# Clinical use of circulating tumor DNA analysis in patients with lymphoma

Bettina Bisig<sup>a</sup>, Karine Lefort<sup>a</sup>, Sylvain Carras<sup>b</sup>, Laurence de Leval<sup>a,*</sup>

<figure><img src="image_1.png" /></figure>

## ARTICLE INFO

### Keywords:

- Lymphoma
- Liquid biopsy
- Circulating tumor DNA (ctDNA)
- Clinical applications
- Genomic profiling

## ABSTRACT

The analysis of circulating tumor DNA (ctDNA) in liquid biopsy specimens has an established role for the detection of predictive molecular alterations and acquired resistance mutations in several tumors. The low-invasiveness of this approach allows for repeated sampling and dynamic monitoring of disease evolution. Originating from the entire body tumor bulk, plasma-derived ctDNA reflects intra- and interlesional genetic heterogeneity. In the management of lymphoma patients, ctDNA quantification at various timepoints of the patient's clinical history is emerging as a complementary tool that may improve risk stratification, assessment of treatment response and early relapse detection during follow-up, most prominently in patients with diffuse large B-cell lymphoma or classic Hodgkin lymphoma. While liquid biopsies have not yet entered standard-of-care treatment protocols in these settings, several trials have provided evidence that at least a subset of lymphoma patients may benefit from the introduction of liquid biopsies into daily clinical care. In parallel, continuous technological developments have enabled highly sensitive ctDNA assessment methods, which span from locus-specific techniques identifying single hotspot mutations, to sequencing panels and genome-wide approaches that explore broader genetic and epigenetic alterations. Here, we provide an overview of current methods and ongoing technical developments for ctDNA evaluation. We also summarize the most important data from a selection of clinical studies that have explored the clinical use of ctDNA in several lymphoma entities.

## 1. Introduction

Liquid biopsies represent a minimally-invasive diagnostic tool that allows exploration of a variety of disease-associated biomarkers through the analysis of blood or other body fluids. Liquid biopsies have increasingly gained in clinical importance over the past few years for the care of cancer patients. In particular, the analysis of circulating tumor DNA (ctDNA) is a well-established approach for the detection of predictive molecular alterations and acquired resistance mechanisms in non-small cell lung cancer and breast cancer, among other applications, and treatment decisions are made based on the results of these analyses [1,2]. Whereas histological subtyping by tissue biopsies or cytological samples remains the gold standard for diagnosis, the development of targeted therapies dictated by the molecular characteristics of the tumor has made it possible to establish treatment plans based on the analysis of ctDNA. This approach applies mainly to disease progression or relapse, but occasionally also at initial presentation, when the available tumor tissue is insufficient for molecular profiling or the lesion not accessible for a biopsy [3].

Unlike leukemias which are usually diagnosed by assessing peripheral blood and/or bone marrow samples, the management of lymphomas requires a tissue biopsy for histological proof of the diagnosis and lymphoma subtyping [4,5]. Nevertheless, liquid biopsies are emerging in this setting as a complementary tool that may support clinical decisions at various timepoints of the disease course, providing invaluable information about tumor-associated genetic alterations and tumor dynamics, with a potential of improving diagnosis, risk stratification, treatment selection and monitoring [6,7].

Main advantages of liquid biopsy over obtaining a tissue biopsy specimen include non-invasiveness, allowing for frequent sampling and real-time monitoring of disease evolution and treatment response [6,7]. In addition, ctDNA is representative of the entire body tumor bulk, reflecting more accurately intra- and interlesional heterogeneity, not encompassing the sampling bias inherent to tissue biopsies, and is able to capture potential clonal evolution [8]. However, several challenges have still to be solved before liquid biopsies can be fully integrated into the daily care of lymphoma patients. Notably, there is need to improve the sensitivity of detection techniques, in particular for minimal residual

<footnote><sup>a</sup> Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland</footnote>

<footnote><sup>b</sup> Institute for Advanced Biosciences (INSERM U1209, CNRS UMR 5309, UGA), Department of Molecular Biology and Department of Oncohematology, University Hospital Grenoble and University Grenoble Alpes, Grenoble, France</footnote>

<footnote>* Corresponding author.
E-mail address: Laurence.deLeval@chuv.ch (L. de Leval).</footnote>
disease (MRD) monitoring, and to validate the most appropriate schedule for ctDNA assessment [6,9].

This paper provides an overview of the current and potential future applications of ctDNA analysis in the management of lymphoma patients. We give practical hints for sample collection and analysis, review current analytical methods and ongoing technological developments, and provide a summary of key findings from a selection of studies that have explored the clinical use of ctDNA in various lymphoma entities.

## 2. Definitions

Liquid biopsies consist of sampling blood or other body fluids (such as cerebrospinal fluid, urine, pleural effusion, ascites, vitreous humor and other specimens) in order to assess cancer-related features. Liquid biopsies represent a low-invasive method compared with tissue biopsies. Besides circulating tumor cells (CTCs), liquid biopsies contain a plethora of analytes that can be used as biomarkers, including circulating nucleic acids (cell-free DNA (cfDNA), cell-free RNA), extracellular vesicles, tumor-educated platelets, proteins and metabolites (Fig. 1).

CfDNA refers to short extracellular double-stranded DNA fragments. It was discovered in 1948 by Mandel and Métais but drew much more attention only in late 1980s when it began to be more studied and described [10]. CfDNA can supposedly originate from every organ or cell in the body although the mechanisms of its release are not yet fully characterized. The major part of cfDNA is derived from degradation products generated by caspase-dependent cleavage during apoptosis, but also by phagocytosis, necrosis, autophagy as well as from active secretion and senescence [11,12]. The half-life of cfDNA in blood is estimated at a few minutes to 2.5 hours due to clearance carried out by the action of DNases in the bloodstream, by Kupffer cells in the liver or filtering in the kidneys, and to a lesser extent by macrophages of the spleen and lymph nodes [13]. Of note, cfDNA is not freely circulating but is bound to nucleosomes protecting them from enzymatic degradation within body fluids.

Amounts of cfDNA in plasma vary considerably between individuals, approximately 1–10 ng/ml in healthy versus 50–200 ng/ml in cancer patients, and strongly correlate with physiological or pathological states

[14,15]. Since cfDNA reflects body-wide degradation products, its quantification and characterization have emerged as potentially valuable biomarkers for disease detection. Noteworthy, in cancer patients only a small fraction of cfDNA – called circulating tumor DNA (ctDNA) – derives from tumor cells and is informative for cancer diagnostic purposes, whereas its major component originates from normal hematopoiesis, as it does in healthy individuals. Non-tumor cfDNA is fragmented around 167 bp, corresponding to DNA wrapped around a mononucleosome plus a linker histone H1 [16]. On the contrary, in cancer patients, the tumor-derived fraction of cfDNA (ctDNA) is consistently of smaller size (≤145 bp) due to the lack of linker histone [17]. Given the low ctDNA fraction expected in cfDNA, highly sensitive techniques have been developed to track genetic and epigenetic alterations in plasma.

## 3. Technologies for ctDNA analysis

### 3.1. Preanalytical considerations

In order to preserve cfDNA and ctDNA quality, liquid biopsies should be performed following particular guidelines (Fig. 1). For blood samples, to prevent contamination with genomic DNA released from cell lysis, dedicated tubes containing a cell-stabilizer (such as PAXgene® or Streck® tubes) are preferred over EDTA tubes when plasma cannot be isolated within less than 4 hours. Cells and debris should be removed by a couple of successive centrifugations (10 min at 1600×g followed, after decantation, by 10 min. at 16,000×g, both at 4 °C) before storage at –80 °C [18]. At least 10 ml of blood are usually used which correspond approximately to 4.7 ml of plasma. Several manual or automated cfDNA extraction kits based on anion exchange, silica membranes or magnetic beads are commercially available [18,19]. Extracted cfDNA should be stored at –20 °C for optimal long-term preservation. Before proceeding with downstream analyses, cfDNA should be quantified on a fluorometer and fragment size distribution evaluated on an appropriate device (i.e. Bioanalyzer, Fragment Analyzer (Agilent Technologies) or equivalent)) to inspect genomic DNA contamination. An example of a representative cfDNA fragment distribution profile is shown in Fig. 1, with the presence

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Schematic representation of cell-free DNA analysis workflow for lymphoma diagnostics, from plasma isolation to downstream technologies. (Partially created in BioRender. Bisig, B. (2024) https://BioRender.com/t76e233)</figcaption></figure>
of one prominent peak around 167 bp, a second peak around 320 bp and possibly an additional peak around 500 bp corresponding respectively to mono/di/tri nucleosome-associated cfDNA fragments. Importantly, samples with higher molecular weight DNA should be discarded or purified to remove genomic DNA that can impinge on downstream analysis.

## 3.2. Technologies developed for ctDNA analysis

Tracking tumor-derived ctDNA in plasma remains highly challenging due to its low fraction among total cfDNA. The notion of limit of detection (LoD) is a critical parameter. It refers to the lowest ctDNA copy number or variant fraction that can be detected, which also depends on the input of total cfDNA. Indeed, considering that 1 ml of plasma may roughly contain between 20 and 2000 tumor-derived haploid genome equivalents (corresponding to 0.066–6.6 ng of ctDNA), and that these

<table><caption>**Table 1**
Technologies used for ctDNA analysis.</caption><thead><tr><th></th><th>Technology/ Method</th><th>Detected alterations</th><th>Sensitivity/ LoD</th><th>cfDNA input<sup>a</sup></th><th>Limitations</th><th>References<sup>b</sup></th></tr></thead><tbody><tr><td>Non NGS</td><td>dPCR (ddPCR, BEAMing)</td><td>Single/limited number of hotspot mutations or CNVs</td><td>0.01%</td><td>A few ng</td><td>Detects only a limited number of mutations<br>Detects only known mutations</td><td>Hattori K, et al. Cancer Sci 2018 [21]; Garcia-Foncillas J, et al. Ann Oncol 2017 [22]; Bettegowda C, et al. Sci Transl Med 2014 [15]</td></tr><tr><td rowspan="5">Targeted NGS</td><td>Targeted NGS panel</td><td>Mutation profile of selected genes, CNVs</td><td>0.1%</td><td>20–50 ng</td><td>Limited sensitivity</td><td>Bohers E, et al. Blood Cancer J 2018 [23]; Agarwal R, et al. Nat Med 2019 [24]</td></tr><tr><td>IG/TR high-throughput sequencing</td><td>VDJ rearrangements of IG/TR genes</td><td>0.005%</td><td>30 ng</td><td>Detects only a single marker<br>Needs to know the clonotype in tissue</td><td>Roschewski M, et al. Lancet Oncol 2015 [25]; Sarkozy C, et al. Oncotarget 2017 [26]; Frank MJ, et al. J Clin Oncol 2021 [27]</td></tr><tr><td>CAPP-Seq</td><td>Mutation profile of selected genes</td><td>0.0025%</td><td>30 ng</td><td>Only suitable for high mutation burden lymphomas</td><td>Newman AM, et al. Nat Med 2014 [28]; Alig S, et al. J Clin Oncol 2021 [29]</td></tr><tr><td>PhasED-Seq</td><td>Mutation profile of selected genes (phased variants)</td><td>0.0001%</td><td>30 ng</td><td>Only suitable for high mutation burden lymphomas</td><td>Kurtz DM, et al. Nat Biotechnol 2021 [30]; Mutter JA, et al. J Clin Oncol 2023 [31]</td></tr><tr><td>EPIC-Seq (TSS hybrid capture at 2000X)</td><td>Fragmentation patterns to infer targeted gene expression profile</td><td>TF >1%</td><td>30 ng</td><td>Requires prior transcriptomic analysis of specific lymphoma subtypes<br>Complex computational analysis</td><td>Esfahani MS, et al. Nat Biotechnol 2022 [32]</td></tr><tr><td>Comprehensive NGS</td><td>Shallow WGS (0.1-2X)</td><td>Tumor fraction estimation based on CNV profile<br>Fragmentation patterns</td><td>TF >3%</td><td>2–20 ng</td><td>Not suitable for variant calling due to low sequencing depth</td><td>Mathios D, et al. Nat Commun 2021 [33]; Raman L, et al. Haematologica 2022 [34]; Buedts L, et al. Blood Adv 2021 [35]; Rickles-Young M, et al. J Mol Diagn 2024 [36]</td></tr><tr><td rowspan="5">Epigenetics</td><td>Microarray</td><td>Targeted cfDNA methylation</td><td>/</td><td>250 ng</td><td>Limited CpG coverage<br>Requires bisulfite conversion</td><td>Moss J, et al. Nat Commun 2018 [37]; Gallardo-Gomez M, et al. Clin Epigenetics 2018 [38]</td></tr><tr><td>5hmC-seal-Seq</td><td>Genome-wide cfDNA methylation</td><td>TF >3%</td><td>A few ng</td><td>Low resolution<br>High sequencing depth due to low frequency of 5hmC</td><td>Chen HY, et al. Clin Epigenetics 2021 [39]; Chiu BC, et al. NPJ Genom Med 2021 [40]</td></tr><tr><td>cfMeDIP-Seq</td><td>Genome-wide cfDNA methylation</td><td>0.001%</td><td>1–10 ng</td><td>Low resolution<br>Biased towards hypermethylated regions<br>Antibody batch effect</td><td>Berchuck JE, et al. Clin Cancer Res 2022 [41]; Li S, et al. Front Genet 2020 [42]; Nuzzo PV, et al. Nat Med 2020 [43]; Nassiri F, et al. Nat Med 2020 [44]</td></tr><tr><td>cfChIP-Seq</td><td>Histone modification profile to infer gene expression profile</td><td>/</td><td>2–3 ml of plasma</td><td>Antibody batch effect<br>Requires the use of machine-learning based approaches</td><td>Sadeh R, et al. Nat Biotechnol 2021 [45]; Baca SC, et al. Nat Med 2023 [46]; Trier Maansson C, et al. Mol Oncol 2023 [47]</td></tr><tr><td>cfATAC-Seq</td><td>Fragmentation patterns to infer transcription factor binding</td><td>TF >5%</td><td>/</td><td>Requires the use of machine-learning based approaches</td><td>Ulz P, et al. Nat Commun 2019 [48]</td></tr><tr><td>3rd generation sequencing</td><td>Nanopore</td><td>Genome wide cfDNA methylation, fragmentation profile, CNV profile, VDJ rearrangements of IG/TR genes</td><td>/</td><td>A few ng</td><td>Not suitable for variant calling due to high rate of sequencing errors</td><td>Katsman E, et al. Genome Biol 2022 [49]; Sampathi S, et al. Front Oncol 2022 [50]; Erblich T, et al. Blood 2023 [51]; Lau BT, et al. Genome Med 2023 [52]; Hench J, et al. Acta Neuropathol 2024 [53]</td></tr></tbody></table>

BEAMing, beads, emulsion, amplification, magnetics; CAPP-Seq, Cancer Personalized Profiling by Deep Sequencing; cfATAC-Seq, cell free Assay for Transposase- Accessible Chromatin using sequencing; cell free cfChIP-Seq, cell free Chromatin Immuno-Precipitation Sequencing; cfMeDIP-Seq, cell free methylated DNA Immuno-Precipitation Sequencing; CNV, Copy Number Variation; CpG; 5'-Cytosine-phosphate-Guanine-3'; ddPCR, droplet digital Polymerase Chain Reaction; dPCR, digital Polymerase Chain Reaction; EPIC-Seq, Epigenetic Expression Inference by Cell-free DNA Sequencing; IG, immunoglobulin; LoD, Limit of Detection; 5hmC-seal-Seq, 5-hydroxymethylcytosine-seal Sequencing; PhasED-Seq, Phased Variant Enrichment and Detection Sequencing; TF, Tumor Fraction; TR, T-Cell Receptor; TSS, Transcription Start Site; WGS, Whole Genome Sequencing.

