Proteomic Features Research Articles

Proteome-wide and matrisome-specific alterations during human pancreas development and maturation

The extracellular matrix (ECM) is unique to each tissue and capable of guiding cell differentiation, migration, morphology, and function. The ECM proteome of different developmental stages has not been systematically studied in the human pancreas. In this study, we apply mass spectrometry-based quantitative proteomics strategies using N,N-dimethyl leucine isobaric tags to delineate proteome-wide and ECM-specific alterations in four age groups: fetal (18-20 weeks gestation), juvenile (5-16 years old), young adults (21-29 years old) and older adults (50-61 years old). We identify 3,523 proteins including 185 ECM proteins and quantify 117 of them. We detect previously unknown proteome and matrisome features during pancreas development and maturation. We also visualize specific ECM proteins of interest using immunofluorescent staining and investigate changes in ECM localization within islet or acinar compartments. This comprehensive proteomics analysis contributes to an improved understanding of the critical roles that ECM plays throughout human pancreas development and maturation.

Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

Progress in precision medicine is limited by insufficient knowledge of transcriptomic or proteomic features in involved tissues that define pathobiological differences between patients. Here, myectomy tissue from patients with obstructive hypertrophic cardiomyopathy and heart failure is analyzed using RNA-Seq, and the results are used to develop individualized protein-protein interaction networks. From this approach, hypertrophic cardiomyopathy is distinguished from dilated cardiomyopathy based on the protein-protein interaction network pattern. Within the hypertrophic cardiomyopathy cohort, the patient-specific networks are variable in complexity, and enriched for 30 endophenotypes. The cardiac Janus kinase 2-Signal Transducer and Activator of Transcription 3-collagen 4A2 (JAK2-STAT3-COL4A2) expression profile informed by the networks was able to discriminate two hypertrophic cardiomyopathy patients with extreme fibrosis phenotypes. Patient-specific network features also associate with other important hypertrophic cardiomyopathy clinical phenotypes. These proof-of-concept findings introduce personalized protein-protein interaction networks (reticulotypes) for characterizing patient-specific pathobiology, thereby offering a direct strategy for advancing precision medicine.

Proteome Characterization of Glaucoma Aqueous Humor

Glaucoma is the leading cause of irreversible blindness worldwide. The proteome characterization of glaucoma is not clearly understood. A total of 175 subjects, including 57 primary acute angle-closure glaucoma (PAACG), 50 primary chronic angle-closure glaucoma (PCACG), 35 neovascular glaucoma (NVG), and 33 cataract patients, were enrolled and comparison proteomic analysis was provided. The samples were randomly divided into discovery group or validation group, whose aqueous humor proteome was analyzed by data-independent acquisition or by parallel reaction monitoring. The common proteome features of three types of glaucoma were immune response, lipid metabolism, and cell death. Three proteins, VTN, SERPIND1, and CD14, showed significant upregulation in glaucoma and could discriminate glaucoma from cataract. Mutual differential proteomic analysis of PAACG, PCACG, and NVG showed different proteome characterization of the three types of glaucoma. NVG was characterized with activated angiogenesis. PAACG was characterized with activation of inflammation response. SERPIND1 was discovered to play vital role in glaucoma occurrences, which is associated with eye transparency decrease and glucose metabolism. This study would provide insights in understanding proteome characterization of glaucoma and benefit the clinical application of AH proteome.

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy.

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n= 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Immune molecular profiling of a multiresistant primary prostate cancer with a neuroendocrine-like phenotype: a case report

BackgroundUnderstanding the drivers of recurrence in aggressive prostate cancer requires detailed molecular and genomic understanding in order to aid therapeutic interventions.We provide here a case report of histological, transcriptional, proteomic, immunological, and genomic features in a longitudinal study of multiple biopsies from diagnosis, through treatment, and subsequent recurrence.Case presentationHere we present a case study of a male in 70 s with high-grade clinically-localised acinar adenocarcinoma treated with definitive hormone therapy and radiotherapy. The patient progressed rapidly with rising PSA and succumbed without metastasis 52 months after diagnosis.We identified the expression of canonical histological markers of neuroendocrine PC (NEPC) including synaptophysin, neuron-specific enolase and thyroid transcription factor 1, as well as intact AR expression, in the recurrent disease only.The resistant disease was also marked by an extremely low immune infiltrate, extensive genomic chromosomal aberrations, and overactivity in molecular hallmarks of NEPC disease including Aurora kinase and E2F, as well as novel alterations in the cMYB pathway. We also observed that responses to both primary treatments (high dose-rate brachytherapy and androgen deprivation therapies) were consistent with known optimal responses—ruling out treatment inefficacy as a factor in relapse.ConclusionsThese data provide novel insights into a case of locally recurrent aggressive prostate cancer harbouring NEPC pathology, in the absence of detected metastasis.

