Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

TL;DR

This study analyzes colorectal cancer data to identify biomarker clusters, revealing that current tumor classifications may not fully reflect immunological factors, suggesting potential for improved treatment strategies.

Contribution

It introduces a clustering approach to colorectal cancer data that highlights discrepancies with existing classifications and suggests new avenues for personalized treatment.

Findings

Existing tumor classifications are largely unrelated to immunological factors.

Clustering reveals potential for re-evaluating treatment options.

Data supports integrating tumor physiology and histochemistry for better prognosis.

Abstract

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.