BreastPRS is a gene expression assay that stratifies intermediate-risk Oncotype DX patients into high- or low-risk for disease recurrence.

Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene Oncotype DX approximation algorithm was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER)-positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P < 0.0001). 59 of 260 (23 %) patients from four previously published studies were classified as intermediate-risk when the 120-gene Oncotype DX approximation algorithm was applied. BreastPRS reclassified the 59 patients into binary risk groups (high- vs. low-risk). 23 (39 %) patients were classified as low-risk and 36 (61 %) as high-risk (P = 0.029, HR: 3.64, 95 % CI: 1.40-9.50). At 10 years from diagnosis, the low-risk group had a 90 % recurrence-free survival (RFS) rate compared to 60 % for the high-risk group. BreastPRS recurrence score is comparable with Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings.

Options available--from start to finish--for obtaining data from DNA microarrays II.

Microarray technology has undergone a rapid evolution. With widespread interest in large-scale genomic research, an abundance of equipment and reagents have now become available and affordable to a large cross section of the scientific community. As protocols become more refined, careful investigators are able to obtain good quality microarray data quickly. In most recent times, however, perhaps one of the biggest obstacles researchers face is not the manufacture and use of microarrays at the bench, but storage and analysis of the array data. This review discusses the most recent equipment, reagents and protocols available to the researcher, as well as describing data analysis and storage options available from the evolving field of microarray informatics.

Implementation of a novel microarray-based diagnostic test for cancer of unknown primary.

Patients with carcinoma of unknown primary (CUP) present with metastatic disease for which the primary site cannot be found, despite extensive standard investigation. Here, we describe the development and implementation of the first clinically available microarray-based test for this cancer type (CUPPrint), based on 633 individual tumors representing 30 carcinoma and 17 noncarcinoma classes. Tissue of origin prediction for either fresh frozen or paraffin-embedded tumor samples is achieved with the use of a custom 8-pack 1.9k microarray and robust classification algorithm. An expression profile of 495 genes was used to predict tumor origin by applying a k-nearest neighbor algorithm. Internal cross-validation and analysis of an independent, previously published, 229-sample dataset revealed that clinically informative predictions were made for up to 94% of samples analyzed. Analysis of 13 previously published CUP specimens yielded predicted tumor origins that supported the clinical suspicion in 12 cases (92%). Microarray profiling presents a promising tool to assist in the identification of the primary tumor and might direct a more tailored treatment for CUP patients.

Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen.

BACKGROUND: Molecular signatures that predict outcome in tamoxifen treated breast cancer patients have been identified. For the first time, we compared these response profiles in an independent cohort of (neo)adjuvant systemic treatment naïve breast cancer patients treated with first-line tamoxifen for metastatic disease. METHODS: From a consecutive series of 246 estrogen receptor (ER) positive primary tumors, gene expression profiling was performed on available frozen tumors using 44K oligoarrays (n = 69). A 78-gene tamoxifen response profile (formerly consisting of 81 cDNA-clones), a 21-gene set (microarray-based Recurrence Score), as well as the HOXB13-IL17BR ratio (Two-Gene-Index, RT-PCR) were analyzed. Performance of signatures in relation to time to progression (TTP) was compared with standard immunohistochemical (IHC) markers: ER, progesterone receptor (PgR) and HER2. RESULTS: In univariate analyses, the 78-gene tamoxifen response profile, 21-gene set and HOXB13-IL17BR ratio were all significantly associated with TTP with hazard ratios of 2.2 (95% CI 1.3-3.7, P = 0.005), 2.3 (95% CI 1.3-4.0, P = 0.003) and 4.2 (95% CI 1.4-12.3, P = 0.009), respectively. The concordance among the three classifiers was relatively low, they classified only 45-61% of patients in the same category. In multivariate analyses, the association remained significant for the 78-gene profile and the 21-gene set after adjusting for ER and PgR. CONCLUSION: The 78-gene tamoxifen response profile, the 21-gene set and the HOXB13-IL17BR ratio were all significantly associated with TTP in an independent patient series treated with tamoxifen. The addition of multigene assays to ER (IHC) improves the prediction of outcome in tamoxifen treated patients and deserves incorporation in future clinical studies.