Aggregation and disaggregation features of the human proteome

Protein aggregates have negative implications in disease. While reductionist experiments have increased our understanding of aggregation processes, the systemic view in biological context is still limited. To extend this understanding, we used mass spectrometry‐based proteomics to characterize aggregation and disaggregation in human cells after non‐lethal heat shock. Aggregation‐prone proteins were enriched in nuclear proteins, high proportion of intrinsically disordered regions, high molecular mass, high isoelectric point, and hydrophilic amino acids. During recovery, most aggregating proteins disaggregated with a rate proportional to the aggregation propensity: larger loss in solubility was counteracted by faster disaggregation. High amount of intrinsically disordered regions were associated with faster disaggregation. However, other characteristics enriched in aggregating proteins did not correlate with the disaggregation rates. In addition, we analyzed changes in protein thermal stability after heat shock. Soluble remnants of aggregated proteins were more thermally stable compared with control condition. Therefore, our results provide a rich resource of heat stress‐related protein solubility data and can foster further studies related to protein aggregation diseases.

First-trimester trophoblasts obtained by chorionic villus sampling maintain tolerogenic and proteomic features in successful pregnancies despite a history of unexplained recurrent pregnancy loss.

While there are several known causes for recurrent pregnancy loss (RPL), about 50% are unexplained (uRPL), and in these cases, an aberrant immune regulation seems to be involved. Although fetally derived trophoblast cells have a key role in immune regulation, it is difficult to study their immune function during pregnancy, and it is not known whether trophoblast function may be an inherent aberration in uRPL or whether it is associated with the outcome of the current pregnancy. Chorionic villus sampling (CVS) was performed for clinical indications at 12weeks of gestation. Superfluous materials, divided in small explants, were cultured for 20-24hours, and supernatants (conditioned medium) were collected from 36 women with singleton normal pregnancies, of whom 9 women had a history of RPL. The secreted immune protein profile was measured by proximity extension assay, and the conditioned medium was further used in functional ex vivo models to assess ability to polarize blood monocytes and CD4+ T cells into immune regulatory phenotypes, as detected by flow cytometry. Conditioned medium from chorionic villi, human fetally derived placental tissue, was able to induce a decidual-type of M2-like macrophages, as well as an expansion of Treg cells ex vivo, both in women with uRPL and in control women. The preserved immunological properties were confirmed by a maintained immune protein profile in RPL compared with controls. Trophoblasts in an ex vivo model maintain tolerogenic and proteomic profile features in successful pregnancies, despite a previous history of RPL.

Abstract LB-232: Deep integrated proteogenomic characterization of clear cell renal cell carcinoma

