Characterizing diversity in the tumor-immune microenvironment of distinct subclasses of gastroesophageal adenocarcinomas.

BACKGROUND: Gastroesophageal adenocarcinomas (GEAs) are heterogeneous cancers where immune checkpoint inhibitors have robust efficacy in heavily inflamed microsatellite instability (MSI) or Epstein-Barr virus (EBV)-positive subtypes. Immune checkpoint inhibitor responses are markedly lower in diffuse/genome stable (GS) and chromosomal instable (CIN) GEAs. In contrast to EBV and MSI subtypes, the tumor microenvironment of CIN and GS GEAs have not been fully characterized to date, which limits our ability to improve immunotherapeutic strategies. PATIENTS AND METHODS: Here we aimed to identify tumor-immune cell association across GEA subclasses using data from The Cancer Genome Atlas (N = 453 GEAs) and archival GEA resection specimen (N = 71). The Cancer Genome Atlas RNAseq data were used for computational inferences of immune cell subsets, which were correlated to tumor characteristics within and between subtypes. Archival tissues were used for more spatial immune characterization spanning immunohistochemistry and mRNA expression analyses. RESULTS: Our results confirmed substantial heterogeneity in the tumor microenvironment between distinct subtypes. While MSI-high and EBV+ GEAs harbored most intense T cell infiltrates, the GS group showed enrichment of CD4+ T cells, macrophages and B cells and, in ∼50% of cases, evidence for tertiary lymphoid structures. In contrast, CIN cancers possessed CD8+ T cells predominantly at the invasive margin while tumor-associated macrophages showed tumor infiltrating capacity. Relatively T cell-rich 'hot' CIN GEAs were often from Western patients, while immunological 'cold' CIN GEAs showed enrichment of MYC and cell cycle pathways, including amplification of CCNE1. CONCLUSIONS: These results reveal the diversity of immune phenotypes of GEA. Half of GS gastric cancers have tertiary lymphoid structures and are therefore promising candidates for immunotherapy. The majority of CIN GEAs, however, exhibit T cell exclusion and infiltrating macrophages. Associations of immune-poor CIN GEAs with MYC activity and CCNE1 amplification may enable new studies to determine precise mechanisms of immune evasion, ultimately inspiring new therapeutic modalities.

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.

Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data.

This paper reports the methods and results of a computer-based search for causal relationships in the gene-regulation pathway of galactose metabolism in the yeast Saccharomyces cerevisiae. The search uses recently published data from cDNA microarray experiments. A Bayesian method was applied to learn causal networks from a mixture of observational and experimental gene-expression data. The observational data were gene-expression levels obtained from unmanipulated "wild-type" cells. The experimental data were produced by deleting ("knocking out") genes and observing the expression levels of other genes. Causal relations predicted from the analysis on 36 galactose gene pairs are reported and compared with the known galactose pathway. Additional exploratory analyses are also reported.

Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.

We demonstrate an integrated approach to build, test, and refine a model of a cellular pathway, in which perturbations to critical pathway components are analyzed using DNA microarrays, quantitative proteomics, and databases of known physical interactions. Using this approach, we identify 997 messenger RNAs responding to 20 systematic perturbations of the yeast galactose-utilization pathway, provide evidence that approximately 15 of 289 detected proteins are regulated posttranscriptionally, and identify explicit physical interactions governing the cellular response to each perturbation. We refine the model through further iterations of perturbation and global measurements, suggesting hypotheses about the regulation of galactose utilization and physical interactions between this and a variety of other metabolic pathways.

Genome sequence of Halobacterium species NRC-1.

We report the complete sequence of an extreme halophile, Halobacterium sp. NRC-1, harboring a dynamic 2,571,010-bp genome containing 91 insertion sequences representing 12 families and organized into a large chromosome and 2 related minichromosomes. The Halobacterium NRC-1 genome codes for 2,630 predicted proteins, 36% of which are unrelated to any previously reported. Analysis of the genome sequence shows the presence of pathways for uptake and utilization of amino acids, active sodium-proton antiporter and potassium uptake systems, sophisticated photosensory and signal transduction pathways, and DNA replication, transcription, and translation systems resembling more complex eukaryotic organisms. Whole proteome comparisons show the definite archaeal nature of this halophile with additional similarities to the Gram-positive Bacillus subtilis and other bacteria. The ease of culturing Halobacterium and the availability of methods for its genetic manipulation in the laboratory, including construction of gene knockouts and replacements, indicate this halophile can serve as an excellent model system among the archaea.

HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins.

We describe a hidden Markov model, HMMSTR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear hidden Markov models used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the protein database and, by representing overlapping motifs in a much more compact form, achieves a great reduction in parameters. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.3 %, backbone torsion angles better than any previously reported method and the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction.

Parking strategies for genome sequencing.

The parking strategy is an iterative approach to DNA sequencing. Each iteration consists of sequencing a novel portion of target DNA that does not overlap any previously sequenced region. Subject to the constraint of no overlap, each new region is chosen randomly. A parking strategy is often ideal in the early stages of a project for rapidly generating unique data. As a project progresses, parking becomes progressively more expensive and eventually prohibitive. We present a mathematical model with a generalization to allow for overlaps. This model predicts multiple parameters, including progress, costs, and the distribution of gap sizes left by a parking strategy. The highly fragmented nature of the gaps left after an initial parking strategy may make it difficult to finish a project efficiently. Therefore, in addition to our parking model, we model gap closing by walking. Our gap-closing model is generalizable to many other strategies. Our discussion includes modified parking strategies and hybrids with other strategies. A hybrid parking strategy has been employed for portions of the Human Genome Project.

Discovery of regulatory interactions through perturbation: inference and experimental design.

We present two methods to be used interactively to infer a genetic network from gene expression measurements. The predictor method determines the set of Boolean networks consistent with an observed set of steady-state gene expression profiles, each generated from a different perturbation to the genetic network. The chooser method uses an entropy-based approach to propose an additional perturbation experiment to discriminate among the set of hypothetical networks determined by the predictor. These methods may be used iteratively and interactively to successively refine the genetic network: at each iteration, the perturbation selected by the chooser is experimentally performed to generate a new gene expression profile, and the predictor is used to derive a refined set of hypothetical gene networks using the cumulative expression data. Performance of the predictor and chooser is evaluated on simulated networks with varying number of genes and number of interactions per gene.

Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.

Although two-color fluorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarray data are still evolving. Here, we report a refined test for differentially expressed genes which does not rely on gene expression ratios but directly compares a series of repeated measurements of the two dye intensities for each gene. This test uses a statistical model to describe multiplicative and additive errors influencing an array experiment, where model parameters are estimated from observed intensities for all genes using the method of maximum likelihood. A generalized likelihood ratio test is performed for each gene to determine whether, under the model, these intensities are significantly different. We use this method to identify significant differences in gene expression among yeast cells growing in galactose-stimulating versus non-stimulating conditions and compare our results with current approaches for identifying differentially-expressed genes. The effect of sample size on parameter optimization is also explored, as is the use of the error model to compare the within- and between-slide intensity variation intrinsic to an array experiment.