The molecular portraits of breast tumors are conserved across microarray platforms

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis

BACKGROUND

The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

RESULTS

A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.

CONCLUSION

Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.

The functional loss of the retinoblastoma tumour suppressor is a common event in basal-like and luminal B breast carcinomas

Introduction

Breast cancers can be classified using whole genome expression into distinct subtypes that show differences in prognosis. One of these groups, the basal-like subtype, is poorly differentiated, highly metastatic, genomically unstable, and contains specific genetic alterations such as the loss of tumour protein 53 (TP53). The loss of the retinoblastoma tumour suppressor encoded by the RB1 locus is a well-characterised occurrence in many tumour types; however, its role in breast cancer is less clear with many reports demonstrating a loss of heterozygosity that does not correlate with a loss of RB1 protein expression.

Methods

We used gene expression analysis for tumour subtyping and polymorphic markers located at the RB1 locus to assess the frequency of loss of heterozygosity in 88 primary human breast carcinomas and their normal tissue genomic DNA samples.

Results

RB1 loss of heterozygosity was observed at an overall frequency of 39%, with a high frequency in basal-like (72%) and luminal B (62%) tumours. These tumours also concurrently showed low expression of RB1 mRNA. p16^INK4a was highly expressed in basal-like tumours, presumably due to a previously reported feedback loop caused by RB1 loss. An RB1 loss of heterozygosity signature was developed and shown to be highly prognostic, and was potentially a predictive marker of response to neoadjuvant chemotherapy.

Conclusions

These results suggest that the functional loss of RB1 is common in basal-like tumours, which may play a key role in dictating their aggressive biology and unique therapeutic responses.

Triple negative breast cancer: molecular profiling and prognostic impact in adjuvant anthracycline-treated patients

BACKGROUND

We analysed the clinical features, distribution of basal markers, prevalence of oncogene amplification, and outcome of triple negative (TN) compared to those of non-TN cancers in a series of adjuvant-anthracycline treated breast cancer patients.

METHODS

We examined the prognostic impact of the TN and BL phenotype in 245 breast cancer patients uniformly treated with adjuvant anthracycline-based chemotherapy following primary surgery, with regards to local relapse-free (LRFS), metastasis free (MFS), and breast cancer specific survival (BCSS). A comparative analysis of the clinicopathological characteristics, expression of basal markers (cytokeratins (Cks) 5/6, 14, 17, EGFR, and caveolin 1 and 2), MIB-1, p53 and topoisomerase II alpha, and prevalence of CCND1, MYC and TOP2A amplification in TN and non-TN breast tumours was performed.

RESULTS

TN cancers were significantly associated with the expression of basal markers (all P < 0.0001). However 19.4% of TN tumours were negative for basal markers, whilst 7.3% of non-TN tumours expressed basal markers. TN phenotype was significantly associated with p53, MIB-1 and topoisomerase II alpha (all, P < 0.01) expression. No TN cancer harboured amplification of CCND1 or TOP2A. In univariate analysis, TN and BL phenotype were significantly associated with shorter MFS (both, P < 0.01) and BCSS (both, P < 0.005) but not LRFS.

CONCLUSIONS

Despite treatment with standard dose anthracycline-based chemotherapy, the clinical outcome of TN and BL cancers remains poor. Alternative chemotherapeutic regimens and/or novel therapeutic approaches are warranted. Although a significant phenotypic overlap exists between TN and basal-like tumours, the TN phenotype is not an ideal surrogate marker for basal-like breast cancers.

A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer

Introduction

Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors.

Methods

Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis.

Results

We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment.

Conclusions

This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.

Combining biological gene expression signatures in predicting outcome in breast cancer: An alternative to supervised classification

INTRODUCTION

Gene expression profiling has been extensively used to predict outcome in breast cancer patients. We have previously reported on biological hypothesis-driven analysis of gene expression profiling data and we wished to extend this approach through the combinations of various gene signatures to improve the prediction of outcome in breast cancer.

METHODS

We have used gene expression data (25.000 gene probes) from a previously published study of tumours from 295 early stage breast cancer patients from the Netherlands Cancer Institute using updated follow-up. Tumours were assigned to three prognostic groups using the previously reported Wound-response and hypoxia-response signatures, and the outcome in each of these subgroups was evaluated.

RESULTS

We have assigned invasive breast carcinomas from 295 stages I and II breast cancer patients to three groups based on gene expression profiles subdivided by the wound-response signature (WS) and hypoxia-response signature (HS). These three groups are (1) quiescent WS/non-hypoxic HS; (2) activated WS/non-hypoxic HS or quiescent WS/hypoxic tumours and (3) activated WS/hypoxic HS. The overall survival at 15 years for patients with tumours in groups 1, 2 and 3 are 79%, 59% and 27%, respectively. In multivariate analysis, this signature is not only independent of clinical and pathological risk factors; it is also the strongest predictor of outcome. Compared to a previously identified 70-gene prognosis profile, obtained with supervised classification, the combination of signatures performs roughly equally well and might have additional value in the ER-negative subgroup. In the subgroup of lymph node positive patients, the combination signature outperforms the 70-gene signature in multivariate analysis. In addition, in multivariate analysis, the WS/HS combination is a stronger predictor of outcome compared to the recently reported invasiveness gene signature combined with the WS.

CONCLUSION

A combination of biological gene expression signatures can be used to identify a powerful and independent predictor for outcome in breast cancer patients.

