Tissue and plasma proteomics for early stage cancer detection

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Biomarker Identification and Risk Prediction Model Development for Differentiated Thyroid Carcinoma Lung Metastasis Based on Primary Lesion Proteomics

PURPOSE

The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis of DTC (LMDTC). This population poses a significant challenge in clinical practice, necessitating the urgent development of effective risk stratification methods and predictive tools for lung metastasis.

EXPERIMENTAL DESIGN

Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and IHC, we identified eight hub proteins as potential biomarkers. By expanding the sample size and conducting statistical analysis on clinical features and hub protein expression, we constructed three risk prediction models.

RESULTS

This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS, and VDAC2-as potential biomarkers for predicting LMDTC risk. We developed and internally validated three risk prediction models incorporating both clinical characteristics and hub protein expression. Our findings demonstrated that the combined prediction model exhibited optimal predictive performance, with the highest discrimination (AUC: 0.986) and calibration (Brier score: 0.043). Application of the combined prediction model within a specific risk threshold (0-0.97) yielded maximal clinical benefit. Finally, we constructed a nomogram based on the combined prediction model.

CONCLUSIONS

As a large sample size study in LMDTC research, the identification of biomarkers through primary lesion proteomics and the development of risk prediction models integrating clinical features and hub protein biomarkers offer valuable insights for predicting LMDTC and establishing personalized treatment strategies.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

circANKRD28 inhibits cisplatin resistance in non-small-cell lung cancer through the miR-221-3p/SOCS3 axis

BACKGROUND

Non-small-cell lung cancer (NSCLC) is a common cancer. Chemotherapeutic drug resistance limits the therapeutic effect of NSCLC and leads to a poor prognosis. As a result, new specific targets may be better identified by studying the mechanism of drug resistance to cisplatin in NSCLC.

METHODS

In the present study, we performed a quantitative real-time polymerase chain reaction and western blotting to detect mRNA and protein levels. The proliferation of cells was analyzed by a Cell Counting Kit-8 and colony formation assays. Cell invasion was measured via the Transwell assay. A scratch assay was performed to measure cell migration in cisplatin (DDP)-resistant NSCLC cells. Apoptosis of cells was examined using flow cytometry.

RESULTS

We found that circANKRD28 was notably decreased in NSCLC. The results showed that circANKRD28 expression was not affected, and its half-life was more than 12 h. Functional experiments revealed that circANKRD28 overexpression inhibited DDP resistance in NSCLC cells in vitro. Mechanistic findings demonstrated that circANKRD28 regulated tumor cell progression and DDP sensitivity through the miR-221-3p/SOCS3 axis.

CONCLUSIONS

The present study revealed the regulatory effects and molecular mechanism of circANKRD28 on the development and cisplatin resistance in NSCLC, which may provide experimental basis and theoretical support to identify new targets for therapy of DDP resistance in NSCLC.

Integrative analysis of multi-omics data for liquid biopsy

The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

High-throughput proteomics: a methodological mini-review

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Screening anlotinib responders via blood-based proteomics in non-small cell lung cancer

Anlotinib has been demonstrated to be effective in advanced non-small cell lung cancer (NSCLC) patients. The response stratification of anlotinib remains unclear. In this study, plasma samples from 28 anlotinib-treated NSCLC patients (discovery cohort: 14 responders and 14 non-responders) were subjected to proteomic analysis, and plasma samples from 35 anlotinib-treated NSCLC patients (validation cohort) were subjected to validation analysis. Liquid chromatography-tandem mass spectrometry analysis was performed on samples with different time points, namely baseline (BL), best response (BR), and progression disease (PD). Bioinformatics analysis was performed to screen for the underlying differential proteins. Enzyme-linked immunosorbent assay was performed to detect plasma ARHGDIB, FN1, CDH1, and KNG1 levels respectively. The Kaplan-Meier survival analysis was used for biomarker-based responsive stratification. Our results indicated that differential proteins between responders and non-responders showed that proteomic technology potentially contributes to biomarker screening in plasma samples at BL. Furthermore, our results suggested that the detection of plasma ARHGDIB, FN1, CDH1, and KNG1 levels have potential predictive value for anlotinib response both in the discovery cohort and validation cohort. Collectively, this study offers novel insights into the value of plasma biomarker screening via proteomic examination and suggests that plasma ARHGDIB, FN1, CDH1, and KNG1 levels could be used as biomarkers for anlotinib stratification in NSCLC patients.

