Guomics papers：2023.4-6
1
前列腺癌（PCa）是男性发病率第二高的恶性肿瘤，也是导致男性癌症相关死亡的第五大原因。一个重要的挑战是确定哪些人群有可能从激素敏感性前列腺癌（HSPC）迅速发展为致命的去势抵抗性前列腺癌（CRPC）。该研究收集了 78 份 HSPC 活检样本，并使用压力循环技术（PCT）和脉冲数据独立采集管道测量了它们的蛋白质组。蛋白质组学数据和临床元数据用于生成模型，用于对 HSPC 患者进行分类并预测每个病例的发展情况。该研究使用 HSPC 活检组织对 7335 种蛋白质进行了定量。共有 251 种蛋白质在长期或短期进展为 CRPC 的患者之间差异表达。利用随机森林模型，确定了7种能显著区分长期病例和短期病例的蛋白质，并以此对前列腺癌患者进行分类。该研究可帮助临床医生预测患者的病情发展，指导个体化临床管理和决策。
Abstract：
Background: Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive PCa (HSPC) to the lethal castration-resistant PCa (CRPC).
Methods: We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. The proteomics data and clinical metadata were used to generate models for classifying HSPC patients and predicting the development of each case.
Results: We quantified 7335 proteins using the HSPC biopsies. A total of 251 proteins were differentially expressed between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term cases, which were used to classify PCa patients with an area under the curve 0.873. Next, one clinical parameter (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression disparities (p-value = 1.3×10-4).
Conclusion: We identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These tools may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.2
肝癌是导致全球癌症死亡的最主要原因之一，根据2020年中国统计的癌症数据，肝癌的死亡率排名第二。该研究对107名肝癌患者的163个肝组织样本进行了基于DIA的蛋白质组学分析，共鉴定并定量到104,489个多肽和8057个蛋白，通过比较HCC（肝细胞癌）和CCA（肝内胆管癌）的定量蛋白质组数据，揭示了不同的蛋白分子特征。作者还开发了一个机器学习分类模型来区分HCC和CCA肿瘤。本研究中提出的HCC和CCA独特的分子特征为这两种主要的原发性肝癌提供了新的见解，有助于开发更有效的诊断方案和新的靶向药物治疗。
Title：
Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic CholangiocarcinomaAbstract：
Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnosis protocols and new targeted drug treatments.链接：https://www.mcponline.org/article/S1535-9476(23)00115-9/fulltext#
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本文重点研究了76个人类乳腺癌症细胞系，通过基于机器学习的多组学模型预测药物敏感性，我们发现纳入蛋白质组学数据后，基于机器学习的多组学模型改善了药物敏感性预测，并提供了对药物作用机制的深入了解。药物扰动的额外纵向蛋白质组学描述了用EGFR/AKT/mTOR抑制剂处理的九个细胞系（五个TNBC，四个非TNBC）的蛋白质组变化。在TNBC中，EGFR/mTOR抑制剂治疗后代谢通路失调，而AKT抑制剂则影响RNA修饰和细胞周期通路。
Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets
Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating prior multi-omics datasets with our proteomic results, we found that including proteomics data improved drug sensitivity predictions and provided insights into mechanism of action. We then profiled the proteome changes in nine cell lines (five TNBC, four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provides insights into adaptive resistance in TNBC.
链接：https://www.mcponline.org/article/S1535-9476(23)00113-5/fulltext
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如今，越来越多的蛋白质组学研究集中在上皮性卵巢癌（EOC）上，试图找出早期疾病生物标志物，建立分子分层并发现新的药物靶点。我们从临床角度系统回顾了EOC的蛋白质组学研究，以及 1990 年以来的约2500项卵巢癌临床试验，这些蛋白质组学的研究进展显示了 EOC 定制疗法的前景。
Mass Spectrometry–Based Proteomics of Epithelial Ovarian Cancers: A Clinical Perspective
Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery of proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990 and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.