Transcriptome analysis of the normal human mammary cell commitment and differentiation process

Mature mammary epithelial cells are generated from undifferentiated precursors through a hierarchical process, but the molecular mechanisms involved, particularly in the human mammary gland, are poorly understood. To address this issue, we isolated highly purified subpopulations of primitive bipotent and committed luminal progenitor cells as well as mature luminal and myoepithelial cells from normal human mammary tissue and compared their transcriptomes obtained using three different methods. Elements unique to each subset of mammary cells were identified, and changes that accompany their differentiation in vivo were shown to be recapitulated in vitro. These include a stage-specific change in NOTCH pathway gene expression during the commitment of bipotent progenitors to the luminal lineage. Functional studies further showed NOTCH3 signaling to be critical for this differentiation event to occur in vitro. Taken together, these findings provide an initial foundation for future delineation of mechanisms that perturb primitive human mammary cell growth and differentiation.

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.

Genes responsive to both oxidant stress and loss of estrogen receptor function identify a poor prognosis group of estrogen receptor positive primary breast cancers

Introduction

Oxidative stress can modify estrogen receptor (ER) structure and function, including induction of progesterone receptor (PR), altering the biology and clinical behavior of endocrine responsive (ER-positive) breast cancer.

Methods

To investigate the impact of oxidative stress on estrogen/ER-regulated gene expression, RNA was extracted from ER-positive/PR-positive MCF7 breast cancer cells after 72 hours of estrogen deprivation, small-interfering RNA knockdown of ER-α, short-term (8 hours) exposure to various oxidant stresses (diamide, hydrogen peroxide, and menadione), or simultaneous ER-α knockdown and oxidant stress. RNA samples were analyzed by high-throughput expression microarray (Affymetrix), and significance analysis of microarrays was used to define gene signatures responsive to estrogen/ER regulation and oxidative stress. To explore the association of these signatures with breast cancer biology, microarray data were analyzed from 394 ER-positive primary human breast cancers pooled from three independent studies. In particular, an oxidant-sensitive estrogen/ER-responsive gene signature (Ox-E/ER) was correlated with breast cancer clinical parameters and disease-specific patient survival (DSS).

Results

From 891 estrogen/ER-regulated probes, a core set of 75 probes (62 unique genes) responsive to all three oxidants were selected (Ox-E/ER signature). Ingenuity pathway analysis of this signature highlighted networks involved in development, cancer, and cell motility, with intersecting nodes at growth factors (platelet-derived growth factor-BB, transforming growth factor-β), a proinflammatory cytokine (tumor necrosis factor), and matrix metalloproteinase-2. Evaluation of the 394 ER-positive primary breast cancers demonstrated that Ox-E/ER index values correlated negatively with PR mRNA levels (r_p = -0.2; P = 0.00011) and positively with tumor grade (r_p = 0.2; P = 9.741 × e^-5), and were significantly higher in ER-positive/PR-negative versus ER-positive/PR-positive breast cancers (t-test, P = 0.0008). Regardless of PR status, the Ox-E/ER index associated with reduced DSS (n = 201; univariate Cox, P = 0.078) and, using the optimized cut-point, separated ER-positive cases into two significantly different DSS groups (log rank, P = 0.0009).

Conclusion

An oxidant-sensitive subset of estrogen/ER-responsive breast cancer genes linked to cell growth and invasion pathways was identified and associated with loss of PR and earlier disease-specific mortality, suggesting that oxidative stress contributes to the development of an aggressive subset of primary ER-positive breast cancers.

An integrative hypothesis about the origin and development of sporadic and familial breast cancer subtypes

Do breast cancer tumours have a common cell origin? Do different breast cancer molecular phenotypes arise from distinct cell types? The studies we have performed during the last few years in familial breast tumours (BRCA1, BRCA2 and non-BRCA1/2) widen questions about the development of sporadic breast cancer to hereditary breast cancer. Array-comparative genomic hybridisation (CGH) studies show universal genomic aberrations in both familial and sporadic breast cancer subtypes that may be selected in the breast tumour development. The inactivation of BRCA1 seems to play a critical role in oestrogen receptor (ER)-negative cancer stem cells (CSCs), driving the tumour development mostly towards a basal-like or, in some cases, to a luminal B phenotype, but other carcinogenetic events are proposed to explain the remaining tumour subtypes. The existence of common genomic alterations in basal-like, ERBB2 and luminal B breast tumours may suggest a common cell origin or clonal selection of these tumour subtypes, arising from an ER-negative CSC or from a progenitor cell (PC). Finally, specific genomic aberrations in ER-positive tumours could provide cellular proliferation advantages when the cells are exposed to oestrogen. We propose a combination of the CSC hypothesis (for the carcinogenesis processes) and the clonal selection model (in terms of tumour development). We uphold that the basal-like-, ERBB2- and luminal B-sporadic and familial tumour subtypes have an ER-negative breast stem/PC origin, whereas luminal A tumours arise from an ER-positive PC, supporting a hierarchical breast carcinogenesis model, whereas crucial genomic imbalances are clonally selected during the tumour development.

PMC42, a breast progenitor cancer cell line, has normal-like mRNA and microRNA transcriptomes

Introduction

The use of cultured cell lines as model systems for normal tissue is limited by the molecular alterations accompanying the immortalisation process, including changes in the mRNA and microRNA (miRNA) repertoire. Therefore, identification of cell lines with normal-like expression profiles is of paramount importance in studies of normal gene regulation.

Methods

The mRNA and miRNA expression profiles of several breast cell lines of cancerous or normal origin were measured using printed slide arrays, Luminex bead arrays, and real-time reverse transcription-polymerase chain reaction.

