An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer

Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment

BACKGROUND

Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity.

METHODS

We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types-CXCR3+CD68+ or CD8+-to profile their spatial relationships with CXCL9+CD68+ cells.

RESULTS

Six unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8.

CONCLUSIONS

Unsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy.

Innovative approaches in colorectal cancer screening: advances in detection methods and the role of artificial intelligence

Colorectal cancer (CRC) is the third most prevalent cancer globally and poses a significant health threat, making early detection crucial. This review paper explored emerging detection methods for early screening of CRC, including gut microbiota, metabolites, genetic markers, and artificial intelligence (AI)-based technologies. Current screening methods have their respective advantages and limitations, particularly in detecting precursors. First, the importance of the gut microbiome in CRC progression is discussed, highlighting how specific microbial alterations can serve as biomarkers for early detection, potentially enhancing diagnostic accuracy when combined with traditional screening methods. Next, research on metabolic reprogramming illustrates the relationship between metabolic changes and CRC, with studies developing metabolite-based detection models that show good sensitivity for early diagnosis. In terms of genetic markers, methylated DNA markers like SEPTIN9 have demonstrated high sensitivity, although further validation across diverse populations is necessary. Lastly, AI technology has shown immense potential in improving adenoma detection rates, significantly enhancing the quality of colonoscopic examinations through image recognition techniques. This review aims to provide a comprehensive perspective on new strategies for CRC screening, emphasizing the potential of noninvasive detection technologies and the prospects of AI and genomics in clinical applications. Despite several challenges, this review advocates for future large-scale prospective studies to validate the effectiveness and cost-effectiveness of these new screening methods while promoting the implementation of screening protocols tailored to individual characteristics.

The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.