Abstract
BackgroundCancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors.Methodology/Principal FindingsWe developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p = 1.7×10−8).ConclusionsOur model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability.SignificancePrognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy.
Highlights
Cancers are complex tissues whose behavior is strongly influenced by dynamic interactions between the cancer cells, the tumor’s stromal cells and the extracellular matrix [1]
We hypothesized that the alterations in gene expression required for tumor cells to survive in vitro might be markers of human cancers that were suited to growth in distant sites, i.e., more likely to invade or metastasize, two processes associated with poor prognosis and foreshortened survival
Since these gene families had been cited in reports analyzing tumor invasion and metastasis [10,11], tumor-progression processes associated with poor prognosis and reduced survival; we decided to test this expression signature on the analysis of human cancer survival based on global gene expression patterns
Summary
Cancers are complex tissues whose behavior is strongly influenced by dynamic interactions between the cancer cells, the tumor’s stromal cells and the extracellular matrix [1]. We undertook the comparison of gene expression profiles between mouse plasma cell tumors (PCTs) growing in mice and PCTs that had been adapted to growth in tissue culture, hoping to gain insights into the genes responsible for the adaptation of this particular tumor to tissue culture conditions. Another goal for this study, which provides the basis for the present paper, was to determine whether these data might be extrapolatable to other tumor types and other species. Prognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity Both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy
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