To examine the relationship between immune activity, PD-L1 expression, and tumor cell signaling, in metastatic melanomas prior to and during treatment with targeted MAPK inhibitors.
Thirty-eight ...tumors from 17 patients treated with BRAF inhibitor (
= 12) or combination BRAF/MEK inhibitors (
= 5) with known PD-L1 expression were analyzed. RNA expression arrays were performed on all pretreatment (PRE,
= 17), early during treatment (EDT,
= 8), and progression (PROG,
= 13) biopsies. HLA-A/HLA-DPB1 expression was assessed by IHC.
Gene set enrichment analysis (GSEA) of PRE, EDT, and PROG melanomas revealed that transcriptome signatures indicative of immune cell activation were strongly positively correlated with PD-L1 staining. In contrast, MAPK signaling and canonical Wnt/-β-catenin activity was negatively associated with PD-L1 melanoma expression. The expression of PD-L1 and immune activation signatures did not simply reflect the degree or type of immune cell infiltration, and was not sufficient for tumor response to MAPK inhibition.
PD-L1 expression correlates with immune cells and immune activity signatures in melanoma, but is not sufficient for tumor response to MAPK inhibition, as many PRE and PROG melanomas displayed both PD-L1 positivity and immune activation signatures. This confirms that immune escape is common in MAPK inhibitor-treated tumors. This has important implications for the selection of second-line immunotherapy because analysis of mechanisms of immune escape will likely be required to identify patients likely to respond to such therapies.
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Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell ...sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.
Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of ...the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relationship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes.
We introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between samples in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their significance. Finally, the response surfaces are generated for the subset of significant relationships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression.
The LC-N2G is practically powerful for identifying the informative nutrition variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information.
Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified ...at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis - none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition.
We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions.
scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney.
Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass ...spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.
V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to
±
. We investigated mechanisms for this and explored whether genotype affects response to ...immunotherapy.
Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with
±
underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti-PD-1 immunotherapy was examined.
Baseline tissue and clinical outcome with
±
were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, -31% vs. -52%,
= 0.154) and shorter progression-free survival (PFS; median, 5.7 vs. 7.1 months,
= 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in
and tumor-suppressor genes. In patients treated with anti-PD-1, V600K (
= 19) had superior outcomes than V600E (
= 84), including response rate (53% vs. 29%,
= 0.059), PFS (median, 19 vs. 2.7 months,
= 0.049), and overall survival (20.4 vs. 11.7 months,
= 0.081).
V600K melanomas appear to benefit less from
±
than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy.
Viruses are well known drivers of several human malignancies. A causative factor for oral cavity squamous cell carcinoma (OSCC) in patients with limited exposure to traditional risk factors, ...including tobacco use, is yet to be identified. Our study aimed to comprehensively evaluate the role of viral drivers in OSCC patients with low cumulative exposure to traditional risk factors. Patients under 50 years of age with OSCC, defined using strict anatomic criteria were selected for WGS. The WGS data was interrogated using viral detection tools (Kraken 2 and BLASTN), together examining >700,000 viruses. The findings were further verified using tissue microarrays of OSCC samples using both immunohistochemistry and RNA in situ hybridisation (ISH). 28 patients underwent WGS and comprehensive viral profiling. One 49-year-old male patient with OSCC of the hard palate demonstrated HPV35 integration. 657 cases of OSCC were then evaluated for the presence of HPV integration through immunohistochemistry for p16 and HPV RNA ISH. HPV integration was seen in 8 (1.2%) patients, all middle-aged men with predominant floor of mouth involvement. In summary, a wide-ranging interrogation of >700,000 viruses using OSCC WGS data showed HPV integration in a minority of male OSCC patients and did not carry any prognostic significance.
The epigenetic modifier EZH2 is part of the polycomb repressive complex that suppresses gene expression via histone methylation. Activating mutations in EZH2 are found in a subset of melanoma that ...contributes to disease progression by inactivating tumor suppressor genes. In this study we have targeted EZH2 with a specific inhibitor (GSK126) or depleted EZH2 protein by stable shRNA knockdown. We show that inhibition of EZH2 has potent effects on the growth of both wild-type and EZH2 mutant human melanoma in vitro particularly in cell lines harboring the EZH2Y646 activating mutation. This was associated with cell cycle arrest, reduced proliferative capacity in both 2D and 3D culture systems, and induction of apoptosis. The latter was caspase independent and mediated by the release of apoptosis inducing factor (AIFM1) from mitochondria. Gene expression arrays showed that several well characterized tumor suppressor genes were reactivated by EZH2 inhibition. This included activating transcription factor 3 (ATF3) that was validated as an EZH2 target gene by ChIP-qPCR. These results emphasize a critical role for EZH2 in the proliferation and viability of melanoma and highlight the potential for targeted therapy against EZH2 in treatment of patients with melanoma.
Heritable trait variation within a population of organisms is largely governed by DNA variations that impact gene transcription and protein function. Identifying genetic variants that affect complex ...functional traits is a primary aim of population genetics studies, especially in the context of human disease and agricultural production traits. The identification of alleles directly altering mRNA expression and thereby biological function is challenging due to difficulty in isolating direct effects of cis-acting genetic variations from indirect trans-acting genetic effects. Allele specific gene expression or allelic imbalance in gene expression (AI) occurring at heterozygous loci provides an opportunity to identify genes directly impacted by cis-acting genetic variants as indirect trans-acting effects equally impact the expression of both alleles. However, the identification of genes showing AI in the context of the expression of all genes remains a challenge due to a variety of technical and statistical issues. The current study focuses on the discovery of genes showing AI using single nucleotide polymorphisms as allelic reporters. By developing a computational and statistical process that addressed multiple analytical challenges, we ranked 5,809 genes for evidence of AI using RNA-Seq data derived from brown adipose tissue samples from a cohort of late gestation fetal lambs and then identified a conservative subgroup of 1,293 genes. Thus, AI was extensive, representing approximately 25% of the tested genes. Genes associated with AI were enriched for multiple Gene Ontology (GO) terms relating to lipid metabolism, mitochondrial function and the extracellular matrix. These functions suggest that cis-acting genetic variations causing AI in the population are preferentially impacting genes involved in energy homeostasis and tissue remodelling. These functions may contribute to production traits likely to be under genetic selection in the population.
Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of ...expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression.
We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons.
Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.
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