Subtype-Specific Metagene-Based Prediction of Outcome after Neoadjuvant and Adjuvant Treatment in Breast Cancer.

PURPOSE: In spite of improvements of average benefit from adjuvant/neoadjuvant treatments, there are still individual patients with early breast cancer at high risk of relapse. We explored the association with outcome of robust gene cluster-based metagenes linked to proliferation, ER-related genes, and immune response to identify those high-risk patients. EXPERIMENTAL DESIGN: A total of 3,847 publicly available gene-expression profiles were analyzed (untreated, N = 826; tamoxifen-treated, N = 685; chemotherapy-treated, N = 1,150). Genes poorly performing in formalin-fixed samples were removed. Outcomes of interest were pathologic-complete response (pCR) and distant metastasis-free survival (DMFS). In ER(+)HER2(-), the proliferation and ER-related metagenes were combined to define three risk groups. In HER2(+) and ER(-)HER2(-) risk groups were defined by tertiles of an immune-related metagene. RESULTS: The high-proliferation/low-ER group of ER(+)HER2(-) breast cancer had significantly higher pCR rate [OR, 5.01 (1.76-17.99), P = 0.005], but poorer outcome [HR = 3.73 (1.63-8.51), P = 0.0018] than the low-proliferation/high-ER. A similar association with outcome applied to patients with residual disease (RD) after neoadjuvant chemotherapy (P = 0.01). In ER(-)HER2(-) and HER2(+) breast cancer, immune metagene in the high tertile was linked to higher pCR [33.7% vs. 11.6% in high and low tertile, respectively; OR, 3.87 (1.79-8.95); P = 0.0009]. In ER(-)HER2(-), after adjuvant/neoadjuvant chemotherapy, 5-year DMFS was 85.4% for high-tertile immune metagene, and 43.9% for low tertile. The outcome association was similar in patients with RD (P = 0.0055). In HER2(+) breast cancer treated with chemotherapy the association with risk of relapse was not significant. CONCLUSIONS: We developed metagene-based predictors able to define low and high risk of relapse after adjuvant/neoadjuvant therapy. High-risk patients so defined should be preferably considered for trials with investigational agents.

Glioblastoma and calcium signaling--analysis of calcium toolbox expression.

The characteristics of a cellular calcium signal (calcium signature) are determined, at least partly, by the expression of a subset of genes encoding proteins involved in calcium entry, calcium uptake and calcium modulation. Our aim in the present work was to characterize the set of genes involved in calcium signal generation that are differentially expressed in normal brain tissues versus brain tumor and/or glioma stem cells. Public datasets were analyzed according to a four step methodology consisting of: 1. detecting the outliers by using principal component analysis of the whole transcriptome; 2. building a calcium toolbox composed of 260 genes involved in the generation and modulation of the calcium signal; 3. analyzing the calcium toolbox transcriptome of different human brain areas and 4. detecting genes from the calcium toolbox preferentially expressed in tumor tissues or tumor cells compared to normal brain tissues. Our approach was validated on normal brain tissue. Tumor sample analysis allowed us to disclose a set of eighteen genes characteristic of glioblastoma tissues or glioma stem cells. Interpreting the set of genes highlighted in the study led us to propose that i) the mechanism of store operated calcium entry is strongly perturbed in cancer cells and tissues, ii) the process of calcium reuptake into mitochondria is more important in cancer cells and tissues than in their normal counterparts and iii) these two mechanisms may be coupled in at least one subgroup of the glioblastoma stem cells.

Lung squamous cell carcinomas with basaloid histology represent a specific molecular entity.

PURPOSE: The basaloid carcinoma (pure) and the (mixed) basaloid variant of lung squamous cell carcinoma (SCC) have a dismal prognosis but their underlying specific molecular characteristics remain obscure and no therapy has proven to be efficient. EXPERIMENTAL DESIGN: To assess their molecular specificity among other lung SCCs we analyzed DNA copy number aberrations and mRNA expression pangenomic profiles of 93 SCCs, including 42 basaloid samples (24 pure, 18 mixed). RESULTS: Supervised analyses reveal that pure basaloid tumors display a specific mRNA expression profile, encoding factors controlling the cell cycle, transcription, chromatin, and splicing, with prevalent expression in germline and stem cells, while genes related to squamous differentiation are underexpressed. From this signature, we derived a 2-genes (SOX4, IVL) immunohistochemistry-based predictor that discriminated basaloid tumors (pure and mixed) from non-basaloid tumors with 94% accuracy in an independent series. The pure basaloid tumors are also distinguished through unsupervised analyses. Using a centroid-based predictor, the corresponding molecular subtype was found in 8 independent public datasets (n = 58/533), and was shown to be associated with a very poor survival as compared with other SCCs (adjusted HR = 2.45; P = 0.000001). CONCLUSION: This study enlightens the heterogeneity of SCCs that can be subclassified in mRNA expression subtypes. This study demonstrates for the first time that basaloid SCCs constitute a distinct histomolecular entity, which justifies its recognition and distinction from non-basaloid SCCs. In addition, their characteristic molecular profile highlights their intrinsic resistance to cytotoxic chemotherapy and could serve as a guide for targeted therapies.

Molecular classification of malignant pleural mesothelioma: identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition.

