B cells are a relatively undercharacterized component of the tumor microenvironment. In human tumors, infiltration with B-cells, as cells and T-cells is often a positive prognostic factor, especially in conjunction with the formation of tertiary lymphoid structures (TLSss). Mechanistically, tumor-infiltrating B-cells (TI-Bs) may act via presentation of BCR-cognate antigens to T-cells (Zhu et al., 2015; Bruno et al., 2017; Nielsen et al., 2012), production of pro- or anti-tumor cytokines Somasundaram et al., 2017; Yang et al., 2013; Ammirante et al., 2010; Pylayeva-Gupta et al., 2016, and production of antibodies, which may be tumor-specific DeFalco et al., 2018 and enhance killing of tumor cells via antibody-dependent cytotoxicity (ADCC) Kurai et al., 2007 and complement-induced cytotoxicity, enhance antigen-presentation by dendritic cells Carmi et al., 2015 or form immune complexes that promote the activation of myeloid-derived suppressor cells (MDSC) (Barbera-Guillem et al., 1999). The effector functions of antibodies are highly dependent on antibody isotype de Taeye et al., 2020 and specificity. For instance, IgG1 is the most efficient factor in ADCC, and IgG3 (together with IgG1) is capable of complement-dependent cytotoxicity (Vidarsson et al., 2014). These functions depend on the ability of an antibody to bind its cognate antigen, which increases during B-cell maturation and somatic hypermutation (Clarke et al., 1985). Isotype switching, SHM, and B-cell proliferation and differentiation are influenced by the tumor microenvironment, neoantigen burden, and presence of certain driver mutations (Isaeva et al., 2019). Therefore, the properties of immunoglobulins produced within the tumor are both a reflection of TME features and a factor that shapes the TME.

The whole population of immunoglobulin receptors and immunoglobulins produced within a certain tissue or cell population is known as a BCR repertoire. BCR repertoire analysis has become an important approach for characterizing adaptive immune responses in health and disease (Cowell, 2020; Bradley and Thomas, 2019; Imkeller and Wardemann, 2018). Starting from genomic DNA or RNA, libraries representing the sequences of variable domains of antibodies can be produced and sequenced using high throughput sequencing (Georgiou et al., 2014; Boyd and Joshi, 2014; Turchaninova et al., 2016; Chaudhary and Wesemann, 2018; Yuzhakova et al., 2020). Alternatively, immune receptor sequences can be derived from RNA-Seq data (Mandric et al., 2020; Bolotin et al., 2017; Wang et al., 2022a; Mose et al., 2016). Then, the properties of the variable parts of immune receptor sequences are studied using bioinformatics tools that are currently quite elaborately developed (Foglierini et al., 2020; Shugay et al., 2015; Avram et al., 2018; Gupta et al., 2015; Gervásio et al., 2023; Yu et al., 2016; Giudicelli et al., 2017).

For BCR repertoires, features that are considered functionally important are clonality, isotype composition, biases in V- and J-gene usage, and extent of SHM (Kitaura et al., 2017; Pineda et al., 2021; Volpe and Kepler, 2009; Bashford-Rogers et al., 2019). In addition, characteristics of the immunoglobulin CDR-H3 region, such as the number of added nucleotides, length, and amino acid physicochemical properties, have functional implications and have been linked to antibody specificity Yu and Guan, 2014 and to the B-cell maturation stage (Grimsholm et al., 2020).

Correspondingly, the characteristics of the immune repertoire have been found to be associated with clinical outcomes (Isaeva et al., 2019; Bolotin et al., 2017; Reuben et al., 2017; Hu et al., 2019; Cha et al., 2017). Specifically, high intratumoral immunoglobulin heavy chain (IGH) expression, high IGH clonality, and a high proportion of IgG1 among all IGH transcripts were strongly correlated with higher overall survival in melanoma (Bolotin et al., 2017). Similarly, high IgG1 mRNA levels are positively associated with improved prognosis in early breast cancer (Larsson et al., 2020). A high intratumoral IgG1 to IgA ratio was associated with improved overall survival in bladder cancer Dyugay et al., 2022 and for KRASmut but not KRASwt lung adenocarcinoma (LUAD), suggesting the first link between driver mutation and B-cell response (Isaeva et al., 2019). In lung adenocarcinoma, breast cancer, and bladder cancer, only a subset of V-segments is associated with improved survival (Iglesia et al., 2016). The co-occurrence of specific antibody motifs and mutated tumor-derived peptides, presumably indicating specificity to particular tumor neoantigens, was also correlated with longer survival in colorectal cancer (Cha et al., 2017).