Abstract Clear cell renal cell carcinoma (ccRCC) is the predominant histology of renal cell carcinoma (RCC), representing 75% of all cases and accounting for the majority of associated deaths. Towards understanding the impact of genomic alterations on the functional modules that drive ccRCC tumorigenesis, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) leveraged a proteogenomic approach to characterize 110 RCC tumors and 84 paired normal adjacent tissue (NAT) samples. Pairing state-of-the-art mass spectrometry based proteomic and phosphoproteomic strategies with comprehensive genomic and transcriptomic analyses, we integrated global and phosphoproteomic measurements with genomic and transcriptomic data to elucidate the dysregulated cellular mechanism resulting from genomic alterations. In addition to delineating chromosomal translocations involving chromosome 3p as a possible mechanism for 3p loss in ccRCC and the altered cellular pathways between tumors and NATs, we also identified several novel proteomic, phosphoproteomic, and immune ccRCC subtypes for personalized, precision-based care. Investigation of the phosphoproteome in our cohort revealed the activation of cell signaling pathways associated with ERK/MAPK, PI3K/AKT, metabolic reprogramming, and G2/M cell cycle regulation. Leveraging FDA-approved kinase inhibitors, prioritized by our phosphoproteomic results, allowed for the examination of the functional consequences of these therapies respective to mechanisms of action in ccRCC cells. Independently, the immunogenicity of ccRCC tumors prompted our exploration of the degree of immune infiltration in our cohort, wherein we identified four subtypes based on immune and stromal tumor microenvironment cell compositions: CD8+ Inflamed, CD8- Inflamed, VEGF Immune Desert, and Metabolic Immune Desert. These subtypes were further discriminated by proteomic and transcriptomic features, as well as displaying distinct predicted outcomes related to patient survival and response to targeted therapeutic intervention. We verified the protein features of these subtypes via orthogonal methodologies, including data-independent acquisition (DIA) and selective reaction monitoring (SRM) mass spectrometry, finding a high degree of correlation between the datasets. Further examination of these proteomic features in an independent cohort found the subtypes robustly identified; providing further evidence of ccRCC inter-tumor heterogeneity and rationale to stratify ccRCC patients for treatment selection. Overall, our study highlights the unique insight that can be gained when integrating multi-level “omics” analyses to broaden our understanding of ccRCC biology, as well as leading to the identification of several new hypothesis-driven investigations for therapeutic intervention in ccRCC. Citation Format: David J. Clark, Clinical Proteomic Tumor Analysis Consortium. Deep integrated proteogenomic characterization of clear cell renal cell carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-232.

Abstract 5311: Integrated proteomic and informatic assessment of ER+/HER2- and ER-/HER2- breast tumors

Abstract Introduction Emerging OMIC technologies enable the comprehensive molecular characterization of breast cancer to continually evolve. Published gene expression-based studies have differentiated ER-positive and ER-negative breast cancer. Herein, we have expanded these investigations utilizing a proteomics approach. Methods Clinical IHC subtyping on core biopsies was used to select available flash-frozen surgical samples for a cohort of 121 primary breast cancer patients. The cohort includes 103 ER+/HER2- and 18 ER-/PR-/HER2- cases. Mass spectrometry-based analyses were performed on these samples for proteomic, metabolomic, and lipidomic features. Comparative analysis was performed on proteomic data between the two groups of samples using Limma with stratified subsampling method and significance is reported at FDR<0.05 and absolute FC>1.5. PCA and hierarchical clustering analysis were applied on our training dataset and independent testing datasets using the identified significantly differentially expressed proteins as a signature to distinguish ER+/HER2- samples from ER-/PR-/HER2- samples. Euclidian distance was used as distance matrix and Ward was used as a linkage method in hierarchical clustering algorithm. Results The cohort was split into a training set (81 cases) and a testing set (40 cases) using stratification randomization. There were 171 differentially expressed proteins identified from the training dataset. 95% cases in our training dataset and 92.5% cases in our testing dataset were clustered correctly using hierarchical clustering method with the 171 proteins. Publicly available datasets from TCGA (RNA-seq, 846 cases) and CPTAC (proteomics, 57 cases) further validated the proteomic results from our cohort. 94.7% cases in CPTAC cohort and 93.2% cases in TCGA RNA-seq cohort were clustered correctly using the 171 proteins. KEGG pathway analysis using the differentially expressed proteins identified two significant pathways at FDR < 0.05: DNA replication and cysteine and methionine metabolism. Independent metabolomic and lipidomic analysis identified profiles supporting our proteomic analysis. These results confirm that ER+/HER2- and ER-/PR-/HER2- tumors are molecularly distinct which can be separated by a panel of expressed proteins. Disclaimers The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense, the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Guisong Wang, Punit Shah, Vladimir Tolstikov, Praveen-Kumar Raj-Kumar, Jianfang Liu, Lori A. Sturtz, J. Leigh Fantacone-Campbell, Rebecca Zingmark, Jeffrey A. Hooke, Mary L. Cutler, Kris Richardson, Leonardo Rodrigues, Valerie Bussberg, Rangaprasad Sarangarajan, Niven R. Narain, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Craig D. Shriver. Integrated proteomic and informatic assessment of ER+/HER2- and ER-/HER2- breast tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5311.

