High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Ultrasensitive Proteomics of Trace Cardiac Tissues with Anchor-Nanoparticles

Pathological cardiac hypertrophy is a complex process that often leads to heart failure. Label-free proteomics has emerged as an important platform to reveal protein variations and to elucidate the mechanisms of cardiac hypertrophy. Endomyocardial biopsy is a minimally invasive technique for sampling cardiac tissue, but it yields only limited amounts of an ethically permissible specimen. After regular pathological examination, the remaining trace samples pose significant challenges for effective protein extraction and mass spectrometry analysis. Herein, we developed trace cardiac tissue proteomics based on the anchor-nanoparticles (TCPA) method. We identified an average of 6666 protein groups using ∼50 μg of myocardial interventricular septum samples by TCPA. We then applied TCPA to acquire proteomics from patients' cardiac samples both diagnosed as hypertrophic hearts and myocarditis controls and identified significant alterations in pathways such as regulation of actin cytoskeleton, oxidative phosphorylation, and cGMP-PKG signaling pathway. Moreover, we found multiple lipid metabolic pathways to be dysregulated in transthyretin cardiac amyloidosis compared to other types of cardiac hypertrophy. TCPA offers a new technique for studying pathological cardiac hypertrophy and can serve as a platform toolbox for proteomic research in other cardiac diseases.

Mass Spectrometry-Based Proteomics of Poly(methylmethacrylate)-Embedded Bone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely employed technique in proteomics research for studying the proteome biology of various clinical samples. Hard tissues, such as bone and teeth, are routinely preserved using synthetic poly(methyl methacrylate) (PMMA) embedding resins that enable histological, immunohistochemical, and morphological examination. However, the suitability of PMMA-embedded hard tissues for large-scale proteomic analysis remained unexplored. This study is the first to report on the feasibility of PMMA-embedded bone samples for LC-MS/MS analysis. Conventional workflows yielded merely limited coverage of the bone proteome. Using advanced strategies of prefractionation by high-pH reversed-phase liquid chromatography in combination with isobaric tandem mass tag labeling resulted in proteome coverage exceeding 1000 protein identifications. The quantitative comparison with cryopreserved samples revealed that each sample preparation workflow had a distinct impact on the proteomic profile. However, workflow replicates exhibited a high reproducibility for PMMA-embedded samples. Our findings further demonstrate that decalcification prior to protein extraction, along with the analysis of solubilization fractions, is not preferred for PMMA-embedded bone. The biological applicability of the proposed workflow was demonstrated using samples of human PMMA-embedded alveolar bone and the iliac crest, which revealed anatomical site-specific proteomic profiles. Overall, these results establish a crucial foundation for large-scale proteomics studies contributing to our knowledge of bone biology.

Chemical investigation of biological trace evidence; toxicological screening of waste residues obtained from DNA extraction processes

In a forensic scenario, if biological stains are found in very small quantities, these are usually collected for DNA analyses, considered essential for the forensic investigation and thus excluding possible investigations by other forensic disciplines as forensic toxicology. We developed an experimental study to evaluate the feasibility of analyzing DNA extraction residues obtained from DNA extraction procedures to perform toxicological analysis, with the aim to extract both genetic and toxicological information without affecting or compromising the genetic sample and/or DNA extraction. DNA extraction from four blood samples (fortified with 5 molecules of interest with a final concentrations of 1 µg/mL, 100 ng/mL, 10 ng/mL and 5 ng/mL, respectively) were analyzed with QIAGEN QIAmp® DNA Mini kit. Three waste residues collected from the DNA extraction were analyzed for the toxicological investigation via Solid-Phase Extraction and High-Performance Liquid Chromatography-Tandem Mass Spectrometry analyses (Thermo Scientific™ TSQ Fortis™ II Triple-Quadrupole Mass Spectrometer). The analytical investigation revealed that our analytes of interest were detected in two different residues of the DNA extraction procedure, allowing both genetic and toxicological analyses without affecting the DNA identification. At last, the experimental protocol was applied to a hypothetical case, with encouraging results and allowing the identification of our molecules of interest.

