Proteogenomic characterization of skull-base chordoma.

Skull-base chordoma is a rare, aggressive bone cancer with a high recurrence rate. Despite advances in genomic studies, its molecular characteristics and effective therapies remain unknown. Here, we conduct integrative genomics, transcriptomics, proteomics, and phosphoproteomics analyses of 187 skull-base chordoma tumors. In our study, chromosome instability is identified as a prognostic predictor and potential therapeutic target. Multi-omics data reveals downstream effects of chromosome instability, with RPRD1B as a putative target for radiotherapy-resistant patients. Chromosome 1q gain, associated with chromosome instability and upregulated mitochondrial functions, lead to poorer clinical outcomes. Immune subtyping identify an immune cold subtype linked to chromosome 9p/10q loss and immune evasion. Proteomics-based classification reveals subtypes (P-II and P-III) with high chromosome instability and immune cold features, with P-II tumors showing increased invasiveness. These findings, confirmed in 17 paired samples, provide insights into the biology and treatment of skull-base chordoma.

ZNF8 Orchestrates with Smad3 to Promote Lung Metastasis by Recruiting SMYD3 in Breast Cancer.

Most deaths in breast cancer patients are attributed to metastasis, and lung metastasis is associated with a particularly poor prognosis; therefore it is imperative to identify potential target for intervention. The transforming growth factor-ß (TGF-ß) pathway plays a vital role in breast cancer metastasis, in which Smad3 is the key mediator and performs specific functions by binding with different cofactors. However, Smad3 cofactors involved in lung metastasis have not yet been identified. This study first establishes the interactome of Smad3 in breast cancer cells and identifies ZNF8 as a novel Smad3 cofactor. Furthermore, the results reveal that ZNF8 is closely associated with breast cancer lung metastasis prognosis, and specifically facilitates TGF-ß pathway-mediated breast cancer lung metastasis by participating in multiple processes. Mechanistically, ZNF8 binds with Smad3 to enhance the H3K4me3 modification and promote the expression of lung metastasis signature genes by recruiting SMYD3. SMYD3 inhibition by BCI121 effectively prevents ZNF8-mediated lung metastasis. Overall, the study identifies a novel cofactor of TGF-ß/Smad3 that promotes lung metastasis in breast cancer and introduces potential therapeutic strategies for the early management of breast cancer lung metastasis.

ZASP: A highly compatible and sensitive ZnCl2-precipitation assisted sample preparation method for proteomic analysis.

Universal sample preparation for proteomic analysis that enables unbiased protein manipulation, flexible reagent use, and low protein loss is required to ensure the highest sensitivity of downstream liquid chromatography-mass spectrometry (LC-MS) analysis. To address these needs, we developed a ZnCl2 precipitation-assisted sample preparation method (ZASP) that depletes harsh detergents and impurities in protein solutions prior to trypsin digestion via 10 min of ZnCl2 and methanol-induced protein precipitation at room temperature (RT). ZASP can remove trypsin digestion and LC-MS incompatible detergents such as SDS, Triton X-100, and urea at high concentrations in solution and unbiasedly recover proteins independent of the amount of protein input. We demonstrated the sensitivity and reproducibility of ZASP in an analysis of samples with 1 µg to 1000 µg of proteins. Compared to commonly used sample preparation methods such as SDC-based in-solution digestion, acetone precipitation, FASP, and SP3, ZASP has proven to be an efficient approach. Here, we present ZASP, a practical, robust, and cost-effective proteomic sample preparation method that can be applied to profile different types of samples.

Genomic Amplification of TBC1D31 Promotes Hepatocellular Carcinoma Through Reducing the Rab22A-Mediated Endolysosomal Trafficking and Degradation of EGFR.

Hepatocellular carcinomas (HCCs) are characterized by a vast spectrum of somatic copy number alterations (CNAs); however, their functional relevance is largely unknown. By performing a genome-wide survey on prognosis-associated focal CNAs in 814 HCC patients by an integrative computational framework based on transcriptomic data, genomic amplification is identified at 8q24.13 as a promising candidate. Further evidence is provided that the 8q24.13 amplification-driven overexpression of Rab GTPase activating protein TBC1D31 exacerbates HCC growth and metastasis both in vitro and in vivo through activating Epidermal growth factor receptor (EGFR) signaling. Mechanistically, TBC1D31 acts as a Rab GTPase activating protein to catalyze GTP hydrolysis for Rab22A and then reduces the Rab22A-mediated endolysosomal trafficking and degradation of EGFR. Notably, overexpression of TBC1D31 markedly increases the resistance of HCC cells to lenvatinib, whereas inhibition of the TBC1D31-EGFR axis can reverse this resistance phenotype. This study highlights that TBC1D31 at 8q24.13 is a new critical oncogene, uncovers a novel mechanism of EGFR activation in HCC, and proposes the potential strategies for treating HCC patients with TBC1D31 amplification or overexpression.