A plasma microRNA biomarker of melanoma as a personalised assessment of treatment response.

New tools for monitoring response to primary melanoma treatment are needed to reduce recurrence rates and patient anxiety. A previously developed plasma-based microRNA signature (MEL38) was measured in four melanoma patient samples obtained before and 12-14 days after treatment (i.e. surgical excision), as well as in two nonmelanoma controls. The value of the MEL38 score and selected individual genes were compared between the time points. The MEL38 scores of the four patients with melanoma became more 'normal like' after tumour excision, with a statistically significant 15% mean reduction. MicroRNAs involved in tumour suppression were upregulated in the postexcision samples and those involved in facilitating treatment resistance and tumour invasion were downregulated. Based on these limited preliminary data, the MEL38 signature may have clinical utility in assessing an individual patient's response to the most common form of melanoma treatment. Additional studies are needed on larger, clinically diverse patient cohorts, sampled over longer periods of time.

An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin.

Gene expression profiling offers a promising new technique for the diagnosis and prognosis of cancer. We have applied this technology to build a clinically robust site of origin classifier with the ultimate aim of applying it to determine the origin of cancer of unknown primary (CUP). A single cDNA microarray platform was used to profile 229 primary and metastatic tumors representing 14 tumor types and multiple histologic subtypes. This data set was subsequently used for training and validation of a support vector machine (SVM) classifier, demonstrating 89% accuracy using a 13-class model. Further, we show the translation of a five-class classifier to a quantitative PCR-based platform. Selecting 79 optimal gene markers, we generated a quantitative-PCR low-density array, allowing the assay of both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue. Data generated using both quantitative PCR and microarray were subsequently used to train and validate a cross-platform SVM model with high prediction accuracy. Finally, we applied our SVM classifiers to 13 cases of CUP. We show that the microarray SVM classifier was capable of making high confidence predictions in 11 of 13 cases. These predictions were supported by comprehensive review of the patients' clinical histories.

Genomic analysis of a spontaneous model of breast cancer metastasis to bone reveals a role for the extracellular matrix.

A clinically relevant model of spontaneous breast cancer metastasis to multiple sites, including bone, was characterized and used to identify genes involved in metastatic progression. The metastatic potential of several genetically related tumor lines was assayed using a novel real-time quantitative RT-PCR assay of tumor burden. Based on this assay, the tumor lines were categorized as nonmetastatic (67NR), weakly metastatic to lymph node (168FARN) or lung (66cl4), or highly metastatic to lymph node, lung, and bone (4T1.2 and 4T1.13). In vitro assays that mimic stages of metastasis showed that highly metastatic tumors lines were more adhesive, invasive, and migratory than the less metastatic lines. To identify metastasis-related genes in this model, each metastatic tumor was array profiled against the nonmetastatic 67NR using 15,000 mouse cDNA arrays. A significant proportion of genes relating to the extracellular matrix had elevated expression in highly metastatic tumors. The role of one of these genes, POEM, was further investigated in the model. In situ hybridization showed that POEM expression was specific to the tumor epithelium of highly metastatic tumors. Decreased POEM expression in 4T1.2 tumors significantly inhibited spontaneous metastasis to the lung, bone, and kidney. Taken together, our data support a role for the extracellular matrix in metastatic progression and describe, for the first time, a role for POEM in this process.

Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small-cell lung cancer.