PURPOSE: Despite research efforts to develop more effective diagnostic and therapeutic approaches, malignant pleural mesothelioma (MPM) prognosis remains poor. The assessment of tumor response to therapy can be improved by a deeper phenotypical classification of the tumor, with emphasis on its clinico-biological heterogeneity. The identification of molecular profiles is a powerful approach to better define MPM subclasses and targeted therapies. EXPERIMENTAL DESIGN: Molecular subclasses were defined by transcriptomic microarray on 38 primary MPM cultures. A three-gene predictor, identified by quantitative reverse transcription PCR, was used to classify an independent series of 108 frozen tumor samples. Gene mutations were determined in BAP1, CDKN2A, CDKN2B, NF2, and TP53. Epithelial-to-mesenchymal transition (EMT) markers were studied at the mRNA and protein levels. RESULTS: Unsupervised hierarchical clustering on transcriptomic data defined two robust MPM subgroups (C1 and C2), closely related to prognosis and partly to histologic subtypes. All sarcomatoid/desmoplastic MPM were included in the C2 subgroup. Epithelioid MPM were found in both subgroups, with a worse survival prognosis in the C2 subgroup. This classification and its association with histologic subtypes and survival were validated in our independent series using the three-gene predictor. Similar subgroups were found after classification of other MPM series from transcriptomic public datasets. C1 subgroup exhibited more frequent BAP1 alterations. Pathway analysis revealed that EMT was differentially regulated between MPM subgroups. C2 subgroup is characterized by a mesenchymal phenotype. CONCLUSIONS: A robust classification of MPM that defines two subgroups of epithelioid MPM, characterized by different molecular profiles, gene alterations, and survival outcomes, was established.

Identity by descent mapping of founder mutations in cancer using high-resolution tumor SNP data.

Dense genotype data can be used to detect chromosome fragments inherited from a common ancestor in apparently unrelated individuals. A disease-causing mutation inherited from a common founder may thus be detected by searching for a common haplotype signature in a sample population of patients. We present here FounderTracker, a computational method for the genome-wide detection of founder mutations in cancer using dense tumor SNP profiles. Our method is based on two assumptions. First, the wild-type allele frequently undergoes loss of heterozygosity (LOH) in the tumors of germline mutation carriers. Second, the overlap between the ancestral chromosome fragments inherited from a common founder will define a minimal haplotype conserved in each patient carrying the founder mutation. Our approach thus relies on the detection of haplotypes with significant identity by descent (IBD) sharing within recurrent regions of LOH to highlight genomic loci likely to harbor a founder mutation. We validated this approach by analyzing two real cancer data sets in which we successfully identified founder mutations of well-characterized tumor suppressor genes. We then used simulated data to evaluate the ability of our method to detect IBD tracts as a function of their size and frequency. We show that FounderTracker can detect haplotypes of low prevalence with high power and specificity, significantly outperforming existing methods. FounderTracker is thus a powerful tool for discovering unknown founder mutations that may explain part of the "missing" heritability in cancer. This method is freely available and can be used online at the FounderTracker website.

PAX3/FOXO1 fusion gene status is the key prognostic molecular marker in rhabdomyosarcoma and significantly improves current risk stratification.

PURPOSE: To improve the risk stratification of patients with rhabdomyosarcoma (RMS) through the use of clinical and molecular biologic data. PATIENTS AND METHODS: Two independent data sets of gene-expression profiling for 124 and 101 patients with RMS were used to derive prognostic gene signatures by using a meta-analysis. These and a previously published metagene signature were evaluated by using cross validation analyses. A combined clinical and molecular risk-stratification scheme that incorporated the PAX3/FOXO1 fusion gene status was derived from 287 patients with RMS and evaluated. RESULTS: We showed that our prognostic gene-expression signature and the one previously published performed well with reproducible and significant effects. However, their effect was reduced when cross validated or tested in independent data and did not add new prognostic information over the fusion gene status, which is simpler to assay. Among nonmetastatic patients, patients who were PAX3/FOXO1 positive had a significantly poorer outcome compared with both alveolar-negative and PAX7/FOXO1-positive patients. Furthermore, a new clinicomolecular risk score that incorporated fusion gene status (negative and PAX3/FOXO1 and PAX7/FOXO1 positive), Intergroup Rhabdomyosarcoma Study TNM stage, and age showed a significant increase in performance over the current risk-stratification scheme. CONCLUSION: Gene signatures can improve current stratification of patients with RMS but will require complex assays to be developed and extensive validation before clinical application. A significant majority of their prognostic value was encapsulated by the fusion gene status. A continuous risk score derived from the combination of clinical parameters with the presence or absence of PAX3/FOXO1 represents a robust approach to improving current risk-adapted therapy for RMS.

Comparison of the latest commercial short and long oligonucleotide microarray technologies.

BACKGROUND: We compared the relative precision and accuracy of expression measurements obtained from three different state-of-the-art commercial short and long-oligonucleotide microarray platforms (Affymetrix GeneChip, GE Healthcare CodeLink and Agilent Technologies). The design of the comparison was chosen to judge each platform in the context of a multi-project program. RESULTS: All wet-lab experiments and raw data acquisitions were performed independently by each commercial platform. Intra-platform reproducibility was assessed using measurements from all available targets. Inter-platform comparisons of relative signal intensities were based on a common and non-redundant set of roughly 3,400 targets chosen for their unique correspondence toward a single transcript. Despite many examples of strong similarities we found several areas of discrepancy between the different platforms. CONCLUSION: We found a higher level of reproducibility from one-color based microarrays (Affymetrix and CodeLink) compared to the two-color arrays from Agilent. Overall, Affymetrix data had a slightly higher level of concordance with sample-matched real-time quantitative reverse-transcriptase polymerase chain reaction (QRT-PCR) data particularly for detecting small changes in gene expression levels.