Integrated pharmaco-proteogenomics defines two subgroups in isocitrate dehydrogenase wild-type glioblastoma with prognostic and therapeutic opportunities

The prognostic and therapeutic relevance of molecular subtypes for the most aggressive isocitrate dehydrogenase 1/2 (IDH) wild-type glioblastoma (GBM) is currently limited due to high molecular heterogeneity of the tumors that impedes patient stratification. Here, we describe a distinct binary classification of IDH wild-type GBM tumors derived from a quantitative proteomic analysis of 39 IDH wild-type GBMs as well as IDH mutant and low-grade glioma controls. Specifically, GBM proteomic cluster 1 (GPC1) tumors exhibit Warburg-like features, neural stem-cell markers, immune checkpoint ligands, and a poor prognostic biomarker, FKBP prolyl isomerase 9 (FKBP9). Meanwhile, GPC2 tumors show elevated oxidative phosphorylation-related proteins, differentiated oligodendrocyte and astrocyte markers, and a favorable prognostic biomarker, phosphoglycerate dehydrogenase (PHGDH). Integrating these proteomic features with the pharmacological profiles of matched patient-derived cells (PDCs) reveals that the mTORC1/2 dual inhibitor AZD2014 is cytotoxic to the poor prognostic PDCs. Our analyses will guide GBM prognosis and precision treatment strategies.

Integrative Proteomic Characterization of Human Lung Adenocarcinoma

Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease's biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD remain poorly understood. We carried out a comprehensive proteomics analysis of 103 cases of LUAD in Chinese patients. Integrative analysis of proteome, phosphoproteome, transcriptome, and whole-exome sequencing data revealed cancer-associated characteristics, such as tumor-associated protein variants, distinct proteomics features, and clinical outcomes in patients at an early stage or with EGFR and TP53 mutations. Proteome-based stratification of LUAD revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Further, we nominated potential drug targets and validated the plasma protein level of HSP 90β as a potential prognostic biomarker for LUAD in an independent cohort. Our integrative proteomics analysis enables a more comprehensive understanding of the molecular landscape of LUAD and offers an opportunity for more precise diagnosis and treatment.

Histopathologic, immunophenotypic, and proteomics characteristics of low-grade phyllodes tumor and fibroadenoma: more similarities than differences

Distinguishing low-grade phyllodes tumor from fibroadenoma is practically challenging due to their overlapping histologic features. However, the final interpretation is essential to surgeons, who base their management on the final pathology report. Patients who receive a diagnosis of fibroadenoma might not undergo any additional intervention while lumpectomy with wide margins is the standard of care for phyllodes tumor, which can have significant cosmetic consequences. We studied the clinical, immunophenotypic, and proteomics profiles of 31 histologically confirmed low-grade phyllodes tumor and 30 fibroadenomas. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) and immunohistochemistry for Ki-67, p53, β-catenin, and E-cadherin were performed on all cases. After the mass spectra for all 31 cases of low-grade phyllodes tumor and 30 cases of fibroadenoma were collected, an average peak value for all cases was generated. There was no significant difference in the overall mass spectra pattern in any of the peaks identified. There was also overlap in the percentage of cells staining positive for Ki-67, p53, β-catenin, and E-cadherin. The two groups of patients showed no statistically significant difference in age, tumor size, or disease-free survival. Neither group developed malignant transformation, distant metastases, or disease-related mortality. We have demonstrated low-grade phyllodes tumor and fibroadenoma to show significant overlapping clinical and proteomics features.

Proteomic and Phosphoproteomic Maps of Lung Squamous Cell Carcinoma From Chinese Patients.

Lung squamous cell carcinoma (LUSC) is one of the leading causes of tumor-driven deaths in the world. To date, studies on the tumor heterogeneity of LUSC at genomic level have only revealed limited therapeutic benefits. Therefore, system-wide research of LUSC at proteomic level may further improve precision medicine strategies on individual demands. To this end, we performed proteomic and phosphoproteomic study for LUSC samples of 25 Chinese patients. From our results, two subgroups (Cluster I and II) based on proteomic data were identified, which were associated with distinct molecular characteristics and clinicopathologic features. Combined with phosphoproteomic data, our result showed that spliceosome pathway was enriched in Cluster I, while focal adhesion pathway, immune-related pathways and Ras signaling pathway were enriched in Cluster II. In addition, we found that lymph node metastasis (LNM) was associated with our proteomic subgroups and cell cycle pathway was enriched in patients with LNM. Further analysis showed that MCM2, a DNA replication licensing factor involved in cell cycle pathway, was highly expressed in patients with poor prognosis, which was further proved by immunohistochemistry (IHC) analysis. In summary, our study provided a resource of the proteomic and phosphoproteomic features of LUSC in Chinese patients.

