胰腺癌早期检测的生物标志物（英文）

胰腺癌症是最难诊断和治疗的恶性肿瘤之一,其特点是发病隐匿、进展迅速、预后差。目前,手术治疗仍然是首选治疗方法。然而由于缺乏早期症状,大约70%的患者在确诊时已经出现局部扩散或远端转移,从而无法进行手术治疗。由此看来,早期检测是提高患者治疗效果和预后的有效途径。临床上使用的成像方法 （CT、MRI、EUS等）通常无法检测早期病变,并且很容易受到操作员的影响。常规临床标志物如CA19-9、CA125、CA242和CEA受到限制,其敏感性或特异性不令人满意。因此,寻找新的具有高敏感性和特异性的标志物是实现胰腺癌早期检测的关键。近年来,对生物标志物的广泛研究主要集中在遗传学、转录组学和蛋白质组学上。特别是由microRNA(miRNA)、long non-coding RNA(lncRNA)和circRNA(circRNA)组成的非蛋白质编码RNA(non-protein coding RNA,ncRNA)为胰腺癌的早期检测提出了许多新思路。然而,其中绝大多数仍处于实验室研究阶段。而一项成熟的生物标志物研究应该整合基因组学、转录组学、蛋白质组学或代谢组学的数据,并结合患者的个体特征（如体重指数...     

Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

MicroRNA-196a and -196b as Potential Biomarkers for the Early Detection of Familial Pancreatic Cancer

Screening programs are recommended for individuals at risk (IAR) from families with familial pancreatic cancer (FPC). However, reliable imaging methods or biomarkers for early diagnosis of pancreatic ductal adenocarcinoma (PC) or its precursor lesions are still lacking. The ability of circulating microRNAs (miRNAs) to discriminate multifocal high-grade precursor lesions or PC from normal was examined. The presence of miRNA-21, -155, -196a, -196b and -210 was analyzed in the serum of transgenic KPC mice to test their ability to distinguish mice with different grades of pancreatic intraepithelial neoplasia (mPanIN1–3) or PC from control mice. Serum levels of miR-196a and -196b were significantly higher in mice with PanIN2/3 lesions (n = 10) or PC (n = 8) as compared to control mice (n = 10) or mice with PanIN1 lesions (n = 10; P = .01). In humans, miR-196a and -196b were also diagnostic. Patients with PC, sporadic (n = 9) or hereditary (n = 10), and IAR with multifocal PanIN2/3 lesions (n = 5) had significantly higher serum levels than patients with neuroendocrine pancreatic tumors (n = 10) or chronic pancreatitis (n = 10), IAR with PanIN1 or no PanIN lesions (n = 5), and healthy controls (n = 10). The combination of both miR-196a and -196b reached a sensitivity of 1 and specificity of 0.9 (area under the curve = 0.99) to diagnose PC or high-grade PanIN lesions. In addition, preoperative elevated serum levels of miR-196a and -196b in patients with PC or multifocal PanIN2/3 lesions dropped to normal after potential curative resection. The combination of miR-196a and -196b may be a promising biomarker test for the screening of IAR for FPC.     

Cerebrospinal Fluid Peptides as Potential Parkinson Disease Biomarkers: A Staged Pipeline for Discovery and Validation

Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson''s Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression.Parkinson disease (PD)¹, the second most common neurodegenerative disease after Alzheimer disease (AD), afflicts roughly 2% of persons over the age of 65 years (1, 2). Currently, PD diagnosis is mainly based on observation of the cardinal motor indicators of the disease, patient response to drug treatment, and medical history (3, 4). There is an appreciable misdiagnosis rate (4), particularly at early disease stages. Additionally, no objective measure of disease progression or treatment effects has been established. Thus, objective, reliable, and reproducible biomarkers are clearly needed to aid in the diagnosis of PD and tracking or predicting the disease progression.The most sensitive tests developed to date are based on imaging modalities, which can detect functional and structural abnormalities even prior to the onset of motor dysfunction (5, 6). However, the usefulness of neuroimaging techniques is limited by high cost, limited accessibility, difficulty in reliable differentiation of PD from other atypical parkinsonian disorders and subjection to confounding factors such as medication and compensatory responses (4–7). Biochemical and molecular markers in cerebrospinal fluid (CSF) and other body fluids have also been actively investigated (5, 8–12). The most extensively studied candidate in CSF is probably α-synuclein, the major protein component of Lewy bodies and Lewy neurites, the pathological hallmarks of PD (2). The current consensus is that CSF α-synuclein concentrations are generally lower in patients with PD compared with controls (5, 8–10); the sensitivity and specificity, however, appear to be only moderate, and no correlation with PD severity or progression has been observed (8, 9). Notably, all these CSF protein markers are measured using antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (5). It should also be stressed that this high variability in immunoassays is not unique to PD, because similar difficulty is encountered in AD and other related disorders (13, 14).One strategy to avoid the inherent technical limitations associated with antibodies is to use alternative techniques in which unique peptides are selected and precisely quantified with mass spectrometry (MS) techniques, for example, accurate inclusion mass screening (AIMS) (15) and selected reaction monitoring (SRM) (16–18). To this end, in the last few years, we and others have utilized proteomic technologies to identify novel proteins and peptides associated with different disease states and stages (5, 6, 19–25). Using brain tissue or CSF, these unbiased proteomic profiling studies have revealed disease-related alterations in hundreds of peptides derived from many proteins (19–25). However, there are no quantitative assays for the majority of these candidate proteins/peptides, and development of such assays is limited by the lack of antibodies available for many of them. Thus, although a large library of potential peptide biomarkers has been developed, the vast majority never reach the stage of validation and clinical testing, hampered by the difficulty of de novo development of immunoassays, a process that is time consuming, prohibitively expensive to develop and very difficult to multiplex.In this study, we aim to establish a PD biomarker identification and verification pipeline, with the goal of prioritizing candidates and swiftly developing reliable quantitative assays. We focused on identifying peptides by SRM and AIMS, because these targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (16) and have become a powerful tool in biomarker discovery because of their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (17, 18). The staged pipeline in the current investigation (Fig. 1) includes: (1) data-dependent and bioinformatic prioritization of thousands of candidate biomarkers identified in our previous profiling studies, (2) de novo development of antibody-free multiplex SRM assays to reliably measure tens to hundreds of peptides simultaneously, and (3) multiplex biomarker verification studies allowing identification and validation of models or panels of candidates in independent sample sets, two of which were used in this study.Open in a separate windowFig. 1.Overview of the workflow used for CSF peptide biomarker discovery and validation. AD, Alzheimer disease; AIMS, accurate inclusion mass screening; CO, healthy controls; DDA, data-dependent acquisition; PD, Parkinson disease; SRM, selected reaction monitoring.     

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow‐up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.     

Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer

BackgroundDetection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.ResultsWe identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.ConclusionOur 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.     

Circulating Exosomal microRNAs as Biomarkers of Colon Cancer

Purpose

Exosomal microRNAs (miRNAs) have been attracting major interest as potential diagnostic biomarkers of cancer. The aim of this study was to characterize the miRNA profiles of serum exosomes and to identify those that are altered in colorectal cancer (CRC). To evaluate their use as diagnostic biomarkers, the relationship between specific exosomal miRNA levels and pathological changes of patients, including disease stage and tumor resection, was examined.

Experimental Design

Microarray analyses of miRNAs in exosome-enriched fractions of serum samples from 88 primary CRC patients and 11 healthy controls were performed. The expression levels of miRNAs in the culture medium of five colon cancer cell lines were also compared with those in the culture medium of a normal colon-derived cell line. The expression profiles of miRNAs that were differentially expressed between CRC and control sample sets were verified using 29 paired samples from post-tumor resection patients. The sensitivities of selected miRNAs as biomarkers of CRC were evaluated and compared with those of known tumor markers (CA19-9 and CEA) using a receiver operating characteristic analysis. The expression levels of selected miRNAs were also validated by quantitative real-time RT-PCR analyses of an independent set of 13 CRC patients.

