Abnormalities in nuclear phenotype and chromosome structure are key features of cancer cells. Investigation of the protein determinants of nuclear subfractions in cancer may yield molecular insights into aberrant chromosome function and chromatin organization and in addition may yield biomarkers for early cancer detection. Here we evaluate a proteomics work flow for profiling protein constituents in subnuclear domains in colorectal cancer tissues and apply this work flow to a comparative analysis of the nuclear matrix fraction in colorectal adenoma and carcinoma tissue samples. First, we established the reproducibility of the entire work flow. In a reproducibility analysis of three nuclear matrix fractions independently isolated from the same colon tumor homogenate, 889 of 1,047 proteins (85%) were reproducibly identified at high confidence (minimally two peptides per protein at 99% confidence interval at the protein level) with an average coefficient of variance for the number of normalized spectral counts per protein of 30%. This indicates a good reproducibility of the entire work flow from biochemical isolation to nano-LC-MS/MS analysis. Second, using spectral counting combined with statistics, we identified proteins that are significantly enriched in the nuclear matrix fraction relative to two earlier fractions (the chromatin-binding and intermediate filament fractions) isolated from six colorectal tissue samples. The total data set contained 2,059 non-redundant proteins. Gene ontology mining and protein network analysis of nuclear matrix-enriched proteins revealed enrichment for proteins implicated in "RNA processing" and "mRNA metabolic process." Finally, an explorative comparison of the nuclear matrix proteome in colorectal adenoma and carcinoma tissues revealed many proteins previously implicated in oncogenesis as well as new candidates. A subset of these differentially expressed proteins also exhibited a corresponding change at the mRNA level. Together, the results show that subnuclear proteomics of tumor tissue is feasible and a promising avenue for exploring oncogenesis.