Absence of miRNA-146a Differentially Alters Microglia Function and Proteome.

Background: MiR-146a is an important regulator of innate inflammatory responses and is also implicated in cell death and survival.Methods: By sorting CNS resident cells, microglia were the main cellular source of miR-146a. Therefore, we investigated microglia function and phenotype in miR-146a knock-out (KO) mice, analyzed the proteome of KO and wild-type (WT) microglia by LC-MS/MS, and examined miR-146a expression in different brain lesions of patients with multiple sclerosis (MS).Results: When stimulated with LPS or myelin in vitro, microglia from KO mice expressed higher levels of IL-1β, TNF, IL-6, IL-10, CCL3, and CCL2 compared to WT. Stimulation increased migration and phagocytosis of WT but not KO microglia. CD11c+ microglia were induced by cuprizone (CPZ) in the WT mice but less in the KO. The proteome of ex vivo microglia was not different in miR-146a KO compared to WT mice, but CPZ treatment induced differential and reduced protein responses in the KO: GOT1, COX5b, CRYL1, and cystatin-C were specifically changed in KO microglia. We explored discriminative features of microglia proteomes: sparse Partial Least Squares-Discriminant Analysis showed the best discrimination when control and CPZ-treated conditions were compared. Cluster of ten proteins separated WT and miR-146a KO microglia after CPZ: among them were sensomes allowing to perceive the environment, Atp1a3 that belongs to the signature of CD11c+ microglia, and proteins related to inflammatory responses (S100A9, Ppm1g). Finally, we examined the expression of miR-146a and its validated target genes in different brain lesions of MS patients. MiR-146 was upregulated in all lesion types, and the highest expression was in active lesions. Nineteen of 88 validated target genes were significantly changed in active lesions, while none were changed in NAWM.Conclusion: Our data indicated that microglia is the major source of miR-146a in the CNS. The absence of miR-146a differentially affected microglia function and proteome, and miR-146a may play an important role in gene regulation of active MS lesions.

P0107DIGITAL PATHOLOGY FOR THE ROUTINE DIAGNOSIS OF RENAL DISEASES: A STANDARD MODEL

Abstract Background and Aims Whole-slide imaging (WSI) and virtual microscopy are useful tools implemented in the routine pathology workflow in the last 10 years, allowing second-opinion on definitive or frozen-section diagnosis (telepathology) and demonstrating a substantial role in multidisciplinary (MDT) meetings and education (Griffin et al, Histopathology. 2017;70(1):134-145). The FDA approval of the clinical employment of this technology led to the progressive digitalization of routine pathological practice. In this setting, a recent work explored the usefulness of this technology applied to renal biopsies, stressing the possibility to create integrate networks to share cases for diagnostic and research purposes (Barisoni et al, Clin Kidney J. 2017;10(2):176–187). Here is discussed the 5-years experience of an Italian Renal Pathology service in which telepathology is a definitive standard of care. Method Renal biopsies were collected in the Anatomic Pathology Department of San Gerardo Hospital, Monza, Italy from 14 different Italian Nephrology centers (9 from the North, 1 from the Center and 4 from the South of Italy) from January 2015 to December 2019. After the routing processing of the tissue, biopsies were scanned with Aperio CS2 device for light microscopy and Aperio ScanScope FL for immunofluorescence (Leica Biosystem, Illinois, USA, Figure 1A). Digital slides were then imported in the Spectrum platform along with the final report and additional pictures (eg. electron micrographs and special stains) such as shown in Figure 1B. Results In 5 years, 704 renal biopsies (interval 110-160) were scanned and analysed with a turnaround time of around 2-3 working days. The most frequent diagnosis was represented by MN (16%, 115 cases, interval 18-28) followed by IgA nephropathy (12%, 84 cases, interval 12-21), FSGS (9%, 66 cases, interval 8-18), MCD (8%, 54 cases, interval 7-13), paucimmune crescentic GN (7%, 48 cases, interval 6-13), diabetic nephropathy (6%, 45 cases, interval 6-12), lupus nephritis (5%, 37 cases, interval 5-11), arterionephrosclerosis (5%, 35 cases, interval 5-13), amyloidosis (5%, 32 cases, interval 3-7) and tubulointerstitial nephritis (4%, 28 cases, interval 4-6). Only 4% of cases (28, interval 4-10) were suboptimal for the diagnosis and the remaining 19% (132 cases, interval 20-36) were characterized by rarer diseases, as detailed in Figure 1C. Conclusion In renal pathology the assessment of biopsies with different techniques (light and electron microscopy and immunofluorescence) is mandatory and can represent an obstacle in the routine employment of digital pathology. In the present experience we demonstrated the feasibility and sustainability of the digital switch in renal pathology routine, thanks to appropriate devices and platform. This led to store cases for teaching and MDT meetings, allowing to maintain the same short turnaround time for the diagnosis. Moreover, the employment of WSI technique can allow to couple the proteomic features of renal tissue with the classic morphological aspects of the diseased parenchyma (L’Imperio et al Proteomics Clin Appl. 2016 Apr;10(4):371-83). This approach, along with the possibility of a multicentric enrollment of patients, led to the publication of 6 scientific papers in highly impacted journals, further confirming the positive role of digital pathology in this field.

