Proteomics technologies for cancer liquid biopsies

Molecular methods in cancer diagnostics: a short review

BACKGROUND

One of the ailments with the greatest fatality rates in the 21st century is cancer. Globally, molecular methods are widely employed to treat cancer-related disorders, and the body of research on this subject is growing yearly. A thorough and critical summary of the data supporting molecular methods for illnesses linked to cancer is required.

OBJECTIVE

In order to guide clinical practice and future research, it is important to examine and summarize the systematic reviews (SRs) that evaluate the efficacy and safety of molecular methods for disorders associated to cancer.

METHODS

We developed a comprehensive search strategy to find relevant articles from electronic databases like PubMed, Google Scholar, Web of Science (WoS), or Scopus. We looked through the literature and determined which diagnostic methods in cancer genetics were particularly reliable. We used phrases like 'cancer genetics', genetic susceptibility, Hereditary cancer, cancer risk assessment, 'cancer diagnostic tools', cancer screening', biomarkers, and molecular diagnostics, reviews and meta-analyses evaluating the efficacy and safety of molecular therapies for cancer-related disorders. Research that only consider treatment modalities that don't necessitate genetic or molecular diagnostics fall under the exclusion criteria.

RESULTS

The results of this comprehensive review clearly demonstrate the transformative impact of molecular methods in the realm of cancer genetics.This review underscores how these technologies have empowered researchers and clinicians to identify and understand key genetic alterations that drive malignancy, ranging from point mutations to structural variations. Such insights are instrumental in pinpointing critical oncogenic drivers and potential therapeutic targets, thus opening the door for methods in precision medicine that can significantly improve patient outcomes.

LIMITATION

The search does not specify a timeframe for publication inclusion, it may have missed recent advancements or changes in the field's landscape of molecular methods for cancer. As a result, it may not have included the most recent developments in the field.

CONCLUSION

After conducting an in-depth study on the molecular methods in cancer genetics, it is evident that these cutting-edge technologies have revolutionized the field of oncology, providing researchers and clinicians with powerful tools to unravel the complexities of cancer at the genetic level. The integration of molecular methods techniques has not only enhanced our understanding of cancer etiology, progression, and treatment response but has also opened new avenues for personalized medicine and targeted therapies, leading to improved patient outcomes.

Efficient isolation of encoded microparticles in a degassed micromold for highly sensitive and multiplex immunoassay with signal amplification

Multiplex detection of low-abundance protein biomarkers in biofluids can contribute to diverse biomedical fields such as early diagnosis and precision medicine. However, conventional techniques such as digital ELISA, microarray, and hydrogel-based assay still face limitations in terms of efficient protein detection due to issues with multiplexing capability, sensitivity, or complicated assay procedures. In this study, we present the degassed micromold-based particle isolation technique for highly sensitive and multiplex immunoassay with enzymatic signal amplification. Using degassing treatment of nanoporous polydimethylsiloxane (PDMS) micromold, the encoded particles are isolated in the mold within 5 min absorbing trapped air bubbles into the mold by air suction capability. Through 10 min of signal amplification in the isolated spaces by fluorogenic substrate and horseradish peroxidase labeled in the particle, the assay signal is amplified with one order of magnitude compared to that of the standard hydrogel-based assay. Using the signal amplification assay, vascular endothelial growth factor (VEGF) and chorionic gonadotropin beta (CG beta), the preeclampsia-related protein biomarkers, are quantitatively detected with a limit of detection (LoD) of 249 fg/mL and 476 fg/mL in phosphate buffer saline. The multiplex immunoassay is conducted to validate negligible non-specific detection signals and robust recovery rates in the multiplex assay. Finally, the VEGF and CG beta in real urine samples are simultaneously and quantitatively detected by the developed assay. Given the high sensitivity, multiplexing capability, and process simplicity, the presented particle isolation-based signal amplification assay holds significant potential in biomedical and proteomic fields.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Secretome analysis using Affinity Proteomics and Immunoassays: a focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system employed, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Associations between genetically predicted concentrations of plasma proteins and the risk of prostate cancer

BACKGROUND

Prostate cancer (PCa) is a leading cause of cancer-related death in men. Understanding the proteomic landscape associated with PCa risk can provide insights into its molecular mechanisms and pave the way for potential therapeutic interventions.

METHODS

A proteome-wide Mendelian randomization (MR) analysis was employed to determine associations between genetically predicted protein concentrations in plasma and PCa risk. From an initial list of 4,364 proteins, significant associations were identified and validated. Multiple sensitivity analyses were also conducted to enhance the robustness of our findings.

RESULTS

Of the 4,364 genetically predicted proteins, 308 exhibited preliminary associations with PCa risk. After rigorous statistical refinement, genetically predicted concentrations of 14 proteins showed positive associations with PCa risk, with odds ratios spanning from 1.55 (95% CI 1.28-1.87) for ATG4B to 2.67 (95% CI 1.94-3.67) for HCN1. In contrast, genetically predicted concentrations of ATG7, B2M, MSMB, and TMEM108 demonstrated inverse associations with PCa. The replication analysis further substantiated positive associations for MDH1 and LSM1, and a negative one for MSMB with PCa. A meta-analysis harmonizing primary and replication data mirrored these findings. Furthermore, the MVMR analysis pinpointed B2M and MSMB as having significant associations with PCa risk.