<footnote>a Numbers indicated are related to DNA amounts except for techniques where the input material is plasma.</footnote>

<footnote>b References related to lymphomas are indicated in bold.</footnote>
may represent only a small fraction of total cfDNA, the methodology used should be sufficiently sensitive to detect mutated ctDNA copies in very low numbers and proportions [20]. The following paragraphs summarize the technologies currently implemented for clinical use, with a focus on the management of lymphoma patients (§ 3.2.1–3.2.3 and Table 1), and more recently developed approaches with still largely exploratory clinical applications (§ 3.2.4–3.2.6).

### 3.2.1. Detection of mutations and copy number variations

Droplet digital PCR (ddPCR) and beads, emulsion, amplification and magnetics (BEAMing) are PCR methods based on the amplification of a defined ctDNA target after partitioning cfDNA fragments into individual droplets, followed by absolute quantification of positive versus total droplets [54,55]. Given the high number of droplets that can be generated, the LoD of mutations or copy number variations (CNVs) can reach 0.01% (i.e. 3 positive partitions over 30,000 total). These rapid assays have a low technical error rate, are relatively low cost but can only be applied to specimens with suspected or known genetic alterations, typically hotspot mutations, e.g. *MYD88 L265P* in primary central nervous system lymphoma (PCNSL) [21,22].

In the meantime, the evolution of high-throughput sequencing has allowed the development of highly sensitive techniques that are able to overcome different challenges such as low abundance of ctDNA and sequencing error rate. There are two ways to deal with low ctDNA fraction: increase the cfDNA input which is limited by the volume of blood draw, or interrogate different loci simultaneously. This latter possibility is offered by NGS-based methods like whole exome sequencing (WES) or targeted panels, which in addition to single nucleotide variants (SNVs) may also explore small insertions/deletions (indels) and CNVs. Whereas WES remains costly and time-consuming, targeted deep-sequencing panels have the advantage of delivering high-coverage results for hundreds of genomic regions within a few days and with limited expense. Moreover, NGS enables the use of molecular as well as bioinformatic tools that can help in increasing analytical sensitivity. Accordingly, the “Cancer personalized profiling by deep sequencing” (CAPP-Seq), based on individual tumor- or tumor subtype-specific panel designs (targeting approximately 100 mutations), has been applied to cancers with recurrently mutated genomic regions, including B-cell lymphoma entities that bear a high number of mutations induced by activation-induced cytidine deaminase (AID) (i.e., aberrant somatic hypermutation, aSHM) [9]. The use of Universal Molecular Identifier (UMI) barcodes, tagging each original DNA molecule, in combination with *in silico* error suppression algorithms, lowering background rate, facilitates reaching a sensitivity of 0.0025% [28,56]. More recently, a sensitivity of 0.0061% has been achieved with the development of the “Phased variant enrichment and detection by sequencing” (PhasED-Seq) technology, based on the enrichment of genomic regions known to harbor a high rate of clustered mutations on a single DNA fragment (*in cis* mutations, or “phased variants”), as defined by publicly available whole genome sequencing (WGS) data of tumors. The detection of individual ctDNA fragments with at least 2 *in cis* variants could thus decrease the background error rate and consequently increase sensitivity [30]. However, these methods can only be applied to tumors with a high mutation burden and/or specific mutational signatures. More widely applicable strategies to improve sensitivity aim at selecting the ctDNA fraction among cfDNA fragments, based on their smaller size (≤145 bp for ctDNA versus 167 bp for non-tumor cfDNA), through *in vitro* and/or *in silico* enrichment steps (reviewed in Ref. [57]). Whereas the *in vitro* approach (e.g. bead-based selection) effectively allows ctDNA enrichment, it can miss rare variants that are undistinguishable from background noise. By contrast, the *in silico* bioinformatic size selection filtering represents a lossless and reversible approach, but it may impinge the read depth required for subsequent analytical steps such as the Integration of VARIants Reads (INVAR) algorithm [57].

Analysis of CNVs through shallow whole genome sequencing (sWGS)

has emerged as an alternative method to overcome the issue of low ctDNA fraction: instead of sequencing selected regions of the genome with a high read depth, the entire genome is sequenced at low coverage (0.1-2X). sWGS is highly specific in identifying tumor-derived DNA when CNVs are detected, since these are restricted to malignant cells, as opposed to SNVs that can be observed in unaffected individuals [58]. Therefore, assessing the CNV profile of cfDNA can be used as a cancer biomarker. Interestingly, sWGS has also recently been employed together with the ichorCNA pipeline to estimate the tumor fraction in cfDNA samples: this assessment may help in interpreting negative cfDNA mutation analyses as true negative or false negative results, and thereby guide subsequent analytic and/or treatment strategies [59,36].

### 3.2.2. Clonality assessment

As the V(D)J rearrangements of immunoglobulin (IG) and T-cell receptor (TR) genes are a reliable feature of lymphoid neoplasms, clonality assessment has been applied to ctDNA evaluation in lymphoma patients. These methods are based on deep-sequencing of cfDNA using a set of locus-specific multiplex PCR (amplificon-based method) or locus-specific probes (capture-based method), covering all possibly rearranged IG (IGH, IGK, IGL) or TR (TRA, TRB, TRG, TRD) gene segments. Of note, the lymphoma-specific clonotype should be first identified in the tumor tissue, so that it can be used for tracking MRD in cfDNA during and after treatment [25]. However, since this approach targets a single genetic marker (i.e. clonotype), its sensitivity for the detection of ctDNA (approx. 0.005%) highly depends on cfDNA input. Moreover, the method implies specific bioinformatic tools like Vidjil [60] and can be challenging, especially in B-cell lymphomas in case of ongoing somatic hypermutations. In T-cell lymphoma diagnosis, clonality assessment in cfDNA may bring significant added value, notably in combination with the analysis of somatic mutations in target genes, allowing improved informativity of cfDNA analysis from about 70 to 90% of cases [61,62].

### 3.2.3. Detection of fusion genes

Fusion genes may represent interesting targets, especially for T-cell lymphomas [63], but their detection is a challenge when considering cfDNA. Major pitfalls in analyzing fusions in cfDNA are represented by the location of breakpoints in intronic regions not usually captured by targeted NGS panels, their heterogeneity among the cases with a need of sequencing large regions, and the need of specific bioinformatic pipelines focusing on split reads to capture the fusion event. One interesting but still experimental approach to analyze fusion genes may be the use of circulating mRNA [64].

### 3.2.4. Epigenetic analysis

Assessment of epigenetic marks includes DNA methylation and post-translational histone modifications. Cancer genomes are characterized by global hypomethylation, with hypermethylation of CpG islets especially in tumor suppressor genes. These events result in changes in chromatin structure and function, eventually leading to abnormal regulation of gene expression and chromosomal instability [65]. Since DNA methylation changes occur in the early steps of carcinogenesis, DNA methylation profiling is a robust method for inferring cell of origin, a concept that may also be applied to liquid biopsies and cfDNA. In addition, screening thousands of CpG sites augments the chances that ctDNA may be detectable in a plasma sample. Thus, considering both the methylation alterations arising upon tumoral processes, and its global conservation from cell of origin, ctDNA methylation may represent a promising cancer biomarker.

Methodologies used can either cover the whole genome or target specific CpG regions. Several techniques have been developed based on i) sodium bisulfite conversion of unmethylated cytosine to uracil, ii) antibodies directed against methylated DNA or iii) enzymatic cleavage (reviewed in Ref. [66]). Bisulfite sequencing remains a gold standard in methylation analysis. Whole-genome bisulfite sequencing (WGBS) allows for comprehensive mapping of methylation across the genome. By
contrast, targeted bisulfite sequencing offers a focused approach, amplifying regions of known relevance to a specific cancer, reducing costs and data complexity [67]. Methylation arrays performed on bisulfite treated cfDNA provide an alternative to sequencing. They allow methylation quantification at predefined sites across the genome and, while not reaching the breadth of sequencing, they offer high-throughput and cost-effective methylation profiling [37,38]. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-Seq) enriches methylated cfDNA fragments using antibodies against 5-methylcytosine (5mC). This method is less comprehensive than bisulfite sequencing, but allows for analysis of methylation-enriched CpG regions, capturing the methylation landscape characteristic of lymphomas. MeDIP-Seq reduces the required DNA input for methylation sequencing, making it suitable for cfDNA methylation detection [68,69]. Recently, the 5-hydroxymethylcytosine (5hmC) signature has been of growing interest since the 5hmC mark is linked to gene activation (while 5mC marks repressed genes) and has the advantage of being at low level in hematopoietic cells (the major contributors of plasma cfDNA). Moreover, due to the low-input of cfDNA required (<10ng) and high sensitivity of the 5hmC-Seal-Seq technology, the 5hmC signature has been successfully used for ctDNA detection, including in lymphomas. In a study by Chiu and colleagues, this approach was able to distinguish follicular lymphomas (FLs) from diffuse large B-cell lymphomas (DLBCLs) [40].

For post-translational modifications of nucleosome histones (acetylation and methylation), antibody-based methods like chromatin immunoprecipitation sequencing (ChIP-Seq) have been developed and may be used for both predictive and treatment monitoring purposes [45, 70,71]. This approach could be particularly interesting in both B- and T-cell lymphomas, considering the high rate of alterations of epigenetic regulators, and should be explored in future.

### 3.2.5. Fragmentomics

Based on the observation that the fragmentation pattern of cfDNA is non-random, the study of fragment length, fragment end motifs and genome-wide cfDNA coverage pattern (fragmentomics), is emerging as a complementary approach to SNV detection (recently reviewed in Refs. [72,73]). These features of cfDNA fragments provide information about releasing cells and their tissues of origin. For instance, the end motifs of cfDNA fragments are specifically imprinted by DNA nuclease. Accordingly, the end motif CCCA, frequently observed in healthy individuals, is decreased in cancer patients who show increased motif diversity [74].

Genome-wide cfDNA coverage pattern is based on chromatin organization pattern, which is directly related to nucleosome or transcription factor occupancy. As such, using WGS, it has been demonstrated that cfDNA derived from cancer patients displays distinct fragmentation profiles compared with the quite uniform pattern in healthy patients. Moreover, several studies have shown that this discrepancy could be harnessed to infer cancer origin by using classifiers [16,75]. Using the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) approach, combined with a bioinformatic pipeline to determine a transcription factor accessibility score, Ulz et al. used fragmentation patterns to infer transcription factor binding profiling, enabling early detection of colon cancer and prediction of prostate cancer subtypes [48].

More recently, epigenetic expression inference from cfDNA sequencing (EPIC-Seq) has been developed based on the observation that fragment lengths are more diverse in actively transcribed genomic regions than in weakly expressed regions. As such, by using hybrid capture-based targeted deep sequencing of transcription start site (TSS) flanking regions in cfDNA, and notably by measuring promoter fragmentation entropy (PFE), the authors could infer selected individual gene expression, helping in detecting cancers and classifying them histologically [32].

To date, despite their promise, these approaches have not been applied to large independent cohorts and their feasibility and robustness

in translational or routine practice remains to be demonstrated.

### 3.2.6. Nanopore sequencing

Nanopore sequencing, a third generation sequencing technology, represents a new powerful and promising tool for studying cfDNA, capable of sequencing native nucleic acids in real time and providing insights into a wide range of genomic and epigenetic alterations, including mutations, rearrangements, CNVs and methylation. This technology measures current changes as nucleic acid molecules pass through nanopores, enabling notably direct cfDNA methylation detection without the need for chemical modifications. Moreover, Nanopore shallow WGS allows rapid multimodal cfDNA analysis by integrating copy number profiles, nucleosome footprints, fragmentation signatures as well as methylation profiles, with several potential uses in cancer patients [49]. In the field of lymphomas, publications have so far been very limited, including a preliminary study exploring methylation and CNV profiles in a small series of DLBCL patients [51], and the report of two PCNSL diagnosed by means of Nanopore sequencing of cfDNA derived from cytology-negative cerebrospinal fluid (CSF) [53].

## 4. Clinical applications of ctDNA in lymphomas

CtDNA evaluation has the potential of improving the diagnosis and management of lymphoma patients in multiple aspects and for several indications. Here we organize these aspects in the following sections according to the possible sources of ctDNA, the various molecular targets used for ctDNA evaluation, and finally according to the timepoint in the disease course and the main purpose of ctDNA assessment, with examples provided for specific lymphoma entities. A selection of studies exploring the current and potential role of ctDNA in lymphomas is summarized in Table 2, highlighting their most important findings for clinical translation.

### 4.1. Source of ctDNA

CtDNA can be assessed in various body fluids. For lymphoma patients, it is most commonly obtained from peripheral blood samples after plasma isolation. Serum also has been used, but has been proven to be less adequate than plasma for ctDNA analysis [9].

In the case of lymphomas involving peculiar sites, other body fluids may contain a higher proportion of ctDNA than blood/plasma. Important examples include PCNSL and primary vitreo-retinal lymphomas (PVRL), in which CSF or vitreous humor may be the only available sample for diagnosis: while these samples are frequently paucicellular, sometimes preventing a reliable cytological evaluation, ctDNA extracted from their supernatants can be subjected to molecular analysis and provide a precious aid to diagnosis (see below) [102,86].

### 4.2. Target of ctDNA evaluation

Depending on the timepoint and purpose of the evaluation, different characteristics of ctDNA can be assessed, as reflected in the variety of targets explored in clinical studies (Table 2).

