULTS

A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival.

CONCLUSIONS

A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.