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Expression profiling to predict outcome in breast cancer: the influence of sample selection.

Gruvberger SK, Ringnér M, Edén P, Borg A, Fernö M, Peterson C, Meltzer PS - Breast Cancer Res. (2002)

Bottom Line: Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients.Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set.We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden.

ABSTRACT
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

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The distribution of clinical characteristics of the 78 sporadic breast tumors used in the training/validation set in the study by van 't Veer et al. [7]. Estrogen receptor-α status is denoted as ER+ and ER-. Clinical outcome for the patients is represented by M+ (distant recurrences within 5 years) and M- (no recurrences within a follow-up period of at least 5 years).
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Figure 3: The distribution of clinical characteristics of the 78 sporadic breast tumors used in the training/validation set in the study by van 't Veer et al. [7]. Estrogen receptor-α status is denoted as ER+ and ER-. Clinical outcome for the patients is represented by M+ (distant recurrences within 5 years) and M- (no recurrences within a follow-up period of at least 5 years).

Mentions: Intriguingly, for the training set of 58 sporadic tumor samples that van't Veer et al. [7] analyzed, their supervised ER-α predictor gene set also had predictive power for outcome (OR = 3.7, 95% CI 1.3–11; P = 0.02), perhaps owing to the predominance (80%) of ER-α-positive tumors in the good prognosis group (Fig. 3). Likewise, in their independent test set, all of the tumors with a good prognosis were ER-α-positive except one (86%; Fig. 4). In addition, 71% of the 231 prognostic genes identified by van't Veer et al. were also listed by them as ER-α status reporter genes, confirming an overlap between the predictors of prognosis and ER-α status. It is important to note, however, that they were able to achieve better prognostic predictions using the 231 selected genes (OR = 15, 95% CI 4–56; P = 0.000004) than with the ER-α predictor gene set. Nevertheless, we believe that the correlation between prognosis and ER-α status in samples reported by van't Veer et al. (Figs 3 and 4) might have led to the selection of a prognostic gene set that may not be broadly applicable to other breast tumor cohorts in which no correlation between ER-α status and prognosis exists.


Expression profiling to predict outcome in breast cancer: the influence of sample selection.

Gruvberger SK, Ringnér M, Edén P, Borg A, Fernö M, Peterson C, Meltzer PS - Breast Cancer Res. (2002)

The distribution of clinical characteristics of the 78 sporadic breast tumors used in the training/validation set in the study by van 't Veer et al. [7]. Estrogen receptor-α status is denoted as ER+ and ER-. Clinical outcome for the patients is represented by M+ (distant recurrences within 5 years) and M- (no recurrences within a follow-up period of at least 5 years).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC154130&req=5

Figure 3: The distribution of clinical characteristics of the 78 sporadic breast tumors used in the training/validation set in the study by van 't Veer et al. [7]. Estrogen receptor-α status is denoted as ER+ and ER-. Clinical outcome for the patients is represented by M+ (distant recurrences within 5 years) and M- (no recurrences within a follow-up period of at least 5 years).
Mentions: Intriguingly, for the training set of 58 sporadic tumor samples that van't Veer et al. [7] analyzed, their supervised ER-α predictor gene set also had predictive power for outcome (OR = 3.7, 95% CI 1.3–11; P = 0.02), perhaps owing to the predominance (80%) of ER-α-positive tumors in the good prognosis group (Fig. 3). Likewise, in their independent test set, all of the tumors with a good prognosis were ER-α-positive except one (86%; Fig. 4). In addition, 71% of the 231 prognostic genes identified by van't Veer et al. were also listed by them as ER-α status reporter genes, confirming an overlap between the predictors of prognosis and ER-α status. It is important to note, however, that they were able to achieve better prognostic predictions using the 231 selected genes (OR = 15, 95% CI 4–56; P = 0.000004) than with the ER-α predictor gene set. Nevertheless, we believe that the correlation between prognosis and ER-α status in samples reported by van't Veer et al. (Figs 3 and 4) might have led to the selection of a prognostic gene set that may not be broadly applicable to other breast tumor cohorts in which no correlation between ER-α status and prognosis exists.

Bottom Line: Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients.Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set.We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden.

ABSTRACT
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

Show MeSH
Related in: MedlinePlus