An individualized protein-based prognostic model to stratify pediatric patients with papillary thyroid carcinoma

Pediatric papillary thyroid carcinomas (PPTCs) exhibit high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collect and evaluate the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantify 10,426 proteins by mass spectrometry. We find 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses show the enhanced activation of the inflammatory and immune system in PM patients compared with the others. Nineteen proteins are selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generates a protein-based personalized prognostic prediction model that can stratify PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Analysis of gliomas DNA methylation: Assessment of pre-analytical variables

Precision oncology is driven by molecular biomarkers. For glioblastoma multiforme (GBM), the most common malignant adult primary brain tumor, O6-methylguanine-DNA methyltransferase ( MGMT ) gene DNA promoter methylation is an important prognostic and treatment clinical biomarker. Time consuming pre-analytical steps such as biospecimen storage before fixing, sampling, and processing are major sources of errors and batch effects, that are further confounded by intra-tumor heterogeneity of MGMT promoter methylation. To assess the effect of pre-analytical variables on GBM DNA methylation, tissue storage/sampling (CryoGrid), sample preparation multi-sonicator (PIXUL) and 5-methylcytosine (5mC) DNA immunoprecipitation (Matrix MeDIP-qPCR/seq) platforms were used. MGMT promoter CpG methylation was examined in 173 surgical samples from 90 individuals, 50 of these were used for intra-tumor heterogeneity studies. MGMT promoter methylation levels in paired frozen and formalin fixed paraffin embedded (FFPE) samples were very close, confirming suitability of FFPE for MGMT promoter methylation analysis in clinical settings. Matrix MeDIP-qPCR yielded similar results to methylation specific PCR (MS-PCR). Warm ex-vivo ischemia (37°C up to 4hrs) and 3 cycles of repeated sample thawing and freezing did not alter 5mC levels at MGMT promoter, exon and upstream enhancer regions, demonstrating the resistance of DNA methylation to the most common variations in sample processing conditions that might be encountered in research and clinical settings. 20-30% of specimens exhibited intratumor heterogeneity in the MGMT DNA promoter methylation. Collectively these data demonstrate that variations in sample fixation, ischemia duration and temperature, and DNA methylation assay technique do not have significant impact on assessment of MGMT promoter methylation status. However, intratumor methylation heterogeneity underscores the need for histologic verification and value of multiple biopsies at different GBM geographic tumor sites in assessment of MGMT promoter methylation. Matrix-MeDIP-seq analysis revealed that MGMT promoter methylation status clustered with other differentially methylated genomic loci (e.g. HOXA and lncRNAs), that are likewise resilient to variation in above post-resection pre-analytical conditions. These MGMT -associated global DNA methylation patterns offer new opportunities to validate more granular data-based epigenetic GBM clinical biomarkers where the CryoGrid-PIXUL-Matrix toolbox could prove to be useful.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Forager-farmer transition at the crossroads of East and Southeast Asia 4900 years ago

The southward expansion of East Asian farmers profoundly influenced the social evolution of Southeast Asia by introducing cereal agriculture. However, the timing and routes of cereal expansion in key regions are unclear due to limited empirical evidence. Here we report macrofossil, microfossil, multiple isotopic (C/N/Sr/O) and paleoproteomic data directly from radiocarbon-dated human samples, which were unearthed from a site in Xingyi in central Yunnan and which date between 7000 and 3300 a BP. Dietary isotopes reveal the earliest arrival of millet ca. 4900 a BP, and greater reliance on plant and animal agriculture was indicated between 3800 and 3300 a BP. The dietary differences between hunter-gatherer and agricultural groups are also evident in the metabolic and immune system proteins analysed from their skeletal remains. The results of paleoproteomic analysis indicate that humans had divergent biological adaptations, with and without farming. The combined application of isotopes, archaeobotanical data and proteomics provides a new approach to documenting dietary and health changes across major subsistence transitions.

Development of a sample preparation method for micro-proteomics analysis of the formaldehyde-fixed paraffin-embedded liver tissue samples

Liver micro-proteomics based on the routinely used formaldehyde-fixed paraffin-embedded (FFPE) samples is valuable for innovative research, but the technical approach for sample preparation is often challenging. In this study, we aimed to develop a method for sample preparation for micro-proteomics on using the FFPE liver samples. We collected 2000 individual cells per batch from FFPE liver slices with laser capture microdissection and used them as test samples. We used the microscale fresh-frozen liver samples or HepG2 cells as control samples. For the FFPE samples, we first established a procedure for protein extraction. 2 h incubation at 95 °C in alkaline amine buffer supplemented with 4% sodium dodecyl sulfate allows improved production, efficiency, and quality of protein extraction. Then, we developed a dedicated protocol HDMSP for the micro-concentrated (<0.05 μg/μL) protein preparation for mass spectrometry (MS) based analysis, in which 2 μg/μL carboxyl magnetic beads and 70% acetonitrile are used to induce protein precipitation. For the 0.01 μg/μL protein control samples, protein recovery rate (PRR) by HDMSP is 72.1%, while the PRR is 5.9% if using a standard method solid phase-enhanced sample preparation. For the FFPE samples, the HDMSP PRR is 88.8%, and the subsequent MS analysis demonstrates increased depth, robustness, and quantitation accuracy for HDMSP relative to the control of in-gel digestion. Moreover, the physicochemical properties and subcellular location of the FFPE liver micro-proteome are comparable to those of the fresh-frozen control samples processed with filter-aided sample preparation (FASP). HDMSP is also comparable to FASP in terms of reproducibility and physicochemical properties in liver subcellular proteomes, and meanwhile reduces the sample preparation time by 15.9% and the experimental cost by 30.8%. Overall, the new method is simple and highly effective for preparing the microscale FFPE liver protein samples for MS analysis. This study provides a useful solution for FFPE liver micro-proteomics.

