Dormancy signatures and metastasis in estrogen receptor positive and negative breast cancer

Breast cancers can recur after removal of the primary tumor and treatment to eliminate remaining tumor cells. Recurrence may occur after long periods of time during which there are no clinical symptoms. Tumor cell dormancy may explain these prolonged periods of asymptomatic residual disease and treatment resistance. We generated a dormancy gene signature from published experimental models and applied it to both breast cancer cell line expression data as well as four published clinical studies of primary breast cancers. We found that estrogen receptor (ER) positive breast cell lines and primary tumors have significantly higher dormancy signature scores (P<0.0000001) than ER- cell lines and tumors. In addition, a stratified analysis combining all ER+ tumors in four studies indicated 2.1 times higher hazard of recurrence among patients whose tumors had low dormancy scores (LDS) compared to those whose tumors had high dormancy scores (HDS) (p<0.000005). The trend was shown in all four individual studies. Suppression of two dormancy genes, BHLHE41 and NR2F1, resulted in increased in vivo growth of ER positive MCF7 cells. The patient data analysis suggests that disseminated ER positive tumor cells carrying a dormancy signature are more likely to undergo prolonged dormancy before resuming metastatic growth. Furthermore, genes identified with this approach might provide insight into the mechanisms of dormancy onset and maintenance as well as dormancy models using human breast cancer cell lines.     

Response Prediction to Neoadjuvant Chemotherapy: Comparison between Pre-Therapeutic Gene Expression Profiles and In Vitro Chemosensitivity Assay

Although the use of (neo-)adjuvant chemotherapy in breast cancer patients has resulted in improved outcome, not all patients benefit equally. We have evaluated the utility of an in vitro chemosensitivity assay in predicting response to neoadjuvant chemotherapy. Pre-therapeutic biopsies were obtained from 30 breast cancer patients assigned to neoadjuvant epirubicin 75 mg/m2 and docetaxel 75 mg/m2 (Epi/Doc) in a prospectively randomized clinical trial. Biopsies were subjected to a standardized ATP-based Epi/Doc chemosensitivity assay, and to gene expression profiling. Patients then received 3 cycles of chemotherapy, and response was evaluated by changes in tumor diameter and Ki67 expression. The efficacy of Epi/Doc in vitro was correlated with differential changes in tumor cell proliferation in response to Epi/Doc in vivo (p = 0.0011; r = 0.73670, Spearmańs rho), but did not predict for changes in tumor size. While a pre-therapeutic gene expression signature identified tumors with a clinical response to Epi/Doc, no such signature could be found for tumors that responded to Epi/Doc in vitro, or tumors in which Epi/Doc exerted an antiproliferative effect in vivo. This is the first prospective clinical trial to demonstrate the utility of a standardized in vitro chemosensitivity assay in predicting the individual biological response to chemotherapy in breast cancer.     

A feature selection approach for identification of signature genes from SAGE data

Background  

One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements.     

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.     

MicroRNA signature predicts survival in papillary thyroid carcinoma

Papillary thyroid cancer (PTC) accounts for the majority of malignant thyroid tumors. Recently, several microRNA (miRNA) expression profiling studies have used bioinformatics to suggest miRNA signatures as potential prognostic biomarkers in various malignancies. However, a prognostic miRNA biomarker has not yet been established for PTC. The aim of the present study was to identify miRNAs with prognostic value for the overall survival (OS) of patients with PTC by analyzing high-throughput miRNA data and their associated clinical characteristics downloaded from The Cancer Genome Atlas database. From our dataset, 150 differentially expressed miRNAs were identified between tumor and nontumor samples; of these miRNAs, 118 were upregulated and 32 were downregulated. Among the 150 differentially expressed miRNAs, a four miRNA signature was identified that reliably predicts OS in patients with PTC. This miRNA signature was able to classify patients into a high-risk group and a low-risk group with a significant difference in OS (P < .01). The prognostic value of the signature was validated in a testing set ( P < .01). The four miRNA signature was an independent prognostic predictor according to the multivariate analysis and demonstrated good performance in predicting 5-year disease survival with an area under the receiver operating characteristic curve area under the curve (AUC) score of 0.886. Thus, this signature may serve as a novel biomarker for predicting the survival of patients with PTC.     

Protein signatures associated with tumor cell dissemination in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common type of cancer worldwide. Strong prognostic indicators that predict development of distant metastases are the presence and number of lymph node metastases in the neck, and extranodal spread. Recently, it was shown in several studies that also the presence of disseminated tumor cells (DTC) in the bone marrow predicts development of distant metastases. We have investigated whether protein signatures could be detected in primary HNSCC that distinguish tumors that disseminate into the bone marrow from those that do not. Therefore, DTC-positive and -negative primary HNSCC tumors were analyzed by 2D-DIGE. A signature consisting of 51 differential protein spots was identified upon stratification for bone marrow status, which allowed a correct classification of DTC-positive and DTC-negative HNSCC tumors in 95% of cases, using hierarchical clustering. The most prominent feature within this signature was the down-regulation of CK19 in DTC-positive tumors. Our data show that tumor cell dissemination to the bone marrow, the onset of hematogenic metastasis, can be deduced from the protein profile in the primary tumor. The highly significant down-regulation of CK19 supports a model of epithelial-mesenchymal transition for tumors that show a high proclivity for hematogenic dissemination.     