Genome mining of the citrus pathogen Elsinoë fawcettii; prediction and prioritisation of candidate effectors, cell wall degrading enzymes and secondary metabolite gene clusters

Elsinoë fawcettii, a necrotrophic fungal pathogen, causes citrus scab on numerous citrus varieties around the world. Known pathotypes of E. fawcettii are based on host range; additionally, cryptic pathotypes have been reported and more novel pathotypes are thought to exist. E. fawcettii produces elsinochrome, a non-host selective toxin which contributes to virulence. However, the mechanisms involved in potential pathogen-host interactions occurring prior to the production of elsinochrome are unknown, yet the host-specificity observed among pathotypes suggests a reliance upon such mechanisms. In this study we have generated a whole genome sequencing project for E. fawcettii, producing an annotated draft assembly 26.01 Mb in size, with 10,080 predicted gene models and low (0.37%) coverage of transposable elements. A small proportion of the assembly showed evidence of AT-rich regions, potentially indicating genomic regions with increased plasticity. Using a variety of computational tools, we mined the E. fawcettii genome for potential virulence genes as candidates for future investigation. A total of 1,280 secreted proteins and 276 candidate effectors were predicted and compared to those of other necrotrophic (Botrytis cinerea, Parastagonospora nodorum, Pyrenophora tritici-repentis, Sclerotinia sclerotiorum and Zymoseptoria tritici), hemibiotrophic (Leptosphaeria maculans, Magnaporthe oryzae, Rhynchosporium commune and Verticillium dahliae) and biotrophic (Ustilago maydis) plant pathogens. Genomic and proteomic features of known fungal effectors were analysed and used to guide the prioritisation of 120 candidate effectors of E. fawcettii. Additionally, 378 carbohydrate-active enzymes were predicted and analysed for likely secretion and sequence similarity with known virulence genes. Furthermore, secondary metabolite prediction indicated nine additional genes potentially involved in the elsinochrome biosynthesis gene cluster than previously described. A further 21 secondary metabolite clusters were predicted, some with similarity to known toxin producing gene clusters. The candidate virulence genes predicted in this study provide a comprehensive resource for future experimental investigation into the pathogenesis of E. fawcettii.

T21. DEVELOPMENT OF PROTEOMIC PREDICTION MODELS FOR OUTCOMES IN THE CLINICAL HIGH RISK STATE AND PSYCHOTIC EXPERIENCES IN ADOLESCENCE: MACHINE LEARNING ANALYSES IN TWO NESTED CASE-CONTROL STUDIES

