Classifying Germinal Center Derived Lymphomas—Navigate a Complex Transcriptional Landscape

Simple Summary

Germinal center-derived B-cell lymphomas constitute a very heterogeneous group of neoplasms with diverse clinical presentations, prognoses, and responses to therapy. They divide into a series of subtypes, such as Diffuse Large B-cell lymphomas (DLBCL), Burkitt Lymphomas (BL), Follicular lymphomas (FL), and further, into several subclasses and rarer subtypes of finer granularity, which makes them one of the most heterogeneous cancer entities. Molecular classification schemes, first of all, derived from whole transcriptome gene expression data largely improved subtyping and functional understanding. Based on a whole transcriptome landscape of B-cell lymphoma, we show that one major caveat in the task of classification is represented by the rather fuzzy distribution of individual tumors without clear-cut borderlines between most of the subtypes, preventing their unambiguous association with clear-cut entities. This landscape is governed by the germinal center (GC) reaction and relates different subtypes to different states along the reaction path. We discuss the relatedness between the expression landscape and classifier signatures and their functional cell of origin background. This view helps to stratify lymphomas in terms of modular building blocks of signature genes and to interpret rarer subclasses in the context of larger ones.

Abstract

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 “double hit lymphomas” (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

1. Introduction

Lymphomas and other cancers are collections of genetic diseases with different pathological, molecular-functional and clinical outcomes even for neoplasms of the same primary site of genesis and growth. The revised 4th Edition of the World Health Organization (WHO, see glossary below) classification of lymphoid neoplasms defined 51 mature B-cell neoplasm entities [1,2], thus representing probably one of the most heterogeneous tumor families. Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. In recent years, the diagnosis of lymphomas has been supplemented by molecular classification schemes, initially based on gene expression profiling (GEP) [3,4,5,6,7,8,9,10] followed by mutational patterns [11,12] and also by combinations of both [13,14,15]. Diffuse large B-cell lymphoma (DLBCL) is the most frequent subtype of mature B-cell lymphomas accounting for around 40% of all lymphomas and features extreme molecular and clinical heterogeneity. The cell-of-origin (COO) classification [16] comprises presently the most common subtyping approach of DLBCL reflecting its developmental stage in the germinal center (GC). It distinguishes germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subgroups as well as a grey-zone, “unclassifiable” (also named Type III) category [17,18]. In addition, a series of alternative models employed GEP for subtyping DLBC based on different molecular contexts such as energy metabolism, proliferation, inflammation [7,9,10,19], tumor microenvironment [20], B-cell phenotypes [21] or distinct pathway activation patterns [3,8] (see also [22] for a review). B-cell lymphoma entities the GEPs of which partially overlap with DLBCL are Burkitt lymphoma (BL) and Follicular lymphoma (FL) as well as related (provisional) entities of “intermediate” character between BL and DLBCL (see below). Novel schemes account for recognition of the continuous nature of transcriptional state space by defining subtypes at a finer granularity scale extracted from the diversity of tumor expression patterns [4] or decomposition of their independent components [23] or from GC-cell states identified by means of single-cell transcriptomics [24,25]. “Now, after 20 years, COO testing for DLBCLs has lost its luster but is not yet obsolete despite its questionable prognostic value and lack of predictive utility” having been recently stated [26].

Molecular classification schemes can utilize single marker genes or sets of them as classifiers whose mRNA levels in tumor samples enable one to predict their class membership after GEP. Only a few of these molecular features, however, have yet been considered in the WHO classification of lymphoid neoplasms [1]. This is presumably due to the lack of available consensus technologies and clinical studies, but also, and probably first of all, to the existence of a series of competing molecular classifiers proposed independently by different groups for the same classification task often without mutual reference and comparison. Users are then left with the problem to choose between classifiers because benchmarking information or contextual details are scarce or even lacking. In our previous studies of lymphomas [4] but also of other tumor entities such as melanomas [27], gliomas [28] and colon cancer [29], we found that independent sets of marker genes published by different authors perform in a similar fashion in our data even if the respective genes sets weakly overlap. Hence, multiple seemingly different classifiers often pop up in the literature for the very same question which obviously reflects a widely distributed redundancy regarding diverging sets of marker genes usually with unclear differences in their prediction accuracy and the underlying biological context.

