Circulating miRNAs as Tumor Biomarkers

C H A P T E R

13 Circulating miRNAs as Tumor Biomarkers Vikas Ghai, Inyoul Lee and Kai Wang Institute for Systems Biology, Seattle, WA, United States

Early detection, diagnosis, and stratification are vital for the proper management and treatment of cancer patients, and as such the development of sensitive, specific, and informative cancer biomarkers is critical. However, the current diagnostic approach is still based on invasive procedures. For example, tissue biopsy is usually needed to confirm and classify cancer type and grade. The advent of so called “liquid biopsies”—to detect and analyze molecules or cells in circulation that are usually tumor-derived, or produced in response to tumor formation—has shown a lot of promise as a noninvasive method to detect and classify various types of cancer. In this chapter we will look at the use of circulating cell-free microRNAs as tumor biomarkers, and reflect on the challenges and opportunities they face gaining traction in the clinic.

MICRORNAs: BIOSYNTHESIS AND FUNCTION There are different classes of ribonucleic acid (RNA) molecules in cells, but recently

Oncogenomics DOI: https://doi.org/10.1016/B978-0-12-811785-9.00013-2

much attention has been focused on understanding the function of noncoding RNA (ncRNAs). In contrast to the well-studied messenger RNAs (mRNAs), ncRNAs do not encode a protein product but rather function as either structural or critical regulators of cellular processes. Of these, microRNAs (miRNAs) are the best characterized. MiRNAs are small (B22 bp) ncRNAs that have been shown to modulate the proteome by targeting the turnover of specific mRNAs. Currently more than 25,000 individual miRNAs have been identified across more than 200 species, with some of the sequences being highly conserved across species, suggesting the role they play in biology is of critical importance. In the nucleus, miRNAs are initially transcribed as part of a larger primary miRNA (pri-miRNA) product, and through a series of modifications (done by a multienzyme complex including the ribonuclease, Drosha, and the RNA binding protein, DGCR8) the 50 and 30 features are trimmed to produce a shorter (B70 bp) precursor miRNA (pre-miRNA). In the cytoplasm, the pre-miRNA is then further

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13. CIRCULATING MIRNAS AS TUMOR BIOMARKERS

processed by the ribonuclease, Dicer, into a 1924 bp mature miRNA duplex. One of the strands (either 23p or 25p) of the duplex then joins with other proteins (including the Argonaut protein, Ago2) to form a ribonucleoprotein structure called the RNA Induced Silencing Complex (RISC). The RISC complex interacts with target mRNA through imperfect base-paring usually at their 30 untranslated regions (UTR). This RISC- miRNA-mRNA interaction promotes degradation of the transcript, or inhibits protein translation by blocking the progression or binding of ribosome.

miRNAs DYSREGULATED IN CANCER Initially, miRNAs were discovered as regulators of heterochrony in Caenorhabditis elegans (Lee, Feinbaum, & Ambros, 1993), but now they have been shown to regulate a wide array of cellular processes including signal transduction, cell-cycle, cell proliferation, cell growth, and metabolism, all of which play central roles in cancer (Huang et al., 2011). Some miRNAs have been shown to be dysregulated in various cancers (coined “OncomiRs”), often due to expansion or deletion of miRNA gene clusters that regulate key processes involved in tumorigenesis, or through aberrant changes to their regulation. For example, the loss of the miR-15a16 miRNA cluster on chromosome 13 has been frequently observed in B-cell chronic lymphocytic leukemia (Cimmino et al., 2005), and the duplication of the miR-17-92 gene cluster has been observed in B-cell lymphomas and lung cancer (Hayashita et al., 2005; Mavrakis et al., 2010). Interestingly, the miRNAs in the miR-1792 miRNA cluster are under the transcriptional control of the oncogene, C-Myc (O’Donnell, Wentzel, Zeller, Dang, & Mendell, 2005). C-Myc not only activates the oncogenic miR-17-92 cluster, but also acts to repress the expression of several tumor-suppressive miRNAs, such as

