Roadmap for developing and validating therapeutically relevant genomic classifiers

Roadmap for developing and validating therapeutically relevant genomic classifiers


The purpose of a validation study is not to see whether redeveloping a classifier with new data results in selection of the same genes, although this is one of the most common misunderstandings of validation. The article by Asgharzadeh et al. The approach taken by the authors allowed them to avoid one of the major pitfalls of developmental studies, which is that they often provide highly biased estimates of accuracy. In a field that is filled with misinformation, hype, and inappropriate cynicism, it is essential for biomedical scientists to obtain the guidance and collaboration of biostatisticians. This principle is especially important for microarray-based studies because the number of candidate predictors genes is generally orders of magnitude greater than the number of cases. Although the expression profiles utilized by Asgharzadeh et al. Another approach to establish clinical utility is that used by Paik et al. Consequently, one should not expect reproducibility in the gene sets selected in developing classifiers for different data sets. In developing a classifier on a reduced data training set, the model development algorithm must be applied from scratch, without using any information based on data not part of that reduced data training set. A classifier that is prognostic for such a mixed group of patients generally has very limited therapeutic relevance. Such patients presumably do not require adjuvant treatment with cytotoxic chemotherapy, which represents clinical utility. Long follow-up will then be required before the results are evaluable. The expression of many genes is correlated, and hence it is to be expected that the genes selected for inclusion in a classifier will not be stable among studies. The fundamental principle is that the same data should not be used for developing a predictive classifier and for evaluating the accuracy of that classifier. Patients classified as having a low risk of recurrence using the gene classifier had a year survival of approximately 0. This classifier was previously developed by investigators at the Netherlands Cancer Institute 10 for a mixed population of node-negative and node-positive patients younger than 55 years of age who had received no systemic therapy. Many studies, by contrast, make the mistake of focusing on the statistical significance of the estimated correlation between clinical outcome and predicted risk group. The studies in this issue, taken together, illustrate many desirable features of gene expression profiling studies for optimizing treatment selection for individual patients. Consequently, their data do not reflect the many potential sources of variation in real-world conditions, with prospective tissue handling, assay drift, and reagent batch effects within an assay laboratory, as well as interlaboratory assay variation. Consequently, most of the patients randomly assigned in the prospective MINDACT trial to evaluate clinical utility of the gene classifier will presumably have ER-positive tumors because few discrepancies in treatment selection would be expected for the ER-negative patients. J Natl Cancer Inst. Nevertheless, the study plans to prospectively accrue patients. They then developed cross-validated Kaplan—Meier curves of progression-free survival and evaluated the statistical significance of the log-rank statistic used as a measure of the separation of the Kaplan—Meier curves by permutational methods, as initially described by Vaselli et al. In this case, the estimate of prediction accuracy pertains to the model that was developed using the full dataset, which is the model that will be used in future studies. If the patients in the first group have better outcomes than those in the second—i. For example, the authors used archived frozen tumor specimens, had them assayed at a single reference laboratory, and limited eligibility to patients less than 61 years of age.

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Roadmap for developing and validating therapeutically relevant genomic classifiers

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Evaluating Inclusion and Exclusion Criteria in Clinical Trials




Such patients presumably do not require adjuvant treatment with cytotoxic chemotherapy, which represents clinical utility. The studies in this issue, taken together, illustrate many desirable features of gene expression profiling studies for optimizing treatment selection for individual patients. Consequently, their data do not reflect the many potential sources of variation in real-world conditions, with prospective tissue handling, assay drift, and reagent batch effects within an assay laboratory, as well as interlaboratory assay variation. What matters, however, is whether a classifier provides accurate prediction for independent data Long follow-up will then be required before the results are evaluable. Validation studies should ideally establish the predictive accuracy and clinical utility of the classifier under conditions that simulate the prospective broad clinical application of the classifier. I believe that the answer is no. This instability is exacerbated by the very stringent significance levels that are generally used for identifying genes whose expression is correlated with patient outcome and are therefore included in the classifier. It might have been advantageous, therefore, to have focused the development and validation of the gene classifier on ER-positive patients who were receiving tamoxifen. Two articles in this issue of the Journal describe clinical studies of gene expression profiling—one a developmental study and the other a validation study.

Roadmap for developing and validating therapeutically relevant genomic classifiers


The purpose of a validation study is not to see whether redeveloping a classifier with new data results in selection of the same genes, although this is one of the most common misunderstandings of validation. The article by Asgharzadeh et al. The approach taken by the authors allowed them to avoid one of the major pitfalls of developmental studies, which is that they often provide highly biased estimates of accuracy. In a field that is filled with misinformation, hype, and inappropriate cynicism, it is essential for biomedical scientists to obtain the guidance and collaboration of biostatisticians. This principle is especially important for microarray-based studies because the number of candidate predictors genes is generally orders of magnitude greater than the number of cases. Although the expression profiles utilized by Asgharzadeh et al. Another approach to establish clinical utility is that used by Paik et al. Consequently, one should not expect reproducibility in the gene sets selected in developing classifiers for different data sets. In developing a classifier on a reduced data training set, the model development algorithm must be applied from scratch, without using any information based on data not part of that reduced data training set. A classifier that is prognostic for such a mixed group of patients generally has very limited therapeutic relevance. Such patients presumably do not require adjuvant treatment with cytotoxic chemotherapy, which represents clinical utility. Long follow-up will then be required before the results are evaluable. The expression of many genes is correlated, and hence it is to be expected that the genes selected for inclusion in a classifier will not be stable among studies. The fundamental principle is that the same data should not be used for developing a predictive classifier and for evaluating the accuracy of that classifier. Patients classified as having a low risk of recurrence using the gene classifier had a year survival of approximately 0. This classifier was previously developed by investigators at the Netherlands Cancer Institute 10 for a mixed population of node-negative and node-positive patients younger than 55 years of age who had received no systemic therapy. Many studies, by contrast, make the mistake of focusing on the statistical significance of the estimated correlation between clinical outcome and predicted risk group. The studies in this issue, taken together, illustrate many desirable features of gene expression profiling studies for optimizing treatment selection for individual patients. Consequently, their data do not reflect the many potential sources of variation in real-world conditions, with prospective tissue handling, assay drift, and reagent batch effects within an assay laboratory, as well as interlaboratory assay variation. Consequently, most of the patients randomly assigned in the prospective MINDACT trial to evaluate clinical utility of the gene classifier will presumably have ER-positive tumors because few discrepancies in treatment selection would be expected for the ER-negative patients. J Natl Cancer Inst. Nevertheless, the study plans to prospectively accrue patients. They then developed cross-validated Kaplan—Meier curves of progression-free survival and evaluated the statistical significance of the log-rank statistic used as a measure of the separation of the Kaplan—Meier curves by permutational methods, as initially described by Vaselli et al. In this case, the estimate of prediction accuracy pertains to the model that was developed using the full dataset, which is the model that will be used in future studies. If the patients in the first group have better outcomes than those in the second—i. For example, the authors used archived frozen tumor specimens, had them assayed at a single reference laboratory, and limited eligibility to patients less than 61 years of age.

Roadmap for developing and validating therapeutically relevant genomic classifiers


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