Improved biomarkers of treatment response are needed for patients with high-grade serous ovarian cancer (HGSC). A challenge is substantial anatomical site-to-site variation in expression. We completed data-independent acquisition–mass spectrometry (DIA-MS) analysis of 404 fresh frozen and 78 formalin-fixed, paraffin-embedded HGSC tissue samples from the ovary (adnexal) and a common secondary site (omentum) in 11 patients. This was compared with mutation testing, gene expression, and whole-genome copy number profiling. Proteins with relatively stable intra- and variable inter-individual expression (n = 1651), included a 52-protein module reflecting interferon-mediated tissue inflammation, indicative of a cGAS-STING pathway cytosolic double-stranded (ds) DNA response. The dsDNA sensing/inflammation score was higher in the omentum compared with the ovary. Ovarian HGSC samples showed marked inter-individual differences in inflammatory and immune responses to DNA damage. Stable discriminative features of the HGSC proteome, a prerequisite for clinical predictive biomarkers, are detectable in ovary (adnexal) tissue samples.

Introduction: Cutaneous melanoma (CM) is one of the most aggressive types of skin cancer, with a poor prognosis for advanced stages. Despite the advances in immunotherapy (IO) treatment, robust predictive biomarkers are still required. The aim of this study was to identify a prognostic and predictive protein-based signature for advanced CM. Methods: A training cohort of 142 pretreatment tumor samples from 71 patients with metastatic CM treated with IO (anti-PD-1) and an independent validation cohort from 67 patients with stage III treatment-naïve CM were analyzed by data-independent acquisition mass spectrometry (DIA-MS). Survival analysis based initially on univariate regression and subsequently on 100 runs of 20-fold cross-validation of multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) was performed to obtain a reduced list of candidate proteins associated with melanoma-specific survival. A risk score was built from the final six proteins. The prognostic ability of this 6-protein signature for melanoma-specific survival was demonstrated by a time-dependent receiver operating characteristic curve (AUROC) and validated by four independent datasets. The predictive ability of the 6-protein signature was also explored in the training dataset using response to IO as the outcome of interest. Finally, differential expression analyses were conducted on the training data to identify the top proteins associated with response to IO. Proteins that were highly correlated (Pearson R2 >0.9) with the 6-protein signature were identified and subjected to pathway enrichment analysis (PEA). Results: Proteomic analyses identified 4298 and 4577 proteins in the training and validation cohorts respectively, with 81.1% overlap between the two sets. Using LASSO multivariate Cox modeling, six proteins were identified in the training cohort, from which a risk score was calculated that dichotomized patients into high- and low-risk groups (Hazard ratio (HR) 2.7, 95% confidence interval (CI) 1.9-3.9, and, AUROC 0.86). The 6-protein signature’s prognostic performance was validated using the RNAseq dataset from the same training cohort (HR 2.7, p<0.001), a second RNASeq dataset from The Cancer Genome Atlas (HR 1.7, p<0.001), a single-cell RNASeq dataset from patients treated with IO (HR 1.2, p<0.001) and finally a proteomic dataset in an independent cohort (stage III treatment-naïve CM) (HR 2.4, p<0.001). The 6-protein signature was also associated with response to IO (HR 2.3, p=0.005). PEA showed that the highly correlated proteins were mostly related to DNA repair and DNA metabolic pathways. PEA revealed activation of immune-related pathways in patients who achieved a good response to IO. Conclusion: A 6-protein signature identified a sub-group of patients with advanced CM who are at higher risk of progression on IO and death from melanoma. Citation Format: Srikanth Manda, Adel T. Aref, Erin K. Sykes, Steven G. Williams, Jennifer M. Koh, Erin M. Humphries, Daniel Bucio-Noble, Daniela Lee-Smith, Natasha Lucas, Dylan Xavier, Alexander Menzies, Ines Da Silva, Felicity Newell, Rosemary Balleine, Peter G. Hains, Graham Mann, Phil J. Robinson, Georgina V. Long, James Wilmott, Qing Zhong, Richard A. Scolyer, Roger R. Reddel. A six-protein signature predicts response and survival in patients with advanced cutaneous melanoma treated with immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5479.

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

Summary The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk , is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

Data-independent-acquisition mass spectrometry (DIA-MS) is a state-of-the-art proteomic technique for high-throughput identification and quantification of peptides and proteins. Interpretation of DIA-MS data relies on the use of a spectral library, which is optimally created from data acquired from the same samples in data-dependent acquisition (DDA) mode. As DIA-MS quantification relies on the spectral libraries, having a high-quality, non-redundant, and comprehensive spectral library is essential. This article describes the major steps for creating a high-quality spectral library using a combination of multiple complementary search engines. We discuss appropriate strategies to control the false discovery rate for the final spectral library as a result of merging multiple searches. © 2021 The Authors Current Protocols © 2021 Wiley Periodicals LLC. Basic Protocol 1: Searching DDA-MS files with multiple search engines Basic Protocol 2: Merging results from multiple search engines Basic Protocol 3: Creating spectral libraries from merged results Alternate Protocol: Using CLI for automating tasks Support Protocol: Creating concatenated FASTA files.