Results

The serum exosomal levels of seven miRNAs (let-7a, miR-1229, miR-1246, miR-150, miR-21, miR-223, and miR-23a) were significantly higher in primary CRC patients, even those with early stage disease, than in healthy controls, and were significantly down-regulated after surgical resection of tumors. These miRNAs were also secreted at significantly higher levels by colon cancer cell lines than by a normal colon-derived cell line. The high sensitivities of the seven selected exosomal miRNAs were confirmed by a receiver operating characteristic analysis.

Conclusion

Exosomal miRNA signatures appear to mirror pathological changes of CRC patients and several miRNAs are promising biomarkers for non-invasive diagnosis of the disease.     

Identification and Validation of Potential Biomarkers for the Detection of Dysregulated microRNA by qPCR in Patients with Colorectal Adenocarcinoma

Colorectal cancer represents a lethal disease that has raised concern and has attracted significant attention. Adenocarcinoma is the most common type of colorectal cancer (CRC). MicroRNAs are thought to be potential biomarkers of CRC. Many researchers have focused on the expression pattern of miRNAs in CRC. However, previous studies did not pay particular attention to the effects of the degree of differentiation of the cancer with respect to the miRNA expression profile. First, this study compared the expression level of 1547 miRNAs by qRT-PCR in Colorectal adenocarcinoma tissues to that in paired normal tissues. In all, 93 miRNAs were identified that were significantly dysregulated in Colorectal adenocarcinoma relative to normal tissues (P<0.05). Then, we analyzed their potential as cancer biomarkers by ROC analysis, and the result revealed that three miRNAs with high sensitivity and specificity are suitable as biomarkers for the diagnosis of CRC (the value of the AUC was greater than 0.7). Interestingly, previous reports of 23 of these miRNAs have been scarce. Furthermore, we wanted to analyze the difference between well- and moderately differentiated cancers, and as expected, 58 miRNAs showed significant dysregulation. Importantly, 32 miRNAs were able to not only distinguish cancer tissues from normal tissues, but they were also able to identify well- and moderately differentiated cancers. In conclusion, the degree of differentiation has an important influence on the miRNA expression pattern. To avoid misdiagnoses and missed diagnoses, tumors of different degrees of differentiation should be treated differently when miRNAs are used as cancer biomarkers.     

Plasma miR-601 and miR-760 Are Novel Biomarkers for the Early Detection of Colorectal Cancer

Background

Colorectal cancer (CRC) is a major cause of death worldwide. Sensitive, non-invasive diagnostic screen methods are urgently needed to improve its survival rates. Stable circulating microRNA offers unique opportunities for the early diagnosis of several diseases, including cancers. Our aim has been to find new plasma miRNAs that can be used as biomarkers for the detection of CRC.

Methodology/Principal Findings

According to the results of miRNA profiling performed on pooling plasma samples form 10 CRC patients or 10 healthy controls, a panel of miRNAs (hsa-miR-10a, -19a, -22*, -24, -92a, 125a-5p, -141, -150, -188-3p, -192, -210, -221, -224*, -376a, -425*, -495, -572, -601, -720, -760 and hsa-let-7a, -7e) were deregulated in CRC plasma with fold changes >5. After large scale validation by qRT-PCR performed on another 191 independent individuals (90 CRC, 43 advanced adenoma and 58 healthy participants), we found that the levels of plasma miR-601 and miR-760 were significantly decreased in colorectal neoplasia (carcinomas and advanced adenomas) compared with healthy controls. ROC curve analysis showed that plasma miR-601 and miR-760 were of significant diagnostic value for advanced neoplasia. These two miRNAs together yield an AUC of 0.792 with 83.3% sensitivity and 69.1% specificity for separating CRC from normal controls, and yield an AUC of 0.683 with 72.1% sensitivity and 62.1% specificity in discriminating advanced adenomas from normal controls.

