Innate Immune Activation by Checkpoint Inhibition in Human Patient-Derived Lung Cancer Tissues

Although Pembrolizumab-based immunotherapy has significantly improved lung cancer patient survival, many patients show variable efficacy and resistance development. A better understanding of the drug’s action is needed to improve patient outcomes. Functional heterogeneity of the tumor microenvironment (TME) is crucial to modulating drug resistance; understanding of individual patients’ TME that impacts drug response is hampered by lack of appropriate models. Lung organotypic tissue slice cultures (OTC) with patients’ native TME procured from primary and brain-metastasized (BM) non-small cell lung cancer (NSCLC) patients were treated with Pembrolizumab and/or beta-glucan (WGP, an innate immune activator). Metabolic tracing with 13C6-Glc/13C5,15N2-Gln, multiplex immunofluorescence, and digital spatial profiling (DSP) were employed to interrogate metabolic and functional responses to Pembrolizumab and/or WGP. Primary and BM PD-1+ lung cancer OTC responded to Pembrolizumab and Pembrolizumab + WGP treatments, respectively. Pembrolizumab activated innate immune metabolism and functions in primary OTC, which were accompanied by tissue damage. DSP analysis indicated an overall decrease in immunosuppressive macrophages and T cells but revealed microheterogeneity in immune responses and tissue damage. Two TMEs with altered cancer cell properties showed resistance. Pembrolizumab or WGP alone had negligible effects on BM-lung cancer OTC but Pembrolizumab + WGP blocked central metabolism with increased pro-inflammatory effector release and tissue damage. In-depth metabolic analysis and multiplex TME imaging of lung cancer OTC demonstrated overall innate immune activation by Pembrolizumab but heterogeneous responses in the native TME of a patient with primary NSCLC. Metabolic and functional analysis also revealed synergistic action of Pembrolizumab and WGP in OTC of metastatic NSCLC

The fate of steroid estrogens: Partitioning during wastewater treatment and onto river sediments

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 Springer Science+Business Media B.V.The partitioning of steroid estrogens in wastewater treatment and receiving waters is likely to influence their discharge to, and persistence in, the environment. This study investigated the partitioning behaviour of steroid estrogens in both laboratory and field studies. Partitioning onto activated sludge from laboratory-scale Husmann units was rapid with equilibrium achieved after 1 h. Sorption isotherms and Kd values decreased in the order 17α-ethinyl estradiol > 17α-estradiol > estrone > estriol without a sorption limit being achieved (1/n >1). Samples from a wastewater treatment works indicated no accumulation of steroid estrogens in solids from primary or secondary biological treatment, however, a range of steroid estrogens were identified in sediment samples from the River Thames. This would indicate that partitioning in the environment may play a role in the long-term fate of estrogens, with an indication that they will be recalcitrant in anaerobic conditions.EPSR

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms

A critical review of the formation of mono- and dicarboxylated metabolic intermediates of alkylphenol polyethoxylates during wastewater treatment and their environmental significance

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 Taylor & Francis.Alkylphenoxyacetic acids, the metabolic biodegradation products of alkylphenol ethoxylates, are commonly found in wastewaters and sewage effluents. These persistent hydrophilic derivatives possess intrinsic estrogenic activity, which can mimic natural hormones. Their concentrations increase through the sewage treatment works as a result of biodegradation and biotransformation, and when discharged can disrupt endocrine function in fish. These acidic metabolites represent the dominant alkylphenolic compounds found in wastewater effluent and their presence is cause for concern as, potentially, through further biotransformation and biodegradation, they can act as sources of nonylphenol, which is toxic and estrogenic. The authors aim to assess the mechanisms of formation as well as elimination of alkylphenoxyacetic acids within conventional sewage treatment works with the emphasis on the activated sludge process. In addition, they evaluate the various factors influencing their degradation and formation in laboratory scale and full-scale systems. The environmental implications of these compounds are considered, as is the need for tertiary treatment processes for their removal

Differential splicing using whole-transcript microarrays

<p>Abstract</p> <p>Background</p> <p>The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events.</p> <p>Results</p> <p>We propose a strategy to score differential splicing by using the auxiliary information from fitting the statistical model, RMA (robust multichip analysis). RMA partitions the probe-level data into probe effects and expression levels, operating robustly so that if a small number of probes behave differently than the rest, they are downweighted in the fitting step. We argue that adjacent poorly fitting probes for a given sample can be evidence of <it>differential </it>splicing and have designed a statistic to search for this behaviour. Using a public tissue panel dataset, we show many examples of tissue-specific alternative splicing. Furthermore, we show that evidence for putative alternative splicing has a strong correspondence between the Gene 1.0 ST and Exon 1.0 ST platforms.</p> <p>Conclusion</p> <p>We propose a new approach, FIRMAGene, to search for differentially spliced genes using the Gene 1.0 ST platform. Such an analysis complements the search for differential expression. We validate the method by illustrating several known examples and we note some of the challenges in interpreting the probe-level data.</p> <p>Software implementing our methods is freely available as an <monospace>R</monospace> package.</p

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

<p>Abstract</p> <p>Background</p> <p>High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.</p> <p>Results</p> <p>We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.</p> <p>Conclusions</p> <p>Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.</p

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes

SummarySomatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC−/− background. XPC−/− cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk