72-Gene Classifier for Predicting Prognosis of Estrogen Receptor–Positive and Node-Negative Breast Cancer Patients Using Formalin-Fixed, Paraffin-Embedded Tumor Tissues

Systematic review and feasibility study on pre-analytical factors and genomic analyses on archival formalin-fixed paraffin-embedded breast cancer tissue

Formalin-fixed paraffin-embedded (FFPE) tissue represents a valuable source for translational cancer research. However, the widespread application of various downstream methods remains challenging. Here, we aimed to assess the feasibility of a genomic and gene expression analysis workflow using FFPE breast cancer (BC) tissue. We conducted a systematic literature review for the assessment of concordance between FFPE and fresh-frozen matched tissue samples derived from patients with BC for DNA and RNA downstream applications. The analytical performance of three different nucleic acid extraction kits on FFPE BC clinical samples was compared. We also applied a newly developed targeted DNA Next-Generation Sequencing (NGS) 370-gene panel and the nCounter BC360® platform on simultaneously extracted DNA and RNA, respectively, using FFPE tissue from a phase II clinical trial. Of the 3701 initial search results, 40 articles were included in the systematic review. High degree of concordance was observed in various downstream application platforms. Moreover, the performance of simultaneous DNA/RNA extraction kit was demonstrated with targeted DNA NGS and gene expression profiling. Exclusion of variants below 5% variant allele frequency was essential to overcome FFPE-induced artefacts. Targeted genomic analyses were feasible in simultaneously extracted DNA/RNA from FFPE material, providing insights for their implementation in clinical trials/cohorts.

Multi-gene assay 95- and 155-gene classifiers for prognosis prediction and chemotherapy omission in lymphnode positive luminal-type breast cancer

BACKGROUND

The prognosis of lymphnode positive breast cancer is worse than that of lymph node negative breast cancer but some cases may not require chemotherapy. We investigated the ability of the new multi-gene assays, 95GC and 155GC, to identify patients with lymphnode positive Luminal-type breast cancer whose chemotherapy can be omitted relatively safely.

PATIENTS AND METHODS

We extracted 1721 cases of lymphnode positive Luminal-type breast cancer from 22 public database Caucasoid cohorts and 3 Asian cohorts, and performed recurrence prognosis analysis with 95GC and 155GC.

RESULTS

Using 95GC, the cases were stratified as the high (n = 917) and low (n = 202) groups according to the prognosis of lymphnode positive Luminal-type endocrine only breast cancer. The 5 years DRFS in the low risk group was relatively good at 90%, and no additional effect of chemotherapy was observed, suggesting omission of chemotherapy. The recurrence prognosis was also significantly dichotomized into the high and low risks by 95GC in 21GC RS 0-25 cases. Here, we found a group with poor prognosis even in post-menopause RS 0-25 and requiring chemotherapy. Additionally, a group in which the prognosis was good in pre-menopause RS 0-25, and the omission of chemotherapy could be considered. Patients in the high-risk group at 155GC had poor prognosis after chemotherapy. 155GC also showed a group that chemotherapy alone was not sufficient.

CONCLUSION

In this study, we demonstrated the possibility of accurately selecting patient groups for which chemotherapy can be omitted from lymphnode positive Luminal-type breast cancer.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

A six-long non-coding RNAs signature as a potential prognostic marker for survival prediction of ER-positive breast cancer patients

Dysregulated expression of lncRNAs has been observed in various human complex diseases (including cancers) by recent transcriptional profiling studies, highlighting potentials of lncRNAs as biomarkers for cancer diagnosis and prognosis. Despite some efforts have been made to search for novel lncRNA signature in breast cancer, the prognostic value of lncRNAs for ER-positive breast cancer patients still needs to be systematically investigated. In this study, we analyzed lncRNA expression profiles in a large of more than 600 breast cancer patients with ER-positive status from The Cancer Genome Atlas (TCGA) and identified six lncRNAs that are significantly associated with survival. Then a linear risk score model comprising six prognostic lncRNAs, termed six-lncRNA signature, was developed to identify high-risk patients from low-risk cases. The results of Kaplan-Meier analysis and ROC curves demonstrated the good sensitivity and specificity in survival prediction both in the training and testing datasets. Multivariate Cox regression analysis and stratified analysis showed that the six-lncRNA signature is an independent prognostic marker in survival prediction for ER-positive breast cancer patients. The GO enrichment analysis suggested that the six-lncRNA might involve with known breast cancer-related biological processes. With further experimental validation, these identified prognostic lncRNAs might have clinical implications for more personalized risk assessment for ER-positive breast cancer patients.

Clinical and microarray analysis of breast cancers of all subtypes from two prospective preoperative chemotherapy studies

Background:

We aimed to analyse clinical and gene expression profiles to predict pathologic complete response and disease-free survival using two consecutive, prospective, preoperative chemotherapy trial cohorts.

Methods:

Clinicopathological and gene expression data were evaluated in a cohort from two consecutive phase II preoperative studies that included patients with stage IIA–IIIC breast cancer of all subtypes. Analysed specimens were obtained before preoperative chemotherapy, and cDNA microarray analyses were performed using the Affymetrix Gene Chip U133 plus 2.0.

Results:

Between December 2005 and December 2010, 122 patients were analysed. The pathologic complete response rate was significantly higher in HER2+ and HR−/HER2− cancers. Age, pathologic complete response, HR−/HER2− status, and lymph node positivity (⩾4) were significant poor prognostic factors for disease-free survival. For the cDNA microarray analyses, sufficient tumour samples were available from 78 of the 107 patients (73%). An 8-gene signature predictive of pathologic complete response and a 17-gene signature predictive of prognosis were identified. Patients were categorised into low-risk (n=45) and high-risk groups (n=33) (HR 70.0, P=0.004).

