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Liquid biopsy and tumor heterogeneity in metastatic solid tumors: the potentiality of blood samples

Abstract

In a large number of cancer types, treatment selection depends on the presence of specific tumor biomarkers. Due to the dynamic nature of cancer, very often these predictive biomarkers are not uniformly present in all cancer cells. Tumor heterogeneity represents indeed one of the main causes of therapeutic failure, and its decoding remains a major ongoing challenge in the field.

Liquid biopsy is the sampling and analysis of non-solid biological tissue often through rapid and non-invasive methods, which allows the assessment in real-time of the evolving landscape of cancer. Samples can be obtained from blood and most other bodily fluids. A blood-based liquid biopsy can capture circulating tumor cells and leukocytes, as well as circulating tumor-derived nucleic acids.

In this review, we discuss the current and possibly future applications of blood-based liquid biopsy in oncology, its advantages and its limitations in clinical practice. We specifically focused on its role as a tool to capture tumor heterogeneity in metastatic cancer patients.

Background

In the last decades, advances in precision medicine have radically changed the therapeutic scenario in medical oncology. The use and efficacy of such tailored therapies, including tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors, often relies on the presence of specific tumor biomarkers, such as activating gene mutations or expression levels of specific proteins [1]. In many cases, however, these biomarkers are not uniformly present in all cancer cells, and such heterogeneity might hinder the therapeutic efficacy of tailored therapies [2].

Tumor heterogeneity refers to the coexistence of different biological, morphological, phenotypic and genotypic profiles, between tumors (inter-tumor heterogeneity) and within tumors (intra-tumor heterogeneity). It exists at multiple levels and may be present within different tumor regions or between primary cancer and metastases (spatial heterogeneity), or during the course of disease progression (temporal heterogeneity). The tumor microenvironment (TME), defined as the complex ecosystem in which cancer cells interact with non-cancerous cells, represents an additional source of intra-tumor heterogeneity. The TME includes proliferating tumor cells, the tumor stroma, surrounding blood vessels, and immune cells. In particular, the dynamic interplay between cancer and immune cells has become an issue of great interest. There is growing recognition that immunoediting, the process whereby the immune system can both counteract and promote tumor development, contributes to cancer heterogeneity and represents a potential source of biomarkers [3,4,5,6].

Tissue biopsy is the most widely used method for categorizing tumors and detecting biomarkers. However, it has a number of limitations: it is an invasive method; it is not always feasible or repeatable; it provides information limited to a single point in space and time, therefore failing to capture the complex tumor heterogeneity.

To overcome these limitations, in recent years there has been an increasing development of liquid biopsy, defined as the sampling and analysis of non-solid biological tissue, such as blood and most other bodily fluids (e.g. urine, saliva, ascites, pleural effusion or cerebrospinal fluid). Liquid biopsy often represents a rapid and non-invasive alternative to tissue biopsy. Additionally, it allows for the longitudinal evaluation of cancer evolution. Blood-based liquid biopsy consists in the isolation and analysis of tumor-derived or tumor-associated components that circulate in the bloodstream (Fig. 1): circulating tumor cells (CTCs), circulating leukocytes, as well as tumor-derived circulating nucleic acids, such as cell-free circulating tumor DNA (ctDNA), microRNA (miRNA) and non-coding RNAs (ncRNAs) [7, 8].

Fig. 1
figure1

Liquid biopsy, non invasive and low-risk procedure, allows to monitor the changing and evolving landscape of cancer in real-time during the course of disease. In blood vessels, circulating tumor DNA (ctDNA), circulating tumor RNA (ctRNA), non-coding RNAs, circulating tumor cells (CTCs), and circulating leukocytes represent promising biomarkers to assess tumor heterogeneity and patients’ treatment response

In this review, we summarize the evidences on the role of blood-based liquid biopsy as a potential tool to capture tumor heterogeneity in metastatic cancer patients, and its current and future role in identifying biomarkers that might contribute to the therapeutic decision-making.

Circulating tumor cells

The longitudinal, non-invasive, and comprehensive analysis of the genetically and phenotypically heterogeneous cancer cells is a major challenge for the modern oncologist. CTCs are defined as cancer cells found in the bloodstream. They are simultaneously shed both by the primary and metastatic sites, possibly providing a direct measure of spatial heterogeneity [9]. Given their rarity (in the range of 0.1–10 CTCs per mL of whole blood), a number of strategies have been developed to detect their presence in blood samples, based on either physical and morphological proprieties (e.g. nuclear irregularity, high nuclear/cytoplasmic ratio), or biological characteristics (e.g. the expression of the epithelial markers such as epithelial cell adhesion molecule, EpCAM, and cytokeratins) [10]. However, currently used CTC assays do not reach 100% of sensitivity and specificity for cancer detection. Indeed, patients with benign inflammatory colon diseases might harbor viable circulating epithelial cells that can be detected with current CTC assays [11]. Conversely, patients with epithelial cancers might present circulating cancer cells expressing mesenchymal rather than epithelial markers, because of epithelial-to-mesenchymal transition (EMT), a phenomenon associated to disease progression [12,13,14]. Moreover, it should be noted that CTC clusters are more difficult to detect with the aforementioned conventional methods, and yet they likely have higher metastatic potential compared to individual CTCs [15, 16]. Despite these limitations, CTCs have shown prognostic implications in a variety of cancer types, including breast cancer (BC), prostate cancer (PC), non-small cell lung cancer (NSCLC), colorectal cancer (CRC) and others [17]. Importantly, one of the main advantages of CTCs compared to other analytes of liquid biopsies is that CTCs can be cultured and expanded ex vivo to perform functional tests or subsequent single-cell sequencing analyses [18, 19].

CTCs and spatial heterogeneity

A number of studies in different diseases have investigated CTCs as a way to gain insights in tumor spatial heterogeneity (Table 1).

Table 1 CTC and Spatial heterogeneity

A different phenotype between primary tumor and CTCs can potentially predict poorer response to conventional anticancer therapy: for example, in metastatic BC (mBC), estrogen receptor (ER)-negative CTCs can be detected in patients diagnosed with ER-positive mBC [20] and HER2-positive CTC clones can be found in patients affected by HER2-negative mBC [21]. The presence of such CTC clones might anticipate failure to established therapies respectively targeting the estrogen axis and HER2.

Similarly, in metastatic castration-resistant PC (mCRPC) patients it is possible to identify CTCs with different levels of regulation of the androgen receptor (AR) signaling pathway [22]. RNA sequencing of individual CTCs showed heterogeneity in the expression of AR alterations, including splice variants, and in the expression of AR-independent pathways, such as glucocorticoid receptor and non-canonical Wnt signaling responsible for resistance to antiandrogen therapies [23].

Moreover, CTCs have shown a possible role as marker of spatial heterogeneity also in metastatic CRC (mCRC). The concordance of the KRAS status between primary tumors and CTCs varies between 50% [26] and 77% [24], supporting the existence of different clones within primary mCRC. Additionally, CTCs from left-side mCRC more frequently display a mesenchymal phenotype coherent with EMT, while CTCs from right-side mCRC show an apoptotic morphology [27].

EMT may be significantly involved in metastasization process of several cancer types; in fact, CTCs from patients affected by Hepatocellular Carcinoma (HCC) display epithelial phenotype at early stages, but they undergo mesenchymal transformation during spreading to metastatic sites [28].

Given the growing interest in the relationship between cancer and immune heterogeneity, immune checkpoint biomarkers have been analyzed on CTCs, in particular in metastatic NSCLC and mBC patients, who show high inter-individual heterogeneity of PD-L1 expression [29]. In particular, CTCs resulted to be more frequently PD-L1 positive in comparison with tissue samples in NSCLC, suggesting that CTCs may reflect spatial heterogeneity better than tissue biopsy [30] or alternatively that PD-L1 positive cells are more likely to acquire features consistent with CTCs.

Finally, a number of studies are exploring whether CTCs-derived information might guide treatment decisions. For example, quantification of the phenotypic heterogeneity of mCRPC CTCs [31] or their expression of nuclear AR splice variant 7 (AR-V7) [25] might help to guide the choice between AR signaling inhibitors and taxanes: lower CTC degree of heterogeneity is associated with better outcomes during AR Signaling Inhibitors (ARSI), while AR-V7 positive CTC predict with better outcomes during taxanes over ARSI.

CTCs and temporal heterogeneity

Although not validated in clinical practice, both the CTCs count and their characterization are being explored as tools for monitoring the evolution of metastatic cancers as well as their sensitivity to anti-neoplastic drugs. For example, the reduction of the number of CTCs during treatment is associated with lower probability of disease progression, and longer progression-free and overall survivals in HER2-positive and HER2-negative mBC patients, but not in triple negative patients [32].

