  It is well known that tumors originating from the same tissue have different
prognosis and sensitivity to treatments. Over the last decade, cancer genomics
consortia like the Cancer Genome Atlas (TCGA) have been generating thousands of
cross-sectional data, for thousands of human primary tumors originated from
various tissues. Thanks to that public database, it is today possible to
analyze a broad range of relevant information such as gene sequences,
expression profiles or metabolite footprints, to capture tumor molecular
heterogeneity and improve patient stratification and clinical management. To
this aim, it is common practice to analyze datasets grouped into clusters based
on clinical observations and/or molecular features. However, the identification
of specific properties of each cluster that may be effectively targeted by
therapeutic drugs still represents a challenging task. We define a method to
generate an activity score for the metabolic reactions of different clusters of
patients based on their transcriptional profile. This approach reduces the
number of variables from many genes to few reactions, by aggregating
transcriptional information associated to the same enzymatic reaction according
to gene-enzyme and enzyme-reaction rules. We also applied the methodology to a
dataset of 244 RNAseq transcriptional profiles taken from patients with
colorectal cancer (CRC). CRC samples are typically divided into two sub-types:
(i) tumors with microsatellite instability (MSI), associated with
hyper-mutation and with CpG island methylation phenotype, and (ii)
microsatellite stable (MSS) tumors, typically endowed with chromosomal
instability. We report some key differences in the central carbon metabolism of
the two clusters. We also show how the method can be used to describe the
metabolism of individual patients and cluster them exclusively based on
metabolic features.