members of the miR-15a, miR-26, miR-29, miR30, and let-7 families (Chang et al., 2008). The p53 transcriptional regulatory network that controls tumorigenesis, and is often mutated in various cancers, also regulates miRNA expression (He et al., 2007). The p53 tumor suppressor guards entry into the cell-cycle through a complex pathway that includes directly regulating the expression of miR-34a, miR-107, miR-605, and miR-1246 (Chang et al., 2007; Hermeking, 2010; Raver-Shapira et al., 2007; Xiao, Lin, Luo, Luo, & Wang, 2011; Yamakuchi et al., 2010; Zhang, Liao, Zeng, & Lu, 2011). Additionally, miR-34a acts to further promote p53 expression by negatively regulating a p53 suppressor, SIRT1 (Yamakuchi & Lowenstein, 2009). Signaling through the transforming growth factor-β (TGFβ) pathway regulates the process of the epithelialmesenchymal transition (EMT), which plays a critical role during metastasis. The miR-200 family acts to repress the expression of ZEB1 and ZEB2 (the downstream transcription targets of TGF-β in the EMT), and is often dysregulated in metastasis in various cancers (Feng, Wang, Fillmore, & Xi, 2014). Epigenetic modifications to miRNAs also lead to their dysregulation in cancer. Hypermethylation of the antitumorigenic miR-148a, and the miR-34b/c cluster results in silencing in cancer cells and increased metastasis in vivo (Lujambio et al., 2008). Similarly, hypermethylation of miR-9-1, miR-124a, and miR-1455p are attributed to decreased expression in breast, lung, and colon carcinomas, respectively (Donzelli et al., 2015; Lehmann et al., 2008; Lujambio, 2007).

EXTRACELLULAR VESICLES AND EXTRACELLULAR miRNAs Interestingly, miRNAs are also present in the extracellular environment and have been detected in various biological fluids such as serum, plasma, urine, tears, saliva, and others (Chen et al., 2008; Lawrie et al., 2008; Mitchell et al.,

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EXTRACELLULAR VESICLES AND EXTRACELLULAR MIRNAS

2008; Valadi et al., 2007; Weber et al., 2010). The specific role of these extracellular miRNAs remains somewhat speculative; however, there is evidence that a subset of these miRNAs may function as intermediaries of paracrine signaling between cells, tissues, or organs, including between a tumor and its microenvironment. The initial observation of these extracellular miRNAs seemed dubious, as purified RNAs are often degraded in the extracellular environment due to excessive ribonuclease activity. To avoid degradation, miRNAs have been observed in complex with RNA binding proteins including AGO2 (Arroyo et al., 2011; Turchinovich, Weiz, Langheinz, & Burwinkel, 2011), NPM1 (Wang, Zhang, Weber, Baxter, & Galas, 2010), hnRNPA2B1 (VillarroyaBeltri et al., 2013), SYNCRIP (Santangelo et al., 2016), and lipoproteins such as HDL (Vickers, Palmisano, Shoucri, Shamburek, & Remaley, 2011; Wagner et al., 2013). However, the release of many of these miRNA-protein complexes (such as those bound to AGO2 and NPM1) are passive and likely the results of cell-death and autophagy, rather than representing actively exported miRNAs that are the result of signaling. MiRNAs have also been found to be encapsulated in lipid vesicles broadly referred to as extracellular vesicles (EVs). EVs include vesicles of different types that differ in size, derivation, and function, including apoptotic bodies (5004000 nm), microvesicles (502000 nm), and exosomes (30100 nm). Among these, exosomes (including related subtypes like the tumor-derived oncosomes) have drawn significant interest in recent years. Exosomes originate from early-endosomederived multivesicle bodies (MVB) inside the cell, and are released through fusion with the plasma membrane (Denzer, Kleijmeer, Heijnen, Stoorvogel, & Geuze, 2000). In addition to miRNAs, exosomes also contain other nucleic acids such as long noncoding RNA (lncRNA), Piwi-interacting RNAs (piRNAs), small

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nucleolar RNAs (snoRNAs), protein coding mRNA, and genomic DNA fragments. Exosomes also contain various proteins, including tetraspanins (such as Alix, CD9, CD63, and CD81) embedded on the vesicle surface that have been used extensively as exosomal surface markers. Oncosomes, which are tumor-derived, have been known to carry their own unique molecular cargo that may be tumor-specific. For example, small oncosomes secreted by glioma cells carry a mutated form of EGFR, EGFRvIII (Al-Nedawi et al., 2008), and large oncosomes derived from prostate cancer tumors carry Cav-1, a well-studied serum marker associated with metastatic prostate cancer (Di Vizio et al., 2012; Morello et al., 2013). While not entirely definitive, several different mechanistic pathways may explain how miRNAs are loaded into exosomes. One potential mechanism is a ceramide-dependent pathway for sorting miRNA into exosomes. Interference of the ceramide biosynthesis, through the targeting of the enzyme neutral sphingomyelinase 2 (nSMase2), affects the distribution of miRNAs present in exosomes (Kosaka et al., 2010). In colorectal cancer (CRC) cell lines, mutations in KRAS affect the miRNA content in exosomes (Cha et al., 2015) and attenuates the tumor microenvironment, which in turn affects tumor cell growth and migration (Demory Beckler et al., 2013). As discussed above, RNA-binding proteins play a role in protecting miRNAs from degradation, but they also play a role in miRNA export into exosomes. Sumoylated hnRNPPA2B1 has been shown to recognize a G/A rich motif present in the 30 UTR of miRNAs in primary T lymphoblasts, and can facilitate their export into exosomes (Villarroya-Beltri et al., 2013). Another member of the hnRNP family, SYNCRIP, also interacts with a “GGCU” motif present in the 30 UTR of miRNAs in hepatocytes cell lines sorting them into exosomes (Santangelo et al., 2016). Similarly, miRNAs with nontemplate

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additions of differing terminal nucleoside bases (uridines more frequently observed in exosomal miRNAs and adenines in cellular miRNAs) can influence export in exosomes (Koppers-Lalic et al., 2014), though the exact mechanism controlling this editing and sorting is unclear.