Quantitative levels of ctDNA and its dynamics are mostly evaluated by NGS techniques, based either on tumor-specific IG or TR clonotypes or tumor-specific mutations, and involving a comparative study with a reference (baseline) DNA, generally extracted from the tumor tissue at diagnosis (tumor-informed approaches) [6,7,9]. Highly sensitive and quantitative methods are required in this setting to allow a meaningful monitoring of MRD, as exemplified by the CAPP-Seq and PhasED-Seq approaches (Table 1).

Other assays are designed to explore specific genetic alterations or global profiles with a diagnostic, prognostic or predictive role, including mutations, copy number variations, translocations/fusions, methylation profiles or fragmentation patterns. These analyses may be performed without the knowledge of the baseline molecular characteristics of the
<table><caption>**Table 2**
Summary of selected ctDNA-based studies in lymphoma patients.</caption><thead><tr><th>Lymphoma entity</th><th>Timepoint of ctDNA evaluation</th><th>Purpose</th><th>Key findings</th><th>Reference</th><th>Target (method of detection)<sup>a</sup></th><th>Cohort size</th></tr></thead><tbody><tr><td rowspan="10">DLBCL</td><td rowspan="10">At presentation</td><td rowspan="2">Prognostication</td><td>Pretreatment ctDNA levels correlate with IPI score, tumor burden, diagnosis-to-treatment interval, and are independently prognostic of outcome.</td><td>Kurtz DM, et al. Blood 2015 [76]<br>Roschewski M, et al. Lancet Oncol 2015 [25]</td><td>Quantitative levels of ctDNA (tumor-specific IGH and IGK clonotypes by LymphoSIGHT-based NGS)</td><td>75<br>126</td></tr><tr><td>Pretreatment 5hmC profiles in cfDNA predict treatment response.</td><td>Kurtz DM, et al. J Clin Oncol 2018 [77]<br>Alig S, et al. J Clin Oncol 2021 [29]</td><td>Quantitative levels of ctDNA (tumor-specific mutations by CAPP-Seq)</td><td>217<br>267</td></tr><tr><td rowspan="3">Genotyping</td><td>Genotyping of pretreatment ctDNA (including COO classification) is concordant with tissue biopsy-based analysis (78-88% agreement).</td><td>Chen HY, et al. Clin Epigenetics 2021 [39]</td><td>Genome-wide 5hmC profiles in cfDNA (5hmC-seal-Seq)</td><td>86</td></tr><tr><td>CNV profiles of ctDNA are highly concordant with tissue biopsy-based analysis (and reliably differentiate DLBCL from cHL).</td><td>Scherer F, et al. Sci Transl Med 2016 [78]<br>Rossi D, et al. Blood 2017 [8]<br>Wang H, et al. Ann Hematol 2024 [79]</td><td>Mutation profiling of ctDNA compared with genomic DNA from tissue biopsy (CAPP-Seq or targeted capture-based NGS)</td><td>76<br>36<br>74</td></tr><tr><td>Acquired resistance mutations in BTK or PLCG2 genes can be detected in ctDNA of ibrutinib-refractory patients.</td><td>Raman L, et al. Haematologica 2022 [34]</td><td>CNV profiling of ctDNA compared with genomic DNA from tissue biopsy (sWGS)</td><td>81</td></tr><tr><td rowspan="3">During treatment</td><td rowspan="2">Interim response assessment</td><td>Detectable interim ctDNA (after 2 cycles) identifies patients at risk of treatment failure.</td><td>Roschewski M, et al. Lancet Oncol 2015 [25]</td><td>Detection of ctDNA (tumor-specific IGH and IGK clonotypes by LymphoSIGHT-based NGS)</td><td>108</td></tr><tr><td>Depth of ctDNA level decrease (after 1 and after 2 cycles) is independently prognostic of outcome.</td><td>Kurtz DM, et al. J Clin Oncol 2018 [77]</td><td>ctDNA dynamics (tumor-specific mutations by CAPP-Seq)</td><td>217</td></tr><tr><td>Resistance mechanisms</td><td>Detection of ultra-low ctDNA levels (after 1 and after 2 cycles) improves stratification of outcome.</td><td>Kurtz DM, et al. Nat Biotechnol 2021 [30]</td><td>ctDNA dynamics (tumor-specific phased variants by PhasED-Seq)</td><td>88</td></tr><tr><td rowspan="3">After treatment</td><td rowspan="2">Post-treatment MRD detection</td><td>Detectable ultra-low ctDNA levels (at end of therapy) improves stratification of outcome.</td><td>Scherer F, et al. Sci Transl Med 2016 [78]<br>Bruscaggin A, et al. Blood Adv 2021 [80]</td><td>Mutation profiling of ctDNA (CAPP-Seq)</td><td>3<br>3</td></tr><tr><td>End-of-therapy cfDNA fragmentation patterns are prognostic of outcome.</td><td>Kurtz DM, et al. Nat Biotechnol 2021 [30]</td><td>Detection of ctDNA (tumor-specific phased variants by PhasED-Seq)</td><td>19</td></tr><tr><td>Early detection of relapse</td><td>Detectable ctDNA during surveillance identifies patients at risk of relapse before clinical evidence of disease.</td><td>Meriranta L, et al. Blood 2022 [81]</td><td>CfDNA fragmentation patterns (targeted capture-based NGS and WGS)</td><td>71</td></tr><tr><td rowspan="2">PMBL</td><td rowspan="2">At presentation</td><td rowspan="2">Genotyping</td><td>Genotyping (mutations and CNVs) of pretreatment ctDNA is concordant with tissue biopsy-based analysis (69-84% agreement).</td><td>Kurtz DM, et al. Blood 2015 [76]<br>Roschewski M, et al. Lancet Oncol 2015 [25]</td><td>Detection of ctDNA (tumor-specific IGH and IGK clonotypes by LymphoSIGHT-based NGS)</td><td>25<br>107</td></tr><tr><td>Detectable ctDNA during surveillance identifies patients at risk of relapse before clinical evidence of disease.</td><td>Camus V, et al., Leuk Lymphoma 2022 [82]<br>Rivas-Delgado, et al. Diagnostics 2022 [83]<br>Wang H, et al. Ann Hematol 2024 [79]</td><td>Mutation ± CNV profiling of ctDNA compared with genomic DNA from tissue biopsy (targeted NGS; sWGS for CNV analysis)</td><td>30<br>20<br>32</td></tr><tr><td rowspan="2">PCNSL/ PVRL</td><td rowspan="2">At presentation</td><td rowspan="2">Early detection of relapse</td><td>Detectable ctDNA during surveillance identifies patients at risk of relapse before clinical evidence of disease.</td><td>Pang D, et al. Blood 2022 [84]</td><td>Detection of ctDNA (targeted NGS)</td><td>16</td></tr><tr><td>Detection of MYD88 L265P mutation in cfDNA from CSF or vitreous fluid supports the diagnosis of PCNSL/PVRL. Variant allele frequency of mutations detected in cfDNA is higher than in matched cell-derived DNA and than in plasma cfDNA.</td><td>Rimelen V, et al. Acta Neuropathol Commun 2019 [85]<br>Bonzheim I, et al. Blood Adv 2022 [86]<br>Gupta M, et al. Blood 2024 [87]</td><td>Mutation ± CNV profiling of cfDNA extracted from CSF or vitreous fluid (targeted NGS; ddPCR or qPCR-based detection of MYD88 L265P; OncoScan for CNV analysis)</td><td>4<br>31<br>10</td></tr><tr><td>FL</td><td>At presentation</td><td>Prognostication</td><td>Pretreatment ctDNA levels correlate with tumor burden and shorter PFS.</td><td>Sarkozy C, et al. Oncotarget 2017 [26]<br>Delfau-Larue MH, et al. Blood Adv 2018 [88]<br>Fernandez-Miranda I, et al. Clin Cancer Res 2023 [89]</td><td>Quantitative levels of ctDNA (tumor-specific IGH and IGK clonotypes by targeted NGS)<br>Quantitative levels of ctDNA (BCL2:: JH translocation by ddPCR)<br>Quantitative levels of ctDNA (mutations by targeted NGS)</td><td>34<br>133<br>36</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Lymphoma entity</th><th>Timepoint of ctDNA evaluation</th><th>Purpose</th><th>Key findings</th><th>Reference</th><th>Target (method of detection)<sup>a</sup></th><th>Cohort size</th></tr></thead><tbody><tr><td rowspan="10">MCL</td><td rowspan="3">At presentation</td><td>Genotyping</td><td>Genotyping of pretreatment ctDNA is moderately concordant with tissue biopsy-based analysis (35%-73% agreement). Detection rate of targetable EZH2 mutations is higher when combining cfDNA and tissue analyses.</td><td>Fernandez-Miranda I, et al. Clin Cancer Res 2023 [89]<br>Jimenez-Ubieto A, et al. Leukemia 2023 [90]<br>Nagy A, et al. J Intern Med 2023 [91]</td><td>Mutation profiling of ctDNA compared with genomic DNA from tissue biopsy (targeted NGS)</td><td>23<br>84<br>114</td></tr><tr><td>Interim response assessment</td><td>Detectable interim ctDNA correlates with POD24+.</td><td>Fernandez-Miranda I, et al. Clin Cancer Res 2023 [89]<br>Jimenez-Ubieto A, et al. Leukemia 2023 [90]</td><td>Detection of ctDNA (mutations by targeted NGS)</td><td>36<br>28</td></tr><tr><td>Post-treatment MRD detection</td><td>Detectable end-of-therapy ctDNA, in combination with PET imaging, correlates with POD24+.</td><td>Fernandez-Miranda I, et al. Clin Cancer Res 2023 [89]<br>Jimenez-Ubieto A, et al. Leukemia 2023 [90]</td><td>Detection of ctDNA (mutations by targeted NGS)</td><td>16<br>42</td></tr><tr><td>During treatment</td><td>Early detection of progression/ transformation</td><td>Increasing ctDNA levels and emergence of new variants predict high grade transformation of FL to DLBCL, and distinguishes this event from FL progression without transformation.</td><td>Scherer F, et al. Sci Transl Med 2016 [78]</td><td>Mutation profiling of ctDNA compared with tissue biopsy at baseline and at progression (CAPP-Seq)</td><td>15</td></tr><tr><td>After treatment</td><td>Early detection of relapse</td><td>Pretreatment ctDNA levels correlate with Ki67 proliferation index, MIPI score and tumor burden.</td><td>Lakhotia R, et al. Blood Adv 2022 [92]</td><td>Quantitative levels of ctDNA (tumor-specific IGH and IGK clonotypes by targeted NGS)</td><td>48</td></tr><tr><td>At presentation</td><td>Interim response assessment</td><td>Detectable interim ctDNA (after 2 cycles of induction therapy) correlates with shorter PFS and OS.</td><td>Zhang W, et al. Int J Lab Hematol 2021 [93]</td><td>Detection of ctDNA (tumor-specific IGH and IGK clonotypes by targeted NGS)</td><td>50</td></tr><tr><td>After treatment</td><td>Early detection of relapse</td><td>Detectable ctDNA during surveillance identifies patients at risk of relapse before clinical evidence of disease.</td><td>Wei C, et al. Br J Haematol 2023 [94]<br>Liang J, et al. Hematol Oncol 2023 [95]</td><td>Quantitative levels of ctDNA (tumor-specific TRB clonotypes by targeted NGS)<br>Quantitative levels of ctDNA (tumor-specific mutations by targeted NGS)</td><td>40</td></tr><tr><td>At presentation</td><td>Prognostication</td><td>Pretreatment ctDNA levels correlate with stage, LDH, IPI score and shorter PFS and OS.</td><td>Sakata-Yanagimoto M, et al. Ann Hematol 2017 [96]<br>Kim SJ, et al. Cancer Res Treat 2023 [62]<br>Wei C, et al. Br J Haematol 2023 [94]<br>Liang J, et al. Hematol Oncol 2023 [95]<br>Sako N, et al. Hematol Oncol 2023 [97]</td><td>Mutation profiling of ctDNA compared with tissue biopsy (targeted NGS)</td><td>23<br>47<br>41<br>14<br>21<br>36<br>36<br>43</td></tr><tr><td>At presentation</td><td>Interim response assessment</td><td>Detectable interim ctDNA correlates with outcome, and may be more accurate than PET/CT in determining residual disease. Persistence of TET2 and DNMT3A mutations may reflect clonal hematopoiesis rather than TFHL ctDNA.</td><td>Miljkovic MD, et al. Blood Adv 2021 [61]<br>Zhang W, et al. Int J Lab Hematol 2021 [93]<br>Sako N, et al. Hematol Oncol 2023 [97]</td><td>ctDNA dynamics (tumor-specific TRB ± TRG clonotypes by targeted NGS)<br>ctDNA dynamics (tumor-specific mutations by targeted NGS)</td><td>24<br>6<br>14</td></tr><tr><td>After treatment</td><td>Post-treatment MRD detection</td><td>Undetectable end-of-therapy ctDNA correlates with continuous complete remission.</td><td>Miljkovic MD, et al. Blood Adv 2021 [61]<br>Liang J, et al. Hematol Oncol 2023 [95]</td><td>Detection of ctDNA (tumor-specific TRB and TRG clonotypes by targeted NGS)<br>Detection of ctDNA (tumor-specific mutations by targeted NGS)</td><td>24<br>41</td></tr><tr><td>After treatment</td><td>Early detection of relapse</td><td>Depth of ctDNA level decrease at end-of-therapy correlates with outcome.</td><td>Kim SJ, et al. Cancer Res Treat 2023 [62]</td><td>ctDNA dynamics (tumor-specific mutations by targeted NGS)</td><td>45</td></tr><tr><td>At presentation</td><td>Prognostication</td><td>Pretreatment ctDNA levels correlate with stage, tumor burden and shorter PFS.</td><td>Miljkovic MD, et al. Blood Adv 2021 [61]</td><td>Detection of ctDNA (tumor-specific TRB and TRG clonotypes by targeted NGS)<br>Quantitative levels of ctDNA (mutations by CAPP-Seq)</td><td>16<br>80<br>309</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Lymphoma entity</th><th>Timepoint of ctDNA evaluation</th><th>Purpose</th><th>Key findings</th><th>Reference</th><th>Target (method of detection)<sup>a</sup></th><th>Cohort size</th></tr></thead><tbody><tr><td rowspan="12"></td><td rowspan="12"></td><td rowspan="12">Genotyping</td><td rowspan="12">Genotyping of pretreatment ctDNA is concordant with and may even outperform tissue biopsy-based analysis (due to low tumor cell content in tissue versus relatively high ctDNA levels). Variant allele frequency of mutations detected in plasma is higher than in matched tissue biopsy.</td><td>Buedts L, et al. Blood Adv 2021 [35]</td><td>Quantitative levels of ctDNA (CNV profiling by sWGS)</td><td>177</td></tr><tr><td>Spina V, et al. Blood 2018 [98]</td><td>Mutation profiling of ctDNA compared (in 15 cases) with genomic DNA from HRS-enriched tissue biopsy (CAPP-Seq)</td><td>80</td></tr><tr><td>Sobesky S, et al. Med 2021 [99]</td><td>Mutation profiling of ctDNA compared (in 8 cases) with genomic DNA from tissue biopsy (targeted NGS)</td><td>121</td></tr><tr><td>Camus V, et al. Haematologica 2021 [100]</td><td>Mutation profiling of ctDNA compared with genomic DNA from tissue biopsy (targeted NGS)</td><td>30</td></tr><tr><td>Camus V, et al. Leuk Lymphoma 2022 [82]</td><td>Mutation profiling of ctDNA compared with genomic DNA from tissue biopsy (targeted NGS)</td><td>20</td></tr><tr><td>Wang H, et al. Ann Hematol 2024 [79]</td><td>Mutation profiling of ctDNA compared with genomic DNA from tissue biopsy (targeted NGS)</td><td>49</td></tr><tr><td>Alig SK, et al. Nature 2024 [12]</td><td>Mutation profiling of ctDNA compared (in 24 cases) with genomic DNA from HRS-enriched tissue biopsy (CAPP-Seq)</td><td>366</td></tr><tr><td rowspan="12">During treatment</td><td rowspan="12">Interim response assessment</td><td rowspan="12">Depth of ctDNA level decrease correlates with outcome, and is more accurate than PET/CT in determining residual disease.</td><td>Vandenberghe P, et al. Lancet Haematol 2015 [101]</td><td>CNV profiling of ctDNA (WGS) compared with FISH analyses in tissue biopsy</td><td>10</td></tr><tr><td>Sobesky S, et al. Med 2021 [99]</td><td>CNV profiling of ctDNA compared (in 8 cases) with genomic DNA from tissue biopsy (targeted NGS)</td><td>121</td></tr><tr><td>Buedts L, et al. Blood Adv 2021 [35]</td><td>CNV profiling of ctDNA (sWGS) compared (in 9 cases) with FISH analyses on cytogenetic specimens</td><td>177</td></tr><tr><td>Raman L, et al. Haematologica 2022 [34]</td><td>CNV profiling of ctDNA compared with genomic DNA from tissue biopsy (sWGS)</td><td>38</td></tr><tr><td>Spina V, et al. Blood 2018 [98]</td><td>ctDNA dynamics (mutations by CAPP-Seq)</td><td>24</td></tr><tr><td>Camus V, et al. Haematologica 2021 [100]</td><td>ctDNA dynamics (mutations detected in pretreatment plasma samples, by targeted NGS)</td><td>41</td></tr><tr><td>Sobesky S, et al. Med 2021 [99]</td><td>ctDNA dynamics (mutations detected in pretreatment plasma samples, by targeted NGS)</td><td>77</td></tr><tr><td>Buedts L, et al. Blood Adv 2021 [35]</td><td>ctDNA dynamics (CNV profiling of ctDNA by sWGS)</td><td>164</td></tr><tr><td>Alig SK, et al. Nature 2024 [12]</td><td>ctDNA dynamics (phased variants detected in pretreatment plasma samples, by PhasED-Seq)</td><td>109</td></tr></tbody></table>