Conclusions/Significance

Plasma miR-601 and miR-760 can potentially serve as promising non-invasive biomarkers for the early detection of CRC.     

Differentially Expressed Androgen-Regulated Genes in Androgen-Sensitive Tissues Reveal Potential Biomarkers of Early Prostate Cancer

Background

Several data favor androgen receptor implication in prostate cancer initiation through the induction of several gene activation programs. The aim of the study is to identify potential biomarkers for early diagnosis of prostate cancer (PCa) among androgen-regulated genes (ARG) and to evaluate comparative expression of these genes in normal prostate and normal prostate-related androgen-sensitive tissues that do not (or rarely) give rise to cancer.

Methods

ARG were selected in non-neoplastic adult human prostatic epithelial RWPE-1 cells stably expressing an exogenous human androgen receptor, using RNA-microarrays and validation by qRT-PCR. Expression of 48 preselected genes was quantified in tissue samples (seminal vesicles, prostate transitional zones and prostate cancers, benign prostatic hypertrophy obtained from surgical specimens) using TaqMan® low-density arrays. The diagnostic performances of these potential biomarkers were compared to that of genes known to be associated with PCa (i.e. PCA3 and DLX1).

Results and Discussion

By crossing expression studies in 26 matched PCa and normal prostate transitional zone samples, and 35 matched seminal vesicle and PCa samples, 14 genes were identified. Similarly, 9 genes were overexpressed in 15 benign prostatic hypertrophy samples, as compared to PCa samples. Overall, we selected 8 genes of interest to evaluate their diagnostic performances in comparison with that of PCA3 and DLX1. Among them, 3 genes: CRYAB, KCNMA1 and SDPR, were overexpressed in all 3 reference non-cancerous tissues. The areas under ROC curves of these genes reached those of PCA3 (0.91) and DLX1 (0.94).

Conclusions

We identified ARG with reduced expression in PCa and with significant diagnostic values for discriminating between cancerous and non-cancerous prostatic tissues, similar that of PCA3. Given their expression pattern, they could be considered as potentially protective against prostate cancer. Moreover, they could be complementary to known genes overexpressed in PCa and included along with them in multiplex diagnostic tools.     

Identification and Validation of Oncologic miRNA Biomarkers for Luminal A-like Breast Cancer

Introduction

Breast cancer is a common disease with distinct tumor subtypes phenotypically characterized by ER and HER2/neu receptor status. MiRNAs play regulatory roles in tumor initiation and progression, and altered miRNA expression has been demonstrated in a variety of cancer states presenting the potential for exploitation as cancer biomarkers. Blood provides an excellent medium for biomarker discovery. This study investigated systemic miRNAs differentially expressed in Luminal A-like (ER+PR+HER2/neu-) breast cancer and their effectiveness as oncologic biomarkers in the clinical setting.

Methods

Blood samples were prospectively collected from patients with Luminal A-like breast cancer (n = 54) and controls (n = 56). RNA was extracted, reverse transcribed and subjected to microarray analysis (n = 10 Luminal A-like; n = 10 Control). Differentially expressed miRNAs were identified by artificial neural network (ANN) data-mining algorithms. Expression of specific miRNAs was validated by RQ-PCR (n = 44 Luminal A; n = 46 Control) and potential relationships between circulating miRNA levels and clinicopathological features of breast cancer were investigated.

Results

Microarray analysis identified 76 differentially expressed miRNAs. ANN revealed 10 miRNAs for further analysis (miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652). The biomarker potential of 4 miRNAs (miR-29a, miR-181a, miR-223 and miR-652) was confirmed by RQ-PCR, with significantly reduced expression in blood of women with Luminal A-like breast tumors compared to healthy controls (p = 0.001, 0.004, 0.009 and 0.004 respectively). Binary logistic regression confirmed that combination of 3 of these miRNAs (miR-29a, miR-181a and miR-652) could reliably differentiate between cancers and controls with an AUC of 0.80.

