Jean Yee Hwa Yang

PD-L1 Negative Status is Associated with Lower Mutation Burden, Differential Expression of Immune-Related Genes, and Worse Survival in Stage III Melanoma

Purpose: Understanding why some melanomas test negative for PD-L1 by IHC may have implications for the application of anti-PD-1 therapies in melanoma management. This study sought to determine somatic mutation and gene expression patterns associated with tumor cell PD-L1 expression, or lack thereof, in stage III metastatic melanoma to better define therapeutically relevant patient subgroups. Experimental Design: IHC for PD-L1 was assessed in 52 American Joint Committee on Cancer stage III melanoma lymph node specimens and compared with specimen-matched comprehensive clinicopathologic, genomic, and transcriptomic data. Results: PD-L1–negative status was associated with lower nonsynonymous mutation (NSM) burden (P = 0.017) and worse melanoma-specific survival [HR = 0.28 (0.12–0.66), P = 0.002] in stage III melanoma. Gene set enrichment analysis identified an immune-related gene expression signature in PD-L1–positive tumors. There was a marked increase in cytotoxic T-cell and macrophage-specific genes in PD-L1–positive melanomas. CD8Ahigh gene expression was associated with better melanoma-specific survival [HR = 0.2 (0.05–0.87), P = 0.017] and restricted to PD-L1–positive stage III specimens. NF1 mutations were restricted to PD-L1–positive tumors (P = 0.041). Conclusions: Tumor negative PD-L1 status in stage III melanoma lymph node metastasis is a marker of worse patient survival and is associated with a poor immune response gene signature. Lower NSM levels were associated with PD-L1–negative status suggesting differences in somatic mutation profiles are a determinant of PD-L1–associated antitumor immunity in stage III melanoma. Clin Cancer Res; 22(15); 3915–23. ©2016 AACR.

Determination of prognosis in metastatic melanoma through integration of clinico‐pathologic, mutation, mRNA, microRNA, and protein information

In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on “‐omics” technologies have focussed on a single high‐throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited clinico‐pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients—gene, protein, and microRNA expression as well as clinical, pathologic and mutation information—to determine their relative impact on prognosis. We used classification frameworks based on pre‐validation and bootstrap multiple imputation to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico‐pathologic information was not out‐performed by any of the various “‐omics” platforms. Rather, a combination of clinico‐pathologic variables and mRNA expression data performed best. Furthermore, a patient‐based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients. This indicates that ongoing development in the individualized evaluation of melanoma patients must take account of the value of both traditional and novel “‐omics” measurements.

Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data

Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.

A dynamic wavelet-based algorithm for pre-processing tandem mass spectrometry data

MOTIVATION Mass spectrometry (MS)-based proteomics is one of the most commonly used research techniques for identifying and characterizing proteins in biological and medical research. The identification of a protein is the critical first step in elucidating its biological function. Successful protein identification depends on various interrelated factors, including effective analysis of MS data generated in a proteomic experiment. This analysis comprises several stages, often combined in a pipeline or workflow. The first component of the analysis is known as spectra pre-processing. In this component, the raw data generated by the mass spectrometer is processed to eliminate noise and identify the mass-to-charge ratio (m/z) and intensity for the peaks in the spectrum corresponding to the presence of certain peptides or peptide fragments. Since all downstream analyses depend on the pre-processed data, effective pre-processing is critical to protein identification and characterization. There is a critical need for more robust pre-processing algorithms that perform well on tandem mass spectra under a variety of different conditions and can be easily integrated into sophisticated data analysis pipelines for practical wet-lab applications. RESULT We have developed a new pre-processing algorithm. Based on wavelet theory, our method uses a dynamic peak model to identify peaks. It is designed to be easily integrated into a complete proteomic analysis workflow. We compared the method with other available algorithms using a reference library of raw MS and tandem MS spectra with known protein composition information. Our pre-processing algorithm results in the identification of significantly more peptides and proteins in the downstream analysis for a given false discovery rate. AVAILABILITY Software available at: http://www.maths.usyd.edu.au/u/penghao/index.html.

OCAP: An Open Comprehensive Analysis Pipeline for iTRAQ

MOTIVATION Mass spectrometry-based iTRAQ protein quantification is a high-throughput assay for determining relative protein expressions and identifying disease biomarkers. Processing and analysis of these large and complex data involves a number of distinct components and it is desirable to have a pipeline to efficiently integrate these together. To date, there are limited public available comprehensive analysis pipelines for iTRAQ data and many of these existing pipelines have limited visualization tools and no convenient interfaces with downstream analyses. We have developed a new open source comprehensive iTRAQ analysis pipeline, OCAP, integrating a wavelet-based preprocessing algorithm which provides better peak picking, a new quantification algorithm and a suite of visualizsation tools. OCAP is mainly developed in C++ and is provided as a standalone version (OCAP_standalone) as well as an R package. The R package (OCAP) provides the necessary interfaces with downstream statistical analysis.

