Next Article in Journal
ILCs—Crucial Players in Enteric Infectious Diseases
Next Article in Special Issue
Re-Sensitizing Cancer Stem Cells to Conventional Chemotherapy Agents
Previous Article in Journal
3K3A-Activated Protein C Prevents Microglia Activation, Inhibits NLRP3 Inflammasome and Limits Ocular Inflammation
Previous Article in Special Issue
Androgen Metabolism and Response in Prostate Cancer Anti-Androgen Therapy Resistance
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 23
Issue 22
10.3390/ijms232214197
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer

by
Caterina Gianni
1,
Michela Palleschi
1,
Filippo Merloni
1,
Giandomenico Di Menna
1,
Marianna Sirico
1,
Samanta Sarti
1,
Alessandra Virga
2,
Paola Ulivi
2,
Lorenzo Cecconetto
1,
Marita Mariotti
1 and
Ugo De Giorgi
1,*
1
Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy
2
Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2022, 23(22), 14197; https://doi.org/10.3390/ijms232214197
Submission received: 29 September 2022 / Revised: 4 November 2022 / Accepted: 16 November 2022 / Published: 17 November 2022
(This article belongs to the Special Issue Combating Drug Resistance in Cancer—from Novel Biomarkers to Enhanced Therapeutic Efficacy)
Browse Figure
Review Reports Versions Notes

Abstract

:
Identifying novel circulating biomarkers predictive of response and informative about the mechanisms of resistance, is the new challenge for breast cancer (BC) management. The integration of omics information will gradually revolutionize the clinical approach. Liquid biopsy is being incorporated into the diagnostic and decision-making process for the treatment of BC, in particular with the analysis of circulating tumor DNA, although with some relevant limitations, including costs. Circulating cell-free DNA (cfDNA) fragmentomics and its integrity index may become a cheaper, noninvasive biomarker that could provide significant additional information for monitoring response to systemic treatments in BC. The purpose of our review is to focus on the available research on cfDNA integrity and its features as a biomarker of diagnosis, prognosis and response to treatments in BC, highlighting new perspectives and critical issues for future applications.

1. Introduction

Breast cancer (BC) is the most prevalent cancer worldwide, with at least 7.8 million patients diagnosed with BC in the last five years [1]. Thanks to new therapies, survival and the possibility of cure have considerably increased in recent times [2]. Disease monitoring has become a priority for millions of patients, and a social issue concerning the proper use of scientific and financial resources. In particular, the identification of novel serum biomarkers, less invasive than standard methods based on tumor tissue analysis, is a real challenge for BC detection and prediction of response to treatments. In this respect, liquid biopsy is revolutionizing clinical practice as a non-invasive diagnostic tool [3]. Applications of liquid biopsy in BC are copious in various settings, from diagnosis (identifying actionable molecular alteration and predictive biomarkers) to treatment, with the objectives of predicting outcome, detecting early progression, causing minimal residual disease, acting as a mechanism of resistance and focusing on treatment management (i.e., tailoring treatment duration, escalation/de-escalation strategies) [3].
Biomarkers detectable using liquid biopsy are many and varied, such as circulating tumor cells, inflammatory cells, mRNA, extracellular vesicles (i.e., exosomes), micro-RNA, circulating DNA, and more [4,5]. The combined analysis of circulating tumor cells and circulating DNA may offer a new approach for the monitoring of disease progression and for individualizing therapy as escalating/de-escalating approaches and/or targeted therapies in patients with advanced BC [6,7,8,9]. In particular, circulating tumor DNA (ctDNA) has emerged as the most useful circulating tool in BC [10]. CtDNA can overcome the bias of analysis in tissue sampling that does not represent tumor heterogeneity, especially in advanced disease, and can detect minimal residual disease or facilitate early diagnosis [11,12]. However, while advanced BC patients may present higher levels of ctDNA (it is detectable in >90% patients with metastatic cancer), it is rarely detectable in early BC [12]. Recent improvements in more sensitive technologies allowed the detection of ctDNA also in early-stage disease, but the application in standard clinical practice is significantly lacking [13,14]. One of the major concerns about ctDNA is the use of mutation-based detection assays (i.e., single-nucleotide variant, somatic copy number alterations, gene amplifications or methylation) particularly useful for differentiating ctDNA from cell-free DNA (cfDNA) [15,16]. However, most ctDNA in blood does not present any mutations, particularly in the earliest stages of the disease, which explains the difficulties of the application in these precise conditions and the low capacity for anticipating the diagnosis of localized cancer [3]. From this point of view, different approaches directed towards epigenetic analysis may be reasonable tools to fully exploit liquid biopsy in various settings.
The examination of fragmentation patterns of cfDNA, also known as “fragmentomics”, has been shown to be a valuable method to extract information from liquid biopsy without the need for mutational information, and it is now an attractive area of cancer biomarker research [17].

2. CfDNA Fragmentomics in BC: Different Features and Applications

CfDNA in the bloodstream (and also in other body fluids) consists of DNA fragments released from cells undergoing apoptosis (in particular hematopoietic cells via caspase-dependent DNA cleavage). The enzymatic cleavage of nucleosome units in normal cells produces DNA fragments of at least 200 base pairs (bp) [18]. Every fragment is constituted of a DNA strand rolled up around nucleosomes (147 bp) and smaller DNA fragments of approximately 20 bp that act as linkers [19]. In the case of cancer cells, many different death processes are involved, such as necrosis, apoptosis, autophagy, necroptosis or ferroptosis [20]. These various events release DNA fragments of different sizes as compared to normal cells; however, long and short fragments may be released by the same mechanisms (e.g., necrosis and apoptosis) [21]. Moreover, cfDNA fragment characteristics and size could be influenced by secretion mechanisms, enzymatic activity, or the preservation process, and also by the tissue of origin [15,22].
The study of fragmentomics includes not only fragment size, but also other properties of cfDNA such as fragment endpoints, specific motifs included in the sequence of the fragment, the nucleosome footprints or position, and the various topologic forms (circular vs. linear) (Figure 1) [17].

