Breast Cancer Prognosis Determined by Gene Expression Profiling: A Quantitative Reverse Transcriptase Polymerase Chain Reaction Study

RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples

Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.

Prediction and interpretation of cancer survival using graph convolution neural networks

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

Three inflammation-related genes could predict risk in prognosis and metastasis of patients with breast cancer

BACKGROUND

Current predictive model is not developed by inflammation-related genes to evaluate clinical outcome of breast cancer patients.

METHODS

With mRNA expression profiling, we identified 3 mRNAs with significant expression between 15 normal samples and 669 breast cancer patients. Using 7 cell lines and 150 paraffin-embedded specimens, we verified the expression pattern by bio-experiments. Then, we constructed a three-mRNA model by Cox regression method and approved its predictive accuracy in both training set (n = 1095) and 4 testing sets (n = 703).

RESULTS

We developed a three-mRNA (TBX21, TGIF2, and CYCS) model to stratify patients into high- and low-risk subgroup with significantly different prognosis. In training set, 5-year OS rate was 84.5% (78.8%-90.5%) vs 73.1% (65.9%-81.2%) for the low- and high-risk group (HR = 1.573 (1.090-2.271); P = 0.016). The predictive value was similar in four independent testing sets (HR>1.600; P < 0.05). This model could assess survival independently with better predictive power compared with single clinicopathological risk factors and any of the three mRNAs. Patients with both low-risk values and any poor prognostic factors had more favorable survival from nonmetastatic status (HR = 1.740 (1.028-2.945), P = 0.039). We established two nomograms for clinical application that integrated this model and another three significant risk factors to forecast survival rates precisely in patients with or without metastasis.

CONCLUSIONS

This model is a dependable tool to predict the disease recurrence precisely and could improve the predictive accuracy of survival probability for breast cancer patients with or without metastasis.

Pathologic diagnosis of breast cancer patients: evolution of the traditional clinical-pathologic paradigm toward "precision" cancer therapy

We present an updated account of breast cancer treatment and of progress toward "precision" cancer therapy; we focus on new developments in diagnostic molecular pathology and breast cancer that have emerged during the past 2 years. Increasing awareness of new prognostic and predictive methodologies, and introduction of next generation sequencing has increased understanding of both tumor biology and clinical behavior, which offers the possibility of more appropriate therapeutic choices. It remains unclear which of these testing methodologies provides the most informative and cost-effective actionable results for predictive and prognostic pathology. It is likely, however, that an integrated "step-wise" approach that uses the traditional clinical-pathologic paradigms coordinated with molecular characterization of breast tumor tissue, will offer the most comprehensive and cost-effective options for individualized, "precision" therapy for patients with breast cancer.

Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status

Background

The aim was to assess and compare prognostic power of nine breast cancer gene signatures (Intrinsic, PAM50, 70-gene, 76-gene, Genomic-Grade-Index, 21-gene-Recurrence-Score, EndoPredict, Wound-Response and Hypoxia) in relation to ER status and follow-up time.

Methods

A gene expression dataset from 947 breast tumors was used to evaluate the signatures for prediction of Distant Metastasis Free Survival (DMFS). A total of 912 patients had available DMFS status. The recently published METABRIC cohort was used as an additional validation set.

Results

Survival predictions were fairly concordant across most signatures. Prognostic power declined with follow-up time. During the first 5 years of followup, all signatures except for Hypoxia were predictive for DMFS in ER-positive disease, and 76-gene, Hypoxia and Wound-Response were prognostic in ER-negative disease. After 5 years, the signatures had little prognostic power. Gene signatures provide significant prognostic information beyond tumor size, node status and histological grade.

Conclusions

Generally, these signatures performed better for ER-positive disease, indicating that risk within each ER stratum is driven by distinct underlying biology. Most of the signatures were strong risk predictors for DMFS during the first 5 years of follow-up. Combining gene signatures with histological grade or tumor size, could improve the prognostic power, perhaps also of long-term survival.

Fluopsin C induces oncosis of human breast adenocarcinoma cells

AIM

Fluopsin C, an antibiotic isolated from Pseudomonas jinanesis, has shown antitumor effects on several cancer cell lines. In the current study, the oncotic cell death induced by fluopsin C was investigated in human breast adenocarcinoma cells in vitro.