Several other examples of dynamic cellular changes that might be monitored over time with CTCs exists: for example, in mCRC patients it is possible to monitor the mutation status of KRAS in CTCs to anticipate changes in therapy [33]. Alternatively, next-generation sequencing (NGS) can be used in CTCs to assess multiple genes associated to resistance to therapies targeting the epidermal growth factor receptor (EGFR) [34]. Similarly, in EGFR-mutated NSCLC cancer progressing to anti-EGFR TKIs, a number of studies have evaluated the expression of resistance mutations and rearrangements [35], such the EGFR T790M secondary mutation [36] or MET amplification [37]. Finally, CTCs can be used to longitudinally assess the presence and intra-patient heterogeneity of PIK3CA mutations [38] which are associated to resistance to anti-HER2 therapies in mBC patients [39] (Table 2).

Table 2 CTC and Temporal heterogeneity

Circulating leukocytes

The number, subsets, and molecular characteristics of leukocytes have been analyzed in cancer patients as prognostic and predictive biomarkers for several decades. Notoriously, the neutrophil-to-lymphocyte ratio has been proposed as an inflammatory biomarker elevated in patients with more advanced or aggressive diseases [40]. T cell receptor (TCR) profiling and surface immunoprofiling of circulating leukocytes are emerging powerful tools to detect the immunological cancer heterogeneity (Table 3).

Table 3 Tumor heterogeneity: potentiality of TCR profiling and circulating leukocytes

TCR profiling of lymphocytes from cancer patients

The TCR is a polymorphic receptor dictating antigen specificity of the T-cell mediated immunity. TCR sequencing has been used as a tool to measure the heterogeneity of the T cells infiltrating tumor samples and of the immunogenic neoantigen burden [55,56,57]. In lung cancer patients, TCR spatial heterogeneity reflected the heterogeneity of the mutational landscape. In particular, the number of ubiquitous and regional TCRs correlated with the number of ubiquitous and regional non-synonymous mutations, respectively [58]. The role of TCR profiling in circulating lymphocytes is less clear. In fact, the TCR repertoires of tumor-infiltrating and matched peripheral lymphocytes only partially overlap [59, 60]. Nevertheless, tumor-specific T cells have been identified in the peripheral blood of cancer patients [61, 62]. Whether these repertoires might predict response to immunotherapy in metastatic cancer patients is still unclear. In melanoma patients, the peripheral TCR repertoire correlated with responses to checkpoint inhibitors [42,43,44], and similar results were reported for pancreatic ductal adenocarcinoma and other solid tumors [41]. Similarly, the evaluation of peripheral TCR repertoire of PD-1 + CD8+ lymphocytes also showed promising results as a non-invasive approach for selecting metastatic NSCLC patients who could benefit from immune checkpoint blockade [51, 52, 63].

Immunoprofiling of peripheral leukocytes from cancer patients

With the technological advancements brought by flow cytometry first and mass cytometry after, our capability to identify rare subsets of circulating/ leukocytes has grown exponentially [64]. The predictive role of specific leukocyte subsets in patients undergoing anti-PD-1 and anti-CTLA4 immunotherapy has been investigated mostly in metastatic melanoma and lung cancer patients.

The baseline frequency of CD14 + CD16-HLA-DRhi monocytes [45], CD69 + MIP-1β + NK cells [46], and PD-1 + CD56+ T cells [47] were reported to be predictors of clinical response in metastatic melanoma patients treated with anti-PD-1 immunotherapy. In the same setting, the post-treatment increase of the specific subset of central memory CD4+ T cells, harboring the CD27 + FAS-CD45RA-CCR7+ phenotype, was associated to prolonged clinical responses [48]. Moreover, the decrease in the percentage of dysfunctional PD-1 + CD38hi CD8+ cells was also correlated with immunotherapy benefit [49]. In advanced lung cancer patients, baseline percentage of HLA-DR monocytes and dendritic cells also correlated to the response to PD-1 inhibitor therapy [53]. In both lung cancer and melanoma patients, elevated baseline frequencies of CD4 + Foxp3- T cells expressing PD-1 and/or lack of their significant down-modulation after PD-1 blockade resulted in a higher risk of death [54].

Finally, after treatment with anti-CTLA4 therapy, the levels of circulating CD33 + CD11b + HLA-DR- myeloid derived suppressor cells correlated with survival [50] in melanoma patients, as well as distinct CD4+ and CD8+ memory T cell subsets [46].

As more evidences accumulates, it emerges that cellular immunoprofiles and changes in TCR repertoires could predict responses to anti-PD1 and anti-CTLA4 therapies. It is yet unclear whether these biomarkers might represent alternative or complementary analyses in addition to more other markers for immunotherapy response (e.g. immunohistochemical analysis of PD-L1 or tumor mutation burden).

Although profiling of circulating leukocytes is a very promising tool, the absence of standardized protocols and the requirement of very sophisticated and expensive technologies to perform such analyses limit its use in the clinical setting.

Circulating DNA

The discovery that blood-derived circulating-free DNA (cfDNA) contains tumor-specific genetic and epigenetic alterations has provided a solid ground to clinical usage of circulating tumor DNA (ctDNA) as a biomarker. Indeed, in the peripheral blood of cancer patients the amount of cfDNA is higher than in healthy subjects and it is partially composed by ctDNA directly released by tumor cells after apoptosis, necrosis or active secretion [65]. Due to the extremely low fraction of ctDNA, highly sensitive and advanced molecular detection technologies, as well as ctDNA-specific isolation methods are required.

The rapid development of new molecular technologies, like NGS and digital PCR (dPCR) has facilitated the clinical applications of ctDNA. In particular, dPCR is widely used for its low costs and high sensitivity that allows detecting mutations whose frequency is less than 0.1%. Nevertheless, PCR-based methods can only screen for known mutations, so their clinical applications are limited. At contrary, NGS platforms are less sensitive (detecting mutations with a frequency < 1%), but they are able to detect unknown genome-wide DNA mutations. Unfortunately, the routine use of NGS platforms in clinic is currently limited by their high costs. A frequently explored solution is the combination of the two techniques, NGS providing a first broad and exploratory view of mutation profile, with dPCR used to validate NGS results and to monitor the identified mutations over time, saving resources [66].

Overall, the quantitation and analysis of ctDNA can provide relevant clinical information about tumor burden, stage, vascularity, and therapy response (Table 4) [67, 68].

Table 4 Tumor Heterogeneity: significance of ctDNA

The potential role of ctDNA in metastatic disease

Several evidences reported that somatic alterations in commonly mutated genes can be identified in ctDNA and, in some cases, ctDNA allows to detect additional mutations not found by sequencing of a single metastatic lesion. In this regard, Chu et al. showed that the sequencing of ctDNA through NGS systems identifies mutations in ESR1, the gene encoding the ER, not detected in the corresponding biopsy of a metastatic lesion in mBC patients [69]. These results support the increasingly evidences of heterogeneity between different metastatic sites within the same patient. In addition, ESR1 mutations found by dPCR in ctDNA of mBC patients were associated with resistance to endocrine therapy suggesting that ctDNA represents a more sensitive strategy to monitor treatment efficacy [70]. dPCR sequencing of baseline plasma DNA from SoFEA and PALOMA-3 trials identified ESR1 mutations that predicted resistance to exemestane and sensitivity to fulvestrant, helping clinicians to choose the best treatment strategy for patients [71]. Similarly, alterations in phosphatidylinositol 3-kinase (PI3K) PIK3CA gene, the most commonly mutated oncogene in BC, have been identified in ctDNA [72]. Since PIK3CA mutational status can change upon disease recurrence, ctDNA analysis might provide an excellent tool to monitor sub-clonal changes in real-time. Indeed, in the PALOMA-3 study a drop in PIK3CA ctDNA levels after 15 days of therapy with fulvestrant and palbociclib, strongly predicted PFS [73]. Notably, ctDNA is currently gaining momentum not only in the endocrine therapy setting, but also in detecting HER2 mutations. In particular, ctDNA analysis of HER2mut variant allele frequency demonstrated to be predictive of response to neratinib with sensitivity of 79% and a specificity of 100%, when compared to tumor tissue analysis [74]. Finally, in triple negative mBC patients, a ≥ 10% ctDNA fraction and the presence of copy number gain or amplification at specific loci was associated with significantly worse outcomes [75, 76].