2.1. Size of cfDNA Fragments and cfDNA Integrity

The first method of determining the size of cfDNA molecules was gel electrophoresis, subsequently replaced by quantitative polymerase chain reaction (qPCR) assay, able to define different fragment-size ranges by using amplicons with various measures [23]. The ratio between shorter and longer fragments is defined as the cfDNA integrity index (cfDI), and gives information about the total amount of tumoral DNA fragments. It is generally hypothesized that DNA fragments in plasma produced by cancer cells are more variable in length than cfDNA from non-cancer cells; however, this is not really been made clear so far, and the debate is open [24].
Most studies in BC have selected the ALU (Arthrobacter luteus) sequences, as they are considered the most frequent and repeated sequences in the human genome, and are commonly approximately 300 nucleotides in length, coinciding with more than 10% of the genome. The DNA integrity index corresponds to the ratio of ALU247/ALU115 [23]. Because the recombining sites of ALU115 are within the ALU247 annealing sites, the ratio of ALU247 to ALU115 is defined by DNA integrity. CfDI characterizes the fragmentation pattern of circulating cfDNA. In these studies, larger fragments, (such as ALU247), are assumed to derive from necrosis/autophagy of cancer cells, whereas shorter fragments (such as ALU115) derive from normal cells [21,25,26]. However, considerations of the actual length of tumor-derived cfDNA fragments are contradictory, and some studies enrolling cancer patients showed the exact opposite [27,28].
In BC, the qPCR technique has been the most widely used in multiple observational studies (Table 1). This method demonstrated that cfDI may easily detect early BC, showing a major level of cfDNA and a higher level of cfDI in BC patients versus healthy controls [26,29].
In this view, the use of cfDNA and cfDI may become in the future a feasible tool to improve early detection of BC and accuracy in cancer diagnosis, according to the cancer stage, after more confirming studies [30]. The analysis conducted by Umetani et al. on serum with a qPCR technique showed that the cfDI corresponding to the ratio ALU 247/115 was significantly associated with the stage at diagnosis, with an increase from stage II to stage IV [26]. Further evidence, using the same method, has been demonstrated by identifying high cfDI as an independent prognostic factor at the time of early BC diagnosis [31]. Iqbal et al. confirmed the correlation between the cfDI value and tumor size, also predicting the overall survival (OS) at 5 years and disease-free survival (DFS) at 4 years using the multivariate analysis [32]. In the same study, the observation of the cfDI trend during BC treatment revealed a significant decrease in DNA integrity in early BC, after surgery [32].
The size distribution of cfDNA and the cfDI variations have been described also under adjuvant/neoadjuvant treatment [33,34,35]. Wang et al. observed that the cfDI increased after neoadjuvant treatment in twenty-nine locally advanced BC patients as a consequence of the tumor shrinkage; in particular, patients undergoing complete pathological response presented a higher cfDI than patients with distant disease-progression after surgery [33]. However, in a similar study that included 65 patients with BC treated with neoadjuvant therapy, the cfDI was not informative, although the data were collected according to the same formulas as Umetani and Wang [36]. In the study conducted by Lehner et al., the absolute levels of ALU 115 showed a downward trend in patients with pCR, whereas they were increased in patients with disease stability. The same trend was observed with longer fragments (ALU 247), confirming other previous findings described for different histologies [36]. This downward trend was observed also by Adusei et al. in 32 female BC patients after three cycles of neoadjuvant chemotherapy [34]. The authors assumed that the reduction of cfDNA and cfDI after three cycles of chemotherapy was due to the decrease in tumor volume and to an improvement in cfDNA clearance; however, there are no clear data demonstrating that these assumptions might be true. In a study involving 41 BC patients in the adjuvant setting, a different variation of the two items was observed in two different groups of patients, independently of the type of treatment received [35]. One group showed an increase in the total amount of cfDNA and a corresponding rise in the cfDI, while the other subgroup revealed the opposite trend.
In the metastatic setting, Cheng et al. and Madhavan et al. reported that high cfDI was correlated as being related to better OS and RFS, in total opposition to results achieved in the early setting [32,37,38]. They used different ALU sequences (ALU 260 and ALU 111bp), and introduced another DNA recurring-element target, LINE-1 (LINE-1 266 and LINE-1 197 bp) [37,38]. Furthermore, Madhavan et al. rejected a possible prognostic value of cfDI in BC patients with non-metastatic disease who were represented in the study by a smaller cohort of 82 cases, as compared to 201 samples from advanced BC patients [37]. Cheng et al. also, surprisingly, observed that cfDI was lower in BC patients developing recurrence (p < 0.001 for ALU and LINE1 cfDI), hypothesizing that a reduction in cfDI could help define the risk of recurrence in association with clinical–biological features [38]. The body of evidence regarding cfDNA quantity and the application of cfDI is still lacking, because of the demonstration of opposite results and the presence of too much hypothesizing, rather than effective data, but the possible application to breast cancer diagnosis and monitoring is intriguing.
Table
Table 1. Studies conducted into fragment size and cfDI in BC with qPCR techniques.
Table 1. Studies conducted into fragment size and cfDI in BC with qPCR techniques.
StudyObjectiveBC Patients Enrolled (n)Results or Status
NCT03474016Diagnosis116Unknown
Agostini et al., 2012 [29]Diagnosis39cfDI (ALU 247/125 ratio) was an accurate circulating biomarker for BC diagnosis (p < 0.0001).
Stötzer et al., 2014 [39]Diagnosis154cfDNA was a valuable biomarker for the detection of localized BC.
Iqbal et al., 2015 [22]Diagnosis148The ALU 247/115 ratio was significantly
higher in BC patients, compared with controls (p < 0.001) and related to stage; cfDI trend during BC treatment revealed a significant decrease in eBC after surgery.
Kamel et al., 2016 [30]Diagnosis95Higher cfDI values were useful for BC diagnosis (p < 0.001).
Elhelaly et al., 2022 [40]Diagnosis50cfDNA and cfDI were markers for early diagnosis of BC, cfDNA concentrations were significantly lower after BC surgery (p < 0.001).
Hussein et al., 2019 [25]Diagnosis40High cfDNA and cfDI (ALU 247/125 ratio) are diagnostic and preoperative prognostic markers for BC.
Umetani et al., 2016 [26]Prediction of risk of relapse83cfDI may easily detect early BC and cfDI trend increase with cancer stage.
Maltoni et al., 2017 [41]Prediction of risk of relapse79No association between cfDNA and prognosis, amplicones ratio between some longer and shorter fragments is higher in BC.
Cheng et al., 2017 [42]Prediction of risk of relapse212 (175 nonrecurrent,
37 recurrent
BC)		ALU260/111 and LINE1-266/97 were lower
in recurrent BC vs. nonrecurrent BC (p < 0.001). Lower cfDI BC patients may develop much more recurrence, compared with patients with higher cfDI (p = 0.020, p = 0.019).
Lamminhao et al., 2021 [31]Prognostic value207High cfDI was an independent prognostic factor for poor OS in BC patients (p = 0.020).
Adusei et al., 2021 [34]Variation of cfDI during neoadjuvant therapy32The study showed a downward trend of cfDNA and cfDI after three cycles of neoadjuvant chemotherapy in BC patients.
Wang et al., 2019 [33]Description of variation of cfDI during neoadjuvant chemotherapy29NACT determined an increase in cfDI (p < 0.05) associated with tumor shrinkage and reduced Ki67 levels (p < 0.05). BC patients with pCR had a higher cfDI than patients with residual disease after surgery.
Lenher et al., 2013 [36]Description of variation of cfDI during neoadjuvant chemotherapy65A downtrend of short and longer ALU amplicons from cycle one to six of neoadjuvant chemotherapy was observed in CR patients, whereas an increase was observed in non-responder (p = 0.033).
Deligezer et al., 2008 [35]Description of variation of cfDI during adjuvant chemotherapy41CfDNA and cfDI varied simultaneously during adjuvant chemotherapy in BC patients.
Cheng et al., 2018 [38]Prognostic value in metastatic BC268CfDI significantly increased in aBC patients after one cycle of chemotherapy (p = 0.00017 for ALU, p = 0.0016 for LINE-1). A higher cfDI (for both ALU
and LINE-1) correlated with a higher PFS and OS.
Madhavan et al., 2014 [37]Prognostic value in metastatic BC283cfDI (with ALU and LINE-1 amplicons) was lower in early BC patients (ALU: p = 0.046; LINE-1 p = 0.041). In aBC, lower values of cfDI were connected to a worse PFS (p = 0.0025 for ALU) and OS (p < 0.0001 for both ALU and LINE-1 fragments).
Another approach to determining the size of cfDNA fragments is based on electron microscopy [43]. However, neither electron microscopy nor qPCR are methods sufficient enough to elaborate a genome-wide study of cfDNA.
The advent of the next-generation sequencing (NGS) method has revealed the chance to use a single-base resolution assay to identify cfDNA. In some studies, cfDNA molecules were sequenced with a paired-end sequencing technique (specifically, bisulfite-based sequencing) [17]. The paired-end reads were lined up with a corresponding genome, to identify the peripheral ends determining the whole fragment size. The limit of this technique is that if the fragments are larger than the read sequence, the sequence located in the middle and not present in the sequencing outcomes might be lost [17]. Among the new techniques, shallow whole genome sequencing (sWGS) has proven to be a useful method for selecting shorter fragments of cfDNA from cancer patients [27]. Mouliere et al. developed a method to identify specific cfDNA fragment sizes and combine them with sWGS, to gain more information about ctDNA [27]. Among the shallow whole-genome techniques, nanopore sequencing may be useful in sequencing very short DNA fragments [44]. Some preclinical data showed that this method could extract multiple information, such as fragmentation, methylation and nucleosomal patterns of DNA from cancer cells from a single run, without the problems observed with commonly used bisulfite-based sequencing for long or very short cfDNA fragments [45,46]. The limitations of PCR and widely-used NGS approaches for the analysis of long fragments may also be overcome by other multiparametric methods such as microfluidic capillary electrophoresis [47]. However, studies on BC patients with these new techniques are awaited.