Results

We demonstrate that the mRNA expression profiles of two breast cell lines are similar to that of normal breast tissue: HB4a, immortalised normal breast epithelium, and PMC42, a breast cancer cell line that retains progenitor pluripotency allowing in-culture differentiation to both secretory and myoepithelial fates. In contrast, only PMC42 exhibits a normal-like miRNA expression profile. We identified a group of miRNAs that are highly expressed in normal breast tissue and PMC42 but are lost in all other cancerous and normal-origin breast cell lines and observed a similar loss in immortalised lymphoblastoid cell lines compared with healthy uncultured B cells. Moreover, like tumour suppressor genes, these miRNAs are lost in a variety of tumours. We show that the mechanism leading to the loss of these miRNAs in breast cancer cell lines has genomic, transcriptional, and post-transcriptional components.

Conclusion

We propose that, despite its neoplastic origin, PMC42 is an excellent molecular model for normal breast epithelium, providing a unique tool to study breast differentiation and the function of key miRNAs that are typically lost in cancer.

The CD44+/CD24- phenotype is enriched in basal-like breast tumors

Introduction

Human breast tumors are heterogeneous and consist of phenotypically diverse cells. Breast cancer cells with a CD44⁺/CD24^- phenotype have been suggested to have tumor-initiating properties with stem cell-like and invasive features, although it is unclear whether their presence within a tumor has clinical implications. There is also a large heterogeneity between tumors, illustrated by reproducible stratification into various subtypes based on gene expression profiles or histopathological features. We have explored the prevalence of cells with different CD44/CD24 phenotypes within breast cancer subtypes.

Methods

Double-staining immunohistochemistry was used to quantify CD44 and CD24 expression in 240 human breast tumors for which information on other tumor markers and clinical characteristics was available. Gene expression data were also accessible for a cohort of the material.

Results

A considerable heterogeneity in CD44 and CD24 expression was seen both between and within tumors. A complete lack of both proteins was evident in 35% of the tumors, while 13% contained cells of more than one of the CD44⁺/CD24^-, CD44^-/CD24⁺ and CD44⁺/CD24⁺ phenotypes. CD44⁺/CD24^- cells were detected in 31% of the tumors, ranging in proportion from only a few to close to 100% of tumor cells. The CD44⁺/CD24^- phenotype was most common in the basal-like subgroup – characterized as negative for the estrogen and progesterone receptors as well as for HER2, and as positive for cytokeratin 5/14 and/or epidermal growth factor receptor, and particularly common in BRCA1 hereditary tumors, of which 94% contained CD44⁺/CD24^- cells. The CD44⁺/CD24^- phenotype was surprisingly scarce in HER2+ tumors, which had a predominantly CD24⁺ status. A CD44⁺/CD24^- gene expression signature was generated, which included CD44 and α₆-integrin (CD49f) among the top-ranked overexpressed genes.

Conclusion

We demonstrate an association between basal-like and particularly BRCA1 hereditary breast cancer and the presence of CD44⁺/CD24^- cells. Not all basal-like tumors and very few HER2+ tumors, however, contain CD44⁺/CD24^- cells, emphasizing that a putative tumorigenic ability may not be confined to cells of this phenotype and that other breast cancer stem cell markers remain to be identified.

Subtypes of breast cancer show preferential site of relapse

We explored whether the five previously reported molecular subtypes in breast cancer show a preference for organ-specific relapse and searched for molecular pathways involved. The "intrinsic" gene list describing the subtypes was used to classify 344 primary breast tumors of lymph node-negative patients. Fisher exact tests were used to determine the association between a tumor subtype and a particular site of distant relapse in these patients who only received local treatment. Modulated genes and pathways were identified in the various groups using Significance Analysis of Microarrays and Global Testing. Bone relapse patients were most abundant in the luminal subtypes but were found less than expected in the basal subtype. The reverse was true for lung and brain relapse patients with the remark that absence of lung relapse was luminal A specific. Finally, a pleura relapse, although rare, was found almost exclusively in both luminal subtypes. Many differentially expressed genes were identified, of which several were in common in a subtype and the site to which the subtype preferentially relapsed. WNT signaling was up-regulated in the basal subtype and in brain-specific relapse, and down-modulated in the luminal B subtype and in bone-specific relapse. Focal adhesion was found up-regulated in the luminal A subtype but down-regulated in lung relapse. The five major molecular subtypes in breast cancer are evidently different with regard to their ability to metastasize to distant organ(s), and share biological features and pathways with their preferred distant metastatic site.

How basal are triple-negative breast cancers?

The basal molecular subtype of breast cancer (BC) is defined by the mRNA expression pattern of an intrinsic approximately 500-gene set. It is the most homogeneous subtype in transcriptional terms, and one of the most aggressive in prognostic terms. Clinical trials testing new systemic therapeutic strategies have been launched in basal BCs. Although no proof of evidence has yet been reported, basal tumors are currently assimilated to and selected as triple-negative (TN) BCs in these trials because of their frequent immunohistochemical (IHC) negativity for hormone and ERBB2 receptors. Here, we have assessed the degrees of correlation and of homogeneity of the TN phenotype (IHC-based definition) and the basal subtype (gene expression-based definition). We analyzed 172 TN BCs defined by gene expression profile as basal (123 cases) and nonbasal (49 cases). Conversely, 160 tumors were defined as basal by their gene expression profile and included 123 TN and 37 non-TN samples. Uni- and multivariate analyses revealed that TN BCs represent a more heterogeneous group than basal BCs, including basal and nonbasal tumors very different both at the histoclinical and molecular level, notably for mRNA expression of molecules targeted by specific therapies under evaluation in clinical trials. These results call for caution in the interpretation of ongoing trials and selection of patients in future trials. They also warrant the identification of molecular markers for basal BCs more clinically applicable than gene expression profiles.

Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype

PURPOSE

Basal-like breast cancer is associated with high grade, poor prognosis, and younger patient age. Clinically, a triple-negative phenotype definition [estrogen receptor, progesterone receptor, and human epidermal growth factor receptor (HER)-2, all negative] is commonly used to identify such cases. EGFR and cytokeratin 5/6 are readily available positive markers of basal-like breast cancer applicable to standard pathology specimens. This study directly compares the prognostic significance between three- and five-biomarker surrogate panels to define intrinsic breast cancer subtypes, using a large clinically annotated series of breast tumors.

EXPERIMENTAL DESIGN

Four thousand forty-six invasive breast cancers were assembled into tissue microarrays. All had staging, pathology, treatment, and outcome information; median follow-up was 12.5 years. Cox regression analyses and likelihood ratio tests compared the prognostic significance for breast cancer death-specific survival (BCSS) of the two immunohistochemical panels.

RESULTS

Among 3,744 interpretable cases, 17% were basal using the triple-negative definition (10-year BCSS, 6 7%) and 9% were basal using the five-marker method (10-year BCSS, 62%). Likelihood ratio tests of multivariable Cox models including standard clinical variables show that the five-marker panel is significantly more prognostic than the three-marker panel. The poor prognosis of triple-negative phenotype is conferred almost entirely by those tumors positive for basal markers. Among triple-negative patients treated with adjuvant anthracycline-based chemotherapy, the additional positive basal markers identified a cohort of patients with significantly worse outcome.

CONCLUSIONS

The expanded surrogate immunopanel of estrogen receptor, progesterone receptor, human HER-2, EGFR, and cytokeratin 5/6 provides a more specific definition of basal-like breast cancer that better predicts breast cancer survival.

An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors

Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear, however, whether these phenotypic similarities reflect the activity of common molecular pathways. Here, we analyze the enrichment patterns of gene sets associated with embryonic stem (ES) cell identity in the expression profiles of various human tumor types. We find that histologically poorly differentiated tumors show preferential overexpression of genes normally enriched in ES cells, combined with preferential repression of Polycomb-regulated genes. Moreover, activation targets of Nanog, Oct4, Sox2 and c-Myc are more frequently overexpressed in poorly differentiated tumors than in well-differentiated tumors. In breast cancers, this ES-like signature is associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcome. The ES signature is also present in poorly differentiated glioblastomas and bladder carcinomas. We identify a subset of ES cell-associated transcription regulators that are highly expressed in poorly differentiated tumors. Our results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell-like phenotypes shown by many tumors.

Characterization of heterotypic interaction effects in vitro to deconvolute global gene expression profiles in cancer

In an effort to deconvolute global gene-expression profiles, an interaction between some breast cancer cells and stromal fibroblasts was found to induce an interferon response, which may be associated with a greater propensity for tumor progression.

Background

Perturbations in cell-cell interactions are a key feature of cancer. However, little is known about the systematic effects of cell-cell interaction on global gene expression in cancer.

Results

We used an ex vivo model to simulate tumor-stroma interaction by systematically co-cultivating breast cancer cells with stromal fibroblasts and determined associated gene expression changes with cDNA microarrays. In the complex picture of epithelial-mesenchymal interaction effects, a prominent characteristic was an induction of interferon-response genes (IRGs) in a subset of cancer cells. In close proximity to these cancer cells, the fibroblasts secreted type I interferons, which, in turn, induced expression of the IRGs in the tumor cells. Paralleling this model, immunohistochemical analysis of human breast cancer tissues showed that STAT1, the key transcriptional activator of the IRGs, and itself an IRG, was expressed in a subset of the cancers, with a striking pattern of elevated expression in the cancer cells in close proximity to the stroma. In vivo, expression of the IRGs was remarkably coherent, providing a basis for segregation of 295 early-stage breast cancers into two groups. Tumors with high compared to low expression levels of IRGs were associated with significantly shorter overall survival; 59% versus 80% at 10 years (log-rank p = 0.001).

Conclusion

In an effort to deconvolute global gene expression profiles of breast cancer by systematic characterization of heterotypic interaction effects in vitro, we found that an interaction between some breast cancer cells and stromal fibroblasts can induce an interferon-response, and that this response may be associated with a greater propensity for tumor progression.

Basal-like breast cancer: a critical review

Recent gene expression profiling of breast cancer has identified specific subtypes with clinical, biologic, and therapeutic implications. The basal-like group of tumors is characterized by an expression signature similar to that of the basal/myoepithelial cells of the breast and is reported to have transcriptomic characteristics similar to those of tumors arising in BRCA1 germline mutation carriers. They are associated with aggressive behavior and poor prognosis, and typically do not express hormone receptors or HER-2 ("triple-negative" phenotype). Therefore, patients with basal-like cancers are unlikely to benefit from currently available targeted systemic therapy. Although basal-like tumors are characterized by distinctive morphologic, genetic, immunophenotypic, and clinical features, neither an accepted consensus on routine clinical identification and definition of this aggressive subtype of breast cancer nor a way of systematically classifying this complex group of tumors has been described. Different definitions are, therefore, likely to produce variable and contradictory results that may hamper consistent identification and development of treatment strategies for these tumors. In this review, we discuss definition, heterogeneity, morphologic spectrum, relation to BRCA1, and clinical significance of this important class of breast cancer.