Several studies reported the detection of EGFR activating mutations in ctDNA of patients with advanced NSCLC [77, 78]. The multicenter ASSESS study demonstrated that ctDNA is a feasible sample type for real-world EGFR mutation testing in metastatic NSCLC patients [79]. In 2015, EMA approved the use of ctDNA for EGFR mutation assessment. A year later, FDA approved COBAS EGFR Mutation Test v2 (Roche Molecular Systems, Inc.) for the detection of EGFR mutations in liquid biopsy, when tissue biopsy is not available, to identify metastatic NSCLC patients eligible for anti-EGFR treatment. Importantly, EGFR-T790M mutation, the most frequent alteration associated with TKI resistance, can be detected in ctDNA as an alternative to tumor DNA derived from a tissue sample [80]. Although the concordance is not full, ctDNA can replace the solid biopsy when the last is not feasible, it can be repeated several times and represents an excellent way for monitoring the treatment with anti-EGFR targeted therapies and for identifying novel resistance mutations [81]. Moreover, in metastatic EGFR mutated NSCLC patients, therapeutic response significantly correlate to the longitudinal quantitative changes in plasma ctDNA [82].

ctDNA has demonstrated a particular utility in monitoring treatment response and identifying mechanisms of resistance in metastatic CRC (mCRC). In particular, a high concordance between the mutational status of KRAS, NRAS and BRAF in the tissue and in ctDNA of CRC patients was found [83]. Interestingly, ctDNA analyses often detected KRAS mutations not detected in the surgical specimen [84]. There are evidences that ctDNA analysis could inform about clonal heterogeneity and subclonal changes in real time [85, 86]. In this regard, Siravegna et al. demonstrated that ctDNA detection allowed to track clonal evolution during therapies with anti-EGFR antibodies. In particular, time-course profiles of ctDNA of patients treated with cetuximab and panitumumab revealed that mutant RAS clones, which rise in blood during EGFR blockade, decline upon withdrawal of EGFR-specific antibodies. In this way, EGFR inhibitor can be rechallenged leading again to patients’ response [84]. In addition, longitudinal analysis of ctDNA showed that several mutations rapidly emerge during EGFR blockades, often before radiological relapse [87]. Recently, Kato S et al., reported that 79% of analyzed advanced CRC patients presented ctDNA genomic alterations in TP53 (51% of patients), KRAS (34%), APC (27%), BRAF (16%), PIK3CA (16%), and EGFR (15%) genes. The authors showed that ctDNA could be helpful for clinicians in the definition of the most appropriate treatment for patients; indeed patients who received the matched targeted-therapy showed better responses compared with patients who received unmatched therapies [88].

ctDNA has demonstrated to be a promising source of clinically relevant information also in PC. A recent study demonstrated that primary tissue and ctDNA share relevant somatic alterations, suggesting that either is suitable for molecular subtyping in de novo metastatic castration-sensitive PC (mCSPC) [89]. Other evidences demonstrated that mutations in the AR gene can be effectively detected in ctDNA of mCRPC patients and provide insights into treatment response and resistance [90, 91].

In conclusion, the determination of EGFR mutations in ctDNA to guide anti-EGFR treatment in NSCLC patients is the first and so far only approved ctDNA assay. The main limitation in this field is related to the low abundance of ctDNA, whose detection requires advanced molecular platforms (NGS or dPCR) still too expensive to be routinely used in the clinics. Nevertheless, the continuous development of cheaper high-throughput sequencing techniques will most likely consolidate the evaluation of ctDNA in liquid biopsies as a promising tool in the next future [66].

Circulating RNA

The development of RNA-based biomarkers is seemingly less successful compared to the other analytes of blood-based liquid biopsies, such as CTCs and ctDNA. Their use is in fact limited by the risk of contamination during sample isolation extraction, which is also present in the case of ctDNA, but also by their higher instability and lower abundance. However, recent studies have shown a broad potential that, if validated, could have important implications in future clinical practice [92,93,94,95]. Circulating RNA, for instance, may help clinicians to track therapy response or resistance that could reflect tumor heterogeneity.

Cancer cells can release RNA into the bloodstream through different mechanisms, some of which are mediated by microvesicles, as exosomes. Exosomes are cell-derived extracellular vesicles released by different cell types, including immune cells and cancer cells. They have a key role in cell-to-cell communication and, more recently, are coming to light as players of tumor-specific process such as proliferation and progression [96,97,98].

Circulating RNAs might provide real-time information on cancer-related events and could have a prognostic role. Among these, circulating mRNAs have been demonstrated to be a useful biomarker in monitoring tumor progression, especially in PC. In particular, the presence of AR splice variant 7 (AR-V7) transcripts in mCRPC patients has been correlated with shorter time to treatment failure. Moreover, AR-V7 RNA showed prognostic value in estimating the OS of these patients [99].

However, due to their higher stability, non-coding RNAs are significantly more abundant than mRNAs in the bloodstream. Therefore, in the last years the research has focused on non-coding RNAs and their role as biomarkers in metastatic cancer diseases.

microRNA

Among non-coding RNAs, miRNAs are the best promising non-invasive biomarker due to the development of NGS technologies that have enabled the sequencing of the complete miRNA profile. Indeed, several studies identified miRNA as potential biomarkers, useful to monitor cancer progression, patients’ outcomes and the development of chemo-resistance [100] (Table 5). However, although miRNAs have been regarded as promising biomarkers for many years, they have never entered the clinics.

Table 5 Tumor Heterogeneity: significance of miRNAs

MiRNAs are endogenous, single-stranded, non-coding small RNAs with length of about 22 nucleotide that exert several cellular biological functions inhibiting their target genes [111]. Several overexpressed miRNAs, called oncomiRs, are involved in tumor onset and metastasis, instead those that decreased in cancer patients were considered tumor suppressors [112].

A recent study reported that the expression levels of different specific miRNAs (miR-21, miR-23b, miR-200b, miR-200c) were found higher in metastatic compared to early BC patients. In addition, miR-23b and miR-190 were correlated with low PFS in de novo metastatic BC patients, whereas high levels of miR-200b were associated to decreased OS in the HER2-negative subgroup [101].

The ability of circulating miRNAs to discriminate patients with lymph nodes and/or distant metastasis has been also examined in advanced stages of CRC. In particular, upregulated serum levels of miR-103 were associated with lymph node metastasis and advanced stage of CRC [102]. Similarly, higher serum levels of miR-29a were found in metastatic CRC patients compared to non-metastatic patients. However, this miRNA did not demonstrate a sufficient accuracy in discriminating these groups of patients, showing a sensitivity of 75% and a specificity of 75% [103].. Another study showed instead that plasma expression of miR-203 and miR-141 are able to discriminate between advanced from early stage CRC patients with a good performance [104]. Finally, Tsukamoto et al. have recently reported the levels of miR-21 are elevated in serum exosomes, primary tumor tissues, and liver metastasis tissues from CRC patients. In particular, exosomal miR-21 showed a correlation with liver metastases and TNM stage and was associated with worse OS and disease free survival [105].

Several evidences showed a potential role of circulating miRNA in identifying NSCLC patients with aggressive advanced disease. Indeed, elevated expression levels of miR-222-3p, miR-23b-3p, miR-10b-3p, and miR-21-5p in serum exosomes were associated with poor OS in NSCLC patients. Among these miRNAs, exosomal miR-21-5p overexpression was also associated with presence of liver metastasis and TNM stage [107, 108].

Since cancer heterogeneity is ultimately associated to drug resistance following prolonged treatments, circulating miRNAs could represent potential biomarkers useful to predict drug resistance and select effective treatment strategies. One example is miR-155-5p, whose expression is directly correlated with chemo-resistance and poor prognosis in pancreatic ductal adenocarcinoma patients receiving gemcitabine [110]. Another example is represented by the evidence of lower levels of miR-1914-3p and miR-1915-3p in the plasma of chemo-resistant CRC patients compared to responders [106]. Finally, lower levels of miR-146-5p are found in serum exosomes of cisplatin-resistant NSCLC patients and they are associated to shorter PFS [109].

Circulating miRNAs could represent non-invasive biomarkers, useful not only for large screening programs for tumors with higher sensitivity, but also to dynamically monitor tumor progression and treatment response [100]. Unfortunately, the complexity of RNA biomarker assays and data interpretation contribute to their low success rate and slow down their use into the clinical practice.

The panorama of other non-coding RNAs

Besides the well-known miRNAs, there are other species of non-coding RNAs that are currently emerging as candidate biomarkers assessable in blood-based liquid biopsies, including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) and PIWI-interacting RNAs (pi-RNAs).