2.2. Nucleosomal Patterns

The size of cfDNA fragments is also determined by epigenetic processes. It is known that hypomethylated cfDNA fragments tend to be shorter than hypermethylated DNA [19,48,49]. The nucleosomal complex is influenced by methylation and, notably, in hypomethylated cfDNA the nucleosomes are fewer; consequently, the DNA is more susceptible to nucleases and the fragments may have a more variable size [50].
The majority of cfDNA fragments are constituted by mono-nucleosomal DNA, but longer fragments may present more nucleosomes (i.e., dinucleosomes, trinucleosomes) [51]. However longer fragments can be degraded to shorter cfDNA fragments, building up the mono-nucleosomal pool, and often tend be excluded by preanalytical procedures that work on the isolation of mono-nucleosomal cfDNA, with biased results [52,53]. Longer fragments often carry more mutational signatures that can get lost with only mono-nucleosomal analysis; instead, di/tri-nucleosomal analysis may increase the detection of ctDNA mutations [54,55].
In consideration of this, nucleosomal patterns may be the determinant for establishing the size and topology of circulating DNA fragments, and may also be informative about the cell of origin, and cell physiology or pathology [19]. The nucleosome occupancy in DNA is different among various tissues and particularly in cancer cells, compared with healthy cells [46]. In particular, the main protein units of the nucleosomal subunit are the histones, which undergo post-translational modifications (PTMs) [56]. These PTMs include acetylation, methylation, phosphorylation, ubiquitination, sumoylation, glycosylation, homocysteinylation and crotonylation [50]. These modifications do not alter the DNA sequence, but can influence the chromatin structure and consequently the production of different-sized fragments [57]. Moreover, the mechanisms of cfDNA release such as apoptosis or necrosis, influence the nucleosomal patterns [58,59]. The analysis of different PTMs proved to be a potential circulating biomarker for cancer. Their presence in the bloodstream was enhanced, due to the rapid cancer-cell turnover after chemotherapy treatments [60,61]. According to this hypothesis, the evaluation of the levels of circulating nucleosomes before and after treatment has been studied [50].
In BC patients undergoing neoadjuvant chemotherapy, it was observed that higher basal levels of nucleosomes corresponded to a worse response [62]. However, the majority of available data derive from preclinical studies, and we will hopefully experience the application of new techniques involving nucleosomes in the near future.

2.3. End-Fragments Signature

Recently, the presence of preferred ends in circulating fragments was demonstrated. The fragmentation of cfDNA molecules does not derive from a random process. Preferred ends of fragments refer to specific ending-sites in the genome, usually in regions with open chromatin (which are more accessible) [63]. Chromatin accessibility differs among various tissues, as do nucleosomal patterns [19]. Of note, it has been observed that there are specific ctDNA fragments end-coordinates among hepatocarcinoma patients, which could serve as biomarkers for cancer [64]. A study on multiple cancer types (including BC) showed that ends of ctDNA fragments tend to be located at different genomic loci, compared with ends of fragments produced by peripheral blood cells. Data from >2700 plasma-DNA samples from patients with 11 different tumors (including healthy controls) were analyzed with WGS, showing a 95% specificity and sensitivity of end signature-analysis for cancer detection, approximately 79% for all cancer types, and in particular 78% for BC; however, this study has not been peer reviewed [65]. If these data are reproduced, this method could become a possible cost-effective way to detect BC at an early stage, thanks to the wide presence of preferred end-coordinates across the genome.
In addition to preferred end size, the composition of end motifs has become increasingly interesting. The end motifs correspond to the number and type of nucleotides proximal to the 5’end of each fragment. Deoxyribonucleases (DNases) are the major actors in cutting fragments and determining the specific end-motifs. Each DNase cuts the fragment into different sequences, for example, DNase1 cleaves protein-free DNA, whereas DNase1L3 cuts chromatin into the nucleosomal linker sequences [66]. In several cancers, including BC, the level of DNase1L3 is lower than in normal cells, resulting in fewer DNase1L3-associated end motifs [67]. Studies have shown a possible clinical application for end motifs in cancer detection [68].
Finally, cDNA fragments were shown to present single-stranded 5′ DNA protrusions denominated “jagged ends”, traced thanks to the use of methylcytosine during DNA end-repair for WGS library development [69]. Approximately 88% of cfDNA fragments present jagged ends, and tumoral DNA fragments seem to have an increased “jaggedness” compared with normal cfDNA [17]. Their formation depends on different DNases activity and nucleosomal footprints [70]. Nowadays, there are few data about the use of jagged ends in cancer diagnosis. Plasma- and urinary-DNA jaggedness has been used for detecting patients with hepatocellular and bladder carcinoma, showing a potential application in oncology (i.e., a surrogate for the analysis of different DNase activities) [71,72]. Further evidence is awaited to investigate jaggedness in patients with BC and other different cancers.