Gene expression profiling of breast cancer

DNA microarray platforms for gene expression profiling were invented relatively recently, and breast cancer has been among the earliest and most intensely studied diseases using this technology. The molecular signatures so identified help reveal the biologic spectrum of breast cancers, provide diagnostic tools as well as prognostic and predictive gene signatures, and may identify new therapeutic targets. Data are best presented in an open access format to facilitate external validation, the most crucial step in identifying robust, reproducible gene signatures suitable for clinical translation. Clinically practical applications derived from full expression profile studies already in use include reduced versions of microarrays representing key discriminatory genes and therapeutic targets, quantitative polymerase chain reaction assays, or immunohistochemical surrogate panels (suitable for application to standard pathology blocks). Prospective trials are now underway to determine the value of such tools for clinical decision making in breast cancer; these efforts may serve as a model for using such approaches in other tumor types.

Using microarray analysis as a prognostic and predictive tool in oncology: focus on breast cancer and normal tissue toxicity

Microarray analysis makes it possible to study the expression levels of tens of thousands of genes in one single experiment and is widely available for research purposes. Gene expression profiling is currently being used in many research projects aimed at identifying gene expression signatures in malignant tumors associated with prognosis and response to therapy. An important goal of such research is to develop gene expression-based diagnostic tests that can be used to guide therapy in cancer patients. Here we provide examples of studies using microarrays, especially focusing on breast cancer, in a wide range of fields including prediction of prognosis, distant metastasis and local recurrence, therapy response to radio- and chemotherapy, and normal tissue response.

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

Integrated analysis of miRNA expression and genomic changes in human breast tumors allows the classification of tumor subtypes.

Background

MicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.

Results

Here we report the analysis of miRNA expression in 93 primary human breast tumors, using a bead-based flow cytometric miRNA expression profiling method. Of 309 human miRNAs assayed, we identify 133 miRNAs expressed in human breast and breast tumors. We used mRNA expression profiling to classify the breast tumors as luminal A, luminal B, basal-like, HER2+ and normal-like. A number of miRNAs are differentially expressed between these molecular tumor subtypes and individual miRNAs are associated with clinicopathological factors. Furthermore, we find that miRNAs could classify basal versus luminal tumor subtypes in an independent data set. In some cases, changes in miRNA expression correlate with genomic loss or gain; in others, changes in miRNA expression are likely due to changes in primary transcription and or miRNA biogenesis. Finally, the expression of DICER1 and AGO2 is correlated with tumor subtype and may explain some of the changes in miRNA expression observed.

Conclusion

This study represents the first integrated analysis of miRNA expression, mRNA expression and genomic changes in human breast cancer and may serve as a basis for functional studies of the role of miRNAs in the etiology of breast cancer. Furthermore, we demonstrate that bead-based flow cytometric miRNA expression profiling might be a suitable platform to classify breast cancer into prognostic molecular subtypes.

High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer

High resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer, and provides a genome-wide list of common copy number alterations associated with aberrant expression and poor prognosis.

Background

The characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.

Results

We performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes ('hotspots'). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.

Conclusion

We present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.

Epidemiology of basal-like breast cancer

Risk factors for the newly identified "intrinsic" breast cancer subtypes (luminal A, luminal B, basal-like and human epidermal growth factor receptor 2-positive/estrogen receptor-negative) were determined in the Carolina Breast Cancer Study, a population-based, case-control study of African-American and white women. Immunohistochemical markers were used to subtype 1,424 cases of invasive and in situ breast cancer, and case subtypes were compared to 2,022 controls. Luminal A, the most common subtype, exhibited risk factors typically reported for breast cancer in previous studies, including inverse associations for increased parity and younger age at first full-term pregnancy. Basal-like cases exhibited several associations that were opposite to those observed for luminal A, including increased risk for parity and younger age at first term full-term pregnancy. Longer duration breastfeeding, increasing number of children breastfed, and increasing number of months breastfeeding per child were each associated with reduced risk of basal-like breast cancer, but not luminal A. Women with multiple live births who did not breastfeed and women who used medications to suppress lactation were at increased risk of basal-like, but not luminal A, breast cancer. Elevated waist-hip ratio was associated with increased risk of luminal A in postmenopausal women, and increased risk of basal-like breast cancer in pre- and postmenopausal women. The prevalence of basal-like breast cancer was highest among premenopausal African-American women, who also showed the highest prevalence of basal-like risk factors. Among younger African-American women, we estimate that up to 68% of basal-like breast cancer could be prevented by promoting breastfeeding and reducing abdominal adiposity.

An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors

Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear, however, whether these phenotypic similarities reflect the activity of common molecular pathways. Here, we analyze the enrichment patterns of gene sets associated with embryonic stem (ES) cell identity in the expression profiles of various human tumor types. We find that histologically poorly differentiated tumors show preferential overexpression of genes normally enriched in ES cells, combined with preferential repression of Polycomb-regulated genes. Moreover, activation targets of Nanog, Oct4, Sox2 and c-Myc are more frequently overexpressed in poorly differentiated tumors than in well-differentiated tumors. In breast cancers, this ES-like signature is associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcome. The ES signature is also present in poorly differentiated glioblastomas and bladder carcinomas. We identify a subset of ES cell-associated transcription regulators that are highly expressed in poorly differentiated tumors. Our results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell-like phenotypes shown by many tumors.

Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists

BACKGROUND

Numerous gene lists or "classifiers" have been derived from global gene expression data that assign breast cancers to good and poor prognosis groups. A remarkable feature of these molecular signatures is that they have few genes in common, prompting speculation that they may use distinct genes to measure the same pathophysiological process(es), such as proliferation. However, this supposition has not been rigorously tested. If gene-based classifiers function by measuring a minimal number of cellular processes, we hypothesized that the informative genes for these processes could be identified and the data sets could be adjusted for the predictive contributions of those genes. Such adjustment would then attenuate the predictive function of any signature measuring that same process.

RESULTS

We tested this hypothesis directly using a novel iterative-subtractive approach. We evaluated five gene expression data sets that sample a broad range of breast cancer subtypes. In all data sets, the dominant cluster capable of predicting metastasis was heavily populated by genes that fluctuate in concert with the cell cycle. When six well-characterized classifiers were examined, all contained a higher than expected proportion of genes that correlate with this cluster. Furthermore, when the data sets were globally adjusted for the cell cycle cluster, each classifier lost its ability to assign tumors to appropriate high and low risk groups. In contrast, adjusting for other predictive gene clusters did not impact their performance.

CONCLUSION

These data indicate that the discriminative ability of breast cancer classifiers is dependent upon genes that correlate with cell cycle progression.

Population differences in breast cancer severity

Breast cancer incidence and mortality vary among different populations. African-American, Hispanic, Asian and Native American women have lower incidence but higher mortality compared with non-Hispanic white women. Explanations for the observed variation include social and economic factors such as education, income level, health insurance coverage, use of mammography, parity, breastfeeding and diet. Breast cancer may be a heterogeneous disease with different subtypes of tumors having different genetic and environmental risk factors. The difference in frequency of particular tumor subtypes between populations may explain some of the differences in incidence and mortality. Known genetic variants explain a small fraction of breast cancer cases, and so far there are no susceptibility genes that explain population differences in incidence and mortality. Studies evaluating the risk for particular tumor subtypes combining genetic and environmental variables and analyzing cases from different populations are needed to understand population differences in the severity of breast cancer.

A phase II study of sequential neoadjuvant gemcitabine plus doxorubicin followed by gemcitabine plus cisplatin in patients with operable breast cancer: prediction of response using molecular profiling

This study examined the pathological complete response (pCR) rate and safety of sequential gemcitabine-based combinations in breast cancer. We also examined gene expression profiles from tumour biopsies to identify biomarkers predictive of response. Indian women with large or locally advanced breast cancer received 4 cycles of gemcitabine 1200 mg m(-2) plus doxorubicin 60 mg m(-2) (Gem+Dox), then 4 cycles of gemcitabine 1000 mg m(-2) plus cisplatin 70 mg m(-2) (Gem+Cis), and surgery. Three alternate dosing sequences were used during cycle 1 to examine dynamic changes in molecular profiles. Of 65 women treated, 13 (24.5% of 53 patients with surgery) had a pCR and 22 (33.8%) had a complete clinical response. Patients administered Gem d1, 8 and Dox d2 in cycle 1 (20 of 65) reported more toxicities, with G3/4 neutropenic infection/febrile neutropenia (7 of 20) as the most common cycle-1 event. Four drug-related deaths occurred. In 46 of 65 patients, 10-fold cross validated supervised analyses identified gene expression patterns that predicted with >or=73% accuracy (1) clinical complete response after eight cycles, (2) overall clinical complete response, and (3) pCR. This regimen shows strong activity. Patients receiving Gem d1, 8 and Dox d2 experienced unacceptable toxicity, whereas patients on other sequences had manageable safety profiles. Gene expression patterns may predict benefit from gemcitabine-containing neoadjuvant therapy.

Comparative expression analysis of four breast cancer subtypes versus matched normal tissue from the same patients

Gene expression studies have been widely used in an effort to identify signatures that can predict clinical progression of cancer. In this study we focused instead on identifying gene expression differences between breast tumors and adjacent normal tissue, and between different subtypes of tumor classified by clinical marker status. We have collected a set of 20 breast cancer tissues, matched with the adjacent pathologically normal tissue from the same patient. The cancer samples representing each subtype of breast cancer identified by estrogen receptor ER(+/-) and Her2(+/-) status and divided into four subgroups (ER+/Her2+, ER+/Her2-, ER-/Her2+, and ER-/Her2-) were hybridized on Affymetrix HG-133 Plus 2.0 microarrays. By comparing cancer samples with their matched normal controls we have identified 3537 overall differentially expressed genes using data analysis methods from Bioconductor. When we looked at the genes in common of the four subgroups, we found 151 regulated genes, some of them encoding known targets for breast cancer treatment. Unique genes in the four subgroups instead suggested gene regulation dependent on the ER/Her2 markers selection. In conclusion, the results indicate that microarray studies using robust analysis of matched tumor and normal samples from the same patients can be used to identify genes differentially expressed in breast cancer tumor subtypes even when small numbers of samples are considered and can further elucidate molecular features of breast cancer.