LncRNAs activate or silence the expression of genes regulating chromatin state, mRNA splicing, transport and translation of RNAs, act as competing endogenous RNAs and influence protein modification [113,114,115]. Intriguingly, among the target loci of lncRNA there are well-known genomic regions with oncogenic or tumor-suppressive functions, suggesting a key role of these RNAs in the control of cancer progression [116].

For example, the aberrant expression of the lncRNA HOTAIR (HOX antisense intergenic RNA) is associated to tumor proliferation, angiogenesis, progression, drug resistance and worse prognosis [117]. Numerous evidences have focused the attention on the potential role of HOTAIR as a circulating marker and therapeutic target is solid tumors [118]. In this regard, Li et al. demonstrated that high levels of HOTAIR were associated to tumor recurrence, radio-resistance and shorter OS in cervical cancer [119].

Similarly, in metastatic HCC patients, plasma levels of lncRNAs XLOC_014172 and LOC149086, allowed to discriminate, with high performance patients with metastatic disease from patients without secondary lesions [120]. Similarly, Wang et al. showed that serum lncRNA-p21 decreased in liver metastatic cancer patients, respect to non-metastatic HCC patients [121].

Several evidences showed the potential prognostic value of some lncRNAs also in CRC. Among these, higher exosomal levels of the oncogenic 91H were found in CRC patients’ serum and these levels were associated with metastatic disease and tumor recurrence [122]. Similarly, higher serum levels of exosomal CRNDE-h were observed in metastatic CRC patients compared to non-metastatic patients and were positively associated with poor OS [123]. Another lncRNA correlated with outcome in metastatic CRC is GNAT1–1, whose serum levels were decreased in patients with worst prognosis [124].

LncRNAs could represent important biomarkers also to monitor drug resistance that could occur in distinct tumor clones, helping clinicians in choosing patients’ best treatment. Increased lncRNA XIST levels, for instance, were found in serum of CRC patients nonresponding to 5-FU [125]. Similarly, reduced serum lncRNA MEG3 expression identified CRC patients resistant to oxaliplatin-based chemotherapy [126].

Circular RNAs (circRNA) are another class of non-coding RNAs that could be implicated with several human diseases, including cancer [127]. CircRNAs are closed circular one-stranded RNAs that act as competing endogenous RNA sponges modulating the activity of their targets (miRNAs, proteins and RNAs) [128]. Specific circRNAs have been detected in high quantities in blood of patients with different advanced solid tumors representing potential biomarkers useful to monitor tumor progression and response to treatments in metastatic stage [129]. In HCC patients, for instance, high serum expression of circ-ZEB1.33 was associated to tumor progression and was also related to patients’ OS [130]. Other circRNAs were associated to tumor stage and presence of metastases in gastric cancer (GC) patients. In particular, Hsa_circ_0000190 was found downregulated in GC patients’ plasma and was correlated to the presence of metastasis, tumor stage and CA19.9 levels [131]. Also in GC patients, decreased expression of plasma hsa_circ_0000745 was associated to TNM stage [132]. Recently, a key role of circRNA was found also in metastatic urothelial cancer patients, where high levels of circPRMT5 in serum exosomes correlated with the presence of metastasis and tumor stage [133].

PiRNAs regulate gene expression, ensure genome integrity of germline cells and control developmental timing. Aberrant piRNA expression was found in blood and is currently being associated to cancer progression and metastases [134]. Recently, a study demonstrated the positive correlation between piR-54,265 levels and tumor stage in CRC patients, with highest levels in the metastatic setting. Moreover, piR-54,265 lower serum levels were also predictive of chemotherapy response and potentially useful to select CRC patients who benefit from chemotherapy [135].

Conclusions

Although blood-based liquid biopsy has shown a huge potential for different purposes in several tumor types, it is validated in clinical practice only for few selected uses. After its approval for the detection of EGFR mutations in NSCLC, it was extensively used to determine the T790M mutation, the main mechanism of resistance to first and second generation TKIs. Thus, many patients avoided additional tissue biopsy and a fraction of them was able to receive an otherwise inaccessible treatment. Its use declined when osimertinib, a third generation TKI, became the standard of care as frontline treatment in EGFR-mutant NSCLC. Nevertheless, this experience represents the proof-of-concept of the potentiality of liquid biopsy to change and improve clinical practice.

To date, liquid biopsy cannot be considered a replacement for tissue biopsy, which remains the standard and undisputed method for the diagnosis and biomarkers detection of all solid tumors, but it has undoubted advantages: it is a non-invasive, rapid, easy, repeatable and real-time test. It can provide a great amount of information, and it is potentially superior to tissue biopsy in its capability to sample tumor heterogeneity especially in the metastatic setting.

Liquid biopsy is usually less expensive than tissue biopsy, but in the case of repeated tests, the cumulative costs can be the same or higher. In addition, some of the technologies and molecular protocols used to detect analytes are very sophisticated and expensive; many of them need to be standardized and require further studies for clinical validation. The other most important barrier preventing the implementation of liquid biopsy in clinical practice is represented by its relatively low accuracy rate [136]. The management of small amounts and easily degradable materials requires extremely sensitive and specific methods. Both circulating tumor cells and DNA are relatively rare compared to the number of other molecules found in a blood sample. Accurate tumor information can therefore be obtained only when the abundance of CTCs or cfDNA is greater than specific thresholds, and a significant number of cancer patients do not meet this criterion [67, 137]. A committee of experts conducted a review of the published clinical ctDNA tests and concluded that the current clinical efficacy of liquid biopsy techniques is very limited [138].

To determine its effectiveness and clinical utility, liquid biopsy requires further and large-scale prospective studies. However, based on available evidence, it appears to have several potential applications: cancer screening and early diagnosis; estimation of the risk for metastatic relapse or metastatic progression; prediction of prognosis; longitudinal monitoring of disease progression and response to treatment; identification of therapeutic targets and resistance mechanisms.

In particular, thanks to the multiplicity of analytes that can be identified and the repeatability of the test, liquid biopsy represents an accessible tool to the decode both the spatial and temporal tumor heterogeneity. In the next future, it may significantly and non-invasively contribute to the clinical management and therapeutic decisions for cancer patients in the era of precision medicine.

Availability of data and materials

Not Applicable.

Abbreviations

CTCs:

Circulating Tumor Cells

ctDNA:

circulating tumor DNA

ctRNA:

circulating tumor RNA

cfDNA:

circulating free DNA

miRNA:

microRNA

non-coding RNA:

ncRNAs

EMT:

Epithelial-to-mesenchymal transition

BC:

Breast Cancer

PC:

Prostate Cancer

CSPC:

Castration Sensitive Prostate Cancer

CRPC:

Castration Resistant Prostate Cancer

AR:

Androgen Receptor

ARSI:

AR Signaling Inhibitors

EGFR:

Epidermal Growth Factor Receptor

TCR:

T Cell Receptor

HLA:

Human Leukocyte Antigen

ER:

Estrogen Receptor

NSCLC:

Non Small Cell Lung Cancer

CRC:

Colorectal Cancer

GC:

Gastric Cancer

PFS:

Progression Free Survival

OS:

Overall Survival

NGS:

Next-generation sequencing

lnRNAs or lincRNAs:

long non-coding RNAs

circRNAs:

circular RNAs

pi-RNAs:

PIWI-interacting RNAs

References

  1. 1.

    La Thangue N, Kerr DJ. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat Rev Clin Oncol. 2011;8(10):587–96.

    PubMed  Google Scholar 

  2. 2.

    Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94. https://doi.org/10.1038/nrclinonc.2017.166.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Hinohara K, Polyak K. Intratumoral heterogeneity: more than just mutations. Trends Cell Biol. 2019;29(7):569–79.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ. Natural innate and adaptive immunity to cancer. Annu Rev Immunol. 2011;29:235–71.

    CAS  PubMed  Google Scholar 

  6. 6.

    Zhou J, Wang G, Chen Y, Wang H, Hua Y, Cai Z. Immunogenic cell death in cancer therapy: present and emerging inducers. J Cell Mol Med. 2019;23(8):4854–65.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Pantel K, Alix-Panabieres C. Real-time liquid biopsy in cancer patients: fact or fiction? Cancer Res. 2013;73(21):6384–8.

    CAS  PubMed  Google Scholar 

  8. 8.

    Zhang W, Xia W, Lv Z, Ni C, Xin Y, Yang L. Liquid biopsy for cancer: circulating tumor cells, circulating free DNA or exosomes? Cell Physiol Biochem. 2017;41:755–68.

    CAS  PubMed  Google Scholar 

  9. 9.

    Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol. 2013;10(8):472–84.

    CAS  PubMed  Google Scholar 

  10. 10.

    Alix-Panabières C, Pantel K. Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov. 2016;6(5):479–91.

    PubMed  Google Scholar 

  11. 11.

    Pantel K, Denève E, Nocca D, Coffy A, Vendrell JP, Maudelonde T, et al. Circulating epithelial cells in patients with benign colon diseases. Clin Chem. 2012;58(5):936–40.

    CAS  PubMed  Google Scholar 

  12. 12.

    Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT, et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013;339(6119):580–4 Erratum in: Science. 2019;363(6425).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Satelli A, Mitra A, Brownlee Z, Xia X, Bellister S, Overman MJ, et al. Epithelial-mesenchymal transitioned circulating tumor cells capture for detecting tumor progression. Clin Cancer Res. 2015;21(4):899–906.

    CAS  PubMed  Google Scholar 

  14. 14.

    Yokobori T, Iinuma H, Shimamura T, Imoto S, Sugimachi K, Ishii H, et al. Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial-mesenchymal transition and is associated with colorectal cancer prognosis. Cancer Res. 2013;73(7):2059–69.

    CAS  PubMed  Google Scholar 

  15. 15.

    Aceto N, Bardia A, Miyamoto DT, Donaldson MC, Wittner BS, Spencer JA, et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell. 2014;158(5):1110–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Aceto N, Toner M, Maheswaran S, Haber DA. En route to metastasis: circulating tumor cell clusters and epithelial-to-mesenchymal transition. Trends Cancer. 2015;1(1):44–52.

    PubMed  Google Scholar 

  17. 17.

    Sundling KE, Lowe AC. Circulating tumor cells: overview and opportunities in cytology. Adv Anat Pathol. 2019;26(1):56–63.

    CAS  PubMed  Google Scholar 

  18. 18.

    Kolostova K, Spicka J, Matkowski R, Bobek V. Isolation, primary culture, morphological and molecular characterization of circulating tumor cells in gynecological cancers. Am J Transl Res. 2015;7(7):1203–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Maheswaran S, Haber DA. Ex vivo culture of CTCs: an emerging resource to guide cancer therapy. Cancer Res. 2015;75(12):2411–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Babayan A, Hannemann J, Spötter J, Müller V, Pantel K, Joosse SA. Heterogeneity of estrogen receptor expression in circulating tumor cells from metastatic breast cancer patients. PLoS One. 2013;8(9):e75038.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Fehm T, Müller V, Aktas B, Janni W, Schneeweiss A, Stickeler E, et al. HER2 status of circulating tumor cells in patients with metastatic breast cancer: a prospective, multicenter trial. Breast Cancer Res Treat. 2010;124(2):403–12.

    CAS  PubMed  Google Scholar 

  22. 22.

    Miyamoto DT, Lee RJ, Stott SL, Ting DT, Wittner BS, Ulman M, et al. Androgen receptor signaling in circulating tumor cells as a marker of hormonally responsive prostate cancer. Cancer Discov. 2012;2(11):995–1003.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Miyamoto DT, Zheng Y, Wittner BS, Lee RJ, Zhu H, Broderick KT, et al. RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science. 2015;349(6254):1351–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Denis JA, Patroni A, Guillerm E, Pépin D, Benali-Furet N, Wechsler J, et al. Droplet digital PCR of circulating tumor cells from colorectal cancer patients can predict KRAS mutations before surgery. Mol Oncol. 2016;10(8):1221–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Scher HI, Lu D, Schreiber NA, Louw J, Graf RP, Vargas HA, et al. Association of AR-V7 on circulating tumor cells as a treatment-specific biomarker with outcomes and survival in castration-resistant prostate cancer. JAMA Oncol. 2016;2(11):1441–9 Erratum in: JAMA Oncol. 2016;2(11):1511.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Fabbri F, Carloni S, Zoli W, Ulivi P, Gallerani G, Fici P, et al. Detection and recovery of circulating colon cancer cells using a dielectrophoresis-based device: KRAS mutation status in pure CTCs. Cancer Lett. 2013;335(1):225–31.

    CAS  PubMed  Google Scholar 

  27. 27.

    Nicolazzo C, Raimondi C, Gradilone A, Emiliani A, Zeuner A, Francescangeli F, et al. Circulating tumor cells in right- and left-sided colorectal cancer. Cancers (Basel). 2019;11(8):E1042.

    Google Scholar 

  28. 28.

    Sun YF, Guo W, Xu Y, Shi YH, Gong ZJ, Ji Y, et al. Circulating tumor cells from different vascular sites exhibit spatial heterogeneity in epithelial and mesenchymal composition and distinct clinical significance in hepatocellular carcinoma. Clin Cancer Res. 2018;24(3):547–59.

    CAS  PubMed  Google Scholar 

  29. 29.

    Mazel M, Jacot W, Pantel K, Bartkowiak K, Topart D, Cayrefourcq L, et al. Frequent expression of PD-L1 on circulating breast cancer cells. Mol Oncol. 2015;9(9):1773–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Guibert N, Delaunay M, Lusque A, Boubekeur N, Rouquette I, Clermont E, et al. PD-L1 expression in circulating tumor cells of advanced non-small cell lung cancer patients treated with nivolumab. Lung Cancer. 2018;120:108–12.

    PubMed  Google Scholar 

  31. 31.

    Scher HI, Graf RP, Schreiber NA, McLaughlin B, Jendrisak A, Wang Y, et al. Phenotypic heterogeneity of circulating tumor cells informs clinical decisions between AR signaling inhibitors and Taxanes in metastatic prostate cancer. Cancer Res. 2017;77(20):5687–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Yan WT, Cui X, Chen Q, Li YF, Cui YH, Wang Y, et al. Circulating tumor cell status monitors the treatment responses in breast cancer patients: a meta-analysis. Sci Rep. 2017;7:43464.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kalikaki A, Politaki H, Souglakos J, Apostolaki S, Papadimitraki E, Georgoulia N, et al. KRAS genotypic changes of circulating tumor cells during treatment of patients with metastatic colorectal cancer. PLoS One. 2014;9(8):e104902.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Onidani K, Shoji H, Kakizaki T, Yoshimoto S, Okaya S, Miura N, et al. Monitoring of cancer patients via next-generation sequencing of patient-derived circulating tumor cells and tumor DNA. Cancer Sci. 2019;110(8):2590–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Yanagita M, Redig AJ, Paweletz CP, Dahlberg SE, O'Connell A, Feeney N, et al. A prospective evaluation of circulating tumor cells and cell-free DNA in EGFR-mutant non-small cell lung cancer patients treated with Erlotinib on a phase II trial. Clin Cancer Res. 2016;22(24):6010–20.

    CAS  PubMed  Google Scholar 

  36. 36.

    Marchetti A, Del Grammastro M, Felicioni L, Malatesta S, Filice G, Centi I, et al. Assessment of EGFR mutations in circulating tumor cell preparations from NSCLC patients by next generation sequencing: toward a real-time liquid biopsy for treatment. PLoS One. 2014;9(8):e103883.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Ilie M, Szafer-Glusman E, Hofman V, Long-Mira E, Suttmann R, Darbonne W, et al. Expression of MET in circulating tumor cells correlates with expression in tumor tissue from advanced-stage lung cancer patients. Oncotarget. 2017;8(16):26112–21.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Pestrin M, Salvianti F, Galardi F, De Luca F, Turner N, Malorni L, et al. Heterogeneity of PIK3CA mutational status at the single cell level in circulating tumor cells from metastatic breast cancer patients. Mol Oncol. 2015;9(4):749–57.

    CAS  PubMed  Google Scholar 

  39. 39.

    Loibl S, von Minckwitz G, Schneeweiss A, Paepke S, Lehmann A, Rezai M, et al. PIK3CA mutations are associated with lower rates of pathologic complete response to anti-human epidermal growth factor receptor 2 (her2) therapy in primary HER2-overexpressing breast cancer. J Clin Oncol. 2014;32(29):3212–20.

    CAS  PubMed  Google Scholar 

  40. 40.

    Guthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol. 2013;88(1):218–30.

    PubMed  Google Scholar 

  41. 41.

    Hopkins AC, Yarchoan M, Durham JN, Yusko EC, Rytlewski JA, Robins HS, et al. T cell receptor repertoire features associated with survival in immunotherapy-treated pancreatic ductal adenocarcinoma. JCI Insight. 2018;3(13):122092.

    PubMed  Google Scholar 

  42. 42.

