Michael Seiler

Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns

Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

Identifying microRNA/mRNA dysregulations in ovarian cancer

BackgroundMicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA).MethodsTCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA.ResultsWe identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms.ConclusionsOur findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

Transcriptomic Characterization of SF3B1 Mutation Reveals Its Pleiotropic Effects in Chronic Lymphocytic Leukemia

Mutations in SF3B1, which encodes a spliceosome component, are associated with poor outcome in chronic lymphocytic leukemia (CLL), but how these contribute to CLL progression remains poorly understood. We undertook a transcriptomic characterization of primary human CLL cells to identify transcripts and pathways affected by SF3B1 mutation. Splicing alterations, identified in the analysis of bulk cells, were confirmed in single SF3B1-mutated CLL cells and also found in cell lines ectopically expressing mutant SF3B1. SF3B1 mutation was found to dysregulate multiple cellular functions including DNA damage response, telomere maintenance, and Notch signaling (mediated through KLF8 upregulation, increased TERC and TERT expression, or altered splicing of DVL2 transcript, respectively). SF3B1 mutation leads to diverse changes in CLL-related pathways.

Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging

With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of analysis can identify biologically distinct disease subclasses of breast cancer with distinct molecular signatures, differential recurrence rates and potentially, very different response to therapy. Image analysis methods are also rapidly finding application in the clinic, complementing the pathologist in quantitative, reproducible, detection, staging, and grading of disease. We will describe novel computerized image analysis techniques and machine learning tools for automated cancer detection from digitized histopathology and how they can be employed for disease diagnosis and prognosis for prostate and breast cancer.

Lactase persistence and lipid pathway selection in the Maasai.

The Maasai are a pastoral people in Kenya and Tanzania, whose traditional diet of milk, blood and meat is rich in lactose, fat and cholesterol. In spite of this, they have low levels of blood cholesterol, and seldom suffer from gallstones or cardiac diseases. Field studies in the 1970s suggested that the Maasai have a genetic adaptation for cholesterol homeostasis. Analysis of HapMap 3 data using Fixation Index (Fst) and two metrics of haplotype diversity: the integrated Haplotype Score (iHS) and the Cross Population Extended Haplotype Homozygosity (XP-EHH), identified genomic regions and single nucleotide polymorphisms (SNPs) as strong candidates for recent selection for lactase persistence and cholesterol regulation in 143–156 founder individuals from the Maasai population in Kinyawa, Kenya (MKK). The non-synonmous SNP with the highest genome-wide Fst was the TC polymorphism at rs2241883 in Fatty Acid Binding Protein 1(FABP1), known to reduce low density lipoprotein and tri-glyceride levels in Europeans. The strongest signal identified by all three metrics was a 1.7 Mb region on Chr2q21. This region contains the genes LCT (Lactase) and MCM6 (Minichromosome Maintenance Complex Component) involved in lactase persistence, and the gene Rab3GAP1 (Rab3 GTPase-activating Protein Catalytic Subunit), which contains polymorphisms associated with total cholesterol levels in a genome-wide association study of >100,000 individuals of European ancestry. Sanger sequencing of DNA from six MKK samples showed that the GC-14010 polymorphism in the MCM6 gene, known to be associated with lactase persistence in Africans, is segregating in MKK at high frequency (∼58%). The Cytochrome P450 Family 3 Subfamily A (CYP3A) cluster of genes, involved in cholesterol metabolism, was identified by Fst and iHS as candidate loci under selection. Overall, our study identified several specific genomic regions under selection in the Maasai which contain polymorphisms in genes associated with lactase persistence and cholesterol regulation.

ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data.

We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster .

H3B-8800, an orally available small-molecule splicing modulator, induces lethality in spliceosome-mutant cancers

Genomic analyses of cancer have identified recurrent point mutations in the RNA splicing factor–encoding genes SF3B1, U2AF1, and SRSF2 that confer an alteration of function. Cancer cells bearing these mutations are preferentially dependent on wild-type (WT) spliceosome function, but clinically relevant means to therapeutically target the spliceosome do not currently exist. Here we describe an orally available modulator of the SF3b complex, H3B-8800, which potently and preferentially kills spliceosome-mutant epithelial and hematologic tumor cells. These killing effects of H3B-8800 are due to its direct interaction with the SF3b complex, as evidenced by loss of H3B-8800 activity in drug-resistant cells bearing mutations in genes encoding SF3b components. Although H3B-8800 modulates WT and mutant spliceosome activity, the preferential killing of spliceosome-mutant cells is due to retention of short, GC-rich introns, which are enriched for genes encoding spliceosome components. These data demonstrate the therapeutic potential of splicing modulation in spliceosome-mutant cancers.

