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Supplemental Figure Legends from Lung Squamous Cell Carcinomas with Basaloid Histology Represent a Specific Molecular Entity

Supplementary Table Legend, Supplementary References, Supplementary Information

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. Clin Cancer Res; 20(5); 1323–34. ©2014 AACR.

XLS file - 11306KB, Pathway analysis.

Supplemental Table 1:A. Characteristics of the antibodies used for immunohistochemical measures B.Clinical characteristics of the CIT cohort of patients. Supplemental Table 2: A .Gene expression related statistics in the CIT cohort. B. immunohistochemistry quickscore measures of SOX4 and IVL and prediction of the basaloid satus Supplemental Table 3: Gene signatures and pathways deregulation analysis in histological subtypes. Supplemental Table 4: DNA copy number aberrations related statistics in the CIT cohort. Supplemental Table 5: 139-gene centroids used for prediction of the CIT molecular subtypes. Supplemental Table 6: Molecular subtype prediction and survival data for each sample of the CIT and 8 validation datasets. Supplemental Table 7: Gene signatures and pathways enrichment analysis in molecular subtypes in the CIT cohort and 4 validation datasets.

XLS file - 212KB, Sample annotations and gene expression.

PDF file - 58KB, Primer sequences used for BAP1 and CDKN2B gene analysis.

PDF file - 356KB, This file contains supplementary figures S1-S7. Figure S1: Flowchart depicting the analyses carried out to establish and validate a molecular classification of MPM; Figure S2: Unsupervised molecular subgroups in the discovery series; Figures S3: Recurrent regions of chromosomal alteration in MPM molecular subgroups; Figures S4: Identification of the three-gene predictor; Figure S5: Unsupervised molecular subgroups in the two public series; Figure S6: Distribution of histological subtypes and overall survival differences between MPM molecular subgroups in the two public series; Figure S7: Differential mRNA expression of epithelial-to-mesenchymal transition (EMT) markers in MPM molecular subgroups.

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. Clin Cancer Res; 20(22); 5777–86. ©2014 AACR.

Supplemental Figure 1: Embryonic stem cell-like gene expression signatures in Basaloid tumors. Supplemental Figure 2: DNA copy number aberrations in the CIT cohort. Supplementary Figure 3: Consensus classification of the expression profiles of tumor samples in the CIT cohort. Supplemental Figure 4: Kaplan-Meier curves in the CIT cohort according to CIT molecular subtypes. Supplemental Figure 5: Kaplan-Meier curves of overall survival in 7 validation public datasets according to CIT molecular subtypes and Wilkerson et al. subtypes.

Table S1: Clinico-pathological features for samples in the PROGNOSTIC collection; Table S2: Clinico-pathological features for samples in the TAM collection; Table S3: Clinico-pathological features for samples in the CHEMO collection; Table S4: Output of immune cluster refinement in ER-HER2- samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S5: Output of immune cluster refinement in HER2+ samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S6: Output of proliferation cluster refinement in 508 ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S7: Output of ER-related cluster refinement in 394 high proliferation ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S8: Univariable Cox regression analysis for the CTM in the combined dataset or in each dataset for ER- or HER2+ cases or separately for ER-HER2- and HER2+ subgroups; Table S9: Univariable and multivariable Cox regression analysis in 205 ER-HER2- and HER2+ samples from the CHEMO collection (all biomarkers were used as continous variables); Table S10: Univariable Cox regression analysis for the combined proliferation and ER-related metagenes in ER+HER2- cases from the combined dataset or from each dataset

XLS file 669K, This file contains supplementary table S4: Convergence between microRNA and mRNA deregulation

Legends of Supplementary Figures and Tables PDF file 134K, This file contains the legends for supplementary figures S1-S7 and tables S1-S4

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. Clin Cancer Res; 22(2); 337–45. ©2015 AACR.

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.

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. Clin Cancer Res; 20(22); 5777–86. ©2014 AACR.

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. Clin Cancer Res; 20(5); 1323–34. ©2014 AACR.

International audience; 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.

BackgroundWe 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.ResultsAll 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.ConclusionWe 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.

With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function1. As elementary constituents of cellular protein complexes and pathways, protein–protein interactions are key determinants of protein function. Here we have built a large-scale protein–protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides2. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein–protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.

Nature 409 , 211 - 215 (2001 ) . The list of interactions between Helicobacter pylori proteins that are described and analysed in this paper are available as Supplementary Information on Nature's World-Wide Web site (http://www.nature.com) or as paper copy from the London editorial office of Nature.Proteins are named according to the nomenclature of The Institute for Genomic Research microbial database.

Hepatocellular carcinomas (HCCs) are a heterogeneous group of tumors that differ in risk factors and genetic alterations. We further investigated transcriptome-genotype-phenotype correlations in HCC. Global transcriptome analyses were performed on 57 HCCs and 3 hepatocellular adenomas and validated by quantitative RT-PCR using 63 additional HCCs. We determined loss of heterozygosity, gene mutations, promoter methylation of CDH1 and CDKN2A, and HBV DNA copy number for each tumor. Unsupervised transcriptome analysis identified 6 robust subgroups of HCC (G1-G6) associated with clinical and genetic characteristics. G1 tumors were associated with low copy number of HBV and overexpression of genes expressed in fetal liver and controlled by parental imprinting. G2 included HCCs infected with a high copy number of HBV and mutations in PIK3CA and TP53. In these first groups, we detected specific activation of the AKT pathway. G3 tumors were typified by mutation of TP53 and overexpression of genes controlling the cell cycle. G4 was a heterogeneous subgroup of tumors including TCF1-mutated hepatocellular adenomas and carcinomas. G5 and G6 were strongly related to beta-catenin mutations that lead to Wnt pathway activation; in particular, G6 tumors were characterized by satellite nodules, higher activation of the Wnt pathway, and E-cadherin underexpression. CONCLUSION: These results have furthered our understanding of the genetic diversity of human HCC and have provided specific identifiers for classifying tumors. In addition, our classification has potential therapeutic implications because 50% of the tumors were related to WNT or AKT pathway activation, which potentially could be targeted by specific inhibiting therapies.