This controversial issue with several competing classifiers directly relates to the multidimensional and complex nature of the underlying data landscape of lymphomas. We recently generated such a holistic transcriptional landscape of lymphomas by applying self-organizing maps (SOM) machine learning to a data set of about 900 lymphomas referring to a wide spectrum of subtypes [4]. In this publication, we aim at evaluating the stability and interpretability of different sets of lymphoma markers taken from the literature [3,5,17,18,30,31] in the context of this landscape. In the first part of the paper, we describe details of this landscape and their biological meanings in terms of subtype-specific transcriptomic portraits for DLBCL, BL and FL as well as for rarer entities such as aggressive lymphoma with simultaneous rearrangements of MYC and BCL2 (so called “double hit lymphomas”; DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and MYC-negative high grade B-cell lymphomas (formerly called Burkitt-like lymphomas) with 11q aberration pattern (mnBLL-11q). We also consider the split of DLBCL into ABC- and GCB-types related to GC-development and discuss footprints of the normal GC-reaction in the lymphoma landscape. In the second part, we extract classifier gene sets for the different lymphoma subtypes (BL, DLBCL, ABC, GCB, DHL) from the expression landscape and compare them with classifiers from the literature. In the last part, we discuss possible avenues to further develop classification of lymphomas.

2. Material and Methods

2.1. Lymphoma Data

We here reanalyzed microarray-based expression values of 873 biopsy specimens of mature B-cell lymphomas and of 40 reference samples (tumor cell lines, sorted B-cells, tonsils). Reference samples were not explicitly addressed in this publication; please see [4] for details, particularly Supplementary Material, Table S1 in [4]. The lymphoma samples divide into ten major strata: (i) diffuse large B-cell lymphoma (DLBCL, 430 cases), (ii) follicular lymphoma (FL, 145 cases), (iii) “intermediate” lymphoma according to [3] (81 cases), (iv) prototypic Burkitt lymphoma (BL, 74 cases), (v) mixed FL/DLBCL and WHO grade 3B FL (48 cases), (vi) mediastinal large B-cell lymphoma (PMBL, 23 cases), (vii) multiple myeloma (MM, 20 cases), (viii) IRF4-rearranged large cell lymphoma (IRF4-LCL, 10 cases), (ix) MYC-negative Burkitt-like lymphomas with 11q aberration (mnBLL-11q, 6 cases), and (x) mantle cell lymphoma (MCL, 4 cases). DLBCL were further stratified into the cell of origin (COO) groups, germinal center B-cell like (GCB, 142 cases), activated B-cell-like (ABC, 133 cases), and unclassified (97 cases) DLBCL and DHL (58 cases) lymphomas. Cases have been classified according to the 4th Edition of the WHO classification, but

Table 1. New nomenclature for genetic entities considered by WHO 2022 [32].

Previous Nomenclature (This Paper)	New Nomenclature (WHO 2022)	Comment
DHL MYC + BCL2	DLBCL/HGBL MYC/BCL2
THL MYC + BCL2 + BCL6	DLBCL/HGBL MYC/BCL2 and BCL6	BCL6 rearrangement is not considered as “hit“ defining an entity
DHL MYC + BCL6	DLBCL or HGBL, NOS
MYC-negative Burkitt-like lymphomas with Chr. 11q aberration pattern (mnBLL-11q)	High-grade B-cell lymphoma with 11q aberration (HGBL-11q)	Reference to BL is removed
IRF4-rearranged large cell lymphoma (IRF4-LCL)	Large B-cell lymphoma with IRF4 rearrangement

provides a translation into the 5th Edition [32].

2.2. Transcriptome Map of Lymphomas and Spot Classifiers

The expression data were used to generate a self-organizing map (SOM) which distributes the genes under study in a 50 × 50-pixel grid such that co-expressed genes across the samples are clustered together in the same or in adjacent pixels called metagenes. The SOM then provides “portraits” of all individual samples and, after group-averaging, of the different lymphoma subtypes by color coding the expression level of the metagenes. Co-expressed, high variant metagenes cluster together according to the self-organizing properties of the SOM forming so-called spot-like areas in the portraits. They were used to extract sets of marker genes as potential classifiers for subtypes showing specific overexpression of the respective spot. Spot genes were selected as described previously [4].

2.3. Reference Classifiers, Gene Set Maps and GSZ-Profiles

We studied reference classifiers taken from six publications developed to discriminate BL-vs-DLBCL, ABC-vs-GCB DLBCL and DHL-vs-GCB DLBCL, respectively [3,5,17,18,30,31] (see Table 2 below). Notably, some of these reference classifiers have been derived and/or validated from data of other quoted classifiers, thus, not all of them can be regarded as independent. The classifiers comprise signature sets of genes, which have been obtained from two-class comparisons based on different lymphoma test data as specified in Table 2. For the sake of simplicity and mutual comparisons, we processed classifier signatures in a standardized way as follows: For each two-group comparison, classifier genes were split into two subsets either upregulated in the one or in the other group of lymphomas: e.g., for BL-versus-DLBCL classification, the signature comprises two sets of genes overexpressed either in BL (BL_up alias DLBCL_down) or DLBCL (DLBCL_up alias BL_down), respectively. We explicitly acknowledge that this way of processing dismisses any information from weighting of single gene expressions. The functional context of the classifier genes was estimated by mapping them into the SOM of the MMML data set (Molecular Mechanisms of Malignant Lymphomas consortium, see [4] for the detailed description). The obtained gene set maps visualize the location of all genes of the reference signatures in the SOM. Their accumulation in or near certain spots reflects co-expression and possible functional association. Each (reference or spot) classifier comprises a set of genes (alias signature). It provides one GSZ (gene set Z-score)-value per lymphoma case which estimates its expression level in this particular sample. GSZ values of the signatures were used for classification and calculation of receiver operator curves (ROC). Additional materials and methods descriptions are provided in Appendix A regarding lymphoma data (Appendix A.1), GSZ Profiles of Classifier Signatures and ROC Characteristics (Appendix A.2), an interactive oposSOM Browser of the MMML Lymphoma Data Set available online (Appendix A.3) and an application of Machine Learning of Transcriptonal Portraits to the data (Appendix A.4).

3. Results

3.1. The Multi-Dimensional Nature of the Lymphoma Transcriptome

To study the diversity of B-cell lymphomas, we applied SOM portrayal to microarray gene expression data of 873 biopsy specimens referring to a large spectrum of lymphoma subtypes collected within the framework of the MMML (see [4] and materials and methods section for details). We utilized the sample portraits to build a pairwise correlation heatmap illustrating mutual similarities (red) and dissimilarity (blue) relations (Figure 1a). One finds roughly three larger groups of BL-like, DLBCL-like and FL-like tumors along the diagonal as red correlation clusters but also a high degree of heterogeneity in terms of red off-diagonal stripes particularly indicating partial BL- or FL-resemblance of part of the DLBCL. Though the clinical data have to been interpreted with caution in this multi-center retrospective of mostly pre-Rituximab series, the major subtypes differ in prognosis and the shapes of the survival curves underlining the need for practicable and robust classification schemes (Figure 1b).

Next, the heatmap was transformed into a similarity tree to visualize the relatedness between the major tumor entities (Figure 1c). FL, DLBCL and BL occupy certain regions along the tree, which, however, considerably overlap. Similar results were obtained for GCB- and ABC-type DLBCL and DHL (Figure 1c, right part). The tumors form a continuum of transcriptional states without clear-cut borderlines between the different strata. Therefore, any sharp delineation of subtypes based on GEP without overlapping grey zones will always be challenging, if not impossible.

Correlation plots of the mean expression of the lymphoma subtypes between functional gene set scores of an epigenetic signature (targets of the polycomb repressive complex 2, PRC2) and cell cycle activity as well as between activity scores of the light zone (LZ) and dark zone (DZ) of the GC are roughly linear between DLBCL (and FL) on the left and BL on the right (Figure 1d). The choice of these characteristics was motivated by recent studies which revealed an antagonistic regulation between proliferative and inflammatory programs in BL- and DLBCL- (and partly also FL-) like lymphomas, respectively. It associates with the activation of MYC-targets and open chromatin states on one hand and with the activation of targets of the repressive PRC2-complex, aberrant DNA-methylation, repressed chromatin states as well as the reprogramming of the machinery of writers and erasers of epigenetic marks on the other one [33,34].

A classifier signature set obtained by means of BL-versus-DLBCL differential expression analysis [3] shows a similar dependence as a DZ-versus-LZ origin would imply (Figure 1d, right plot). These virtually one-dimensional changes, however, oversimplify the real picture of transcriptional covariations. The multidimensional nature can be visualized in terms of transcriptomic “portraits” of the subtypes (Figure 1e). The portraits reveal modules of co-expressed genes by means of red (upregulated) and blue (downregulated) “spot”-like clusters labeled by capital letters for assignment (Figure 1e, modules are also called “spots’ in the following). The activated modules in the portraits provide fingerprint patterns of the respective transcriptional state. In BL, activated modules enrich genes related to proliferation (spot D) and DNA processing (spot B); DLBCL display higher expression of genes which are associated with immune response, inflammation and extracellular matrix (spot F and G), and FL have increased expression of genes of stromal characteristics [35] (spot I). ABC-DLBCL specifically activate genes in spot H related to plasma cell maturation, also activated in MM [4]. The different spots contain marker genes upregulated in the respective subtypes such as MYC (BL and part of DLBCL, spot D), ID3 and TCF3 (BL, spot B) or PRDM1 and IRF4 (ABC-DLBCL, spot H). Expression profiles of the spot modules are shown in Figure A3 and accumulation of functional signatures in and around the spots in Figure A4. Note that the scores used in the bi-plots in Figure 1d can be substituted by functional signatures accumulating in spots D and F such “MYC-targets” or “stroma”, respectively, without changing the plots significantly. The spot-landscape can be summarized as overexpression summary map which divides into “lands” containing spots upregulated in the different subtypes (Figure 1f).