2.4. Epigenomic Modifications of cfDNA Fragments: Methylation and Hydroxymethylation

Changes in epigenomic features are typically observed in cancer also in the early stages of the disease, motivating their analysis as a new tool for early diagnosis or prognostic biomarkers. The methylation profiling of cfDNA fragments is a new method for identifying patterns of cancer tissue [73,74]. Methylation patterns are unique to cancer cells, with hypermethylation for the silencing of tumor-suppressor genes and the hypomethylation of oncogene to enhance oncogenic activity. The realization of reference methylomes from normal cells and cancer cells allowed for the definition of new non-invasive methods to discriminate against cancer or other diseases [19].
Various techniques have been used to study methylation patterns. The “cell-free DNA methylation immunoprecipitation” (cfMeDIP-seq) method was used to find methylations on a genome-wide scale, using liquid biopsy to identify cancers that do not usually release useful amounts of ctDNA (such as renal cancer of intracranial tumors) [75,76].
Regarding BC, “The Cancer Genome Atlas” (TCGA) network identified five subgroups of BC which were different from each other, based on DNA-methylation patterns, with one of them presenting a CpG island methylator phenotype (CIMP) [77]. Some clinical trials have started, to determine a potential use for methylation in BC (Table 2).
In a study conducted by Liu et al. involving 80 patients with malignant BC and 80 patients with a benign breast-tumor, the analysis of differentially methylated regions of cfDNA was combined with other fragment information (such as size) and radiologic information, to anticipate the detection of malignant BC [78]. The cfDNA of each patient was studied with whole-genome bisulfite sequencing (WGBS), highlighting the fact that the quantity of cfDNA in different genomic regions is negatively related to their CpG density (genomic sites which are usually methylated) in cancer patients [78]. Of note, in another study it was observed that the methylation profile of cfDNA can change years before BC is clinically identified [79].
“CMethDNA assay” is a further method that potentially retains more CpG-rich informative fragments [74]. This method has been used to apply methylation information to predict survival outcomes in advanced BC patients [80]. It is a 10-gene panel of cfDNA methylation markers using a quantitative PCR assay, derived from known BC hypermethylated genes identified from DNA-methylation patterns in breast tissue (such as methylated RASSF1A, APC and BRCA1) [80,81]. Using this technique, Visvanathan et al. tested methylation patterns in 141 metastatic BC patients at the baseline, 4 weeks after treatment initiation and at the first radiological disease-assessment. There was a significant association between an elevated cumulative-methylation-index (CMI), and both a shorter PFS and OS after 4 weeks of treatment [82]. Panagopoulou et al. used a similar method consisting of capillary electrophoresis (a multiparametric method). [83]. They considered five other frequently methylated genes (KLK10, SOX17, WNT5A, MSH2, GATA3) in the cfDNA of BC patients, which were found to be more methylated than those in the healthy controls [83]. In the same study, the authors observed simultaneously many features of cfDNA fragments, from methylation patterns to size (with a SYBR Green-based/Qubit assay), enhancing a more fragmented pattern in larger tumors. They also found a correlation between the quantification of cfDNA with a worse PFS, higher death risk and non-response to pharmacotherapy, in particular in the metastatic setting [83].
Table
Table 2. Registered clinical trials assessing methylation patterns in cfDNA in breast cancer.
Table 2. Registered clinical trials assessing methylation patterns in cfDNA in breast cancer.
TrialObjectivePatients Enrolled (n)Type Status or Available Results
NCT03480659Early detection400 ObservationalTerminated (technical problem with plasma blood samples obtained from the patients).
NCT03863522
Visvanathan et al., 2017 [82]		Early detection447InterventionalCompleted: elevated cumulative methylation index is significantly associated with both shorter PFS and OS after 4 weeks of treatment in advanced BC.
Agostini et al.,
2012 [29]		Diagnosis and early detection39ObservationalCompleted: cfDNA methylatation (RASSF1A, MAL and SFRP1) is a phenotypic feature of BC, and can be used for cancer diagnosis.
NCT03184090Prediction of treatment benefit33 InterventionalCompleted, no result posted.
NCT04996836Prediction of treatment benefit200Observational Active, not recruiting yet.
NCT00698477Prediction of treatment benefit and mechanism of resistance30ObservationalUnknown.
NCT03205761Prediction of treatment benefit 34InterventionalActive, not recruiting.
Methylation patterns have also been evaluated as biomarkers for the prediction of treatment response [84]. In a study conducted by Zurita et al., the serum levels of methylated gene-promoter 14-3-3-σ were observed in 34 metastatic BC patients during chemotherapy, and 77 patients previously treated for BC [84]. In the study, the variation of the methylation was considered the “biomarker response ratio”, and connected to response and prognosis. The power of methylation analysis in predicting treatment response has also been studied by Fackler et al. and Legendre et al. who, with different techniques (such as cMethDNA) hypothesized an application in therapy stratification or the identification of recurrent metastatic BC [80,85].
Furthermore the oxidation of 5mC, catalyzed by the ten-eleven translocation enzymes (TETs), is considered one of the active mechanisms for DNA demethylation, obtaining 5-hydroxymethylcytosine (5hmC) [86]. The presence of low quantities of 5hmC has been observed in different cancers as a consequence of altered TETs activity [87]. The analysis of this epigenetic alteration is under study for cancer diagnosis and stage classification, but there are still not many data available on BC [88]. Recently, it has also been demonstrated that 5hmC variations occur first in tumorigenesis, and tend not to change during cancer progression at the various stages [89].
Despite the intriguing results, these findings need further validation to determine the clinical utility of methylation analysis in BC.

3. Limitations and Critical Issues

First of all, one of the major critical issues for the use of fragment analysis in BC is the wide discrepancy among authors concerning ctDNA-fragment length. Some studies assume that ctDNA is made of longer amplicons, compared with healthy cfDNA, while others suggest the complete opposite [23]. This debate also reflects on the value of cfDI. Most of the studies presented assume that a higher cfDI is related to the presence of BC, higher stages, and worse outcome after treatments [26,33,34,35]. However, this is not verified in the advanced setting, where the opposite was demonstrated [37,38]. As a clear example, Umetani et al. proposed that an increased cfDI could be a prediction of metastasization, but afterwards Cheng et al. demonstrated that cfDI was lower in BC patients developing a recurrence [26,38]. The reason for these controversial results may derive from the different methods used for evaluating cfDI considering different fragments (e.g., ALU sequences and others), and the different population of patients taken into account.
It is well known that the pre-analytical collection, processing of blood samples, centrifugation protocols and long-term storage can have significant quantitative and qualitative effects on cfDNA (such as hematopoietic lysis, cfDNA damage and contamination) [31,90]. Methods of DNA extraction, different kits and the use of plasma versus serum in the different studies may influence the results, with different amounts of cfDNA analyzed. Moreover the intrinsic limits of qPCR (the most used technique in studies on BC and cfDI) are probably also responsible.
Not less important are the small sample sizes of the studies available and the necessity for wide prospective-validation cohorts. Another consideration concerns the necessity for standard reference-markers to adequately compare studies [91]. Actually, there is no agreement on standardized methods for pre-analytical studies, and this reduces the strength of the body of evidence.
To date, there is still a scarcity of application of some new techniques derived from genome sequencing, and research in oncology into some aspects of fragmentomics (i.e., jagged end, end motif) is only at the beginning (compared with other application fields such as transplantology and noninvasive prenatal diagnostics) [17].
Finally, the most important aspect in transferring all these findings to BC clinical practice will be the acquisition of analytical validity (from an evaluation of accuracy, reliability and reproducibility), and clinical validity (the power to identify populations with significantly specific clinical-outcomes) from studies that are still overdue in this field [51].

4. Conclusions

The advent of overwhelming new data from “omics” in cancer diagnosis will progressively change the therapeutic and diagnostic approach in cancer. Fragmentomics analysis, in particular fragment size and cfDI, is establishing itself as a non-invasive, cost-effective new informative method in BC. In particular, it will change the approach to liquid biopsy, gaining helpful information independently from the mutational signature. Epigenetic features are more dynamic, and global fragmentation-patterns could be combined as different biomarkers, thanks to new bioinformatic techniques (i.e., DNA evaluation of fragments for early interception, DELFI) to reflect BC disease [22].
The potential applications seem to be numerous, from cancer detection and the anticipation of recurrence and the evaluation of minimal residual disease, to responsiveness or resistance to treatments. However, the gap between research and clinical practice is still deep, and larger studies are needed to fill in the missing points. Further results from fragmentomics in BC are awaited, and we look forward to new opportunities for personalized cancer treatment.

Author Contributions

Conceptualization, C.G and U.D.G.; methodology, C.G.; formal analysis, C.G. and U.D.G.; data curation, C.G.; writing C.G.; review and editing, C.G., M.P., F.M., G.D.M., M.S., S.S., A.V., P.U., L.C., M.M. and U.D.G.; supervision, U.D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We thank Margherita Gianni for the editorial assistance and language revision. This work was partly supported thanks to the contribution of Ricerca Corrente by the Italian Ministry of Health within the research line L2 (Innovative therapies, phase I-III clinical trials).