Tissue-Based Proteomic Profiling in Patients with Hyperplasia and Endometrial Cancer

Uterine cancers are among the most prevalent gynecological malignancies, and endometrial cancer (EC) is the most common in this group. This study used tissue-based proteomic profiling analysis in patients with endometrial cancer and hyperplasia, and control patients. Conventional 2D gel electrophoresis, followed by a mass spectrometry approach with bioinformatics, including a network pathway analysis pipeline, was used to identify differentially expressed proteins and associated metabolic pathways between the study groups. Thirty-six patients (twelve with endometrial cancer, twelve with hyperplasia, and twelve controls) were enrolled in this study. The mean age of the participants was 46–75 years. Eighty-seven proteins were significantly differentially expressed between the study groups, of which fifty-three were significantly differentially regulated (twenty-eight upregulated and twenty-five downregulated) in the tissue samples of EC patients compared to the control (Ctrl). Furthermore, 26 proteins were significantly dysregulated (8 upregulated and 18 downregulated) in tissue samples of hyperplasia (HY) patients compared to Ctrl. Thirty-two proteins (nineteen upregulated and thirteen downregulated) including desmin, peptidyl prolyl cis-trans isomerase A, and zinc finger protein 844 were downregulated in the EC group compared to the HY group. Additionally, fructose bisphosphate aldolase A, alpha enolase, and keratin type 1 cytoskeletal 10 were upregulated in the EC group compared to those in the HY group. The proteins identified in this study were known to regulate cellular processes (36%), followed by biological regulation (16%). Ingenuity pathway analysis found that proteins that are differentially expressed between EC and HY are linked to AKT, ACTA2, and other signaling pathways. The panels of protein markers identified in this study could be used as potential biomarkers for distinguishing between EC and HY and early diagnosis and progression of EC from hyperplasia and normal patients.

Proteomics analyses of Xiaopi granules in MNNG-induced gastric epithelial dysplasia rat model by LC-MS

OBJECTIVE

Xiaopi granules have been shown to ameliorate gastric epithelial dysplasia in patients. However, the therapeutic mechanism is unclear. Herein, the proteomics method was applied to identify the differentially expressed proteins and related pathways.

METHODS

Sixty male Sprague-Dawley (SD) rats were randomly divided into four groups: control (C group, n=10), model (M group), Xiaopi granules (X group), and vitacoenzyme (V group). The rat gastric epithelial dysplasia model was established by intragastrically administering N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) and ranitidine, and drinking 0.05% ammonia solution. After 12 weeks, the stomach tissue was analyzed by H&E staining and proteomics analyses. Western blot analysis was applied to further validate the proteomics results.

RESULTS

Compared to the M group, levels of 326 and 350 proteins were altered significantly in the X and V groups (1.5-fold, P<0.05), which were significantly enriched in digestion, metabolism, coagulation, and cell apoptosis. CELA2A, GHRL, NDUFB9, and PGC were significantly upregulated (P<0.0001), while CLCA1, PLG, and DAC2 were downregulated (P<0.001 or P<0.0001) in the M group vs. the C group. The change in the above proteins could be reversed after the treatment of Xiaopi granules or vitacoenzyme tablets.

CONCLUSION

Xiaopi granules improve ameliorated gastric epithelial dysplasia by intervening in digestion, metabolism, blood coagulation, cell apoptosis, and other related pathways.

Potential biomarkers for inherited thrombocytopenia 2 identified by plasma proteomics

Inherited thrombocytopenia 2 (THC2) is difficult to diagnose due to the lack of specific clinical characteristics and diagnostic methods. To identify potential plasma protein biomarkers for THC2, we collected the plasma samples from a THC2 family (9 THC2 and 15 non-THC2 members), enriched the medium and low abundant proteins using Proteominer and analyzed the protein profiles using the liquid chromatography-mass spectrometry in data independent acquisition mode. Initially, we detected 784 proteins in the plasma samples of this family and identified 27 up-regulated and 36 down-regulated in the THC2 group compared to the non-THC2 group (|log₂ ratio| >1 and p-value <0.05). To improve the predictive power, top eight dysregulated proteins (B7Z2B4, LTF, HP, ERN1, IGHV1-8, A0A0X9V9C4, VH6DJ, and D3JV41) were selected by an area under the curve-based random forest process to construct a clinical model. Multivariate analysis with random forest and support vector machine showed that the prediction model provided high discrimination ability for THC2 diagnosis (AUC: 1.000 and 0.967, respectively). The potential plasma protein biomarkers will be tested in more THC2 patients and other thrombocytopenia patients to further validate their specificity and sensitivity.

