Recommendations from the EGAPP Working Group: can tumor gene expression profiling improve outcomes in patients with breast cancer?

Testing a deliberative democracy method with citizens of African ancestry to weigh pros and cons of targeted screening for hereditary breast and ovarian cancer risk

Background

Democratic deliberation (DD), a strategy to foster co-learning among researchers and communities, could be applied to gain informed public input on health policies relating to genomic translation.

Purpose

We evaluated the quality of DD for gaining informed community perspectives regarding targeting communities of African Ancestry (AAn) for Hereditary Breast and Ovarian Cancer (HBOC) screening in Georgia.

Methods

We audiotaped a 2.5 day conference conducted via zoom in March 2021 to examine indicators of deliberation quality based on three principles: (1) inclusivity (diverse viewpoints based on participants' demographics, cancer history, and civic engagement), (2) consideration of factual information (balanced and unbiased expert testimonies, participant perceived helpfulness), and (3) deliberation (speaking opportunities, adoption of a societal perspective on the issue, reasoned justification of ideas, and participant satisfaction).

Results

We recruited 24 participants who reflected the diversity of views and life experiences of citizens of AAn living in Georgia. The expert testimony development process we undertook for creating balanced factual information was endorsed by experts' feedback. Deliberation process evaluation showed that while participation varied (average number of statements = 24, range: 3-62), all participants contributed. Participants were able to apply expert information and take a societal perspective to deliberate on the pros and cons of targeting individuals of AAn for HBOC screening in Georgia.

Conclusions

The rigorous process of public engagement using deliberative democracy approach can successfully engage a citizenry with diverse and well-informed views, do so in a relatively short time frame and yield perspectives based on high quality discussion.

ExhauFS: exhaustive search-based feature selection for classification and survival regression

Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS-the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e., turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L₁-regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS.

miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).

Cost-effectiveness of Molecular Profile Patient Selection for First-line Treatment of Recurrent/Metastatic Head and Neck Cancer

PURPOSE

Patients with recurrent and/or metastatic head and neck squamous cell cancer are offered platinum-based chemotherapy plus cetuximab; however, this strategy is not cost-effective. We evaluated the cost-effectiveness of a hypothetical predictive molecular test to identify and treat only patients potentially responsive to cetuximab (C) added to platinum-fluorouracil (PF) (PF + C POS) versus the administration of PF + C to all patients (PF + C ALL).

METHODS

A Markov model has been developed to estimate health outcomes (quality-adjusted life years [QALYs]; life years [LYs]) and costs of the 2 strategies on a time horizon of 3 years from the Italian health care perspective. For the response to treatment, a definition, including partial or complete response, has been applied. In the base-case scenario, molecular test sensitivity, specificity, and cost have been assumed equal to 85%, 70%, and €4000, respectively.

FINDINGS

The model estimated 0.5285 QALYs (0.9245 LYs) and 0.5666 QALYs (0.9949 LYs) for PF + C POS and PF + C ALL, respectively. The incremental cost-utility ratio of PF + C ALL versus PF + C POS was €112,462/QALY, suggesting the administration of PF + C only to patients who would be responsive to it.

IMPLICATIONS

The use of cetuximab with chemotherapy could be a cost-effective choice in first-line recurrent and/or metastatic head and neck squamous cell cancer if based on a molecular selection able to identify which patients will achieve partial or complete response to the treatment. The developed model may be usefully applied to new emerging treatments, such as immunotherapeutic agents in the same setting.

Oncoproteomic and gene expression analyses identify prognostic biomarkers for second primary malignancy in patients with head and neck squamous cell carcinoma

Patients with head and neck squamous cell carcinoma are at increased risk of developing a second primary malignancy, which is associated with poor prognosis and early death. To help improve clinical outcome, we aimed to identify biomarkers for second primary malignancy risk prediction using the routinely obtained formalin-fixed paraffin-embedded tissues of the index head and neck cancer. Liquid chromatography-tandem mass spectrometry was initially performed for candidate biomarker discovery in 16 pairs of primary cancer tissues and their matched normal mucosal epithelia from head and neck squamous cell carcinoma patients with or without second primary malignancy. The 32 candidate proteins differentially expressed between head and neck cancers with and without second primary malignancy were identified. Among these, 30 selected candidates and seven more from literature review were further studied using NanoString nCounter gene expression assay in an independent cohort of 49 head and neck cancer patients. Focusing on the p16-negative cases, we showed that a multivariate logistic regression model comprising the expression levels of ITPR3, KMT2D, EMILIN1, and the patient's age can accurately predict second primary malignancy occurrence with 88% sensitivity and 75% specificity. Furthermore, using Cox proportional hazards regression analysis and survival analysis, high expression levels of ITPR3 and DSG3 were found to be significantly associated with shorter time to second primary malignancy development (log-rank test P = 0.017). In summary, we identified a set of genes whose expressions may serve as the prognostic biomarkers for second primary malignancy occurrence in head and neck squamous cell carcinomas. In combination with the histopathologic examination of index tumor, these biomarkers can be used to guide the optimum frequency of second primary malignancy surveillance, which may lead to early diagnosis and better survival outcome.

Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients

Background

CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast.

Methods

All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively.

Results

CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested.

Conclusions

The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5443-5) contains supplementary material, which is available to authorized users.

Pathologist's health-care value in the triage of Oncotype DX® testing: a value-based pathology study of tumour biology with outcomes

AIMS

Pathologists provide expert tissue assessment of breast cancer, yet their value to guide the appropriate use of breast cancer gene expression profile tests (GEPT) is underutilised. The specific aims of this study are to report morpho-immunohistological characteristics of breast tumours with Oncotype DX^® (ODx) recurrence scores (RS) of 10 or fewer (ultra-low risk) and 25 or fewer (low risk) in order to determine if pathologists can identify prospectively patient tumours that do not require ODx testing.

METHODS AND RESULTS

Oncotype DX^® cases with RS < 10 from 2005 to 2010 comprised 441 of 2594 (17%) of clinical cases; this cohort had 5 years' follow-up and was treated with endocrine therapy alone. Tumours were analysed for tumour type, Nottingham grade, mitosis score (MS) semi-quantitative (H-score) hormone receptor content and Magee equation 3. Knowledge derived from this data set was used to develop algorithms in order to identify prospectively tumours with RS of 10 or fewer or 25 or fewer. Thirty-four per cent of tumours were low-grade special types, while the remainder were enriched with high hormone receptor content with MS of 1. These algorithmic selection criteria identified correctly all patient cases below the chemotherapy cut-point, i.e. RS < 25, indicating that these oncotype test orders were an unnecessary cost.