ZNF8 promotes progression of gastrointestinal cancers via a p53-dependent mechanism.

p53 is a critical tumor suppressor, and the disruption of its normal function is often a prerequisite for the development or progression of tumors. Our previous works revealed that multiple members of Krüppel-associated box (KRAB) domain zinc-finger proteins (KZFPs) family regulate p53 transcriptional activity by interacting with it. But the tumor biology functions of these members have not been fully elucidated. Here, the pan-cancer analysis related to gastrointestinal cancers (GICs) revealed that ZNF8, a p53-interacting protein, is an unfavorable prognostic factor for patients with malignancies. ZNF8 interacts with p53 and further depresses its transcriptional activity in colon cancer cells. The knockdown of ZNF8 or the overexpression of ZNF8 inhibits or facilitates the in vitro colony formation, migration, invasion, and angiogenesis of p53+/+ colon cancer HCT116 cells, HepG2 cells and EC109 cells rather than p53-/- colon cancer HCT116 cells and p53-knockout HepG2 cells, respectively. Xenograft experiments conducted in vivo also showed that the knockdown of ZNF8 in p53+/+ but not in p53-/- HCT116 cells curbs the tumor growth and metastasis to lung, leading to an extended life span for tumor-bearing mice. Clinically, two independent immunohistochemistry cohorts of colon cancer and esophageal cancer also indicated that ZNF8 is higher expression in carcinoma tissues than adjacent tissues and this is associated with worse overall survival outcomes in patients without harboring p53 mutation. Together, our results provide insight into the p53-specific tumor oncogenic function of ZNF8. ZNF8 may prove to be a potential target for GICs treatment.

OrganogenesisDB: A Comprehensive Database Exploring the Cell-Type Identities and Gene Expression Dynamics during Organogenesis.

Organogenesis, the phase of embryonic development that starts at the end of gastrulation and continues until birth is the critical process for understanding cellular differentiation and maturation during organ development. The rapid development of single-cell transcriptomics technology has led to many novel discoveries in understanding organogenesis while also accumulating a large quantity of data. To fill this gap, OrganogenesisDB (http://organogenesisdb.com/), which is a comprehensive database dedicated to exploring cell-type identification and gene expression dynamics during organogenesis, is developed. OrganogenesisDB contains single-cell RNA sequencing data for more than 1.4 million cells from 49 published datasets spanning various developmental stages. Additionally, 3324 cell markers are manually curated for 1120 cell types across 9 human organs and 4 mouse organs. OrganogenesisDB leverages various analysis tools to assist users in annotating and understanding cell types at different developmental stages and helps in mining and presenting genes that exhibit specific patterns and play key regulatory roles during cell maturation and differentiation. This work provides a critical resource and useful tool for deciphering cell lineage determination and uncovering the mechanisms underlying organogenesis.

Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma.

Stable infiltration of myeloid cells, especially tumor-associated M2 macrophages, acts as one of the essential features of the tumor immune microenvironment by promoting the malignant progression of hepatocellular carcinoma (HCC). However, the factors affecting the infiltration of M2 macrophages are not fully understood. In this study, we found the molecular subtypes of HCC with the worst prognosis are characterized by immune disorders dominated by myeloid cell infiltration. Myeloid cell nuclear differentiation antigen (MNDA) was significantly elevated in the most aggressive subtype and exhibited a positively correlation with M2 infiltration and HCC metastasis. Moreover, MNDA functioned as an independent prognostic predictor and has a good synergistic effect with some existing prognostic clinical indicators. We further confirmed that MNDA was primarily expressed in tumor M2 macrophages and contributed to the enhancement of its polarization by upregulating the expression of the M2 polarization enhancers. Furthermore, MNDA could drive the secretion of M2 macrophage-derived pro-metastasis proteins to accelerate HCC cells metastasis both in vivo and in vitro. In summary, MNDA exerts a protumor role by promoting M2 macrophages polarization and HCC metastasis, and can serve as a potential biomarker and therapeutic target for HCC.

Proteomics Identifies LUC7L3 as a Prognostic Biomarker for Hepatocellular Carcinoma.

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan-Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors.

To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p = 1.19 × 10-9) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

From PROTAC to TPD: Advances and Opportunities in Targeted Protein Degradation.