Conflicts of Interest

This research received no external funding. M.P. has received advisory-board fees from Novartis. U.D.G. has received advisory-board or consultant fees from Merck Sharp and Dohme, Bristol Myers Squibb, Janssen, Astellas, Astrazeneca, Bayer, Pfizer, Ipsen, Novartis, and Pharmamar and institutional research grants from Astrazeneca, Sanofi, and Roche. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Harbeck, N.; Gnant, M. Breast cancer. Lancet 2017, 389, 1134–1150. [Google Scholar] [CrossRef]
  3. Alix-Panabières, C.; Pantel, K. Liquid Biopsy: From Discovery to Clinical Application. Cancer Discov. 2021, 11, 858–873. [Google Scholar] [CrossRef] [PubMed]
  4. Mego, M.; Gao, H.; Cohen, E.; Anfossi, S.; Giordano, A.; Sanda, T.; Fouad, T.; De Giorgi, U.; Giuliano, M.; Woodward, W.; et al. Circulating Tumor Cells (CTC) Are Associated with Defects in Adaptive Immunity in Patients with Inflammatory Breast Cancer. J. Cancer 2016, 7, 1095–1104. [Google Scholar] [CrossRef] [Green Version]
  5. De Giorgi, U.; Mego, M.; Scarpi, E.; Giordano, A.; Giuliano, M.; Valero, V.; Alvarez, R.H.; Ueno, N.T.; Cristofanilli, M.; Reuben, J.M. Association between circulating tumor cells and peripheral blood monocytes in metastatic breast cancer. Ther. Adv. Med. Oncol. 2019, 11, 1758835919866065. [Google Scholar] [CrossRef] [Green Version]
  6. de Freitas, A.J.A.; Causin, R.L.; Varuzza, M.B.; Calfa, S.; Filho, C.M.T.H.; Komoto, T.T.; Souza, C.D.P.; Marques, M.M.C. Liquid Biopsy as a Tool for the Diagnosis, Treatment, and Monitoring of Breast Cancer. Int. J. Mol. Sci. 2022, 23, 9952. [Google Scholar] [CrossRef]
  7. Pedrazzoli, P.; Ferrante, P.; Kulekci, A.; Schiavo, R.; De Giorgi, U.; Carminati, O.; Marangolo, M.; Demirer, T.; Siena, S.; Rosti, G. European Group for Blood and Marrow Transplantation (EBMT), Solid Tumors Working Party. Autologous hematopoietic stem cell transplantation for breast cancer in Europe: Critical evaluation of data from the European Group for Blood and Marrow Transplantation (EBMT) Registry 1990–1999. Bone Marrow Transplant. 2003, 32, 489–494. [Google Scholar]
  8. De Giorgi, U.; Mego, M.; Rohren, E.M.; Liu, P.; Handy, B.C.; Reuben, J.M.; Macapinlac, H.A.; Hortobagyi, G.N.; Cristofanilli, M.; Ueno, N.T. 18F-FDG PET/CT Findings and Circulating Tumor Cell Counts in the Monitoring of Systemic Therapies for Bone Metastases from Breast Cancer. J. Nucl. Med. 2010, 51, 1213–1218. [Google Scholar] [CrossRef] [Green Version]
  9. Addanki, S.; Meas, S.; Sarli, V.N.; Singh, B.; Lucci, A. Applications of Circulating Tumor Cells and Circulating Tumor DNA in Precision Oncology for Breast Cancers. Int. J. Mol. Sci. 2022, 23, 7843. [Google Scholar] [CrossRef]
  10. Sant, M.; Bernat-Peguera, A.; Felip, E.; Margelí, M. Role of ctDNA in Breast Cancer. Cancers 2022, 14, 310. [Google Scholar] [CrossRef]
  11. Peng, Y.; Mei, W.; Ma, K.; Zeng, C. Circulating Tumor DNA and Minimal Residual Disease (MRD) in Solid Tumors: Current Horizons and Future Perspectives. Front. Oncol. 2021, 11, 763790. [Google Scholar] [CrossRef] [PubMed]
  12. Ma, F.; Guan, Y.; Yi, Z.; Chang, L.; Li, Q.; Chen, S.; Zhu, W.; Guan, X.; Li, C.; Qian, H.; et al. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int. J. Cancer 2020, 146, 1359–1368. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Bettegowda, C.; Sausen, M.; Leary, R.J.; Kinde, I.; Wang, Y.; Agrawal, N.; Bartlett, B.R.; Wang, H.; Luber, B.; Alani, R.M.; et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 2014, 6, 224ra24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Croessmann, S.; Park, B.H. Circulating tumor DNA in early-stage breast cancer: New directions and potential clinical applications. Clin. Adv. Hematol. Oncol. 2021, 19, 155–161. [Google Scholar] [PubMed]
  15. van der Pol, Y.; Mouliere, F. Toward the Early Detection of Cancer by Decoding the Epigenetic and Environmental Fingerprints of Cell-Free DNA. Cancer Cell 2019, 36, 350–368. [Google Scholar] [CrossRef]
  16. Keller, L.; Belloum, Y.; Wikman, H.; Pantel, K. Clinical relevance of blood-based ctDNA analysis: Mutation detection and beyond. Br. J. Cancer 2021, 124, 345–358. [Google Scholar] [CrossRef]
  17. Ding, S.C.; Lo, Y.M.D. Cell-Free DNA Fragmentomics in Liquid Biopsy. Diagnostics 2022, 12, 978. [Google Scholar] [CrossRef]
  18. Giacona, M.B.; Ruben, G.C.; Iczkowskim, K.A.; Roos, T.B.; Porter, D.M.; Sorenson, G.D. Cell-free DNA in human blood plasma: Length measurements in patients with pancreatic cancer and healthy controls. Pancreas 1998, 17, 89–97. [Google Scholar] [CrossRef]
  19. Angeles, A.K.; Janke, F.; Bauer, S.; Christopoulos, P.; Riediger, A.L.; Sültmann, H. Liquid Biopsies beyond Mutation Calling: Genomic and Epigenomic Features of Cell-Free DNA in Cancer. Cancers 2021, 13, 5615. [Google Scholar] [CrossRef]
  20. Strasser, A.; Vaux, D.L. Cell Death in the Origin and Treatment of Cancer. Mol. Cell 2020, 78, 1045–1054. [Google Scholar] [CrossRef]
  21. Jahr, S.; Hentze, H.; Englisch, S.; Hardt, D.; Fackelmayer, F.O.; Hesch, R.D.; Knippers, R. DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001, 61, 1659–1665. [Google Scholar] [PubMed]
  22. Cristiano, S.; Leal, A.; Phallen, J.; Fiksel, J.; Adleff, V.; Bruhm, D.C.; Jensen, S.Ø.; Medina, J.E.; Hruban, C.; White, J.R.; et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 2019, 570, 385–389. [Google Scholar] [CrossRef] [PubMed]
  23. Sobhani, N.; Generali, D.; Zanconati, F.; Bortul, M.; Scaggiante, B. Cell-free DNA integrity for the monitoring of breast cancer: Future perspectives? World J. Clin. Oncol. 2018, 9, 26–32. [Google Scholar] [CrossRef] [PubMed]
  24. Jiang, P.; Lo, Y.M.D. The Long and Short of Circulating Cell-Free DNA and the Ins and Outs of Molecular Diagnostics. Trends Genet. 2016, 32, 360–371. [Google Scholar] [CrossRef]
  25. Hussein, N.A.; Mohamed, S.N.; Ahmed, M.A. Plasma ALU-247, ALU-115, and cfDNA Integrity as Diagnostic and Prognostic Biomarkers for Breast Cancer. Appl. Biochem. Biotechnol. 2019, 187, 1028–1045. [Google Scholar] [CrossRef]
  26. Umetani, N.; Giuliano, A.E.; Hiramatsu, S.H.; Amersi, F.; Nakagawa, T.; Martino, S.; Hoon, D.S.B. Prediction of breast tumor progression by integrity of free circulating DNA in serum. J. Clin. Oncol. 2006, 24, 4270–4276. [Google Scholar] [CrossRef]