CIRCULATING miRNAs AS POTENTIAL BIOMARKERS FOR CANCERS Many of the current tumor biomarkers are proteins or sugars/glycans that circulate in body fluids like whole blood or serum. For example, the use of Alpha-fetoprotein (AFP) to screen for liver cancers, CA15-3 for breast cancer, CA19-9 for pancreatic cancer, CA-125 for ovarian cancer, Carcinoembryonic antigen (CEA) for CRC, and Prostate Specific Antigen (PSA) for prostate cancer. While results from these biomarkers are clinically useful when used in the appropriate manner, many cannot detect cancers at an early stage, lack specificity, and have high false negative/positive rates (Diamandis, 2012; Hanash, 2011). There are also many technical challenges, such as low protein abundancy in samples and difficulties in developing high affinity capture reagents (Fu¨ze´ry, Levin, Chan, & Chan, 2013). MiRNAs offer many advantages over their protein counterparts in many of these regards, as they are stable, abundant in biofluids, and can be easily measured using amplification or hybridization based approaches. Historically, extracellular miRNAs were first observed and characterized in cancer cell lines and the serum of cancer patients (Chen et al., 2008; Mitchell et al., 2008; Valadi et al., 2007), thus significant effort has gone towards identifying (1) circulating miRNAs involved in or associated with various forms of cancer, and (2) their potential influence on microenvironment. Here we provide some updates on the current status and

challenges of identifying circulating miRNAs as biomarkers for several different types of cancers.

Breast Cancer While there has been considerable research focused on genetic risk factors for developing breast cancer (e.g., mutations in BRCA1 or 2) or how breast tumors respond to therapy (presence or absence of ER, PR, and HER2 receptors), work to identify circulating miRNA that may have diagnostic potential is still emerging (Table 13.1). There have been two primary studies looking at the changes of miRNAs in whole blood among breast cancer patients. One group found that the concentration of miR-195, let-7, and miR-155 were elevated in the blood of breast cancer patients compared to controls or other cancer patients using qRT-PCR, and found these three miRNAs to have a discriminative power of 94% in this capacity (Heneghan, Miller, Kelly, Newell, & Kerin, 2010). Another study used microarrays to detect changes in the blood of miRNAs in both breast cancer patients and healthy controls, as well as in a mammary tumorigenesis murine model. In the patient samples, they found 77 miRNAs that exhibited concentration changes, and validated the increase of miR-138 concentration in both human blood and mouse tissue (Waters et al., 2014). Profiling of miRNAs in plasma has also been done in breast cancer patients. Cuk et al. used qRT-PCR and identified several miRNAs (miR-148b, miR-376c, miR-409-3p, and miR801) that were increased in plasma of early stage (I and II) breast cancer patients compared to healthy controls, suggesting these miRNAs should be used in early breast cancer detection (Cuk et al., 2013). The increase of plasma miR148b (along with miR-133a) levels was also confirmed using qRT-PCR by an independent study, which also found that miR-148 is actively secreted by breast cancer cells in

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TABLE 13.1 Subtype

Studies Examining Circulating miRNAs in Breast Cancer Affected Circulating miRNA (up)

Affected Circulating miRNA (down)

Assay

Sample

References

miR-195, let-7, -155

qRT-PCR

W.blood

Heneghan et al. (2010)

miR-138

qRT-PCR

W. blood

Waters et al. (2014)

miR-148b, miR-376c, miR-409-3p, miR-801

qRT-PCR

Plasma

Cuk et al. (2013)

miR-148b, miR-133a

qRT-PCR

Plasma

Shen et al. (2014)

miR-10b, miR-373

qRT-PCR

Plasma/ tumor

Chen et al. (2013)

qRT-PCR

Plasma

Madhavan et al. (2016)

miR-155

qRT-PCR

Serum

Zhu et al. (2009)

miR-21, miR-126, miR-155, miR-199a, miR-335

qRT-PCR

Serum/ tumor

Wang et al. (2010)

Nonmetastatic/ metastatic

miR-10b, miR-34a, miR-155

qRT-PCR

Serum

Roth et al. (2010)