5
结直肠癌(CRC)是世界上最常见的恶性肿瘤之一，是导致癌症死亡的第二大原因。该研究通过整合转录组学、蛋白质组学和代谢组学数据引入了一个IMS系统。确定了三种亚型对预后不同的CRC患者进行分层。C3亚型具有高S100A9+巨噬细胞丰度，与不良预后相关。在C3亚型CRC中，S100A9+巨噬细胞显示出通过细胞-细胞影响T细胞的能力。靶向S100A9+巨噬细胞和免疫检查点的联合策略可能为逆转某些特征性免疫代谢亚型CRC患者的免疫逃逸和提高免疫治疗疗效开辟了广阔的前景。
An immunometabolism subtyping system identifies S100A9+ macrophage as an immune therapeutic target in colorectal cancer based on multiomics analysis
Summary：
Immunometabolism in the tumor microenvironment (TME) and its influence on the immunotherapy response remain uncertain in colorectal cancer (CRC). We perform immunometabolism subtyping (IMS) on CRC patients in the training and validation cohorts. Three IMS subtypes of CRC, namely, C1, C2, and C3, are identified with distinct immune phenotypes and metabolic properties. The C3 subtype exhibits the poorest prognosis in both the training cohort and the in-house validation cohort. The single-cell transcriptome reveals that a S100A9+ macrophage population contributes to the immunosuppressive TME in C3. The dysfunctional immunotherapy response in the C3 subtype can be reversed by combination treatment with PD-1 blockade and an S100A9 inhibitor tasquinimod. Taken together, we develop an IMS system and identify an immune tolerant C3 subtype that exhibits the poorest prognosis. A multiomics-guided combination strategy by PD-1 blockade and tasquinimod improves responses to immunotherapy by depleting S100A9+ macrophages in vivo.
6
我们在 Life Medicine 上发表了一篇technical highlight，讲述了长新冠蛋白质组学研究方法的现状与展望。
Proteomics approaches to long COVID: status and outlooks
7
新冠大流行是人类历史上最为严重的大流行病之一，应用血液生态系统理念剖析奥密克戎病毒感染，对其诊疗与防控具有重要意义。该研究使用多组学因子分析（MOFA），对Omicron感染患者的1000多份血细胞/血浆样本的临床表型、转录组、蛋白质组、代谢组和免疫组库进行了系统分析。利用无偏差的因子分析多组学数据，剖析了宿主在多个疾病阶段的反应动态，揭示了血液中的分子和细胞景观，对Omicron突破性感染者的血液生态系统进行了不同疾病阶段的研究。研究人员还整合临床指标、血浆蛋白质组和代谢组，开发了一组机器学习模型，利用该模型所发现的与宿主免疫反应密切相关一组血浆分子生物标志物，可以准确预测Omicron患者复阳的概率。
该研究进一步展示了抗体或血浆疗法在预防和治疗Omicron复阳患者方面的潜力，可能会激发研究系统性疾病和新发公卫问题的范式转变。
Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection
Summary：
Although host responses to the ancestral SARS-CoV-2 strain are well described, those to the new Omicron variants are less resolved. We profiled the clinical phenomes, transcriptomes, proteomes, metabolomes, and immune repertoires of >1,000 blood cell or plasma specimens from SARS-CoV-2 Omicron patients. Using in-depth integrated multi-omics, we dissected the host response dynamics during multiple disease phases to reveal the molecular and cellular landscapes in the blood. Specifically, we detected enhanced interferon-mediated antiviral signatures of platelets in Omicron-infected patients, and platelets preferentially formed widespread aggregates with leukocytes to modulate immune cell functions. In addition, patients who were re-tested positive for viral RNA showed marked reductions in B cell receptor clones, antibody generation, and neutralizing capacity against Omicron. Finally, we developed a machine learning model that accurately predicted the probability of re-positivity in Omicron patients. Our study may inspire a paradigm shift in studying systemic diseases and emerging public health concerns.
