Quantitative proteomics revealed novel proteins associated with molecular subtypes of breast cancer

Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, marked by poor outcomes and dismal prognosis. Due to the absence of targetable receptors, chemotherapy still represents the main therapeutic option. Therefore, current research is now focusing on understanding the specific molecular pathways implicated in TNBC, in order to identify novel biomarker signatures and develop targeted therapies able to improve its clinical management. With the aim of identifying novel molecular features characterizing TNBC, elucidating the mechanisms by which these molecular biomarkers are implicated in the tumor development and progression, and assessing the impact on cancerous cells following their inhibition or modulation, we conducted a literature search from the earliest works to December 2023 on PubMed, Scopus, and Web Of Science. A total of 146 studies were selected. The results obtained demonstrated that TNBC is characterized by a heterogeneous molecular profile. Several biomarkers have proven not only to be characteristic of TNBC but also to serve as potential effective therapeutic targets, holding the promise of a new era of personalized treatments able to improve its prognosis. The pre-clinical findings that have emerged from our systematic review set the stage for further investigation in forthcoming clinical trials.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Proteoglycans in breast cancer, identification and characterization by LC-MS/MS assisted proteomics approach: A review

PURPOSE

Proteoglycans (PGs) are negatively charged macromolecules containing a core protein and single or several glycosaminoglycan chains attached by covalent bond. They are distributed in all tissues, including extracellular matrix (ECM), cell surface, and basement membrane. They are involved in major pathways and cell signalling cascades which modulate several vital physiological functions of the body. They have also emerged as a target molecule for cancer treatment and as possible biomarkers for early cancer detection. Among cancers, breast cancer is a highly invasive and heterogenous type and has become the major cause of mortality especially among women. So, this review revisits the studies on PGs characterization in breast cancer using LC-MS/MS-based proteomics approach, which will be further helpful for identification of potential PGs-based biomarkers or therapeutic targets.

EXPERIMENTAL DESIGN

There is a lack of comprehensive knowledge on the use of LC-MS/MS-based proteomics approaches to identify and characterize PGs in breast cancer.

RESULTS

LC-MS/MS assisted PGs characterization in breast cancer revealed the vital PGs in breast cancer invasion and progression. In addition, comprehensive profiling and characterization of PGs in breast cancer are efficiently carried out by this approach.

CONCLUSIONS

Proteomics techniques including LC-MS/MS-based identification of proteoglycans is effectively carried out in breast cancer research. Identification of expression at different stages of breast cancer is a major challenge, and LC-MS/MS-based profiling of PGs can boost novel strategies to treat breast cancer, which involve targeting PGs, and also aid early diagnosis using PGs as biomarkers.

Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma

Background

Autophagy, a key regulator of programmed cell death, is critical for maintaining the stability of the intracellular environment. Increasing evidence has revealed the clinical importance of interactions between autophagy and immune status in lung adenocarcinoma. The present study evaluated the potential of autophagy-immune-derived biomarkers to predict prognosis and therapeutic response in patients with lung adenocarcinoma.

Methods

Patients from the GSE72094 dataset were randomized 7:3 to a training set and an internal validation set. Three independent cohorts, TCGA, GSE31210, and GSE37745, were used for external verification. Unsupervised hierarchical clustering based on autophagy- and immune-associated genes was used to identify autophagy- and immune-associated molecular patterns, respectively. Significantly prognostic autophagy-immune genes were identified by LASSO analysis and by univariate and multivariate Cox regression analyses. Differences in tumor immune microenvironments, functional pathways, and potential therapeutic responses were investigated to differentiate high-risk and low-risk groups.

Results

High autophagy status and high immune status were associated with improved overall survival. Autophagy and immune subtypes were merged into a two-dimensional index to characterize the combined prognostic classifier, with 535 genes defined as autophagy-immune-related differentially expressed genes (DEGs). Four genes (C4BPA, CD300LG, CD96, and S100P) were identified to construct an autophagy-immune-related prognostic risk model. Survival and receiver operating characteristic (ROC) curve analyses showed that this model was significantly prognostic of survival. Patterns of autophagy and immune genes differed in low- and high-risk patients. Enrichment of most immune infiltrating cells was greater, and the expression of crucial immune checkpoint molecules was higher, in the low-risk group. TIDE and immunotherapy clinical cohort analysis predicted that the low-risk group had more potential responders to immunotherapy. GO, KEGG, and GSEA function analysis identified immune- and autophagy-related pathways. Autophagy inducers were observed in patients in the low-risk group, whereas the high-risk group was sensitive to autophagy inhibitors. The expression of the four genes was assessed in clinical specimens and cell lines.

