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1.  Testing a Multigene Signature of Prostate Cancer Death in the Swedish Watchful Waiting Cohort 
While prostate cancer is a leading cause of cancer death, most men die with and not from their disease, underscoring the urgency to distinguish potentially lethal from indolent prostate cancer. We tested the prognostic value of a previously identified multigene signature of prostate cancer progression to predict cancer-specific death. The Örebro Watchful Waiting Cohort included 172 men with localized prostate cancer of whom 40 died of prostate cancer. We quantified protein expression of the markers in tumor tissue by immunohistochemistry, and stratified the cohort by quintiles according to risk classification. We accounted for clinical parameters (age, Gleason, nuclear grade, tumor volume) using Cox regression, and calculated Receiver Operator Curves to compare discriminatory ability. The hazard ratio of prostate cancer death increased with increasing risk classification by the multigene model, with a 16-fold greater risk comparing highest versus lowest risk strata, and predicted outcome independent of clinical factors (p=0.002). The best discrimination came from combining information from the multigene markers and clinical data, which perfectly classified the lowest risk stratum where no one developed lethal disease; using the two lowest risk groups as referent, the hazard ratio (95% confidence interval) was 11.3 (4.0–32.8) for the highest risk group and difference in mortality at 15 years was 60% (50–70%). The combined model provided greater discriminatory ability (AUC 0.78) than the clinical model alone (AUC 0.71), p=0.04. Molecular tumor markers can add to clinical parameters to help distinguish lethal and indolent prostate cancer, and hold promise to guide treatment decisions.
doi:10.1158/1055-9965.EPI-08-0044
PMCID: PMC2536630  PMID: 18583469
2.  Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts 
Breast Cancer Research  2005;7(6):R953-R964.
Introduction
Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction.
Methods
We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined.
Results
Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively.
Conclusion
We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.
doi:10.1186/bcr1325
PMCID: PMC1410752  PMID: 16280042

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