Clinical and pathologic aspects of basal-like breast cancers

Gene-expression profiling of breast cancers has shown that distinct molecular subclasses are present within tumors that are apparently morphologically similar. The molecular subclasses of cohorts classified by the 'intrinsic' gene set include the luminal A and B, erbB-2+, normal-breast-like, and basal-like tumors. Basal-like breast cancers have been reported to be associated with worse overall and disease-free survival compared with the luminal A subtype. In addition, there is an immunohistochemical surrogate for the basal-like profile, which has considerably facilitated their study in non-specialty laboratories. Basal-like breast carcinomas have markedly reduced expression of genes related to estrogen receptors and erbB-2, and express proteins that are characteristic of the normal myoepithelial cell. This Review appraises the current state of knowledge on the clinical and pathologic features of breast cancers classified as 'basal-like' by gene-expression profiling and/or immunohistochemical criteria. These tumors seem to be relatively heterogeneous according to a multitude of clinicopathologic parameters, which indicates that their most prognostically relevant subsets have yet to be defined. Similarly to tumors of luminal epithelial differentiation, carcinomas of the 'basal' type have a spectrum of morphologic and clinical characteristics.

Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas

INTRODUCTION

Breast cancer is a heterogeneous group of tumors, and can be subdivided on the basis of histopathological features, genetic alterations and gene-expression profiles. One well-defined subtype of breast cancer is characterized by a lack of HER2 gene amplification and estrogen and progesterone receptor expression ('triple-negative tumors'). We examined the histopathological and gene-expression profile of triple-negative tumors to define subgroups with specific characteristics, including risk of developing distant metastases.

METHODS

97 triple-negative tumors were selected from the fresh-frozen tissue bank of the Netherlands Cancer Institute, and gene-expression profiles were generated using 35K oligonucleotide microarrays. In addition, histopathological and immunohistochemical characterization was performed, and the findings were associated to clinical features.

RESULTS

All triple-negative tumors were classified as basal-like tumors on the basis of their overall gene-expression profile. Hierarchical cluster analysis revealed five distinct subgroups of triple-negative breast cancers. Multivariable analysis showed that a large amount of lymphocytic infiltrate (HR = 0.30, 95% CI 0.09-0.96) and absence of central fibrosis in the tumors (HR = 0.14, 95% CI 0.03-0.62) were associated with distant metastasis-free survival.

CONCLUSION

Triple-negative tumors are synonymous with basal-like tumors, and can be identified by immunohistochemistry. Based on gene-expression profiling, basal-like tumors are still heterogeneous and can be subdivided into at least five distinct subgroups. The development of distant metastasis in basal-like tumors is associated with the presence of central fibrosis and a small amount of lymphocytic infiltrate.

Aging impacts transcriptomes but not genomes of hormone-dependent breast cancers

Introduction

Age is one of the most important risk factors for human malignancies, including breast cancer; in addition, age at diagnosis has been shown to be an independent indicator of breast cancer prognosis. Except for inherited forms of breast cancer, however, there is little genetic or epigenetic understanding of the biological basis linking aging with sporadic breast cancer incidence and its clinical behavior.

Methods

DNA and RNA samples from matched estrogen receptor (ER)-positive sporadic breast cancers diagnosed in either younger (age ≤ 45 years) or older (age ≥ 70 years) Caucasian women were analyzed by array comparative genomic hybridization and by expression microarrays. Array comparative genomic hybridization data were analyzed using hierarchical clustering and supervised age cohort comparisons. Expression microarray data were analyzed using hierarchical clustering and gene set enrichment analysis; differential gene expression was also determined by conditional permutation, and an age signature was derived using prediction analysis of microarrays.

Results

Hierarchical clustering of genome-wide copy-number changes in 71 ER-positive DNA samples (27 younger women, 44 older women) demonstrated two age-independent genotypes; one with few genomic changes other than 1q gain/16q loss, and another with amplifications and low-level gains/losses. Age cohort comparisons showed no significant differences in total or site-specific genomic breaks and amplicon frequencies. Hierarchical clustering of 5.1 K genes variably expressed in 101 ER-positive RNA samples (53 younger women, 48 older women) identified six transcriptome subtypes with an apparent age bias (P < 0.05). Samples with higher expression of a poor outcome-associated proliferation signature were predominantly (65%) younger cases. Supervised analysis identified cancer-associated genes differentially expressed between the cohorts; with younger cases expressing more cell cycle genes and more than threefold higher levels of the growth factor amphiregulin (AREG), and with older cases expressing higher levels of four different homeobox (HOX) genes in addition to ER (ESR1). An age signature validated against two other independent breast cancer datasets proved to have >80% accuracy in discerning younger from older ER-positive breast cancer cases with characteristic differences in AREG and ESR1 expression.

Conclusion

These findings suggest that epigenetic transcriptome changes, more than genotypic variation, account for age-associated differences in sporadic breast cancer incidence and prognosis.

Merging two gene-expression studies via cross-platform normalization

MOTIVATION

Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms.

RESULTS

The article makes two contributions to the problem. The first is a simple cross-study normalization method, which is based on linked gene/sample clustering of the given datasets. The second is the introduction and description of several general validation measures that can be used to assess and compare cross-study normalization methods. The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures.