5hmC, 5-hydroxymethylcytosine; CAPP-Seq, cancer personalized profiling by deep sequencing; cfDNA, cell-free DNA; cHL, classic Hodgkin lymphoma; CNV, copy number variation; COO, cell of origin; CSF, cerebrospinal fluid; CT, computed tomography; ctDNA, circulating tumor DNA; ddPCR, droplet digital PCR; DLBCL, diffuse large B-cell lymphoma; FISH, Fluorescence in situ hybridization; FL, follicular lymphoma; HRS, Hodgkin and Reed-Sterberg cells; IPI, international prognostic index; LDH, lactate dehydrogenase; MCL, mantle cell lymphoma; MIPI, mantle cell lymphoma international prognostic index; NGS, next generation sequencing; OS, overall survival; PCNSL, primary central nervous system lymphoma; PET, positron emission tomography; PFS, progression-free survival; PhasED-Seq, phased variant enrichment and detection sequencing; PMBL, primary mediastinal B-cell lymphoma; POD24+, progression of disease within 24 months; PTCLs, peripheral T-cell lymphomas; PVRL, primary vitreoretinal lymphoma; qPCR, quantitative PCR; sWGS, shallow whole genome sequencing; TFHL, follicular helper T-cell lymphoma.

tumor (tumor-agnostic approaches). The analytical methods used are very different depending on the clinical question and the associated requirements in terms of sensitivity and comprehensiveness. Single hotspot mutations, such as the MYD88 L265P mutation, can be assessed by focused ddPCR assays or similar locus-specific techniques. Broader genotyping and epigenetic analyses, exploring multiple genes, pathways or signatures, need more comprehensive NGS panels or genome-wide approaches (see §3.2 and Table 1).

### 4.3. Timepoint and purpose of ctDNA evaluation, with applications in specific lymphoma entities

#### 4.3.1. At presentation (before treatment)

##### 4.3.1.1. Diagnostic use.
Obtaining a tissue biopsy specimen is the established gold standard for lymphoma diagnosis and histological subtyping. Infrequently, cytological samples are the main source, namely for lymphomas presenting as effusions in body cavities or compartments (e.g. primary effusion lymphoma, breast implant-associated anaplastic large cell lymphoma, PVRL). In exceptional situations, when an adequate tissue or cytological specimen is not available due to limited accessibility or insufficient representativity, the analysis

<footnote>a If not otherwise specified, the source of ctDNA is blood (mostly plasma; serum in Roschewski M, et al. Lancet Oncol 2015 [25]; serum or plasma in Kurtz DM, et al. J Clin Oncol 2018 [77], and Alig S, et al. J Clin Oncol 2021 [29]).</footnote>
of ctDNA can provide important diagnostic clues. In the specific setting of an inaccessible central nervous system (CNS) lesion suspicious for PCNSL, the identification of a *MYD88* L265P mutation in the cfDNA extracted from CSF may establish the diagnosis and thereby accelerate time to treatment [87]. This hotspot mutation is present in approximately 70% of PCNSL cases, is highly specific for this diagnosis among CNS tumors, and its detection in the CSF ctDNA is more sensitive than cytomorphologic and flow cytometry assessment, in particular when combined with high IL-10 concentrations [87,103-105]. The *MYD88* L265P mutation is also a highly recurrent mutation in PVRL (75% of cases) and can reliably be detected in the cfDNA extracted from vitreous humor aspirates [86]. Interestingly, both in CSF and vitreous humor it has been demonstrated that the *MYD88* L265P variant allele frequency (VAF) is higher in cfDNA than in matched cell-derived DNA [86,85]. In addition, the blood-brain barrier limits the “contamination” of CNS sites by cfDNA derived from normal hematopoiesis, explaining the higher *MYD88* L265P VAFs observed in CSF and vitreous humor compared with plasma samples [21,85,106]. Finally, CSF or vitreous humor analysis reduces the risk of “biological false positive” results that could be due to a concurrent subclinical *MYD88*-mutated hematological neoplasm in the peripheral blood, such as IgM-MGUS [107].

#### 4.3.1.2. Genotyping.
In selected settings, broader molecular profiling (tumor genotyping) is routinely performed on DNA extracted from tumor tissue to confirm or refine a diagnosis, or for molecular subtyping. The latter may have prognostic implications or predict benefit from targeted therapies, such as in DLBCL [108,109]. In DLBCL, it has been shown that non-invasive genotyping, based on pretreatment ctDNA, reliably recapitulates genetic subtyping and cell of origin (COO) classification, achieving highly concordant results with tissue-based assays and a similar prognostic stratification (78–88% agreement) [8,78,79, 110]. Good levels of concordance between ctDNA-based and tissue-based mutation profiling have also been reached for primary mediastinal B-cell lymphoma (PMBL, 69–84% agreement) [79,82,83]. In classic Hodgkin lymphoma (cHL), ctDNA is even outperforming tissue-extracted DNA for tumor genotyping, since the tumor cell fraction is usually low in cHL tissue biopsy specimens, while the high turnover rate of Hodgkin/Reed-Sternberg (HRS) cells and the high activity of specific DNases (such as DNASE1L3) contribute to relatively high levels of plasma ctDNA, in a range similar to that of more cellular aggressive lymphomas [12,79,82,98–100]. In addition to enabling mutational profiling, ctDNA analysis may inform on copy number variations, including the detection of CD274 amplification at 9p24.1 [34,35,101], an aberration found in approximately 40% of cHL, which correlates with PD-L1 expression and response to PD-1 inhibitors [111,112].

A few studies have compared ctDNA-based and tissue-based genotyping for peripheral T-cell lymphomas (PTCLs), with concordance rates ranging from 56% to 83% [62,94–97]. Moreover, ctDNA has allowed the identification of mutations that were not detected in tumor tissue, overcoming the issue of spatial heterogeneity [95]. An additional challenge that should be considered in this setting is the distinction of tumor-associated mutations from clonal hematopoiesis, especially in T follicular helper (TFH)-cell lymphomas (TFHL). These neoplasms feature a characteristic mutational landscape, including recurrent mutations in genes such as *RHOA* and *IDH2* or targeting the TCR pathway, which are restricted to the neoplastic TFH-cell compartment, in addition to *TET2* and *DNMT3A* mutations, mediating epigenetic deregulation, and believed to derive from underlying clonal hematopoiesis [113]. *TET2* and *DNMT3A* mutations may therefore be detected in the plasma of TFHL patients at diagnosis and follow-up but belong to the myeloid compartment [97].

For indolent lymphomas, such as FL, the proportion and absolute levels of ctDNA are generally lower when compared to aggressive lymphomas [20]. The sensitive analysis of basal cfDNA in FL patients recovers 35%–73% of tumor tissue-associated genetic alterations [89,90],

and may occasionally deliver genomic information missed by single-site tissue biopsies. A relevant example is provided by Nagy and colleagues, who showed that targetable *EZH2* mutations are detected in a higher proportion of FL patients when combining liquid and tissue biopsy results. In their study, 37.8% of 114 FL patients had *EZH2* mutations at diagnosis, of which 7.9% and 5.3% were exclusively found in liquid and tissue biopsy specimens, respectively [91].

Indeed, the molecular alterations of lymphomas may be subclonal, differing within the same lesion or between synchronous lesions at distant sites [114,115]. This divergence can also involve TP53 mutations, with relevant prognostic consequences [116]. While tissue biopsies introduce sampling bias, the intra- and intertumoral heterogeneity may be captured more faithfully by analysis of ctDNA, which is shed from all tumor sites throughout the body [8].

#### 4.3.1.3. Prognostication.
The quantitative level of pretreatment ctDNA has been shown to have a prognostic value in several lymphoma entities and could thus be used to refine baseline risk stratification, complementing established prognostic factors. In DLBCL patients, high levels of baseline ctDNA correlate with advanced stage, serum lactate dehydrogenase (LDH) and International Prognostic Index (IPI) score, representing a surrogate of overall tumor burden; moreover, it appears to be independently prognostic of outcome [25,29,76,77]. Similarly, FL and cHL patients with elevated ctDNA levels have higher total metabolic tumor volume (TMTV) and shorter progression-free survival (PFS) [12, 98,35,26,88]; and basal ctDNA levels at diagnosis of FL can be used to predict treatment response and the risk of disease progression within 2 years after first-line therapy [89]. In mantle cell lymphoma (MCL) patients, higher ctDNA levels have been shown in cases with blastoid compared to classic histology, and shown to correlate with established prognostic factors such as Ki67 proliferation index, MCL IPI (MIPI) and TMTV. In spite of these data, no statistically significant association could be shown between pre-treatment ctDNA levels and PFS or overall survival (OS) in MCL patients [92]. Also in PTCL patients, pretreatment ctDNA levels have been shown to correlate with tumor burden indicators such as stage, LDH and IPI score, and higher levels were associated with inferior PFS and OS [94,95,93].

Moving away from the quantitative measurement of ctDNA levels, several groups have studied genome-wide methylation patterns in cfDNA from lymphoma patients, to thereby extract prognostic or predictive information. As a relevant example, Chen and colleagues were able to identify a series of 13 specific 5hmC markers that could predict response to R-CHOP treatment in DLBCL patients [39]. Similarly, Tian and colleagues established a prognostic prediction model for extranodal NK/T-cell lymphomas, based on ctDNA methylation markers, which was highly accurate in predicting therapeutic response [117]. Another genome-wide feature of cfDNA, i.e. the size profile of fragments (“fragmentomics”, see § 3.2.5), has also been shown to correlate with prognosis: in a series of PTCLs, higher IPI scores and non-complete response were associated with a shift towards shorter cfDNA size [95].

### 4.3.2. During treatment

#### 4.3.2.1. Interim response assessment.
In patients with DLBCL or cHL treated with curative intent, assessment of treatment response is traditionally performed ad interim by PET/CT scan (metabolic) imaging, typically after 2 cycles of chemotherapy, providing important prognostic information and allowing subsequent therapy schemes to be adapted to the kinetics of disease reduction. This approach is suboptimal, however, as evidenced by frequent false positive/false negative imaging results [107]. Several works have demonstrated that the quantitative level and dynamics of ctDNA decrease could represent a more reliable and sensitive marker for monitoring of treatment response, allowing for serial real-time assessments. This interim molecular MRD evaluation could thus be applied as a complementary tool to metabolic imaging, and
patients identified at high risk of treatment failure be offered early therapy intensification [6,12,30,25,98–100,35,77].

Interestingly, in DLBCL patients it has been shown that a very high sensitivity of the technical approach used for MRD detection is clinically relevant: at each step of increased analytical sensitivity, the prognostic stratification of patients could be improved. Thus, although the CAPP-Seq technique is already highly sensitive, MRD-negative patients with that approach could further be stratified for EFS by the even more sensitive PhasED-Seq (Table 1) [30].

Interim evaluation of treatment response through molecular MRD detection in liquid biopsies, in combination with PET/CT assessment, has also been applied to FL, allowing identification of those patients at higher risk of progression during the first 2 years (POD24+). This group of patients, representing 15–20% of FLs, are characterized by a poor prognosis and shorter OS, and could benefit from risk-adapted therapy optimization [89,90]. In a similar way, MCL patients with detectable ctDNA after 2 cycles of induction therapy had significantly shorter PFS and OS [92].