Conclusions:

This study yielded preliminary data on the expression of specific genes predicting pathologic complete response and disease-free survival in a cohort of chemonaïve breast cancer patients. Further validation may distinguish those who would benefit most from perioperative chemotherapy as well as those needing further intervention.

Straightforward and sensitive RT-qPCR based gene expression analysis of FFPE samples

Fragmented RNA from formalin-fixed paraffin-embedded (FFPE) tissue is a known obstacle to gene expression analysis. In this study, the impact of RNA integrity, gene-specific reverse transcription and targeted cDNA preamplification was quantified in terms of reverse transcription polymerase chain reaction (RT-qPCR) sensitivity by measuring 48 protein coding genes on eight duplicate cultured cancer cell pellet FFPE samples and twenty cancer tissue FFPE samples. More intact RNA modestly increased gene detection sensitivity by 1.6 fold (earlier detection by 0.7 PCR cycles, 95% CI = 0.593-0.850). Application of gene-specific priming instead of whole transcriptome priming during reverse transcription further improved RT-qPCR sensitivity by a considerable 4.0 fold increase (earlier detection by 2.0 PCR cycles, 95% CI = 1.73-2.32). Targeted cDNA preamplification resulted in the strongest increase of RT-qPCR sensitivity and enabled earlier detection by an average of 172.4 fold (7.43 PCR cycles, 95% CI = 6.83-7.05). We conclude that gene-specific reverse transcription and targeted cDNA preamplification are adequate methods for accurate and sensitive RT-qPCR based gene expression analysis of FFPE material. The presented methods do not involve expensive or complex procedures and can be easily implemented in any routine RT-qPCR practice.

Multi-gene classifiers for prediction of recurrence in breast cancer patients

Accurate prediction of recurrence risk is of vital importance for tailoring adjuvant chemotherapy for individual breast cancer patients. Although recurrence risk has been assessed by means of examination of histological data and biomarkers (ER, PR, HER2, Ki67), such conventional examinations are not accurate enough to select subsets of patients who are at sufficiently low risk of recurrence to be spared adjuvant chemotherapy without comprising the prognosis. In the past two decades or so, comprehensive gene expression analysis technology has rapidly developed and made it possible to construct recurrence prediction models for breast cancer based on multi-gene expression in tumor tissues. These models include MammaPrint, Oncotype DX, PAM50 ROR, GGI, EndoPredict, BCI, and Curebest 95GC. In clinical practice, these multi-gene classifiers are mostly used for ER-positive and node-negative breast cancer patients for whom deciding the indication of adjuvant chemotherapy based on conventional histological examination findings alone is often difficult. This article briefly reviews these multi-gene expression-based classifiers with special emphasis on Curebest™ 95GC, which was developed by us for ER-positive and node-negative breast cancer patients.

Prognostic stromal gene signatures in breast cancer

Introduction

Global gene expression analysis of tumor samples has been a valuable tool to subgroup tumors and has the potential to be of prognostic and predictive value. However, tumors are heterogeneous, and homogenates will consist of several different cell types. This study was designed to obtain more refined expression data representing different compartments of the tumor.

Methods

Formalin-fixed paraffin-embedded stroma-rich triple-negative breast cancer tumors were laser-microdissected, and RNA was extracted and processed to enable microarray hybridization. Genes enriched in stroma were identified and used to generate signatures by identifying correlating genes in publicly available data sets. The prognostic implications of the signature were analyzed.

Results

Comparison of the expression pattern from stromal and cancer cell compartments from three tumors revealed a number of genes that were essentially specifically expressed in the respective compartments. The stroma-specific genes indicated contribution from fibroblasts, endothelial cells, and immune/inflammatory cells. The gene set was expanded by identifying correlating mRNAs using breast cancer mRNA expression data from The Cancer Genome Atlas. By iterative analyses, 16 gene signatures of highly correlating genes were characterized. Based on the gene composition, they seem to represent different cell types. In multivariate Cox proportional hazard models, two immune/inflammatory signatures had opposing hazard ratios for breast cancer recurrence also after adjusting for clinicopathological variables and molecular subgroup. The signature associated with poor prognosis consisted mainly of C1Q genes and the one associated with good prognosis contained HLA genes. This association with prognosis was seen for other cancers as well as in other breast cancer data sets.

Conclusions

Our data indicate that the molecular composition of the immune response in a tumor may be a powerful predictor of cancer prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0530-2) contains supplementary material, which is available to authorized users.

An ER-associated miRNA signature predicts prognosis in ER-positive breast cancer

Background

Breast cancer patients with positive estrogen receptor (ER) have a better prognosis. However, no prognostic miRNA signature was reported in the ER-positive breast cancer. The aim of the study was to identify and assess the prognostic significance of a miRNA signature in ER-positive breast cancer.

Methods

Two cohorts from The Cancer Genome Atlas (TCGA) dataset were used as training (n =596) and testing set (n =319). Differential expression profiling was identified in the training set. And the prognostic value of the miRNA signature was then assessed in the two cohorts.

Results

A total of 14 miRNAs were observed to be associated with the status of ER by significance analysis of microarrays (SAM) in the training set. Patients were characterized as high score or low score group according to the calculated risk scores from each miRNA. And patients in high score group had worse overall survival compared with those in low score group both in the training and testing set.

Conclusions

Our study revealed a miRNA signature including 14 miRNAs associated with ER status which could act as a prognostic marker in ER-positive breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-014-0094-5) contains supplementary material, which is available to authorized users.