Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy.

Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p < 0.05) and more than 76% of the common genes were highly correlated (r > 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation>0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r > 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.

Stratification of Pancreatic Ductal Adenocarcinoma: Combinatorial Genetic, Stromal, and Immunologic Markers.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is associated with an immunosuppressive milieu that supports immune system evasion and disease progression. Here, we interrogated genetic, stromal, and immunologic features of PDAC to delineate impact on prognosis and means to more effectively employ immunotherapy.Experimental Design: A cohort of 109 PDAC cases annotated for overall survival was utilized as a primary discovery cohort. Gene expression analysis defined immunologic subtypes of PDAC that were confirmed in the Cancer Genome Atlas dataset. Stromal and metabolic characteristics of PDAC cases were evaluated by histologic analysis and immunostaining. Enumeration of lymphocytes, as well as staining for CD8, FOXP3, CD68, CD163, PDL1, and CTLA4 characterized immune infiltrate. Neoantigens were determined by analysis of whole-exome sequencing data. Random-forest clustering was employed to define multimarker subtypes, with univariate and multivariate analyses interrogating prognostic significance.Results: PDAC cases exhibited distinct stromal phenotypes that were associated with prognosis, glycolytic and hypoxic biomarkers, and immune infiltrate composition. Immune infiltrate was diverse among PDAC cases and enrichment for M2 macrophages and select immune checkpoints regulators were specifically associated with survival. Composite analysis with neoantigen burden, immunologic, and stromal features defined novel subtypes of PDAC that could have bearing on sensitivity to immunologic therapy approaches. In addition, a subtype with low levels of neoantigens and minimal lymphocyte infiltrate was associated with improved overall survival.Conclusions: The mutational burden of PDAC is associated with distinct immunosuppressive mechanisms that are conditioned by the tumor stromal environment. The defined subtypes have significance for utilizing immunotherapy in the treatment of PDAC. Clin Cancer Res; 23(15); 4429-40. ©2017 AACR.

An Expression Signature as an Aid to the Histologic Classification of Non-Small Cell Lung Cancer.

PURPOSE: Most non-small cell lung cancers (NSCLC) are now diagnosed from small specimens, and classification using standard pathology methods can be difficult. This is of clinical relevance as many therapy regimens and clinical trials are histology dependent. The purpose of this study was to develop an mRNA expression signature as an adjunct test for routine histopathologic classification of NSCLCs. EXPERIMENTAL DESIGN: A microarray dataset of resected adenocarcinomas (ADC) and squamous cell carcinomas (SCC) was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used for validation. Another microarray dataset of ADCs and matched nonmalignant lung was used as the learning set for a tumor versus nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. RESULTS: We developed a 62-gene expression signature that contained many genes used in immunostains for NSCLC typing. It includes 42 genes that distinguish ADC from SCC and 20 genes differentiating nonmalignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93%-95%). In addition, a prediction score was derived that correlates both with histologic grading and prognosis. We developed a practical version of the Classifier using the HTG EdgeSeq nuclease protection-based technology in combination with next-generation sequencing that can be applied to formalin-fixed paraffin-embedded (FFPE) tissues and small biopsies. CONCLUSIONS: Our RNA classifier provides an objective, quantitative method to aid in the pathologic diagnosis of lung cancer. Clin Cancer Res; 22(19); 4880-9. ©2016 AACR.

Stressing out over tRNA cleavage.

A conserved response to stress involves endonucleolytic cleavage of cytoplasmic transfer RNAs (tRNAs) by ribonucleases that are normally secreted or sequestered. Ribonuclease activation or release is an intriguing new aspect of cellular stress responses, with a potential impact on translation, apoptosis, cancer, and disease progression.

The RNase Rny1p cleaves tRNAs and promotes cell death during oxidative stress in Saccharomyces cerevisiae.

The cellular response to stress conditions involves a decision between survival or cell death when damage is severe. A conserved stress response in eukaryotes involves endonucleolytic cleavage of transfer RNAs (tRNAs). The mechanism and significance of such tRNA cleavage is unknown. We show that in yeast, tRNAs are cleaved by the RNase T2 family member Rny1p, which is released from the vacuole into the cytosol during oxidative stress. Rny1p modulates yeast cell survival during oxidative stress independently of its catalytic ability. This suggests that upon release to the cytosol, Rny1p promotes cell death by direct interactions with downstream components. Thus, detection of Rny1p, and possibly its orthologues, in the cytosol may be a conserved mechanism for assessing cellular damage and determining cell survival, analogous to the role of cytochrome c as a marker for mitochondrial damage.

tRNA cleavage is a conserved response to oxidative stress in eukaryotes.

Recent results have identified a diversity of small RNAs in a wide range of organisms. In this work, we demonstrate that Saccharomyces cerevisiae contains a small RNA population consisting primarily of tRNA halves and rRNA fragments. Both 5' and 3' fragments of tRNAs are detectable by Northern blot analysis, suggesting a process of endonucleolytic cleavage. tRNA and rRNA fragment production in yeast is most pronounced during oxidative stress conditions, especially during entry into stationary phase. Similar tRNA fragments are also observed in human cell lines and in plants during oxidative stress. These results demonstrate that tRNA cleavage is a conserved aspect of the response to oxidative stress.

Cytoplasmic decay of intergenic transcripts in Saccharomyces cerevisiae.

Eukaryotes produce a number of noncoding transcripts from intergenic regions. In Saccharomyces cerevisiae, such cryptic unstable transcripts (CUTs) are thought to be degraded in the nucleus by a process involving polyadenylation and 3'-to-5' degradation by the nuclear exosome. In this work, we examine the degradation pathway of the RNA SRG1, which is produced from an intergenic region and contributes to the regulation of the SER3 gene by promoter occlusion during SRG1 transcription. Although there is some effect on SRG1 transcript levels when the nuclear exosome is compromised, the bulk of the SRG1 RNA is degraded in the cytoplasm by decapping and 5'-to-3' exonucleolytic digestion. Examination of other CUTs suggests that individual CUTs can be degraded by a variety of different mechanisms, including nuclear decay, cytoplasmic decapping and 5'-to-3' decay, and nonsense-mediated decay. Moreover, some CUTs appear to be associated with polyribosomes. These results indicate that some CUTs can be exported from the nucleus and enter translation before being degraded, identifying a potential mechanism for the evolution of new protein-encoding genes.

Novel lethal mouse mutants produced in balancer chromosome screens.

Mutagenesis screens are a valuable method to identify genes that are required for normal development. Previous mouse mutagenesis screens for lethal mutations were targeted at specific time points or for developmental processes. Here we present the results of lethal mutant isolation from two mutagenesis screens that use balancer chromosomes. One screen was localized to mouse chromosome 4, between the STS markers D4Mit281 and D4Mit51. The second screen covered the region between Trp53 and Wnt3 on mouse chromosome 11. These screens identified all lethal mutations in the balancer regions, without bias towards any phenotype or stage of death. We have isolated 19 lethal lines on mouse chromosome 4, and 59 lethal lines on chromosome 11, many of which are distinct from previous mutants that map to these regions of the genome. We have characterized the mutant lines to determine the time of death, and performed a pair-wise complementation cross to determine if the mutations are allelic. Our data suggest that the majority of mouse lethal mutations die during mid-gestation, after uterine implantation, with a variety of defects in gastrulation, heart, neural tube, vascular, or placental development. This initial group of mutants provides a functional annotation of mouse chromosomes 4 and 11, and indicates that many novel developmental phenotypes can be quickly isolated in defined genomic intervals through balancer chromosome mutagenesis screens.