In summary, lymphomas distribute in a complex transcriptomic similarity landscape. Subtype-related portraits show clear differences which associate with specific biological functions. One major caveat in the task of classification, however, is represented by the rather fuzzy distribution of individual cases without clear-cut borderlines between most of the subtypes preventing their unambiguous association with clear-cut entities.

3.2. Beyond the Canonical DZ-LZ Dualism

In a simplified view, GC B-cells divide into two canonical populations, showing either dark zone (DZ) or light zone (LZ) phenotypes [38,39,40]. Within the active GC, orchestrated molecular programs must balance proliferation and selection to provide effective humoral immunity and to protect against genomic instability and neoplastic transformation (Figure 2a). The GC reaction is based on complex spatial and temporal dynamics that are still only partially elucidated. The GC is also the site in which most mature B cell lymphomas originate [41], suggesting that the process of malignant transformation disturbs the physiology of GC and post-GC differentiation by stalling lymphoma cells at various stages of the GC reaction [42] and/or de-synchronizing their transcriptional programs [43]. For example, BL and DLBCL on the average show high and low DZ scores, respectively, which associate with proliferative activity and epigenetic re-programming, respectively (Figure 1d) [37,41,44].

Recent single-cell transcriptome studies provided a more detailed view, segregating the GC into more than the two morphologically distinguishable compartments [24,25,43,45,46]. Of note, two distinct B-cell populations have been described by Kennedy et al. [25,47] in the DZ that differ both with respect to function and location: Proliferating DZ (DZp) cells transform into differentiating DZ (DZd) cells before they exit the DZ and re-enter the LZ (Figure 2b, [25,47]). Interestingly, gene signatures of the three compartments taken from [25] are found in/near spot areas F (LZ_up), D (DZp_up), and B (DZd_up) (Figure 2e and Figure A5), suggesting that our transcriptomic state space well recapitulates this tri-partition of the GC (Figure 2d). Key genes of these compartments, namely, MYC and TCF3, locate in different spot modules D and B, respectively. The former is a driver of proliferation in DZp while the latter acts as primer for E2A targets in DZd [25]. MYC is a global cancer driver deregulated in virtually all BL (typically, via an IG::MYC translocation) but also in roughly 30–40% of DLBCL featuring increased proliferation-associated transcription as reflected by upregulated expression of spot D (Figure 2d, see also [48] and references cited therein). Genes of the TCF3 pathway (TCF3, ID3, CCND3) constitute targets of BL-specific second mutation hits in around 90% of BL, following MYC-translocation [49] rendering BL differently from MYC-translocated DLBCL. These three TCF3-pathway genes locate in or near spot B which specifically upregulates in BL due to second hits and thus possibly reflects stalling of BL in the DZd (and/or DZp) compartment(s) (Figure A5).

Another single-cell transcriptomic study identified thirteen GC B-cell states which distribute over the DZ, LZ, an intermediate phenotype of B-cells (INT), and include also B-cells that just exit the DZ (DZexit), and in addition, precursors of memory B-cells (PreM) and plasmablasts (PBL) [24] (Figure 2c). We considered signatures of the thirteen states referring to DZ (DZa-c), INT (INTa-d), LZ (LZa,b) as well as PreM and PLB (PLBa,b) as provided in [24] and analyzed their GSZ profiles, distribution in the SOM landscape, mean expression across the lymphoma subtypes as well their accumulation in the different spots (Figure 3). Key genes of PBL such as PRDM1 and IRF4 locate in spot H in our landscape upregulated in ABC-DLBCL (Figure 1e) and MM [4]. Signature genes of the DZ, INT and DZexit states are found in spots D, B and A, K, respectively (Figure 2d,e and Figure 3b,e). Overall, the transcriptomic states of these different GC B-cell phenotypes form a closed trajectory in the expression state space as provided by the spot summary map. It reflects B-cell development in the GC by connecting LZ (spot K and F), proliferative DZp (spot D), developmental DZd (spot B and A), and INT (spot A and K) expression states, while PreM and PBL (spot H) form a side branch featuring plasma cell maturation (Figure 2d and Figure 3). This side branch links with the final LZb state, combining expression characteristics of DZa (spot D, proliferation), LZa (inflammation, spot F) and PBL (spot H, Figure 3). Notably, this trajectory is not characterized by the strict serial activation of the spots but mostly by the combinatorial activation of two to four spots along the trajectory in each of the GC-states (Figure 3a,b,e) in correspondence with our previous pattern-type (PAT) analytics of lymphoma heterogeneity [4] (see Figure A6b).

