Immune Infiltration in Tumor and Adjacent Non-Neoplastic Regions Codetermines Patient Clinical Outcomes in Early-Stage Lung Cancer

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

Integrated profiling identifies DXS253E as a potential prognostic marker in colorectal cancer

BACKGROUND

Increasing evidence suggests that DXS253E is critical for cancer development and progression, but the function and potential mechanism of DXS253E in colorectal cancer (CRC) remain largely unknown. In this study, we evaluated the clinical significance and explored the underlying mechanism of DXS253E in CRC.

METHODS

DXS253E expression in cancer tissues was investigated using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Kaplan-Meier plot was used to assess the prognosis of DXS253E. The cBioPortal, MethSurv, and Tumor Immune Estimation Resource (TIMER) databases were employed to analyze the mutation profile, methylation, and immune infiltration associated with DXS253E. The biological functions of DXS253E in CRC cells were determined by CCK-8 assay, plate cloning assay, Transwell assay, flow cytometry, lactate assay, western blot, and qRT-PCR.

RESULTS

DXS253E was upregulated in CRC tissues and high DXS253E expression levels were correlated with poor survival in CRC patients. Our bioinformatics analyses showed that high DXS253E gene methylation levels were associated with the favorable prognosis of CRC patients. Furthermore, DXS253E levels were linked to the expression levels of several immunomodulatory genes and an abundance of immune cells. Mechanistically, the overexpression of DXS253E enhanced proliferation, migration, invasion, and the aerobic glycolysis of CRC cells through the AKT/mTOR pathway.

CONCLUSIONS

We demonstrated that DXS253E functions as a potential role in CRC progression and may serve as an indicator of outcomes and a therapeutic target for regulating the AKT/mTOR pathway in CRC.

Tissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosis

Tissue-resident memory T cells (TRM) are a specialized T cell population residing in peripheral tissues. The presence and potential impact of TRM in the tumor immune microenvironment (TIME) remain to be elucidated. Here, we systematically investigated the relationship between TRM and melanoma TIME based on multiple clinical single-cell RNA-seq datasets and developed signatures indicative of TRM infiltration. TRM infiltration is associated with longer overall survival and abundance of T cells, NK cells, M1 macrophages, and memory B cells in the TIME. A 22-gene TRM-derived risk score was further developed to effectively classify patients into low- and high-risk categories, distinguishing overall survival and immune activation, particularly in T cell-mediated responses. Altogether, our analysis suggests that TRM abundance is associated with melanoma TIME activation and patient survival, and the TRM-based machine learning model can potentially predict prognosis in melanoma patients.

Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study

BACKGROUND

Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes.

METHODS

A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The OCDPI was developed using formalin-fixed, paraffin-embedded (FFPE) WSIs from the TCGA-OV cohort, and was externally validated in two independent cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and Harbin Medical University Cancer Hospital (HMUCH).

RESULTS

The OCDPI showed prognostic ability for overall survival prediction in the PLCO (HR, 1.916; 95% CI, 1.380-2.660; log-rank test, P < 0.001) and HMUCH (HR, 2.796; 95% CI, 1.404-5.568; log-rank test, P = 0.0022) cohorts. Patients with low OCDPI experienced better survival benefits and lower recurrence rates following adjuvant therapy compared to those with high OCDPI. Multivariable analyses, adjusting for clinicopathological factors, consistently identified OCDPI as an independent prognostic factor across all cohorts (all P < 0.05). Furthermore, OCDPI performed well in patients with low-grade tumors or fresh-frozen slides, and could differentiate between HRD-deficient or HRD-intact patients with and without sensitivity to adjuvant therapy.

CONCLUSION

The results from this multicenter cohort study indicate that the OCDPI may serve as a valuable and labor-saving tool to improve prognostic and predictive clinical decision-making in patients with OV.