Despite less mature data, a similar trend for ctDNA dynamics holds true for PTCL patients: a more favorable prognosis has been associated with ctDNA clearance at interim evaluation, although the difference was not statistically significant in all cohorts [61,97,93]. Of note, in TFHL patients with *TET2* and *DNMT3A* mutations detected in pretreatment cfDNA, these mutations persisted while other gene mutations disappeared in responding patients, supporting the hypothesis that *TET2* and *DNMT3A* mutations are not restricted to neoplastic TFH cells but belong to a broader hematopoietic clone, with limited interest in MRD analysis [97].

#### 4.3.2.2. Resistance mechanisms.
Resistance to therapies targeting specific mutations or pathways may be acquired during treatment, typically in lymphoma subclones. These acquired resistance mutations may be more accurately detected via ctDNA analysis than by tumor biopsies, supporting a real-time tailoring of the treatment [118,119]. Examples are provided by mutations in the *BTK* (C481S) and *PLCG2* genes, which confer resistance to the BTK inhibitor ibrutinib and have been detected in the plasma of lymphoma patients progressing under this treatment, witnessing the emergence of resistant subclones [78,80].

### 4.3.3. After treatment/During follow-up

#### 4.3.3.1. Post-treatment MRD detection.
As for interim response assessment, detection of ctDNA after treatment completion (post-treatment MRD) has also been shown to correlate with outcome in several lymphoma entities and to represent an independent predictor of PFS and OS. It is therefore a promising complementary tool to metabolic imaging [6].

Indeed, approximately 15% of DLBCL patients relapse despite a complete metabolic response by PET/CT, and, conversely, a subset of patients with positive end-of-therapy imaging are eventually cured. In contrast, detectable post-treatment ctDNA was shown to uniformly identify DLBCL patients with poor PFS, while in the case of undetectable MRD, long-term survival depended on the sensitivity of the technique employed: using the most sensitive PhasED-Seq approach, 99% of MRD-negative DLBCL patients were in complete remission at last follow-up [30,81,120,121].

While most studies have based MRD detection on cfDNA mutation analysis, Meriranta and colleagues explored a mutation-independent MRD approach, based on cfDNA fragmentation patterns. They demonstrated that the end-of therapy cfDNA fragmentome differed between cured and relapsing patients and could be used to predict their outcome [81].

For FL patients, in whom already pretreatment ctDNA levels are lower compared to aggressive lymphomas, highly sensitive techniques are needed for reliable detection of post-treatment MRD [122]. This technical limitation may explain the suboptimal performance of ctDNA

assessment in this indication, characterized by a globally lower sensitivity for end-of-therapy residual disease detection compared with imaging [89,90,122].

A few studies have explored the predictive value of end-of-therapy ctDNA in PTCLs, demonstrating a trend towards better prognosis for those patients with undetectable ctDNA after treatment [61,62,95]. Interestingly, Miljkovic and colleagues reported two patients with durable remission (10 years follow-up) despite persistent TR clonotypes in cfDNA. Given the demonstration of multiple TR clonotypes in pretreatment cfDNA in a few patients (referred to as clonotypic heterogeneity), the latter observation may possibly reflect the presence of circulating non-tumor-associated clonotypes [61].

#### 4.3.3.2. Early detection of relapse.
Monitoring of ctDNA through serial blood samples allows for detection of disease recurrence several months before clinical relapse/imaging positivity, in particular for DLBCL patients [6,25,76]. Molecular relapse of MCL or PTCL could also be observed before clinical relapse in some patients [61,92]. As for MRD applications, this is also a setting requiring highly sensitive techniques for ctDNA detection, since it becomes relevant to distinguish very low ctDNA levels from complete negativity [121].

#### 4.3.3.3. Early detection of progression/transformation.
A peculiar indication of ctDNA analysis during follow-up is represented by monitoring of FL patients, for early detection of progression or high-grade transformation [6,78,122]. While progression of FL is generally mediated by growing clones that are already present at diagnosis, histological transformation to DLBCL is believed to be the consequence of a divergent branched evolution from a common progenitor cell, translating into the acquisition of additional genetic alterations and the expansion of a novel clone [123,124]. Based on this knowledge, Scherer and colleagues demonstrated that the clonal dynamics, revealed by increasing ctDNA levels and the emergence of new variants in serial plasma samples, may reliably mirror and even predict tissue transformation, with the additional advantage of recapitulating in one blood draw the various events occurring at multiple distant tumor sites [78].

## 5. Conclusion and future directions

CtDNA is a powerful emerging biomarker in lymphoma diagnosis and clinical patient management. It promises to improve patient outcome in several lymphoma entities, by means of refined baseline prognostication, dynamic monitoring of response and real-time tailoring of treatment strategies to the patient's individual needs. Numerous clinical studies on this topic have already been published, mostly concordant in demonstrating the benefits of adding ctDNA evaluation to current stratification criteria of patients with lymphoma. Several prospective clinical trials are ongoing that include the analysis of ctDNA in their treatment protocols (https://clinicaltrials.gov/).

The prospect of the upcoming translation of liquid biopsies into routine clinical practice for lymphoma patients poses several challenges. Firstly, laboratories involved in the analysis of liquid biopsies need to adapt their logistic setup, from timely preanalytical handling of blood samples to quality control and biobanking of plasma and cfDNA samples. These specimens are likely to be numerous, since patients are likely to receive repeated blood draws over an extended period of time, possibly including long-term surveillance. Secondly, methods for cfDNA analysis are very diverse and continuously evolving, both in terms of technologies and bioinformatic pipelines. Their implementation for clinical use requires an experienced team and significant resources to ensure proper assay validations and continuous monitoring of high-quality performance metrics. The establishment of internationally recognized guidelines to standardize these metrics remains an unmet need. Finally, it is not yet clear how the advent of liquid biopsies in lymphoma patients will be integrated into and potentially transform
current diagnostic procedures and future clinical trials. CtDNA analysis is not a substitute for histopathological examination of tissue biopsy specimens, but promises to become a precious complement for molecular profiling of lymphomas at presentation and further, also considering its ability to capture tumor heterogeneity and clonal evolution.

In addition to the established methods used to quantify lymphoma ctDNA and to characterise common alterations, epigenetic analyses and genome-wide approaches will certainly significantly gain importance and may allow to infer broader tumor features such as cell of origin, transcription factor occupancy and gene expression data. These complex procedures are still exploratory and their potential clinical applications need to be determined in future translational research.

## CRediT authorship contribution statement

**Bettina Bisig:** Conceptualization, Writing – original draft, Writing – review & editing. **Karine Lefort:** Writing – original draft, Writing – review & editing. **Sylvain Carras:** Writing – review & editing. **Laurence de Leval:** Conceptualization, Writing – review & editing.

## Disclosures

The authors have no conflicts of interest to disclose.

## Funding/support

No funding or support is associated with this study.

## References

[1] Heitzer E, van den Broek D, Denis MG, Hofman P, Hubank M, Mouliere F, Paz-Ares L, Schuurig E, Sultmann H, Vainer G, Verstraaten E, de Visser L, Cortinovis D. Recommendations for a practical implementation of circulating tumor DNA mutation testing in metastatic non-small-cell lung cancer. ESMO Open 2022;7:100399.

[2] Burstein HJ, DeMichele A, Somerfield MR, Henry NL, Biomarker T, Endocrine. Targeted therapy in metastatic breast cancer expert P. Testing for ESR1 mutations to guide therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative metastatic breast cancer: ASCO guideline rapid recommendation update. J Clin Oncol 2023;41:3423-5.

[3] Pascual J, Attard G, Bidard FC, Curigliano G, De Mattos-Arruda L, Diehn M, Italiano A, Lindberg J, Merker JD, Montagut C, Normanno N, Pantel K, Penthoudakis G, Popat S, Reis-Filho JS, Tie J, Seoane J, Tarazona N, Yoshino T, Turner NC. ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: a report from the ESMO Precision Medicine Working Group. Ann Oncol 2022;33:750-68.

[4] Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, Brousse P, Cerroni L, de Leval L, Dirnhofe S, Dogan A, Feldman AL, Fend F, Friedberg JW, Gaulard P, Ghia P, Horwitz SM, King RL, Salles G, San-Miguel J, Seymour JF, Treon SP, Vose JM, Zucca E, Advani R, Ansell S, Au WY, Barrionuevo C, Bergsagel L, Chan WC, Cohen JI, d'Amore F, Davies A, Falini B, Ghibrial IM, Goodlad JR, Gribben JG, Hsi ED, Kahl BS, Kim WS, Kumar S, LaCasce AS, Laurent C, Lenz G, Leonard JP, Link MP, Lopez-Guilermo A, Mateos MV, Macintyre E, Melnick AM, Morschhauser F, Nakamura S, Narbaitz M, Pavlovsky A, Pileri SA, Piris M, Pro B, Rajkumar V, Rosen ST, Sander B, Sehn L, Shipp MA, Smith SM, Staudt LM, Thiebumont C, Toussey T, Wilson WH, Yoshino T, Zinzani PL, Dreyling M, Scott DW, Winter JN, Zelenetz AD. The international consensus classification of mature lymphoid neoplasms: a report from the clinical advisory committee. Blood 2022;140:1229-53.

[5] Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, Bhagat G, Borges AM, Boyer D, Calaminici M, Chadburn A, Chan JKC, Cheuk W, Chng WJ, Choi JK, Chuang SS, Coupland SE, Czader M, Dave SS, de Jong D, Du MQ, Elenitoba-Johnson KS, Ferry J, Geyer J, Gratzinger D, Guitart J, Gujral S, Harris M, Harrison CJ, Hartmann S, Hochhaus A, Jansen PM, Karube K, Kempf W, Khoury J, Kimura H, Klapper W, Kovach AE, Kumar S, Lazar AJ, Lazzi S, Leoncini L, Leung N, Leventaki V, Li XQ, Lim MS, Liu WP, Louissaint Jr A, Marcogliese A, Medeiros LJ, Michal M, Miranda RN, Mitteldorf C, Montes-Moreno S, Morice W, Nardi V, Naresh KN, Natkunam Y, Ng SB, Oschlies I, Ott G, Parrens M, Pulitzer M, Rajkumar SV, Rawstron AC, Rech K, Rosenwald A, Said J, Sarkozy C, Sayed S, Saygin C, Schuh A, Sewell W, Siebert J, Sohani AR, Tooze R, Traverse-Glehen A, Vega F, Vergier B, Wechalekar AD, Wood B, Xerri L, Xiao W. The 5th edition of the world health organization classification of haematolymphoid tumours: lymphoid neoplasms. Leukemia 2022;36:1720-48.

[6] Huet S, Salles G. Potential of circulating tumor DNA for the management of patients with lymphoma. JCO Oncol Pract 2020;16:561-8.

[7] Schroers-Martin JG, Alig S, Garofalo A, Tessoulin B, Sugio T, Alizadeh AA. Molecular monitoring of lymphomas. Annu Rev Pathol 2023;18:149-80.

[8] Rossi D, Diop F, Spaccarotella E, Monti S, Zanni M, Rasi S, Deambrogi C, Spina V, Bruscaggin A, Favini C, Serra R, Ramponi A, Boldorini R, Foa R, Gaidano G. Diffuse large B-cell lymphoma genotyping on the liquid biopsy. Blood 2017;129: 1947-57.

[9] Roschewski M, Rossi D, Kurtz DM, Alizadeh AA, Wilson WH. Circulating tumor DNA in lymphoma: principles and future directions. Blood Cancer Discov 2022;3: 5-15.

[10] Mandel P, Metais P. [Nuclear acids in human blood plasma]. C R Seances Soc Biol Fil 1948;142:241-3.

[11] Kustanovich A, Schwartz R, Peretz T, Grinshpun A. Life and death of circulating cell-free DNA. Cancer Biol Ther 2019;20:1057-67.

[12] Alig SK, Shahrokh Esfahani M, Garofalo A, Li MY, Rossi C, Flerlage T, Flerlage JE, Adams R, Binkley MS, Shukla N, Jin MC, Olsen M, Telenius A, Mutter JA, Schroers-Martin JG, Sworder BJ, Rai S, King DA, Schultz A, Bogeholz J, Su S, Kathuria KR, Liu CL, Kang X, Strohband MJ, Langfitt D, Pobre-Piza KF, Surman S, Tian F, Spina V, Tousseyn T, Buedts L, Hoppe R, Natkunam Y, Fornecker LM, Castellino SM, Advani R, Rossi D, Lynch R, Ghesquieres H, Casasnovas O, Kurtz DM, Marks LJ, Link MP, Andre M, Vandenberghe P, Steidl C, Diehn M, Alizadeh AA. Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling. Nature 2024;625:778-87.

[13] Sanchez-Herrero E, Serna-Blasco R, Robado de Lope L, Gonzalez-Rumayor V, Romero A, Provencio M. Circulating tumor DNA as a cancer biomarker: an overview of biological features and factors that may impact on ctDNA analysis. Front Oncol 2022;12:943253.

[14] Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 2017;17:223-38.

[15] Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM, Antonarakis ES, Azad NS, Bardelli A, Brem H, Cameron JL, Lee CC, Fecher LA, Gallia GL, Gibbs P, Le D, Giuntoli RL, Goggins M, Hogarty MD, Holdhoff M, Hong SM, Jiao Y, Juhl HH, Kim JJ, Siravegna G, Laheru DA, Lauricella C, Lim M, Lipson EJ, Marie SK, Netto GJ, Oliner KS, Olivari A, Olsson L, Riggis GJ, Sartore-Bianchi A, Schmidt K, Shih I M, Oba-Shinjo SM, Siena S, Theodorescu D, Tie J, Harkins TT, Veronese S, Wang TL, Weingart JD, Wolfgang CL, Wood LD, Xing D, Hruban RH, Wu J, Allen PJ, Schmidt CM, Choti MA, Velculescu VE, Kinzler KW, Vogelstein B, Papadopoulos N, Diaz Jr LA. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 2014;6:224ra224.

[16] Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 2016;164: 57-68.

[17] Mouliere F, Chandrananda D, Piskorz AM, Moore EK, Morris J, Ahlborn LB, Mair R, Goranova T, Marass F, Heider K, Wan JCM, Supernat A, Hudecova I, Gounaris I, Ros S, Jimenez-Linan M, Garcia-Corbacho J, Patel K, Ostrup O, Murphy S, Eldridge MD, Gale D, Stewart GD, Burge J, Cooper VN, van der Heijden MS, Massie CE, Watts C, Corrie P, Pacey S, Brindle KM, Baird RD, Mau-Sorensen M, Parkinson CA, Smith CG, Brenton JD, Rosenfeld N. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med 2018;10.

