Biomarkers for early detection of breast cancer: what, when, and where?

Early detection of breast cancer reduces the suffering and cost to society associated with the disease. A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. The earlier and more accurate the diagnostic biomarker can predict disease onset, the more valuable it becomes. Here, a brief review of existing and emerging approaches for breast cancer biomarker identification and analysis is presented. Those biomarkers found in biological fluids, blood in particular, apparently hold the best promise for fast development of screening assays. Autoantibodies and abnormal tumor-specific DNA methylation found in cell-free plasma DNA may provide the best opportunity for constructing multiplexed and highly redundant tests, which will be sufficiently specific and sensitive for early detection of breast cancer. It is expected that technologies developed for breast cancer detection will be useful for other types of cancer.     

A Plasma Long Noncoding RNA Signature for Early Detection of Lung Cancer

The early detection of lung cancer is a major clinical challenge. Long noncoding RNAs (lncRNAs) have important functions in tumorigenesis. Plasma lncRNAs directly released from primary tumors or the circulating cancer cells might provide cell-free cancer biomarkers. The objective of this study was to investigate whether the lncRNAs could be used as plasma biomarkers for early-stage lung cancer. By using droplet digital polymerase chain reaction, we determined the diagnostic performance of 26 lung cancer–associated lncRNAs in plasma of a development cohort of 63 lung cancer patients and 33 cancer-free individuals, and a validation cohort of 39 lung cancer patients and 28 controls. In the development cohort, 7 of the 26 lncRNAs were reliably measured in plasma. Two (SNHG1 and RMRP) displayed a considerably high plasma level in lung cancer patients vs. cancer-free controls (all P?<?.001). Combined use of the plasma lncRNAs as a biomarker signature produced 84.13% sensitivity and 87.88% specificity for diagnosis of lung cancer, independent of stage and histological type of lung tumor, and patients' age and sex (all P?>?.05). The diagnostic value of the plasma lncRNA signature for lung cancer early detection was confirmed in the validation cohort. The plasma lncRNA signature may provide a potential blood-based assay for diagnosing lung cancer at the early stage. Nevertheless, a prospective study is warranted to validate its clinical value.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Autoantibody biomarker opens a new gateway for cancer diagnosis

The list of cancer markers of current interest has grown considerably, but none of the markers used in clinical work is a true tumor marker. These cancer biomarkers are based on the determination of tumor antigens. Here, we report a single method of autoantibody enzyme immunoassay (EIA) screens for a spectrum of serum tumor markers. A comparison of the autoantibody-based EIA to conventional antigen EIA kits, using receiver operating characteristic (ROC) plots, showed that the autoantibody EIA can significantly enhance the sensitivity and specificity of tumor markers. The detection of serum autoantibodies for a spectrum of serum tumor markers, as demonstrated here, suggests that most, if not all, serum cancer biomarkers produce autoantibodies. A unique autoantibody biomarker screening method, as presented here, might therefore facilitate achieving the accurate and early diagnosis of cancer.     

AGR3 in Breast Cancer: Prognostic Impact and Suitable Serum-Based Biomarker for Early Cancer Detection

Blood-based early detection of breast cancer has recently gained novel momentum, as liquid biopsy diagnostics is a fast emerging field. In this study, we aimed to identify secreted proteins which are up-regulated both in tumour tissue and serum samples of breast cancer patients compared to normal tissue and sera. Based on two independent tissue cohorts (n = 75 and n = 229) and one serum cohort (n = 80) of human breast cancer and healthy serum samples, we characterised AGR3 as a novel potential biomarker both for breast cancer prognosis and early breast cancer detection from blood. AGR3 expression in breast tumours is significantly associated with oestrogen receptor α (P<0.001) and lower tumour grade (P<0.01). Interestingly, AGR3 protein expression correlates with unfavourable outcome in low (G1) and intermediate (G2) grade breast tumours (multivariate hazard ratio: 2.186, 95% CI: 1.008-4.740, P<0.05) indicating an independent prognostic impact. In sera analysed by ELISA technique, AGR3 protein concentration was significantly (P<0.001) elevated in samples from breast cancer patients (n = 40, mainly low stage tumours) compared to healthy controls (n = 40). To develop a suitable biomarker panel for early breast cancer detection, we measured AGR2 protein in human serum samples in parallel. The combined AGR3/AGR2 biomarker panel achieved a sensitivity of 64.5% and a specificity of 89.5% as shown by receiver operating characteristic (ROC) curve statistics. Thus our data clearly show the potential usability of AGR3 and AGR2 as biomarkers for blood-based early detection of human breast cancer.     