BackgroundIndividuals at clinical high risk (CHR) of psychosis have an approximately 20% probability of developing psychosis within 2 years, as well as an associated risk of non-psychotic disorders and functional impairment. People with subclinical psychotic experiences (PEs) are also at risk of future psychotic and non-psychotic disorders and decreased functioning. It is difficult to accurately predict outcomes in individuals at risk of psychosis on the basis of symptoms alone. Biomarkers for accurate prediction of outcomes could inform the clinical management of this group.MethodsWe conducted two nested case-control studies.Study 1, a clinical high-risk (CHR) sample, was nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) cohort study. The sample comprised 133 CHR participants who were followed clinically for up to 6 years, of whom 49 transitioned to psychosis and 84 did not.Study 2, a general population sample, was nested within the Avon Longitudinal Study of Parents and Children (ALSPAC). The sample comprised 121 participants who did not report psychotic experiences (PEs) at age 12, of whom 55 later reported PEs at age 18 and 66 did not.We employed discovery-based proteomic methods to analyse protein expression in baseline plasma samples in EU-GEI and age 12 plasma samples in ALSPAC using liquid chromatography mass spectrometry.Differential expression of quantified proteomic markers was determined by analyses of covariance (with false discovery rate of 5%) comparing expression levels for each marker between those who did not and did not develop psychosis in Study 1 (adjusting for age, gender, body mass index and years in education), and between those who did and did not develop PEs in Study 2 (adjusting for gender, body mass index and maternal social class).Support vector machine algorithms were used to develop models for prediction of transition vs. non-transition (as determined by the Comprehensive Assessment of At Risk Mental States) and poor vs. good functional outcome at 2 years in Study 1 (General Assessment of Functioning: Disability subscale score </=60 vs. >60). Similar algorithms were used to develop a model for prediction of PEs vs. no PEs at age 18 in Study 2 (as determined by the Psychosis Like Symptoms Interview).ResultsIn Study 1, 35 of 166 quantified proteins were significantly differentially expressed between CHR participants who did and did not develop psychosis. Functional enrichment analysis provided evidence for particular implication of the complement and coagulation cascade (false discovery rate-adjusted Fisher’s exact test p=2.23E-21). Using 65 clinical and 166 proteomic features a model demonstrated excellent performance for prediction of transition status (area under the receiver-operating curve [AUC] 0.96, positive predictive value [PPV] 83.0%, negative predictive value [NPV] 93.8%). A model based on the ten most predictive proteins accurately predicted transition status in training (AUC 0.96, PPV 87.5%, NPV 95.8%) and withheld data (AUC 0.92, PPV 88.9%, NPV 91.4%). A model using the same 65 clinical and 166 proteomic features predicted 2-year functional outcome with AUC 0.72 (PPV 67.6%, NPV 47.6%).In Study 2, 5 of 265 quantified proteins were significantly differentially expressed between participants who did and did not report PEs at age 18. A model using 265 proteomic features predicted PEs at age 18 with AUC 0.76 (PPV 69.1%, NPV 74.2%).DiscussionWith external validation, models incorporating proteomic data may contribute to improved prediction of clinical outcomes in individuals at risk of psychosis.

Proteomic landscape of human coronary artery atherosclerosis.

In order to investigate novel biomarkers for the detection of coronary artery disease for effective therapeutic targets, a comprehensive understanding of the protein networks and protein expression abundance in coronary artery samples is required. This was established by means of liquid chromatography (LC)-mass spectrometry (MS)/MS analysis in the present study. A total of 20 human coronary artery specimens from 2 autopsied adults were employed in the present study. The natural history and histological classification of the atherosclerotic lesions of the coronary artery samples were analyzed by hematoxylin and eosin (H&E) staining, and the human coronary arterial proteome and proteomics features were characterized by MS analysis. The present study identified 2,135 proteins in the 20 coronary artery segments samples from the 2 cases. Combined with the results of H&E staining of the coronary artery samples, a total of 174 proteins, including 4 upregulated proteins and 164 downregulated proteins (excluding 6 proteins with inconsistent expression tendencies), were shown to be associated with coronary artery disease. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment of the differentially expressed proteins revealed that the mitochondrial energy metabolism may be responsible for the occurrence and development of coronary artery atherosclerosis. The human coronary arterial proteome can be considered as a complex network whose architectural characteristics vary considerably as a function of the presence or absence, and histological classification of coronary artery atherosclerosis. These data thus suggest that the prevention of mitochondrial dysfunction via the retrieval of the mitochondrial associated proteins expression may be a promising target in coronary artery disease.