The tumor immune repertoire may be used as a source of tumor-antigen-specific antibodies for therapy development (Zhang et al., 1995; Kotlan et al., 2015; Pavoni et al., 2007). For instance, in melanoma 30–80% of the total BCR repertoire (Bolotin et al., 2017) may constitute one or several dominant B cell clones. Corresponding antibodies can be produced, verified for tumor reactivity (Pavoni et al., 2007), and further employed in chimeric antigen receptor T cell (CAR-T) therapy or other therapeutic approaches. The knowledge of antigen specificity of cancer-associated antibodies is currently insufficient, and it is mostly limited to autoantigens and some tumor-associated antigens (Wu et al., 2018; Anderson et al., 2010; Yadav et al., 2019; Hoshino et al., 2020; Kunizaki et al., 2016; Budiu et al., 2011).

However, even in the absence of knowledge of cognate antigens, certain hypotheses may be derived from BCR repertoire characteristics. Some individual IGHV-genes and subgroups have been associated with autoimmunity (Matsuda et al., 1998). Likewise, an increase in the BCR clonal expansion index (Bashford-Rogers et al., 2013), dominated by IgA and IgM isotypes, was associated with systemic lupus erythematosus (SLE) and Crohn’s disease (Bashford-Rogers et al., 2019).

The ability to accurately characterize the B cell repertoire in the tumor microenvironment is of vital importance for both fundamental and clinical challenges. However, tumor tissue may not always be available for analysis; therefore, it would be beneficial to derive information about the tumor BCR repertoire from peripheral blood or draining LNs. It is not known, however, whether tumor-dominant BCR clonotypes can be found in the peripheral blood of cancer patients and at what frequencies. The ability to detect these clonotypes in a patient’s peripheral blood could have a predictive value for disease prognosis. In addition, it is not known how the BCR repertoire characteristics of peripheral blood B-cells and tumor-draining LNs relate to the repertoire of TI-Bs.

Therefore, in the current study, we aimed to comprehensively and systematically evaluate tumor, lymph node, and peripheral blood BCR repertoires and their interrelationships. We show that the tumor BCR repertoire is closely related to that of draining LNs, both in clonal composition and isotype proportion. Furthermore, we observed that different LNs from one draining lymph node pool may be differentially involved in the interaction with the tumor, as reflected by the similarity of the BCR repertoire clonal composition. CDR-H3 properties indicate a less mature and less specific BCR repertoire of tumor-infiltrating B cells compared to circulating B cells. BCR clonotypes that expand in a tumor relative to other tissues are, on average, less hypermutated than non-expanded (ubiquitous) clonotypes.

We performed a multi-localization study of BCR repertoires in cancer patients using an unbiased BCR library preparation technique complemented by deep repertoire sequencing. We analyzed these repertoires from two complementary perspectives. At the level of individual BCR clonotypes, we studied various repertoire features and their differences between tissues of origin, as well as repertoire heterogeneity within tumor or lymph node tissue. At the level of B-cell clonal lineages, we tracked lineage sharing between tissues, and examined differences in clonotype features between clonotypes belonging to any clonal lineage, and those that do not.

