Hamid Bolouri

Substantial interindividual and limited intraindividual genomic diversity among tumors from men with metastatic prostate cancer

Tumor heterogeneity may reduce the efficacy of molecularly guided systemic therapy for cancers that have metastasized. To determine whether the genomic alterations in a single metastasis provide a reasonable assessment of the major oncogenic drivers of other dispersed metastases in an individual, we analyzed multiple tumors from men with disseminated prostate cancer through whole-exome sequencing, array comparative genomic hybridization (CGH) and RNA transcript profiling, and we compared the genomic diversity within and between individuals. In contrast to the substantial heterogeneity between men, there was limited diversity among metastases within an individual. The number of somatic mutations, the burden of genomic copy number alterations and aberrations in known oncogenic drivers were all highly concordant, as were metrics of androgen receptor (AR) activity and cell cycle activity. AR activity was inversely associated with cell proliferation, whereas the expression of Fanconi anemia (FA)-complex genes was correlated with elevated cell cycle progression, expression of the E2F transcription factor 1 (E2F1) and loss of retinoblastoma 1 (RB1). Men with somatic aberrations in FA-complex genes or in ATM serine/threonine kinase (ATM) exhibited significantly longer treatment-response durations to carboplatin than did men without defects in genes encoding DNA-repair proteins. Collectively, these data indicate that although exceptions exist, evaluating a single metastasis provides a reasonable assessment of the major oncogenic driver alterations that are present in disseminated tumors within an individual, and thus may be useful for selecting treatments on the basis of predicted molecular vulnerabilities.

Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast.

Transcriptional noise is known to be an important cause of cellular heterogeneity and phenotypic variation. The extent to which molecular interaction networks may have evolved to either filter or exploit transcriptional noise is a much debated question. The yeast genetic network regulating galactose metabolism involves two proteins, Gal3p and Gal80p, that feed back positively and negatively, respectively, on GAL gene expression. Using kinetic modeling and experimental validation, we demonstrate that these feedback interactions together are important for (i) controlling the cell-to-cell variability of GAL gene expression and (ii) ensuring that cells rapidly switch to an induced state for galactose uptake.

Expression and functional profiling reveal distinct gene classes involved in fatty acid metabolism

Cells respond to fatty acid exposure by metabolic reorganization and proliferation of peroxisomes. Described here is the development and application of a genome‐wide screen to identify nonessential yeast genes necessary for efficient metabolism of myristic and oleic acids. Comparison of the resultant fitness data set with an integrated data set of genes transcriptionally responsive to fatty acids revealed very little overlap between the data sets. Furthermore, the fitness data set enriched for genes involved in peroxisome biogenesis and other processes related to cell morphology, whereas the expression data set enriched for genes related to metabolism. These data suggest that in response to fatty acid exposure, transcriptional control is biased towards metabolic reorganization, and structural changes tend to be controlled post‐transcriptionally. They also suggest that fatty acid responsive metabolic networks are more robust than those related to cell structure. Statistical analyses of these and other global data sets suggest that the utilization of distinct control mechanisms for the execution of morphological versus metabolic responses is widespread.

ERG Activates the YAP1 Transcriptional Program and Induces the Development of Age-Related Prostate Tumors

The significance of ERG in human prostate cancer is unclear because mouse prostate is resistant to ERG-mediated transformation. We determined that ERG activates the transcriptional program regulated by YAP1 of the Hippo signaling pathway and found that prostate-specific activation of either ERG or YAP1 in mice induces similar transcriptional changes and results in age-related prostate tumors. ERG binds to chromatin regions occupied by TEAD/YAP1 and transactivates Hippo target genes. In addition, in human luminal-type prostate cancer cells, ERG binds to the promoter of YAP1 and is necessary for YAP1 expression. These results provide direct genetic evidence of a causal role for ERG in prostate cancer and reveal a connection between ERG and the Hippo signaling pathway.

Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells.

To identify therapeutic targets for glioblastoma (GBM), we performed genome-wide CRISPR-Cas9 knockout (KO) screens in patient-derived GBM stem-like cells (GSCs) and human neural stem/progenitors (NSCs), non-neoplastic stem cell controls, for genes required for their in vitro growth. Surprisingly, the vast majority GSC-lethal hits were found outside of molecular networks commonly altered in GBM and GSCs (e.g., oncogenic drivers). In vitro and in vivo validation of GSC-specific targets revealed several strong hits, including the wee1-like kinase, PKMYT1/Myt1. Mechanistic studies demonstrated that PKMYT1 acts redundantly with WEE1 to inhibit cyclin B-CDK1 activity via CDK1-Y15 phosphorylation and to promote timely completion of mitosis in NSCs. However, in GSCs, this redundancy is lost, most likely as a result of oncogenic signaling, causing GBM-specific lethality.