PROTAC is a rapidly developing engineering technology for targeted protein degradation using the ubiquitin-proteasome system, which has promising applications for inflammatory diseases, neurodegenerative diseases, and malignant tumors. This paper gives a brief overview of the development and design principles of PROTAC, with a special focus on PROTAC-based explorations in recent years aimed at achieving controlled protein degradation and improving the bioavailability of PROTAC, as well as TPD technologies that use other pathways such as autophagy and lysosomes to achieve targeted protein degradation.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Proteomic Characterization Identifies Clinically Relevant Subgroups of Gastrointestinal Stromal Tumors.

BACKGROUND & AIMS: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the gastrointestinal tract, and it has high metastatic and recurrence rates. We aimed to characterize the proteomic features of GIST to understand biological processes and treatment vulnerabilities. METHODS: Quantitative proteomics and phosphoproteomics analyses were performed on 193 patients with GIST to reveal the biological characteristics of GIST. Data-driven hypotheses were tested by performing functional experiments using both GIST cell lines and xenograft mouse models. RESULTS: Proteomic analysis revealed differences in the molecular features of GISTs from different locations or with different histological grades. MAPK7 was identified and functionally proved to be associated with tumor cell proliferation in GIST. Integrative analysis revealed that increased SQSTM1 expression inhibited the patient response to imatinib mesylate. Proteomics subtyping identified 4 clusters of tumors with different clinical and molecular attributes. Functional experiments confirmed the role of SRSF3 in promoting tumor cell proliferation and leading to poor prognosis. CONCLUSIONS: Our study provides a valuable data resource and highlights potential therapeutic approaches for GIST.

Repopulating Kupffer cells originate directly from hematopoietic stem cells.

BACKGROUND: Kupffer cells (KCs) originate from yolk-sac progenitors before birth. Throughout adulthood, they self-maintain independently from the input of circulating monocytes (MOs) at a steady state and are replenished within 2 weeks after having been depleted, but the origin of repopulating KCs in adults remains unclear. The current paradigm dictates that repopulating KCs originate from preexisting KCs or monocytes, but there remains a lack of fate-mapping evidence. METHODS: We first traced the fate of preexisting KCs and that of monocytic cells with tissue-resident macrophage-specific and monocytic cell-specific fate-mapping mouse models, respectively. Secondly, we performed genetic lineage tracing to determine the type of progenitor cells involved in response to KC-depletion in mice. Finally, we traced the fate of hematopoietic stem cells (HSCs) in an HSC-specific fate-mapping mouse model, in the context of chronic liver inflammation induced by repeated carbon tetrachloride treatment. RESULTS: By using fate-mapping mouse models, we found no evidence that repopulating KCs originate from preexisting KCs or MOs and found that in response to KC-depletion, HSCs proliferated in the bone marrow, mobilized into the blood, adoptively transferred into the liver and differentiated into KCs. Then, in the chronic liver inflammation context, we confirmed that repopulating KCs originated directly from HSCs. CONCLUSION: Taken together, these findings provided in vivo fate-mapping evidence that repopulating KCs originate directly from HSCs, which presents a completely novel understanding of the cellular origin of repopulating KCs and shedding light on the divergent roles of KCs in liver homeostasis and diseases.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

RNF149 Promotes HCC Progression through Its E3 Ubiquitin Ligase Activity.

Hepatocellular carcinoma (HCC) accounts for over 80% of cases among liver cancer, with high incidence and poor prognosis. Thus, it is of valuable clinical significance for discovery of potential biomarkers and drug targets for HCC. In this study, based on the proteomic profiling data of paired early-stage HCC samples, we found that RNF149 was strikingly upregulated in tumor tissues and correlated with poor prognosis in HCC patients, which was further validated by IHC staining experiments of an independent HCC cohort. Consistently, overexpression of RNF149 significantly promoted cell proliferation, migration, and invasion of HCC cells. We further proved that RNF149 stimulated HCC progression via its E3 ubiquitin ligase activity, and identified DNAJC25 as its new substrate. In addition, bioinformatics analysis showed that high expression of RNF149 was correlated with immunosuppressive tumor microenvironment (TME), indicating its potential role in immune regulation of HCC. These results suggest that RNF149 could exert protumor functions in HCC in dependence of its E3 ubiquitin ligase activity, and might be a potential prognostic marker and therapeutic target for HCC treatment.

RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples.

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 µg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1-2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research.

Digital image analysis allows objective stratification of patients with silent PIT1-lineage pituitary neuroendocrine tumors.

Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)-lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1-lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06-6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1-lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60-93.5), p = 0.015]. We obtained three-tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high-risk profile presented the highest recurrence rate compared with those in the medium-risk and low-risk profiles [HR: 3.54, (95% CI, 1.40-8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1-lineage tumors. Typical morphological characteristics of high-risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1-lineage hormones.

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics.

BACKGROUND: Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS: Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION: Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.