First, we compared BCR repertoires from PBMCs and bulk tumor infiltrates and the corresponding sorted CD19⁺CD20^-CD38^high plasma cells, and confirmed that the bulk RNA-based BCR repertoire is dominated by plasma cells across different peripheral compartments. This finding is in line with our expectations, given that the IG expression level is 50–500 times higher in plasma cells than in memory B-cells (Turchaninova et al., 2016). However, we observed a higher proportion of IgM in PCs within the tumor and a lower proportion of IgG in PCs within the lymph nodes. Higher proportion of IgM in PCs relative to the bulk repertoire may indicate that some IgM-producing plasma cells, while having an appropriate plasmablast/PC surface marker phenotype, produce relatively low levels of antibodies, which leads to them being masked in the bulk repertoire by non-IgM non-PC B cells. A higher level of antibody production correlates with the differentiation of plasmablasts/short-lived plasma cells into long-lived plasma cells. Therefore, one could hypothesize that tumor-infiltrating IgM-expressing antibody-producing cells are skewed towards a less differentiated B-cell compartment compared to other isotypes. Similar logic applies to the IgG-producing plasma cells in the LNs: a lower proportion of IgG-clonotypes in PCs compared to the bulk repertoire indicates that some of the most frequent IgG clonotypes in the bulk repertoire belong to cells with a non-PC/plasmablast phenotype, presumably stimulated memory B-cells.

The overlap in BCR repertoires and the correlation of isotype proportions between tumor and lymph node samples were higher than those between tumor and peripheral blood samples.A higher repertoire overlap indicates a higher proportion of tumor-related BCR clonotypes in the draining LNs than in the peripheral blood. This underscores the crucial role of draining LNs in the development of the anti-tumor immune response. In addition, consistent with previous studies, our data confirmed that the tumor BCR repertoire is more clonal than that of LNs or PBMCs. We also showed that LNs within the same draining LN pool may be differentially involved in tumor interaction. Overall, LNs are a better source of material to study anti-tumor B-cell response than PBMC, and a better source of material for a potential cancer B-cell or antibody therapy, if tumor tissue is unavailable.

The mean CDR-H3 length of the most frequent BCR clonotypes in tumors was higher than in other tissues. This finding may indicate that tumors tend to be infiltrated with less mature B-cells compared to other compartments (Grimsholm et al., 2020). It may also suggest a higher proportion of autoreactive and polyreactive (Prigent, 2016; Boughter et al., 2020) antibodies, consistent with previous research. However, in colorectal cancer, the mean CDR-H3 length in the IgA repertoire was lower than that in melanoma. This may indicate that the IgA repertoire in colorectal cancer is less influenced by the TME than in other cancers that we studied. Differences in other CDR-H3 properties, such as mean kf3 (b-sheet preference), kf4 (inversely correlated with hydrophobicity), predicted interaction strength, and charge between different tissues, including increased central-CDR-H3 charge in the tumor compared to normal tissue, may suggest a less mature and less specific BCR repertoire of tumor-infiltrating B-cells.

Clonal groups representing hypermutating B-cell lineages differ in their isotype and tissue composition. In colorectal cancer, clonal groups dominated by IgA (>60% of IgA clonotypes) tend to be shared between tumors and blood, whereas IgG-dominated (>60% of IgG clonotypes) clonal groups tend to be shared between tumors and draining LNs. The probability of detection of a given clone in a given location is dependent on the BCR expression level, as well as on the time that these cells spend in a given location. Assuming that BCR is expressed at the same level in all members of a certain clonal population, the proportion of clones within a clonal group derived from a certain tissue is then a snapshot of their preference for the particular tissue of residence or circulation. By comparing IgA- and IgG-dominated clonal groups, we observed subtle differences in the behavior of B cells during ongoing clonal selection and interaction with the tumor. Whereas IgG clonal groups preferentially reside in tumors and draining LNs, IgA-dominated clonal groups show a greater preference for circulation. An obvious question is whether this corresponds to the proportion of isotypes at the cellular level (by surface or intracellular staining). The relative frequencies of antibody-secreting B cells in peripheral blood were IgA1 >IgG1>IgA2>IgM > IgG4 >IgG2>IgG3 (Lee et al., 1991), which corresponds to our observations of the peripheral blood bulk repertoire (total IgA >total IgG >total IgM). Therefore, the observed clonal group sharing between tissues may simply be due to the higher number of clonotypes, with a certain isotype detected in a certain tissue. However, for tissue distribution triangle plots, the data were normalized for isotype usage between tissues (i.e. equal numbers of IgG/IgA/IgM most frequent clonotypes were used for PBMC, LN, and blood) to correct for quantitative bias. Therefore, the trend for tissue preference reflects true clonal behavior and not simply quantitative prevalence.