BACKGROUND: Improved methods are needed for predicting prognosis and the benefit of delivering adjuvant chemotherapy (ACT) in patients with non-small-cell lung cancer (NSCLC). METHODS: A novel prognostic algorithm was identified using genomic profiles from 332 stage I-III adenocarcinomas and independently validated on a separate series of 264 patients with stage I-II tumors, compiled from five previous studies. The prognostic algorithm was used to interrogate genomic data from a series of patients treated with adjuvant chemotherapy. Those genes associated with outcome in the adjuvant treatment setting, independent to prognosis were used to train an algorithm able to classify a patient as either a responder or non-responder to ACT. The performance of this signature was independently validated on a separate series of genomic profiles from patients enrolled in a randomized controlled trial of cisplatin/vinorelbine vs. observation alone (JBR.10). RESULTS: NSCLC patients exhibiting the high-risk, poor-prognosis form of the 160-gene prognosis signature experienced a 2.80-times higher rate of 5-year disease specific death (log rank P < 0.0001) compared to those with the low-risk, good prognosis profile, adjusted for covariates. The prognosis signature was found to especially accurate at identifying early stage patients at risk of disease specific death within 24 months of diagnosis when compared to traditional methods of outcome prediction.Separately, NSCLC patients with the 37-gene ACT-response signature (n = 70, 64 %), benefited significantly from cisplatin/vinorelbine (adjusted HR: 0.23, P = 0.0032). For those patients predicted to be responders, receiving this form of ACT conferred a 25 % improvement in the probability of 5-year-survival, compared to observation alone and adjusted for covariates. Conversely, in those patients predicted to be non-responders, ACT was observed to offer no significant survival benefit (adjusted HR: 0.55, P = 0.32).The two gene signatures overlap by one gene only SPSB3, which interacts with the oncogene MET. In this study, higher levels of SPSB3 which were associated with favorable prognosis and benefit from ACT. CONCLUSIONS: These complimentary prognostic and predictive gene signatures may assist physicians in their management and treatment of patients with early stage lung cancer.

Design and multiseries validation of a web-based gene expression assay for predicting breast cancer recurrence and patient survival.

Gene expression analysis is a valuable tool for determining the risk of disease recurrence and overall survival of an individual patient with breast cancer. The purpose of this study was to create and validate a robust prognostic algorithm and implement it within an online analysis environment. Genomic and clinical data from 477 clinically diverse patients with breast cancer were analyzed with Cox regression models to identify genes associated with outcome, independent of standard prognostic factors. Percentile-ranked expression data were used to train a "metagene" algorithm to stratify patients as having a high or low risk of recurrence. The classifier was applied to 1016 patients from five independent series. The 200-gene algorithm stratifies patients into risk groups with statistically and clinically significant differences in recurrence-free and overall survival. Multivariate analysis revealed the classifier to be the strongest predictor of outcome in each validation series. In untreated node-negative patients, 88% sensitivity and 44% specificity for 10-year recurrence-free survival was observed, with positive and negative predictive values of 32% and 92%, respectively. High-risk patients appear to significantly benefit from systemic adjuvant therapy. A 200-gene prognosis signature has been developed and validated using genomic and clinical data representing a range of breast cancer clinicopathological subtypes. It is a strong independent predictor of patient outcome and is available for research use.

Gene expression profiling to identify the histogenetic origin of metastatic adenocarcinomas of unknown primary.