[18] Meddeb R, Pisareva E, Thierry AR. Guidelines for the preanalytical conditions for analyzing circulating cell-free DNA. Clin Chem 2019;65:623-33.

[19] Lehle S, Emons J, Hack CC, Heindl F, Hein A, Preuss C, Seitz K, Zahn AL, Beckmann MW, Fasching PA, Ruebner M, Huebner H. Evaluation of automated techniques for extraction of circulating cell-free DNA for implementation in standardized high-throughput workflows. Sci Rep 2023;13:373.

[20] Bourbon E, Alcazer V, Cheli E, Huet S, Sujobert P. How to obtain a high quality ctDNA in lymphoma patients: preanalytical tips and tricks. Quality Biotechnol (Basel) 2021;14.

[21] Hattori K, Sakata-yanagimoto M, Suehara Y, Yokoyama Y, Kato T, Kurita N, Nishikii H, Obara N, Takano S, Ishikawa E, Matsumura A, Hasegawa Y, Chiba S. Clinical significance of disease-specific MYD88 mutations in circulating DNA in primary central nervous system lymphoma. Cancer Sci 2018;109:225-30.

[22] Garcia-Foncillas J, Alba E, Aranda E, Diaz-Rubio E, Lopez-Lopez R, Tabernero J, Vivancos A. Incorporating BEAMing technology as a liquid biopsy into clinical practice for the management of colorectal cancer patients: an expert taskforce review. Ann Oncol 2017;28:2943-9.

[23] Bohers E, Viailly PJ, Becker S, Marchand V, Ruminy P, Maingonnat C, Bertrand P, Etancelin P, Picquenot JM, Camus V, Menard AL, Lemasle E, Contentin N, Lepretre S, Lenain P, Stamatoullas A, Lanic H, Libraire J, Vaudaux S, Pepin LF, Vera P, Tilly H, Jardin F. Non-invasive monitoring of diffuse large B-cell lymphoma by cell-free DNA high-throughput targeted sequencing: analysis of a prospective cohort. Blood Cancer J 2018 Aug;8(8):74.

[24] Agarwal R, Chan YC, Tam CS, Hunter T, Vassiliadis D, Teh CE, Thijssen R, Yeh P, Wong SQ, Ftouni S, Lam EYN, Anderson MA, Pott C, Gilan O, Bell CC, Knezevic K, Blombery P, Rayeroux K, Zordan A, Li J, Huang DCS, Wall M, Seymour JF, Gray DHD, Roberts AW, Dawson MA, Dawson SJ. Dynamic molecular monitoring reveals that SWI-SNF mutations mediate resistance to ibrutinib plus venetoclax in mantle cell lymphoma. Nat Med 2019 Jan;25(1):119-29.

[25] Roschewski M, Dunleavy K, Pittaluga A, Moorhead M, Pepin F, Kong K, Shovlin M, Jaffe ES, Staudt LM, Lai C, Steinberg SM, Chen CC, Zheng J, Willis TD, Faham M, Wilson WH. Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study. Lancet Oncol 2015;16:541-9.

[26] Sarkozy C, Huet S, Carlton VE, Fabiani B, Delmer A, Jardin F, Delfau-Larue MH, Hacini M, Ribrag V, Guides S, Faham M, Salles G. The prognostic value of clonal heterogeneity and quantitative assessment of plasma circulating clonal IG-VDJ
sequences at diagnosis in patients with follicular lymphoma. OncoRETarget 2017;8:8765-74.

[27] Frank MJ, Hossain NM, Bukhari A, Dean E, Spiegel JY, Claire GK, Kirsch I, Jacob AP, Mullins CD, Lee LW, Kong KA, Craig J, Macklin CL, Rapoport AP, Jain MD, Dahiya S, Locke FL, Miklos DB. Monitoring of Circulating Tumor DNA Improves Early Relapse Detection After Axicabtagene Ciloleucel Infusion in Large B-Cell Lymphoma: Results of a Prospective Multi-Institutional Trial. J Clin Oncol 2021;39(27):3034-43.

[28] Newman JW, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, Liu CL, Neal JW, Wakelee HA, Merritt RE, Shrager F, Loo Jr BW, Alizadeh AA, Diehn M. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med 2014;20:548-57.

[29] Alig S, Macaulay CW, Kurtz DM, Duhrsen U, Huttmann A, Schmitz C, Jin MC, Sworder BJ, Garofalo A, Shahrokh Esfahani M, Nabet BY, Soo J, Scherer F, Craig AFM, Casasnovas O, Westin JR, Gaidano G, Rossi D, Roschewski M, Wilson WH, Meignan M, Diehn M, Alizadeh AA. Short diagnosis-to-treatment interval is associated with higher circulating tumor DNA levels in diffuse large B-cell lymphoma. J Clin Oncol 2021;39:2605-16.

[30] Kurtz DM, Soo J, Co Ting Keh L, Alig S, Chabon JJ, Sworder BJ, Schultz A, Jin MC, Scherer F, Garofalo A, Macaulay CW, Hamilton EG, Chen B, Olsen M, Schroers-Martin JG, Craig AFM, Moding EJ, Esfahani MS, Liu CL, Duhrsen U, Huttmann A, Casasnovas RO, Westin JR, Roschewski M, Wilson WH, Gaidano G, Rossi D, Diehn M, Alizadeh AA. Enhanced detection of minimal residual disease by targeted sequencing of phased variants in circulating tumor DNA. Nat Biotechnol 2021;39:1537-47.

[31] Mutter JA, Alig SK, Esfahani MS, Lauer EM, Mitschke J, Kurtz DM, Kühn J, Bleu S, Olsen M, Liu CL, Jin MC, Macaulay CW, Neidert N, Volk T, Eisenblaetter M, Rauer S, Heiland DH, Finke J, Duyster J, Wehrle M, Prinz M, Ilerhaus G, Reinacher PC, Schorb E, Diehn M, Alizadeh AA, Scherer F. Circulating Tumor DNA Profiling for Detection, Risk Stratification, and Classification of Brain Lymphomas. J Clin Oncol 2023;41(9):1684-94.

[32] Esfahani MS, Hamilton EG, Mehrmohamedi M, Nabet BY, Alig SK, King DA, Steen CB, Macaulay CW, Schultz A, Nesselbush MC, Soo J, Schroers-Martin JG, Chen B, Binkley MS, Stehr H, Chabon JJ, Sworder BJ, Hui AB, Frank MJ, Moding EJ, Liu CL, Newman AM, Isbell JM, Rudin CM, Li BT, Kurtz DM, Diehn M, Alizadeh AA. Inferring gene expression from cell-free DNA fragmentation profiles. Nat Biotechnol 2022;40:585-97.

[33] Mathios D, Johansen JS, Cristiano S, Medina JE, Phallen J, Larsen KR, Bruhm DC, Nikfa N, Ferreira LE, Adleff V, Chiao JY, Leal A, Noe M, White JR, Arun AS, Hruban C, Annapragada AV, Jensen SØ, Ørntoft MW, Madsen AH, Carvalho B, de Wit M, Carey J, Dracopoli NC, Maddala T, Fang KC, Hartman AR, Forde PM, Anagnostov V, Brahmer JR, Fijneman RJA, Nielsen HJ, Meijer GA, Andersen CL, Mellemgard A, Bojesen SE, Scharpf RB, Velculescu VE. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun 2021;12(1):5060.

[34] Raman L, Van der Linden M, De Vriendt C, Van den Broeck B, Muylle K, Deeren D, Dedeurwaerdere F, Verbeke S, Dendooen A, De Grove K, Baert S, Claes K, Menten B, Offner F, Van Dorpe J. Shallow-depth sequencing of cell-free DNA for Hodgkin and diffuse large B-cell lymphoma (differential diagnosis: a standardized approach with underappreciated potential. Haematologica 2022; 107:211-20.

[35] Buedts L, Wlodarska I, Finalet-Ferreiro J, Gheysens O, Dehaspe L, Tousseyn T, Fornecker LM, Lazarovici J, Casasnovas RO, Gac AC, Bonnet C, Bouabdallah K, Copie-Bergman C, Fabiani B, Dierickx D, Marcellis L, Vermeesch J, Andre M, Vandenberghe P. The landscape of copy number variations in classical Hodgkin lymphoma: a joint KU Leuven and LYSA study on cell-free DNA. Blood Adv 2021; 5:1991-2002.

[36] Rickles-Young M, Tinoco G, Tsuji J, Pollock S, Haynam M, Lefebvre H, Glover K, Owen DH, Collier KA, Ha G, Adalsteinsson VA, Cibulskis D, Lennon NJ, Stover DG. Assay validation of cell-free DNA shallow whole-genome sequencing to determine tumor fraction in advanced cancers. J Mol Diagn 2024;26:413-22.

[37] Moss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, Samet Y, Maoz M, Druid H, Arner P, Fu KY, Kiss E, Spalding KL, Landesberg G, Zick A, Grinshpun A, Shapiro AMJ, Grompe M, Wittenberg ADV, Glaser R, Frank R, Kaplan T, Dor Y. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun 2018;9: 5068.

[38] Gallardo-Gomez M, Moran S, Paez de la Cadena M, Martinez-Cubizano VS, Rodriguez-Berrocal FJ, Rodriguez-Girondo M, Esteller M, Cubiella J, Bujanda L, Castells A, Balaguer F, Jover R, De Chiara L. A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples. Clin Epigenetics 2018;10:53.

[39] Chen HY, Zhang WL, Zhang L, Yang P, Li F, Yang ZR, Wang J, Pang M, Hong Y, Yan C, Li W, Liu J, Xu N, Chen L, Xiao XB, Qin Y, He XH, Liu H, Zhu HC, He C, Lin J, Jing HM. 5-Hydroxymethylcytosine profiles of cfDNA are highly predictive of R-CHOP treatment response in diffuse large B cell lymphoma patients. Clin Epigenetics 2021;13:33.

[40] Chiu BC, Chen C, You Q, Chiu R, Venkataraman G, Zeng G, Zhang Z, Cui X, Smith SM, He C, Zhang W. Alterations of 5-hydroxymethylation in circulating cell-free DNA reflect molecular distinctions of subtypes of non-Hodgkin lymphoma. NPJ Genom Med 2021;6:11.

[41] Berchuck JE, Baca SC, McClure HM, Korthauer K, Tsai HK, Nuzzo PV, Kelleher KM, He M, Steinharter JA, Zacharia S, Spisak S, Seo JH, Conteduca V, Elemento O, Auh J, Sigouros M, Corey E, Hirsch MS, Taplin ME, Choueiri TK, Pomerantz MM, Beltran H, Freedman ML. Detecting Neuroendocrine Prostate

Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis. Clin Cancer Res 2022;28(5):928-38.

[42] Li S, Wang L, Zhao Q, Wang Z, Lu S, Kang Y, Jin G, Tian J. Genome-Wide Analysis of Cell-Free DNA Methylation Profiling for the Early Diagnosis of Pancreatic Cancer. Front Genet 2020;11:596078.

[43] Nuzzo PV, Berchuck JE, Korthauer K, Spisak S, Nassar AH, Abou Alaiwi S, Chakravarthy A, Shen SY, Bakouny Z, Boccardo F, Steinharter J, Bouchard G, Curran CR, Pan W, Baca SC, Seo JH, Lee GM, Michaelson MD, Chang SL, Waikar SS, Sonpayde G, Irizarry RA, Pomerantz M, De Carvalho DD, Choueiri TK, Freedman ML. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat Med 2020 Jul;26(7):1041-3.

[44] Nassiri F, Chakravarthy A, Feng S, Shen SY, Nejad R, Zuccato JA, Voisin MR, Patil V, Horbinski C, Aldape K, Zadeh G, De Carvalho DD. Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes. Nat Med 2020 Jul;26(7):1044-7.

[45] Sadeh R, Sharkia I, Fialkoff G, Rahat A, Gutin J, Chappleboim N, Nitzan M, Fox-Fisher I, Neiman D, Meler G, Kamari Z, Yaish D, Peretz T, Hubert A, Cohen JE, Salah A, Temper M, Grinshpun A, Maoz M, Abu-Gazala S, Ben Ya'acov A, Shteyer E, Safadi R, Kaplan T, Shemer P, Planer D, Galun D, Glaser A, Zick A, Dor Y, Friedman N. ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin. Nat Biotechnol 2021;39:586-98.

[46] Baca SC, Seo JH, Davidsohn MP, Fortunato B, Semaan K, Sotudias S, Lakshminarayanan G, Diossy M, Qiu X, El Zarif T, Savignano H, Canniff J, Madueke I, Saliby RM, Zhang Z, Li R, Jiang Y, Taing L, Awad M, Chau CH, DeCaprio JA, Figg WD, Greten TF, Hata AN, Hodi FS, Hughes ME, Ligon KL, Lin N, Ng K, Oser MG, Meador C, Parsons HA, Pomerantz MM, Rajan A, Ritz J, Thakuria M, Tolaney SM, Wen PY, Long H, Berchuck JE, Szallasi Z, Choueiri TK, Freedman ML. Liquid biopsy epigenomic profiling for cancer subtyping. Nat Med. 2023 Nov;29(11):2737-41.

[47] Trier Maansson C, Meldgaard P, Stougaard P, Nielsen AL, Sorensen BS. Cell-free chromatin immunoprecipitation can determine tumor gene expression in lung cancer patients. Mol Oncol 2023 May 17(5):722-36.

[48] Ulz P, Perakis S, Zhou Q, Moser T, Belic J, Lazzeri I, Wolfler A, Zebisch A, Gerger A, Prastuz G, Petru E, White B, Roberts CES, John JS, Schimek MG, Geigl JB, Bauernhofer T, Sill H, Bock C, Heitzer E, Speicher MR. Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection. Nat Commun 2019;10:4666.

[49] Katsman E, Orlanski S, Martignano F, Fox-Fisher I, Shemer R, Dor Y, Zick A, Eden A, Petriini I, Conticello SG, Berman BP. Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing. Genome Biol 2022;23:158.

[50] Sampath S, Chernyavskaya Y, Haney MG, Moore LH, Snyder IA, Cox AH, Fuller BL, Taylor TJ, Yan D, Badgett TC, Blackburn JS. Nanopore sequencing of clonal IGH rearrangements in cell-free DNA as a biomarker for acute lymphoblastic leukemia. Front Oncol 2022;12:958673.

[51] Erblich T, Sauer C, Muyas F, Ficz G, Cortes-Ciriano I, Gribben J. A novel approach: nanopore sequencing of native cell-free DNA in diffuse-large B-cell lymphoma patients. Blood 2023;142:7170.