It is well known that only draining LNs show signs of interaction with tumor antigens (Marzo et al., 1999). In murine models, non-draining usually occurs contralateral to the tumor site, which is anatomically distant. However, whether LNs within the same anatomical site may differ in terms of their interaction with the tumor has yet to be determined. For some of the patients in this study, we were able to obtain more than one lymph node from the same tumor-draining pool, which allowed us to address this question directly on the level of BCR repertoires. A higher overlap between the tumor and LN repertoire signifies a more intensive interaction with the tumor and its antigens. In at least one patient, the LNs studied were significantly different in this regard, with one of the LNs being significantly more similar to the tumor in its BCR repertoire. This observation was also reproduced at the clonal group level, where the lymph node that was in closer interaction with the tumor in terms of clonal overlap also contained more clonal groups that included tumor-derived clonotypes and were confined to this lymph node only. These clonal groups represent antibody maturation and selection processes involving tumors, and occur preferentially in the LN, which is in tight interaction with the tumor, but not in the others. Whether this also reflects the response to tumor antigens and the maturation of tumor-specific antibodies remains to be tested.

It has been previously shown that within a single lymph node, individual germinal centers (GCs) share their immunoglobulin clonal composition to some extent, and the offspring of one B-cell clone may be found in several individual germinal centers (Bende et al., 2007). During GC reactions, individual GCs become oligoclonal (containing 4–13 major B cell clones with functional IgVH, as detected in the pre-NGS era) due to an affinity selection process (Roers et al., 2000; Küppers et al., 1993; Jacob et al., 1993). However, in the case of chronic antigen stimulation, as in cancer, repeated cycles of antigen exposure, memory B-cell reactivation, and germinal center reactions should lead to uniform involvement of the whole LN in interaction with tumors and production of tumor-specific immune responses. Again, we sought to study the spatial heterogeneity of LNs at the level of individual immunoglobulin clonotypes and clonal lineages. We found that the BCR repertoires derived from fragments of a single lymph node were significantly different in their similarity to the pooled tumor BCR repertoire (F2 metric). We conclude that despite chronic antigen exposure, despite the ability of memory B cells to leave and re-enter germinal center reactions and form new germinal centers in subsequent rounds of affinity maturation, the BCR landscape of a lymph node remains significantly heterogeneous in space.

Likewise, for tumor immune infiltrates, it was shown that the TCR repertoire is heterogeneous in space, and this heterogeneity correlates with subclonal mutations within the tumor tissue Joshi et al., 2019 and likely represents expanded mutation-specific T cell clones. BCRs are expected to be even more clonal, both in LNs and tumors. However, for BCRs, heterogeneity analysis is complicated by the orders-of-magnitude difference in immunoglobulin expression between plasma cells and other B-cells (Turchaninova et al., 2016). A rare plasma cell will have a low probability of sampling at the cellular level but will produce a sufficient amount of immunoglobulin RNA to be reliably detected in the BCR repertoire. Therefore, appropriate use of biological (cellular) replicates sequenced at comparable depths is crucial for accurate detection of differentially expanded clonotypes (Yuzhakova et al., 2020). Following the logic described above, we were able to analyze repertoire overlap between tumor fragments and detect BCR clonotypes that were differentially expanded in individual fragments. We observed that, in some patients, the most abundant BCR clonotypes were different in different fragments. In this case, the most expanded clonotypes rarely overlapped between fragments, thus probably representing clonotypes that recognize subclonal tumor antigens. This needs to be tested directly and, if proven true, may potentially be a reliable method for the identification of tumor-specific antibodies. In other cases, the tumor may be relatively homogeneous, without significant expansion, and with highly overlapping repertoires of individual fragments. These observations are mostly reproduced at the level of clonal groups, where in heterogeneous tumors, we observed clonal groups that concentrated almost entirely in single tumor fragments, whereas in homogenous tumors, clonal groups mostly include clonotypes derived from all fragments of the tumor.

