Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis

Abdo Alnabulsi, Tiehui Wang, Wei Pang, Marius Ionescu, Stephanie G. Craig, Matthew P. Humphries, Kris McCombe, Manuel Salto Tellez, Ayham Alnabulsi, Graeme Murray*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer-related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistry-based biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p-cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (chi(2) = 53.183, p < 0.001) and as a cluster variable (chi(2) = 67.625, p < 0.001). This signature was also significantly independent of age, extramural vascular invasion, tumour stage, and lymph node metastasis (Wald = 32.898, p < 0.001). Assessment of the results in an external cohort showed that the signature was significantly associated with prognosis (chi(2) = 14.217, p = 0.007). This study developed and optimised an innovative discovery approach which could be adapted for the discovery of biomarkers and molecular interactions in a range of biological and clinical studies. Furthermore, this study identified a protein signature that can be utilised as an independent prognostic method and for potential therapeutic interventions.

Original languageEnglish
Number of pages12
JournalJournal of pathology clinical research
Early online date18 Jan 2022
DOIs
Publication statusE-pub ahead of print - 18 Jan 2022

Keywords

  • biomarker
  • colorectal cancer
  • combinatorial analysis
  • combinatorial algorithm
  • immunohistochemistry
  • prognosis
  • tissue microarray
  • CONSENSUS MOLECULAR SUBTYPES
  • EXPRESSION
  • CELL

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