Skip to content

Research at St Andrews

Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer

Research output: Contribution to journalArticlepeer-review

Abstract

Both immune profiling and tumor budding significantly correlate with colorectal cancer (CRC) patient outcome, but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II CRC. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3+CD8+ T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: n = 114, validation cohort 1: n = 56, validation cohort 2: n = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs (HR = 5.899, 95% CI, 1.875 - 18.55), low CD3+ 11 T cell density (HR = 9.964, 95% CI 3.156 - 31.46), and low mean number of CD3+CD8+ T cells within 50 μm of TBs (HR = 8.907, 95% CI 2.834 - 28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75, 95% CI 6.46–54.43), than TBs, Immunoscore, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27, 95% CI 3.524–42.73, HR = 15.61, 95% CI 4.692-51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II CRC through an automated image analysis and machine learning workflow.
Close

Details

Original languageEnglish
Pages (from-to)609-620
JournalCancer Immunology Research
Volume7
Issue number4
Early online date7 Mar 2019
DOIs
Publication statusPublished - Apr 2019

    Research areas

  • Digital image analysis, Digital pathology, Tumor budding, Immunoscore, Prognosis, Colorectal cancer

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning

    Gavriel, C., Dimitriou, N., Brieu, N., Nearchou, I. P., Arandelovic, O., Schmidt, G., Harrison, D. J. & Caie, P. D., 1 Apr 2021, In: Cancers. 13, 7, 1624.

    Research output: Contribution to journalArticlepeer-review

  2. Automated detection and classification of desmoplastic reaction at the colorectal tumour front using deep learning

    Nearchou, I. P., Ueno, H., Kajiwara, Y., Lillard, K., Mochizuki, S., Takeuchi, K., Harrison, D. J. & Caie, P. D., 31 Mar 2021, In: Cancers. 13, 7, 1615.

    Research output: Contribution to journalArticlepeer-review

  3. Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients

    Nearchou, I. P., Gwyther, B. M., Georgiakakis, E. C. T., Gavriel, C., Lillard, K., Kajiwara, Y., Ueno, H., Harrison, D. J. & Caie, P. D., 15 May 2020, In: npj Digital Medicine. 3, 10 p., 71.

    Research output: Contribution to journalArticlepeer-review

ID: 257559242

Top