CONCLUSION

The genetic evidence unveils a refined set of proteins associated with PCa risk. The findings underscore the potential of these proteins as molecular markers or therapeutic targets for PCa, calling for deeper mechanistic studies and exploration into their translational relevance.

Oligo cyc-DEP: On-chip cyclic immunofluorescence profiling of cell-derived nanoparticles

We present a follow-on technique for the cyclic-immunofluorescence profiling of suspension particles isolated using dielectrophoresis. The original lab-on-chip technique ("cyc-DEP" [cyclic immunofluorescent imaging on dielectrophoretic chip]) was designed for the multiplex surveillance of circulating biomarkers. Nanoparticles were collected from low-volume liquid biopsies using microfluidic dielectrophoretic chip technology. Subsequent rounds of cyclic immunofluorescent labeling and quenching were imaged and quantified with a custom algorithm to detect multiple proteins. While cyc-DEP improved assay multiplicity, long runtimes threatened its clinical adoption. Here, we modify the original cyc-DEP platform to reduce assay runtimes. Nanoparticles were formulated from human prostate adenocarcinoma cells and collected using dielectrophoresis. Three proteins were labeled on-chip with a mixture of short oligonucleotide-conjugated antibodies. The sample was then incubated with complementary fluorophore-conjugated oligonucleotides, which were dehybridized using an ethylene carbonate buffer after each round of imaging. Oligonucleotide removal exhibited an average quenching efficiency of 98 ± 3% (n = 12 quenching events), matching the original cyc-DEP platform. The presented "oligo cyc-DEP" platform achieved clinically relevant sample-to-answer times, reducing the duration for three rounds of cyclic immunolabeling from approximately 20 to 6.5 h-a 67% decrease attributed to rapid fluorophore removal and the consolidated co-incubation of antibodies.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma

BACKGROUND/AIMS

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.

METHODS

We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.

RESULTS

Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.

CONCLUSION

Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods

In accordance with the World Health Organization data, cancer remains at the forefront of fatal diseases. An upward trend in cancer incidence and mortality has been observed globally, emphasizing that efforts in developing detection and treatment methods should continue. The diagnostic path typically begins with learning the medical history of a patient; this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy. Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization. Thus, there is a need for novel cancer detection methods such as liquid biopsy, elastography, synthetic biosensors, fluorescence imaging, and reflectance confocal microscopy. Conventional therapeutic methods, although still common in clinical practice, pose many limitations and are unsatisfactory. Nowadays, there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy, exosome-based therapy, nanotechnology, dendritic cells, chimeric antigen receptors, immune checkpoint inhibitors, natural product-based therapy, tumor-treating fields, and photodynamic therapy. The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions. As evidenced, modern methods are not without drawbacks; there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity, specificity, safety, and efficacy. Nevertheless, an appropriate route has been taken, as confirmed by the approval of some modern methods by the Food and Drug Administration.

Genetic Biomarkers in Astrocytoma: Diagnostic, Prognostic, and Therapeutic Potential

Astrocytoma is the most common adult brain tumor, with glioblastoma being the deadliest neuro-related malignancy. Despite advances in oncology, the prognosis for astrocytoma, especially glioblastoma, remains poor, and tracking disease progression is challenging due to a lack of robust biomarkers. Genetic biomarkers, including microRNAs, cell-free DNA, circulating tumor DNA, circular RNA, and long noncoding RNA, can serve as potential diagnostic and therapeutic targets. In this review, we examine the existing literature, analyzing the various less established liquid and tumor genetic biomarkers and their potential to act as diagnostic, prognostic, and therapeutic targets. We highlight the clinical challenges and limitations in implementing liquid biopsy strategies in clinical practice. The article discusses the potential of liquid biopsies as valuable tools for personalized astrocytoma management while emphasizing the need for standardized protocols and further advancements to establish their clinical utility and therapeutic application.

An electron density clustering based adaptive segmentation method for protein Raman spectrum calculation

Raman spectroscopy is a powerful technique for protein detection, but the calculation of Raman spectrum is a longstanding challenging problem due to the large sizes and complex structures of protein molecules. Dividing proteins into fragments can greatly accelerate the calculation, but this usually introduces large errors originating from ignored interactions between fragments into obtained spectra. In this paper, we proposed a new adaptive segmentation method based on the strength of interactions and molecular shapes and structures, i.e., electron density clustering, to divide proteins. It can reduce errors of obtained Raman spectra by about 20% compared to the uniform segmentation method without a significant increase in computational cost. This method can facilitate the validation and analysis of detected Raman spectra of proteins and promote the application of Raman spectroscopy in biological detection.

Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects

Firefighters are frequently exposed to a variety of chemicals formed from smoke, which pose a risk for numerous diseases, including cancer. Comparative urine proteome profiling could significantly improve our understanding of the early detection of potential cancer biomarkers. In this study, for the first time, we conducted a comparative protein profile analysis of 20 urine samples collected from ten real-life firefighters prior to and following emergency fire-induced smoke. Using a label-free quantitative proteomics platform, we identified and quantified 1325 unique protein groups, of which 45 proteins showed differential expressions in abundance in response to fire-smoke exposure (post) compared to the control (pre). Pathway analysis showed proteins associated with epithelium development (e.g., RHCG, HEG1, ADAMTSL2) and Alzheimer's disease (SORL1) were significantly increased in response to smoke exposure samples. A protein-protein-network study showed a possible link between these differentially abundant proteins and the known cancer gene (TP53). Moreover, a cross-comparison analysis revealed that seven proteins-ALDH1A1, APCS, POMC, COL2A1, RDX, DDAH2, and SDC4 overlapped with the previously published urine cancer proteome datasets, suggesting a potential cancer risk. Our findings demonstrated that the discovery proteomic platform is a promising analytical technique for identifying potential non-invasive biomarkers associated with fire-smoke exposure in firefighters that may be related to cancer.

Utilizing nullomers in cell-free RNA for early cancer detection

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.

Quantitative Proteomic Analysis Reveals Functional Alterations of the Peripheral Immune System in Colorectal Cancer

Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.

The plasma-soluble CSF1R level is a promising prognostic indicator for pediatric Langerhans cell histiocytosis

Langerhans cell histiocytosis (LCH) is a rare hematologic neoplasm characterized by the clonal proliferation of Langerhans-like cells. Colony-stimulating factor 1 receptor (CSF1R) is a membrane-bound receptor that is highly expressed in LCH cells and tumor-associated macrophages. In this study, a soluble form of CSF1R protein (sCSF1R) was identified by plasma proteome profiling, and its role in evaluating LCH prognosis was explored. We prospectively measured plasma sCSF1R levels in 104 LCH patients and 10 healthy children using ELISA. Plasma sCSF1R levels were greater in LCH patients than in healthy controls (p < .001) and significantly differed among the three disease extents, with the highest level in MS RO+ LCH patients (p < .001). Accordingly, immunofluorescence showed the highest level of membrane-bound CSF1R in MS RO+ patients. Furthermore, the plasma sCSF1R concentration at diagnosis could efficiently predict the prognosis of LCH patients treated with standard first-line treatment (AUC = 0.782, p < .001). Notably, dynamic monitoring of sCSF1R levels could predict relapse early in patients receiving BRAF inhibitor treatment. In vitro drug sensitivity data showed that sCSF1R increased resistance to Ara-C in THP-1 cells expressing ectopic BRAF-V600E. Overall, the plasma sCSF1R level at diagnosis and during follow-up is of great clinical importance in pediatric LCH patients.

Panel of serum long non-coding RNAs as potential non-invasive biomarkers for gallbladder carcinoma

Gallbladder carcinoma (GBC) is a common malignancy and is usually diagnosed in the late stages of the disease. The identification of new effective early diagnostic biomarkers could represent an effective approach in reducing mortality in GBC. Altered expression of long non-coding RNAs (lncRNAs) is believed to be associated with the emergence and development of GBC. Our study aims to identify the expression of a range of circulating lncRNAs, including HOTAIR, ANRIL, H19, CCAT1 and MEG3, in matched serum and tissues of GBC for diagnosis and its association with clinicopathological features. The case and control study included matched serum and tissues from 63 GBC, 19 cholecystitis (CC), and 46 normal controls (NC). RNA extraction and cDNA synthesis from serum and fresh tissue match were performed using commercially available kits. Relative expression was assessed using SYBR Green real-time quantitative polymerase chain reaction. Circulating lncRNA levels including HOTAIR, ANRIL and H19 were upregulated in serum samples, while MEG3 and CCAT1 were downregulated in GBC compared to controls. The trend towards upregulation and downregulation was comparable in the tissue. HOTAIR and MEG3 levels were significantly different between serum CC and early-stage GBC (p = 0.0373, 0.0020), while H19 was significantly upregulated comparing early-stage GBC to advanced-stage GBC (p = 0.018). The expression of ANRIL was significant with M stage (p = 0.0488), H19 with stage (p = 0.009), M stage (p=<0.0001) & stage (0.009) and CCAT1 with M stage (0.044). When distinguishing GBC and NC, AUC for HOTAIR was 0.75, ANRIL 0.78, H19 0.74, CCAT1 0.80 and 0.96 for MEG3. The combination sensitivity for lncRNAs ranged from 84.13% (CI: 72.74-92.12%) to 100.0% (CI: 94.31-100.0%). Significant diagnostic value in discriminating pathologic stage was observed for ANRIL and MEG3 (p = 0.022, p = 0.0005). LncRNA show a significant change in expression in GBC and in discrimination of early stage from late-stage disease. The detection of 2 lncRNAs in panels, in coordination with radiology, could represent a potential serum-based biomarker for early-stage GBC diagnosis.