CONCLUSIONS

This unique study demonstrates that (i) pathologists add great value to triage breast cancer for GEPT; and (ii) can identify prospectively low-grade tumour biology with high sensitivity and high specificity for those cases which do not require chemotherapy (RS < 25) using MS and hormone receptor content.

Characterizing Molecular Variants and Clinical Utilization of Next-generation Sequencing in Advanced Breast Cancer

Women with advanced breast carcinomas have few therapeutic options. Recent advances in genomic profiling represent a new paradigm of cancer classification and treatment, but experience with genomic testing in a clinical setting remains limited. We retrospectively determined the genomic variants and correlate these with histology [histomorphologic subtype, nuclear grade, standard immunohistochemistry (IHC)] and clinical utilization (ordering, turnaround time, report review, and targeted therapy). Among 48 patients, 2 showed no genetic alterations, 11 (23%) showed variants of unclear significance only and 35 (73%) showed variant(s) affecting function (VaF) and/or variants of unclear significance. Overall, 119 variants were observed in 20 of 50 tested genes. Each patient had a unique molecular profile, with numerous (n=58) variants not previously reported in breast cancer. VaF detected in more than 2 patients included: TP53 (n=21), PIK3CA (n=20), and FGFR1 (n=3). VaF comprised 46 single nucleotide variants (79%), 7 amplifications (12%), 3 frameshifts (5%), 1 insertion (2%), and 1 deletion (2%). The tested samples had very high Ki67 index (average 57%±23%) and approximately half were hormone receptor and HER2 negative (25/46, 54%). Metastatic breast carcinomas showed a higher average VaF versus breast-localized tumors (1.3±0.99 vs. 0.18±0.60, P<0.05). Next-generation sequencing reports were promptly reported and reviewed (average 1 to 2 d) and 7 (∼25%) of potentially eligible patients received targeted therapy. Advanced breast cancers show unique landscapes of genetic variants. Most testing was done in late disease, often in metastatic and receptor-negative carcinomas. Next-generation sequencing results were promptly reported and reviewed, but the utilization of targeted therapies was limited.

Comprehensive network of miRNA-induced intergenic interactions and a biological role of its core in cancer

MicroRNAs (miRNAs) are a family of short noncoding RNAs that posttranscriptionally regulate gene expression and play an important role in multiple cellular processes. A significant percentage of miRNAs are intragenic, which is often functionally related to their host genes playing either antagonistic or synergistic roles. In this study, we constructed and analyzed the entire network of intergenic interactions induced by intragenic miRNAs. We further focused on the core of this network, which was defined as a union of nontrivial strongly connected components, i.e., sets of nodes (genes) mutually connected via directed paths. Both the entire network and its core possessed statistically significant non-random properties. Specifically, genes forming the core had high expression levels and low expression variance. Furthermore, the network core did not split into separate components corresponding to individual signalling or metabolic pathways, but integrated genes involved in key cellular processes, including DNA replication, transcription, protein homeostasis and cell metabolism. We suggest that the network core, consisting of genes mutually regulated by their intragenic miRNAs, could coordinate adjacent pathways or homeostatic control circuits, serving as a horizontal inter-circuit link. Notably, expression patterns of these genes had an efficient prognostic potential for breast and colorectal cancer patients.

Clinical verification of genetic results returned to research participants: findings from a Colon Cancer Family Registry

BACKGROUND

The extent to which participants act to clinically verify research results is largely unknown. This study examined whether participants who received Lynch syndrome (LS)-related findings pursued researchers' recommendation to clinically verify results with testing performed by a CLIA-certified laboratory.

METHODS

The Fred Hutchinson Cancer Research Center site of the multinational Colon Cancer Family Registry offered non-CLIA individual genetic research results to select registry participants (cases and their enrolled relatives) from 2011 to 2013. Participants who elected to receive results were counseled on the importance of verifying results at a CLIA-certified laboratory. Twenty-six (76.5%) of the 34 participants who received genetic results completed 2- and 12-month postdisclosure surveys; 42.3% of these (11/26) participated in a semistructured follow-up interview.

RESULTS

Within 12 months of result disclosure, only 4 (15.4%) of 26 participants reported having verified their results in a CLIA-certified laboratory; of these four cases, all research and clinical results were concordant. Reasons for pursuing clinical verification included acting on the recommendation of the research team and informing future clinical care. Those who did not verify results cited lack of insurance coverage and limited perceived personal benefit of clinical verification as reasons for inaction.

CONCLUSION

These findings suggest researchers will need to address barriers to seeking clinical verification in order to ensure that the intended benefits of returning genetic research results are realized.

Choosing not to undergo predictive genetic testing for hereditary colorectal cancer syndromes: expanding our understanding of decliners and declining

While medical research continues to investigate the genetic basis of cancer, and personalised prevention gains momentum, little research has been conducted with the individuals who decline predictive genetic testing for cancer. We recruited individuals who had been offered genetic testing for Lynch syndrome or bi-allelic MUTYH mutations due to their participation in a large, population-based, Australia-wide colorectal cancer study. Thirty-three individuals in mutation-carrying families, unaffected by cancer, who had actively or passively declined testing at one of four decision-making points, took part in a qualitative interview about their decision. Data analysis revealed a typology of 'decliners': (1) uninformed about genetic testing; (2) a weak intention to undergo genetic testing; (3) conditionally declining; and (4) unconditionally declining testing. In this population we found substantial barriers to achieving the benefits promised by predictive genetic testing; a lack of knowledge of the availability of genetic testing; a lack of trust in genetic test information; a desire to see a stronger benefit from genetic testing before proceeding; and a sense that there may be more negative than positive outcomes from genetic testing. These discourses must be addressed if medical research on the genetic basis of cancer continues to be funded, and personalised prevention of cancer continues to be recommended by experts.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature

INTRODUCTION

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making.

METHODS

We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer.

RESULTS AND CONCLUSIONS

Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy.