The human hookworm Necator americanus infects more than 400 million people worldwide, contributing substantially to the poverty in these regions. Adult stage N. americanus live in the small intestine of the human host where they inject excretory/secretory (ES) products into the mucosa. ES products have been characterized at the proteome level for a number of animal hookworm species, but until now, the difficulty in obtaining sufficient live N. americanus has been an obstacle in characterizing the secretome of this important human pathogen. Herein we describe the ES proteome of N. americanus and utilize this information along with RNA Seq data to conduct the first proteogenomic analysis of a parasitic helminth, significantly improving the available genome and thereby generating a robust description of the parasite secretome. The genome annotation resulted in a revised prediction of 3,425 fewer genes than initially reported, accompanied by a significant increase in the number of exons and introns, total gene length and the percentage of the genome covered by genes. Almost 200 ES proteins were identified by LC-MS/MS with SCP/TAPS proteins, hypothetical proteins and proteases among the most abundant families. These proteins were compared to commonly used model species of human parasitic infections, including Ancylostoma caninum, Nippostrongylus brasiliensis and Heligmosomoides polygyrus. SCP/TAPS proteins are immunogenic in nematode infections, so we expressed four of those identified in this study in recombinant form and showed that they are all recognized to varying degrees by serum antibodies from hookworm-infected subjects from a disease-endemic area of Brazil. Our findings provide valuable information on important families of proteins with both known and unknown functions that could be instrumental in host-parasite interactions, including protein families that might be key for parasite survival in the onslaught of robust immune responses, as well as vaccine and diagnostic targets.

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Whole exome sequencing, owing to its low cost and computational burden, has become the standard for causal gene detection in disease and treatment management. This article provides a brief overview of exome data generation and discusses the computational pipeline involved in utilizing exome sequencing data to identify specific variants, as well as its applications in clinical settings. In particular, we present the steps necessary to facilitate relevant data analysis practices, and survey relevant computational tools to streamline these analyses. This article is aimed to serve as a quick guide for researchers working on exome sequencing data.

Background: X-linked retinoschisis (XLRS) is a vitreoretinal degenerative disorder causing vision deterioration, due to structural defects in retina. The hallmark of this disease includes radial streaks arising from the fovea and splitting of inner retinal layers (schisis). Although these retinal changes are attributed to mutations in the retinoschisin gene, schisis is also observed in patients who do not carry mutations. In addition, the origin of intraschisis fluid, the triggering point of schisis formation and its progression are largely unknown still. So far, there is no report on the complete proteomic analysis of this fluid. Schisis fluid proteome could reflect biochemical changes in the disease condition, helping in better understanding and management of retinoschisis. Therefore it was of interest to investigate the intraschisis fluid proteome using high-resolution mass spectrometry.Methods: Two male XLRS patients (aged 4 and 40 years) underwent clinical and genetic evaluation followed by surgical extraction of intraschisis fluids. The two fluid samples were resolved on a SDS-PAGE and the processed peptides were analyzed by Q-Exactive plus hybrid quadrupole-Orbitrap mass spectrometry. Functional annotation of the identified proteins was performed using Ingenuity pathway analysis software.Results: Mass spectrometry analysis detected 770 nonredundant proteins in the intraschisis fluid. Retinol dehydrogenase 14 was found to be abundant in the schisis fluid. Gene ontology based analysis indicated that 19% of the intraschisis fluid proteins were localized to the extracellular matrix and 15% of the proteins were involved in signal transduction. Functional annotation identified three primary canonical pathways to be associated with the schisis fluid proteome viz., LXR/RXR activation, complement system and acute phase response signalling, which are involved in immune and inflammatory responses. Collectively, our results show that intraschisis fluid comprises specific inflammatory proteins which highly reflect the disease environment.Conclusion: Based on our study, it is suggested that inflammation might play a key role in the pathogenesis of XLRS. To our knowledge, this is the first report describing the complete proteome of intraschisis fluid, which could serve as a template for future research and facilitate the development of therapeutic modalities for XLRS.

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions,192 novel protein start sites,19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

Long non-coding RNAs (lncRNAs) have been shown to possess a wide range of functions in both cellular and developmental processes including cancers. Although some of the lncRNAs have been implicated in the regulation of the immune response, the exact function of the large majority of lncRNAs still remains unknown. In this study, we characterized the lncRNAs in human primary monocytes, an essential component of the innate immune system. We performed RNA sequencing of monocytes from four individuals and combined our data with eleven other publicly available datasets. Our analysis led to identification of ~8000 lncRNAs of which >1000 have not been previously reported in monocytes. PCR-based validation of a subset of the identified novel long intergenic noncoding RNAs (lincRNAs) revealed distinct expression patterns. Our study provides a landscape of lncRNAs in monocytes, which could facilitate future experimental studies to characterize the functions of these molecules in the innate immune system.