Super-resolution ultrasound and microvasculomics: a consensus statement

This is a summary of a consensus statement on the introduction of "Ultrasound microvasculomics" produced by The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The evaluation of microvessels is a very important part for the assessment of diseases. Super-resolution ultrasound (SRUS) microvascular imaging surpasses traditional ultrasound imaging in the morphological and functional analysis of microcirculation. SRUS microvascular imaging relies on contrast microbubbles to gain sensitivity to microvessels and improves the spatial resolution of ultrasound blood flow imaging for a more detailed depiction of vascular structures and hemodynamics. This method has been applied in preclinical animal models and pilot clinical studies, involving areas including neurology, oncology, nephrology, and cardiology. However, the current quantitative parameters of SRUS images are not enough for precise evaluation of microvessels. Therefore, by employing omics methods, more quantification indicators can be obtained, enabling a more precise and personalized assessment of microvascular status. Ultrasound microvasculomics - a high-throughput extraction of image features from SRUS images - is one novel approach that holds great promise but needs further validation in both bench and clinical settings. CLINICAL RELEVANCE STATEMENT: Super-resolution Ultrasound (SRUS) blood flow imaging improves spatial resolution. Ultrasound microvasculomics is possible to acquire high-throughput information of features from SRUS images. It provides more precise and abundant micro-blood flow information in clinical medicine. KEY POINTS: This consensus statement reviews the development and application of super-resolution ultrasound (SRUS). The shortcomings of the current quantification indicators of SRUS and strengths of the omics methodology are addressed. "Ultrasound microvasculomics" is introduced for a high-throughput extraction of image features from SRUS images.

Ribosomal Dysregulation in Metastatic Laryngeal Squamous Cell Carcinoma: Proteomic Insights and CX-5461's Therapeutic Promise

One of the main barriers to the successful treatment of laryngeal squamous cell carcinoma (LSCC) is postoperative progression, primarily due to tumor cell metastasis. To systematically investigate the molecular characteristics and potential mechanisms underlying the metastasis in laryngeal cancer, we carried out a TMT-based proteomic analysis of both cancerous and adjacent non-cancerous tissues from 10 LSCC patients with lymph node metastasis (LNM) and 10 without. A total of 5545 proteins were quantified across all samples. We identified 57 proteins that were downregulated in LSCC with LNM, which were enriched in cell adhesion pathways, and 69 upregulated proteins predominantly enriched in protein production pathways. Importantly, our data revealed a strong correlation between increased ribosomal activity and the presence of LNM, as 18 ribosomal subunit proteins were found to be upregulated, with RPS10 and RPL24 being the most significantly overexpressed. The potential of ribosomal proteins, including RPS10 and RPL24, as biomarkers for LSCC with LNM was confirmed in external validation samples (six with LNM and six without LNM) using Western blotting and immunohistochemistry. Furthermore, we have confirmed that the RNA polymerase I inhibitor CX-5461, which impedes ribosome biogenesis in LSCC, also decreases the expression of RPS10, RPL24, and RPS26. In vitro experiments have revealed that CX-5461 moderately reduces cell viability, while it significantly inhibits the invasion and migration of LSCC cells. It can enhance the expression of the epithelial marker CDH1 and suppress the expression of the mesenchymal markers CDH2, VIM, and FN at a dose that does not affect cell viability. Our study broadens the scope of the proteomic data on laryngeal cancer and suggests that ribosome targeting could be a supplementary therapeutic strategy for metastatic LSCC.

Peptide set test: a peptide-centric strategy to infer differentially expressed proteins

MOTIVATION

The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.

RESULTS

We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.

AVAILABILITY AND IMPLEMENTATION

Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

Activation of the YY1-UGT2B7 Axis Promotes Mammary Estrogen Homeostasis Dysregulation and Exacerbates Breast Tumor Metastasis

Metastasis is the most common pathway of cancer death. The lack of effective predictors of breast cancer metastasis is a pressing issue in clinical practice. Therefore, exploring the mechanism of breast cancer metastasis to uncover reliable predictors is very important for the clinical treatment of breast cancer patients. In this study, tandem mass tag quantitative proteomics technology was used to detect protein content in primary breast tumor tissue samples from patients with metastatic and nonmetastatic breast cancer at diagnosis. We found that the high expression of yin-yang 1(YY1) is strongly associated with poor prognosis in high-grade breast cancer. YY1 expression was detected in both clinical tumor tissue samples and tumor tissue samples from mammary-specific polyomavirus middle T antigen overexpression mouse model mice. We demonstrated that upregulation of YY1 expression was closely associated with breast cancer metastasis and that high YY1 expression could promote the migratory invasive ability of breast cancer cells. Mechanistically, YY1 directly binds to the UGT2B7 mRNA initiation sequence ATTCAT, thereby transcriptionally regulating the inhibition of UGT2B7 expression. UGT2B7 can regulate the development of breast cancer by regulating estrogen homeostasis in the breast, and the abnormal accumulation of estrogen, especially 4-OHE2, promotes the migration and invasion of breast cancer cells, ultimately causing the development of breast cancer metastasis. In conclusion, YY1 can regulate the UGT2B7-estrogen metabolic axis and induce disturbances in estrogen metabolism in breast tumors, ultimately leading to breast cancer metastasis. Disturbances in estrogen metabolism in the breast tissue may be an important risk factor for breast tumor progression and metastasis SIGNIFICANCE STATEMENT: In this study, we propose for the first time a regulatory relationship between YY1 and the UGT2B7/estrogen metabolism axis and explore the molecular mechanism. Our study shows that the YY1/UGT2B7/estrogen axis plays an important role in the development and metastasis of breast cancer. This study further elucidates the potential mechanisms of YY1-mediated breast cancer metastasis and the possibility and promise of YY1 as a predictor of cancer metastasis.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

The wedish initiative for the stdy of ary sclerosing cholangitis (SUPRIM)

Background

Despite more than 50 years of research and parallel improvements in hepatology and oncology, there is still today neither a treatment to prevent disease progression in primary sclerosing cholangitis (PSC), nor reliable early diagnostic tools for the associated hepatobiliary cancers. Importantly, the limited understanding of the underlying biological mechanisms in PSC and its natural history not only affects the identification of new drug targets but implies a lack of surrogate markers that hampers the design of clinical trials and the evaluation of drug efficacy. The lack of easy access to large representative well-characterised prospective resources is an important contributing factor to the current situation.