PURPOSE: Patients with adenocarcinoma of unknown primary origin (ACUP) constitute approximately 4% of all malignancies. For effective treatment of these patients, it is considered optimal to identify the primary tumor origins. Currently, the success rate of the diagnostic work-up is only 20% to 30%. Our goal was to evaluate the contribution of gene expression profiling for routine clinical practice in patients with ACUP. PATIENTS AND METHODS: Formalin-fixed, paraffin-embedded (FFPE) samples were obtained from 84 patients with a known primary adenocarcinoma and from 38 patients with ACUP. An extensive immunohistochemical panel classified 16 of the patients with ACUP, whereas 22 patients remained unclassified for their histogenetic origin. Information about staging procedures and clinical follow-up were available in all patient cases. The expression data were analyzed in relation to clinicopathologic variables and immunohistochemical results. RESULTS: The gene expression-based assay classified the primary site correctly in 70 (83%) of 84 patient cases of primary and metastatic tumors of known origin, with good sensitivity for the majority of the tumor classes and relatively poor sensitivity for primary lung adenocarcinoma. Gene expression profiling identified 15 (94%) of 16 patients with initial ACUP who were classified by immunohistochemistry, and it made a valuable contribution to a potential site of origin in 14 of the 22 patients with ACUP. CONCLUSION: The gene expression platform can classify correctly from FFPE samples the majority of tumors classes both in patients with known primary and in patients with ACUP. Therefore, gene expression profiling represents an additional analytic approach to assist with the histogenetic diagnosis of patients with ACUP.

Location of, immunogenicity of and relationships between neutralization epitopes on the attachment protein (G) of Hendra virus.

Epitopes involved in a protective immune response to Hendra virus (HeV) (Henipavirus, Paramxyoviridae) were investigated by generating five neutralizing monoclonal antibodies (mAbs) to the virus attachment protein (G) of HeV (HeV G) and sequencing of the G gene of groups of neutralization-escape variants selected with each mAb. Amino acid substitutions occurred at eight distinct sites on HeV G. Relationships between these sites were investigated in binding and neutralization assays using heterologous combinations of variants and mAbs. The sites were also mapped to a proposed structural model for the attachment proteins of Paramyxoviridae. Their specific locations and the nature of their interactions with the mAb panel provided the first functional evidence that HeV G in fact resembled the proposed structure. Four sites (aa 183-185, 417, 447 and 570) contributed to a major discontinuous epitope, on the base of the globular head, that was similar to immunodominant virus neutralization sites found in other paramyxoviruses. Amino acid similarity between HeV and Nipah virus was relatively highly conserved at these sites but decreased significantly at the other sites identified in this study. These included another discontinuous epitope on the base of the head region defined by sites aa 289 and 324 and well separated epitopes on the top of the head at sites aa 191-195 and 385-356. The latter epitope corresponded to immunodominant neutralization sites found in Rinderpest virus and Measles virus.

Identification and functional significance of genes regulated by structurally different histone deacetylase inhibitors.

Histone deacetylase inhibitors (HDACis) inhibit tumor cell growth and survival, possibly through their ability to regulate the expression of specific proliferative and/or apoptotic genes. However, the HDACi-regulated genes necessary and/or sufficient for their biological effects remain undefined. We demonstrate that the HDACis suberoylanilide hydroxamic acid (SAHA) and depsipeptide regulate a highly overlapping gene set with at least 22% of genes showing altered expression over a 16-h culture period. SAHA and depsipeptide coordinately regulated the expression of several genes within distinct apoptosis and cell cycle pathways. Multiple genes within the Myc, type beta TGF, cyclin/cyclin-dependent kinase, TNF, Bcl-2, and caspase pathways were regulated in a manner that favored induction of apoptosis and decreased cellular proliferation. APAF-1, a gene central to the intrinsic apoptotic pathway, was induced by SAHA and depsipeptide and shown to be important, but not essential, for HDACi-induced cell death. Overexpression of p16(INK4A) and arrest of cells in G(1) can suppress HDACi-mediated apoptosis. Although p16(INK4A) did not affect the genome-wide transcription changes mediated by SAHA, a small number of apoptotic genes, including BCLXL and B-MYB, were differentially regulated in a manner consistent with attenuated HDACi-mediated apoptosis in arrested cells. We demonstrate that different HDACi alter transcription of a large and common set of genes that control diverse molecular pathways important for cell survival and proliferation. The ability of HDACi to target multiple apoptotic and cell proliferation pathways may provide a competitive advantage over other chemotherapeutic agents because suppression/loss of a single pathway may not confer resistance to these agents.