Adoption of Gene Expression Profiling for Breast Cancer in US Oncology Practice for Women Younger Than 65 Years

BACKGROUND

A number of practice guidelines incorporate the use of gene expression profiling (GEP) tests for early-stage, hormone receptor-positive, HER2-negative breast tumors. Few studies describe factors associated with GEP testing in US oncology practice. We assessed the relationship between clinical, demographic, and group-level socioeconomic variables and test use in women younger than 65 years.

PATIENTS AND METHODS

Data from 5 state cancer registries were linked with insurance claims data and GEP test results. We assessed rates of testing and variables associated with test use in an incident cohort of 9,444 commercially insured women younger than 65 years, newly diagnosed with stage I or II hormone receptor-positive breast cancer from 2006 through 2012.

RESULTS

Rates of testing for women with N0 disease increased from 20.4% in 2006 to 35.2% in 2011. Variables associated with higher rates of testing, beyond clinical factors such as nodal status (P<.001), included being diagnosed from 2008 through 2012 versus 2006 through 2007 (adjusted odds ratio [OR], 1.67; 95% CI, 1.47-1.90), having preexisting comorbidities (adjusted OR, 1.35; 95% CI, 1.14-1.59), and higher out-of-pocket pharmacy costs (adjusted OR, 1.66; 95% CI, 1.40-1.97). Women younger than 50 years were more likely to be tested if they had stage I versus stage II disease (P<.0001).

CONCLUSIONS

In an insured population of women younger than 65 years, GEP testing increased after its inclusion in clinical practice guidelines and mounting evidence. Additional research is needed to better understand oncologists' decision not to order GEP testing for their patients who are otherwise eligible.

Signatures of breast cancer metastasis at a glance

Gene expression profiling has yielded expression signatures from which prognostic tests can be derived to facilitate clinical decision making in breast cancer patients. Some of these signatures are based on profiling of whole tumor tissue (tissue signatures), which includes all tumor and stromal cells. Prognostic markers have also been derived from the profiling of metastasizing tumor cells, including circulating tumor cells (CTCs) and migratory-disseminating tumor cells within the primary tumor. The metastasis signatures based on CTCs and migratory-disseminating tumor cells have greater potential for unraveling cell biology insights and mechanistic underpinnings of tumor cell dissemination and metastasis. Of clinical interest is the promise that stratification of patients into high or low metastatic risk, as well as assessing the need for cytotoxic therapy, might be improved if prognostics derived from these two types of signatures are used in a combined way. The aim of this Cell Science at a Glance article and accompanying poster is to navigate through both types of signatures and their derived prognostics, as well as to highlight biological insights and clinical applications that could be derived from them, especially when they are used in combination.

The current use and attitudes towards tumor genome sequencing in breast cancer

There is increasing availability of technologies that can interrogate the genomic landscape of an individual tumor; however, their impact on daily practice remains uncertain. We conducted a 28-item survey to investigate the current attitudes towards the integration of tumor genome sequencing in breast cancer management. A link to the survey was communicated via newsletters of several oncological societies, and dedicated mailing by academic research groups. Multivariable logistic regression modeling was carried out to determine the relationship between predictors and outcomes. 215 physicians participated to the survey. The majority were medical oncologists (88%), practicing in Europe (70%) and working in academic institutions (66%). Tumor genome sequencing was requested by 82 participants (38%), of whom 21% reported low confidence in their genomic knowledge, and 56% considered tumor genome sequencing to be poorly accessible. In multivariable analysis, having time allocated to research (OR 3.37, 95% CI 1.84–6.15, p < 0.0001), working in Asia (OR 5.76, 95% CI 1.57 – 21.15, p = 0.01) and having institutional guidelines for molecular sequencing (OR 2.09, 95% 0.99–4.42, p = 0.05) were associated with a higher probability of use. In conclusion, our survey indicates that tumor genome sequencing is sometimes used, albeit not widely, in guiding management of breast cancer patients.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Prognostic ability of EndoPredict compared to research-based versions of the PAM50 risk of recurrence (ROR) scores in node-positive, estrogen receptor-positive, and HER2-negative breast cancer. A GEICAM/9906 sub-study

There are several prognostic multigene-based tests for managing breast cancer (BC), but limited data comparing them in the same cohort. We compared the prognostic performance of the EndoPredict (EP) test (standardized for pathology laboratory) with the research-based PAM50 non-standardized qRT-PCR assay in node-positive estrogen receptor-positive (ER+) and HER2-negative (HER2−) BC patients receiving adjuvant chemotherapy followed by endocrine therapy (ET) in the GEICAM/9906 trial. EP and PAM50 risk of recurrence (ROR) scores [based on subtype (ROR-S) and on subtype and proliferation (ROR-P)] were compared in 536 ER+/HER2− patients. Scores combined with clinical information were evaluated: ROR-T (ROR-S, tumor size), ROR-PT (ROR-P, tumor size), and EPclin (EP, tumor size, nodal status). Patients were assigned to risk-categories according to prespecified cutoffs. Distant metastasis-free survival (MFS) was analyzed by Kaplan–Meier. ROR-S, ROR-P, and EP scores identified a low-risk group with a relative better outcome (10-year MFS: ROR-S 87 %; ROR-P 89 %; EP 93 %). There was no significant difference between tests. Predictors including clinical information showed superior prognostic performance compared to molecular scores alone (10-year MFS, low-risk group: ROR-T 88 %; ROR-PT 92 %; EPclin 100 %). The EPclin-based risk stratification achieved a significantly improved prediction of MFS compared to ROR-T, but not ROR-PT. All signatures added prognostic information to common clinical parameters. EPclin provided independent prognostic information beyond ROR-T and ROR-PT. ROR and EP can reliably predict risk of distant metastasis in node-positive ER+/HER2− BC patients treated with chemotherapy and ET. Addition of clinical parameters into risk scores improves their prognostic ability.

A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds

Background

Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim

To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods

Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina’s Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results

Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion

The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Recommendations from the EGAPP Working Group: does the use of Oncotype DX tumor gene expression profiling to guide treatment decisions improve outcomes in patients with breast cancer?

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RECOMMENDATIONS

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group found insufficient evidence to recommend for or against the use of Oncotype DX testing to guide chemotherapy treatment decisions in women with hormone receptor-positive, lymph node-negative, or lymph node-positive early breast cancer who are receiving endocrine therapy. This recommendation statement updates a 2009 EGAPP statement on the use of gene expression profiling tests in breast cancer. Evidence of clinical validity for Oncotype DX was confirmed as adequate. With regard to clinical utility, although there was evidence from prospective retrospective studies that the Oncotype DX test predicts benefit from chemotherapy, and there was adequate evidence that the use of Oncotype DX gene expression profiling in clinical practice changes treatment decisions regarding chemotherapy, no direct evidence was found that the use of Oncotype DX testing leads to improved clinical outcomes.