METHODS

Human breast adenocarcinoma cell lines MCF-7 and MD-MBA-231 were used. The cytotoxicity was evaluated using MTT assay. Time-lapse microscopy and transmission electron microscopy were used to observe the morphological changes. Cell membrane integrity was assessed with propidium iodide (PI) uptake and lactate dehydrogenase (LDH) assay. Flow cytometry was used to measure reactive oxygen species (ROS) level and mitochondrial membrane potential (Δψm). A multimode microplate reader was used to analyze the intracellular ATP level. The changes in cytoskeletal system were investigated with Western blotting and immunostaining.

RESULTS

Fluopsin C (0.5-8 μmol/L) reduced the cell viability in dose- and time-dependent manners. Its IC50 values in MCF-7 and MD-MBA-231 cells at 24 h were 0.9 and 1.03 μmol/L, respectively. Fluopsin C (2 μmol/L) induced oncosis in both the breast adenocarcinoma cells characterized by membrane blebbing and swelling, which was blocked by pretreatment with the pan-caspase inhibitor Z-VAD-fmk. In MCF-7 cells, fluopsin C caused PI uptake into the cells, significantly increased LDH release, induced cytoskeletal system degradation and ROS accumulation, decreased the intracellular ATP level and Δψm. Noticeably, fluopsin C exerted comparable cytotoxicity against the normal human hepatocytes (HL7702) and human mammary epithelial cells with the IC50 values at 24 h of 2.7 and 2.4 μmol/L, respectively.

CONCLUSION

Oncotic cell death was involved in the anticancer effects of fluopsin C on human breast adenocarcinoma cells in vitro. The hepatoxicity of fluopsin C should not be ignored.

The present and future of gene profiling in breast cancer

Gene signatures can provide prognostic and predictive information to help in the treatment of early-stage breast cancer. Although many of these signatures have been described, only a few have been properly validated. MammaPrint and OncoType offer prognostic information and identify low-risk patients who do not benefit from adjuvant chemotherapy. With regard to prediction of response, molecular subtypes of breast cancer differ in their sensitivity to chemotherapy, although further studies are needed in this field. Cost, small sample size, and the need to use central laboratories are common limitations to the widespread use of these tools.

Early developments in gene-expression profiling of breast tumors: potential for increasing black-white patient disparities in breast cancer outcomes?

New prognostic tests, such as gene-expression profiling (GEP) of breast tumors, are expected to prolong survival and improve the quality of life for many breast cancer patients. In this article, we argue that GEP has not been adequately validated in minority populations, and that both biological and social factors might affect the broad utility of these tests in diverse populations. We suggest that the widespread use of this technology could potentially lead to suboptimal treatment for black women, resulting in a further increase in black-white patient disparities in treatment response, morbidity and mortality rates. We argue for the need to build a large and diverse evidence base for GEP and other emerging technologies in personalized medicine.

Quantitative determination of insulin-like growth factor 1 receptor mRNA in formalin-fixed paraffin-embedded tissues of invasive breast cancer

BACKGROUND

Insulin-like growth factor 1 receptor (IGF1R) has recently received much attention due to its role in initiation and progression of breast cancer. Previously analysis of its gene expression has been restricted to fresh-frozen (FF) samples, but application of this technique to routinely processed formalin-fixed paraffin-embedded (FFPE) samples could facilitate larger retrospective studies correlating IGF1R expression with prognosis and therapeutic response.

METHODS

A series of 77 paired FFPE and FF specimens of breast tumors was used to evaluate the possibility of quantifying IGF1R gene expression with FFPE samples and to compare the results obtained from FFPE and FF samples. The feasibility and prognostic value of analyzing IGF1R gene expression using FFPE samples was evaluated in a cohort of 260 primary breast tumors.

RESULTS

Total RNA was extracted from 95.4% of the FFPE samples with concentration at least 30 ng/μL. Real-time PCR based on Taqman methodology was successful in 90% of the FFPE samples. IGF1R gene expression showed strong correlation not only between FFPE and FF (Spearman ρ = 0.74), but also with IGF1R protein expression in both types of specimen. Kaplan-Meier analysis showed that higher IGF1R mRNA expression was associated with longer recurrence-free survival (P = 0.009) and breast cancer-specific survival (P = 0.0002).