Risk stratification of papillary thyroid cancers using multidimensional machine learning

BACKGROUND

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.

MATERIALS AND METHODS

The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system.

RESULTS

The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively.

CONCLUSION

This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Multitissue Circadian Proteome Atlas of WT and Per1-/-/Per2-/- Mice

The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.

α-SMA+ cancer-associated fibroblasts increased tumor enhancement ratio on contrast-enhanced multidetector-row computed tomography in stages I-III colon cancer

BACKGROUND AND AIM

Our prior research revealed that the tumor enhancement ratio (TER) on triphasic abdominal contrast-enhanced MDCT (CE-MDCT) scans was a prognostic factor for patients with stages I-III colon cancer. Building upon this finding, the present study aims to investigate the proteomic changes in colon cancer patients with varying TER values.

METHODS

TER was analyzed on preoperative triphasic CE-MDCT scans of 160 stages I-III colon cancer patients. The survival outcomes of those in the low-TER and high-TER groups were compared. Proteomic analysis on colon cancer tissues was performed by mass spectrometry (MS) and verified by immune-histological chemistry (IHC) assays. In vivo, mouse xenograft models were employed to test the function of target proteins identified through the MS. CE-MDCT scans were conducted on mice xenografts, and the TER values were compared.

RESULTS

Patients in the high-TER group had a significantly worse prognosis than those in the low-TER group. Proteomic analysis of colon cancer tissues revealed 153 differentially expressed proteins between the two groups. A correlation between TER and the abundance of α-SMA protein in tumor tissue was observed. IHC assays further confirmed that α-SMA protein expression was significantly increased in high-TER colon cancer, predominantly in cancer-associated fibroblasts (CAFs) within the cancer stroma. Moreover, CAFs promoted the growth of CRC xenografts in vivo and increased TER.

CONCLUSIONS

Our study identified the distinct protein changes in colon cancer with low and high TER for the first time. The presence of CAFs may promote the growth of colon cancer and contribute to an increased TER.

Routine Workflow of Spatial Proteomics on Micro-formalin-Fixed Paraffin-Embedded Tissues

In the era of single-cell biology, spatial proteomics has emerged as an important frontier. However, it still faces several challenges in technology. Formalin-fixed paraffin-embedded (FFPE) tissues are an important material in spatial proteomics, in which fixed tissues are excised using laser capture microdissection (LCM), followed by protein identification with mass spectrometry. For a satisfied spatial proteomics upon FFPE tissues, the excision area is expected to be as small as possible, and the identified proteins are countered upon as much as possible. For a general laboratory for spatial proteomics, a routine workflow is required, not relying on any special device, and is easily operating. In view of these challenges in technology, we initiated a technology evaluation throughout the entire procedure of proteomic analysis with micro-FFPE tissues. In contrast to the protocols reported previously, several innovations in technology were proposed and conducted, such as removal of destaining, decross-linking with "hang-down", solution simplification for peptide generation and balancing to excision area, and capture rate of micro-FFPE tissues. After optimization of all the necessary steps, a routine workflow was established, in which the minimized area for protein identification was 0.002 mm2, while the excision area for a consistent proteomic analysis was 0.05 mm2. Using the developed workflow and collecting the micro-FFPE tissues continuously, for the first time, a spatial proteomic atlas of mouse brain was preliminarily constructed, which exhibited the typical characteristics of spatial-dependent protein abundance and functional enrichment.

A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes

Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study

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 prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression 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 progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (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 differences (p-value = 1.3×10-4). To conclude, 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 models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

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 our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and 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 provide insights into adaptive resistance in TNBC.

Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

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 diagnostic approaches and new targeted drug treatments.

DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.

A proteome-level view of brain tumors for a better understanding of novel diagnosis, prognosis, and therapy

INTRODUCTION

Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation.

AREAS COVERED

This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches.

EXPERT OPINION

Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.

The Need to Set up a Biobank Dedicated to Lymphoid Malignancies: Experience of a Single Center (Laboratory of Clinical and Experimental Pathology, University Côte d'Azur, Nice, France)

Several therapies to improve the management of lymphoma are currently being investigated, necessitating the development of new biomarkers. However, this requires high-quality and clinically annotated biological material. Therefore, we established a lymphoma biobank including all available biological material (tissue specimens and matched biological resources) along with associated clinical data for lymphoma patients diagnosed, according to the WHO classification, between 2005 and 2022 in the Laboratory of Clinical and Experimental Pathology, Nice, France. We retrospectively included selected cases in a new collection at the Côte d'Azur Biobank, which contains 2150 samples from 363 cases (351 patients). The male/female ratio was 1.3, and the median age at diagnosis was 58 years. The most common lymphoma types were classical Hodgkin lymphoma, diffuse large B-cell lymphoma, and extra-nodal marginal zone lymphoma of MALT tissue. The main sites of lymphoma were the mediastinum, lymph node, Waldeyer's ring, and lung. The Côte d'Azur Biobank is ISO 9001 and ISO 20387 certified and aims to provide high quality and diverse biological material to support translational research projects into lymphoma. The clinico-pathological data generated by this collection should aid the development of new biomarkers to enhance the survival of patients with lymphoid malignancies.

A High-Throughput Workflow for FFPE Tissue Proteomics

Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background

The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies.

Methods

Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression.

Results

MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Conclusions

The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

Expanding individualized therapeutic options via genoproteomics

Clinical next-generation sequencing (NGS)² tests have enabled treatment recommendations for cancer patients with driver gene mutations. Targeted therapy options for patients without driver gene mutations are currently unavailable. Herein, we performed NGS and proteomics tests on 169 formalin-fixed paraffin-embedded (FFPE)³ samples of non-small cell lung cancers (NSCLC, 65),⁴ colorectal cancers (CRC, 61),⁵ thyroid carcinomas (THCA, 14),⁶ gastric cancers (GC, 2),⁷ gastrointestinal stromal tumors (GIST, 11),⁸ and malignant melanomas (MM, 6).⁹ Of the 169 samples, NGS detected 14 actionable mutated genes in 73 samples, providing treatment options for 43% of the patients. Proteomics identified 61 actionable clinical drug targets approved by the FDA or undergoing clinical trials in 122 samples, providing treatment options for 72% of the patients. In vivo experiments demonstrated that the Mitogen-Activated Protein Kinase (MEK)¹⁰ inhibitor induced the overexpression of MEK1 (Map2k1) to block lung tumor growth in mice. Therefore, protein overexpression is a potentially feasible indicator for guiding targeted therapies. Collectively, our analysis suggests that combining NGS and proteomics (genoproteomics) could expand the targeted treatment options to 85% of cancer patients.

An immunometabolism subtyping system identifies S100A9+ macrophage as an immune therapeutic target in colorectal cancer based on multiomics analysis

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.

ASAP─Automated Sonication-Free Acid-Assisted Proteomes─from Cells and FFPE Tissues

Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for retrospective studies, but protein extraction and subsequent sample processing steps have been shown to be challenging for mass spectrometry (MS) analysis. Streamlined high-throughput sample preparation workflows are essential for efficient peptide extraction from complex clinical specimens such as fresh frozen tissues or FFPE. Overall, proteome analysis has gained significant improvements in the instrumentation, acquisition methods, sample preparation workflows, and analysis pipelines, yet even the most recent FFPE workflows remain complex and are not readily scalable. Here, we present an optimized workflow for automated sonication-free acid-assisted proteome (ASAP) extraction from FFPE sections. ASAP enables efficient protein extraction from FFPE specimens, achieving similar proteome coverage as established methods using expensive sonicators, resulting in reduced sample processing time. The broad applicability of ASAP on archived pediatric tumor FFPE specimens resulted in high-quality data with increased proteome coverage and quantitative reproducibility. Our study demonstrates the practicality and superiority of the ASAP workflow as a streamlined, time- and cost-effective pipeline for high-throughput FFPE proteomics of clinical specimens.