Nonmetastatic/ metastatic

miR-10b, miR-17, miR-34a, miR-93, miR-155, miR-373

qRT-PCR

Serum

Eichelser et al. (2013)

miR-10b, miR-21, miR-125b, miR-145, miR-155, miR-191, miR-382

qRT-PCR

Serum

Mar-Aguilar et al. (2013)

Early-stage

Metastatic Metastatic

Early-stage

miR-141, miR-144, miR-193b, miR-200a, miR-200b, miR-200c, miR-203, miR-210, miR-215, miR-365, miR-375, miR-429, miR-486-5p, miR-801, miR-1260, miR-1274a

miR-505-5p, miR-96-5p

miR-505-5p (with treatment)

Microarray Serum

Matamala et al. (2015)

miR-1246, miR-1307-3p, miR-6861-5p

miR-4634, miR-6875-5p

Microarray Serum

Hamam et al. (2017)

Microarray Serum

Shimomura et al. (2016)

sRNAseq

Zhu et al. (2014)

miR-4270, miR-1225-5p, miR -188-5p, miR-1202, miR-4281, miR-1207-5p, miR-642b-3p, miR-1290, and miR -3141 miR-184, miR-1246, miR-224-5p

miR-598-3p, miR-323b-3p, miR485-5p, miR-382-3p, miR-34c-5p, miR-125b-1-3p, miR-132-5p

Serum/ tumor

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culture (Shen et al., 2014). Looking at the potential correlation between miRNA concentration changes in plasma and in metastatic breast cancer tissue, one study found miR-10b and miR-373 to be overexpressed in breastlymph node metastases, and elevated in plasma from preoperative breast cancer patients with lymph node metastasis compared with patients without metastasis and healthy controls (Chen, Cai, Zhang, Barekati, & Zhong, 2013). Recently, additional circulating plasma miRNAs associated with overall survival of patients with metastatic breast cancer have been identified, including several members of the miR-200 family, which are involved in promoting metastasis and tumor progression (Feng et al., 2014; Madhavan et al., 2016). The majority of the circulating miRNA studies for breast cancer have been done using serum. One of the first groups to look at circulating miRNAs in breast cancer patients used qRT-PCR to measure several miRNAs in the serum of breast cancer patients and found that miR-155 was elevated in cancer patients compared to controls (Zhu, Qin, Atasoy, & Sauter, 2009). Wang et al. followed up with these results and validated miR-155 (Wang, Zheng, Guo, & Ding, 2010). They also found the concentration changes for several additional miRNAs (miR-21, miR-126, miR-199a and miR335) in serum and tumor tissues, and these changes are closely associated with clinical features such as tumor grades and receptor expression status (Wang et al., 2010). Similarly, another study looking at serum miRNAs that might be associated with breast cancer identified miR-10b, miR-34a, and miR-155 to be elevated in breast cancer patients and also associated with tumor stage and metastasis (Roth et al., 2010). An expanded set of these miRNAs (miR-10b, miR-17, miR-34a, miR-93, miR-155, and miR-373) were later used by the same group to show that they can be used to either discriminate breast cancer patients from healthy controls, or further stratify patients

based on different stages of metastasis and HER2 tumor expression status (Eichelser, Flesch-Janys, Chang-Claude, Pantel, & Schwarzenbach, 2013). A similar seven miRNA signature (miR-10b, miR-21, miR-125b, miR145, miR-155, miR-191, and miR-382) was increased in the serum of breast cancer patients, and a panel comprised of miR-145, miR-155, and miR-382 was used to discriminate breast cancer patients from healthy controls with high sensitivity and specificity (Mar-Aguilar et al., 2013). Several recent studies used high-throughput methods to profile circulating miRNAs and identified several previously uncharacterized breast cancer associated miRNAs in serum. As measured by microarrays, the concentrations of miR-505-5p and miR-96-5p were also identified as being significantly increased in the serum of early stage breast cancer patients versus controls, and miR-505-5p levels decreased in early stage breast cancer patients that had undergone treatment (Matamala et al., 2015). Another microarray experiment identified several uncharacterized serum miRNAs that were elevated in breast cancer patients compared to controls, and correlated well with stage and molecular subtype (Hamam et al., 2017). A similar study used microarrays to identify additional serum miRNAs that also performed well in discriminating breast cancer patients from healthy controls, with very high sensitivity (97.3%), specificity of (82.9%), and accuracy (89.7%), even at early stages (Shimomura et al., 2016). Taking a next-generation sequencing (NGS) approach, Zhu et al. used small RNAseq (sRNAseq) to analyze the spectrum of miRNA in breast cancer tumors/healthy tissues and serum samples between breast cancer patients and healthy controls. Initially, they identified many dysregulated miRNAs in tumors (174 between breast cancer tumors and healthy tissue) and in serum (109 between breast cancer patients and healthy controls), but found only 10 miRNAs had common changes between

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CIRCULATING MIRNAS AS POTENTIAL BIOMARKERS FOR CANCERS

tumors and serum (Zhu et al., 2014). Several of these had been previously identified as showing concentration changes in serum (miR-125b and miR-382) (Mar-Aguilar et al., 2013) while others were previously uncharacterized (miR323b, miR-598, and miR-184).

