Application of distance matrices to define associations between acute toxicities in colorectal cancer patients receiving chemotherapy.

Background: Colorectal cancer patients undergoing chemotherapy (CT) are likely to experience multiple concurrent toxicities that, rather than appearing singularly, may be associated with one another. Graphic and tabular representations of distance matrices were used to identify associations between toxicities and to define the strengths of these relations.
Methods: Using a standardized data collection tool, electronic medical charts of 300 consecutive patients receiving either the combination of leucovorin, 5-fluorouracil (5-FU), and oxaliplatin (FOLFOX); the combination of leucovorin, 5-FU, and irinotecan (FOLFIRI); or 5-FU) were retrospectively reviewed to record baseline demographic and clinical information. Treatment-related toxicities were recorded using National Cancer Institute Common Toxicity Criteria during the first cycle of CT. Using a distance matrix approach, an analysis of CT-induced toxicity associations was elaborated.
Results: The graphic analysis, in which associations between toxicities were represented as links, identified 6 major hubs (fever, dehydration, fatigue, anorexia, pain, and weight loss), defined as central nodes with more connections than expected by chance. These were highly linked with minor nodes and provided evidence suggesting the existence of symptom clusters associated with CT-induced toxicities.
Conclusions: The application of distance matrix analyses to define CT-induced toxicity associations is new. The technique was effective in defining the global landscape of the binary relations among toxicities associated with Cycle 1 therapy. The coherent clinical picture emerging from the network provides a strong suggestion that the toxicities in each cluster share a common pathobiologic basis, which may provide an opportunity for intervention. These findings could become useful for the early prediction of co-occurring toxicities and, in the future, as a phenotyping framework for the pharmacogenomic analysis of individual responses to chemotherapy.