RATIONALE

In women with early-stage invasive breast cancer, gene expression profiling is increasingly being used as an aid to estimate the likely benefit from chemotherapy treatment. In a previous recommendation statement, the EGAPP Working Group (EWG) found adequate evidence for clinical validity of some gene expression profiling tests in predicting distant disease recurrence in women with early-stage, hormone receptor-positive, lymph-node-negative breast cancer who are treated with tamoxifen, but insufficient evidence that use of these tests for decisions about chemotherapy treatment has clinical utility. The current recommendation statement updates these findings for Oncotype DX and extends them to the population of women with lymph node-positive disease, using evidence from recent systematic reviews and other sources.

ANALYTIC VALIDITY

The previous recommendation statement found that evidence was inadequate to enable quantitative determination of the analytic validity of Oncotype DX. Analytic validity was not reconsidered in the updated recommendation statement because there remains no gold-standard test for comparison.

CLINICAL VALIDITY

The EWG found that new evidence published since the original evidence review supports the clinical validity of Oncotype DX in predicting risk of distant metastases in women with hormone receptor-positive, early-stage breast cancer that is either node-negative or node-positive.

CLINICAL UTILITY

No direct evidence was found that use of Oncotype DX tumor gene expression profiling to guide treatment decisions improves clinical outcomes in women with early breast cancer. There is indirect evidence, from prospective retrospective studies on archived tissue samples from randomized controlled trials, that the Oncotype DX test can predict benefit from chemotherapy. Large, prospective, randomized, controlled trials currently in progress may provide evidence of clinical utility.

CONTEXTUAL ISSUES

Until definitive evidence for clinical utility is available, clinicians must decide on a case-by-case basis whether to offer the test to patients. Although Oncotype DX testing has been reported, on the basis of economic modeling studies, to be cost-effective in several different health-care systems and to save costs in the US health-care setting, studies were based on assumptions regarding the clinical utility of the test that require confirmation by clinical trial results.Genet Med 18 8, 770-779.

Cost effectiveness of personalized treatment in women with early breast cancer: the application of OncotypeDX and Adjuvant! Online to guide adjuvant chemotherapy in Austria

A Breast Cancer Outcomes model was developed at the ONCOTYROL research center to evaluate personalized test-treatment strategies in Austria. The goal was to evaluate the cost-effectiveness of a new 21-gene assay (ODX) when used in conjunction with the Adjuvant! Online (AO) decision aid to support personalized decisions about use of adjuvant chemotherapy in early-stage breast cancer patients in Austria. We applied a validated discrete-event-simulation model to a hypothetical cohort of 50 years old women over a lifetime horizon. The test-treatment strategies of interest were defined using three-letter acronyms. The first (second, third) letter indicates whether patients with a low (intermediate, high) risk according to AO were tested using ODX (Y yes, N no). The main outcomes were life-years gained, quality-adjusted life-years (QALYs), costs and cost effectiveness. Robustness of the results was tested in sensitivity analyses. Results were compared to a Canadian analysis conducted by the Toronto Health Economics and Technology Assessment Collaborative (THETA). Five of eight strategies were dominated (i.e., more costly and less effective: NNY, NYN, YNN, YNY, YYN). The base-case analysis shows that YYY (ODX provided to all patients) is the most effective strategy and is cost effective with an incremental cost-effectiveness ratio of 15,700 EUR per QALY gained. These results are sensitive to changes in the probabilities of distant recurrence, age and costs of chemotherapy. The results of the base-case analysis were comparable to the THETA results. Based on our analyses, using ODX in addition to AO is effective and cost effective in all women in Austria. The development of future genetic tests may require alternative or additional test-treatment strategies to be evaluated.

Use and Costs for Tumor Gene Expression Profiling Panels in the Management of Breast Cancer From 2006 to 2012: Implications for Genomic Test Adoption Among Private Payers

Gene expression profiling has diffused into clinical practice. Reimbursements by insurers have increased, and average out-of-pocket costs to patients have decreased, seemingly driven by improved coverage for testing over time.

SOCIO-ETHICAL ISSUES IN PERSONALIZED MEDICINE: A SYSTEMATIC REVIEW OF ENGLISH LANGUAGE HEALTH TECHNOLOGY ASSESSMENTS OF GENE EXPRESSION PROFILING TESTS FOR BREAST CANCER PROGNOSIS

OBJECTIVES

There have been multiple calls for explicit integration of ethical, legal, and social issues (ELSI) in health technology assessment (HTA) and addressing ELSI has been highlighted as key in optimizing benefits in the Omics/Personalized Medicine field. This study examines HTAs of an early clinical example of Personalized Medicine (gene expression profile tests [GEP] for breast cancer prognosis) aiming to: (i) identify ELSI; (ii) assess whether ELSIs are implicitly or explicitly addressed; and (iii) report methodology used for ELSI integration.

METHODS

A systematic search for HTAs (January 2004 to September 2012), followed by descriptive and qualitative content analysis.

RESULTS

Seventeen HTAs for GEP were retrieved. Only three (18%) explicitly presented ELSI, and only one reported methodology. However, all of the HTAs included implicit ELSI. Eight themes of implicit and explicit ELSI were identified. "Classical" ELSI including privacy, informed consent, and concerns about limited patient/clinician genetic literacy were always presented explicitly. Some ELSI, including the need to understand how individual patients' risk tolerances affect clinical decision-making after reception of GEP results, were presented both explicitly and implicitly in HTAs. Others, such as concern about evidentiary deficiencies for clinical utility of GEP tests, occurred only implicitly.

CONCLUSIONS

Despite a wide variety of important ELSI raised, these were rarely explicitly addressed in HTAs. Explicit treatment would increase their accessibility to decision-makers, and may augment HTA efficiency maximizing their utility. This is particularly important where complex Personalized Medicine applications are rapidly expanding choices for patients, clinicians and healthcare systems.

The value of personalizing medicine: medical oncologists' views on gene expression profiling in breast cancer treatment

OBJECTIVES

Guidelines recommend gene-expression profiling (GEP) tests to identify early-stage breast cancer patients who may benefit from chemotherapy. However, variation exists in oncologists' use of GEP. We explored medical oncologists' views of GEP tests and factors impacting its use in clinical practice.