Label-free proteome quantification and evaluation

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

Proteomic and single-cell landscape reveals novel pathogenic mechanisms of HBV-infected intrahepatic cholangiocarcinoma

Despite the epidemiological association between intrahepatic cholangiocarcinoma (ICC) and hepatitis B virus (HBV) infection, little is known about the relevant oncogenic effects. A cohort of 32 HBV-infected ICC and 89 non-HBV-ICC patients were characterized using whole-exome sequencing, proteomic analysis, and single-cell RNA sequencing. Proteomic analysis revealed decreased cell-cell junction levels in HBV-ICC patients. The cell-cell junction level had an inverse relationship with the epithelial-mesenchymal transition (EMT) program in ICC patients. Analysis of the immune landscape found that more CD8 T cells and Th2 cells were present in HBV-ICC patients. Single-cell analysis indicated that transforming growth factor beta signaling-related EMT program changes increased in tumor cells of HBV-ICC patients. Moreover, ICAM1⁺ tumor-associated macrophages are correlated with a poor prognosis and contributed to the EMT in HBV-ICC patients. Our findings provide new insights into the behavior of HBV-infected ICC driven by various pathogenic mechanisms involving decreased cell junction levels and increased progression of the EMT program.

Biomarker Analysis of Formalin-Fixed Paraffin-Embedded Clinical Tissues Using Proteomics

The relatively recent developments in mass spectrometry (MS) have provided novel opportunities for this technology to impact modern medicine. One of those opportunities is in biomarker discovery and diagnostics. Key developments in sample preparation have enabled a greater range of clinical samples to be characterized at a deeper level using MS. While most of these developments have focused on blood, tissues have also been an important resource. Fresh tissues, however, are difficult to obtain for research purposes and require significant resources for long-term storage. There are millions of archived formalin-fixed paraffin-embedded (FFPE) tissues within pathology departments worldwide representing every possible tissue type including tumors that are rare or very small. Owing to the chemical technique used to preserve FFPE tissues, they were considered intractable to many newer proteomics techniques and primarily only useful for immunohistochemistry. In the past couple of decades, however, researchers have been able to develop methods to extract proteins from FFPE tissues in a form making them analyzable using state-of-the-art technologies such as MS and protein arrays. This review will discuss the history of these developments and provide examples of how they are currently being used to identify biomarkers and diagnose diseases such as cancer.

Meta-Analysis of MS-Based Proteomics Studies Indicates Interferon Regulatory Factor 4 and Nucleobindin1 as Potential Prognostic and Drug Resistance Biomarkers in Diffuse Large B Cell Lymphoma

The prognosis of diffuse large B cell lymphoma (DLBCL) is inaccurately predicted using clinical features and immunohistochemistry (IHC) algorithms. Nomination of a panel of molecules as the target for therapy and predicting prognosis in DLBCL is challenging because of the divergences in the results of molecular studies. Mass spectrometry (MS)-based proteomics in the clinic represents an analytical tool with the potential to improve DLBCL diagnosis and prognosis. Previous proteomics studies using MS-based proteomics identified a wide range of proteins. To achieve a consensus, we reviewed MS-based proteomics studies and extracted the most consistently significantly dysregulated proteins. These proteins were then further explored by analyzing data from other omics fields. Among all significantly regulated proteins, interferon regulatory factor 4 (IRF4) was identified as a potential target by proteomics, genomics, and IHC. Moreover, annexinA5 (ANXA5) and nucleobindin1 (NUCB1) were two of the most up-regulated proteins identified in MS studies. Functional enrichment analysis identified the light zone reactions of the germinal center (LZ-GC) together with cytoskeleton locomotion functions as enriched based on consistent, significantly dysregulated proteins. In this study, we suggest IRF4 and NUCB1 proteins as potential biomarkers that deserve further investigation in the field of DLBCL sub-classification and prognosis.

Neurooncology: 2023 update

This article presents some of the author's neuropathological highlights in the field on neuro-oncology research encountered in 2022. Major advances were made in the development of more precise, faster, easier, less invasive and unbiased diagnostic tools ranging from immunohistochemical prediction of 1p/19q loss in diffuse glioma, methylation analyses in CSF samples, molecular profiling for CNS lymphoma, proteomic analyses of recurrent glioblastoma, integrated molecular diagnostics for better stratification in meningioma, intraoperative profiling making use of Raman effect or methylation analysis, to finally, the assessment of histological slides by means of machine learning for the prediction of molecular tumor features. In addition, as the discovery of a new tumor entity may also be a highlight for the neuropathology community, the newly described high-grade glioma with pleomorphic and pseudopapillary features (HPAP) has been selected for this article. Regarding new innovative treatment approaches, a drug screening platform for brain metastasis is presented. Although diagnostic speed and precision is steadily increasing, clinical prognosis for patients with malignant tumors affecting the nervous system remains largely unchanged over the last decade, therefore future neuro-oncological research focus should be put on how the amazing developments presented in this article can be more sustainably applied to positively impact patient prognosis.

