Gene expression profile assays as predictors of recurrence-free survival in early-stage breast cancer: a metaanalysis

Economic issues involved in integrating genomic testing into clinical care: the case of genomic testing to guide decision-making about chemotherapy for breast cancer patients

The use of taxanes to treat node-positive (N+) breast cancer patients is associated with heterogeneous benefits as well as with morbidity and financial costs. This study aimed to assess the economic impact of using gene expression profiling to guide decision-making about chemotherapy, and to discuss the coverage/reimbursement issues involved. Retrospective data on 246 patients included in a randomised trial (PACS01) were analyzed. Tumours were genotyped using DNA microarrays (189-gene signature), and patients were classified depending on whether or not they were likely to benefit from chemotherapy regimens without taxanes. Standard anthracyclines plus taxane chemotherapy (strategy AT) was compared with the innovative strategy based on genomic testing (GEN). Statistical analyses involved bootstrap methods and sensitivity analyses. The AT and GEN strategies yielded similar 5-year metastasis-free survival rates. In comparison with AT, GEN was cost-effective when genomic testing costs were less than 2,090€. With genomic testing costs higher than 2,919€, AT was cost-effective. Considering a 30% decrease in the price of docetaxel (the patent rights being about to expire), GEN was cost-effective if the cost of genomic testing was in the 0€-1,139€, range; whereas AT was cost-effective if genomic testing costs were higher than 1,891€. The use of gene expression profiling to guide decision-making about chemotherapy for N+ breast cancer patients is potentially cost-effective. Since genomic testing and the drugs targeted in these tests yield greater well-being than the sum of those resulting from separate use, questions arise about how to deal with extra well-being in decision-making about coverage/reimbursement.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.

Evaluating use characteristics for the oncotype dx 21-gene recurrence score and concordance with chemotherapy use in early-stage breast cancer

PURPOSE

Oncotype Dx 21-gene assay recurrence score (RS) predicts recurrence of early-stage breast cancer (ESBC). We investigated whether patient, tumor, or practice characteristics drive its use and explored Oncotype DX RS and chemotherapy use in subgroups.

METHODS

Patients with ESBC with documented estrogen receptor-positive, lymph node-negative, human epidermal growth factor receptor 2-negative tumors registered within McKesson Specialty Health's iKnowMed electronic health record were included. Patient and practice characteristics by region and size were analyzed. The association between Oncotype DX RS value and use of chemotherapy were assessed.

RESULTS

The study included 6,229 patients. Of these, 1,822 (29%) had an Oncotype DX RS result. Test use was 36%, 38%, 34%, 25%, and 6%, respectively, in patients age ≤ 45, 46-55, 56-65, 66-75, and ≥ 76 years; 33%, 25%, and 9% in patients with Eastern Cooperative Oncology Group performance status of 0, 1, and ≥ 2; 7%, 9%, 25%, 38%, 27%, and 10% in T1mic, T1a, T1b, T1c, T2, and T3 tumors; and 26%, 32%, and 33% for grades 1, 2, and 3 tumors. Of the 1,822 patients with available Oncotype DX RS, adjuvant chemotherapy use was 6%, 42%, and 84% in the low-, intermediate-, and high-risk groups.

CONCLUSION

Patients who were younger, had better ECOG performance status, or had higher grade tumors were more likely to undergo RS testing. It appears that the RS test may have influenced the decision about whether to administer adjuvant chemotherapy: a low RS score was associated with lower chemotherapy use and a high RS score was associated with higher chemotherapy use.

Identification of new hematopoietic cell subsets with a polyclonal antibody library specific for neglected proteins

The identification of new markers, the expression of which defines new phenotipically and functionally distinct cell subsets, is a main objective in cell biology. We have addressed the issue of identifying new cell specific markers with a reverse proteomic approach whereby approximately 1700 human open reading frames encoding proteins predicted to be transmembrane or secreted have been selected in silico for being poorly known, cloned and expressed in bacteria. These proteins have been purified and used to immunize mice with the aim of obtaining polyclonal antisera mostly specific for linear epitopes. Such a library, made of about 1600 different polyclonal antisera, has been obtained and screened by flow cytometry on cord blood derived CD34+CD45dim cells and on peripheral blood derived mature lymphocytes (PBLs). We identified three new proteins expressed by fractions of CD34+CD45dim cells and eight new proteins expressed by fractions of PBLs. Remarkably, we identified proteins the presence of which had not been demonstrated previously by transcriptomic analysis. From the functional point of view, looking at new proteins expressed on CD34+CD45dim cells, we identified one cell surface protein (MOSC-1) the expression of which on a minority of CD34+ progenitors marks those CD34+CD45dim cells that will go toward monocyte/granulocyte differentiation. In conclusion, we show a new way of looking at the membranome by assessing expression of generally neglected proteins with a library of polyclonal antisera, and in so doing we have identified new potential subsets of hematopoietic progenitors and of mature PBLs.