CONCLUSIONS

Quantitative analysis of IGF1R gene expression in FFPE tissues can be feasibly and reliably conducted, and provides information relevant to the characteristics and outcome of invasive breast cancer.

Comparison of gene expression profiling by reverse transcription quantitative PCR between fresh frozen and formalin-fixed, paraffin-embedded breast cancer tissues

Recent reports demonstrate the feasibility of quantifying gene expression by using RNA isolated from blocks of formalin-fixed, paraffin-embedded (FFPE) tumor tissue. The development of molecular tests for clinical use based on archival materials would be of great utility in the search for and validation of important genes or gene expression profiles. In this study, we compared the performance of different normalization strategies in the correlation of quantitative data between fresh frozen (FF) and FFPE samples and analyzed the parameters that characterize such correlation for each gene. Total RNA extracted from FFPE samples presented a shift in raw cycle threshold (Cq) values that can be explained by its extensive degradation. Proper normalization can compensate for the effects of RNA degradation in gene expression measurements. We show that correlation between normalized expression values is better for moderately to highly expressed genes whose expression varies significantly between samples. Nevertheless, some genes had no correlation. These genes should not be included in molecular tests for clinical use based on FFPE samples. Our results could serve as a guide when developing clinical diagnostic tests based on RT-qPCR analyses of FFPE tissues in the coming era of treatment decision-making based on gene expression profiling.

An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

BACKGROUND

Gene expression profiling may improve prognostic accuracy in patients with early breast cancer. Our objective was to demonstrate that it is possible to develop a simple molecular signature to predict distant relapse.

METHODS

We included 153 patients with stage I-II hormonal receptor-positive breast cancer. RNA was isolated from formalin-fixed paraffin-embedded samples and qRT-PCR amplification of 83 genes was performed with gene expression assays. The genes we analyzed were those included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Index. The association among gene expression, clinical variables and distant metastasis-free survival was analyzed using Cox regression models.

RESULTS

An 8-gene prognostic score was defined. Distant metastasis-free survival at 5 years was 97% for patients defined as low-risk by the prognostic score versus 60% for patients defined as high-risk. The 8-gene score remained a significant factor in multivariate analysis and its performance was similar to that of two validated gene profiles: the 70-Gene Signature and the Recurrence Score. The validity of the signature was verified in independent cohorts obtained from the GEO database.

CONCLUSIONS

This study identifies a simple gene expression score that complements histopathological prognostic factors in breast cancer, and can be determined in paraffin-embedded samples.

Combinatorial biomarker expression in breast cancer

Current clinical management of breast cancer relies on the availability of robust clinicopathological variables and few well-defined biological markers. Recent microarray-based expression profiling studies have emphasised the importance of the molecular portraits of breast cancer and the possibility of classifying breast cancer into biologically and molecularly distinct groups. Subsequent large scale immunohistochemical studies have demonstrated that the added value of studying the molecular biomarker expression in combination rather than individually. Oestrogen (ER) and progesterone (PR) receptors and HER2 are currently used in routine pathological assessment of breast cancer. Additional biomarkers such as proliferation markers and 'basal' markers are likely to be included in the future. A better understanding of the prognostic and predictive value of combinatorial assessment of biomarker expression could lead to improved breast cancer management in routine clinical practice and would add to our knowledge concerning the variation in behaviour and response to therapy. Here, we review the evidence on the value of assessing biomarker expression in breast cancer individually and in combination and its relation to the recent molecular classification of breast cancer.

Protein and gene expression of estrogen receptor alpha and nuclear morphology of two breast cancer cell lines after different fixation methods

We assessed morphology and ERalpha protein and gene expression of two breast cancer cell lines after three different fixatives: neutral-buffered 10% formaldehyde, LN-FIX and FineFIX and varying fixation times. We found that the cell morphology was best preserved in cells fixed with LN-FIX. Two commercial fixatives used in this study shrank cells less than formalin. In immunohistochemical assay samples were stained with two different ERalpha antibodies, clone 1D5 and clone SP1. All tested fixatives were suitable for immunohistochemistry. Staining was more intensive and the number of stained cells was larger with the clone 1D5 than with the clone SP1. Our gene expression analysis showed that formalin and LN-FIX preserve the ERalpha better than FineFIX, which is advertised to be optimal for molecular analysis. Our study suggests that tissues fixed with formalin are suitable also for molecular biology assays. This makes possible to research formalin-fixed paraffin-embedded archival tissues also with molecular techniques.