METHODS

We used a qualitative design, comprising telephone interviews with medical oncologists (n = 14; 10 academic, 4 in the community) recruited through oncology clinics, professional advertisements, and referrals. Interviews were analyzed for anticipated and emergent themes using the constant comparative method including searches for disconfirming evidence.

RESULTS

Some oncologists considered GEP to be a tool that enhanced confidence in their established approach to risk assessments, whereas others described it as "critical" to resolving their uncertainty about whether to recommend chemotherapy. Some community oncologists also valued the test in interpreting what they considered variable practice and accuracy across pathology reports and testing facilities. However, concerns were also raised about GEP's cost, overuse, inappropriate use, and over-reliance on the results within the medical community. In addition, although many oncologists said it was simple to explain the test to patients, paradoxically, they remained uncertain about patients' understanding of the test results and their treatment implications.

CONCLUSION

Oncologists valued the test as a treatment-decision support tool despite their concerns about its cost, over-reliance, overuse, and inappropriate use by other oncologists, as well as patients' limited understanding of GEP. The results identify a need for decision aids to support patients' understanding and clinical practice guidelines to facilitate standardized use of the test.

Predictive markers in breast cancer: An update on ER and HER2 testing and reporting

Gene expression profiling of human tumors has provided a new paradigm for classifying breast carcinomas, predicting response to treatment, and risk of recurrence. Estrogen receptor (ER), human epidermal growth factor 2 (HER2) receptor, and proliferation-related genes are the main drivers of classification in many of the gene expression profiling tests for breast cancer. However, ER, progesterone receptor (PR), and HER2 receptor status remain essential in determining the need and type of adjuvant therapy. These biomarkers are routinely tested for in all invasive breast carcinomas; ER testing is also performed on cases of ductal carcinoma in situ (DCIS). This article will provide an update on current guidelines from the American Society of Clinical Oncologists (ASCO) and the College of American Pathologists (CAP) for ER and HER2 testing by immunohistochemistry (IHC) and in situ hybridization (ISH). The populations to be tested, antibody selection, criteria for interpretation, and reporting are discussed. The molecular alterations that correlate with IHC results, alternative methods of testing, and the current approach to complex aspects of HER2 testing, including heterogeneity and polysomy, also are summarized.

Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs

PURPOSE

The aim of this study was to compare guidance for genetic testing in US Food and Drug Administration (FDA)-approved drug labels in oncology to those of drugs for other therapeutic areas.

METHODS

We reviewed labels of all the FDA-approved drugs with labels containing pharmacogenomic information. We assessed whether genetic testing was required or recommended before prescription and, if not, the reason for pharmacogenomic labeling.

RESULTS

We included 140 drugs corresponding to 158 drug-biomarker pairs. Overall, 46 (29%) of 158 pairs stated a requirement or recommendation for genetic biomarker testing in the label. This proportion was higher in oncology than in other areas (62 vs. 12%; P < 0.001). For the 112 drug-biomarker pairs (including 20 in oncology) without recommendation or requirement for genetic testing, the main reasons for pharmacogenomic labeling were change in pharmacologic end points (32%) and higher risk of toxicity (30%). For 11 (10%) pairs (including 1 in oncology), a genetic biomarker was mentioned only to inform that it was not relevant. In oncology, the main reasons for pharmacogenomic labeling were higher risk of toxicity (55%) and definition of the mechanism of action (25%).

CONCLUSION

Inclusion of biomarkers in drug labels does not always correspond to required or recommended genetic testing, especially outside oncology.Genet Med 17 9, 733-738.

Clinical utility of gene-expression profiling in women with early breast cancer: an overview of systematic reviews

PURPOSE

This overview systematically evaluates the clinical utility of using Oncotype DX and MammaPrint gene-expression profiling tests to direct treatment decisions in women with breast cancer. The findings are intended to inform an updated recommendation from the Evaluation of Genomic Applications in Practice and Prevention Working Group.

METHODS

Evidence reported in systematic reviews evaluating the clinical utility of Oncotype DX and MammaPrint, as well as the ability to predict treatment outcomes, change in treatment decisions, and cost-effectiveness, was qualitatively synthesized.

RESULTS

Five systematic reviews found no direct evidence of clinical utility for either test. Indirect evidence showed Oncotype DX was able to predict treatment effects of adjuvant chemotherapy, whereas no evidence of predictive value was found for MammaPrint. Both tests influenced a change in treatment recommendations in 21 to 74% of participants. The cost-effectiveness of Oncotype DX varied with the alternative compared. For MammaPrint, lack of evidence of the predictive value led to uncertainty in the cost-effectiveness.

CONCLUSION

No studies were identified that provided direct evidence that using gene-expression profiling tests to direct treatment decisions improved outcomes in women with breast cancer. Three ongoing studies may provide direct evidence for determining the clinical utility of gene-expression profiling testing.

Overview of a chemoresponse assay in ovarian cancer

The objective of this review is to summarize recent scientific and medical literature regarding chemoresponse assays or chemotherapy sensitivity and resistance assays (CSRAs), specifically as applied to epithelial ovarian cancer. A total of sixty-seven articles, identified through PubMed using the key words “in vitro chemoresponse assay,” “chemo sensitivity resistance assay,” “ATP,” “HDRA,” “EDR,” “MiCK,” and “ChemoFx,” were reviewed. Recent publications on marker validation, including relevant clinical trial designs, were also included. Recent CSRA research and clinical studies are outlined in this review. Published findings demonstrate benefits regarding patient outcome with respect to recent CSRAs. Specifically, analytical and clinical validations, as well as clinical utility and economic benefit, of the most common clinically used CSRA in the United States support its use to aid in making effective, individualized clinical treatment selections for patients with ovarian cancer.

Access to personalized medicine: factors influencing the use and value of gene expression profiling in breast cancer treatment

Genomic information is increasingly being used to personalize health care. One example is gene expression profiling (gep) tests, which estimate recurrence risk to inform chemotherapy decisions in breast cancer. Recently, gep tests were publicly funded in Ontario. We explored the perceived utility of gep tests, focusing on the factors influencing their use and value in treatment decision-making by patients and oncologists.