In summary, recent GC B-cell single-cell transcriptomics in different settings revealed several DZ B-cell types such as the DZ, DZexit, partly intermediate [24], DZp and DZd [25], which all group along a closed ‘GC reaction trajectory’ in the expression landscape. The expression profiles of the spots along the GC trajectory are discriminative for distinguishing different lymphoma subtypes (Figure 2e) and partly resemble signatures of GC B-cell populations in agreement with a cell-of-origin classification derived from single-cell expression phenotypes [24]. Overall, these results provide indications for the cyclic progression of B cells in the GC which extends the complexity of the GC reaction beyond the DZ–LZ dualism with possible impact on lymphoma subtype specifics. Of note, the gene expression signatures associated with the cell subpopulations of GC B-cells provided sc-COO classifiers for novel—putatively prognostic—subgroups of DLBCL with relation to LZ (light zone) and DZ (dark zone) GC functions [24,25].

3.3. Provisonal Genetic Entities: mnBLL-11q, IRF4-Break-LCL and HGBL-DH

Subtyping schemes do not only pursue the major goal of describing recognized and reproducibly diagnosable entities but also aim at the recognition of putative novel categories that require further studies: High-grade B-cell lymphomas with MYC and BCL2 or BCL6 (double/triple hit) rearrangements (HGBL-DH/TH), High-grade B-cell lymphomas, not otherwise specified (HGBL, NOS), Large B-cell lymphoma with IRF4 rearrangement (IRF4-LCL) and high grade B-cell (previously Burkitt-like) lymphoma with 11q aberrations (mn-BLL11q) were introduced, still awaiting their definite positioning in the molecular landscape of lymphomas [4,5,6,30,51,52,53]. The pairwise correlation heatmap of the expression portraits of these entities indicates the relatedness between them (Figure 3c). One observes a cluster of BL-resemblance which includes DHL-BCL2 and DHL-BCL6, mnBLL-11q, and partly, IRF4-breakL, as well as a DLBCL-like and an FL-like cluster with a certain overlap between them reflecting a transition range between DLBCL and transformed FL [54,55]. The expression of the subtypes across the GC states further refines the relatedness between them (Figure 3d). BL, intermediate L, mn-BLL11q and DHL form a DZ-like cluster, GCB-DLBCL and FL/DLBCL are more LZ-like while FL distributes mostly across INT and LZ states, IRF4-break LCL are bimodally distributed between DZ and LZ (and partly, PBLb) and ABC-DLBCL show PBL properties. Note also that the proliferative late LZb state resembles DZ-like expression except in ABC-DLBCL, which supports the view that the paths of re-entry into DZ and towards plasma blasts split in the LZb compartment.

Transcriptomic portraits of IRF4-break LCL and mnBLL-11q partly resemble the portraits of ABC-DLBCL and BL, respectively (Figure 4a). Difference portraits between IRF4-breakL and ABC- as well as GCB-DLBCL reveal increased levels of spot D (proliferation) and E (PIM1) and decreased levels of spot H (PRDM1, IRF4) and F (inflammation) compared with their IRF4 break negative counterparts in agreement with [51]. A model of “kinetic control” [56] suggests that IRF4 regulates immunoglobulin class switch recombination and plasma cell differentiation by mutually antagonistic cell fate decisions into B- and plasma-cell transcriptional programs. mnBL11q feature DLBCL signatures such as elevated expression of spots E (PIM1), F (inflammation) and K (B-cell activation, BCL6), reflecting intermediate characteristics between BL and DLBCL. Detailed inspection of gene expression along chromosome 11 revealed marked activation along the q-arm associating with chromosomal gains [57], however, with considerable variation between individual cases (Figure 4a). Hence, consideration of specific genetic lesions increases granularity of subtyping schemes towards the “intermediate” range between DLBCL and BL. Details of the molecular mechanisms leading to “BL-resemblance” remain, however, unclear in many details.

Consideration of transcriptome changes along the GC reaction provides a few insights into molecular mechanisms of mnBLL-11q. Strikingly, these MYC-negative lymphomas resemble BL in transcriptional features but lack recurrent mutations in the ID3-TCF3 axis [58,59] (see above). However, GNA13, a gene mutated in around 25% of BL and also in GCB-DLBCL ([60] and references cited therein) is mutated in 50% mnBLL-11q. GNA13 mutated GC B-cells seem to persist in the GC B differentiation state because of uncoupling between affinity selection and cell survival. Another gene recurrently mutated in mnBLL-11q, NFRKB, locates at Chr.11q24, a region of chromosomal deletions and downregulated expression compared with BL (Figure 4a) which presumably affects transcriptional regulation by chromatin remodeling as a cofactor in the INO80 complex in parallel and in analogy to SWI/SNF, a chromatin remodeler frequently deregulated in BL [58]. Both GNA13 and NFKRB genes locate in/near spots D and B activated in mnBLL-11q, however, at a lower level than in BL (compare main, individual, and difference portraits in Figure 4a). The difference portrait of mnBLL-11q compared with BL also indicates an increased expression of spot F (inflammation) reflecting a slight shift towards LZ resemblance, and thus, towards DLBCL and/or high-grade B-cell lymphomas [59]. Hence, mnBLL-11q seem to be locked in a DZp resembling transcriptional state in partial analogy to BL, which, however, is driven by different (epi-)genetic determinants and shows slightly enhanced inflammatory, LZ-resembling characteristics in partial analogy with highly proliferative DLBCL cases.