Comparison of prognostic gene profiles using qRT-PCR in paraffin samples: a retrospective study in patients with early breast cancer

Introduction

Gene profiling may improve prognostic accuracy in patients with early breast cancer, but this technology is not widely available. We used commercial assays for qRT-PCR to assess the performance of the gene profiles included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Ratio.

Methods

153 patients with early breast cancer and a minimum follow-up of 5 years were included. All tumours were positive for hormonal receptors and 38% had positive lymph nodes; 64% of patients received adjuvant chemotherapy. RNA was extracted from formalin-fixed paraffin-embedded (FFPE) specimens using a specific kit. qRT-PCR amplifications were performed with TaqMan Gene Expression Assays products. We applied the three gene-expression-based models to our patient cohort to compare the predictions derived from these gene sets.

Results

After a median follow-up of 91 months, 22% of patients relapsed. The distant metastasis-free survival (DMFS) at 5 years was calculated for each profile. For the 70-Gene Signature, DMFS was 95% -good prognosis- versus 66% -poor prognosis. In the case of the Recurrence Score, DMFS was 98%, 81% and 69% for low, intermediate and high-risk groups, respectively. Finally, for the Two-Gene Ratio, DMFS was 86% versus 70%. The 70-Gene Signature and the Recurrence Score were highly informative in identifying patients with distant metastasis, even in multivariate analysis.

Conclusion

Commercially available assays for qRT-PCR can be used to assess the prognostic utility of previously published gene expression profiles in FFPE material from patients with early breast cancer. Our results, with the use of a different platform and with different material, confirm the robustness of the 70-Gene Signature and represent an independent test for the Recurrence Score, using different primer/probe sets.

Recent advances in systemic therapy: new diagnostics and biological predictors of outcome in early breast cancer

The key to optimising our approach in early breast cancer is to individualise care. Each patient has a tumour with innate features that dictate their chance of relapse and their responsiveness to treatment. Often patients with similar clinical and pathological tumours will have markedly different outcomes and responses to adjuvant intervention. These differences are encoded in the tumour genetic profile. Effective biomarkers may replace or complement traditional clinical and histopathological markers in assessing tumour behaviour and risk. Development of high-throughput genomic technologies is enabling the study of gene expression profiles of tumours. Genomic fingerprints may refine prediction of the course of disease and response to adjuvant interventions. This review will focus on the role of multiparameter gene expression analyses in early breast cancer, with regards to prognosis and prediction. The prognostic role of genomic signatures, particularly the Mammaprint and Rotterdam signatures, is evolving. With regard to prediction of outcome, the Oncotype Dx multigene assay is in clinical use in tamoxifen treated patients. Extensive research continues on predictive gene identification for specific chemotherapeutic agents, particularly the anthracyclines, taxanes and alkylating agents.

Confirmation of gene expression-based prediction of survival in non-small cell lung cancer

PURPOSE

It is a critical challenge to determine the risk of recurrence in early stage non-small cell lung cancer (NSCLC) patients. Accurate gene expression signatures are needed to classify patients into high- and low-risk groups to improve the selection of patients for adjuvant therapy.

EXPERIMENTAL DESIGN

Multiple published microarray data sets were used to evaluate our previously identified lung cancer prognostic gene signature. Expression of the signature genes was further validated with real-time reverse transcription-PCR and Western blot assays of snap-frozen lung cancer tumor tissues.

RESULTS

Our previously identified 35-gene signature stratified 264 patients with NSCLC into high- and low-risk groups with distinct overall survival rates (P < 0.05, Kaplan-Meier analysis, log-rank tests). The 35-gene signature further stratified patients with clinical stage 1A diseases into poor prognostic and good prognostic subgroups (P = 0.0007, Kaplan-Meier analysis, log-rank tests). This signature is independent of other prognostic factors for NSCLC, including age, sex, tumor differentiation, tumor grade, and tumor stage. The expression of the signature genes was validated with real-time reverse transcription-PCR analysis of lung cancer tumor specimens. Protein expression of two signature genes, TAL2 and ILF3, was confirmed in lung adenocarcinoma tumors by using Western blot analysis. These two biomarkers showed correlated mRNA and protein overexpression in lung cancer development and progression.