Automated Sample Preparation for Mass Spectrometry-Based Clinical Proteomics

Mass spectrometry (MS)-based proteomics is a rapidly maturing discipline, thus gaining momentum for routine molecular profiling of clinical specimens to improve disease classification, diagnostics, and therapy development. Yet, hurdles need to be overcome to enhance reproducibility in preanalytical sample processing, especially in large, quantity-limited sample cohorts. Therefore, automated sonication and single-pot solid-phase-enhanced sample preparation (autoSP3) was developed as a streamlined workflow that integrates all tasks from tissue lysis and protein extraction, protein cleanup, and proteolysis. It enables the concurrent processing of 96 clinical samples of any type (fresh-frozen or FFPE tissue, liquid biopsies, or cells) on an automated liquid handling platform, which can be directly interfaced to LC-MS for proteome analysis of clinical specimens with high sensitivity, high reproducibility, and short turn-around times.

A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

BACKGROUND

High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR).

METHODS

A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves.

RESULTS

Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients' age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72-0.92).

CONCLUSIONS

We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients' care.

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

Proteome Analysis of Temporomandibular Joint with Disc Displacement

Disc displacement without reduction is a common disorder of the temporomandibular joint, causing clinical symptoms and sometimes condylar degeneration. In some cases, bone regeneration is detected following disc-repositioning procedures. Until now, however, systems-wide knowledge of the protein levels for condylar outcome with disc position is still lacking. Here, we performed comprehensive expression profiling of synovial fluid from 109 patients with disc displacement without reduction using high-resolution data-independent acquisition mass spectrometry and characterized differences in 1,714 proteins. Based on magnetic resonance imaging, samples were divided into groups with versus without condylar absorption and subgroups with versus without new bone. For the proteomic analysis, 32 proteins in groups presented with statistical significance (>2-fold, P < 0.05). Pathways such as response to inorganic substances, blood coagulation, and estrogen signaling were significantly expressed in the group with bone absorption as compared with pathways such as regulation of body fluid levels, vesicle-mediated transport, and focal adhesion, which were enriched in the group without bone absorption. In subgroup analysis, 45 proteins of significant importance (>2-fold, P < 0.05) were associated with pathways including would healing, glycolysis and gluconeogenesis, and amino acid metabolism. Combined with clinical examination, molecules such as acetyl-CoA carboxylase beta (ACACB) and transforming growth factor beta 1 (TGFB1) were related to features such as visual analog scale and maximum interincisal opening (P < 0.05). In addition, 7 proteins were examined by Western blotting, including progesterone immunomodulatory binding factor 1 (PIBF1), histidine-rich glycoprotein (HRG), and protein kinase C and casein kinase substrate in neurons 2 (PACSIN2). In conclusion, this study provides the first proteome analysis of condylar absorption at disc displacement without reduction and postoperative new bone formation after disc reposition. Integrated with clinical data, this analysis provides an important insight into the proteomics of condylar modification at disc position.

Artificial intelligence defines protein-based classification of thyroid nodules

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

High-throughput proteomic sample preparation using pressure cycling technology

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1–2 mg is placed in a 150 μL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors.

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. This protocol describes pressure cycling technology (PCT)-assisted sample preparation of biopsy tissues.

Quantitative proteomic landscapes of primary and recurrent glioblastoma reveal a protumorigeneic role for FBXO2-dependent glioma-microenvironment interactions

BACKGROUND

Recent efforts have described the evolution of glioblastoma from initial diagnosis to post-treatment recurrence on a genomic and transcriptomic level. However, the evolution of the proteomic landscape is largely unknown.

METHODS

Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to characterize the quantitative proteomes of two independent cohorts of paired newly diagnosed and recurrent glioblastomas. Recurrence-associated proteins were validated using immunohistochemistry and further studied in human glioma cell lines, orthotopic xenograft models, and human organotypic brain slice cultures. External spatial transcriptomic, single-cell, and bulk RNA sequencing data were analyzed to gain mechanistic insights.