Conclusion

This study provides insight into the underlying molecular portrait of Luminal A-like breast cancer subtype. From an initial 76 miRNAs, 4 were validated with altered expression in the blood of women with Luminal A-like breast cancer. The expression profiles of these 3 miRNAs, in combination with mammography, has potential to facilitate accurate subtype-specific breast tumor detection.     

Circulating Tumor DNA as Biomarkers for Cancer Detection

Detection of circulating tumor DNAs (ctDNAs) in cancer patients is an important component of cancer precision medicine ctDNAs. Compared to the traditional physical and biochemical methods, blood-based ctDNA detection offers a non-invasive and easily accessible way for cancer diagnosis, prognostic determination, and guidance for treatment. While studies on this topic are currently underway, clinical translation of ctDNA detection in various types of cancers has been attracting much attention, due to the great potential of ctDNA as blood-based biomarkers for early diagnosis and treatment of cancers. ctDNAs are detected and tracked primarily based on tumor-related genetic and epigenetic alterations. In this article, we reviewed the available studies on ctDNA detection and described the representative methods. We also discussed the current understanding of ctDNAs in cancer patients and their availability as potential biomarkers for clinical purposes. Considering the progress made and challenges involved in accurate detection of specific cell-free nucleic acids, ctDNAs hold promise to serve as biomarkers for cancer patients, and further validation is needed prior to their broad clinical use.     

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose

Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.

Methods

A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.

Results

Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.

Conclusion

A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.     

Prediagnostic Serum Biomarkers as Early Detection Tools for Pancreatic Cancer in a Large Prospective Cohort Study

BackgroundThe clinical management of pancreatic cancer is severely hampered by the absence of effective screening tools.MethodsSixty-seven biomarkers were evaluated in prediagnostic sera obtained from cases of pancreatic cancer enrolled in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO).ResultsThe panel of CA 19-9, OPN, and OPG, identified in a prior retrospective study, was not effective. CA 19-9, CEA, NSE, bHCG, CEACAM1 and PRL were significantly altered in sera obtained from cases greater than 1 year prior to diagnosis. Levels of CA 19-9, CA 125, CEA, PRL, and IL-8 were negatively associated with time to diagnosis. A training/validation study using alternate halves of the PLCO set failed to identify a biomarker panel with significantly improved performance over CA 19-9 alone. When the entire PLCO set was used for training at a specificity (SP) of 95%, a panel of CA 19-9, CEA, and Cyfra 21-1 provided significantly elevated sensitivity (SN) levels of 32.4% and 29.7% in samples collected <1 and >1 year prior to diagnosis, respectively, compared to SN levels of 25.7% and 17.2% for CA 19-9 alone.ConclusionsMost biomarkers identified in previously conducted case/control studies are ineffective in prediagnostic samples, however several biomarkers were identified as significantly altered up to 35 months prior to diagnosis. Two newly derived biomarker combinations offered advantage over CA 19-9 alone in terms of SN, particularly in samples collected >1 year prior to diagnosis. However, the efficacy of biomarker-based tools remains limited at present. Several biomarkers demonstrated significant velocity related to time to diagnosis, an observation which may offer considerable potential for enhancements in early detection.     

Aptamer-Based Detection of Disease Biomarkers in Mouse Models for Chagas Drug Discovery

Drug discovery initiatives, aimed at Chagas treatment, have been hampered by the lack of standardized drug screening protocols and the absence of simple pre-clinical assays to evaluate treatment efficacy in animal models. In this study, we used a simple Enzyme Linked Aptamer (ELA) assay to detect T. cruzi biomarker in blood and validate murine drug discovery models of Chagas disease. In two mice models, Apt-29 ELA assay demonstrated that biomarker levels were significantly higher in the infected group compared to the control group, and upon Benznidazole treatment, their levels reduced. However, biomarker levels in the infected treated group did not reduce to those seen in the non-infected treated group, with 100% of the mice above the assay cutoff, suggesting that parasitemia was reduced but cure was not achieved. The ELA assay was capable of detecting circulating biomarkers in mice infected with various strains of T. cruzi parasites. Our results showed that the ELA assay could detect residual parasitemia in treated mice by providing an overall picture of the infection in the host. They suggest that the ELA assay can be used in drug discovery applications to assess treatment efficacy in-vivo.     