Conclusions

The autophagy-immune-based gene signature represents a promising tool for risk stratification in patients with lung adenocarcinoma, guiding individualized targeted therapy or immunotherapy.

High-throughput Proteomic Profiling of Male Breast Cancer Tissue

BACKGROUND/AIM

Until now, little emphasis has been placed on the protein expression profile of male breast cancer (MBC) tumors, due to the rarity of the disease. The present study aimed to identify a proteomic pattern that is characteristic for malignant male breast tissue epithelium.

MATERIALS AND METHODS

The protein content of four male breast tumors and corresponding adjacent healthy (control) tissues was analyzed by high-throughput nano-liquid chromatography-MS/MS technology.

RESULTS

A total of 2,352 proteins were identified, that correspond to 1,249 single gene products, with diverse biological roles. Of those, a panel of 119 differentially expressed tissue proteins was identified in MBC samples compared to controls; 90 were found to be over-expressed in MBC tissues, while 29 were down-regulated. Concurrently, 844 proteins were detected only in MBC tumors and 197 were expressed exclusively in control mammary samples.

CONCLUSION

Differential proteomic expression was found in MBC tissue, leading to improved understanding of MBC pathology and highlighting the need for personalized management of male patients.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Recent advances of transcriptomics and proteomics in triple-negative breast cancer prognosis assessment

Triple-negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating 'big data' and biological systems, which is referred to as 'omics', as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple-negative breast cancers, which provides a powerful engine for TNBC's therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple-negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.

cPLA2 blockade attenuates S100A7-mediated breast tumorigenicity by inhibiting the immunosuppressive tumor microenvironment

BACKGROUND

Molecular mechanisms underlying inflammation-associated breast tumor growth are poorly studied. S100A7, a pro-inflammatory molecule has been shown to enhance breast cancer growth and metastasis. However, the S100A7-mediated molecular mechanisms in enhancing tumor growth and metastasis are unclear.

METHODS

Human breast cancer tissue and plasma samples were used to analyze the expression of S100A7, cPLA2, and PGE2. S100A7-overexpressing or downregulated human metastatic breast cancer cells were used to evaluate the S100A7-mediated downstream signaling mechanisms. Bi-transgenic mS100a7a15 overexpression, TNBC C3 (1)/Tag transgenic, and humanized patient-derived xenograft mouse models and cPLA2 inhibitor (AACOCF3) were used to investigate the role of S100A7/cPLA2/PGE2 signaling in tumor growth and metastasis. Additionally, CODEX, a highly advanced multiplexed imaging was employed to delineate the effects of S100A7/cPLA2 inhibition on the recruitment of various immune cells.

RESULTS

In this study, we found that S100A7 and cPLA2 are highly expressed and correlate with decreased overall survival in breast cancer patients. Further mechanistic studies revealed that S100A7/RAGE signaling promotes the expression of cPLA2 to mediate its oncogenic effects. Pharmacological inhibition of cPLA2 suppressed S100A7-mediated tumor growth and metastasis in multiple pre-clinical models including transgenic and humanized patient-derived xenograft (PDX) mouse models. The attenuation of cPLA2 signaling reduced S100A7-mediated recruitment of immune-suppressive myeloid cells in the tumor microenvironment (TME). Interestingly, we discovered that the S100A7/cPLA2 axis enhances the immunosuppressive microenvironment by increasing prostaglandin E2 (PGE2). Furthermore, CO-Detection by indEXing (CODEX) imaging-based analyses revealed that cPLA2 inhibition increased the infiltration of activated and proliferating CD4⁺ and CD8⁺ T cells in the TME. In addition, CD163⁺ tumor associated-macrophages were positively associated with S100A7 and cPLA2 expression in malignant breast cancer patients.