RESULTS

Although overall proteomic changes were heterogeneous across patients, we identified BCAS1, INF2, and FBXO2 as consistently upregulated proteins at recurrence and validated these using immunohistochemistry. Knockout of FBXO2 in human glioma cells conferred a strong survival benefit in orthotopic xenograft mouse models and reduced invasive growth in organotypic brain slice cultures. In glioblastoma patient samples, FBXO2 expression was enriched in the tumor infiltration zone and FBXO2-positive cancer cells were associated with synaptic signaling processes.

CONCLUSIONS

These findings demonstrate a potential role of FBXO2-dependent glioma-microenvironment interactions to promote tumor growth. Furthermore, the published datasets provide a valuable resource for further studies.

Unbiased spatial proteomics with single-cell resolution in tissues

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multi-Omics Analysis

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumor type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. Here, we constructed two independent cohorts from two clinic centers. A comprehensive multi-omics analysis of ICC via proteomic, whole-exome sequencing (WES), and single-cell RNA sequencing (scRNA-seq) was performed. Novel ICC tumor subtypes were derived in the training cohort (n=110) using proteomic signatures and their associated activated pathways, which was further validated in a validation cohort (n=41). Three molecular subtypes, chromatin remodeling, metabolism, and chronic inflammation, with distinct prognoses in ICC were identified. The chronic inflammation subtype associated with a poor prognosis. Our random forest algorithm revealed that mutation of lysine methyltransferase 2D (KMT2D) frequently occurred in the metabolism subtype and associated with lower inflammatory activity. scRNA-seq further identified an APOE+C1QB+ macrophage subtype, which showed the capacity to reshape the chronic inflammation subtype and contribute to a poor prognosis in ICC. Altogether, with single-cell transcriptome-assisted multi-omics analysis, we identified novel molecular subtypes of ICC and validated APOE+C1QB+ tumor-associated macrophages (TAMs) as potential immunotherapy targets against ICC.

Potential Biomarkers of Acute Ischemic Stroke Etiology Revealed by Mass Spectrometry-Based Proteomic Characterization of Formalin-Fixed Paraffin-Embedded Blood Clots

Background and Aims

Besides the crucial role in the treatment of acute ischemic stroke (AIS), mechanical thrombectomy represents a unique opportunity for researchers to study the retrieved clots, with the possibility of unveiling biological patterns linked to stroke pathophysiology and etiology. We aimed to develop a shotgun proteomic approach to study and compare the proteome of formalin-fixed paraffin-embedded (FFPE) cardioembolic and large artery atherosclerotic (LAA) clots.

Methods

We used 16 cardioembolic and 15 LAA FFPE thrombi from 31 AIS patients. The thrombus proteome was analyzed by label-free quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS). MaxQuant v1.5.2.8 and Perseus v.1.6.15.0 were used for bioinformatics analysis. Protein classes were identified using the PANTHER database and the STRING database was used to predict protein interactions.

Results

We identified 1,581 protein groups as part of the AIS thrombus proteome. Fourteen significantly differentially abundant proteins across the two etiologies were identified. Four proteins involved in the ubiquitin-proteasome pathway, blood coagulation or plasminogen activating cascade were identified as significantly abundant in LAA clots. Ten proteins involved in the ubiquitin proteasome-pathway, cytoskeletal remodeling of platelets, platelet adhesion or blood coagulation were identified as significantly abundant in cardioembolic clots.

Conclusion

Our results outlined a set of 14 proteins for a proof-of-principle characterization of cardioembolic and LAA FFPE clots, advancing the proteome profile of AIS human thrombi and understanding the pathophysiology of ischemic stroke.

Targeted transcriptomic analyses of RNA isolated from formalin-fixed and paraffin-embedded human menisci

Formalin-fixed and paraffin-embedded (FFPE) biospecimens are a valuable and widely-available resource for diagnostic and research applications. With biobanks of tissue samples available in many institutions, FFPE tissues could prove to be a valuable resource for translational orthopaedic research. The purpose of this study was to characterize the molecular profiles and degree of histologic degeneration on archival fragments of FFPE human menisci obtained during arthroscopic partial meniscectomy. We used FFPE menisci for multiplexed gene expression analysis using the NanoString nCounter® platform, and for histological assessment using a quantitative scoring system. In total, 17 archival specimens were utilized for integrated histologic and molecular analyses. The median patient age was 22 years (range: 14-62). We found that the genes with the highest normalized counts were those typically expressed in meniscal fibrocartilage. Gene expression differences were identified in patient cohorts based on age (≤40 years), including genes associated with the extracellular matrix and tissue repair. The majority of samples showed mild to moderate histologic degeneration. Based on these data, we conclude that FFPE human menisci can be effectively utilized for molecular evaluation following a storage time as long as 11 years. Statement of Clinical Significance: The integration of histological and transcriptomic analyses described in this study will be useful for future studies investigating the basis for biological classification of meniscus specimens in patients. Further exploration into the genes and pathways uncovered by this study may suggest targets for biomarker discovery and identify patients at greater risk for osteoarthritis once the meniscus is torn.