Metformin: A Potential Therapeutic Agent for Recurrent Colon Cancer

Accumulating evidence suggests that metformin, a biguanide class of anti-diabetic drugs, possesses anti-cancer properties. However, most of the studies to evaluate therapeutic efficacy of metformin have been on primary cancer. No information is available whether metformin could be effectively used for recurrent cancer, specifically colorectal cancer (CRC) that affects up to 50% of patients treated by conventional chemotherapies. Although the reasons for recurrence are not fully understood, it is thought to be due to re-emergence of chemotherapy-resistant cancer stem/stem-like cells (CSCs/CSLCs). Therefore, development of non-toxic treatment strategies targeting CSCs would be of significant therapeutic benefit.In the current investigation, we have examined the effectiveness of metformin, in combination with 5-fluorouracil and oxaliplatin (FuOx), the mainstay of colon cancer therapeutics, on survival of chemo-resistant colon cancer cells that are highly enriched in CSCs/CSLCs. Our data show that metformin acts synergistically with FuOx to (a) induce cell death in chemo resistant (CR) HT-29 and HCT-116 colon cancer cells, (b) inhibit colonospheres formation and (c) enhance colonospheres disintegration. In vitro cell culture studies have further demonstrated that the combinatorial treatment inhibits migration of CR colon cancer cells. These changes were associated with increased miRNA 145 and reduction in miRNA 21. Wnt/β-catenin signaling pathway was also down-regulated indicating its pivotal role in regulating the growth of CR colon cancer cells. Data from SCID mice xenograft model of CR HCT-116 and CR HT-29 cells show that the combination of metformin and FuOX is highly effective in inhibiting the growth of colon tumors as evidenced by ∼50% inhibition in growth following 5 weeks of combination treatment, when compared with the vehicle treated controls. Our current data suggest that metformin together with conventional chemotherapy could be an effective treatment regimen for recurring colorectal cancer (CRC).     

Circulating microRNAs as Specific Biomarkers for Breast Cancer Detection

Background

We previously showed microRNAs (miRNAs) in plasma are potential biomarkers for colorectal cancer detection. Here, we aimed to develop specific blood-based miRNA assay for breast cancer detection.

Methodology/Principal Findings

TaqMan-based miRNA profiling was performed in tumor, adjacent non-tumor, corresponding plasma from breast cancer patients, and plasma from matched healthy controls. All putative markers identified were verified in a training set of breast cancer patients. Selected markers were validated in a case-control cohort of 170 breast cancer patients, 100 controls, and 95 other types of cancers and then blindly validated in an independent set of 70 breast cancer patients and 50 healthy controls. Profiling results showed 8 miRNAs were concordantly up-regulated and 1 miRNA was concordantly down-regulated in both plasma and tumor tissue of breast cancer patients. Of the 8 up-regulated miRNAs, only 3 were significantly elevated (p<0.0001) before surgery and reduced after surgery in the training set. Results from the validation cohort showed that a combination of miR-145 and miR-451 was the best biomarker (p<0.0001) in discriminating breast cancer from healthy controls and all other types of cancers. In the blind validation, these plasma markers yielded Receiver Operating Characteristic (ROC) curve area of 0.931. The positive predictive value was 88% and the negative predictive value was 92%. Altered levels of these miRNAs in plasma have been detected not only in advanced stages but also early stages of tumors. The positive predictive value for ductal carcinoma in situ (DCIS) cases was 96%.

Conclusions

These results suggested that these circulating miRNAs could be a potential specific biomarker for breast cancer screening.