[52] Lau BT, Almeda A, Schauer M, McNamara M, Bai X, Meng Q, Partha M, Grimes SM, Lee H, Heestand GM, Ji HP. Single-molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing. Genome Med 2023;15(1):33.

[53] Hench J, Hultschig C, Bratic Hench I, Sadasivan H, Yaldizli O, Hutter G, Dirhofer S, Tzankov A, Frank S. Rapid brain lymphoma diagnostics through nanopore sequencing of cytology-negative cerebrospinal fluid. Acta Neuropathol 2024;148:36.

[54] Drandi D, Genuardi E, Dogliotti I, Ferrara M, Jimenez C, Guerrini F, Schirico ML, Mantoa B, Muccia V, Lia G, Zaccaria GM, Omeide P, Passera R, Orsucci L, Benevolo G, Cavallo F, Galimberti S, Sanz RG, Boccadoro M, Ladetto M, Ferrero S. Highly sensitive MYD88(L265P) mutation detection by droplet digital polymerase chain reaction in Waldenstrom macroglobulinemia. Haematologica 2018;103: 1029-37.

[55] Dressman D, Yan H, Traverso G, Kinzler KW, Vogelstein B. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A 2003;100:8817-22.

[56] Newman AM, Lovejoy AF, Klass DM, Kurtz DM, Chabon JJ, Scherer F, Stehr H, Liu CL, Bratman SV, Say C, Zhou L, Carter JN, West RB, Sledge GW, Shrager JB, Loo Jr BW, Neal JW, Wakelee HA, Diehn M, Alizadeh AA. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol 2016;34:547-55.

[57] Underhill HR. Leveraging the fragment length of circulating tumour DNA to improve molecular profiling of solid tumour malignancies with next-generation sequencing: a pathway to advanced non-invasive diagnostics in precision oncology? Mol Diagn Ther 2021;25:389-408.

[58] Heitzer E, Auer M, Ulz P, Geigl JB, Speicher MR. Circulating tumor cells and DNA as liquid biopsies. Genome Med 2013;5:73.

[59] Adalsteinsson VA, Ha G, Freeman SS, Choudhury AD, Stover DG, Parsons HA, Gydush G, Reed SC, Rotem D, Rhoades J, Loginov D, Livitz D, Rosebrock D, Leshchiner I, Kim J, Stewart C, Rosenberg M, Francis JM, Zhang CZ, Cohen O, Oh C, Ding H, Polak P, Lloyd M, Mahmud S, Helvie K, Merrill MS, Santiago RA, O'Connor EP, Jeong SH, Leeson R, Barry RM, Kramkowski JF, Zhang Z, Polacek L, Lohr JG, Schleicher M, Lipscomb E, Saltzman A, Oliver NM, Marini L, Waks AG, Harshman LC, Tolaney SM, Van Allen EM, Winer EP, Lin NU, Nakabayashi M, Taplin ME, Johannessen CM, Garraway LA, Golub TR, Boers JS, Wagle N, Getz G, Love JC, Meyerson M. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 2017;8:1324.
[60] Duez M, Giraud M, Herbert R, Rocher T, Salson M, Thonier F. Vidjil: a web platform for analysis of high-throughput repertoire sequencing. PLoS One 2016; 11:e0166126.

[61] Miljkovic MD, Melani C, Pittaluga S, Lakhotia R, Lucas N, Jacob A, Yusko E, Gaffé ES, Wilson WH, Roschewski M. Next-generation sequencing-based monitoring of circulating tumor DNA reveals clonotypic heterogeneity in untreated PTCL. Blood Adv 2021;5:4198–210.

[62] Kim SJ, Kim YJ, Yoon SE, Ryu KJ, Park B, Park D, Cho D, Kim HY, Cho J, Ko YH, Park WY, Kim WS. Circulating tumor DNA-based genotyping and monitoring for predicting disease relapses of patients with peripheral T-cell lymphomas. Cancer Res Treat 2023;55:291–303.

[63] Drieux F, Ruminy P, Sater V, Marchand V, Fattacoli V, Lanic MD, Viennot M, Viailly PJ, Sako N, Robe C, Dupuy A, Vallois D, Veresez L, Poullot E, Picquenot JM, Bossard C, Parrens M, Lemonnier F, Jardin F, de Leval L, Gaulard P. Detection of gene fusion transcripts in peripheral T-cell lymphoma using a multiplexed targeted sequencing assay. J Mol Diagn 2021;23:929–40.

[64] Albitar M, Zhang H, Charifa A, Ip A, Ma W, McCloskey J, Donato M, Siegel D, Waintraub S, Gutierrez M, Pecora A, Goy A. Combining cell-free RNA with cell-free DNA in liquid biopsy for hematologic and solid tumors. Heliomy 2023;9:e16261.

[65] Nishiyama A, Nakanishi M. Navigating the DNA methylation landscape of cancer. Trends Genet 2021;37:1031–1037.

[66] Huang J, Wang L. Cell-free DNA methylation profiling analysis-technologies and bioinformatics. Cancers (Basel) 2019;11.

[67] Beck S, Rakayan VK. The methylation: approaches for global DNA methylation profiling. Trends Genet 2008;24:231–7.

[68] Weber M, Davies JJ, Wittig D, Oakeley JE, Haase M, Lam WL, Schubeler D. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 2005;37:853–62.

[69] Taiwo O, Wilson GA, Morris T, Seisenberger S, Reik W, Pearce D, Beck S, Taiwo LM. Methylome analysis using MeDIP-seq with low DNA concentrations. Nat Protoc 2012;7:617–36.

[70] Van den Ackerveker P, Lobbens A, Pamart D, Kotronoulas C, Romelaere G, Eccleston M, Herzog M. Epigenetic profiles of elevated cell free circulating H3.1 nucleosomes as potential biomarkers for non-Hodgkin lymphoma. Sci Rep 2023;13:16335.

[71] Salani F, Eccleston M, Palmieri L, Pernot S, Cousin S, Masi G, Crea F, Italiano A. Circulating H3K27me3 modified nucleosomes as a biomarker to monitor anti EZH2-based treatment in advanced solid tumour patients: translational analyses from CAIRE trial. Cancer Res 2024;84:5128.

[72] Thierry AR. Circulating DNA fragmentomics and cancer screening. Cell Genom 2023;3:100242.

[73] Loy C, Ahmann L, De Vlaminck I, Gu W. Liquid biopsy based on cell-free DNA and RNA. Annu Rev Biomed Eng 2024;26:169–95.

[74] Jiang P, Sun K, Peng W, Cheng SH, Ni M, Yeung PON, Heung MMS, Xie T, Shang H, Zhou Z, Chan RWY, Wong J, Wong VWS, Poon LC, Leung TY, Lam WKJ, Chan JYK, Chan HLY, Chan KCA, Chiu RWK, Lo YMD. Plasma DNA end-motif profiling as a fragmentomeric marker in cancer, pregnancy, and transplantation. Cancer Discov 2020;10:664–73.

[75] Cristiano S, Leal A, Phallen J, Fiksel J, Adleff V, Bruhm DC, Jensen SO, Medina JE, Hruban C, White JR, Palsgrove DN, Romelaore V, Forde P, Naidoo J, Marrone C, Brahman J, Woodward BD, Husain H, van Rooijen KL, Orntoft MW, Madsen AH, van de Velde CJH, Verheij M, Cabs A, Punt CJA, Vink GR, van Grieken NCT, Koopman M, Fijneman RJA, Johansen JS, Nielsen HJ, Meijer GA, Andersen CL, Scharpf RB, Velculescu VE. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 2019;570:385–9.

[76] Kurtz DM, Green MR, Bratman SV, Scherer F, Liu CL, Kunder CA, Takahashi K, Glover C, Keane C, Kihira S, Visser B, Callahan J, Kong KA, Faham M, Corbetti KS, Miklos D, Advani RH, Levy R, Hicks RJ, Hertzberg M, Ohgami RS, Gandhi MK, Diehn M, Alizadeh AA. Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing. Blood 2015;125:3679–87.

[77] Kurtz DM, Scherer F, Jin MC, Soo J, Craig AFM, Esfahani MS, Chabon JJ, Stehr H, Liu CL, Tibshirani R, Maeda LS, Gupta NK, Khodadous MS, Advani RH, Levy R, Newman AM, Duhrsen U, Huttmann A, Meignan M, Casasnovas RO, Westin RJ, Roschewski M, Wilson WH, Gaidano G, Rossi D, Diehn M, Alizadeh AA. Circulating tumor DNA measurements as early outcome predictors in diffuse large B-cell lymphoma. J Clin Oncol 2018;36:2845–53.

[78] Scherer F, Kurtz DM, Newman AM, Stehr H, Craig AF, Esfahani MS, Lovejoy AF, Chabon JJ, Klass DM, Liu CL, Zhou L, Glover C, Visser BC, Poultsides GA, Advani RH, Maeda LS, Gupta NK, Levy R, Ohgami RS, Kunder CA, Diehn M, Alizadeh AA. Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA. Sci Transl Med 2016;8:364ra155.

[79] Wang H, Wang Z, Zhu S, Li Z, Yang H, Sun P, Zhu M, Zhao X, Shen L, Ou Q, Yang H, Li ZM. Circulating tumor DNA assisting lymphoma genetic feature profiling and identification. Ann Hematol 2024;103:4135–4144.

[80] Bruscaggin A, di Bergamo LT, Spina V, Hodkinson B, Forestieri G, Bonfiglio F, Condoluci A, Wu W, Pirosa MC, Faderl MR, Koch R, Schaffer M, Alvarez JD, Fourneau N, Gerber B, Stussi G, Zucca A, Balasubramanian S, Rossi D. Circulating tumor DNA for noninvasive monitoring of lymphoma treated with ibrutinib plus volumulab. Blood Adv 2021;5:4674–85.

[81] Meriranta L, Alkodsi A, Pasanen A, Lepisto M, Mapar P, Blaker YN, Jorgensen J, Karjalainen-Lindsberg ML, Fiskvik I, Mikalsen LTG, Autio M, Bjorkholm M, Jerkeman M, Fluge O, Brown P, Jyrkki S, Holte H, Pitkanen E, Ellonen P, Leppa S. Molecular features encoded in the ctDNA reveal heterogeneity and

predict outcome in high-risk aggressive B-cell lymphoma. Blood 2022;139:1863–77.

[82] Camus V, Viennot M, Leveque E, Viailly PJ, Tonnelet D, Veresezan EL, Drieux F, Etancelin P, Dubois S, Stamatoulas A, Tilly H, Bohers E, Jardin F. Circulating tumor DNA in primary mediastinal large cell lymphoma versus classical Hodgkin lymphoma: a retrospective study. Leuk Lymphoma 2022;63:834–44.

[83] Rivas-Delgado A, Nadeu F, Andrade-Campos M, Lopez-Campos L, Enjuanes A, Mozas P, Frigola G, Colomo L, Sanchez-Gonzalez B, Villamor N, Bea S, Campo E, Salar A, Gine E, Lopez-Guillermo A, Bellosillo B. Cell-free DNA for genomic analysis in primary mediastinal large B-cell lymphoma. Diagnostics 2022;12.

[84] Pang D, Jiang X, Huang L, Teng Y, Liu S, Chen F, Wei X, Guo H, Li W. Ctdna monitoring in predicting relapse of primary mediastinal B-cell lymphoma. Blood 2020;136:36.

[85] Rimelen V, Ahle G, Pencreach E, Zinniger N, Debliquis A, Zalmai L, Harzallah I, Hurstel R, Alamome I, Lamy F, Voirin J, Drenou B. Tumor cell-free DNA detection in CSF for primary CNS lymphoma diagnosis. Acta Neuropathol Commun 2019;7:43.

[86] Bonzheim I, Sander P, Salmeron-Villalobos J, Susskind D, Szurman P, Gekeler F, Spitzer MS, Steinhilber J, Kohler E, Bussgen M, Schittenheim J, Salaverria I, Campo E, Coupland SE, Quintanilla-Martinez L, Fend F. The molecular hallmarks of primary and secondary vitreoretinal lymphoma. Blood Adv 2022;6:1598–607.

[87] Gupta M, Bradley J, Massaad E, Burns E, Georgantos NZ, Baron G, Batten J, Gallagher A, Thierauf J, Nayyar N, Gordon A, Jones S, Pisapia M, Sun Y, Jones PS, Barker 2nd FG, Curry W, Gupta R, Romero J, Wang N, Brastianos P, Martinez-Lage M, Tateishi K, Forst DA, Nahed BV, Batchelor TT, Ritterhouse LL, Iser F, Kessler T, Jordan JT, Dietrich J, Meyerson ML, Cahill DP, Lennerz JK, Carter B, Shankar GM. Rapid tumor DNA analysis of cerebrospinal fluid accelerates treatment of central nervous system lymphoma. Blood 2024;144:1093–1100.

[88] Delfau-Larue MH, van der Gucht A, Dupuis J, Jais JP, Nel I, Beldi-Ferchou A, Hamdane S, Benmaad I, Laboure G, Verret B, Haioun C, Copie-Bergman C, Berriolo-Riedinger A, Robert P, Casasnovas RO, Itti E. Total metabolic tumor volume, circulating tumor cells, cell-free DNA detection: distinct prognostic value in follicular lymphoma. Blood Adv 2018;2:807–16.

[89] Fernandez-Miranda I, Pedrosa L, Llanos M, Franco FF, Gomez S, Martin-Acosta P, Garcia-Arroyo FR, Guma J, Horcajo B, Ballesteros AK, Galvez L, Martinez N, Marin M, Sequero S, Navarro M, Yanguas-Casas N, Calvo V, Rueda-Dominguez A, Provencio M, Sanchez-Beato M. Monitoring of circulating tumor DNA predicts response to treatment and early progression in follicular lymphoma: results of a prospective pilot study. Clin Cancer Res 2023;29:209–20.

[90] Jimenez-Ubieto A, Poza M, Martin-Munoz A, Ruiz-Heredia Y, Dorado S, Figaredo G, Rosa-Rosa JM, Rodriguez A, Barcena C, Navamuel LP, Carrillo J, Sanchez R, Rufian L, Juarez A, Rodriguez A, Mwang C, de Toledo P, Grande C, Mollejo M, Casado LF, Calbacho M, Baumann T, Rapado I, Gallardo M, Sarandeses P, Ayala R, Martinez-Lopez J, Barrio S. Real-life disease monitoring in follicular lymphoma patients using liquid biopsy ultra-deep sequencing and PET/CT. Leukemia 2023;37:659–69.