Methods

We here present the SUPRIM cohort, a national multicentre prospective longitudinal study of unselected PSC patients capturing the representative diversity of PSC phenotypes. We describe the 10-year effort of inclusion and follow-up, an intermediate analysis report including original results, and the associated research resource. All included patients gave written informed consent (recruitment: November 2011-April 2016).

Findings

Out of 512 included patients, 452 patients completed the five-year follow-up without endpoint outcomes. Liver transplantation was performed in 54 patients (10%) and hepatobiliary malignancy was diagnosed in 15 patients (3%). We draw a comprehensive landscape of the multidimensional clinical and biological heterogeneity of PSC illustrating the diversity of PSC phenotypes. Performances of available predictive scores are compared and perspectives on the continuation of the SUPRIM cohort are provided.

Interpretation

We envision the SUPRIM cohort as an open-access collaborative resource to accelerate the generation of new knowledge and independent validations of promising ones with the aim to uncover reliable diagnostics, prognostic tools, surrogate markers, and new treatment targets by 2040.

Funding

This work was supported by the Swedish Cancer Society, Stockholm County Council, and the Cancer Research Funds of Radiumhemmet.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Optical nanomaterial-based detection of biomarkers in liquid biopsy

Liquid biopsy, which is a minimally invasive procedure as an alternative to tissue biopsy, has been introduced as a new diagnostic/prognostic measure. By screening disease-related markers from the blood or other biofluids, it promises early diagnosis, timely prognostication, and effective treatment of the diseases. However, there will be a long way until its realization due to its conceptual and practical challenges. The biomarkers detected by liquid biopsy, such as circulating tumor cell (CTC) and circulating tumor DNA (ctDNA), are extraordinarily rare and often obscured by an abundance of normal cellular components, necessitating ultra-sensitive and accurate detection methods for the advancement of liquid biopsy techniques. Optical biosensors based on nanomaterials open an important opportunity in liquid biopsy because of their enhanced sensing performance with simple and practical properties. In this review article, we summarized recent innovations in optical nanomaterials to demonstrate the sensitive detection of protein, peptide, ctDNA, miRNA, exosome, and CTCs. Each study prepares the optical nanomaterials with a tailored design to enhance the sensing performance and to meet the requirements of each biomarker. The unique optical characteristics of metallic nanoparticles (NPs), quantum dots, upconversion NPs, silica NPs, polymeric NPs, and carbon nanomaterials are exploited for sensitive detection mechanisms. These recent advances in liquid biopsy using optical nanomaterials give us an opportunity to overcome challenging issues and provide a resource for understanding the unknown characteristics of the biomarkers as well as the mechanism of the disease.

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study

OBJECTIVES

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making.

METHODS

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models.

RESULTS

Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk.

CONCLUSIONS

Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

Insights on Three Dimensional Organoid Studies for Stem Cell Therapy in Regenerative Medicine

Regenerative medicine has developed as a promising discipline that utilizes stem cells to address limitations in traditional therapies, using innovative techniques to restore and repair damaged organs and tissues. One such technique is the generation of three-dimensional (3D) organoids in stem cell therapy. Organoids are 3D constructs that resemble specific organs' structural and functional characteristics and are generated from stem cells or tissue-specific progenitor cells. The use of 3D organoids is advantageous in comparison to traditional two-dimensional (2D) cell culture by bridging the gap between in vivo and in vitro research. This review aims to provide an overview of the advancements made towards regenerative medicine using stem cells to generate organoids, explore the techniques used in generating 3D organoids and their applications and finally elucidate the challenges and future directions in regenerative medicine using 3D organoids.

Advances of aptamer-based small-molecules sensors in body fluids detection

The field of aptamer-based sensing has evolved considerably over the past three decades. The aptamer sensor-based detection of small-molecule targets in body fluids is designed for real-time or rapid, low-cost, non- or minimally invasive tracking and diagnosis of human health status. It can be achieved by specifically monitoring biomarkers or metabolites excreted from various body fluids, including blood, urine, cerebrospinal fluid, saliva, ect. This article reviews a comprehensive collection of aptamer-based sensors for detecting small-molecule in various body fluids. A comparative analysis of aptamer features, emerging chemistry, advanced sensing materials, transduction techniques, and detection performance is conducted, and the strengths and pitfalls of each approach are discussed. Finally, the development process and application challenges of aptamer-based sensors in the detection of small-molecule in body fluids are presented and discussed.

Blood-based liquid biopsy in advanced prostate cancer

Prostate cancer is characterized by several genetic alterations which could impact prognosis and therapeutic decisions in the advanced disease. Tissue biopsy is still considered the gold standard approach for molecular characterization in prostate cancer, but it has several limitations, including the possibility of insufficient/inadequate tumor tissue to be analyzed. Blood-based liquid biopsy is a non-invasive method to investigate tumor cell derivatives in the bloodstream, being a valid alternative to tissue biopsy for molecular characterization but also for predictive and/or prognostic purposes. In this review, we analyze the most relevant evidence in this field, focusing on clinically relevant targets such as HRD genetic alterations and also focusing on the differences between tissue and liquid biopsy in light of the data from the latest clinical trials.

CD99 Modulates the Proteomic Landscape of Ewing Sarcoma Cells and Related Extracellular Vesicles

Ewing sarcoma (EWS) is an aggressive pediatric bone tumor characterized by unmet clinical needs and an incompletely understood epigenetic heterogeneity. Here, we considered CD99, a major surface molecule hallmark of EWS malignancy. Fluctuations in CD99 expression strongly impair cell dissemination, differentiation, and death. CD99 is also loaded within extracellular vesicles (EVs), and the delivery of CD99-positive or CD99-negative EVs dynamically exerts oncogenic or oncosuppressive functions to recipient cells, respectively. We undertook mass spectrometry and functional annotation analysis to investigate the consequences of CD99 silencing on the proteomic landscape of EWS cells and related EVs. Our data demonstrate that (i) the decrease in CD99 leads to major changes in the proteomic profile of EWS cells and EVs; (ii) intracellular and extracellular compartments display two distinct signatures of differentially expressed proteins; (iii) proteomic changes converge to the modulation of cell migration and immune-modulation biological processes; and (iv) CD99-silenced cells and related EVs are characterized by a migration-suppressive, pro-immunostimulatory proteomic profile. Overall, our data provide a novel source of CD99-associated protein biomarkers to be considered for further validation as mediators of EWS malignancy and as EWS disease liquid biopsy markers.

Targeted Phosphoproteomics of Human Saliva Extracellular Vesicles via Multiple Reaction Monitoring Cubed (MRM3)

Oral squamous cell carcinoma (OSCC) has become a global health problem due to its increasing incidence and high mortality rate. Early intervention through monitoring of the diagnostic biomarker levels during OSCC treatment is critical. Extracellular vesicles (EVs) are emerging surrogates in intercellular communication through transporting biomolecule cargo and have recently been identified as a potential source of biomarkers such as phosphoproteins for many diseases. Here, we developed a multiple reaction monitoring cubed (MRM3) method coupled with a novel sample preparation strategy, extracellular vesicles to phosphoproteins (EVTOP), to quantify phosphoproteins using a minimal amount of saliva (50 μL) samples from OSCC patients with high specificity and sensitivity. Our results established differential patterns in the phosphopeptide content of healthy, presurgery, and postsurgery OSCC patient groups. Notably, we discovered significantly increased salivary phosphorylated alpha-amylase (AMY) in the postsurgery group compared to the presurgery group. We hereby present the first targeted MS method with extremely high sensitivity for measuring endogenous phosphoproteins in human saliva EVs.

Peptide absent sequences emerging in human cancers

Early diagnosis of cancer can significantly improve survival of cancer patients; however sensitive and highly specific biomarkers for cancer detection are currently lacking for most cancer types. Nullpeptides are short peptides that are absent from the human proteome. Here, we examined the emergence of nullpeptides during cancer development. We analyzed 3,600,964 somatic mutations across 10,064 whole exome sequencing tumor samples spanning 32 cancer types. We analyze RNA-seq data from primary tumor samples to identify the subset of nullpeptides that emerge in highly expresed genes. We show that nullpeptides, and particularly the subset that is highly recurrent across cancer patients, can be identified in tumor biopsy samples. We find that cancer genes show an excess of nullpeptides and detect nullpeptide hotspots in specific loci of oncogenes and tumor suppressors. We also observe that recurrent nullpeptides are more likely to be found in neoantigens, which have been shown to be effective targets for immunotherapy, suggesting that they can be used to prioritize candidates. Our findings provide evidence for the utility of nullpeptides as cancer detection and therapeutic biomarkers.

O-Glycoproteomics Sample Preparation and Analysis Using NanoHPLC and Tandem MS

Glycosylation refers to the biological processes that covalently attach carbohydrates to the peptide backbone after the synthesis of proteins. As one of the most common post-translational modifications (PTMs), glycosylation can greatly affect proteins' features and functions. Moreover, aberrant glycosylation has been linked to various diseases. There are two major types of glycosylation, known as N-linked and O-linked glycosylation. Here, we focus on O-linked glycosylation and thoroughly describe a bottom-up strategy to perform O-linked glycoproteomics studies. The experimental section involves enzymatic digestions using trypsin and O-glycoprotease at 37 °C. The prepared samples containing O-glycopeptides are analyzed using nanoHPLC coupled with tandem mass spectrometry (MS) for accurate identification and quantification.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Adaptive Control of Tumor Growth

Cancer treatment optimizations select the most optimum combinations of drugs, sequencing schedules, and appropriate doses that would limit toxicity and yield an improved patient quality of life. However, these optimizations often lack an adequate consideration of cancer's near-infinite potential for evolutionary adaptation to therapeutic interventions. Adapting cancer therapy based on monitored tumor burden and clonal composition is an intuitively sound approach to the treatment of cancer as an inherently complex and adaptive system. The adaptation would be driven by clinical outcome setpoints embodying the aims to thwart therapeutic resistance and maintain a long-term management of the disease or even a cure. However, given the nonlinear, stochastic dynamics of tumor response to therapeutic interventions, adaptive therapeutic strategies may at least need a one-step-ahead prediction of tumor burden to maintain their control over tumor growth dynamics. The article explores the feasibility of adaptive cancer treatment driven by tumor state feedback assuming cell adaptive fitness to be the underlying source of phenotypic plasticity and pathway entropy as a biomarker of tumor growth trajectory. The exploration is undertaken using deterministic and stochastic models of tumor growth dynamics.