Colorectal Cancer Noninvasive blood biomarkers, such as CEA, have been used for CRC diagnosis, however CEA has very little specificity since its levels also changed in patients with other cancers. While miRNAs have primarily been investigated in CRC tumor biopsies (Boisen et al., 2015; Chang, Mestdagh, Vandesompele, Kerin, & Miller, 2010; Eriksen et al., 2016; Peltier & Latham, 2008; Yang et al., 2016) and stool samples (Ahmed et al., 2009; Ahmed et al., 2013; Wu et al., 2012; Zhu et al., 2016), studies have also been examining the changes of circulating miRNAs in CRC patients. In one of the first studies looking at circulating miRNAs in CRC, Ng et al. found elevated levels of miR-135b, miR-95, miR-222, and miR-17-3p in serum, and miR-92 in the plasma of CRC patients compared to healthy controls (Ng et al., 2009). Further work confirmed the elevated levels of miR-92 (along with miR-29a) in a different cohort of CRC patients (Huang et al., 2010; Zheng et al., 2013). Interestingly, elevated miR-29a and miR-141 serum levels can be used to discriminate metastatic CRC patients from controls (Wang & Gu, 2012). A recent study found that whole serum, serum exosome, and corresponding colon and rectal tumor levels of miR-19a-3p, miR-21-5p, and miR-425-5p were elevated in CRC patients compared to controls, and a panel of all three had good discriminatory ability in a separate validation set of CRC patients and controls (AUC of 0.87) (Zhu et al., 2017). In a similar study, Ogata-Kawata et al. used microarrays to measure exosomal miRNAs isolated from both CRC patient serum and CRC cell lines, and identified several miRNAs (let-7a,

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miR-1229, miR-1246, miR-150, miR-21, miR-223, and miR-23a) that were elevated (Ogata-Kawata et al., 2014). Similar studies have been undertaken for plasma miRNA profiling in CRC patients. As in serum, the level of circulating miR-141 can also be used as a prognostic marker, as elevated plasma miR-141 is associated with poor outcomes in metastatic CRC patients (Cheng et al., 2011). From pooled plasma samples, one group used qRT-PCR profiling to identify nine miRNAs (miR-18a, miR-20a, miR-21, miR-29a, miR-92a, miR-106b, miR-133a, miR-143, and miR-145) that were elevated in CRC plasma samples compared to neoplasm-free controls, and had moderate discriminatory function (AUC of 0.745) in a separate validation set of CRC patients and controls (Luo, Stock, Burwinkel, & Brenner, 2013). Gira´ldez et al. used sRNAseq to profile miRNAs from the plasma of CRC patients and healthy controls, and identified 13 miRNAs exhibiting concentration changes, and were able to validate six (miR-18a, miR-19a, miR-19b, miR-15b, miR-29a, and miR-335) with qRT-PCR. These miRNAs had moderate discriminatory power (AUC of 0.80) to identify CRC patients in an independent validation cohort (Gira´ldez et al., 2013). Some miRNAs have been shown to have prognostic application in CRC. For example, Hansen et al. showed that miR-126 levels in plasma can be used to evaluate responses to treatment in metastatic CRC patients undergoing chemotherapy (Hansen, Carlsen, Heegaard, Sørensen, & Jakobsen, 2015), and Toiyama et al. demonstrated that miR-200c levels in serum are associated with evaluating lymph node metastasis and long term survival in CRC patients (Toiyama et al., 2014) (Table 13.2).

Lung Cancer Nonsmall cell lung cancer (NSCLC) accounts for the majority of cases of lung cancers globally,

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TABLE 13.2 Studies Examining Circulating miRNAs in Colorectal Cancer Subtype

Metastatic

Metastatic

Affected Circulating miRNA (up)