    Postow MA, Manuel M, Wong P, Yuan J, Dong Z, Liu C, et al. Peripheral T cell receptor diversity is associated with clinical outcomes following ipilimumab treatment in metastatic melanoma. J Immunother Cancer. 2015;3:23.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Hogan SA, Courtier A, Cheng PF, Jaberg-Bentele NF, Goldinger SM, Manuel M, et al. Peripheral blood TCR repertoire profiling may facilitate patient stratification for immunotherapy against melanoma. Cancer Immunol Res. 2019;7(1):77–85.

    CAS  PubMed  Google Scholar 

  44. 44.

    Arakawa A, Vollmer S, Tietze J, Galinski A, Heppt MV, Bürdek M, et al. Clonality of CD4(+) blood T cells predicts longer survival with CTLA4 or PD-1 checkpoint inhibition in advanced melanoma. Front Immunol. 2019;10:1336.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM, et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med. 2018;24(2):144–53.

    CAS  PubMed  Google Scholar 

  46. 46.

    Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M, Zhou J, et al. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer. 2018;6(1):18.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Bochem J, Zelba H, Amaral T, Spreuer J, Soffel D, Eigentler T, et al. Peripheral PD-1+CD56+ T-cell frequencies correlate with outcome in stage IV melanoma under PD-1 blockade. PLoS One. 2019;14(8):e0221301.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Takeuchi Y, Tanemura A, Tada Y, Katayama I, Kumanogoh A, Nishikawa H. Clinical response to PD-1 blockade correlates with a sub-fraction of peripheral central memory CD4+ T cells in patients with malignant melanoma. Int Immunol. 2018;30(1):13–22.

    CAS  PubMed  Google Scholar 

  49. 49.

    Verma V, Shrimali RK, Ahmad S, Dai W, Wang H, Lu S, et al. PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1 (+) CD38(hi) cells and anti-PD-1 resistance. Nat Immunol. 2019;20(9):1231–43.

    CAS  PubMed  Google Scholar 

  50. 50.

    Sade-Feldman M, Kanterman J, Klieger Y, Ish-Shalom E, Olga M, Saragovi A, et al. Clinical significance of circulating CD33+CD11b+HLA-DR- myeloid cells in patients with stage IV melanoma treated with Ipilimumab. Clin Cancer Res. 2016;22(23):5661–72.

    CAS  PubMed  Google Scholar 

  51. 51.

    Han J-F, Wang Z, Bai H, Chen S, Wang Y, Duan J, et al. A novel noninvasive biomarker based on peripheral PD-1posi CD8 T-cell receptor repertoire correlated with clinical outcomes to immunotherapy in non-small cell lung cancer. J Clin Oncol. 2019;37(15_suppl):e14174.

    Google Scholar 

  52. 52.

    Matsumoto S, Matsutani T, Fujita Y, Kitaura K, Nakamura Y, Nakamichi T, et al. Correlation between peripheral CD8+PD-1+ T cells diversity, tumor mutation burden (TMB) and T cell clones with anti-PD-1 antibody treatment of lung cancer patients: TCR repertoire as a novel predictive biomarker. J Clin Oncol. 2019;37(15_suppl):e14041.

    Google Scholar 

  53. 53.

    Möller M, Turzer S, Schütte W, Seliger B, Riemann D. Blood immune cell biomarkers in patient with lung cancer undergoing treatment with checkpoint blockade. J Immunother. 2019;43(2):57.

    PubMed Central  Google Scholar 

  54. 54.

    Zappasodi R, Budhu S, Hellmann MD, Postow MA, Senbabaoglu Y, Manne S, et al. Non-conventional inhibitory CD4 (+) Foxp3 (−) PD-1 (hi) T cells as a biomarker of immune checkpoint blockade activity. Cancer Cell. 2018;33(6):1017–1032.e7.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Shitaoka K, Hamana H, Kishi H, Hayakawa Y, Kobayashi E, Sukegawa K, et al. Identification of Tumoricidal TCRs from tumor-infiltrating lymphocytes by single-cell analysis. Cancer Immunol Res. 2018;6(4):378–88.

    CAS  PubMed  Google Scholar 

  56. 56.

    Kirsch I, Vignali M, Robins H. T-cell receptor profiling in cancer. Mol Oncol. 2015;9(10):2063–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Hosoi A, Takeda K, Nagaoka K, Iino T, Matsushita H, Ueha S, et al. Increased diversity with reduced “diversity evenness” of tumor infiltrating T-cells for the successful cancer immunotherapy. Sci Rep. 2018;8(1):1058.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Joshi K, Robert de Massy M, Ismail M, Reading JL, Uddin I, Woolston A, et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat Med. 2019;25(10):1549–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Emerson RO, Sherwood AM, Rieder MJ, Guenthoer J, Williamson DW, Carlson CS, et al. High-throughput sequencing of T-cell receptors reveals a homogeneous repertoire of tumour-infiltrating lymphocytes in ovarian cancer. J Pathol. 2013;231(4):433–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Cui C, Tian X, Wu J, Zhang C, Tan Q, Guan X, et al. T cell receptor β-chain repertoire analysis of tumor-infiltrating lymphocytes in pancreatic cancer. Cancer Sci. 2019;110(1):61–71.

    CAS  PubMed  Google Scholar 

  61. 61.

    Cohen CJ, Gartner JJ, Horovitz-Fried M, Shamalov K, Trebska-McGowan K, Bliskovsky VV, et al. Isolation of neoantigen-specific T cells from tumor and peripheral lymphocytes. J Clin Invest. 2015;125(10):3981–91.

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Gros A, Parkhurst MR, Tran E, Pasetto A, Robbins PF, Ilyas S, et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat Med. 2016;22(4):433–8.

    CAS  PubMed  Google Scholar 

  63. 63.

    Li Y, Jiao SC, Wu LL, Sun SJ, Wei HF, Liao SY. T cell receptor β-chain repertoire analysis to reveal potential predictive biomarker for the use of immune checkpoint blockade in patients with advanced solid tumors. J Clin Oncol. 2019;37(15_suppl):e14152.

    Google Scholar 

  64. 64.

    Simoni Y, Chng MHY, Li S, Fehlings M, Newell EW. Mass cytometry: a powerful tool for dissecting the immune landscape. Curr Opin Immunol. 2018;51:187–96.

    CAS  PubMed  Google Scholar 

  65. 65.

    Stroun M, Lyautey J, Lederrey C, Olson-Sand A, Anker P. About the possible origin and mechanism of circulating DNA apoptosis and active DNA release. Clin Chim Acta. 2001;313(1–2):139–42.

    CAS  PubMed  Google Scholar 

  66. 66.

    Chen M, Zhao H. Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum Genomics. 2019 Aug 1;13(1):34. https://doi.org/10.1186/s40246-019-0220-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6(224):224ra24.

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Jahr S, Hentze H, Englisch S, Hardt D, Fackelmayer FO, Hesch RD, et al. DNA fragments in the blood plasma of cancer patients: quantification and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001;61(4):1659–65.

    CAS  Google Scholar 

  69. 69.

    Chu D, Paoletti C, Gersch C, VanDenBerg DA, Zabransky DJ, Cochran RL, et al. ESR1 mutations in circulating plasma tumor DNA from metastatic breast cancer patients. Clin Cancer Res. 2016;22(4):993–9.

    CAS  PubMed  Google Scholar 

  70. 70.

    Schiavon G, Hrebien S, Garcia-Murillas I, Cutts RJ, Pearson A, Tarazona N, et al. Analysis of ESR1 mutation in circulating tumor DNA demonstrates evolution during therapy for metastatic breast cancer. Sci Transl Med. 2015;7(313):313ra182.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Fribbens C, O'Leary B, Kilburn L, Hrebien S, Garcia-Murillas I, Beaney M, et al. Plasma ESR1 mutations and the treatment of estrogen receptor-positive advanced breast cancer. J Clin Oncol. 2016;34(25):2961–8.

    CAS  PubMed  Google Scholar 

  72. 72.

    Higgins MJ, Jelovac D, Barnathan E, Blair B, Slater S, Powers P, et al. Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood. Clin Cancer Res. 2012;18(12):3462–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    O’Leary B, Hrebien S, Morden JP, Beaney M, Fribbens C, Huang X, et al. Early circulating tumor DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer. Nat Commun. 2018;9(1):896.

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Ma CX, Bose R, Gao F, Freedman RA, Telli ML, Kimmick G, et al. Neratinib efficacy and circulating tumor DNA detection of HER2 mutations in HER2 nonamplified metastatic breast cancer. Clin Cancer Res. 2017;23(19):5687–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Adalsteinsson VA, Ha G, Freeman SS, Choudhury AD, Stover DG, Parsons HA, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun. 2017;8(1):1324.