Splicing modulators act at the branch point adenosine binding pocket defined by the PHF5A–SF3b complex

Pladienolide, herboxidiene and spliceostatin have been identified as splicing modulators that target SF3B1 in the SF3b subcomplex. Here we report that PHF5A, another component of this subcomplex, is also targeted by these compounds. Mutations in PHF5A-Y36, SF3B1-K1071, SF3B1-R1074 and SF3B1-V1078 confer resistance to these modulators, suggesting a common interaction site. RNA-seq analysis reveals that PHF5A-Y36C has minimal effect on basal splicing but inhibits the global action of splicing modulators. Moreover, PHF5A-Y36C alters splicing modulator-induced intron-retention/exon-skipping profile, which correlates with the differential GC content between adjacent introns and exons. We determine the crystal structure of human PHF5A demonstrating that Y36 is located on a highly conserved surface. Analysis of the cryo-EM spliceosome Bact complex shows that the resistance mutations cluster in a pocket surrounding the branch point adenosine, suggesting a competitive mode of action. Collectively, we propose that PHF5A–SF3B1 forms a central node for binding to these splicing modulators.

Evasion of immunosurveillance by genomic alterations of PPARγ/RXRα in bladder cancer

Muscle-invasive bladder cancer (MIBC) is an aggressive disease with limited therapeutic options. Although immunotherapies are approved for MIBC, the majority of patients fail to respond, suggesting existence of complementary immune evasion mechanisms. Here, we report that the PPARγ/RXRα pathway constitutes a tumor-intrinsic mechanism underlying immune evasion in MIBC. Recurrent mutations in RXRα at serine 427 (S427F/Y), through conformational activation of the PPARγ/RXRα heterodimer, and focal amplification/overexpression of PPARγ converge to modulate PPARγ/RXRα-dependent transcription programs. Immune cell-infiltration is controlled by activated PPARγ/RXRα that inhibits expression/secretion of inflammatory cytokines. Clinical data sets and an in vivo tumor model indicate that PPARγHigh/RXRαS427F/Y impairs CD8+ T-cell infiltration and confers partial resistance to immunotherapies. Knockdown of PPARγ or RXRα and pharmacological inhibition of PPARγ significantly increase cytokine expression suggesting therapeutic approaches to reviving immunosurveillance and sensitivity to immunotherapies. Our study reveals a class of tumor cell-intrinsic “immuno-oncogenes” that modulate the immune microenvironment of cancer.Muscle-invasive bladder cancer (MIBC) is a potentially lethal disease. Here the authors characterize diverse genetic alterations in MIBC that convergently lead to constitutive activation of PPARgamma/RXRalpha and result in immunosurveillance escape by inhibiting CD8+ T-cell recruitment.

Widespread alternative exon usage in clinically distinct subtypes of Invasive Ductal Carcinoma

Cancer cells can have different patterns of exon usage of individual genes when compared to normal tissue, suggesting that alternative splicing may play a role in shaping the tumor phenotype. The discovery and identification of gene variants has increased dramatically with the introduction of RNA-sequencing technology, which enables whole transcriptome analysis of known, as well as novel isoforms. Here we report alternative splicing and transcriptional events among subtypes of invasive ductal carcinoma in The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Alternative exon usage was widespread, and although common events were shared among three subtypes, ER+ HER2−, ER− HER2−, and HER2+, many events on the exon level were subtype specific. Additional RNA-seq analysis was carried out in an independent cohort of 43 ER+ HER2− and ER− HER2− primary breast tumors, confirming many of the exon events identified in the TCGA cohort. Alternative splicing and transcriptional events detected in five genes, MYO6, EPB41L1, TPD52, IQCG, and ACOX2 were validated by qRT-PCR in a third cohort of 40 ER+ HER2− and ER− HER2− patients, showing that these events were truly subtype specific.