The translational values of TRIM family in pan-cancers: From functions and mechanisms to clinics

Cancer is the second leading cause of human death across the world. Tripartite motif (TRIM) family, with E3 ubiquitin ligase activities in majority of its members, is reported to be involved in multiple cellular processes and signaling pathways. TRIM proteins have critical effects in the regulation of biological behaviors of cancer cells. Here, we discussed the current understanding of the molecular mechanism of TRIM proteins regulation of cancer cells. We also comprehensively reviewed published studies on TRIM family members as oncogenes or tumor suppressors in the oncogenesis, development, and progression of a variety of types of human cancers. Finally, we highlighted that certain TRIM family members are potential molecular biomarkers for cancer diagnosis and prognosis, and potential therapeutic targets.

Dermatopontin as a potential pathogenic factor in endometrial cancer

The present study aimed to determine the differential expression profiles of proteins in endometrial carcinoma and to screen the proteins associated with the occurrence and development of endometrial cancer (EC). In total, 15 samples of human EC and paracancerous tissues were selected for proteomic analysis using a label-free quantification method based on liquid chromatography-tandem mass spectrometry. The differential proteins were analysed using bioinformatics and verified using reverse transcription-quantitative PCR (RT-qPCR) and western blotting. Finally, the expression of differential proteins in 75 endometrial carcinoma samples and 30 normal endometrial tissue samples were detected using immunohistochemical staining, and the associations between differential protein expression and clinicopathological features were analysed. In total, 579 up-regulated proteins and 346 down-regulated proteins were identified between the two groups and seven proteins with the most significant differences were selected; these proteins included interferon-induced protein with tetratricopeptide repeats 3, poly(ADP-ribose) polymerase family member 9, solute carrier family 34 member 2, cytochrome b5 reductase 1, protein tyrosine phosphatase non-receptor type 1, dermatopontin (DPT) and secretory leukocyte peptidase inhibitor. RT-qPCR and western blotting showed that DPT expression was down-regulated (P<0.001), which was consistent with the mass spectrometry results. The immunohistochemical staining results showed that the positive expression of DPT in EC and normal endometrial tissues was statistically significant (P<0.001). The positive expression of DPT was significantly decreased in poorly differentiated, late stage, lymph node metastasis and myometrial invasion depth ≥1/2 samples (P<0.05). DPT expression was significantly lower in EC, which might play role in the pathogenesis of EC.

Induced volatolomics of pathologies

Volatolomics allows us to elucidate cell metabolic processes in real time. In particular, a volatile organic compound (VOC) excreted from our bodies may be specific for a certain disease, such that measuring this VOC may afford a simple, fast, accessible and safe diagnostic approach. Yet, finding the optimal endogenous volatile marker specific to a pathology is non-trivial because of interlaboratory disparities in sample preparation and analysis, as well as high interindividual variability. These limit the sensitivity and specificity of volatolomics and its applications in biological and clinical fields but have motivated the development of induced volatolomics. This approach aims to overcome issues by measuring VOCs that result not from an endogenous metabolite but, rather, from the pathogen-specific or metabolic-specific enzymatic metabolism of an exogenous biological or chemical probe. In this Review, we introduce volatile-compound-based probes and discuss how they can be exploited to detect and discriminate pathogenic infections, to assess organ function and to diagnose and monitor cancers in real time. We focus on cases in which labelled probes have informed us about metabolic processes and consider the potential and drawbacks of the probes for clinical trials. Beyond diagnostics, VOC-based probes may also be effective tools to explore biological processes more generally.

Update on uveal melanoma: Translational research from biology to clinical practice (Review)

Uveal melanoma is the most common type of intraocular cancer with a low mean annual incidence of 5‑10 cases per million. Tumours are located in the choroid (90%), ciliary body (6%) or iris (4%) and of 85% are primary tumours. As in cutaneous melanoma, tumours arise in melanocytes; however, the characteristics of uveal melanoma differ, accounting for 3‑5% of melanocytic cancers. Among the numerous risk factors are age, sex, genetic and phenotypic predisposition, the work environment and dermatological conditions. Management is usually multidisciplinary, including several specialists such as ophthalmologists, oncologists and maxillofacial surgeons, who participate in the diagnosis, treatment and complex follow‑up of these patients, without excluding the management of the immense emotional burden. Clinically, uveal melanoma generates symptoms that depend as much on the affected ocular globe site as on the tumour size. The anatomopathological study of uveal melanoma has recently benefited from developments in molecular biology. In effect, disease classification or staging according to molecular profile is proving useful for the assessment of this type of tumour. Further, the improved knowledge of tumour biology is giving rise to a more targeted approach to diagnosis, prognosis and treatment development; for example, epigenetics driven by microRNAs as a target for disease control. In the present study, the main epidemiological, clinical, physiopathological and molecular features of this disease are reviewed, and the associations among all these factors are discussed.