Abstract P4-06-01: Using multi-omic profiling to unravel the complexity of triple-negative breast cancer (TNBC)

Abstract Introduction: TNBCs show marked heterogeneity in their spectrum of genomic alterations, patterns of gene expression, proteomic profiles, and immune microenvironments. Although TNBC heterogeneity has been characterized at each of these levels individually, how variation at one level is associated with differences at other levels is poorly understood. As new targeted and immune-based therapies are developed for TNBC, there is a critical need to better understand this interplay. In this study, we performed “multi-omic” profiling on a cohort of TNBCs in order to determine how various DNA, RNA, protein, and immunologic parameters are correlated. Methods: A cohort of 95 formalin-fixed, paraffin-embedded tumor specimens from primary and metastatic TNBCs was curated. The following parameters in each tumor specimen were quantified: stromal tumor-infiltrating lymphocytes (sTILs) assessed on hematoxylin and eosin stained sections; expression of programmed death-ligand 1 (PD-L1), androgen receptor (AR) and the retinoblastoma tumor suppressor protein (RB) by immunohistochemistry (IHC); and expression of 776 breast cancer-related transcripts (using the nCounter Breast Cancer 360 panel). Targeted tumor DNA sequencing was performed in a subset of cases and tumor mutational burden (TMB) and DNA mismatch repair (MMR) status were determined. Gene expression patterns were analyzed using customized algorithms and we performed descriptive statistics to evaluate associations between DNA, RNA, and protein variables. Results: All 95 TNBC specimens were analyzed for sTILs; IHC for RB, AR, and PD-L1; and transcriptomic profiling. DNA sequencing was performed on 68 cases. The genomic, transcriptomic, and proteomic classification of the cohort is provided in Table 1. AR+ TNBCs (&amp;gt;1% tumor cells on IHC) were more likely to be non-basal (p&amp;lt;0.001), showed higher levels of AR (p&amp;lt;0.001) and FOXA1 (p&amp;lt;0.01) transcripts, were enriched for expression of genes related to mammary differentiation (p&amp;lt;0.001), depleted for expression of genes related to proliferation (p=0.018), “BRCAness” (p&amp;lt;0.001), and homologous recombination deficiency (HRD) (p=0.045), and showed significant upregulation of the immune inhibitory checkpoint molecule TIGIT (p=0.004). RB-positivity (&amp;gt;50% tumor cells on IHC) correlated with higher RB1 transcript levels but not with RB1 deletion/mutation status. RB+ tumors were depleted for expression of genes associated with HRD and BRCAness (p=0.029 and p=0.071). TNBCs showing widespread upregulation of gene expression signatures related to numerous immune processes included those with: (i) higher levels of sTILs, (ii) &amp;gt;1% PD-L1+ stromal mononuclear cells and/or tumor cells, (iii) tumors without RB1 or TP53 alterations. Genomic aberration in the PI3K pathway was associated with increased expression of AR and FOXA1 but not with alterations in immune gene expression. Conclusion: Patterns exist connecting TNBC heterogeneity across the DNA, RNA, and protein levels. Further analyses determining associations between genomic, transcriptomic, and proteomic features in this cohort of TNBC will be presented at the meeting. Genomic, transcriptomic, and proteomic classification of tumors in the entire TNBC cohortTumor site (n=95)Primary64 (67%)Regional nodal4 (4%)Distant nodal or visceral27 (28%)Stromal TILs (n=93)&amp;lt;10%50 (53%)10-49%30 (32%)&amp;gt;50%13 (14%)RB staining (n=95)Positive (&amp;gt;50% tumor cells)52 (55%)AR staining (n=95)Positive (&amp;gt;1% tumor cells)34 (36%)PD-L1 staining (stromal, n=94)Positive (&amp;gt;1% mononuclear cells)34 (37%)PD-L1 staining (tumor, n=94)&amp;gt;1% tumor cells27 (29%)PAM50 subtype (n=90)Basal72 (80%)HER2-enriched15 (17%)Luminal B3 (3%)TNBC subtype (n=91)Basal-like immune activated (BLIA)76 (80%)Basal-like immunosuppressed (BLIS)7 (7%)Mesenchymal (MES)8 (8%)Luminal androgen receptor (LAR)4 (4%)DNA alterations (n=68)TP53 alterations54 (80%)BRCA1/2 mutations14 (21%)RB1 alterations21 (31%)PI3K pathway alterations31 (46%) Citation Format: Shom Goel, Heather Ann Brauer, Yuqi Ren, Kara Gorman, Wafa Osmani, Jessica Pittenger, Chelsea Andrews, Edward T Richardson III, Elizabeth A Mittendorf, MD PhD, Eric P Winer, Stuart Schnitt, Sara M Tolaney. Using multi-omic profiling to unravel the complexity of triple-negative breast cancer (TNBC) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-06-01.