Hypoxia-induced tRNA-derived fragments,novel regulatory factor for doxorubicin resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of epithelial breast malignancy, and chemoresistance is the major obstacle for cancer therapy. TNBC is associated with a hypoxic phenotype, and hypoxia contributes to the chemoresistance in breast cancer. Transfer RNA-derived fragments (tDRs) represent a new class of small noncoding RNAs that can be induced specifically by hypoxia. Here, we conducted a comparative analysis of the aberrant expression of tDRs in hypoxia-treated TNBC cell lines through the use of high-throughput sequencing technique. Quantitative real-time polymerase chain reaction was used to validate the differently expressed tDRs between two samples. The results showed that tDR-0009 [derived from transfer RNA (tRNA)^Gly-GCC-1-1] and tDR-7336 (derived from tRNA ^{Gly-GCC-1–2}) were significantly upregulated when the SUM-1315 cell lines were stimulated by hypoxia. Gene ontology (GO) and pathway analysis indicated that these two upregulated tDRs were mainly involved in maintenance of stem cell population and cellular response to interleukin (IL)-6, which may be the underlying mechanism of hypoxia-induced tDRs that facilitate the doxorubicin resistance in TNBC. The protein–protein interaction network for predicted target genes established by the STRING database manifested that tDR-0009 (tDR-7336) might be involved in the chemoresistance of TNBC via regulation of the activation of phosphorylation of STAT3. In summary, our study provided a comprehensive analysis of the deviant expression profiling of tDRs in hypoxia-treated TNBC cell lines. Specific tDRs may be a new class of regulatory factors involved in the hypoxia-induced chemoresistance in TNBC, and they could serve as potential biomarkers and intervention targets.     

Current and proposed biomarkers of anthracycline cardiotoxicity in cancer: emerging opportunities in oxidative damage and autophagy

A biomarker is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention". Biomarkers can be utilized to detect disease, evaluate treatment risks, or determine treatment effectiveness. In the case of cancer, anthracyclines such as doxorubicin are front-line therapy to treat a number of different malignancies including breast cancer. However, a significant fraction of patients experience drug-induced cardiotoxicity. This toxicity is dose-limiting and can cause long-term morbidity or mortality. There is an unmet medical need to identify patients who are at risk for doxorubicin-induced cardiotoxicity, to detect cardiac damage early so that patient risk can be minimized, and to monitor the success of cardioprotective strategies. Therefore, doxorubicin treatment of cancer is an excellent example of the need for biomarkers to indicate drug safety in addition to drug efficacy. In this review we will discuss the mechanism of doxorubicinassociated cardiotoxicity, as well as other cancer therapies that induce cardiac toxicity by causing oxidative damage. We will also evaluate established and proposed biomarkers for cardiotoxicity based on our evolving knowledge of oxidative damage and subsequent autophagy. Finally, we will discuss advantages of combining oxidative damage- and autophagy-based protein biomarkers with current biomarkers such as troponins to facilitate early detection and mitigation of cardiotoxicity induced by cancer therapeutic agents.     

Advances in theranostic biomarkers for tumor immunotherapy

Cancer treatment has known a revolution with the emergence of immune checkpoint inhibitors. However, accurate theranostic biomarkers are lacking. In this review, we discuss different types of biomarkers currently under investigation. First, we focus on tissue biomarkers including PD-L1 expression by immunohistochemistry—the first Food and Drug Administration—approved biomarker—despite conflicting results. In addition, we report on novel biomarkers, including protein-based, molecular (tumor mutational load, immune signature…), circulating (neutrophil-to-lymphocyte ratio, serum cytokines…), and imaging-based biomarkers (radiomic signatures and positron-emission tomography using radiolabeled antibodies). We highlight the limitations of each candidate biomarker and finally discuss combinatorial approaches for their use and the opportunity to switch from a predictive strategy of biomarker research to an adaptive one in the field of cancer immunotherapy.     

The cancer cell secretome: A good source for discovering biomarkers?

Cancer is a leading cause of death. Early detection is usually associated with better clinical outcomes. Recent advances in genomics and proteomics raised hopes that new biomarkers for diagnosis, prognosis or monitoring therapeutic response will soon be discovered. Proteins secreted by cancer cells, referred also as “the cancer cell secretome”, is a promising source for biomarker discovery. In this review we will summarize recent advances in cancer cell secretome analysis, focusing on the five most fatal cancers (lung, breast, prostate, colorectal, and pancreatic). For each cancer type we will describe the proteomic approaches utilized for the identification of novel biomarkers. Despite progress, identification of markers that are superior to those currently used has proven to be a difficult task and very few, if any, newly discovered biomarker has entered the clinic the last 10 years.     