Affected Circulating miRNA (down) Assay

Sample

References

miR-135b, miR-95, miR-222, miR-17-3p, miR-92

qRT-PCR

Serum

Ng et al. (2009)

miR-92, miR-29a

qRT-PCR

Serum

Huang et al. (2010)

miR-92

qRT-PCR

Serum

Zheng et al. (2013)

miR-29a, miR-141

qRT-PCR

Serum

Wang and Gu (2012)

miR-19a-3p, miR-21-5p, miR-425-5p

qRT-PCR

Serum/ tumor

Zhu et al. (2017)

let-7a, miR-1229, miR-1246, miR-150, miR-21, miR-223, miR-23a

microarray

Serum exosomes

Ogata-Kawata et al. (2014)

miR-141

qRT-PCR

Plasma

Cheng et al. (2011)

miR-18a, miR-20a, miR-21, miR-29a, miR-92a, miR-106b, miR-133a, miR-143, miR-145

qRT-PCR

Plasma

Luo et al. (2013)

miR-18a, miR-19a, miR-19b, miR-15b, miR-29a, miR-335

sRNAseq/ qRT-PCR

Plasma

Gira´ldez et al. (2013)

qRT-PCR

Plasma

Hansen et al. (2015)

Metastatic

miR-126 (nonresponders to therapy)

miR-126 (responders to therapy)

Metastatic

miR-200c (metastatsis)

miR-200c (long termsurvival)

so major effort has been taken to identify biomarkers for NSCLC. MiRNA signatures present in tumor samples are a valuable tool in predicting survival and relapse in NSCLC (Yu et al., 2008). Accordingly, similar studies looking at their counterparts present in circulation as a noninvasive alternative have been undertaken. In serum, Chen et al. made one of the first observational reports of cell-free miRNAs in circulation, and found the increase of miR-24 and miR-223 levels in samples from NSCLC patients versus healthy controls. Another group also found the increase of miR-24 (along with miR-21, miR-205, and miR-30d) levels in the sera of NSCLC patients compared to controls (Le et al., 2012). Not only did this panel of four

miRNAs (miR-24, miR-21, miR-205 and miR30d) have good performance at discriminating between NSCLC and controls, but it also performed better than the traditional serum biomarker, CAE in the same samples. Interestingly, serum levels of miR-21 and miR-24 decreased in postoperative NSCLC patients in the same study. Hu et al. used sRNAseq and RT-qPCR platforms, and identified several serum miRNAs (miR-1, miR-30d, miR-499, and miR-486) that were associated with either short or long term survival in NSCLC patients (Hu et al., 2010). In a separate study, serum miR-486 was decreased in NSCLC patients compared to controls based on results from microarrays and qRT-PCR (Petriella et al., 2015). Another group used qRT-PCR profiling of

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serum miRNAs to develop a 34-miRNA diagnostic panel (Table 13.3) for early stage NSCLC that could accurately discriminate NSCLC patients at different stages (Bianchi et al., 2011). Similar studies have been done to profile plasma miRNAs in NSCLC patients. Shen et al. identified a signature of six miRNAs (miR-21, miR-182, miR-210, miR-126, and miR-486-5p) that exhibited similar concentration changes

between plasma and corresponding tumor tissues in NSCLC patients, and was able to discriminate NSCLC patients from healthy controls (Shen et al., 2011). As discussed previously, both miR-21 and miR-486 show the same serum concentration changes in NSCLC patients in other studies (Table 13.3). Interestingly, elevated serum, plasma, and whole blood levels of miR-21 have been shown

TABLE 13.3 Studies Examining Circulating miRNAs in Nonsmall Cell Lung Cancer Subtype

Affected Circulating miRNA (up)

Affected Circulating miRNA (down)

Assay

Sample

References

qRT-PCR

Serum

Chen et al. (2008)

qRT-PCR

Serum

Le et al. (2012)

NGS, qRT-PCR

Serum

Hu et al. (2010)

miR-486-5p

Microarray, qRT-PCR

Serum

Petriella et al. (2015)

miR-32, miR-133b, miR-566, miR-432*, miR-223, miR-29a, miR-148a, miR-142-5p, miR-140-5p

miR-92a, miR-484, miR-328, miR-191, miR-376a, miR-342-3p, miR-331-2p, miR-30c, miR-285p, miR-98, miR-17, miR-26b, miR-374a, miR-30b, miR-26a, miR-134-3p, miR-103, miR-126, let-7a, let-7d, let-7b, miR-22. miR-148, miR-139

qRT-PCR

Serum

Bianchi et al. (2011)

miR-21, miR-182, miR-210

miR-126, miR-486-5p

qRT-PCR

Plasma/ tumor

Shen et al. (2011)

Microarray, qRT-PCR

Plasma/ tumor

Boeri et al. (2011)

miR-218, miR-566, miR-661, miR-485-3p, miR-203, miR-517b, miR-122, miR-182, miR-193a, miR-411, miR-450b-5p

qRT-PCR

plasma

Wozniak et al. (2015)

miR-139-5p, miR-378a, miR-379,

qRT-PCR

Plasma Cazzoli et al. exosomes (2013)

qRT-PCR

W. blood Li et al. (2011)

miR-24, miR-223 miR-21, miR-205, miR-30d, and miR-24

miR-21, miR-24 (postoperative)

miR-1, miR-499 (long survivial); miR-30d, miR-486 (short survivial) Metastatic Early stage

miR-221, miR-660, miR-486-5p, miR-28-3p, miR-197, miR-106a, miR-451, miR-140-5p, miR-16 Metastatic let-7c, let-7b, miR-192, miR-200c, miR-155, miR-519a, miR-641, miR-520f, miR-206, miR-304, miR-1243, miR-720, miR-543, miR-1276 miR-200b-5p miR-21