Deep proteome profiling of SW837 cells treated by photodynamic therapy (PDT) reveals the underlying mechanisms of metronomic and acute PDTs

AIM

Metronomic photodynamic therapy (mPDT) with a longer irradiation time and lower energy compared with acute (or classic) photodynamic therapy (aPDT) is a more effective treatment than aPDT for tumor cells, especially colorectal cancer. However, the underlying mechanisms of the superior effects of mPDT are unknown.

METHODS

we used SWATH-MS (sequential window acquisition of all theoretical mass spectra) to identify differentially expressed proteins (DEPs) specific to aPDT (conventional fluence rate, 20 mW/cm², 4 min 10 s), mPDT (metronomic fluence rate, 0.4 mW/cm², 3.5 h), and control groups of SW837 cells. The photosensitizer used in both PDT methods was aminolevulinic acid which were incubated with the cells before irradiation.

RESULTS

A total of 6805 proteins were identified in the three groups of SW837 cells. aPDT induced 333 DEPs and mPDT induced 1716 DEPs compared with the control. We identified 185 common DEPs in the two PDT groups, 148 different DEPs in the aPDT group, and 1531 different DEPs in the mPDT group. Most of the 185 common DEPs were involved in the extracellular component, participated in the processes of vesicle transport and secretion, binding, and hydrolase/catalytic activity. They were also involved in PI3K-Akt, cGMP-PKG, RAS, and aAMP signaling pathways. In addition, the 1531 different DEPs in the mPDT group participated in similar processes and molecular functions, but in a more complex manner than those in the aPDT group.

CONCLUSION

our proteome data suggest that mPDT has a complex tumor destruction mechanism with more involved proteins compared with aPDT, which may explain the better tumor killing effect of mPDT.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models.

Predictive value and clinical significance of increased SSAT-1 activity in healthy adults

AIM

Spermidine/spermine N1-acetyltransferase (SSAT-1) regulates cell growth, proliferation and death. Amantadine is converted by SSAT-1 to acetylamantadine (AA). In our earlier studies, although SSAT-1 was activated in patients with cancer, a number of ostensibly healthy adult volunteers had higher than expected AA concentration. This study was therefore undertaken to examine the outlier group.

MATERIALS & METHODS

A follow up of urine analysis for AA by liquid chromatography-tandem mass spectrometry as well as clinical assessments and additional blood analyses were conducted.

RESULTS

In some of the outlier controls, higher than expected AA concentration was linked to increased serum carcinoembryonic antigen. Clinical and radiographic assessments revealed underlying abnormalities in other cases that could represent premalignant conditions. Hematology tests revealed elevations in white blood cells and platelets, which are markers of inflammation.

CONCLUSION

High urine concentration of AA could be used as a simple and useful test for screening of cancer in high-risk populations.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

Quantitative proteomics identifies brain acid soluble protein 1 (BASP1) as a prognostic biomarker candidate in pancreatic cancer tissue

BACKGROUND

Pancreatic cancer is a heterogenous disease with a poor prognosis. This study aimed to discover and validate prognostic tissue biomarkers in pancreatic cancer using a mass spectrometry (MS) based proteomics approach.

METHODS

Global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers.

FINDINGS

In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. Brain acid soluble protein 1 (BASP1) was identified as a new biomarker candidate for pancreatic cancer possessing largely unknown biological and clinical functions and was selected for further analysis. Importantly, bioinformatic analysis indicated that BASP1 interacts with Wilms tumour protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257-0.852, p = .013) and predicted favourable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083-2.473, p = .019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336-9.362, p = .011).

INTERPRETATION

We here describe an MS-based proteomics platform for developing biomarkers for pancreatic cancer. Bioinformatic analysis and clinical data from our study suggest that BASP1 and its putative interaction partner WT1 can be used as biomarkers for predicting outcomes in pancreatic cancer patients.