Combining immunoscore and tumor budding in colon cancer: an insightful prognostication based on the tumor-host interface

Background: Tumor Budding (TB) and Immunoscore are independent prognostic markers in colon cancer (CC). Given their respective representation of tumor aggressiveness and immune response, we examined their combination in association with patient disease-free survival (DFS) in pTNM stage I-III CC. Methods: In a series of pTNM stage I-III CCs (n = 654), the Immunoscore was computed and TB detected automatically using a deep learning network. Two-tiered systems for both biomarkers were used with cut-offs of 25% and ten buds for Immunoscore and TB according to clinical guidelines, respectively. Associations of Immunoscore with TB with 5-year DFS were examined using Kaplan-Meier survival analysis in addition to multivariable modeling and relative contribution analysis using Cox regression. Results: Immunoscore and TB independently are prognostic with hazard ratio (HR) = 2.0, 95% confidence interval (CI) 1.4-2.8 and HR 2.5, with 95% CI 1.4-4.5, respectively; P value < 0.0001. By combining Immunoscore with TB, patients with Immunoscore Low, TB High tumors had a significantly poorer DFS (HR 5.6, 95% CI 2.6-12.0; P value < 0.0001) than those with Immunoscore High, TB Low tumors. The combined Immunoscore with TB score was independently prognostic (P value = 0.009) in comparison to N-stage, T-stage, and MSI. Immunoscore with TB had the highest relative contribution (35%) to DFS in pTNM stage I-II CCs. Conclusions: The association of Immunoscore and TB with patient survival suggests that both biomarkers are complementary and should be interpreted in combination to identify high-risk Stage I-II patients who should be considered for adjuvant therapy or further diagnostic testing.

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Open Access CRIS of the University of Bern
Published
2024-12-02
DOI
https://doi.org/10.48620/77179
Primary Topic
Cell Image Analysis Techniques
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article

Combining immunoscore and tumor budding in colon cancer: an insightful prognostication based on the tumor-host interface

Open Access CRIS of the University of Bern
Cell Image Analysis Techniques
article

Combining immunoscore and tumor budding in colon cancer: an insightful prognostication based on the tumor-host interface

article en

Abstract

Background: Tumor Budding (TB) and Immunoscore are independent prognostic markers in colon cancer (CC). Given their respective representation of tumor aggressiveness and immune response, we examined their combination in association with patient disease-free survival (DFS) in pTNM stage I-III CC. Methods: In a series of pTNM stage I-III CCs (n = 654), the Immunoscore was computed and TB detected automatically using a deep learning network. Two-tiered systems for both biomarkers were used with cut-offs of 25% and ten buds for Immunoscore and TB according to clinical guidelines, respectively. Associations of Immunoscore with TB with 5-year DFS were examined using Kaplan-Meier survival analysis in addition to multivariable modeling and relative contribution analysis using Cox regression. Results: Immunoscore and TB independently are prognostic with hazard ratio (HR) = 2.0, 95% confidence interval (CI) 1.4-2.8 and HR 2.5, with 95% CI 1.4-4.5, respectively; P value < 0.0001. By combining Immunoscore with TB, patients with Immunoscore Low, TB High tumors had a significantly poorer DFS (HR 5.6, 95% CI 2.6-12.0; P value < 0.0001) than those with Immunoscore High, TB Low tumors. The combined Immunoscore with TB score was independently prognostic (P value = 0.009) in comparison to N-stage, T-stage, and MSI. Immunoscore with TB had the highest relative contribution (35%) to DFS in pTNM stage I-II CCs. Conclusions: The association of Immunoscore and TB with patient survival suggests that both biomarkers are complementary and should be interpreted in combination to identify high-risk Stage I-II patients who should be considered for adjuvant therapy or further diagnostic testing.

Open Access CRIS of the University of Bern
Tissue Genetics (United States) (US)
No poverty
Openalex Percentile: Top 61%
Cell Image Analysis Techniques
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