METHODS

We conducted interviews with oncologists (n = 14) and interviews and a focus group with early-stage breast cancer patients (n = 28) who underwent gep testing. Both groups were recruited through oncology clinics in Ontario. Data were analyzed using the content analysis and constant comparison techniques.

RESULTS

Narratives from patients and oncologists provided insights into various factors facilitating and restricting access to gep. First, oncologists are positioned as gatekeepers of gep, providing access in medically appropriate cases. However, varying perceptions of appropriateness led to perceived inequities in access and negative impacts on the doctor-patient relationship. Second, media attention facilitated patient awareness of gep, but also complicated gatekeeping. Third, the dedicated administration attached to gep was burdensome and led to long waits for results and also to increased patient anxiety and delayed treatment. Collectively, because of barriers to access, those factors inadvertently heightened the perceived value of gep for patients relative to other prognostic indicators.

CONCLUSIONS

Our study delineates the factors facilitating and restricting access to gep, and highlights the roles of media and organization of services in the perceived value and utilization of gep. The results identify a need for administrative changes and practice guidelines to support streamlined and standardized use of gep tests.

Stakeholder consultation insights on the future of genomics at the clinical-public health interface

In summer 2011, the Centers for Disease Control and Prevention Office of Public Health Genomics conducted a stakeholder consultation, administered by the University of Michigan Center for Public Health and Community Genomics, and Genetic Alliance, to recommend priorities for public health genomics from 2012 through 2017. Sixty-two responses from health professionals, administrators, and members of the public were pooled with 2 sets of key informant interviews and 3 discussion groups. NVivo 9 and manual methods were used to organize themes. This review offers an interim analysis of progress with respect to the final recommendations, which demonstrated a strong interest in moving genomic discoveries toward implementation and comparative effectiveness (T3/T4) translational research. A translational research continuum exists with familial breast and ovarian cancer at one end and prostate cancer at the other. Cascade screening for inherited arrhythmia syndromes and hypercholesterolemia lags stakeholder recommendations in the United States but not in Europe; implementation of health service-based screening for Lynch syndrome, and integration into electronic health information systems, is on pace with the recommended timeline. A number of options exist to address deficits in the funding of translational research, particularly for oncogenomic gene expression profiling. The goal of personalized risk assessment necessitates both research progress (eg, in whole genome sequencing, as well as provider education in the differentiation of low- vs high-risk status. The public health approach supports an emphasis on genetic test validation while endorsing clinical translation research inclusion of an environmental and population-based perspective.

Prioritizing genomic applications for action by level of evidence: a horizon-scanning method

As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.

Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens

Background

NanoString’s Prosigna™ Breast Cancer Prognostic Gene Signature Assay is based on the PAM50 gene expression signature. The test outputs a risk of recurrence (ROR) score, risk category, and intrinsic subtype (Luminal A/B, HER2-enriched, Basal-like). The studies described here were designed to validate the analytical performance of the test on the nCounter Analysis System across multiple laboratories.

Methods

Analytical precision was measured by testing five breast tumor RNA samples across 3 sites. Reproducibility was measured by testing replicate tissue sections from 43 FFPE breast tumor blocks across 3 sites following independent pathology review at each site. The RNA input range was validated by comparing assay results at the extremes of the specified range to the nominal RNA input level. Interference was evaluated by including non-tumor tissue into the test.

Results

The measured standard deviation (SD) was less than 1 ROR unit within the analytical precision study and the measured total SD was 2.9 ROR units within the reproducibility study. The ROR scores for RNA inputs at the extremes of the range were the same as those at the nominal input level. Assay results were stable in the presence of moderate amounts of surrounding non-tumor tissue (<70% by area).

Conclusions

The analytical performance of NanoString’s Prosigna assay has been validated using FFPE breast tumor specimens across multiple clinical testing laboratories.

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics

BACKGROUND

Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups.

METHODS

We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase PCR was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier.

RESULTS

In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% luminal A, 20.5% luminal B, 13.0% HER2-enriched, 9.8% basal-like, and 3.6% normal-like. Among low-risk endocrine-positive tumors (i.e., estrogen and progesterone receptor positive by immunohistochemistry, HER2 negative, and low histologic grade), only 76.5% were categorized as luminal A by PAM50. Continuous-scale luminal A, luminal B, HER2-enriched, and normal-like scores from PAM50 were mutually positively correlated. Basal-like score was inversely correlated with other subtypes. The proportion with non-luminal A subtype decreased with older age at diagnosis, P Trend < 0.0001. Compared with non-Hispanic Whites, African American women were more likely to have basal-like tumors, age-adjusted OR = 4.4 [95% confidence intervals (CI), 2.3-8.4], whereas Asian and Pacific Islander women had reduced odds of basal-like subtype, OR = 0.5 (95% CI, 0.3-0.9).

CONCLUSIONS

Our data indicate that over 50% of breast cancers treated in the community have luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathologic criteria to higher-risk subtypes.

IMPACT

Subtyping in a population-based cohort revealed distinct profiles by age and race.

Predictive and prognostic biomarkers with therapeutic targets in breast, colorectal, and non-small cell lung cancers: a systemic review of current development, evidence, and recommendation

Appropriate evidence-based roles of prognostic and predictive biomarkers of known therapeutic targets in breast, colorectal, and non-small cell lung cancers in adults are reviewed, with summary of evidence for use and recommendation. Current development in biomarker studies is also discussed. Computerized literature searches of PubMed (National Library of Medicine), the Cochrane Collaboration Library, and commonly accepted US and international guidelines (American Society of Clinical Oncology, European Society for Medical Oncology, and National Comprehensive Cancer Network) were performed from 2001 to 2012. Literature published before 2001 was noted for historical interest but not evaluated. Literature review was focused on available systematic reviews and meta-analyses of published predictive (associated with treatment response and/or efficacy) and prognostic (associated with disease outcome) biomarkers of known therapeutic targets in colorectal, breast, and non-small cell lung cancers. In general, significant health outcomes (e.g. predicted response to therapy, overall survival, disease-free survival, quality of life, lesser toxicity, and cost-effectiveness) were used for making recommendations. Four breast cancer biomarkers were evaluated, two of which (2D6 genotyping, Oncotype Dx) were considered emerging with insufficient evidence. Seven colorectal cancer biomarkers were evaluated, five of which (EGFR gene expression, K-ras G13D gene mutation, B-raf V600E gene mutation, dihydropyrimidine dehydrogenase deficiency, and UGT1A1 genotyping) were considered emerging. Seven non-small cell lung cancer biomarkers were evaluated, five of which were emerging (EGFR gene expression, ERCC gene expression, RRM1 gene expression, K-ras gene mutation, and TS gene expression). Of all 18 biomarkers evaluated, the following showed evidence of clinical utility and were recommended for routine use in practice: ER/PR and HER2 for breast cancer; K-ras gene mutation (except G13D gene mutation) for colorectal cancer; mismatch repair deficiency or microsatellite instability for colorectal cancer; and EGFR and EML4-ALK gene mutations for non-small cell lung. Not all recommendations for these biomarkers were uniformly supported by all guidelines.