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本文开发了一种新的以谱系为中心的方法：Dear-DIAXMBD。该方法将深度变分自编码器（VAE）和其他机器学习算法相结合，无需借助 DDA 实验即可检测 DIA 数据中前体和片段之间的对应关系。在不同仪器平台获得的不同物种的DIA数据上，Dear-DIAXMBD都表现出了优异的性能。由于其强大的泛化能力，我们认为 Dear-DIAXMBD 是一款有价值的 DIA 蛋白组学开源软件。
Title：
Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition ProteomicsAbstract：
Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIAXMBD.链接：https://spj.science.org/doi/10.34133/research.0179
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宿主-菌群互作失调在克罗恩病（CD）的发生发展中起着关键作用，然而肠道及其附属组织的空间分布和相互作用网络尚不清楚。该研究应用空间蛋白质组学和微生物组学，分析了来自30名CD患者的肠粘膜、粘膜下-肌层-浆膜、肠系膜脂肪组织、肠系膜和肠系膜淋巴结的540份样本中的宿主蛋白和组织微生物，并在空间上解读了宿主-微生物的相互作用。该研究发现CD期间跨多组织的失调蛋白功能簇、异常抗菌免疫和代谢过程，并可确定细菌传播以及改变的菌群和生态模式。此外，该研究还确定了几种宿主蛋白和微生物之间的候选相互作用对，这些相互作用对与CD中肠炎的持续存在及细菌在多组织间的迁移有关。
Integrative multi-omics deciphers the spatial characteristics of host-gut microbiota interactions in Crohn’s disease
Dysregulated host-microbial interactions play critical roles in initiation and perpetuation of gut inflammation in Crohn’s disease (CD). However, the spatial distribution and interaction network across the intestine and its accessory tissues are still elusive. Here, we profile the host proteins and tissue microbes in 540 samples from the intestinal mucosa, submucosa-muscularis-serosa, mesenteric adipose tissues, mesentery, and mesenteric lymph nodes of 30 CD patients and spatially decipher the host-microbial interactions. We observe aberrant antimicrobial immunity and metabolic processes across multi-tissues during CD and determine bacterial transmission along with altered microbial communities and ecological patterns. Moreover, we identify several candidate interaction pairs between host proteins and microbes associated with perpetuation of gut inflammation and bacterial transmigration across multi-tissues in CD. Signature alterations in host proteins (e.g., SAA2 and GOLM1) and microbes (e.g., Alistipes and Streptococcus) are further imprinted in serum and fecal samples as potential diagnostic biomarkers, thus providing a rationale for precision diagnosis.
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胚胎干细胞（ESCs）具有体外无限自我更新和向各种谱系分化的能力。深入理解ESCs多能性维持和命运决定机制对于推动多能干细胞的潜在临床应用具有重要的意义。本研究发现了USP7在mESCs维持中去泛素化酶活性非依赖的非经典功能，为USP7在其它生物学过程如肿瘤中的研究提供了新的方向与思路。
USP7 represses lineage differentiation genes in mouse embryonic stem cells by both catalytic and noncatalytic activities
Abstract：
USP7, a ubiquitin-specific peptidase (USP), plays an important role in many cellular processes through its catalytic deubiquitination of various substrates. However, its nuclear function that shapes the transcriptional network in mouse embryonic stem cells (mESCs) remains poorly understood. We report that USP7 maintains mESC identity through both catalytic activity–dependent and –independent repression of lineage differentiation genes. Usp7 depletion attenuates SOX2 levels and derepresses lineage differentiation genes thereby compromising mESC pluripotency. Mechanistically, USP7 deubiquitinates and stabilizes SOX2 to repress mesoendodermal (ME) lineage genes. Moreover, USP7 assembles into RYBP-variant Polycomb repressive complex 1 and contributes to Polycomb chromatin–mediated repression of ME lineage genes in a catalytic activity–dependent manner. USP7 deficiency in its deubiquitination function is able to maintain RYBP binding to chromatin for repressing primitive endoderm–associated genes. Our study demonstrates that USP7 harbors both catalytic and noncatalytic activities to repress different lineage differentiation genes, thereby revealing a previously unrecognized role in controlling gene expression for maintaining mESC identity.
Guomics
郭天南研究员课题组 (https://www.guomics.com) 长期从事蛋白质组学相关研究，联合人工智能，解析生物过程的原理，助力疾病诊疗。团队诚邀优秀研究生及博士后研究人员加盟！