[91] Nagy A, Batai B, Kiss S, Kiraly PA, Jona A, Demeter J, Santa H, Batai A, Pettendi P, Szendrei T, Plander M, Korosmezey G, Alizadeh H, Kajtar B, Mehes G, Krenacs L, Timar B, Csomor J, Toth E, Schneider T, Mikala G, Matolcsy A, Alpar D, Masszi A, Bodor C. Parallel testing of liquid biopsy (ctDNA) and tissue biopsy samples reveals a higher frequency of EZH2 mutations in follicular lymphoma. J Intern Med 2023;294:295–313.

[92] Lakhotia R, Melani C, Dunleavy K, Pittaluga S, Saba N, Lindenberg L, Mena E, Bergvall E, Lucas AN, Jacob A, Yusko E, Steinberg SM, Jaffe ES, Wiestner A, Wilson WH, Roschewski M. Circulating tumor DNA predicts therapeutic outcome in mantle cell lymphoma. Blood Adv 2022;6:2667–80.

[93] Zhang W, Wang W, Han X, Gan Y, Qian L, Zhang Y, Zhang C, Wang Y, Guan Y, Yang L, Zhou D. Circulating tumor DNA by high-throughput sequencing of T cell receptor monitored treatment response and predicted treatment failure in T cell lymphomas. Int J Lab Hematol 2021;43:1041–9.

[94] Wei C, Wang W, Zhang Y, Zhao D, Zhang W, Zhou D. Mutation profiling, tumour burden assessment, outcome prediction and disease monitoring by circulating tumour DNA in peripheral T-cell lymphoma. Br J Haematol 2023;202:86–95.

[95] Liang J, Hua W, Liang J, Shen H, Yin H, Wu J, Li Y, Wang L, Li J, Xu W. Molecular features possessed in the ctDNA reveal heterogeneity and predict outcome in newly diagnosed peripheral T-cell lymphoma. Hematol Oncol 2023;41:336–8.

[96] Sakata-Yanagimoto M, Nakamoto-Matsubara R, Komori D, Nguyen TB, Hattori K, Nanmoku T, Kato T, Kurita N, Yokoyama Y, Obara N, Hasegawa Y, Shinagawa A, Chiba S. Detection of the circulating tumor DNAs in angioimmunoblastic T-cell lymphoma. Ann Hematol 2017;96:1471–5.

[97] Sako N, Delfau-Larue MH, Bachy E, Morschhauser F, Cartron G, Casasnovas O, Gressin R, Daguidinau N, Tournilhac O, Bouabdallah F, Le Guilli S, Andre M, Robe C, Dupuis J, de Leval L, Gaulard P, Lemonnier F. Cell-free DNA sequencing allows the identification of the mutational profile of TFH lymphomas and has a predictive value: a LYSA study. Hematol Oncol 2023;41:338–9.

[98] Spina V, Bruscaggin A, Cuccaro A, Martini M, Di Trani M, Forestieri G, Manzoni M, Condoluci A, Arribas A, Terzi-Di-Bergamo L, Locatelli SL, Cupelli E, Ceriani L, Moccia AA, Stathis A, Nassi L, Deambrogi O, Diop F, Guidetti F, Cocomazzi A, Annunziata S, Rufini V, Giordano A, Neri A, Diop L, Bertoni R, Bertoni F, Ghielmini M, Stussi G, Santoro A, Cavalli F, Zucca E, Larocca LM, Gaidano G, Hohaus S, Carlo-Stella C, Rossi D. Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma. Blood 2018;131:2413–25.

[99] Sobesky S, Mammadova L, Cirillo M, Drees EEE, Mattlener J, Dorr H, Altmuller J, Shi Z, Brockelmann PJ, Weiss J, Kreissl S, Sasse S, Ullrich RT, Reinke S, Klapper W, Gerhard-Hartmann E, Rosenwald A, Roemer MGM, Nurnberg P,
Hagenbeek A, Zijlstra JM, Pegtel DM, Engert A, Borchmann P, von Tresckow B, Borchmann S. In-depth cell-free DNA sequencing reveals genomic landscape of Hodgkin's lymphoma and facilitates ultrasensitive residual disease detection. Med 2021;2:e1111. 1171-1193.

[100] Camus V, Viennot M, Lequesne J, Viaillay PJ, Bohers E, Bessi L, Marcq B, Etancelin P, Dubois S, Picquenot JM, Veresezan EL, Comic M, Burel L, Loret J, Becker S, Decazes P, Lenain P, Lepretre S, Lemisle E, Lanic H, Menard AL, Contentin N, Tilly H, Stamatoullas A, Jardin F. Targeted genotyping of circulating tumor DNA for classical Hodgkin lymphoma monitoring: a prospective study. Haematologica 2021;106:154-62.

[101] Vandenberghe P, Wlodarska I, Tousseyn T, Dehaspe L, Dierickx D, Verheecke M, Uyttebroeck A, Bechter O, Delforge M, Vandecaveye V, Brison N, Verhoef GE, Legius E, Amant F, Vermeesch JR. Non-invasive detection of genomic imbalances in Hodgkin/Reed-Sternberg cells in early and advanced stage Hodgkin's lymphoma by sequencing of circulating cell-free DNA: a technical proof-of-principle study. Lancet Haematol 2015;2:e55-65.

[102] Olszewski AJ, Chorzalska AD, Petersen M, Ollila TA, Zayac A, Kurt H, Treaba DO, Reagan JL, Hsu A, Egan PC, Butera J, Niroula R, Vatkevich J, Robison J, Sahin I, Jacob AP, Mullins CD, Dubielecka PM. Detection of clonotypic DNA in the cerebrospinal fluid as a marker of central nervous system invasion in lymphoma. Blood Adv 2021;5:5525-35.

[103] Radke J, Ishaque N, Koll R, Gu Z, Schumann E, Sieverling L, Uhrig S, Hubschmann D, Toprak UH, Lopez C, Hostench XP, Borgoni S, Juraeva D, Pritsch F, Paramasivam N, Balasubramanian GP, Schlesner M, Sahay S, Weniger M, Pehl D, Radbruch H, Osterloh A, Korfel A, Misch M, Onken J, Faust K, Vajkoczy P, Moskopp D, Wang Y, Jodicke A, Trumper L, Anagnostopoulos I, Lenze D, Kuppers R, Hummel M, Schmitt CA, Wiestler OD, Wolf S, Unterberg A, Eils R, Herold-Mende C, Brors B, Consortium IM-S, Siebert R, Wiemann S, Heppner FL. The genomic and transcriptional landscape of primary central nervous system lymphoma. Nat Commun 2022;13:2558.

[104] Loser V, Segot A, de Leval L, Bisig B, Brouland JP, Hewer E, Barcena C, Hottinger AF, Pot C. Primary diffuse large B-cell lymphoma of the central nervous system identified with CSF biomarkers. BMC Neurol 2024;24:250.

[105] Calimeri T, Anzalone N, Cangi MG, Fiore P, Gagliardi F, Miserochi E, Ponzoni M, Ferreri AJM. Molecular diagnosis of primary CNS lymphoma in 2024 using MYD88(Leu265Pro) and IL-10. Lancet Haematol 2024;11:e540-9.

[106] Fontanilles M, Marguet F, Bohers E, Viaillay PJ, Dubois S, Bertrand P, Camus V, Mareschal S, Ruminy P, Maingonnat C, Lepretre S, Veresezan EL, Derrey S, Tilly H, Picquenot JM, Laquerriere A, Jardin F. Non-invasive detection of somatic mutations using next-generation sequencing in primary central nervous system lymphoma. Oncotarget 2017;8:48157-68.

[107] Rossi D, Spina V, Bruscaggin A, Gaidano G. Liquid biopsy in lymphoma. Haematologica 2019;104:648-52.

[108] Schmitz R, Wright GW, Huang DW, Johnson CA, Phelan JD, Wang JQ, Roulland S, Kasbekar M, Young RM, Shaffer AL, Hodson DJ, Xiao W, Yu X, Yang Y, Zhao H, Xu W, Liu X, Zhou B, Du W, Chan WC, Jaffe ES, Gascoyne RD, Connors JM, Campo E, Lopez-Guillermo A, Rosenwald A, Ott G, Delabie J, Rimsza LM, Tay Kuang Wei K, Zelenetz AD, Leonard JP, Bartlett NL, Tran B, Shetty J, Zhao Y, Soppet DR, Pittaluga S, Wilson WH, Staudt LM. Genetics and pathogenesis of diffuse large B-cell lymphoma. N Engl J Med 2018;378:1396-407.

[109] Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, Lawrence MS, Roemer MGM, Li AJ, Ziepert M, Staiger AM, Wala JA, Ducar MD, Leshchiner I, Rheinbay E, Taylor-Weiner A, Coughlin CA, Hess JM, Pedamallu CS, Livitz D, Rosebrock D, Rosenberg M, Tracy AA, Horn H, van Hummelen P, Feldman AL, Link BK, Novak AJ, Cerhan JR, Habermann TM, Siebert R, Rosenwald A, Thorner AR, Meyerson ML, Golub TR, Beroukhim R, Wulf GG, Ott G, Rodig SJ, Monti S, Neuberg DS, Loeffler M, Pfreundschuh M, Trumper L, Getz G, Shipp MA. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med 2018;24:679-90.

[110] Esfahani MS, Alig S, Kurtz DM, Soo J, Jin MC, Macaulay C, Craig AF, Garofalo A, Steen CB, Scherer F, Sworder B, Diehn M, Alizadeh AA. Towards non-invasive classification of DLBCL genetic subtypes by ctdna profiling. Blood 2019;134:551.

[111] Roemer MG, Advani RH, Ligon AH, Natkunam Y, Redd RA, Homer H, Connelly CF, Sun HH, Daadi SE, Freeman GJ, Armand P, Chapuy B, de Jong D, Hoppe RT, Neuberg DS, Rodig SJ, Shipp MA. PD-L1 and PD-L2 genetic alterations define classical Hodgkin lymphoma and predict outcome. J Clin Oncol 2016;34:2690-7.

[112] Roemer MGM, Redd RA, Cader FZ, Pak CJ, Abdelrahman S, Ouyang J, Sasse S, Younes A, Fanale M, Santoro A, Zinzani PL, Timmerman J, Collins GP, Ramchandren R, Cohen JB, De Boer JP, Kuruvilla J, Savage KJ, Trneny M, Ansell S, Kato K, Farsaci B, Sumbul A, Armand P, Neuberg DS, Pinkus GS, Ligon AH, Rodig SJ, Shipp MA. Major histocompatibility complex class II and programmed death ligand 1 expression predict outcome after programmed death 1 blockade in classic Hodgkin lymphoma. J Clin Oncol 2018;36:942-50.

[113] Bisig B, Savage KJ, De Leval L. Pathobiology of nodal peripheral T-cell lymphomas: current understanding and future directions. Haematologica 2023;108:3227-43.

[114] Araf S, Wang J, Korfi K, Pangault C, Kotsiou E, Rio-Machin A, Rahim T, Heward J, Clear A, Iqbal S, Davies JK, Johnson P, Calaminici M, Montoto S, Auer R, Chelala C, Gribben JG, Graham TA, Fest T, Fitzgibbon J, Okosun J. Genomic profiling reveals spatial intra-tumor heterogeneity in follicular lymphoma. Leukemia 2018;32:1261-5.

[115] Magnes T, Wagner S, Thorner AR, Neureiter D, Klieser E, Rinnerthaler G, Weiss L, Huemer F, Schlick K, Zaborsky N, Steiner M, Greil R, Egle A, Melchardt T. Spatial heterogeneity in large resected diffuse large B-cell lymphoma bulks analysed by massively parallel sequencing of multiple synchronous biopsies. Cancers (Basel) 2021;13.

[116] Hersby DS, Schejbel L, Breinholt MF, Hogdall E, Norgaard P, Dencker D, Nielsen TH, Pedersen LM, Gang AO. Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma. Leuk Lymphoma 2023;64:1527-35.

[117] Tian XP, Zhang YC, Lin NJ, Wang L, Li ZH, Guo HG, Ma SY, An MJ, Yang J, Hong YH, Wang XH, Zhou H, Li YJ, Rao HL, Li M, Hu SX, Lin TY, Li ZM, Huang H, Liang Y, Xia ZJ, Lv Y, Liu YY, Duan ZH, Chen QY, Wang JN, Cai J, Xie Y, Ong CK, Liu F, Liu YY, Yan Z, Huang L, Tao R, Li WY, Huang HQ, Cai QQ. Diagnostic performance and prognostic value of circulating tumor DNA methylation marker in extranodal natural killer/T cell lymphoma. Cell Rep Med 2023;4:100859.

[118] Melani C, Wilson WH, Roschewski M. Liquid biopsy in non-Hodgkin's lymphoma. Hematol Oncol 2019;37(Suppl 1):70-4.

[119] Roschewski M, Staudt LM, Wilson WH. Dynamic monitoring of circulating tumor DNA in non-Hodgkin lymphoma. Blood 2016;127:3127-32.

[120] Roschewski M, Kurtz DM, Westin J, Lynch RC, Macaulay C, Kuffer C, Hogan GJ, Close S, Chabon JJ, Rossi D, Diehn M, Alizadeh AA, Schultz A. MRD-negativity as a potential surrogate endpoint after frontline DLBCL therapy: pooled analysis of trials & implications for clinical trial design. Blood 2022;140:785-6.

[121] Cherng HJ, Herrera A. Circulating tumor DNA in diffuse large B-cell lymphoma: from bench to bedside? Curr Treat Options Oncol 2024;25:659-78.

[122] Lakhotia R, Roschewski M. Clinical applications of circulating tumor DNA in indolent B-cell lymphomas. Semin Hematol 2023;60:164-72.

[123] Kridel R, Chan FC, Mottok A, Boyle M, Farinha P, Tan K, Meissner B, Bashashati A, McPherson A, Roth A, Shumansky K, Yap D, Ben-Neriah S, Rosner J, Smith MA, Nielsen C, Gine E, Telenius A, Ennishi D, Mungall A, Moore R, Morin RD, Johnson NA, Sehn LH, Tousseyn T, Dogan A, Connors JM, Scott DW, Steidl C, Marra MA, Gascoyne RD, Shah SP. Histological transformation and progression in follicular lymphoma: a clonal evolution study. PLoS Med 2016;13:e1002197.

[124] Pasqualucci L, Khiabanian H, Fangazio M, Vasishtha M, Messina M, Holmes AB, Ouillette P, Trifonov V, Rossi D, Tabbo F, Ponzoni M, Chadburn A, Murty VV, Bhagat G, Gaidano G, Inghirami G, Malek SN, Rabadan R, Dalla-Favera R. Genetics of follicular lymphoma transformation. Cell Rep 2014;6:130-40.