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Stover DG, Parsons HA, Ha G, Freeman SS, Barry WT, Guo H, et al. Association of Cell-Free DNA tumor fraction and somatic copy number alterations with survival in metastatic triple-negative breast cancer. J Clin Oncol. 2018;36(6):543–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Karachaliou N, Mayo-de las Casas C, Queralt C, de Aguirre I, Melloni B, Cardenal F, et al. Association of EGFR L858R mutation in circulating free DNA with survival in the EURTAC trial. JAMA Oncol. 2015;1(2):149–57.

    PubMed  Google Scholar 

  78. 78.

    Douillard JY, Ostoros G, Cobo M, Ciuleanu T, Cole R, McWalter G, et al. Gefitinib treatment in EGFR mutated caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status. J Thorac Oncol. 2014;9(9):1345–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Reck M, Hagiwara K, Han B, Tjulandin S, Grohé C, Yokoi T, et al. ctDNA determination of EGFR mutation status in European and Japanese patients with advanced NSCLC: the ASSESS study. J Thorac Oncol. 2016;11(10):1682–9.

    PubMed  Google Scholar 

  80. 80.

    Mok T, Wu YL, Lee JS, Yu CJ, Sriuranpong V, Sandoval-Tan J, et al. Detection and dynamic changes of EGFR mutations from circulating tumor DNA as a predictor of survival outcomes in NSCLC patients treated with first-line intercalated Erlotinib and chemotherapy. Clin Cancer Res. 2015;21(14):3196–203.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Ortiz-Cuaran S, Scheffler M, Plenker D, Dahmen L, Scheel AH, Fernandez-Cuesta L, et al. Heterogeneous mechanisms of primary and acquired resistance to third-generation EGFR inhibitors. Clin Cancer Res. 2016;22(19):4837–47.

    CAS  PubMed  Google Scholar 

  82. 82.

    Taus Á, Camacho L, Rocha P, Hardy-Werbin M, Pijuan L, Piquer G, et al. Dynamics of EGFR mutation load in plasma for prediction of treatment response and disease progression in patients with EGFR-mutant lung adenocarcinoma. Clin Lung Cancer. 2018;19(5):387–394.e2.

    CAS  PubMed  Google Scholar 

  83. 83.

    Thierry AR, El Messaoudi S, Mollevi C, Raoul JL, Guimbaud R, Pezet D, et al. Clinical validation of the detection of KRAS and BRAF mutations from circulating tumor DNA. Nat Med. 2014;20(4):430–5.

    CAS  Google Scholar 

  84. 84.

    Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015;21(7):827.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Murtaza M, Dawson SJ, Pogrebniak K, Rueda OM, Provenzano E, Grant J, et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat Commun. 2015;6:8760.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Mattos-Arruda L, Weigelt B, Cortes J, Won HH, Ng CKY, Nuciforo P, et al. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann Oncol. 2018;29(11):2268.

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Misale S, Yaeger R, Hobor S, Scala E, Janakiraman M, Liska D, et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature. 2012;486(7404):532–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Kato S, Schwaederlé MC, Fanta PT, Okamura R, Leichman L, Lippman SM, et al. Genomic assessment of blood-derived circulating tumor DNA in patients with colorectal cancers: correlation with tissue sequencing, therapeutic response, and survival. JCO Precis Oncol. 2019;3. https://doi.org/10.1200/PO.18.00158.

  89. 89.

    Vandekerkhove G, Struss WJ, Annala M, Kallio HML, Khalaf D, Warner EW, et al. Circulating tumor DNA abundance and potential utility in de novo metastatic prostate cancer. Eur Urol. 2019;75(4):667–75.

    CAS  PubMed  Google Scholar 

  90. 90.

    Azad AA, Volik SV, Wyatt AW, Haegert A, Le Bihan S, Bell RH, et al. Androgen receptor gene aberrations in circulating cell-free DNA: biomarkers of therapeutic resistance in castration-resistant prostate cancer. Clin Cancer Res. 2015;21(10):2315–24.

    CAS  PubMed  Google Scholar 

  91. 91.

    Wyatt AW, Azad AA, Volik SV, Annala M, Beja K, McConeghy B, et al. Genomic alterations in cell-free DNA and enzalutamide resistance in castration-resistant prostate cancer. JAMA Oncol. 2016;2(12):1598–606.

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Glinge C, Clauss S, Boddum K, Jabbari R, Jabbari J, Risgaard B, et al. Stability of circulating blood-based MicroRNAs – pre-analytic methodological considerations. PLoS One. 2017;12:2.

    Google Scholar 

  93. 93.

    Sorber L, Zwaenepoel K, Deschoolmeester V, Van Schil PE, Van Meerbeeck J, Lardon F, et al. Circulating cell-free nucleic acids and platelets as a liquid biopsy in the provision of personalized therapy for lung cancer patients. Lung Cancer. 2017;107:100–7.

    CAS  PubMed  Google Scholar 

  94. 94.

    Feng H, Qin Z, Zhang X. Opportunities and methods for studying alternative splicing in cancer with RNA-Seq. Cancer Lett. 2013;340:179–91.

    CAS  PubMed  Google Scholar 

  95. 95.

    Lau D, Bobe AM, Khan AA. RNA sequencing of the tumor microenvironment in precision cancer immunotherapy. Trends Cancer. 2019;5(3):149–56.

    CAS  PubMed  Google Scholar 

  96. 96.

    Zheng H, Zhan Y, Liu S, Lu J, Luo J, Feng J, et al. The roles of tumor-derived exosomes in non-small cell lung cancer and their clinical implications. J Exp Clin Cancer Res. 2018;37(1):226. https://doi.org/10.1186/s13046-018-0901-5 Review.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Falcone G, Felsani A, D'Agnano I. Signaling by exosomal microRNAs in cancer. J Exp Clin Cancer Res. 2015;34:32. https://doi.org/10.1186/s13046-015-0148-3 Review.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Ruivo CF, Adem B, Silva M, Melo SA. The biology of cancer exosomes: insights and new perspectives. Cancer Res. 2017;77(23):6480–8.

    CAS  PubMed  Google Scholar 

  99. 99.

    Qu F, Xie W, Nakabayashi M, Zhang H, Jeong SH, Wang X, et al. Association of AR-V7 and prostate-specific antigen RNA levels in blood with efficacy of Abiraterone acetate and enzalutamide treatment in men with prostate cancer. Clin Cancer Res. 2017;23(3):726–34.

    CAS  PubMed  Google Scholar 

  100. 100.

    Ma R, Jiang T, Kang X. Circulating microRNAs in cancer: origin, function and application. J Exp Clin Cancer Res. 2012;31:38. https://doi.org/10.1186/1756-9966-31-38.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Papadaki C, Stoupis G, Tsalikis L, Monastirioti A, Papadaki M, Maliotis N, et al. Circulating miRNAs as a marker of metastatic disease and prognostic factor in metastatic breast cancer. Oncotarget. 2019;10(9):966–81.

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Giráldez MD, Lozano JJ, Ramírez G, Hijona E, Bujanda L, Castells A, et al. Circulating microRNAs as biomarkers of colorectal cancer: results from a genome-wide profiling and validation study. Clin Gastroenterol Hepatol. 2013;11:681–8.

    PubMed  Google Scholar 

  103. 103.

    Wang LG, Gu J. Serum microRNA-29a is a promising novel marker for early detection of colorectal liver metastasis. Cancer Epidemiol. 2012;36(1):e61–7.

    CAS  PubMed  Google Scholar 

  104. 104.

    Sun Y, Liu Y, Cogdell D, Calin GA, Sun B, Kopetz S, et al. Examining plasma microRNA markers for colorectal cancer at different stages. Oncotarget. 2016;7:11434–49.

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Tsukamoto M, Iinuma H, Yagi T, Matsuda K, Hashiguchi Y. Circulating exosomal microRNA-21 as a biomarker in each tumor stage of colorectal cancer. Oncology. 2017;92:360–70.

    CAS  PubMed  Google Scholar 

  106. 106.

    Hu J, Cai G, Xu Y, Cai S. The plasma microRNA miR-1914* and −1915 suppresses chemoresistant in colorectal cancer patients by down-regulating NFIX. Curr Mol Med. 2016;16:70–82.

    CAS  PubMed  Google Scholar 

  107. 107.

    Wei F, Ma C, Zhou T, Dong X, Luo Q, Geng L, et al. Exosomes derived from gemcitabine-resistant cells transfer malignant phenotypic traits via delivery of miRNA-222-3p. Mol Cancer. 2017;16:1.