Identification of novel dysregulated circular RNAs in early-stage breast cancer

Breast cancer is a major cause of cancer-related death in women worldwide. Non-coding RNAs are a potential resource to be used as an early diagnostic biomarker for breast cancer. Circular RNAs are a recently identified group of non-coding RNA with a significant role in disease development with potential utility in diagnosis/prognosis in cancer. In this study, we identified 26 differentially expressed circular RNAs associated with early-stage breast cancer. RNA sequencing and two circRNA detection tools (find_circ and DCC) were used to understand the circRNA expression signature in breast cancer. We identified hsa_circ_0006743 (circJMJD1C) and hsa_circ_0002496 (circAPPBP1) to be significantly up-regulated in early-stage breast cancer tissues. Co-expression analysis identified four pairs of circRNA-miRNA (hsa_circ_0023990 : hsa-miR-548b-3p, hsa_circ_0016601 : hsa_miR-1246, hsa_circ_0001946 : hsa-miR-1299 and hsa_circ_0000117:hsa-miR-502-5p) having potential interaction. The miRNA target prediction and network analysis revealed mRNA possibly regulated by circRNAs. We have thus identified circRNAs of diagnostic implications in breast cancer and also observed circRNA-miRNA interaction which could be involved in breast cancer development.     

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Finding cancer biomarkers from mass spectrometry data by decision lists.

Finding accurate biomarkers is key to early diagnosis and successful treatment of many otherwise incurable diseases. In this work, we study the problem of finding biomarkers through mass spectrometry (SELDI-TOF) spectra from cancerous and normal tissues. In contrast to the common practice of using vague methods such as genetic algorithms, or uninterpretable methods such as Support Vector Machines, we look for a method that is simple, intuitive, interpretable, usable, and more accurate. We introduce decision lists to this domain. Our experiments on clinical cancer datasets demonstrate that decision lists can achieve more accurate results than other methods. More interestingly, the resulting decision lists are more interpretable for possible causal relationship between cancer and differentially expressed proteins, and directly usable in clinical biomarker design. In particular, our approach is capable of finding multiple biomarkers with high sensitivity and specificity. Such a feature will provide clues for medical experts to thoroughly investigate the roles of protein in cancer development and progression.     

Challenges to developing proteomic-based breast cancer diagnostics

Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.     

Quantitative profiling of serum samples using TMT protein labelling, fractionation and LC-MS/MS

Blood-borne biomarkers are urgently required for the early detection, accurate diagnosis and prognosis of disease. Additionally, improved methods of profiling serum and plasma proteins for biomarker discovery efforts are needed. Herein, we report a quantitative method based on amino-group labelling of serum proteins (rather than peptides) with isobaric tandem mass tags (TMT) and incorporating immune-based depletion, gel-based and strong anion exchange separation of proteins prior to differential endoproteinase treatment and liquid chromatography tandem mass spectrometry. We report a generally higher level of quantitative coverage of the serum proteome compared to other peptide-based isobaric tagging approaches and show the potential of the method by applying it to a set of unique samples that pre-date the diagnosis of pancreatic cancer.     

Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection

Introduction

MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast cancer patients and healthy controls.

Methods

We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718).

Results

Using the significance level of p<0.05, we found that 59 miRNAs were differentially expressed in whole blood of early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically significant differences in miRNA-expression were found for miR-202.

Conclusions

MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but there are still challenges that need to be addressed to establish these new biomarkers in clinical use.     

tRFs结构及其生物学功能

tRFs（tRNA-derived RNA fragments）是来源于tRNA的小分子非编码RNA,由前体tRNA或成熟tRNA经加工和修饰而成,在生物界中广泛存在。tRFs深度测序结果表明,tRFs可能并不是由tRNA随机裂解产生的,而是通过某个特定机制生成。根据来源不同,tRFs可被分为tRF-1、tRF-2、tRF-3、tRF-5和tiR。tRFs具有类似于miRNA的调控功能,并能参与调控基因转录和翻译,细胞增殖以及细胞应激反应。新近研究表明,乳腺癌细胞中某些特异性的tRFs（如tRF^GlyTCC和tRF^AspGTC）,可通过与Y-box结合蛋白1（Y box binding protein 1, YB-1）结合进而抑制癌细胞的生长和转移。另有研究表明,tRFs还可通过细胞色素c介导的信号转导途径来发挥其抑制癌细胞凋亡的功能。由此可见,tRFs在调控癌症发生发展过程中也具有重要调控作用,然而其机制仍不清楚。本文综述了tRFs结构和分布、生物学功能及其作用机制的研究现状,旨在为tRFs相关研究提供参考。