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to be associated with poor prognosis of NSCLC patients in several studies (Gao et al., 2010; Li, Li, Ouyang, Hu, & Tang, 2011; Liu et al., 2012; Wang et al., 2011) and is elevated in the sputum of NSCLC patients (Xie et al., 2010). Like with serum, several groups have used plasma miRNA profiling to develop larger panels of diagnostic miRNAs that can be used to discriminate NSCLC patients from heathy controls, and correlate concentration changes in plasma to expression changes in tumor samples (Boeri et al., 2011; Wozniak et al., 2015). Cazzoli et al. specifically looked at miRNAs from plasma exosomes in lung cancer patients, and identified a six miRNA signature (miR151a-5p, miR-30a-3p, miR-200b-5p, miR-629, miR-100, and miR-154-3p) that discriminates lung cancer patients from healthy controls with an AUC of 0.90, and patients with lung adenocarcinoma from those with granuloma with an AUC of 0.760 (Cazzoli et al., 2013).

Pancreatic Cancer Pancreatic cancer is one of the deadliest cancers due to the lack of early stage clinical symptoms and diagnostic tools. The 5-year survival rate for the most common type of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) is only about 5%. MiRNA studies for PDAC have not only been done in circulation (plasma, serum, whole blood), but also in pancreatic biofluids, such as bile and pancreatic juice. In plasma, the levels of four miRNAs (miR-21, miR-155, miR-210, and miR-196a) were shown to be increased in the plasma of PDAC patients versus healthy controls when measured by qRT-PCR (Wang et al., 2009). Another group validated the increase of plasma miR-21 levels in PDAC patients, and found that when combined with miR-483-3p, it performs better than CA19-9 in discriminating PDAC patients

from heathy controls (Abue et al., 2015). Liu et al. also validated the elevated levels of miR196a in plasma, and found when combined with miR-16 (also elevated in PDAC patient plasma) and CA 19-9, results provided the most effective diagnosis of PDAC. Moreover, an elevated miR-196a concentration in plasma correlates with a lower survival rate in advanced stage PDAC patients (Kong et al., 2011). Other studies have highlighted additional plasma miRNAs that could have prognostic application. For example, the miR-18a level in plasma is elevated in preoperative PDAC patients, but decreased after surgery (whereas CA 19-9 level did not change) (Morimura et al., 2011). The concentration of miR-182 is increased in PDAC patient plasma samples and the level inversely correlates with overall survival and disease-free survival (Chen, Yang, Xiao, Zhu, & Li, 2014). In serum and whole blood, several similar diagnostic cell-free miRNAs for PDAC have been developed, for example, the miR-21 level is also increased in serum and whole blood in PDAC patients. Liu et al. identified a seven miRNA panel (miR-20a, miR-21, miR-24, miR25, miR-99a, miR-185, and miR-191) in serum from PDAC patients that can be used to discriminate PDAC from chronic pancreatitis (CP) patients (Liu et al., 2012), where CA 19-9 lacks this capability. Like in plasma, combining the level of miR-21 in serum with other miRNAs such as miR-34a can enhance its diagnostic performance (Alemar et al., 2016). Looking at whole blood, Schultz et al. identified a miRNA biomarker panel (see Table 13.4) that outperformed CA 19-9 in discriminating PDAC patients from healthy controls, but performed better when used in combination with CA 19-9 measurement (Schultz, Dehlendorff, & Jensen, 2014). The pancreas secretes proximal biofluid, including pancreatic juice and bile, which offer an opportunity to examine the local tumor microenvironment more closely (though often

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TABLE 13.4 Studies Examining Circulating miRNAs in Pancreatic Ductal Adenocarcinoma Subtype Affected Circulating miRNA (up)

Affected Circulating miRNA (down)