  27. Mouliere, F.; Chandrananda, D.; Piskorz, A.M.; Moore, E.K.; Morris, J.; Ahlborn, L.B.; Mair, R.; Goranova, T.; Marass, F.; Heider, K.; et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci. Transl. Med. 2018, 10, eaat4921. [Google Scholar] [CrossRef] [Green Version]
  28. Underhill, H.R.; Kitzman, J.O.; Hellwig, S.; Welker, N.C.; Daza, R.; Baker, D.N.; Gligorich, K.M.; Rostomily, R.C.; Bronner, M.P.; Shendure, J. Fragment Length of Circulating Tumor DNA. PLoS Genet. 2016, 12, e1006162. [Google Scholar] [CrossRef] [Green Version]
  29. Agostini, M.; Enzo, M.; Bedin, C.; Belardinelli, V.; Goldin, E.; Del Bianco, P.; Maschietto, E.; D’Angelo, E.; Izzi, L.; Saccani, A.; et al. Circulating cell-free DNA: A promising marker of regional lymphonode metastasis in breast cancer patients. Cancer Biomark. 2012, 11, 89–98. [Google Scholar] [CrossRef]
  30. Kamel, A.M.; Teama, S.; Fawzy, A.; El Deftar, M. Plasma DNA integrity index as a potential molecular diagnostic marker for breast cancer. Tumor Biol. 2016, 37, 7565–7572. [Google Scholar] [CrossRef]
  31. Lamminaho, M.; Kujala, J.; Peltonen, H.; Tengström, M.; Kosma, V.M.; Mannermaa, A. High Cell-Free DNA Integrity Is Associated with Poor Breast Cancer Survival. Cancers 2021, 13, 4679. [Google Scholar] [CrossRef] [PubMed]
  32. Iqbal, S.; Vishnubhatla, S.; Raina, V.; Sharma, S.; Gogia, A.; Deo, S.S.V.; Mathur, S.R.; Shukla, N.K. Circulating cell-free DNA and its integrity as a prognostic marker for breast cancer. SpringerPlus 2015, 4, 265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Wang, W.; Zhang, W.; Su, L.; Sang, J.; Wang, S.; Yao, Y. Plasma cell-free DNA integrity: A potential biomarker to monitor the response of breast cancer to neoadjuvant chemotherapy. Transl. Cancer Res. 2019, 8, 1531–1539. [Google Scholar] [CrossRef] [PubMed]
  34. Adusei, E.; Ahenkorah, J.; Adu-Aryee, N.A.; Adutwum-Ofosu, K.K.; Tagoe, E.A.; Koney, N.K.; Nkansah, E.; Aryee, N.; Blay, R.; Hottor, B.; et al. Reduced Serum Circulation of Cell-Free DNA Following Chemotherapy in Breast Cancer Patients. Med. Sci. 2021, 9, 37. [Google Scholar] [CrossRef] [PubMed]
  35. Deligezer, U.; Eralp, Y.; Akisik, E.Z.; Akisik, E.E.; Saip, P.; Topuz, E.; Dalay, N. Effect of Adjuvant Chemotherapy on Integrity of Free Serum DNA in Patients with Breast Cancer. Ann. N. Y. Acad. Sci. 2008, 1137, 175–179. [Google Scholar] [CrossRef] [PubMed]
  36. Lehner, J.; Stötzer, O.J.; Fersching, D.; Nagel, D.; Holdenrieder, S. Circulating plasma DNA and DNA integrity in breast cancer patients undergoing neoadjuvant chemotherapy. Clin. Chim. Acta 2013, 425, 206–211. [Google Scholar] [CrossRef] [PubMed]
  37. Madhavan, D.; Wallwiener, M.; Bents, K.; Zucknick, M.; Nees, J.; Schott, S.; Cuk, K.; Riethdorf, S.; Trumpp, A.; Pantel, K.; et al. Plasma DNA integrity as a biomarker for primary and metastatic breast cancer and potential marker for early diagnosis. Breast Cancer Res. Treat. 2014, 146, 163–174. [Google Scholar] [CrossRef]
  38. Cheng, J.; Holland-Letz, T.; Wallwiener, M.; Surowy, H.; Cuk, K.; Schott, S.; Trumpp, A.; Pantel, K.; Sohn, C.; Schneeweiss, A.; et al. Circulating free DNA integrity and concentration as independent prognostic markers in metastatic breast cancer. Breast Cancer Res. Treat. 2018, 169, 69–82. [Google Scholar] [CrossRef]
  39. Stötzer, O.J.; Lehner, J.; Fersching-Gierlich, D.; Nagel, D.; Holdenrieder, S. Diagnostic relevance of plasma DNA and DNA integrity for breast cancer. Tumor Biol. 2014, 35, 1183–1191. [Google Scholar] [CrossRef]
  40. Elhelaly, R.; Effat, N.; Hegazy, M.A.E.-F.; Abdelwahab, K.; Hamdy, O.; Hashem, E.M.A.; Elzehery, R.R. Circulating Cell Free DNA and DNA Integrity Index as Discriminating Tools between Breast Cancer and Benign Breast Disease. Asian Pac. J. Cancer Prev. 2022, 23, 545–552. [Google Scholar] [CrossRef]
  41. Maltoni, R.; Casadio, V.; Ravaioli, S.; Foca, F.; Tumedei, M.M.; Salvi, S.; Martignano, F.; Calistri, D.; Rocca, A.; Schirone, A.; et al. Cell-free DNA detected by “liquid biopsy” as a potential prognostic biomarker in early breast cancer. Oncotarget 2017, 8, 16642–16649. [Google Scholar] [CrossRef]
  42. Cheng, J.; Cuk, K.; Heil, J.; Golatta, M.; Schott, S.; Sohn, C.; Schneeweiss, A.; Burwinkel, B.; Surowy, H. Cell-free circulating DNA integrity is an independent predictor of impending breast cancer recurrence. Oncotarget 2017, 8, 54537–54547. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Davis, R.W.; Simon, M.; Davidson, N. Electron Microscope Heteroduplex Methods for Mapping Regions of Base Sequence Homology in Nucleic Acids. Methods Enzymol. 1971, 21, 413–428. [Google Scholar]
  44. Wilson, B.D.; Eisenstein, M.; Soh, H.T. High-Fidelity Nanopore Sequencing of Ultra-Short DNA Targets. Anal. Chem. 2019, 91, 6783–6789. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Katsman, E.; Orlanski, S.; Martignano, F.; Fox-Fisher, I.; Shemer, R.; Dor, Y.; Zick, A.; Eden, A.; Petrini, I.; Conticello, S.G.; et al. Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing. Genome Biol. 2022, 23, 158. [Google Scholar] [CrossRef]
  46. Yu, S.C.Y.; Jiang, P.; Peng, W.; Cheng, S.H.; Cheung, Y.T.T.; Tse, O.Y.O.; Shang, H.; Poon, L.C.; Leung, T.Y.; Chan, K.C.A.; et al. Single-molecule sequencing reveals a large population of long cell-free DNA molecules in maternal plasma. Proc. Natl. Acad. Sci. USA 2021, 118, e2114937118. [Google Scholar] [CrossRef]
  47. Schneider, L.; Tripathi, A. Sequence to size-based separation using microfluidic electrophoresis for targeted cell-free DNA analysis. Anal. Biochem. 2022, 649, 114691. [Google Scholar] [CrossRef]
  48. Jensen, T.J.; Kim, S.K.; Zhu, Z.; Chin, C.; Gebhard, C.; Lu, T.; Deciu, C.; van den Boom, D.; Ehrich, M. Whole genome bisulfite sequencing of cell-free DNA and its cellular contributors uncovers placenta hypomethylated domains. Genome Biol. 2015, 16, 78. [Google Scholar] [CrossRef] [Green Version]
  49. Lun, F.M.; Chiu, R.W.; Sun, K.; Leung, T.Y.; Jiang, P.; Chan, K.C.; Sun, H.; Lo, Y.M.D. Noninvasive prenatal methylomic analysis by genomewide bisulfite sequencing of maternal plasma DNA. Clin. Chem. 2013, 59, 1583–1594. [Google Scholar] [CrossRef] [Green Version]
  50. McAnena, P.; Brown, J.A.L.; Kerin, M.J. Circulating Nucleosomes and Nucleosome Modifications as Biomarkers in Cancer. Cancers 2017, 9, 5. [Google Scholar] [CrossRef] [Green Version]
  51. Hasenleithner, S.O.; Speicher, M.R. A clinician’s handbook for using ctDNA throughout the patient journey. Mol. Cancer 2022, 21, 81. [Google Scholar] [CrossRef] [PubMed]