Mutant IDH1 regulates the tumor-associated immune system in gliomas

Gliomas harboring mutations in isocitrate dehydrogenase 1/2 (IDH1/2) have the CpG island methylator phenotype (CIMP) and significantly longer patient survival time than wild-type IDH1/2 (wtIDH1/2) tumors. Although there are many factors underlying the differences in survival between these two tumor types, immune-related differences in cell content are potentially important contributors. In order to investigate the role of IDH mutations in immune response, we created a syngeneic pair mouse model for mutant IDH1 (muIDH1) and wtIDH1 gliomas and demonstrated that muIDH1 mice showed many molecular and clinical similarities to muIDH1 human gliomas, including a 100-fold higher concentration of 2-hydroxygluratate (2-HG), longer survival time, and higher CpG methylation compared with wtIDH1. Also, we showed that IDH1 mutations caused down-regulation of leukocyte chemotaxis, resulting in repression of the tumor-associated immune system. Given that significant infiltration of immune cells such as macrophages, microglia, monocytes, and neutrophils is linked to poor prognosis in many cancer types, these reduced immune infiltrates in muIDH1 glioma tumors may contribute in part to the differences in aggressiveness of the two glioma types.

Modeling genomic regulatory networks with big data

High-throughput sequencing, large-scale data generation projects, and web-based cloud computing are changing how computational biology is performed, who performs it, and what biological insights it can deliver. I review here the latest developments in available data, methods, and software, focusing on the modeling and analysis of the gene regulatory interactions in cells. Three key findings are: (i) although sophisticated computational resources are increasingly available to bench biologists, tailored ongoing education is necessary to avoid the erroneous use of these resources. (ii) Current models of the regulation of gene expression are far too simplistic and need updating. (iii) Integrative computational analysis of large-scale datasets is becoming a fundamental component of molecular biology. I discuss current and near-term opportunities and challenges related to these three points.

Oncogenic Signaling Is Dominant to Cell of Origin and Dictates Astrocytic or Oligodendroglial Tumor Development from Oligodendrocyte Precursor Cells

Stem cells, believed to be the cellular origin of glioma, are able to generate gliomas, according to experimental studies. Here we investigated the potential and circumstances of more differentiated cells to generate glioma development. We and others have shown that oligodendrocyte precursor cells (OPCs) can also be the cell of origin for experimental oligodendroglial tumors. However, the question of whether OPCs have the capacity to initiate astrocytic gliomas remains unanswered. Astrocytic and oligodendroglial tumors represent the two most common groups of glioma and have been considered as distinct disease groups with putatively different origins. Here we show that mouse OPCs can give rise to both types of glioma given the right circumstances. We analyzed tumors induced by K-RAS and AKT and compared them to oligodendroglial platelet-derived growth factor B-induced tumors in Ctv-a mice with targeted deletions of Cdkn2a (p16Ink4a−/−, p19Arf−/−, Cdkn2a−/−). Our results showed that glioma can originate from OPCs through overexpression of K-RAS and AKT when combined with p19Arf loss, and these tumors displayed an astrocytic histology and high expression of astrocytic markers. We argue that OPCs have the potential to develop both astrocytic and oligodendroglial tumors given loss of p19Arf, and that oncogenic signaling is dominant to cell of origin in determining glioma phenotype. Our mouse data are supported by the fact that human astrocytoma and oligodendroglioma display a high degree of overlap in global gene expression with no clear distinctions between the two diagnoses.

Transcriptional noise and cellular heterogeneity in mammalian macrophages

Transcriptional noise is known to play a crucial role in heterogeneity in bacteria and yeast. Mammalian macrophages are known to exhibit cell-to-cell variation in their responses to pathogens, but the source of this heterogeneity is not known. We have developed a detailed stochastic model of gene expression that takes into account scaling effects due to cell size and genome complexity. We report the results of applying this model to simulating gene expression variability in mammalian macrophages, demonstrating a possible molecular basis for heterogeneity in macrophage signalling responses. We note that the nature of predicted transcriptional noise in macrophages is different from that in yeast and bacteria. Some molecular interactions in yeast and bacteria are thought to have evolved to minimize the effects of the high-frequency noise observed in these species. Transcriptional noise in macrophages results in slow changes to gene expression levels and would not require the type of spike-filtering circuits observed in yeast and bacteria.

Big data visualization identifies the multidimensional molecular landscape of human gliomas.

Significance We demonstrate that computational visualization of large-scale molecular and clinical datasets can delineate molecularly defined groups of highly similar patients that are well separated from other subgroups. We show that our approach is applicable to multiple data types (sequence, expression, DNA methylation), and that it provides the ability to discover clusters of tumors with targetable lesions. Our methods are generally applicable to all diseases and provide an intuitive means for physicians and bench scientists to work directly with “big” biomedical data. We show that visualizing large molecular and clinical datasets enables discovery of molecularly defined categories of highly similar patients. We generated a series of linked 2D sample similarity plots using genome-wide single nucleotide alterations (SNAs), copy number alterations (CNAs), DNA methylation, and RNA expression data. Applying this approach to the combined glioblastoma (GBM) and lower grade glioma (LGG) The Cancer Genome Atlas datasets, we find that combined CNA/SNA data divide gliomas into three highly distinct molecular groups. The mutations commonly used in clinical evaluation of these tumors are regionally distributed in these plots. One of the three groups is a mixture of GBM and LGG that shows similar methylation and survival characteristics to GBM. Altogether, our approach identifies eight molecularly defined glioma groups with distinct sequence/expression/methylation profiles. Importantly, we show that regionally clustered samples are enriched for specific drug targets.