Assay

Sample

References

miR-21, miR-210, miR-155, miR-196a

qRT-PCR

Plasma

Wang et al. (2009)

miR-21, miR-483-3p

qRT-PCR

Plasma

Abue et al. (2015)

miR-16, miR-196a

qRT-PCR

Plasma

Liu et al. (2012), Kong et al. (2011)

miR-18a

qRT-PCR

Plasma

Morimura et al. (2011)

miR-182

qRT-PCR

Plasma

Chen et al. (2014)

miR-20a, miR-21, miR-24, miR-25, miR-99a, miR-185, miR-191

NGS, qRTPCR

Serum

Liu et al. (2012)

miR-21, miR-34a

qRT-PCR

Serum

Alemar et al. (2016)

miR-145, miR-150, miR-223, miR-636, miR-26b, miR-34a, miR-122, miR-126-5p, miR-145, miR-150, miR-223, miR-505, miR-636, miR-885.5p

qRT-PCR

W. blood

Schultz et al. (2014)

miR-10b, miR-155, miR-106b, miR-30c, miR-212

qRT-PCR

Bile

Cote et al. (2014)

miR-205, miR-210, miR-492, miR-1247

microarray, qRT-PCR

Pancreatic juice

Wang et al. (2014)

at the expense of increased invasiveness). One study used microarrays and qRT-PCR to measure miRNAs in pancreatic juice, and found the concentrations of miR-205, miR-210, miR492, and miR-1247 were not only increased in PDAC patients, but were also able to discriminate between PDAC and non-PDAC controls alone, or with increased sensitivity when combined with CA 19-9 results (Wang et al., 2014). Using the bile of PDAC patients, Cote et al. examined several miRNAs known to be involved in PDAC and found five (miR-10b, miR-155, miR-106b, miR-30c, and miR-212) that were elevated and the five miRNAs showed better diagnostic performance in bile compared to plasma, suggesting that miRNA in bile may offer more precise diagnosis in detecting PDAC.

CHALLENGES AND OPPORTUNITIES FOR miRNA BASED BIOMARKERS Challenges: There are number of factors that could affect the spectrum of circulating miRNA. While some of these factors are associated with samples such as sample types, sample collection time, and gender or state of the subject, other causes are experimental procedure related including sample processing, storage condition, RNA purification methods, or measurement platform (Lee, Baxter, Lee, Scherler, & Wang, 2017; Weber et al., 2010) To ensure sample consistency, several steps can be taken during sample collection and preparation, including limiting contamination from other elements (such as platelets or RBCs when

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13. CIRCULATING MIRNAS AS TUMOR BIOMARKERS

preparing plasma or serum), and collecting samples under the similar conditions (controlling for collection time and diet). The measurement platform greatly impacts the miRNA spectra and can contribute to bias in what miRNAs are prominently detected (Git et al., 2010; Wang et al., 2012). This is even true for intraplatform-specific differences (that is the same platform, but using reagents from different vendors) (Mestdagh et al., 2014). Some of the common platforms discussed in this chapter include qRT-PCR, microarrays, and NGS. qRTpCR is the most common measurement platform used in literature (see Tables 13.113.4) and has been considered the “gold standard” of assaying miRNAs as it is sensitive, quantifiable, and relatively low per assay cost. However, limitations include that it is low throughput, cannot detect novel miRNAs, and is affected by primer design. Microarrays have an advantage over qRT-PCR, as they can assay many miRNAs simultaneously, however they require larger amounts of RNA input, and also cannot identify novel miRNAs. Microarray probe design also affects its signal. NGS-based miRNA measurement (sRNAseq) allows for the identification of novel miRNAs, and avoids the sequencedependent primer and probe issues of qRT-PCR and microarrays. However, the sequencing of miRNAs from biofluids can be challenging, as inconsistencies exist between sequencing library construction kits, and computational support is needed to analyze the data (Yuan et al., 2016). Opportunities: Although we do not fully understand all the factors that could impact the level of miRNA in circulation, there is compelling evidence indicating that circulating RNAs play important roles in cancer diagnosis. Some cancer associated miRNAs along with other molecules—large RNAs, proteins, lipids— encapsulated in EVs may serve as biomarkers to reflect the status of cells in the disease site. While this chapter has primarily discussed miRNAs as potential biomarkers, other RNA species, like mRNAs, might also provide

insights to diseases, as there is a large diversity of cell-free mRNAs present in circulation, some of which have been proven to be useful in the detection and monitoring of various cancers (Garcı´a et al., 2008; Garcia et al., 2009; MarchVillalba et al., 2012). Additionally, while the majority of studies discussed here do not specifically look at EVs like exosomes or oncosomes, there has been considerable interest in using EVs as a source for circulating RNA biomarkers, as some of these vesicles are derived from tumors/CTCs. Further work detailing the intricate molecular cargo of these EVs is essential to understand the true source of these EVs, what their final targets may be, and how this may influence the local tumor microenvironment.

Acknowledgments We appreciate critical comments from Shannon Fallen. This work is supported by research contracts from DOD (W81XWH-16-1-0301 and W911NF-17-2-0086) and DTRA (HDTRA1-13-C-0055), and NIH grants (U01HL126496-02, R56HL133887, U01CA213330 and R01 DA040395).

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