Essential elements of personalized medicine

OBJECTIVES

Genomic information has been promoted as the basis for "personalized" health care. We considered the benefits provided by genomic testing in context of the concept of personalized medicine.

MATERIALS AND METHODS

We evaluated current and potential uses of genomic testing in health care, using prostate cancer as an example, and considered their implications for individualizing or otherwise improving health care.

RESULTS AND CONCLUSIONS

Personalized medicine is most accurately seen as a comprehensive effort to tailor health care to the individual, spanning multiple dimensions. While genomic tests will offer many potential opportunities to improve the delivery of care, including the potential for genomic research to offer opportunities to improve prostate cancer screening and treatment, such advances do not in themselves constitute a paradigm shift in the delivery of health care. Rather, personalized medicine is based on a partnership between clinician and patient that utilizes shared decision making to determine the best health care options among the available choices, weighing the patient's personal values and preferences together with clinical findings. This approach is particularly important for difficult clinical decisions involving uncertainty and trade-offs, such as those involved in prostate cancer screening and management. The delivery of personalized medicine also requires adequate health care access and assurance that basic health needs have been met. Substantial research investment will be needed to identify how genomic tests can contribute to this effort.

Progressing the utilisation of pharmacogenetics and pharmacogenomics into clinical care

Understanding human genetic variation and how it impacts on gene function is a major focus in genomic-based research. Translation of this knowledge into clinical care is exemplified by pharmacogenetics/pharmacogenomics. The identification of particular gene variants that might influence drug uptake, metabolism, distribution or excretion promises a more effective personalised medicine approach in choosing the right drug or its dose for any particular individual. Adverse drug responses can then be avoided or mitigated. An understanding of germline or acquired (somatic) DNA mutations can also be used to identify drugs that are more likely to be therapeutically beneficial. This represents an area of growing interest in the treatment of cancer.

From High-Throughput Microarray-Based Screening to Clinical Application: The Development of a Second Generation Multigene Test for Breast Cancer Prognosis

Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of a second generation multigene assay, the EndoPredict test, a prognostic multigene expression test for estrogen receptor (ER) positive, human epidermal growth factor receptor (HER2) negative (ER+/HER2−) breast cancer patients. The EndoPredict gene signature was initially established in a large high-throughput microarray-based screening study. The key steps for biomarker identification are discussed in detail, in comparison to the establishment of other multigene signatures. After biomarker selection, genes and algorithms were transferred to a diagnostic platform (reverse transcription quantitative PCR (RT-qPCR)) to allow for assaying formalin-fixed, paraffin-embedded (FFPE) samples. A comprehensive analytical validation was performed and a prospective proficiency testing study with seven pathological laboratories finally proved that EndoPredict can be reliably used in the decentralized setting. Three independent large clinical validation studies (n = 2,257) demonstrated that EndoPredict offers independent prognostic information beyond current clinicopathological parameters and clinical guidelines. The review article summarizes several important steps that should be considered for the development process of a second generation multigene test and offers a means for transferring a microarray signature from the research laboratory to clinical practice.

The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis

The impact of the Oncotype Dx (ODX) breast cancer assay on adjuvant chemotherapy (ACT) treatment decisions has been evaluated in many previous studies. However, it can be difficult to interpret the collective findings, which were conducted in diverse settings with limited sample sizes. We conducted a systematic review and meta-analysis to synthesize the results and provide insights about ODX utility. Studies, identified from PubMed, Embase, ASCO, and SABCS, were included if patients had ER+, node -, early-stage breast cancer, reported use of ODX to inform actual ACT decisions. Information was summarized and pooled according to: (1) distribution of ODX recurrence scores (RS), (2) impact of ODX on ACT recommendations, (3) impact of ODX on ACT use, and (4) proportion of patients following the treatment suggested by the ODX RS. A total of 23 studies met inclusion criteria. The distribution of RS categories was 48.8 % low, 39.0 % intermediate, and 12.2 % high (21 studies, 4,156 patients). ODX changed the clinical-pathological ACT recommendation in 33.4 % of patients (8 studies, 1,437 patients). In patients receiving ODX, receipt of ACT were: 28.2 % overall, 5.8 % low, 37.4 % intermediate, and 83.4 % high. Low RS patients were significantly more likely to follow the treatment suggested by ODX versus high RS patients RR: 1.07 (1.01–1.14) [corrected].The pooled results are consistent with most individual studies to date. The increased proportion of intermediate scores relative to original estimates may have implications for the clinical utility and cost impacts of testing. In addition, low versus high RS patients were significantly more likely to follow the ODX results, suggesting a tendency toward less aggressive treatment, despite a high ODX RS. Finally, there was a lack of studies on the impact of ODX on ACT use versus standard approaches, suggesting that additional studies are warranted.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

The EGAPP initiative: lessons learned

The Evaluation of Genomic Applications in Practice and Prevention Working Group was first convened in 2005 to develop and test evidence-based methods for the evaluation of genomic tests in transition from research to clinical and public health practice. Over the ensuing years, the Working Group has met 26 times, publishing eight recommendation statements, two methods papers, and one outcomes paper, as well as planning and serving as technical experts on numerous associated systematic reviews. Evaluation of Genomic Applications in Practice and Prevention methods have evolved to address implications of the proliferation of genome-wide association studies and are currently expanding to face challenges expected from clinical implementation of whole-genome sequencing tests. In this article, we review the work of the Evaluation of Genomic Applications in Practice and Prevention Working Group over the first 8 years of its existence with an emphasis on lessons learned throughout the process. It is hoped that in addition to the published methods of the Working Group, the lessons we have learned along the way will be informative to others who are producers and consumers of evidence-based guidelines in the field of genomic medicine.