  52. Malkin, E.Z.; De Michino, S.; Lambie, M.; Gill, R.G.; Zhao, Z.; Rostami, A.; Arruda, A.; Minden, M.D.; Bratman, S.V. Cell-free DNA topology is unique to its sub-cellular and cellular origins in cancer. JCI Insight 2022, 7, e159590. [Google Scholar] [CrossRef] [PubMed]
  53. Ungerer, V.; Bronkhorst, A.J.; Uhlig, C.; Holdenrieder, S. Cell-Free DNA Fragmentation Patterns in a Cancer Cell Line. Diagnostics 2022, 12, 1896. [Google Scholar] [CrossRef]
  54. Ganesamoorthy, D.; Robertson, A.J.; Chen, W.; Hall, M.B.; Cao, M.D.; Ferguson, K.; Lakhani, S.R.; Nones, K.; Simpson, P.T.; Coin, L.J.M. Whole genome deep sequencing analysis of cell-free DNA in samples with low tumour content. BMC Cancer 2022, 22, 85. [Google Scholar] [CrossRef] [PubMed]
  55. Snyder, M.W.; Kircher, M.; Hill, A.J.; Daza, R.M.; Shendure, J. Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin. Cell 2016, 164, 57–68. [Google Scholar] [CrossRef] [Green Version]
  56. Buocikova, V.; Rios-Mondragon, I.; Pilalis, E.; Chatziioannou, A.; Miklikova, S.; Mego, M.; Pajuste, K.; Rucins, M.; El Yamani, N.; Longhin, E.M.; et al. Epigenetics in Breast Cancer Therapy—New Strategies and Future Nanomedicine Perspectives. Cancers 2020, 12, 3622. [Google Scholar] [CrossRef]
  57. Hosseini, A.; Minucci, S. Alterations of histone modifications in cancer. In Epigenetics in Human Disease, 2nd ed.; Academic Press: Cambridge, MA, USA, 2018; pp. 141–217. [Google Scholar]
  58. Ungerer, V.; Bronkhorst, A.J.; Van den Ackerveken, P.; Herzog, M.; Holdenrieder, S. Serial profiling of cell-free DNA and nucleosome histone modifications in cell cultures. Sci. Rep. 2021, 11, 9460. [Google Scholar] [CrossRef]
  59. Rostami, A.; Lambie, M.; Yu, C.W.; Stambolic, V.; Waldron, J.N.; Bratman, S.V. Senescence, Necrosis, and Apoptosis Govern Circulating Cell-free DNA Release Kinetics. Cell Rep. 2020, 31, 107830. [Google Scholar] [CrossRef]
  60. Holdenrieder, S.; Stieber, P.; Bodenmüller, H.; Busch, M.; Fertig, G.; Fürst, H.; Schalhorn, A.; Schmeller, N.; Untch, M.; Seidel, D. Nucleosomes in serum of patients with benign and malignant diseases. Int. J. Cancer 2001, 95, 114–120. [Google Scholar] [CrossRef]
  61. Kuroi, K.; Tanaka, C.; Toi, M. Plasma Nucleosome Levels in Node-Negative Breast Cancer Patients. Breast Cancer 1999, 6, 361–364. [Google Scholar] [CrossRef]
  62. Stoetzer, O.J.; Fersching, D.M.; Salat, C.; Steinkohl, O.; Gabka, C.J.; Hamann, U.; Braun, M.; Feller, A.-M.; Heinemann, V.; Siegele, B.; et al. Prediction of response to neoadjuvant chemotherapy in breast cancer patients by circulating apoptotic biomarkers nucleosomes, DNAse, cytokeratin-18 fragments and survivin. Cancer Lett. 2013, 336, 140–148. [Google Scholar] [CrossRef]
  63. Oberhofer, A.; Bronkhorst, A.J.; Uhlig, C.; Ungerer, V.; Holdenrieder, S. Tracing the Origin of Cell-Free DNA Molecules through Tissue-Specific Epigenetic Signatures. Diagnostics 2022, 12, 1834. [Google Scholar] [CrossRef] [PubMed]
  64. Jiang, P.; Sun, K.; Tong, Y.K.; Cheng, S.H.; Cheng, T.H.T.; Heung, M.M.S.; Wong, J.; Wong, V.W.S.; Chan, H.L.Y.; Chan, K.C.A.; et al. Preferred end coordinates and somatic variants as signatures of circulating tumor DNA associated with hepatocellular carcinoma. Proc. Natl. Acad. Sci. USA 2018, 115, E10925–E10933. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Budhraja, K.; McDonald, B.; Stephens, M.D.; Contente-Cuomo, T.; Markus, H.; Farooq, M.; Favaro, P.F.; Connor, S.; Byron, S.A.; Egan, J.B.; et al. Analysis of fragment ends in plasma DNA from patients with cancer. medRxiv 2021. [Google Scholar] [CrossRef]
  66. Serpas, L.; Chan, R.W.Y.; Jiang, P.; Ni, M.; Sun, K.; Rashidfarrokhi, A.; Soni, C.; Sisirak, V.; Lee, W.-S.; Cheng, S.H.; et al. Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA. Proc. Natl. Acad. Sci. USA 2019, 116, 641–649. [Google Scholar] [CrossRef] [Green Version]
  67. Jiang, P.; Sun, K.; Peng, W.; Cheng, S.H.; Ni, M.; Yeung, P.C.; Heung, M.M.; Xie, T.; Shang, H.; Zhou, Z.; et al. Plasma DNA End-Motif Profiling as a Fragmentomic Marker in Cancer, Pregnancy, and Transplantation. Cancer Discov. 2020, 10, 664–673. [Google Scholar] [CrossRef] [Green Version]
  68. Zhitnyuk, Y.V.; Koval, A.P.; Alferov, A.A.; Shtykova, Y.A.; Mamedov, I.Z.; Kushlinskii, N.E.; Chudakov, D.M.; Shcherbo, D.S. Deep cfDNA fragment end profiling enables cancer detection. Mol. Cancer 2022, 21, 26. [Google Scholar] [CrossRef]
  69. Liu, Y. At the dawn: Cell-free DNA fragmentomics and gene regulation. Br. J. Cancer 2022, 126, 379–390. [Google Scholar] [CrossRef]
  70. Avgeris, M.; Marmarinos, A.; Gourgiotis, D.; Scorilas, A. Jagged Ends of Cell-Free DNA: Rebranding Fragmentomics in Modern Liquid Biopsy Diagnostics. Clin. Chem. 2021, 67, 576–578. [Google Scholar] [CrossRef]
  71. Jiang, P.; Xie, T.; Ding, S.C.; Zhou, Z.; Cheng, S.H.; Chan, R.W.Y.; Lee, W.-S.; Peng, W.; Wong, J.; Wong, V.W.S.; et al. Detection and characterization of jagged ends of double-stranded DNA in plasma. Genome Res. 2020, 30, 1144–1153. [Google Scholar] [CrossRef]
  72. Zhou, Z.; Cheng, S.H.; Ding, S.C.; Heung, M.M.S.; Xie, T.; Cheng, T.H.T.; Lam, W.K.J.; Peng, W.; Teoh, J.Y.C.; Chiu, P.K.F.; et al. Jagged Ends of Urinary Cell-Free DNA: Characterization and Feasibility Assessment in Bladder Cancer Detection. Clin. Chem. 2021, 67, 621–630. [Google Scholar] [CrossRef] [PubMed]
  73. Leal, A.; Sidransky, D.; Brait, M. Tissue and Cell-Free DNA-Based Epigenomic Approaches for Cancer Detection. Clin. Chem. 2020, 66, 105–116. [Google Scholar] [CrossRef] [PubMed]
  74. Shen, S.Y.; Singhania, R.; Fehringer, G.; Chakravarthy, A.; Roehrl, M.H.A.; Chadwick, D.; Zuzarte, P.C.; Borgida, A.; Wang, T.T.; Li, T.; et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 2018, 563, 579–583. [Google Scholar] [CrossRef] [PubMed]
  75. Nuzzo, P.V.; Berchuck, J.E.; Korthauer, K.; Spisak, S.; Nassar, A.H.; Alaiwi, S.A.; Chakravarthy, A.; Shen, S.Y.; Bakouny, Z.; Boccardo, F.; et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat. Med. 2020, 26, 1041–1043. [Google Scholar] [CrossRef] [PubMed]
  76. Nassiri, F.; Chakravarthy, A.; Feng, S.; Shen, S.Y.; Nejad, R.; Zuccato, J.A.; Voisin, M.R.; Patil, V.; Horbinski, C.; Aldape, K.; et al. Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes. Nat. Med. 2020, 26, 1044–1047. [Google Scholar] [CrossRef]
  77. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar] [CrossRef] [Green Version]