HGBL-DH/TH (high grade B-cell lymphoma with double/triple hits) harbor simultaneous translocations of MYC and BCL2 (DHL BCL2) or MYC and BCL6 (DHL BCL6), which associate with aggressive clinical course and inferior prognosis [6,61]. Comparison of the mean SOM expression portraits revealed that tumors classified as BL, mnBLL-11q and DHL BCL2 form a cluster of closest BL resemblance while DHL BCL6, although also similar, form a distinct side branch (Figure 3c). For a more detailed view, we compared the expression portraits of the different types of DH/TH and single hit lymphomas (SHL, Figure 4b). SHL MYC, DHL BCL2 and THL form a cluster of BL resemblance overexpressing, first of all, spot A, while the other entities distribute over the FL- and DLBCL-like clusters. DHL BCL2 contrast with DHL BCL6 in the overexpression of spot A and under-expression of spot F (inflammation) while both HGBL types show enhanced proliferative activity (spot D), however, distinctly lower compared with BL and mnBLL-11q (Figure 4a,b). In HGBL, the presence of a MYC translocation seems to dominate the resulting phenotype conferring BL resemblance. The marked singular overexpression of spot A specifically characterizes DHL BCL2 and THL but it is also observed in BL (see portraits in Figure 4b) and, to a lesser degree, in FL, thus constituting an “overlap” expression feature of BL and FL. Overexpression of spot A suggests that DHL BCL2 combine dark zone and especially DZd and partly DZexit features of the GC reaction with FL characteristics, while expression of DHL BCL6 is more similar to the proliferative DZp state (Figure 3d). Hence, these lymphoid tumor types take distinct intermediate positions in between the core subtypes caused by specific genetic lesions. The 5th edition of the WHO classification of haemato-lymphoid tumors 2022 acknowledges these similarity relations between the lymphoma entities and segregates DH BCL6 as a subtype of DLBCL, NOS or HGBL, NOS apart from the DH BCL2 (Table 1).

3.4. Gene Expression Classifiers of BL and DLBCL

The different lymphoma subtypes show distinct SOM expression portraits which are characterized by specific activation patterns of one or more spot-modules of co-expressed genes. In the next step of our analysis, we asked how the genes from these spots perform as classifiers for a certain subtype, how they compare with GEP classifier signatures selected from the literature, how the literature classifiers map into the SOM expression landscape, and how they fit into the functional context of this landscape, particularly with respect to the GC-reaction and tumor microenvironment. The expression portraits in Figure 5a illustrate such subtype-specific activation patterns (see [4] for details). Difference portraits visualize and identify differentially expressed spots distinguishing subtypes in pairwise comparisons (Figure 5a, second row of portraits). For example, BL and DLBCL differ in the expression of spots A, B, D (BL_up: upregulated in BL) and F (DLBCL_up), while ABC- and GCB-DLBCL show differential expression of spots H (ABC_up) and A (GCB_up). Overexpression of spot module A also distinguishes DHL BCL2 versus non-DHL (DHL_up, see heatmap in Figure 5b for an overview of spot expression in the different subtypes).

Numerous gene signatures derived from differential GEP analyses have been published to distinguish BL-vs-DLBCL [3,18,30], ABC-vs-GCB DLBCL [17,31] and DHL-vs-non-DHL DLBCL [5] (Table 2, see also [22] for a review). In part, these were derived from each other. In the following, we have analyzed how these signatures perform in the MMML-data set. By that, we take into account that some of the classifiers were derived from parts of the MMML dataset like the Hummel et al. dataset [3]. Moreover, for sake of simplicity we only take into account the direction of gene expression differences (up and down) and dismiss any weighting of genes as applied for some classifiers. The published gene sets not only differ in the number of genes but do also show different degrees of mutual gene overlap. While the largest BL_up and DLBCL_up sets (Hummel et al. [3]) include the smaller ones (Sha et al. [30] and Masque-Soler et al. [18]), the two latter BL_up sets do only partly overlap (Figure 5c). Such a lack of agreement does not surprise because of “methodical noise” owing to, e.g., different pre-processing and classification methods and test data, which introduces instability of the selecting marker genes (Table 2).