Can survival prediction be improved by merging gene expression data sets?

BACKGROUND

High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.

RESULTS

Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1.

CONCLUSIONS

Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

Genetic prognostic and predictive markers in colorectal cancer

Despite many studies of the likely survival outcome of individual patients with colorectal cancer, our knowledge of this subject remains poor. Until recently, we had virtually no understanding of individual responses to therapy, but the discovery of the KRAS mutation as a marker of probable failure of epidermal growth factor receptor (EGFR)-targeted therapy is a first step in the tailoring of treatment to the individual. With the application of molecular analyses, as well as the ability to perform high-throughput screens, there has been an explosive increase in the number of markers thought to be associated with prognosis and treatment outcome in this disease. In this Review, we attempt to summarize the sometimes confusing findings, and critically assess those markers already in the public domain.

Transcriptomics and micronutrient research

This review examines the extent to which transcriptomic methods have lived up to their promise in the context of nutrition research, placing particular emphasis on examples from micronutrient research. A case is made that the high quality platform technologies now available, together with established standards and systems for data storage and exchange and powerful new methods of data analysis, mean that microarrays have reached a level of technical maturity at which they can be exploited to their full potential. In the context of nutrition and micronutrient research, transcriptomic methods have already been widely applied, albeit primarily in studies using cell lines and animal models. Using this type of approach, a multitude of genes regulated at the mRNA level by dietary components has been identified and this, in turn, has provided new insights into the biological processes affected by nutritional parameters. Evidence from the very limited number of published transcriptomics-based nutritional studies performed in human volunteers suggests that, with appropriate study design, it is feasible to apply transcriptomic methods successfully in dietary intervention trials. On the other hand, gene expression-based biomarker development still poses a major challenge. Here the use of expression profile 'signatures', rather than single genes, may provide a solution. Approaches designed to identify such 'signatures' are being developed and tested widely, primarily in the context of medical research. The applicability and power of such approaches should also be evaluated in the context of nutrition.

The modern histopathologist: in the changing face of time

The molecular age histopathologist of today is practicing pathology in a totally different scenario than the preceding generations did. Histopathologists stand, as of now, on the cross roads of a traditional 'visible' morphological science and an 'invisible' molecular science. As molecular diagnosis finds more and more applicability in histopathological diagnosis, it is time for the policy makers to reframe the process of accreditation and re-accreditation of the modern histopathologist in context to the rapid changes taking place in this science. Incorporation of such 'molecular' training viv-a-vis information communication technology skills viz. telemedicine and telepathology, digital imaging techniques and photography and a sound knowledge of the economy that the fresh entrant would ultimately become a part of would go a long way to produce the Modern Histopathologist. This review attempts to look at some of these aspects of this rapidly advancing 'art of science.'

Adjuvant chemotherapy--yes or no? Prognostic markers in early breast cancer

The question of whether to offer adjuvant chemotherapy to patients with early-stage breast cancer continues to challenge clinicians on a daily basis. Many patients with node-negative or low-risk breast cancer could be spared the trauma of receiving chemotherapy, but more reliable prognostic markers are still needed to aid our therapy decision making. Gene expression profiling is a welcome product of the 'omic' era and several multi-gene expression panels ('signatures') have now been developed that show promise in predicting which breast cancer patients are likely to develop metastatic disease if adjuvant chemotherapy is not administered. The value of gene expression profiling as a prognostic tool in clinical practice is currently being appraised more fully in two large, prospective, randomised studies--TAILORx and MINDACT. These studies should provide level I evidence of the prognostic power of gene expression profiling and will hopefully allow us to one day quantify risk of progression in the individual patient and tailor treatment accordingly. Genetic profiling of circulating tumour cells and micrometastases should further enhance our understanding of breast cancer biology and allow us to personalise therapy based on functional maps of critical tumour pathways. Close collaboration between clinicians and scientists will be essential to achieve this goal.

Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation

BACKGROUND

Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays.

RESULTS

We gathered expression data from 9 published microarray studies examining estrogen receptor positive (ER+) and estrogen receptor negative (ER-) BrCa tumor cases from the Oncomine database. We performed a meta-analysis and identified genes that were universally up or down regulated with respect to ER+ versus ER- tumor status. We surveyed both the proximal promoter and 3' untranslated regions (3'UTR) of our top-ranking genes in each expression group to test whether common sequence elements may contribute to the observed expression patterns. Utilizing a combination of known transcription factor binding sites (TFBS), evolutionarily conserved mammalian promoter and 3'UTR motifs, and microRNA (miRNA) seed sequences, we identified numerous motifs that were disproportionately represented between the two gene classes suggesting a common regulatory network for the observed gene expression patterns.

CONCLUSION

Some of the genes we identified distinguish key transcripts previously seen in array studies, while others are newly defined. Many of the genes identified as overexpressed in ER- tumors were previously identified as expression markers for neoplastic transformation in multiple human cancers. Moreover, our motif analysis identified a collection of specific cis-acting target sites which may collectively play a role in the differential gene expression patterns observed in ER+ versus ER- breast cancer tumors. Importantly, the gene sets and associated DNA motifs provide a starting point with which to explore the mechanistic basis for the observed expression patterns in breast tumors.

The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?

Advances in genomics have led to mounting expectations in regard to their impact on health care and disease prevention. In light of this fact, a comprehensive research agenda is needed to move human genome discoveries into health practice in a way that maximizes health benefits and minimizes harm to individuals and populations. We present a framework for the continuum of multidisciplinary translation research that builds on previous characterization efforts in genomics and other areas in health care and prevention. The continuum includes four phases of translation research that revolve around the development of evidence-based guidelines. Phase 1 translation (T1) research seeks to move a basic genome-based discovery into a candidate health application (e.g., genetic test/intervention). Phase 2 translation (T2) research assesses the value of a genomic application for health practice leading to the development of evidence-based guidelines. Phase 3 translation (T3) research attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research. Phase 4 translation (T4) research seeks to evaluate the "real world" health outcomes of a genomic application in practice. Because the development of evidence-based guidelines is a moving target, the types of translation research can overlap and provide feedback loops to allow integration of new knowledge. Although it is difficult to quantify how much of genomics research is T1, we estimate that no more than 3% of published research focuses on T2 and beyond. Indeed, evidence-based guidelines and T3 and T4 research currently are rare. With continued advances in genomic applications, however, the full continuum of translation research needs adequate support to realize the promise of genomics for human health.

Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer

BACKGROUND

The prognostic accuracy for distant recurrence-free survival using a 21-gene reverse-transcriptase polymerase chain reaction (RT-PCR) assay underwent validation in 668 lymph node-negative, estrogen receptor-positive women with early-stage breast cancer receiving tamoxifen on National Surgical Adjuvant Breast Program (NSABP) B-14. The predictive accuracy for treatment efficacy also underwent validation in 651 patients randomized on NSABP B-20 and 645 patients on NSABP B-14.

METHODS

Patients were classified as high (recurrence score [RS] >or= 31), intermediate (RS 18-30), or low (RS < 18) risk for distant recurrence at 10 years. Cost-effectiveness ratios were estimated for RS-guided treatment compared with either tamoxifen alone or the combined chemotherapy and tamoxifen.

RESULTS

Distant recurrence was reported in RS low-risk, intermediate-risk, and high-risk patients at 10 years in 3.7%, 17.8%, and 38.3% receiving tamoxifen alone and 5.0%, 10.1%, and 11.1% receiving the chemotherapy and tamoxifen. RS-guided therapy is associated with a gain in individual life expectancy of 2.2 years compared with tamoxifen alone, whereas it is associated with similar life expectancy to that seen with the chemotherapy and tamoxifen strategy. RS-guided therapy is estimated to provide a net cost savings of $2256 compared with chemotherapy and tamoxifen with an incremental cost-effectiveness ratio of $1944 per life year saved compared with tamoxifen alone.

CONCLUSIONS

Treatment decisions based on RS-guided therapy compared with tamoxifen alone are associated with greater efficacy with acceptable cost-effectiveness ratios, and associated with similar efficacy and lower cost compared with chemotherapy and tamoxifen for patients with lymph node-negative, estrogen receptor-positive early-stage breast cancer.