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Updated: Jun 27, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
The Heterogeneity of Mucinous Colorectal Adenocarcinoma-Histologic and Molecular Phenotypes Drive Prognostic Outcomes
Daniel W Wilsdon1, Yoohyun Park1, Kelly Harper2
1Division of Diagnostic & Molecular Pathology, University of Ottawa & The Ottawa Hospital, Ottawa, ON K1H 8L6, Canada.
Background/Objectives:
The prognostic significance of mucinous colorectal adenocarcinoma (MAC) is controversial. Some studies report good outcomes relative to conventional colorectal adenocarcinoma (CRC) as is similarly described for MACs in, e.g., the breast, lung, pancreas and prostate. However, other studies refute this, proclaiming either no difference or worse outcomes. Herein, we proffer additional insights into the biology of MAC to explain these conflicting findings.
Methods:
A literature search was undertaken using keywords pertaining to MAC. Archival cases from our database were analyzed to provide context for our findings.
Main Findings:
The unifying histologic feature of MACs is their >50% content of extracellular mucin, but they should not be viewed as a monolithic entity, as is commonly portrayed in databases. Instead, MAC is a heterogenous disease as defined by histologic and molecular phenotypes. For example, MACs arising from adenoma-like CRC have relatively good outcomes unlike those from traditional serrated adenomas. Likewise, other factors such as histologic grade (grade 1-3), genomics (e.g., BRAF, KRAS, TP53), microsatellite instability (MSI-H, MSI-L), consensus molecular subtypes (CMS1-CMS4), and mucin types (MUC2, MUC5AC) significantly influence prognosis. These pathophysiologic features, demographics (age and sex) and specific anatomic regions/topography (right/left colon/rectum) can be captured and used to improve prognostic stratification.
Conclusions:
In contrast to previous studies that largely demarcated MAC as a discrete entity, this paper shows the limitations of this approach by highlighting the various sub-entities comprising MAC. Recognition of this heterogeneity may help to inform future treatment algorithms.
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