Functional genomics approaches for the study of sporadic adrenal tumor pathogenesis: clinical implications

Although sporadic adrenal tumors are frequently encountered in the general population their pathogenesis is not well elucidated. The advent of functional genomics/bioinformatics tools enabling large scale comprehensive genome expression profiling should contribute to significant progress in this field. Some studies have already been published describing gene expression profiles of benign and malignant adrenocortical tumors and phaeochromocytomas. Several genes coding for growth factors and their receptors, enzymes involved in steroid hormone biosynthesis, genes related to the regulation of cell cycle, cell proliferation, adhesion and intracellular metabolism have been found to be up- or downregulated in various tumors. Some alterations in gene expression appear so specific for certain tumor types that their application in diagnosis, determination of prognosis and the choice of therapy can be envisaged. In this short review, the authors will present a synopsis of these recent findings that seem to open new perspectives in adrenal tumor pathogenesis, with emphasis on changes in steroidogenic enzyme expression profiles and highlighting possible clinical implications.     

MicroRNA expression and function in cancer

MicroRNAs are small non-coding RNAs of 19-24 nucleotides in length that downregulate gene expression during various crucial cell processes such as apoptosis, differentiation and development. Recent work supports a role for miRNAs in the initiation and progression of human malignancies. Large high-throughput studies in patients revealed that miRNA profiling have the potential to classify tumors with high accuracy and predict outcome. Functional studies, some of which involve animal models, indicate that miRNAs act as tumor suppressors and oncogenes. Here, we summarize miRNA-profiling studies in human malignancies and examine the role of miRNAs in the pathogenesis of cancer. We also discuss the implications of these findings for the diagnosis and treatment of cancer.     

DNA microarrays in clinical cancer research

The recent sequencing of the human genome, coupled with advances in biotechnology, is enabling the comprehensive molecular "profiling" of human tissues. In particular, DNA microarrays are powerful tools for obtaining global views of human tumor gene expression. Complex information from tumor "expression profiling" studies can, in turn, be used to create novel molecular cancer diagnostics. We discuss the utility of DNA microarray-based tumor profiling in clinical cancer research, highlight some important recent studies, and identify future avenues of research in this evolving field.     

Developing a Prognostic Micro-RNA Signature for Human Cervical Carcinoma

Cervical cancer remains the third most frequently diagnosed and fourth leading cause of cancer death in women worldwide. We sought to develop a micro-RNA signature that was prognostic for disease-free survival, which could potentially allow tailoring of treatment for cervical cancer patients. A candidate prognostic 9-micro-RNA signature set was identified in the training set of 79 frozen specimens. However, three different approaches to validate this signature in an independent cohort of 87 patients with formalin-fixed paraffin-embedded (FFPE) specimens, were unsuccessful. There are several challenges and considerations associated with developing a prognostic micro-RNA signature for cervical cancer, namely: tumour heterogeneity, lack of concordance between frozen and FFPE specimens, and platform selection for global micro-RNA expression profiling in this disease. Our observations provide an important cautionary tale for future miRNA signature studies for cervical cancer, which can also be potentially applicable to miRNA profiling studies involving other types of human malignancies.     

Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues

A critical and difficult part of studying cancer with DNA microarrays is data interpretation. Besides the need for data analysis algorithms, integration of additional information about genes might be useful. We performed genome-wide expression profiling of 36 types of normal human tissues and identified 2503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by reanalyzing a large collection of published DNA microarray datasets. We observed that the expression level of liver-specific genes in hepatocellular carcinoma (HCC) correlates with the clinically defined degree of tumor differentiation. Through unsupervised clustering of tissue-specific genes differentially expressed in tumors, we extracted expression patterns that are characteristic of individual cell types, uncovering differences in cell lineage among tumor subtypes. We were able to detect the expression signature of hepatocytes in HCC, neuron cells in medulloblastoma, glia cells in glioma, basal and luminal epithelial cells in breast tumors, and various cell types in lung cancer samples. We also demonstrated that tissue-specific expression signatures are useful in locating the origin of metastatic tumors. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage, and metastasis.     

Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data

A common task for the cancer pathologist is to determine, in a patient suffering from cancer, whether a new tumor in a distinct anatomic site from the primary is an independent occurrence of cancer or a metastasis. As mutational profiling of tumors becomes more widespread in routine clinical practice, this diagnostic task can be greatly enhanced by comparing mutational profiles of the tumors to determine if they are sufficiently similar to conclude that the tumors are clonally related, that is, one is a metastasis of the other. We present here a likelihood ratio test for clonal relatedness in this setting and provide evidence of its validity. The test is unusual in that there are two possible alternative hypotheses, representing the two anatomic sites from which the single clonal cell could have initially emerged. Although evidence for clonal relatedness is largely provided by the presence of exact mutational matches in the two tumors, we show that it is possible to observe data where the test is statistically significant even when no matches are observed. This can occur when the mutational profile of one of the tumors is closely aligned with the anatomic site of the other tumor, suggesting indirectly that the tumor originated in that other site. We exhibit examples of this phenomenon and recommend a strategy for interpreting the results of these tests in practice.