Transcriptomic analysis of Nodal- and BMP-associated genes during juvenile development of the sea urchin Heliocidaris erythrogramma

Understanding the unusual radial body plan of echinoderms and its relationship to the bilateral plan of other deuterostomes remains a challenge. The molecular processes of embryonic and early larval development in sea urchins are well characterised, but those giving rise to the adult and its radial body remain poorly studied. We used the developmental transcriptome generated for Heliocidaris erythrogramma, a species that forms the juvenile soon after gastrulation, to investigate changes in gene expression underlying radial body development. As coelomogenesis is key to the development of pentamery and juvenile formation on the left side of the larva, we focussed on genes associated with the nodal and BMP2/4 network that pattern this asymmetry. We identified 46 genes associated with this Nodal and BMP2/4 signalling network, and determined their expression profiles from the gastrula, through to rudiment development, metamorphosis and the fully formed juvenile. Genes associated with Nodal signalling shared similar expression profiles, indicating that they may have a regulatory relationship in patterning morphogenesis of the juvenile sea urchin. Similarly, many genes associated with BMP2/4 signalling had similar expression profiles through juvenile development. Further examination of the roles of Nodal- and BMP2/4-associated genes is required to determine function and whether the gene expression profiles seen in H. erythrogramma are due to ongoing activity of gene networks established during early development, or to redeployment of regulatory cassettes to pattern the adult radial body plan.

PD-L1 Expression and Immune Escape in Melanoma Resistance to MAPK Inhibitors

Purpose: 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. Experimental Design: Thirty-eight tumors from 17 patients treated with BRAF inhibitor (n = 12) or combination BRAF/MEK inhibitors (n = 5) with known PD-L1 expression were analyzed. RNA expression arrays were performed on all pretreatment (PRE, n = 17), early during treatment (EDT, n = 8), and progression (PROG, n = 13) biopsies. HLA-A/HLA-DPB1 expression was assessed by IHC. Results: 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. Conclusions: 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. Clin Cancer Res; 23(20); 6054–61. ©2017 AACR.

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.

BackgroundLarge 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.ResultsWe 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.ConclusionIntegration 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.

Nodal and BMP expression during the transition to pentamery in the sea urchin Heliocidaris erythrogramma : insights into patterning the enigmatic echinoderm body plan

BackgroundThe molecular mechanisms underlying the development of the unusual echinoderm pentameral body plan and their likeness to mechanisms underlying the development of the bilateral plans of other deuterostomes are of interest in tracing body plan evolution. In this first study of the spatial expression of genes associated with Nodal and BMP2/4 signalling during the transition to pentamery in sea urchins, we investigate Heliocidaris erythrogramma, a species that provides access to the developing adult rudiment within days of fertilization.ResultsBMP2/4, and the putative downstream genes, Six1/2, Eya, Tbx2/3 and Msx were expressed in the earliest morphological manifestation of pentamery during development, the five hydrocoele lobes. The formation of the vestibular ectoderm, the specialized region overlying the left coelom that forms adult ectoderm, involved the expression of putative Nodal target genes Chordin, Gsc and BMP2/4 and putative BMP2/4 target genes Dlx, Msx and Tbx. The expression of Nodal, Lefty and Pitx2 in the right ectoderm, and Pitx2 in the right coelom, was as previously observed in other sea urchins.ConclusionThat genes associated with Nodal and BMP2/4 signalling are expressed in the hydrocoele lobes, indicates that they have a role in the developmental transition to pentamery, contributing to our understanding of how the most unusual body plan in the Bilateria may have evolved. We suggest that the Nodal and BMP2/4 signalling cascades might have been duplicated or split during the evolution to pentamery.

KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis

Mass spectrometry (MS)‐based quantitative phosphoproteomics has become a key approach for proteome‐wide profiling of phosphorylation in tissues and cells. Traditional experimental design often compares a single treatment with a control, whereas increasingly more experiments are designed to compare multiple treatments with respect to a control. To this end, the development of bioinformatic tools that can integrate multiple treatments and visualise kinases and substrates under combinatorial perturbations is vital for dissecting concordant and/or independent effects of each treatment. Here, we propose a hypothesis driven kinase perturbation analysis (KinasePA) to annotate and visualise kinases and their substrates that are perturbed by various combinatorial effects of treatments in phosphoproteomics experiments. We demonstrate the utility of KinasePA through its application to two large‐scale phosphoproteomics datasets and show its effectiveness in dissecting kinases and substrates within signalling pathways driven by unique combinations of cellular stimuli and inhibitors. We implemented and incorporated KinasePA as part of the “directPA” R package available from the comprehensive R archive network (CRAN). Furthermore, KinasePA also has an interactive web interface that can be readily applied to annotate user provided phosphoproteomics data (http://kinasepa.pengyiyang.org).