Integrative proteomic characterization of trace FFPE samples in early-stage gastrointestinal cancer

Background

The surveillance and therapy of early-stage cancer would be better for patients’ prognosis. However, the extreme trace amount of tissue samples in different stages have limited in portraying the characterization of early-stage cancer. Therefore, we focused on and presented comprehensive proteomic and phosphoproproteomic profiling of the trace FFPE samples from early-stage gastrointestinal cancer, and then explored the potential biomarkers of early-stage gastrointestinal cancer.

Methods

In this study, a quantitative proteomic method with chromatography with mass spectrometry (LC-MS/MS) was used to analyse the proteomic difference between the trace early-stage esophageal squamous cell carcinoma (EESCC) and early-stage duodenum adenocarcinoma cancer (EDAC).

Results

We identified ~ 6000 proteins and > 10,000 phosphosites in single trace FFPE samples. Comparative analysis disclosed the diverse proteomic features of tumor tissues compared with paired normal tissue of EESCC and EDAC, and revealed the difference of EESCC and EDAC was derived from their origin normal tissue. The distinct separation of EESCC and EDAC illustrated the functions of cell cycle (RB1 T373, EGFR T693) in EESCC, and the positive impacts of apoptosis, metabolic processes (MTOR and MTOR S1261) in EDAC. Furthermore, we deconvoluted the immune infiltration of early-stage gastrointestinal cancer, in which higher immune cell signatures were detected in EDAC, and showed the specific cytokines in EESCC and EDAC. We performed kinases-substates relationship analysis and elucidated the specific proteomic kinase characterization of EESCC and EDAC, and proposed the medicative effects and corresponding drugs for EESCC and EDAC at the clinic.

Conclusion

We disclosed the specific immune characterization of the early-stage gastrointestinal cancer, and presented potential makers of EESCC (EGFR, PDGFRB, CDK4, WEE1) and EDAC (MTOR, MAP2K1, MAPK3). This study represents a major stepping stone towards investigating the carcinogenesis mechanism of gastrointestinal cancer, and providing a rich resource for medicative strategy in the clinic.

Graphical Abstract

All-Ion Monitoring-Directed Low-Abundance Protein Quantification Reveals CALB2 as a Key Promoter in Hepatocellular Carcinoma Metastasis

Because of the wide abundance range of the proteome, achieving high-coverage quantification of low-abundance proteins is always a major challenge. In this study, a complete pipeline focused on all-ion monitoring (AIM) is first constructed with the concept of untargeted parallel-reaction monitoring, including the seamless connection of protein sample preparation, liquid chromatography mass spectrometry (LC-MS) acquisition, and algorithm development to enable the in-depth quantitative analysis of low-abundance proteins. This pipeline significantly improves the reproducibility and sensitivity of sample preparation and LC-MS acquisition for low-abundance proteins, enabling all the precursors ions fragmented and collected. Contributed by the advantages of the AIM method with all the target precursor acquisition by the data-dependent acquisition (DDA) approach, together with the ability of data-independent acquisition to fragment all precursor ions, the quantitative accuracy and precision of low-abundance proteins are greatly enhanced. As a proof of concept, this pipeline is employed to discover the key differential proteins in the mechanism of hepatocellular carcinoma (HCC) metastasis. On the basis of the superiority of AIM, an extremely low-abundance protein, CALB2, is proposed to promote HCC metastasis in vitro and in vivo. We also reveal that CALB2 activates the TRPV2-Ca²⁺-ERK1/2 signaling pathway to induce HCC cell metastasis. In summary, we provide a universal AIM pipeline for the high-coverage quantification of low-abundance functional proteins to seek novel insights into the mechanisms of cancer metastasis.

Multi-omics in COVID-19: Seeing the unseen but overlooked in the clinic

COVID-19 is an ongoing pandemic of global concern and is unlikely to disappear. This commentary discusses how multi-omics technologies have helped uncover the molecular processes and dynamics underlying COVID-19 initiation, progression, and transmission, and how lack of standardization has limited their application in clinical settings.