CONCLUSIONS

The results indicate that the identified 35-gene signature is an accurate predictor of survival in NSCLC. It provides independent prognostic information in addition to traditional clinicopathologic criteria.

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

Introduction

Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance.

Method

To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures.

Results

Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER^-/HER2^- (basal-like), the HER2⁺ (HER2-like), and the low- and high-proliferation ER⁺/HER2^- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER^- status (basal-like) and ERBB2⁺ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes.

Conclusion

This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Ku70 predicts response and primary tumor recurrence after therapy in locally advanced head and neck cancer

5-Fluorouracil and cisplatin-based induction chemotherapy (IC) is commonly used to treat locally advanced head and neck squamous cell carcinoma (HNSCC). The role of nonhomologous end joining (NHEJ) genes (Ku70, Ku80 and DNA-PKcs) in double-strand break (DSB) repair, genomic instability and apoptosis suggest a possible impact on tumor response to radiotherapy, 5-fluorouracil or cisplatin, as these agents are direct or indirect inductors of DSBs. We evaluated the relationship between Ku80, Ku70 or DNA PKcs mRNA expression in pretreatment tumor biopsies, and tumor response to IC or local recurrence, in 50 patients with HNSCC. Additionally, in an independent cohort of 75 patients with HNSCC, we evaluated the relationship between tumor Ku70 protein expression and the same clinical outcomes or patient survival. Tumors in the responder group had significantly higher mRNA levels for Ku70, Ku80 and DNA-PKcs than those in the nonresponder group. Ku70 mRNA was the marker most significantly associated with response to IC. Moreover, high tumor Ku70 mRNA expression was associated with significantly longer local recurrence-free survival (LRFS). Ku70 protein expression was also significantly related to response, and patients with higher percentage of tumor cells expressing Ku70 had longer LRFS. In addition, the percentage of Ku70 positive cells, tumor localization and node involvement were significantly associated with overall survival of patient. Therefore, Ku70 expression is a candidate predictive marker that could distinguish patients who are likely to benefit from chemoradiotherapy or radiotherapy after the induction chemotherapy treatment, suggesting a contribution of the NHEJ system in HNSCC clinical outcome.

Implications of applied research for prognosis and therapy of breast cancer

Breast cancer is the one of leading causes of cancer-related deaths in women within economically developed regions of the world. The heterogeneity of the natural history of breast cancer complicates patient management in that there is tremendous variability in response to treatment and for survival. More recently, several biomarkers (hormone receptor status and HER2 expression) have been added to the risk evaluation and therapeutic assessments. Evolving knowledge of molecular biology and newer techniques, such as genomics and proteomics, offer the potential to better define the biologic nature of the disease process, both for risk and therapy. This review discusses classical as well as new prognostic and predictive techniques. These are leading to a paradigm shift from empirical treatment to an individually tailored approach, which may soon become a realistic option for patients, based on specific molecular profiles.

American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer

PURPOSE

To update the recommendations for the use of tumor marker tests in the prevention, screening, treatment, and surveillance of breast cancer.

METHODS

For the 2007 update, an Update Committee composed of members from the full Panel was formed to complete the review and analysis of data published since 1999. Computerized literature searches of MEDLINE and the Cochrane Collaboration Library were performed. The Update Committee's literature review focused attention on available systematic reviews and meta-analyses of published tumor marker studies. In general, significant health outcomes (overall survival, disease-free survival, quality of life, lesser toxicity, and cost-effectiveness) were used for making recommendations. Recommendations and

CONCLUSIONS

Thirteen categories of breast tumor markers were considered, six of which were new for the guideline. The following categories showed evidence of clinical utility and were recommended for use in practice: CA 15-3, CA 27.29, carcinoembryonic antigen, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, urokinase plasminogen activator, plasminogen activator inhibitor 1, and certain multiparameter gene expression assays. Not all applications for these markers were supported, however. The following categories demonstrated insufficient evidence to support routine use in clinical practice: DNA/ploidy by flow cytometry, p53, cathepsin D, cyclin E, proteomics, certain multiparameter assays, detection of bone marrow micrometastases, and circulating tumor cells.