[Liquid Biopsy - A new diagnostic concept in oncology]

The analysis of tumor cells circulating in the blood or of products of tumor cells circulating in other body fluids has gained increasing attention in recent years and is summarized under the term liquid biopsy (LB). LB includes the analysis of circulating tumor cells, cell-free circulating tumor-associated nucleic acids, extracellular vesicles, proteins, or other products that are released into the peripheral bloodstream by the primary or metastatic tumor. For a huge number of solid tumor entities, LB has already been successfully applied in preclinical and clinical studies for the detection, risk stratification, treatment monitoring and relapse detection. LB provides valuable real-time information on tumor cell development, therapeutic targets, and mechanisms of therapy resistance using a non-invasive peripheral blood test. In this article, the most important LB analytes and the current state of research are presented. In addition, the remaining obstacles and the diverse efforts to implement LB in clinical routine are critically discussed.

Plasma/Serum Proteomics based on Mass Spectrometry

Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.

Vitreous Humor Proteomic Profile in Patients With Vitreoretinal Lymphoma

Purpose

Vitreoretinal lymphoma is a high-grade malignant non-Hodgkin lymphoma with poor prognosis. The objective of this study was to elucidate the proteome profile of the vitreous in patients with vitreoretinal lymphoma (VRL), aiming to advance understanding of the pathophysiology of VRL.

Methods

Comprehensive proteomic analyses of vitreous humor using liquid chromatography with tandem mass spectrometry were performed for 10 patients with VRL, 10 control patients with idiopathic epiretinal membrane or macular hole, and 10 patients with ocular sarcoidosis. Differentially expressed proteins (DEPs) were identified by comparing VRL with controls and sarcoidosis, and functional pathway analysis was performed. Finally, vitreous concentrations of representative DEPs that were significantly upregulated in proteomics study were measured by ELISA using a separate cohort.

Results

In total, 1594 proteins were identified in the vitreous humor of VRL, control, and sarcoidosis samples. Also, 282 DEPs were detected in VRL, 249 upregulated and 33 downregulated, compared with controls. Enrichment pathway analysis showed alterations in proteasome-related pathways. Compared to controls and sarcoidosis, 14 DEPs in VRL showed significant upregulation. In the validation study, ELISA confirmed significantly higher vitreous concentrations of PSAT1, YWHAG, and 20S/26S proteasome complex in VRL compared with controls and sarcoidosis. Among the upregulated DEPs, vitreous PITHD1 and NCSTN concentrations correlated positively with vitreous IL-10 concentrations.

Conclusions

This study highlights aberrations in protein expression pattern in the vitreous of patients with VRL. The DEPs identified in this study may play pivotal roles in VRL pathogenesis, providing insights to enhance understanding of VRL pathophysiology and contribute to the development of VRL biomarkers.

Extracellular vesicle-based liquid biopsies in cancer: Future biomarkers for oral cancer

Oral cancer is the sixth most common cancer worldwide, with approximately 530,000 new cases and 300,000 deaths each year. The process of carcinogenesis is complex, and survival rates have not changed significantly in recent decades. Early detection of cancer, prognosis prediction, treatment selection, and monitoring of progression are important to improve survival. With the recent significant advances in analytical technology, liquid biopsy has made it possible to achieve these goals. In this review, we report new results from clinical and cancer research applications of liquid biopsy, focusing on extracellular vesicles (EVs) among the major targets of liquid biopsy, namely, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and EVs. In addition, the potential application of EVs derived from gram-negative bacteria (outer membrane vesicles; OMVs) among oral bacteria, which have recently attracted much attention, to liquid biopsy for oral cancer will also be addressed.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Single-molecule systems for detection and monitoring of plasma circulating nucleosomes and oncoproteins in Diffuse Midline Glioma

The analysis of cell-free tumor DNA (ctDNA) and proteins in the blood of cancer patients potentiates a new generation of non-invasive diagnostics and treatment monitoring approaches. However, confident detection of these tumor-originating markers is challenging, especially in the context of brain tumors, in which extremely low amounts of these analytes circulate in the patient's plasma. Here, we applied a sensitive single-molecule technology to profile multiple histone modifications on millions of individual nucleosomes from the plasma of Diffuse Midline Glioma (DMG) patients. The system reveals epigenetic patterns that are unique to DMG, significantly differentiating this group of patients from healthy subjects or individuals diagnosed with other cancer types. We further develop a method to directly capture and quantify the tumor-originating oncoproteins, H3-K27M and mutant p53, from the plasma of children diagnosed with DMG. This single-molecule system allows for accurate molecular classification of patients, utilizing less than 1ml of liquid-biopsy material. Furthermore, we show that our simple and rapid detection strategy correlates with MRI measurements and droplet-digital PCR (ddPCR) measurements of ctDNA, highlighting the utility of this approach for non-invasive treatment monitoring of DMG patients. This work underscores the clinical potential of single-molecule-based, multi-parametric assays for DMG diagnosis and treatment monitoring.