  78. Liu, J.; Zhao, H.; Huang, Y.; Xu, S.; Zhou, Y.; Zhang, W.; Ming, Y.; Wang, X.; Zhao, S.; Li, K.; et al. Genome-wide cell-free DNA methylation analyses improve accuracy of non-invasive diagnostic imaging for early-stage breast cancer. Mol. Cancer 2021, 20, 36. [Google Scholar] [CrossRef]
  79. Xu, Z.; Sandler, D.P.; Taylor, J.A. Blood DNA Methylation and Breast Cancer: A Prospective Case-Cohort Analysis in the Sister Study. J. Natl. Cancer Inst. 2020, 112, 87–94. [Google Scholar] [CrossRef]
  80. Fackler, M.J.; Bujanda, Z.L.; Umbricht, C.; Teo, W.W.; Cho, S.; Zhang, Z.; Visvanathan, K.; Jeter, S.; Argani, P.; Wang, C.; et al. Novel Methylated Biomarkers and a Robust Assay to Detect Circulating Tumor DNA in Metastatic Breast Cancer. Cancer Res. 2014, 74, 2160–2170. [Google Scholar] [CrossRef] [Green Version]
  81. Müller, H.M.; Fiegl, H.; Widschwendter, A.; Widschwendter, M. Prognostic DNA methylation marker in serum of cancer patients. Ann. N. Y. Acad. Sci. 2004, 1022, 44–49. [Google Scholar] [CrossRef]
  82. Visvanathan, K.; Fackler, M.S.; Zhang, Z.; Lopez-Bujanda, Z.A.; Jeter, S.C.; Sokoll, L.J.; Garrett-Mayer, E.; Cope, L.M.; Umbricht, C.B.; Euhus, D.M.; et al. Monitoring of Serum DNA Methylation as an Early Independent Marker of Response and Survival in Metastatic Breast Cancer: TBCRC 005 Prospective Biomarker Study. J. Clin. Oncol. 2017, 35, 751–758. [Google Scholar] [CrossRef] [PubMed]
  83. Panagopoulou, M.; Karaglani, M.; Balgkouranidou, I.; Biziota, E.; Koukaki, T.; Karamitrousis, E.; Nena, E.; Tsamardinos, I.; Kolios, G.; Lianidou, E.; et al. Circulating cell-free DNA in breast cancer: Size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers. Oncogene 2019, 38, 3387–3401. [Google Scholar] [CrossRef] [PubMed]
  84. Zurita, M.; Lara, P.C.; del Moral, R.; Torres, B.; Linares-Fernández, J.L.; Arrabal, S.R.; Martínez-Galán, J.; Oliver, F.J.; de Almodóvar, J.M.R. Hypermethylated 14-3-3-sigma and ESR1 gene promoters in serum as candidate biomarkers for the diagnosis and treatment efficacy of breast cancer metastasis. BMC Cancer 2010, 10, 217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Legendre, C.; Gooden, G.C.; Johnson, K.; Martinez, R.A.; Liang, W.S.; Salhia, B. Whole-genome bisulfite sequencing of cell-free DNA identifies signature associated with metastatic breast cancer. Clin. Epigenetics 2015, 7, 100. [Google Scholar] [CrossRef] [Green Version]
  86. Pfeifer, G.P.; Xiong, W.; Hahn, M.A.; Jin, S.G. The role of 5-hydroxymethylcytosine in human cancer. Cell Tissue Res. 2014, 356, 631–641. [Google Scholar] [CrossRef] [Green Version]
  87. Jin, S.-G.; Jiang, Y.; Qiu, R.; Rauch, T.A.; Wang, Y.; Schackert, G.; Krex, D.; Lu, Q.; Pfeifer, G.P. 5-Hydroxymethylcytosine Is Strongly Depleted in Human Cancers but Its Levels Do Not Correlate with IDH1 Mutations. Cancer Res 2011, 71, 7360–7365. [Google Scholar] [CrossRef] [Green Version]
  88. Li, W.; Zhang, X.; Lu, X.; You, L.; Song, Y.; Luo, Z.; Zhang, J.; Nie, J.; Zheng, W.; Xu, D.; et al. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res. 2017, 27, 1243–1257. [Google Scholar] [CrossRef] [Green Version]
  89. Feng, F.; Ning, Y.; Xue, Y.; Friedl, V.; Hann, D.; Gibb, B.; Bergamaschi, A.; Guler, G.; Hazen, K.; Scott, A.; et al. 69MO 5-Hydroxymethycytosine analysis reveals stable epigenetic changes in tumor tissue that enable cfDNA cancer predictions. Ann. Oncol. 2022, 33, S573. [Google Scholar] [CrossRef]
  90. Parpart-Li, S.; Bartlett, B.; Popoli, M.; Adleff, V.; Tucker, L.; Steinberg, R.; Georgiadis, A.; Phallen, J.; Brahmer, J.R.; Azad, N.; et al. The Effect of Preservative and Temperature on the Analysis of Circulating Tumor DNA. Clin. Cancer Res. 2017, 23, 2471–2477. [Google Scholar] [CrossRef] [Green Version]
  91. Su, S.; Xuan, Y.; Fan, X.; Bao, H.; Tang, H.; Lv, X.; Ren, W.; Chen, F.; Wu, X.; Shao, Y.; et al. 1681P Testing the generalizability of cfDNA fragmentomic features across different studies for cancer early detection. Ann. Oncol. 2022, 33 (Suppl. S7), S1310–S1311. [Google Scholar] [CrossRef]
Ijms 23 14197 g001 550
Figure 1. Fragmentomics features in BC. The study of fragmentomics in BC includes various aspects such as the evaluation of fragment size and cfDI, the study of fragment ends with specific motifs included in the sequence of the fragment and the nucleosome footprints or position. The potentiality of fragmentomics covers a wide range of applications in BC, from the early stage with a role in cancer detection and evaluation of the minimal residual disease, to advanced stages, where it may be useful in the prediction of treatment efficacy or resistance and the evaluation of response to therapies. Created with BioRender.com.
Figure 1. Fragmentomics features in BC. The study of fragmentomics in BC includes various aspects such as the evaluation of fragment size and cfDI, the study of fragment ends with specific motifs included in the sequence of the fragment and the nucleosome footprints or position. The potentiality of fragmentomics covers a wide range of applications in BC, from the early stage with a role in cancer detection and evaluation of the minimal residual disease, to advanced stages, where it may be useful in the prediction of treatment efficacy or resistance and the evaluation of response to therapies. Created with BioRender.com.
Ijms 23 14197 g001
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Gianni, C.; Palleschi, M.; Merloni, F.; Di Menna, G.; Sirico, M.; Sarti, S.; Virga, A.; Ulivi, P.; Cecconetto, L.; Mariotti, M.; et al. Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer. Int. J. Mol. Sci. 2022, 23, 14197. https://doi.org/10.3390/ijms232214197

AMA Style

Gianni C, Palleschi M, Merloni F, Di Menna G, Sirico M, Sarti S, Virga A, Ulivi P, Cecconetto L, Mariotti M, et al. Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer. International Journal of Molecular Sciences. 2022; 23(22):14197. https://doi.org/10.3390/ijms232214197

Chicago/Turabian Style

Gianni, Caterina, Michela Palleschi, Filippo Merloni, Giandomenico Di Menna, Marianna Sirico, Samanta Sarti, Alessandra Virga, Paola Ulivi, Lorenzo Cecconetto, Marita Mariotti, and et al. 2022. "Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer" International Journal of Molecular Sciences 23, no. 22: 14197. https://doi.org/10.3390/ijms232214197

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop