Cell-autonomous immune gene expression is repressed in pulmonary neuroendocrine cells and small cell lung cancer.

Small cell lung cancer (SCLC) is classified as a high-grade neuroendocrine (NE) tumor, but a subset of SCLC has been termed "variant" due to the loss of NE characteristics. In this study, we computed NE scores for patient-derived SCLC cell lines and xenografts, as well as human tumors. We aligned NE properties with transcription factor-defined molecular subtypes. Then we investigated the different immune phenotypes associated with high and low NE scores. We found repression of immune response genes as a shared feature between classic SCLC and pulmonary neuroendocrine cells of the healthy lung. With loss of NE fate, variant SCLC tumors regain cell-autonomous immune gene expression and exhibit higher tumor-immune interactions. Pan-cancer analysis revealed this NE lineage-specific immune phenotype in other cancers. Additionally, we observed MHC I re-expression in SCLC upon development of chemoresistance. These findings may help guide the design of treatment regimens in SCLC.

MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples.

MOTIVATION: Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance. RESULTS: We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data. AVAILABILITY AND IMPLEMENTATION: R code available at https://github.com/S-YIN/MIXnorm. CONTACT: swang@smu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data.

Formalin-fixed paraffin-embedded (FFPE) samples have great potential for biomarker discovery, retrospective studies, and diagnosis or prognosis of diseases. Their application, however, is hindered by the unsatisfactory performance of traditional gene expression profiling techniques on damaged RNAs. NanoString nCounter platform is well suited for profiling of FFPE samples and measures gene expression with high sensitivity, which may greatly facilitate realization of scientific and clinical values of FFPE samples. However, methodological development for normalization, a critical step when analyzing this type of data, is far behind. Existing methods designed for the platform use information from different types of internal controls separately and rely on an overly-simplified assumption that expression of housekeeping genes is constant across samples for global scaling. Thus, these methods are not optimized for the nCounter system, not mentioning that they were not developed for FFPE samples. We construct an integrated system of random-coefficient hierarchical regression models to capture main patterns and characteristics observed from NanoString data of FFPE samples, and develop a Bayesian approach to estimate parameters and normalize gene expression across samples. Our method, labeled RCRnorm, incorporates information from all aspects of the experimental design and simultaneously removes biases from various sources. It eliminates the unrealistic assumption on housekeeping genes and offers great interpretability. Furthermore, it is applicable to freshly frozen or like samples that can be generally viewed as a reduced case of FFPE samples. Simulation and applications showed the superior performance of RCRnorm.

Genetic and Epigenetic Features of Rapidly Progressing IDH-Mutant Astrocytomas.

IDH-mutant astrocytomas are significantly less aggressive than their IDH-wildtype counterparts. We analyzed The Cancer Genome Atlas dataset (TCGA) and identified a small group of IDH-mutant, WHO grade II-III astrocytomas (n = 14) with an unexpectedly poor prognosis characterized by a rapid progression to glioblastoma and death within 3 years of the initial diagnosis. Compared with IDH-mutant tumors with the typical, extended progression-free survival in a control group of age-similar patients, the tumors in the rapidly progressing group were characterized by a markedly increased level of overall copy number alterations ([CNA]; p = 0.006). In contrast, the mutation load was similar, as was the methylation pattern, being consistent with IDH-mutant astrocytoma. Two of the gliomas (14%) in the rapidly progressing, IDH-mutant group but none of the other grade II-III gliomas in the TCGA (n = 283) had pathogenic mutations in genes (FANCB and APC) associated with maintaining chromosomal stability. These results suggest that chromosomal instability can negate the beneficial effect of IDH mutations in WHO II-III astrocytomas. The mechanism of the increased CNA is unknown but in some cases appears to be due to mutations in genes with a role in chromosomal stability. Increased CNA could serve as a biomarker for tumors at risk for rapid progression.

Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database.

Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Programs such as PolyPhen-2 predict the relative severity of damage caused by missense mutations, but not the actual probability that a mutation will reduce/eliminate protein function. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified "probably damaging", "possibly damaging", or "probably benign" mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state. We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. We estimate the proportion of essential autosomal genes in Mus musculus (C57BL/6J) and show that viable mutations in essential genes are more likely to induce phenotype than mutations in non-essential genes.

A permutation-based non-parametric analysis of CRISPR screen data.

BACKGROUND: Clustered regularly-interspaced short palindromic repeats (CRISPR) screens are usually implemented in cultured cells to identify genes with critical functions. Although several methods have been developed or adapted to analyze CRISPR screening data, no single specific algorithm has gained popularity. Thus, rigorous procedures are needed to overcome the shortcomings of existing algorithms. METHODS: We developed a Permutation-Based Non-Parametric Analysis (PBNPA) algorithm, which computes p-values at the gene level by permuting sgRNA labels, and thus it avoids restrictive distributional assumptions. Although PBNPA is designed to analyze CRISPR data, it can also be applied to analyze genetic screens implemented with siRNAs or shRNAs and drug screens. RESULTS: We compared the performance of PBNPA with competing methods on simulated data as well as on real data. PBNPA outperformed recent methods designed for CRISPR screen analysis, as well as methods used for analyzing other functional genomics screens, in terms of Receiver Operating Characteristics (ROC) curves and False Discovery Rate (FDR) control for simulated data under various settings. Remarkably, the PBNPA algorithm showed better consistency and FDR control on published real data as well. CONCLUSIONS: PBNPA yields more consistent and reliable results than its competitors, especially when the data quality is low. R package of PBNPA is available at: https://cran.r-project.org/web/packages/PBNPA/ .

Exome Sequencing of Normal and Isogenic Transformed Human Colonic Epithelial Cells (HCECs) Reveals Novel Genes Potentially Involved in the Early Stages of Colorectal Tumorigenesis.

BACKGROUND: We have generated a series of isogenically derived immortalized human colonic epithelial cell (HCEC 1CT and HCEC 2CT) lines, including parental un-immortalized normal cell strains. The CDK4 and hTERT immortalized colonic epithelial cell line (HCEC 1CT) is initially karyotypically normal diploid and expresses a series of epithelial cell markers including stem cell markers. Under stressful tissue culture conditions, a spontaneous aneuploidy event occurred in the HCEC 1CT line, resulting in a single chromosomal change leading to a stable trisomy 7 cell line (1CT7). Trisomy 7 occurs in about 40% of all benign human adenomas (polyps) and thus this specific chromosomal change in diploid HCEC 1CT cells appears to be non random. In addition, we have partially transformed the HCEC 1CT line by introducing stable knockdown of wild type APC and TP53, and ectopically introducing a mutant Krasv12 and a mutant version of APC (A1309), all commonly found mutations in colorectal cancer (CRC). METHODS: Whole exome sequencing and bioinformatic analyses were performed to comprehensively examine the genetic background of these isogenic cell lines. RESULTS: Exome sequencing of these experimentally progressed cell lines recapitulates a list of genes previously reported to be involved in CRC tumorigenesis. In addition, sequencing revealed a collection of novel genes specifically detected in 1CT7 and A1309 cells but not normal diploid 1CT cells. CONCLUSION: This study demonstrates the utility of using isogenic experimentally derived HCEC lines as a model to recapitulate CRC initiation and progression. Exome sequencing reveals a collection of novel genes that may play important roles in CRC tumorigenesis.

Receptor-interacting protein kinase 2 promotes triple-negative breast cancer cell migration and invasion via activation of nuclear factor-kappaB and c-Jun N-terminal kinase pathways.

INTRODUCTION: Metastasis is the main cause of breast cancer morbidity and mortality. Processes that allow for tumor cell migration and invasion are important therapeutic targets. Here we demonstrate that receptor-interacting protein kinase 2 (RIP2), a kinase known to be involved in inflammatory processes, also has novel roles in cancer cell migration and invasion. METHODS: A total of six breast cancer expression databases, including The Cancer Genome Atlas, were assessed for RIP2 expression among various clinical subtypes and its role as a prognostic biomarker. mRNA fluorescence in situ hybridization (FISH) for RIP2 was performed on 17 stage III breast cancers to determine if there was a correlation between RIP2 expression and lymph node involvement. RNA-interference was used to knock-down RIP2 expression in MDA-MB-231, Htb126, SUM149PT, MCF7, T47D, and HCC1428 cells. Cell migration and invasion were measured in vitro by scratch/wound healing and transwell migration assays. A xenograft mouse model was used to assess tumor growth and chemosensitivity to docetaxel in vivo in MDA-MB-231 cells with and without RIP2 small hairpin RNA knockdown. Western blot and immunofluorescence imaging were used to evaluate protein expressions. RESULTS: Interrogation of expression databases showed that RIP2 expression is significantly over-expressed in triple-negative breast cancers (TNBC: estrogen-receptor (ER) negative, progesterone-receptor (PR) negative, Her2/neu- (Her2) negative), compared to other clinical subtypes. High RIP2 expression correlates with worse progression-free survival using a combined breast cancer expression array dataset consisting of 946 patients. Multivariate analysis shows RIP2 as an independent prognostic biomarker. Knock-down of RIP2 significantly decreases migration in both scratch/wound healing and transwell migration assays in MDA-MB-231, Htb126, SUM149PT, MCF7, and T47D cells and is correlated with decreased Nuclear Factor-kappaB and c-Jun N-terminal kinase (JNK) activation. Finally, RIP2 knock-down leads to increased sensitivity to docetaxel and decreased tumor mass and lung metastases in a xenograft mouse model. CONCLUSION: These results highlight RIP2 as a pro-metastasis kinase in patients with advanced breast cancer. These results also illustrate a novel role for this kinase in addition to its known role in inflammation, and suggest that targeting RIP2 may improve outcomes in advanced breast cancer patients, in which it is overexpressed.

Emerging roles of SIRT6 on telomere maintenance, DNA repair, metabolism and mammalian aging.

With the characterization of Sir2 gene in yeast aging, its mammalian homologs Sirtuins 1­7 have been attracting attention from scientists with various research backgrounds. Among Sirtuins, SIRT1 is the most extensively studied. Recent progress on mammalian Sirtuins has shown that SIRT6 as a histone deacetylase may also play a critical role in regulating mammalian aging. This review summarizes recent advances on SIRT6 as a key modulator of telomere structure, DNA repair, metabolism, and NF-kappa B pathway in aging. In addition, we discuss the challenges that remain to be studied in SIRT6 biology.