Is Molecular Profiling Ready For Use In Clinical Decision Making?

Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece.
The Oncologist (Impact Factor: 4.87). 04/2007; 12(3):301-11. DOI: 10.1634/theoncologist.12-3-301
Source: PubMed


Molecular profiling, the classification of tissue or other specimens for diagnostic, prognostic, and predictive purposes based on multiple gene expression, is a technology that holds major promise for optimizing the management of patients with cancer. However, the use of these tests for clinical decision making presents many challenges to overcome. Assay development and data analysis in this field have been largely exploratory, and leave numerous possibilities for the introduction of bias. Standardization of profiles remains the exception. Classifier performance is usually overinterpreted by presenting the results as p-values or multiplicative effects (e.g., relative risks), while the absolute sensitivity and specificity of classification remain modest at best, especially when tested in large validation samples. Validation has often been done with suboptimal attention to methodology and protection from bias. The postulated classifier performance may be inflated compared to what these profiles can achieve. With the exception of breast cancer, we have little evidence about the incremental discrimination that molecular profiles can provide versus classic risk factors alone. Clinical trials have started to evaluate the utility of using molecular profiles for breast cancer management. Until we obtain data from these trials, the impact of these tests and the net benefit under real-life settings remain unknown. Optimal incorporation into clinical practice is not straightforward. Finally, cost-effectiveness is difficult to appreciate until these other challenges are addressed. Overall, molecular profiling is a fascinating and promising technology, but its incorporation into clinical decision making requires careful planning and robust evidence.

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    • "All three of these gene expression signatures were defined based on the ability to predict distant recurrence whereas the PAM50 genes were defined based on the ability to identify the underlying biology defined by the four intrinsic breast cancer subtypes, which are themselves predictive of distant recurrence. The Oncotype DX Recurrence Score is considered by some to be predictive of chemotherapy benefit[42]; however, some controversy exists regarding the bioinformatics approach used to make this claim[43]and the relevance of the chemotherapy regimens and patient population used within the clinical trials tested[44]. There are ongoing clinical trials to assess the clinical utility of the MammaPrint RS[45], Oncotype DX® Recurrence Score®[46]and Prosigna ROR[47]within the ER-positive, Her2-negative intended use population using modern chemotherapy regimens. "
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    ABSTRACT: Background: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. Methods: 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. Results: The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. Conclusions: The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.
    Full-text · Article · Aug 2015 · BMC Medical Genomics
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    • "Precise and reproducible measurement of the amount of a specific protein or RNA species in a tissue specimen is therefore necessary for reliable administration of therapeutic course for individual cancer patients. Preanalytic factors, related to the tumor itself but associated with the procedures used for specimen collection, handling and preservation, may introduce variation and potentially bias analytical results obtained via immunohistochemistry or gene expression assays (Abdullah-Sayani et al., 2006; Ioannidis, 2007; Sparano and Solin, 2010). In an effort to address this issue, guidelines have been established for standardizing the handling of clinical specimens with the goal to minimize the potential impact of preanalytical factors on the quality of the biospecimen (Hammond et al., 2010; Wolff et al., 2007). "
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    ABSTRACT: Background Tissue handling can alter global gene expression potentially affecting the analytical performance of genomic signatures, but such effects have not been systematically evaluated. Methods Tissue samples from 11 previously untreated breast tumors were minced and aliquots were either snap frozen or placed in RNAlater immediately or after 20, 40, 60, 120 or 180 minutes at room temperature. RNA was profiled on Affymetrix HG-U133A arrays. We used probe-set-wise hierarchical models to evaluate the effect of preservation method on transcript expression and linear mixed effects models to assess the effect of cold ischemic delay on the expression of individual probe sets. Gene set enrichment analysis identified pathways overrepresented in the affected transcripts. We combined the levels of 41 most sensitive transcripts to develop an index of ischemic stress. Results Concordance in global gene expression between the baseline and 40 min delay was higher for samples preserved in RNAlater (average concordance correlation coefficient CCC = 0.92 compared to 0.88 for snap frozen). Overall, 481 transcripts (3%) were significantly affected by the preservation method, most of them involved in processes important in cancer. Prolonged cold ischemic delay of up to 3 hours induced marginal global gene expression changes (average CCC=0.90 between baseline and 3 hour delay). However 41 transcripts were significantly affected by cold ischemic delay. Among the induced transcripts were stress response genes, apoptotic response genes; among the downregulated were genes involved in metabolism, protein processing and cell cycle regulation. An index combining the expression levels of these genes was proportional to the cold ischemic delay. Conclusions Prolonged cold ischemia induces significant transcriptional changes in a small subset of transcripts in the tissue. Furthermore, the expression level of about 3% of the transcripts is affected by the preservation method. These sensitive transcripts should not be included in genomic signatures for more reliable analytical performance.
    Full-text · Article · May 2014 · Molecular oncology
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    • "Today biological samples become an increasingly important tool for biomedical research into human diseases [1], [2]. High quality biosample with RNA closely representing the amount of transcripts in vivo can ensure a more accurate downstream molecular assay [3]–[5]. However, some pre-analytical factors like biosample collection, handling, or processing can affect the RNA quality [6]–[9]. "
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    ABSTRACT: Some biosamples obtained from biobanks may go through thawing before processing. We aim to evaluate the effects of thawing at room temperature for different time periods on gene expression analysis. A time course study with four time points was conducted to investigate the expression profiling on 10 thawed normal mice renal tissue samples through Affymetrix GeneChip mouse gene 2.0 st array. Microarray results were validated by quantitative real time polymerase chain reactions (qPCR) on 6 candidate reference genes and 11 target genes. Additionally, we used geNorm plus and NormFinder to identify the most stably expressed reference genes over time. The results showed RNA degraded more after longer incubation at room temperature. However, microarray results showed only 240 genes (0.91%) altered significantly in response to thawing at room temperature. The signal of majority altered probe sets decreased with thawing time, and the crossing point (Cp) values of all candidate reference genes correlated positively with the thawing time (p<0.05). The combination of B2M, ACTB and PPIA was identified as the best choice for qPCR normalization. We found most target genes were stable by using this normalization method. However, serious gene quantification errors were resulted from improper reference genes. In conclusion, thirty minutes of thawing at room temperature has a limited impact on microarray and qPCR analysis, gene expression variations due to RNA degradation in early period after thawing can be largely reduced by proper normalization.
    Full-text · Article · Mar 2014 · PLoS ONE
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