Evaluation of state comprehensive cancer control plans for genomics content

INTRODUCTION

Comprehensive Cancer Control (CCC) plans address cancer burden at the state level through consolidation of activities and collaboration among stakeholders. Public health genomics strategies are increasingly important in prevention and treatment of cancer. The objectives of this study were to assess the extent to which CCC plans have incorporated genomics-related terms since 2005, determine which of the 3 core public health functions were fulfilled by genomics components, and identify facilitators of and barriers to integration of genomics.

METHODS

We reviewed 50 CCC plans in 2010 to assess use of 22 genomics-related terms. Among plans that used the term genetics or genomics, we examined the plan for inclusion of genomics-related goals, objectives, or strategies and documented the 3 core public health functions (assessment, policy development, and assurance) fulfilled by them. We surveyed plan coordinators about factors affecting incorporation of genomic strategies into plans.

RESULTS

Forty-seven of 50 (94%) plans included at least 1 genomics-related term. Thirty-two of 50 (64%) plans included at least 1 genomics-related goal, objective, or strategy, most encompassing the core function of assurance; 6 state plans encompassed all 3 core functions. Plan coordinators indicated that genomics is a low priority in state public health; barriers to incorporation included lack of sufficient staff and funding.

CONCLUSION

Incorporation of genomic terms into state CCC plans increased from 60% in 2005 to 94% in 2010, but according to plan coordinators, genomics has not grown as a priority. Identification of partnerships and resources may help increase the priority, encourage incorporation, and guide the eventual success of public health genomics in state plans. Strong partnerships with state public health departments, health care providers, and the research community are useful for integration.

An optimized workflow for improved gene expression profiling for formalin-fixed, paraffin-embedded tumor samples

BACKGROUND

Whole genome microarray gene expression profiling is the 'gold standard' for the discovery of prognostic and predictive genetic markers for human cancers. However, suitable research material is lacking as most diagnostic samples are preserved as formalin-fixed, paraffin-embedded tissue (FFPET). We tested a new workflow and data analysis method optimized for use with FFPET samples.

METHODS

Sixteen breast tumor samples were split into matched pairs and preserved as FFPET or fresh-frozen (FF). Total RNA was extracted and tested for yield and purity. RNA from FFPET samples was amplified using three different commercially available kits in parallel, and hybridized to Affymetrix GeneChip® Human Genome U133 Plus 2.0 Arrays. The array probe set was optimized in silico to exclude misdesigned and misannotated probes.

RESULTS

FFPET samples processed using the WT-Ovation™ FFPE System V2 (NuGEN) provided 80% specificity and 97% sensitivity compared with FF samples (assuming values of 100%). In addition, in silico probe set redesign improved sequence detection sensitivity and, thus, may rescue potentially significant small-magnitude gene expression changes that could otherwise be diluted by the overall probe set background.

CONCLUSION

In conclusion, our FFPET-optimized workflow enables the detection of more genes than previous, nonoptimized approaches, opening new possibilities for the discovery, validation, and clinical application of mRNA biomarkers in human diseases.

Cost-effectiveness of the 21-gene recurrence score assay in the context of multifactorial decision making to guide chemotherapy for early-stage breast cancer

PURPOSE

New evidence is available regarding the utility of the 21-gene recurrence score assay in guiding chemotherapy use for node-negative, estrogen receptor-positive breast cancer. We applied this evidence in a decision-analytic model to re-evaluate the cost-effectiveness of the assay.

METHODS

We cross-classified patients by clinicopathologic characteristics from the Adjuvant! risk index and by recurrence score risk group. For non-recurrence score-guided treatment, we assumed patients receiving hormonal therapy alone had low-risk characteristics and patients receiving chemotherapy and hormonal therapy had higher-risk characteristics. For recurrence score-guided treatment, we assigned chemotherapy probabilities conditional on recurrence score risk group and clinicopathologic characteristics.

RESULTS

An estimated 40.4% of patients in the recurrence score-guided strategy and 47.3% in the non-recurrence score-guided strategy were expected to receive chemotherapy. The incremental gain in quality-adjusted life-years was 0.16 (95% confidence interval, 0.08-0.28) with the recurrence score-guided strategy. Lifetime medical costs to the health system were $2,692 ($1,546-$3,821) higher with the recurrence score-guided strategy, for an incremental cost-effectiveness ratio of $16,677/quality-adjusted life-year ($7,613-$37,219). From a societal perspective, the incremental cost-effectiveness was $10,788/quality-adjusted life-year ($6,840-$30,265).

CONCLUSION

The findings provide supportive evidence for the economic value of the 21-gene recurrence score assay in node-negative, estrogen receptor-positive breast cancer.

Gene expression profiling in breast cancer: a clinical perspective

Gene expression profiling tests are used in an attempt to determine the right treatment for the right person with early-stage breast cancer that may have spread to nearby lymph nodes but not to distant parts of the body. These new diagnostic approaches are designed to spare people who do not need additional treatment (adjuvant therapy) the side effects of unnecessary treatment, and allow people who may benefit from adjuvant therapy to receive it. In the present review we discuss in detail the major diagnostic tests available such as MammaPrint dx, Oncotype dx, PAM50, Mammostrat, IHC4, MapQuant DX, Theros-Breast Cancer Gene Expression Ratio Assay, and their potential clinical applications.

Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement

BACKGROUND

We critically evaluated the available evidence on genomic tests in breast cancer to define their prognostic ability and likelihood to determine treatment benefit.

DESIGN

Independent evaluation of six genomic tests [Oncotype Dx™, MammaPrint(®), Genomic Grade Index, PAM50 (ROR-S), Breast Cancer Index, and EndoPredict] was carried out by a panel of experts in three parameters: analytical validity, clinical validity, and clinical utility based on the principles of the EGAPP criteria. PANEL STATEMENTS: The majority of the working group members found the available evidence on the analytical and clinical validity of Oncotype Dx™ and MammaPrint(®) to be convincing. None of the genomic tests demonstrated robust evidence of clinical utility: it was not clear from the current evidence that modifying treatment decisions based on the results of a given genomic test could result in improving clinical outcome.

CONCLUSIONS

The IMPAKT 2012 Working Group proposed the following recommendations: (i) a need to develop models that integrate clinicopathologic factors along with genomic tests; (ii) demonstration of clinical utility should be made in the context of a prospective randomized trial; and (iii) the creation of registries for patients who are subjected to genomic testing in the daily practice.