    Google Scholar 

  108. 108.

    Liu Q, Yu Z, Yuan S, Xie W, Li C, Hu Z, et al. Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer. Oncotarget. 2017;8:13048–58.

    PubMed  Google Scholar 

  109. 109.

    Yuwen DL, Sheng BB, Liu J, Wenyu W, Shu YQ. MiR-146a-5p level in serum exosomes predicts therapeutic effect of cisplatin in non-small cell lung cancer. Eur Rev Med Pharmacol Sci. 2017;21(11):2650–2.

    PubMed  Google Scholar 

  110. 110.

    Papaconstantinou IG, Manta A, Gazouli M, Lyberopoulou A, Lykoudis PM, Polymeneas G, et al. Expression of microRNAs in patients with pancreatic cancer and its prognostic significance. Pancreas. 2013;42:67–71.

    CAS  PubMed  Google Scholar 

  111. 111.

    Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature. 2008;455:64–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Bracken CP, Scott HS, Goodall GJ. A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet. 2016;17:719–32.3.

    CAS  PubMed  Google Scholar 

  113. 113.

    Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: insights into functions. Nat Rev Genet. 2009;10:155–9.

    CAS  PubMed  Google Scholar 

  114. 114.

    Deniz E, Erman B. Long noncoding RNA (lincRNA), a new paradigm in gene expression control. Funct Integr Genomics. 2017;17:135–47.

    CAS  PubMed  Google Scholar 

  115. 115.

    Subramanian S. Competing endogenous RNAs (ceRNAs): new entrants tothe intricacies of gene regulation. Front Genet. 2014;5:1–9.

    Google Scholar 

  116. 116.

    Huarte M. The emerging role of lncRNAs in cancer. Nat Med. 2015;21:1253.

    CAS  PubMed  Google Scholar 

  117. 117.

    Tang Q, Hann SS. HOTAIR: an oncogenic Long non-coding RNA in human cancer. Cell Physiol Biochem. 2018;47(3):893–913.

    CAS  PubMed  Google Scholar 

  118. 118.

    Botti G, Marra L, Malzone MG, Anniciello A, Botti C, Franco R, et al. LncRNA HOTAIR as prognostic circulating marker and potential therapeutic target in patients with tumor diseases. Curr Drug Targets. 2017;18(1):27–3.

    CAS  PubMed  Google Scholar 

  119. 119.

    Li J, Wang Y, Yu J, Dong R, Qiu H. A high level of circulating HOTAIR is associated with progression and poor prognosis of cervical cancer. Tumor Biol. 2015;36(3):1661–5.

    CAS  Google Scholar 

  120. 120.

    Tang J, Jiang R, Deng L, Zhang X, Wang K, Sun B. Circulation long non-coding RNAs act as biomarkers for predicting tumorigenesis and metastasis in hepatocellular carcinoma. Oncotarget. 2015;6(6):4505–15.

    PubMed  PubMed Central  Google Scholar 

  121. 121.

    Wang S, Jiang J, Zhang C, Zhang X, Wang C. Serum lincRNA-p21 expression in primary liver diseases and liver metastatic diseases. Pathol Res Pract. 2019;215(4):779–83.

    CAS  PubMed  Google Scholar 

  122. 122.

    Gao T, Liu X, He B, Nie Z, Zhu C, Zhang P, et al. Exosomal lncRNA 91H is associated with poor development in colorectal cancer by modifying HNRNPK expression. Cancer Cell Int. 2018;18:11.

    PubMed  PubMed Central  Google Scholar 

  123. 123.

    Liu T, Zhang X, Gao S, Jing F, Yang Y, Du L, et al. Exosomal long noncoding RNA CRNDE-h as a novel serum-based biomarker for diagnosis and prognosis of colorectal cancer. Oncotarget. 2016;7(51):85551–63.

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Ye C, Shen Z, Wang B, Li Y, Li T, Yang Y, et al. A novel long non-coding RNA lnc-GNAT1-1 is low expressed in colorectal cancer and acts as a tumor suppressor through regulating RKIP-NF-κB-snail circuit. J Exp Clin Cancer Res. 2016;35(1):187.

    PubMed  PubMed Central  Google Scholar 

  125. 125.

    Xiao Y, Yurievich UA, Yosypovych SV. Long noncoding RNA XIST is a prognostic factor in colorectal cancer and inhibits 5-fluorouracil-induced cell cytotoxicity through promoting thymidylate synthase expression. Oncotarget. 2017;8(47):83171–82.

    PubMed  PubMed Central  Google Scholar 

  126. 126.

    Li L, Shang J, Zhang Y, Liu S, Peng Y, Zhou Z, et al. MEG3 is a prognostic factor for CRC and promotes chemosensitivity by enhancing oxaliplatin-induced cell apoptosis. Oncol Rep. 2017;38(3):1383–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Zhang Y, Liang W, Zhang P, Chen J, Qian H, Zhang X, et al. Circular RNAs: emerging cancer biomarkers and targets. J Exp Clin Cancer Res. 2017;36(1):152. https://doi.org/10.1186/s13046-017-0624-z Review.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495:333–8.

    CAS  PubMed  Google Scholar 

  129. 129.

    Cui X, Wang J, Guo Z, Li M, Liu S, Liu H, et al. Emerging function and potential diagnostic value of circular RNAs in cancer. Mol Cancer. 2018;17(1):123.

    PubMed  PubMed Central  Google Scholar 

  130. 130.

    Gong Y, Mao J, Wu D, Wang X, Li L, Zhu L, et al. Circ-ZEB1.33 promotes the proliferation of human HCC by sponging miR-200a-3p and upregulating CDK6. Cancer Cell Int. 2018;18:116.

    PubMed  PubMed Central  Google Scholar 

  131. 131.

    Chen S, Li T, Zhao Q, Xiao B, Guo J. Using circular RNA hsa_circ_0000190 as a new biomarker in the diagnosis of gastric cancer. Clin Chim Acta. 2017;466:167–71.

    CAS  PubMed  Google Scholar 

  132. 132.

    Huang M, He YR, Liang LC, Huang Q, Zhu ZQ. Circular RNA hsa_circ_0000745 may serve as a diagnostic marker for gastric cancer. World J Gastroenterol. 2017;23:6330–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Chen X, Chen RX, Wei WS, Li YH, Feng ZH, Tan L, et al. PRMT5 circular RNA promotes metastasis of urothelial carcinoma of the bladder through sponging miR-30c to induce epithelial-mesenchymal transition. Clin Cancer Res. 2018;24:6319–30.

    CAS  PubMed  Google Scholar 

  134. 134.

    Ponnusamy M, Yan KW, Liu CY, Li PF, Wang K. PIWI family emerging as a decisive factor of cell fate: an overview. Eur J Cell Biol. 2017;96(8):746–57.

    CAS  PubMed  Google Scholar 

  135. 135.

    Mai D, Ding P, Tan L, Zhang J, Pan Z, Bai R, et al. PIWI-interacting RNA-54265 is oncogenic and a potential therapeutic target in colorectal adenocarcinoma. Theranostics. 2018;8(19):5213–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136.

    Bai Y, Zhao H. Liquid biopsy in tumors: opportunities and challenges. Ann Transl Med. 2018;6(Suppl 1):S89. https://doi.org/10.21037/atm.2018.11.31.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Murtaza M, Dawson SJ, Tsui DW, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013;497:108–12. https://doi.org/10.1038/nature12065.

    CAS  Article  PubMed  Google Scholar 

  138. 138.

    Merker JD, Oxnard GR, Compton C, et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J Clin Oncol. 2018;36:1631–41. https://doi.org/10.1200/JCO.2017.76.8671.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Conceptualization, M.R., A.N., and D.S; drafting of the text M. R, M.I., G.R., S.S; figure M.I., G.R., S.S., A.N. and F.C.; tables F.C., C.A., M.I., G.R. and S.S. All authors gave substantial contributions to the preparation of the draft, revised the manuscript and gave their approval to the final version.

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Correspondence to Giulia Ribelli.

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Russano, M., Napolitano, A., Ribelli, G. et al. Liquid biopsy and tumor heterogeneity in metastatic solid tumors: the potentiality of blood samples. J Exp Clin Cancer Res 39, 95 (2020). https://doi.org/10.1186/s13046-020-01601-2

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Keywords

  • Liquid biopsy
  • Tumor heterogeneity
  • Circulating tumor cells
  • Peripheral blood mononuclear cells
  • Circulating tumor DNA
  • microRNA