CONCLUSIONS

Our study provides new mechanistic insights on the cross-talk between S100A7/cPLA2 in enhancing breast tumor growth and metastasis by generating an immunosuppressive TME that inhibits the infiltration of cytotoxic T cells. Furthermore, our studies indicate that S100A7/cPLA2 could be used as novel prognostic marker and cPLA2 inhibitors as promising drugs against S100A7-overexpressing aggressive breast cancer.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Analysis of H3K4me3-ChIP-Seq and RNA-Seq data to understand the putative role of miRNAs and their target genes in breast cancer cell lines

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)‒specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3′-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

Lipopolysaccharide from the commensal microbiota of the breast enhances cancer growth: role of S100A7 and TLR4

The role of commensal bacterial microbiota in the pathogenesis of human malignancies has been a research field of incomparable progress in recent years. Although breast tissue is commonly assumed to be sterile, recent studies suggest that human breast tissue may contain a bacterial microbiota. In this study, we used an immune‐competent orthotopic breast cancer mouse model to explore the existence of a unique and independent bacterial microbiota in breast tumors. We observed some similarities in breast cancer microbiota with skin; however, breast tumor microbiota was mainly enriched with Gram‐negative bacteria, serving as a primary source of lipopolysaccharide (LPS). In addition, dextran sulfate sodium (DSS) treatment in late‐stage tumor lesions increased LPS levels in the breast tissue environment. We also discovered an increased expression of S100A7 and low level of TLR4 in late‐stage tumors with or without DSS as compared to early‐stage tumor lesions. The treatment of breast cancer cells with LPS increased the expression of S100A7 in breast cancer cells in vitro. Furthermore, S100A7 overexpression downregulated TLR4 and upregulated RAGE expression in breast cancer cells. Analysis of human breast cancer samples also highlighted the inverse correlation between S100A7 and TLR4 expression. Overall, these findings suggest that the commensal microbiota of breast tissue may enhance breast tumor burden through a novel LPS/S100A7/TLR4/RAGE signaling axis.

Protein Corona Gold Nanoparticles Fingerprinting Reveals a Profile of Blood Coagulation Proteins in the Serum of HER2-Overexpressing Breast Cancer Patients

Breast cancer (BC) is a molecularly heterogeneous disease that encompasses five major molecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B HER2 positive (LB+), HER2 positive (HER2+) and triple negative breast cancer (TNBC)). BC treatment mainly depends on the identification of the specific subtype. Despite the correct identification, therapies could fail in some patients. Thus, further insights into the genetic and molecular status of the different BC subtypes could be very useful to improve the response of BC patients to the range of available therapies. In this way, we used gold nanoparticles (AuNPs, 12.96 ± 0.72 nm) as a scavenging tool in combination with Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) to quantitatively analyze the serum proteome alterations in the different breast cancer intrinsic subtypes. The differentially regulated proteins specific of each subtype were further analyzed with the bioinformatic tools STRING and PANTHER to identify the major molecular function, biological processes, cellular origin, protein class and biological pathways altered due to the heterogeneity in proteome of the different BC subtypes. Importantly, a profile of blood coagulation proteins was identified in the serum of HER2-overexpressing BC patients.

Comparative analysis of intact glycopeptides from mannose receptor among different breast cancer subtypes using mass spectrometry

Breast cancer is a highly heterogeneous disease, encompassing a number of biologically distinct entities with specific pathologic features and biological behaviors. In the preliminary experiments, we identified several glycosylation sites of mannose receptors in different breast cancer subtypes and showed that the mannose receptors could be a potential marker for breast cancer. However, the glycan composition on each site is still unknown because the glycan was removed by PNGase F in previous work. Analysis of intact glycopeptides can provide the information of both the glycan composition and the glycosylation site, which can further help to reveal the difference of glycosylation in the four subtypes of breast cancer. In this work, we analyzed the intact glycopeptides of the mannose receptors in serum from breast cancer patients using isobaric tags for relative and absolute quantitation (iTRAQ) and LC-MS/MS. In total, 7 glycosylation sites and 12 glycan types corresponding to 26 intact glycopeptides were characterized from the four subtypes of breast cancer. Among them, 11 glycopeptides can be used to differentiate the subtypes of breast cancer, which further supported the previous conclusion that mannose receptor can be used as a potential marker for the identification of breast cancer subtypes.