The BL_up signature of Sha et al. reveals a slight bias towards proliferative gene functions as indicated by specific enrichment of signature genes in spot D compared with the other BL_up signature sets showing consistent accumulation in spot B (Figure A7 and Figure A8). The ABC_up signatures reflect a systematic bias either towards plasma cell-related (spot H, Wright et al. [31], Scott et al. [17], Figure A9) or inflammation and reactive B-cell-related transcriptional programs (Masque-Soler et al. [18]). Genes accumulating in spot A are discriminative for BL-vs-DLBCL (BL_up), ABC-vs-GCB (GCB_up) and DHL-vs-DLBCL (DHL_up) as well, which makes them ambiguous and requires careful pre-selection in terms of inclusion criteria of tumors (Figure A11). This ambiguity reflects overlapping functional and cellular programs of different lymphoma subtypes partly related to different stages of the GC reaction (Figure 5b).

We further compared the literature signatures with the spot modules containing considerably more genes (between 129 and 1154 in spot E and D, respectively, Figure 5b) than the classifiers under study (maximum 47 genes in BL_up, Table 2) and showed different degrees of overlap (Figure 5d). For BL-vs-DLBCL classification, BL_up literature classifiers and spot B perform nearly equally providing virtually equal area-under-the-curve (AUC)-values above 98% (Figure 5e). Similar results were obtained for DLBCL_up classifiers and spot F (AUC >96%), for ABC_up sets and spot H (AUC >92%) and DHL_up and spot A (AUC >82%). For GCB selection, spot A performs slightly inferior to the classifiers taken from Scott et al. and Wright et al., but similarly to that of Masque-Soler et al. (Figure 5e and Figure A10). Taken together, sets of classifier genes taken from the literature largely compare to spot-modules of co-expressed genes extracted from the multidimensional expression landscape. We show below that marker genes can be taken out from these modules in different ways without marked loss of classification power.

Table 2. Classifier signatures used for evaluation. An interactive classifier tool is implemented in the opoSOM browser (Appendix B and [62] for a short description).

Classifier (a),(f)	Reference	Classifier Sets (b)	Sample Size and Platform (c)	Comment (d)	Spot (e)
BL-vs-DLBCL (& intermediate) (f)	Hummel et al. [3]	BL_up: 47 DLBCL_up: 18	BL, DLBCL (N = 221) Array (HG U133A)	Linear model (shrunken centroids [63])	BL_UP: B DLBCL_UP: F
BL-vs-DLBCL	Sha et al. [30]	BL_up: 16 DLBCL_up: 11	BL, DLBCL (N = 1177) Array (HG U133A, lymphochip)	Support vector machine (SVM)
BL-vs-DLBCL (& intermediate)	Masque-Soler at al. [18]	BL_up: 6 DLBCL_up: 4	BL, DLBCL (N = 90) Multiplex count (nCounter)	Linear classification
ABC-vs-GCB (& unclassified)	Masque-Soler at al. [18]	ABC_up: 9 GCB_up: 11	DLBCL (N = 90) Multiplex count (nCounter)	Linear classification	ABC_UP: H GCB_UP: A
ABC-vs-GCB (& unclassified)	Scott et al. [17]	ABC_up: 7 GCB_up: 6	DLBCL (N = 119) Array (Nanostring)	Weighted average
ABC-vs-GCB (& unclassified)	Wright et al. [31]	ABC_up: 13 GCB_up: 7	DLBCL (N = 274) Array (HG U133A, Lymphochip)	Linear classification g)
DHL-vs-non DHL (DHL-BCL2)	Ennishi et al. [5]	DHL_up: 31 Non-DHL_up: 47	GCB DLBCL (N = 157) RNA sequencing	Weighted average	DHL_UP: A n-DHL_UP: F

^(a): Classifier derived for two-group-comparisons, “grey zone” intermediate/unclassifiable groups are indicated, if considered. ^(b): Number of upregulated transcripts (Ensemble-IDs) in the classifier sets for each of the groups. For the sake of simplicity and comparison, we considered all signatures as unidirectionally upregulated (between the groups) classifier sets of genes. For distinguishing, e.g., BL-vs-DLBCL we split the list of signature genes into genes up- or downregulated in BL and subsumed them as “BL_up” and “DLBCL_up” sets, respectively. ^(c): Number of cases (tumor samples) and experimental platform used to derive the classifiers. The number of transcripts/genes measured varies from about 800 (nCounter multiplexing and NanoString arrays) to 15,000–20,000 (microarrays and sequencing). ^(d): Method used in the original publication. ^(e): Spot module accumulating the classifier genes. This classification used part of the MMML-data as test data, namely BL: 44 out of 74 cases; DLBCL: 176 out of 430 cases. However, raw chip data were processed completely differently here, by applying hook-calibration and standard SOM-pre- and post-processing which differ from the methods used in [3]. ^(f): The method uses a weighted sum of (log-)expression values as classifier. No difference between weighted and non-weighted sum was found in our data (not shown).

Table 2. Classifier signatures used for evaluation. An interactive classifier tool is implemented in the opoSOM browser (Appendix B and [62] for a short description).

