H. Lee Moffitt Cancer Center and Research Institute, Inc.

University of South Florida

Gregory C Bloom

Timothy J Yeatman

Steven A Eschrich

Jeff Xiwu Zhou

Barbara A Centeno

The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

Methods and systems for predicting cancer outcome

A two-tiered classification system that can be integrated with the current algorithm used by pathologists for identification of the site of origin for 'malignancy with unknown primary' is presented. In use, morphology, immunohistochemical (IHC) studies, and microarray-based top tier gene expression classifiers first subclassify cytokeratin positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used in conjunction with microarray-based second tier gene expression classifiers to assign the primary site of origin to the sample. This new hybrid approach combines IHC with a hierarchy of quantitative gene expression based classifiers into an algorithmic method that can assist pathologists to further refine and support their decision making process.

Hybrid model for the classification of carcinoma subtypes

Here the inventors describe a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction.

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Tampa, FL, United States of America

Gregory C Bloom