The plasma peptides of breast versus ovarian cancer

Background

There is a need to demonstrate a proof of principle that proteomics has the capacity to analyze plasma from breast cancer versus other diseases and controls in a multisite clinical trial design. The peptides or proteins that show a high observation frequency, and/or precursor intensity, specific to breast cancer plasma might be discovered by comparison to other diseases and matched controls. The endogenous tryptic peptides of breast cancer plasma were compared to ovarian cancer, female normal, sepsis, heart attack, Alzheimer's and multiple sclerosis along with the institution-matched normal and control samples collected directly onto ice.

Methods

Endogenous tryptic peptides were extracted from individual breast cancer and control EDTA plasma samples in a step gradient of acetonitrile, and collected over preparative C18 for LC-ESI-MS/MS with a set of LTQ XL linear quadrupole ion traps working together in parallel to randomly and independently sample clinical populations. The MS/MS spectra were fit to fully tryptic peptides or phosphopeptides within proteins using the X!TANDEM algorithm. The protein observation frequency was counted using the SEQUEST algorithm after selecting the single best charge state and peptide sequence for each MS/MS spectra. The observation frequency was subsequently tested by Chi Square analysis. The log₁₀ precursor intensity was compared by ANOVA in the R statistical system.

Results

Peptides and/or phosphopeptides of common plasma proteins such as APOE, C4A, C4B, C3, APOA1, APOC2, APOC4, ITIH3 and ITIH4 showed increased observation frequency and/or precursor intensity in breast cancer. Many cellular proteins also showed large changes in frequency by Chi Square (χ² > 100, p < 0.0001) in the breast cancer samples such as CPEB1, LTBP4, HIF-1A, IGHE, RAB44, NEFM, C19orf82, SLC35B1, 1D12A, C8orf34, HIF1A, OCLN, EYA1, HLA-DRB1, LARS, PTPDC1, WWC1, ZNF562, PTMA, MGAT1, NDUFA1, NOGOC, OR1E1, OR1E2, CFI, HSA12, GCSH, ELTD1, TBX15, NR2C2, FLJ00045, PDLIM1, GALNT9, ASH2L, PPFIBP1, LRRC4B, SLCO3A1, BHMT2, CS, FAM188B2, LGALS7, SAT2, SFRS8, SLC22A12, WNT9B, SLC2A4, ZNF101, WT1, CCDC47, ERLIN1, SPFH1, EID2, THOC1, DDX47, MREG, PTPRE, EMILIN1, DKFZp779G1236 and MAP3K8 among others. The protein gene symbols with large Chi Square values were significantly enriched in proteins that showed a complex set of previously established functional and structural relationships by STRING analysis. An increase in mean precursor intensity of peptides was observed for QSER1 as well as SLC35B1, IQCJ-SCHIP1, MREG, BHMT2, LGALS7, THOC1, ANXA4, DHDDS, SAT2, PTMA and FYCO1 among others. In contrast, the QSER1 peptide QPKVKAEPPPK was apparently specific to ovarian cancer.

Conclusion

There was striking agreement between the breast cancer plasma peptides and proteins discovered by LC-ESI-MS/MS with previous biomarkers from tumors, cells lines or body fluids by genetic or biochemical methods. The results indicate that variation in plasma peptides from breast cancer versus ovarian cancer may be directly discovered by LC-ESI-MS/MS that will be a powerful tool for clinical research. It may be possible to use a battery of sensitive and robust linear quadrupole ion traps for random and independent sampling of plasma from a multisite clinical trial.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.

Plasma/serum proteomics: depletion strategies for reducing high-abundance proteins for biomarker discovery

Plasma and serum are widely used for proteomics-based biomarker discovery. However, analysis of these biofluids is highly challenging due to the complexity and wide dynamic range of their proteomes. Notably, highly abundant proteins tend to obscure the detection of potential biomarkers that are usually of lower concentrations. Among the strategies to resolve this problem are: depletion of high-abundance proteins, enrichment of low abundant proteins of interest and prefractionation. In this review, we focus on current and emerging depletion techniques used to enhance the detection and identification of the less abundant proteins in plasma and serum. We discuss the applications and contributions of these methods to proteomics analysis of plasma and serum alongside their limitations and future perspectives.