Liquid Biopsy as an Adjunct to Lung Screening Imaging

Current lung cancer screening protocols use low-dose computed tomography scans in selected high-risk individuals. Unfortunately, utilization is low, and the rate of false-positive screens is high. Peripheral biomarkers carry meaningful promise in diagnosing and monitoring cancer with added potential advantages reducing invasive procedures and improving turnaround time. Herein, the use of such blood-based assays is considered as an adjunct to further utilization and accuracy of lung cancer screening.

Bile liquid biopsy in biliary tract cancer

Biliary tract cancers are heterogeneous in etiology, morphology and molecular characteristics thus impacting disease management. Diagnosis is complex and prognosis poor. The advent of liquid biopsy has provided a unique approach to more thoroughly understand tumor biology in general and biliary tract cancers specifically. Due to their minimally invasive nature, liquid biopsy can be used to serially monitor disease progression and allow real-time monitoring of tumor genetic profiles as well as therapeutic response. Due to the unique anatomic location of biliary tract cancer, bile provides a promising biologic fluid for this purpose. This review focuses on the composition of bile and the use of these various components, ie, cells, extracellular vesicles, nucleic acids, proteins and metabolites as potential biomarkers. Based on the disease characteristics and research status of biliary tract cancer, considerable effort should be made to increase understanding of this disease, promote research and development into early diagnosis, develop efficient diagnostic, therapeutic and prognostic markers.

Improvement of untargeted proteomics workflow for surfaceome profiling and its evaluation through the implementation of quality controls: Application to multiple myeloma

BACKGROUND

Comprehensive surfaceome profiling of cancer cells using mass spectrometry (MS)-based technologies is a valuable approach to identify new antigens that could be targeted by immunotherapies. Multiple myeloma (MM) is an incurable hematological malignancy in which patients suffer from multiple relapses associated with drug resistance. Nevertheless, only three MM-specific antigens are currently targeted by approved immunotherapies which restrain the availability of efficient treatments for severe refractory patients affected by aggressive forms of the disease. Therefore, the discovery of new antigens in this context could open new perspectives for those patients.

RESULTS

In this study, the first objective was to improve a MS-based untargeted proteomics workflow in order to handle limited patient samples. For this purpose, a highly sensitive and robust miniaturized separation system (LC-Chip) coupled with drift tube ion mobility spectrometry and high-resolution MS was integrated in our workflow to maximize protein identification. As sample preparation can strongly influence the detectability of membrane-associated proteins, the critical steps in sample preparation were carefully optimized. As a result, 4.5 times more membrane-associated proteins were identified and experimental throughput was also drastically improved. In addition to workflow performance, particular attention was paid to assess the quality of the generated data. Indeed, several quality controls (QC) were implemented to assess data quality. Finally, the optimized workflow as well as selected QCs were evaluated in the analysis of samples containing limited number of cells.

SIGNIFICANCE

This work allowed the improvement of an untargeted proteomics workflow for surfaceome profiling in terms of performance. Besides, the reliability of the obtained data was evaluated through the introduction of QCs in the workflow. The applicability of the improved workflow as well as the implemented QCs for the analysis of MM primary cells obtained from patients was confirmed.

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Introduction

Proteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers.

Methods

In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models.

Results

We constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model.

Discussion

Our study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Serum autoantibody profiling of oral squamous cell carcinoma patients reveals NUBP2 as a potential diagnostic marker

Introduction

Oral Squamous Cell Carcinoma (OSCC), a common malignancy of the head and neck region, is frequently diagnosed at advanced stages, necessitating the development of efficient diagnostic methods. Profiling autoantibodies generated against tumor-associated antigens have lately demonstrated a promising role in diagnosis, predicting disease course, and response to therapeutics and relapse.

Methods

In the current study, we, for the first time, aimed to identify and evaluate the diagnostic value of autoantibodies in serum samples of patients with OSCC using autoantibody profiling by an immunome protein array. The utility of anti-NUBP2 antibody and tissue positivity in OSCC was further evaluated.

Results and discussion

We identified a total of 53 autoantibodies with significant differential levels between OSCC and control groups, including 25 that were increased in OSCC and 28 that were decreased. These included autoantibodies against Thymidine kinase 1 (TK1), nucleotide-binding protein 2 (NUBP2), and protein pyrroline-5-carboxylate reductase 1 (PYCR1), among others. Immunohistochemical validation indicated positive staining of NUBP2 in a large majority of cases (72%). Further, analysis of OSCC data available in TCGA revealed higher NUBP2 expression correlated with better disease-free patient survival. In conclusion, the differential serum autoantibodies identified in the current study, including those for NUBP2, could be used as potential biomarkers for early diagnosis or as screening biomarkers for OSCC pending investigation in a larger cohort.

[Molecular tumor diagnostics as the driving force behind precision oncology]

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.