Somatic L1 retrotransposition events have been shown to occur in epithelial cancers. Here, we attempted to determine how early somatic L1 insertions occurred during the development of gastrointestinal (GI) cancers. Using L1-targeted resequencing (L1-seq), we studied different stages of four colorectal cancers arising from colonic polyps, seven pancreatic carcinomas, as well as seven gastric cancers. Surprisingly, we found somatic L1 insertions not only in all cancer types and metastases but also in colonic adenomas, well-known cancer precursors. Some insertions were also present in low quantities in normal GI tissues, occasionally caught in the act of being clonally fixed in the adjacent tumors. Insertions in adenomas and cancers numbered in the hundreds, and many were present in multiple tumor sections, implying clonal distribution. Our results demonstrate that extensive somatic insertional mutagenesis occurs very early during the development of GI tumors, probably before dysplastic growth.

Tamoxifen, an estrogen receptor- (ER) antagonist, is an important agent for the treatment of breast cancer. However, this therapy is complicated by the fact that a substantial number of patients exhibit either de novo or acquired resistance. To characterize the signaling mechanisms underlying this resistance, we treated the MCF7 breast cancer cell line with tamoxifen for over six months and showed that this cell line acquired resistance to tamoxifen in vitro and in vivo. We performed SILAC-based quantitative phosphoproteomic profiling on the tamoxifen resistant and vehicle-treated sensitive cell lines to quantify the phosphorylation alterations associated with tamoxifen resistance. From >5600 unique phosphopeptides identified, 1529 peptides exhibited hyperphosphorylation and 409 peptides showed hypophosphorylation in the tamoxifen resistant cells. Gene set enrichment analysis revealed that focal adhesion pathway was one of the most enriched signaling pathways activated in tamoxifen resistant cells. Significantly, we showed that the focal adhesion kinase FAK2 was not only hyperphosphorylated but also transcriptionally up-regulated in tamoxifen resistant cells. FAK2 suppression by specific siRNA knockdown or a small molecule inhibitor repressed cellular proliferation in vitro and tumor formation in vivo. More importantly, our survival analysis revealed that high expression of FAK2 is significantly associated with shorter metastasis-free survival in estrogen receptor-positive breast cancer patients treated with tamoxifen. Our studies suggest that FAK2 is a potential therapeutic target for the management of hormone-refractory breast cancers.

Cell and protein arrays have demonstrated remarkable utility in the high-throughput evaluation of biological responses; however, they lack the complexity of native tissue and organs. Here we spotted tissue extracellular matrix (ECM) particles as two-dimensional (2D) arrays or incorporated them with cells to generate three-dimensional (3D) cell-matrix microtissue arrays. We then investigated the responses of human stem, cancer and immune cells to tissue ECM arrays originating from 11 different tissues. We validated the 2D and 3D arrays as representative of the in vivo microenvironment by means of quantitative analysis of tissue-specific cellular responses, including matrix production, adhesion and proliferation, and morphological changes after culture. The biological outputs correlated with tissue proteomics, and network analysis identified several proteins linked to cell function. Our methodology enables broad screening of ECMs to connect tissue-specific composition with biological activity, providing a new resource for biomaterials research and further understanding of regeneration and disease mechanisms.

BACKGROUND:Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.RESULTS:Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome.CONCLUSIONS:We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.

A filter is a key and critical component for all types of wireless communication receivers, which are used to reject the noises (unwanted frequencies) in accordance with the applications. Band pass filters enable operation of any given module in a particular frequency range. In this paper, a compact microstrip band pass filter is proposed with operating range from 2.7 to 10.7 GHz for Ultra-Wide band (3.1 to 10.6 GHz) applications. The Chebychev elliptic function filtering method is employed. Two open circuited stubs are used to achieve the upper transmission loss and coupled micro strip structure is used to reduce transmission loss. The designed filter has insertion loss greater than -1 dB and return loss lower than -10 dB in pass band. The filter is designed with dimensions of 10mm x 9mm x 1.6mm on FR4 (εr = 4.3) substrate. The sharp cut-off at lower and upper stop bands has been achieved by proper tuning of the widths and lengths of the open circuited stubs. Bandwidth of about 8 GHz and a flat group delay in the pass band have been achieved. In view of its compactness, this filter can be easily integrated with all communication devices. The designed band pass filter has potential and promising application in UWB (Wi-Fi, Wi-Max, WLAN and ITU) applications. Keywords: Compact BPF, Elliptic Function, Microstrip, Open Circuited Stubs, UWB Filter