Low E2F1 transcript levels are a strong determinant of favorable breast cancer outcome

Introduction

We investigated whether mRNA levels of E2F1, a key transcription factor involved in proliferation, differentiation and apoptosis, could be used as a surrogate marker for the determination of breast cancer outcome.

Methods

E2F1 and other proliferation markers were measured by quantitative RT-PCR in 317 primary breast cancer patients from the Stiftung Tumorbank Basel. Correlations to one another as well as to the estrogen receptor and ERBB2 status and clinical outcome were investigated. Results were validated and further compared with expression-based prognostic profiles using The Netherlands Cancer Institute microarray data set reported by Fan and colleagues.

Results

E2F1 mRNA expression levels correlated strongly with the expression of other proliferation markers, and low values were mainly found in estrogen receptor-positive and ERBB2-negative phenotypes. Patients with low E2F1-expressing tumors were associated with favorable outcome (hazard ratio = 4.3 (95% confidence interval = 1.8–9.9), P = 0.001). These results were consistent in univariate and multivariate Cox analyses, and were successfully validated in The Netherlands Cancer Institute data set. Furthermore, E2F1 expression levels correlated well with the 70-gene signature displaying the ability of selecting a common subset of patients at good prognosis. Breast cancer patients' outcome was comparably predictable by E2F1 levels, by the 70-gene signature, by the intrinsic subtype gene classification, by the wound response signature and by the recurrence score.

Conclusion

Assessment of E2F1 at the mRNA level in primary breast cancer is a strong determinant of breast cancer patient outcome. E2F1 expression identified patients at low risk of metastasis irrespective of the estrogen receptor and ERBB2 status, and demonstrated similar prognostic performance to different gene expression-based predictors.

Interpretation of microarray data in cancer

Microarray studies aim at identifying homogeneous subtypes of cancer patients, searching for differentially expressed genes in tumours with different characteristics, or predicting the prognosis of patients. Using breast cancer as an example, we discuss the hypotheses underlying these studies, their power, and the validity and the clinical usefulness of the findings.

A new molecular breast cancer subclass defined from a large scale real-time quantitative RT-PCR study

Background

Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour.

Methods

To this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures.

Results

Hierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up.

Conclusion

The analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features.

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

BACKGROUND

Several investigators have reported efforts to define gene expression signatures based on prediction of survival in early-stage breast cancer. The analysis reported here reviews test performance characteristics of reported gene expression signatures in women with breast cancer.

PATIENTS AND METHODS

All published reports of gene expression profiling in breast cancer were sought through an extensive search of the published literature. Seventeen cohorts of patients with primary breast cancer were identified reporting on the relationship between a gene expression signature and recurrence-free survival. Several measures of test performance were evaluated including sensitivity, specificity, likelihood ratio, predictive value, and the diagnostic odds ratio as an overall measure of test performance.

RESULTS

Reported series included 2908 patients ranging from 20 to 668 per study. Seven cohorts were evaluated using cross validation techniques, and 10 were studied in independent cohorts. Overall, 52.6% of patients were classified as high risk and 20.5% experienced disease recurrence. The false negative rate was > 20% in 8 studies (47%) and false positive rate was > 50% in 6 (35%). The number of genes in the assay correlated with assay sensitivity (rsp= 0.537; P = 0.032), the positive predictive value (rsp = 0.501; P = 0.048), and the diagnostic odds ratio (rsp = 0.532; P = 0.041).

CONCLUSION

Gene expression profiles based on microarray analysis show early promise for predicting survival in patients with breast cancer. However, the use of these assays in therapeutic decision-making must consider the limitations of assay test performance and the specific patient population being evaluated.

International Agency for Research on Cancer workshop on 'Expression array analyses in breast cancer taxonomy'

In May 2006, a workshop on Expression array analyses in breast cancer taxonomy was held at the International Agency for Research on Cancer (IARC). The workshop covered an array of topics from the validity of the currently defined breast tumor subtypes and other expression profile-based signatures to the technical limitations of expression analysis and the types of platforms on which these omics results will eventually reach clinical practice. Overall, the workshop participants believed firmly that tumor taxonomy is likely to yield improved prognostic and predictive markers. Even so, further standardization and validation are required before clinical trials are set in motion.

Gene Expression Profiling and Clinical Outcome in Breast Cancer

Pathologic and clinical heterogeneity of breast cancer reflects the poorly documented, complex, and combinatory molecular basis of the disease and is in part responsible for therapeutic failures. The DNA microarray technique allows the analysis of RNA expression of several thousands of genes simultaneously in a sample. There are multiple potential applications of the technique in cancer research. A number of recent studies have shown the promising role of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters and new prognostic and/or predictive gene signatures, whose predictive impact is superior to conventional histoclinical prognostic factors. In this review we describe current use of DNA microarrays in the prognosis of breast cancer. We also discuss issues that need to be addressed in the near future to allow the method to reach its full potential.

Prediction of recurrence in Ta urothelial cell carcinoma by real-time quantitative PCR analysis: a microarray validation study

Accurate prediction of tumor recurrence in patients with superficial urothelial cell carcinoma (UCC) might result in a significant reduction of invasive follow-up cystoscopies. A recent study identified a panel of 26 genes from a large cDNA microarray analysis of bladder tumors that discriminated between early- and late-recurring patients with superficial Ta tumors (Dyrskjøt et al., Nat Genet 2003;33:90-6). We aimed to validate this panel of genes in 44 primary Ta UCCs (23 and 21 tumors from patients with short or prolonged recurrence-free periods, respectively), by real-time quantitative PCR. Statistical analysis showed marginal significant different mRNA expression levels between the 2 patient groups. To evaluate a supplementary effect of genes for the identification of patients with short or prolonged recurrence-free intervals, forward logistic regression analysis was applied. This revealed that a combination of the expression profiles of the genes HNRPK, LTB4DH and ANP32B resulted in the best performance, although the combination only marginally increased the predictive value of HNRPK alone. Comparing the receiver-operating-characteristic curves for HNRPK expression among patients with short or prolonged recurrence-free periods, revealed an area under the curve of 0.696 (95% CI, 0.537-0.855). Using the median HNRPK expression level as cut-off, a sensitivity of 69.6% and a specificity of 71.4% were obtained for the identification of patients with short or prolonged recurrence-free periods, respectively. In conclusion, we were not able to confirm the microarray gene expression pattern of the 26 genes shown by Dyrskjøt et al. The discovery of accurate recurrence predictive markers, therefore, remains a challenge.

Molecular biology of breast cancer

Although Breast Cancer (BC) has been considered for many years as a single entity with a common management and treatment, it is actually a extremely heterogeneous disease which includes at least 4 or 5 very different subtypes. The first step in the recognition of the heterogeneity of BC was the demonstration of the presence of functional hormonal receptors (HR) in nearly two thirds of breast cancer specimens. This finding, which established a first classification of BC in two clear subtypes (HR-positive and HR-negative) was followed by the demonstration of many other differential features. The her2/neu gene alteration, present in nearly 20% of BC tumors, is probably the most relevant of them, but certainly not the only one. The development of new technologies and, in particular, the use of complementary DNA (cDNA) microarrays will allow us now the simultaneous analysis of thousands of genes and the establishment of new, more refined BC subtypes based on gene expression profiles/genetic fingerprints. This review discusses the practical applications of molecular analysis of BC, which can be classified in four categories: 1. Establishment of a new molecular taxonomy of breast cancer. 2. Definition of prognostic factors/prognostic indexes based on molecular/genetic peculiarities. 3. Prediction of response to diverse antitumoral treatments. 4. Identification of molecular targets that allows the development of new tailored antitumor treatments.

High-throughput techniques in breast cancer: A clinical perspective

High-throughput technologies such as DNA-microarrays, RT-PCR and proteomics can improve the prognostic and predictive information acquired from classical parameters. Unlike information gathered by classical methods, high-throughput technologies can accurately inform clinicians on patient response to adjuvant therapy or those who will resist the effect of that therapy. Studies performed in breast cancer with high-throughput techniques have focused on tumour biology, prognosis, prediction of response to a few agents and, more recently, early diagnosis. However, further refinement is needed before these techniques become part of clinical routine. In the meantime, they will be used in clinical investigation, particularly in the areas of hormonal therapy and adjuvant chemotherapy, where modest improvements in the capacity of prediction can benefit many women. Close cooperation among clinicians, pathologists and basic investigators is essential to take high-throughput techniques to daily practice. New diagnostic tools will be complex but they will provide valuable patient information.