Proteomic analysis of cerebrospinal fluid in pediatric acute lymphoblastic leukemia patients: a pilot study

Purpose

Involvement of central nervous system in acute lymphoblastic leukemia (CNSL) remains one of the major causes of pediatric acute lymphoblastic leukemia (ALL) treatment failure. However, the current understanding of the pathological process of CNSL is still limited. This study aimed to better understand the protein expression in cerebrospinal fluid (CSF) of ALL and discover valuable prognostic biomarkers.

Materials and methods

CSF samples were obtained from ALL patients and healthy controls. Comparative proteomic profiling using label-free liquid chromatography-tandem mass spectrometry was performed to detect differentially expressed proteins.

Results

In the present study, 51 differentially expressed proteins were found. Among them, two core clusters including ten proteins (TIMP1, LGALS3BP, A2M, FN1, AHSG, HRG, ITIH4, CF I, C2, and C4a) might be crucial for tumorigenesis and progression of ALL and can be potentially valuable indicators of CNSL.

Conclusion

These differentially expressed proteins of ALL children with central nervous system involvement and normal children may work as diagnostic and prognostic factors of ALL patients.

Estudio proteómico 2DE-DIGE en plasma sanguíneo de pacientes en etapa infantil con leucemia linfoblástica aguda

En Colombia, durante la última década la leucemia linfoblástica aguda (LLA) ha sido el cáncer con mayor incidencia, siendo más del 40% de las muertes por cáncer en menores de edad atribuidas a esta enfermedad. Entre los factores que influyen en estas cifras, el diagnóstico tardío es tal vez el factor más sensible que afecta de manera negativa el éxito del tratamiento. Esta investigación se centró en el estudio del proteoma plasmático de niños colombianos diagnosticados con LLA tipo B, dada su alta incidencia, en comparación con controles en la búsqueda de proteínas que podrían tener potencialidad a ser clasificadas como biomarcadores de diagnóstico. Ahora bien, en vista de los avances en las herramientas proteómicas y de espectrometría de masas y sabiendo que son una alternativa para abordar la complejidad molecular de enfermedades como el cáncer, utilizamos una aproximación proteómica basada en una separación por electroforesis bidimensional diferencial (2DE-DIGE) con posterior separación por cromatografía líquida acoplada a espectrometría de masas en tándem. Se encontraron 8 proteínas con expresión diferencial en plasma de pacientes con LLA-B, entre las cuales resaltan la serotransferrina, la Alfa-1-antitripsina, la haptoglobina, la α2-glicoproteína de zinc y la complemento C3.

Identification of plasma biomarkers for diffuse axonal injury in rats by iTRAQ-coupled LC-MS/MS and bioinformatics analysis

DAI is a serious and complex brain injury associated with significant morbidity and mortality. The lack of reliable objective diagnostic modalities for DAI delays administration of therapeutic interventions. Hence, identifying reliable biomarkers is urgently needed to enable early DAI diagnosis in the clinic. Herein, we established a rat model of DAI and applied an isobaric tags for a relative and absolute quantification (iTRAQ) coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) proteomics approach to screen differentially expressed plasma proteins associated with DAI. A total of 58 proteins were found to be significantly modulated in blood plasma samples of the injury group in at least one time point compared to controls. Bioinformatics analysis of the differentially expressed proteins revealed that the pathogenesis of axonal injury underlying DAI is multi-stage biological process involved. Two significantly changed proteins, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and hemopexin (Hpx), were identified as potential diagnostic biomarkers for DAI, and were successfully confirmed by further western blot analysis. This proteomic profiling study not only identified novel plasma biomarkers that may facilitate the development of clinically diagnostic for DAI, but also provided enhanced understanding of the molecular mechanisms underlying DAI.

Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation

Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

Tumor tissue protein signatures reflect histological grade of breast cancer

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.