AVAILABILITY

The supplementary materials and XPN Matlab code are publicly available at website: https://genome.unc.edu/xpn

Transgenic models to study actions of prolactin in mammary neoplasia

Transgenic models to explore the role of prolactin and its interactions with other factors in mammary oncogenesis have begun to reveal the dynamic contributions of prolactin to the development and progression of this disease. Targeting prolactin to mammary epithelial cells mimics the local production of this hormone that is prominent in women, and permits studies in the absence of effects on the ovarian steroid milieu. These models have demonstrated that local production of prolactin is sufficient to induce mammary tumors after a long latency. Prolactin also can potentiate actions of other oncogenic stimuli, decreasing tumor latency and increasing incidence in several models. Augmented proliferation, without alteration of apoptosis, is a consistent feature. Pathways in addition to the well-characterized Jak2-Stat5 pathway, including ERK1/2 and Akt1/2, are implicated in these actions. These studies have also revealed a complex relationship with estrogen; while prolactin increases ERalpha expression, it does not require estrogenic ligand for lesion development, and indeed, in combination with the EGFR ligand, TGFalpha, prolactin can contribute to estrogen insensitivity. These studies highlight the utility of these models to decipher the interplay between prolactin and other oncogenic factors in breast cancer, with implications for preventative and therapeutic strategies.

A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of postgenomic clinical trials on cancer

This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.

Basal-like subtype of breast cancer: a review of its unique characteristics and their clinical significance

Subtyping of breast cancers by means of DNA microarray analyses has given rise to the new concept of the basal-like subtype; this subtype is in effect the equivalent of so-called "triple-negative" breast cancer. Basal-like breast cancer has aggressive characteristics, such as high histological grade, mutation of the TP53 gene, and negative hormone receptors. It tends to occur in relatively young women and is highly correlated with suppression of BRCA1 function. The EGFR gene is often overexpressed in this subtype. Here, research carried out in the last few years into the basal-like subtype of breast cancer will be reviewed.

An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

A feature selection method was used in an analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in estrogen receptor-negative breast cancer, showing that it is a heterogeneous disease with at least four main subtypes.

Background

Estrogen receptor (ER)-negative breast cancer specimens are predominantly of high grade, have frequent p53 mutations, and are broadly divided into HER2-positive and basal subtypes. Although ER-negative disease has overall worse prognosis than does ER-positive breast cancer, not all ER-negative breast cancer patients have poor clinical outcome. Reliable identification of ER-negative tumors that have a good prognosis is not yet possible.

Results

We apply a recently proposed feature selection method in an integrative analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in ER-negative breast cancer. We find a subclass of basal tumors, characterized by over-expression of immune response genes, which has a better prognosis than the rest of ER-negative breast cancers. Moreover, we show that, in contrast to ER-positive tumours, the majority of prognostic markers in ER-negative breast cancer are over-expressed in the good prognosis group and are associated with activation of complement and immune response pathways. Specifically, we identify an immune response related seven-gene module and show that downregulation of this module confers greater risk for distant metastasis (hazard ratio 2.02, 95% confidence interval 1.2-3.4; P = 0.009), independent of lymph node status and lymphocytic infiltration. Furthermore, we validate the immune response module using two additional independent datasets.

Conclusion

We show that ER-negative basal breast cancer is a heterogeneous disease with at least four main subtypes. Furthermore, we show that the heterogeneity in clinical outcome of ER-negative breast cancer is related to the variability in expression levels of complement and immune response pathway genes, independent of lymphocytic infiltration.

Clinical application of the 70-gene profile: the MINDACT trial

The 70-gene profile is a new prognostic tool that has the potential to greatly improve risk assessment and treatment decision making for early breast cancer. Its prospective validation is currently ongoing through the MINDACT (Microarray in Node-Negative Disease May Avoid Chemotherapy) trial, a 6,000-patient randomized, multicentric trial. This article reviews the several steps in the development of the profile from its discovery to its clinical validation.

Understanding systemic sclerosis through gene expression profiling

PURPOSE OF REVIEW

Genomic analysis has rapidly become commonplace in the study and treatment of complex disease. Several recent studies of gene expression profiling in systemic sclerosis have demonstrated its value in diagnosis and illustrate the potential for this technique in prognostication, as well as the elucidation of the underlying pathogenesis.

RECENT FINDINGS

Skin biopsies from patients with systemic sclerosis show robust changes in gene profile that precede clinically detectable involvement. Current results suggest that clinically indistinguishable subgroups may be identified with different pathogenesis and outcome. Expression profiling studies of animal models of systemic sclerosis and explanted fibroblasts have helped to reveal the utility and deficiencies of these surrogates in the study of systemic sclerosis.

SUMMARY

Gene profiling is likely to provide valuable prognostic information in systemic sclerosis patients. Recent advances in sample collection and standardization of analysis mean that longitudinal collection of samples for gene profiling, even in small numbers of patients from different clinical centers, will contribute enormously to our understanding of the disease.

A future of cancer prevention and cures: highlights of the Centennial Meeting of the American Association for Cancer Research

The Centennial Meeting of the American Association for Cancer Research (AACR) was held from 14-18 April 2007 at the Los Angeles Convention Center. This meeting brought together a diverse group of over 18 000 researchers working in the fields of basic and applied cancer sciences, and explored how cancer research could be used most effectively to prevent and cure cancer at the earliest possible stage. The goal of the AACR Annual Meeting was to stimulate the dialog between basic and clinical researchers so that the translation of new discoveries might be speeded up for the benefit of cancer patients. Advances in the clinical application of genomics, epigenomics and proteomics to diagnose, monitor and prognosticate cancer development led to a dramatic increase in the number of presentations with a translational focus at this year's meeting. Several remarkable areas were particularly highlighted in this report, including The Cancer Genome Atlas, cancer stem cells, microRNA and siRNA, targeted therapy and individualized treatment. This article tries to bring attention to some hot topics in the program that are both new and noteworthy. For those who did not attend the meeting, this report may serve as a highlight of this important international cancer research meeting.