The physicochemical properties of the CDR-H3 region differ between clonotypes that represent clonal lineages, and thus, are actively hypermutating, and those that do not. The most prominent difference was observed for kf4 (inversely related to hydrophobicity) in PBMC and tumors; clonotypes that undergo hypermutation are, on average, less hydrophobic, which indicates higher binding specificity. However, there was no correlation between kf4 and the extent of hypermutation, even in large full-length Ig-profiling datasets. One possible explanation may be that the selection of less hydrophobic CDR-H3s occurred before the onset of hypermutation.

Interestingly, analysis of silent vs. non-silent hypermutations (dNdS) suggests that in the absence of a defined time-controlled antigenic stimulus, such as vaccination, in steady-state equilibrium, only a few immunoglobulin clonal groups show definitive signs of positive selection. It would be interesting to systematically explore the specificity of these clones in patients with cancer as a potential source of tumor-reactive antibodies.

In general, this study has several limitations. One is the small number of patients, both per cancer type and overall. Another is that our 5’RACE library preparation protocol, which was used for most samples in this study, does not allow for an accurate distinction between the IgA and IgG isotype subtypes. Regarding clonal lineage structure analysis, we found that our study design has a major limitation of a relatively short (150 bp) read length, which does not provide sufficient information for hypermutation analysis.

Nevertheless, our observations contribute to the understanding of the interactions among the tumor immune microenvironment, tumor-draining LNs, and peripheral blood.

BCR clonesets are deposited on Figshare under the following link https://doi.org/10.6084/m9.figshare.22340302.v1. Code is deposited on github under the following link https://github.com/vsevolodovna/bcr_in_three_cancers/tree/main (copy archived at vsevolodovna, 2025).

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Author details

1. Sofia V Krasik

   1. Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
   2. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation

   Contribution

   Software, Investigation, Visualization, Writing - original draft

   Contributed equally with

   Ekaterina A Bryushkova

   Competing interests

   No competing interests declared

2. Ekaterina A Bryushkova

   1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
   2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation
   3. Department of Molecular Biology, Lomonosov Moscow State University, Moscow, Russian Federation

   Contribution

   Investigation, Methodology, Writing - original draft

   Contributed equally with

   Sofia V Krasik

   Competing interests

   No competing interests declared

3. George V Sharonov

   1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
   2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation
   3. Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8610-5054

4. Daria S Myalik

   1. Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation
   2. Nizhny Novgorod Regional Clinical Cancer Hospital, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Elizaveta V Shurganova

   Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Dmitry V Komarov

   Volga Regional Medical Centre Under Federal Medical and Biological Agency, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Irina A Shagina

   1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
   2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Polina S Shpudeiko

   Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Maria A Turchaninova

   1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
   2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Maria T Vakhitova

    1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
    2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Igor V Samoylenko

    Federal State Budgetary Institution "N.N. Blokhin National Medical Research Center of Oncology" of the Ministry of Health of Russian Federation, Moscow, Russian Federation

    Contribution

    Resources, Investigation, Methodology

    Competing interests

    No competing interests declared

12. Dimitr T Marinov

    Federal State Budgetary Institution "N.N. Blokhin National Medical Research Center of Oncology" of the Ministry of Health of Russian Federation, Moscow, Russian Federation

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

13. Lev V Demidov

    Federal State Budgetary Institution "N.N. Blokhin National Medical Research Center of Oncology" of the Ministry of Health of Russian Federation, Moscow, Russian Federation

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

14. Vladimir E Zagaynov

    1. Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation
    2. Nizhny Novgorod Regional Clinical Cancer Hospital, Nizhny Novgorod, Russian Federation

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

15. Dmitriy M Chudakov

    1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
    2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation
    3. Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0430-790X

16. Ekaterina O Serebrovskaya

    1. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russian Federation
    2. Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russian Federation

    Contribution

    Supervision, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

    For correspondence

    katyaakts@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4967-7165

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