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

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

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)

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 (χ2 = 53.183, p < 0.001) and as a cluster variable (χ2 = 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 (χ2 = 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
Pages (from-to)245-256
Number of pages12
JournalJournal of Pathology: Clinical Research
Volume8
Issue number3
Early online date18 Jan 2022
DOIs
Publication statusPublished - May 2022

Keywords

  • biomarker
  • colorectal cancer
  • combinatorial algorithm
  • combinatorial analysis
  • immunohistochemistry
  • prognosis
  • tissue microarray

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

Fingerprint

Dive into the research topics of 'Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis'. Together they form a unique fingerprint.

Cite this