Classifier (a),(f)	Reference	Classifier Sets (b)	Sample Size and Platform (c)	Comment (d)	Spot (e)
BL-vs-DLBCL (& intermediate) (f)	Hummel et al. [3]	BL_up: 47 DLBCL_up: 18	BL, DLBCL (N = 221) Array (HG U133A)	Linear model (shrunken centroids [63])	BL_UP: B DLBCL_UP: F
BL-vs-DLBCL	Sha et al. [30]	BL_up: 16 DLBCL_up: 11	BL, DLBCL (N = 1177) Array (HG U133A, lymphochip)	Support vector machine (SVM)
BL-vs-DLBCL (& intermediate)	Masque-Soler at al. [18]	BL_up: 6 DLBCL_up: 4	BL, DLBCL (N = 90) Multiplex count (nCounter)	Linear classification
ABC-vs-GCB (& unclassified)	Masque-Soler at al. [18]	ABC_up: 9 GCB_up: 11	DLBCL (N = 90) Multiplex count (nCounter)	Linear classification	ABC_UP: H GCB_UP: A
ABC-vs-GCB (& unclassified)	Scott et al. [17]	ABC_up: 7 GCB_up: 6	DLBCL (N = 119) Array (Nanostring)	Weighted average
ABC-vs-GCB (& unclassified)	Wright et al. [31]	ABC_up: 13 GCB_up: 7	DLBCL (N = 274) Array (HG U133A, Lymphochip)	Linear classification g)
DHL-vs-non DHL (DHL-BCL2)	Ennishi et al. [5]	DHL_up: 31 Non-DHL_up: 47	GCB DLBCL (N = 157) RNA sequencing	Weighted average	DHL_UP: A n-DHL_UP: F

^(a): Classifier derived for two-group-comparisons, “grey zone” intermediate/unclassifiable groups are indicated, if considered. ^(b): Number of upregulated transcripts (Ensemble-IDs) in the classifier sets for each of the groups. For the sake of simplicity and comparison, we considered all signatures as unidirectionally upregulated (between the groups) classifier sets of genes. For distinguishing, e.g., BL-vs-DLBCL we split the list of signature genes into genes up- or downregulated in BL and subsumed them as “BL_up” and “DLBCL_up” sets, respectively. ^(c): Number of cases (tumor samples) and experimental platform used to derive the classifiers. The number of transcripts/genes measured varies from about 800 (nCounter multiplexing and NanoString arrays) to 15,000–20,000 (microarrays and sequencing). ^(d): Method used in the original publication. ^(e): Spot module accumulating the classifier genes. This classification used part of the MMML-data as test data, namely BL: 44 out of 74 cases; DLBCL: 176 out of 430 cases. However, raw chip data were processed completely differently here, by applying hook-calibration and standard SOM-pre- and post-processing which differ from the methods used in [3]. ^(f): The method uses a weighted sum of (log-)expression values as classifier. No difference between weighted and non-weighted sum was found in our data (not shown).

3.5. Distinguishing BL

For a closer look, we applied three BL_up classifier signatures [3,18,30] to the MMML-data set. Their gene set Z (GSZ)-profiles strongly and specifically upregulate in BL (red bars) and downregulate in DLBCL (light and dark blue and cyan bars in Figure 6a for the Hummel et al. and Figure A8 for the other signatures) as expected. The signature genes accumulate in and near spots A, B and D (Figure 6b). The degree of accumulation in B is consistently high for all three BL_up signatures studied, while proliferative spot D also accumulating MYC targets, is slightly overrepresented for Sha et al. (Figure 6c and Figure A8).

ROC curves (Figure 6f) and AUC values (Figure 6g) are very similar for the different literature and the spot-module signatures as well (Figure 6d,e). The proliferative module D-signature performs slightly inferior because it overexpresses also in proliferative DLBCL cases. Reduction of the numbers of genes by random selection from the module sets does virtually not decrease the AUC values (Figure 6g, part below). This result implies that the number of similarly performing co-expressed genes is in the order of several dozen at minimum. In other words, there is a reservoir of suited classifier genes exceeding the number of genes included in the literature signatures.

3.6. Distinguishing DLBCL: ABC, GCB and DHL

Next, we analyzed DLBCL_up (alias BL_down) signatures as well as classifier signatures for ABC-versus-GCB-DLBCL [16,64] and DHL-versus-nonDHL-DLBCL [5]. Overlap and distribution of tumors and of signature genes show a diverse pattern (Figure 7a,b, respectively), where BL cases specifically associate with BL_up (spot B) and ABC-DLBCL with ABC_up (spot H), while other features are more common, such as spot D upregulated in BL and in part of DLBCL due to overlapping proliferative characteristics (see above). Spot A ambiguously upregulates in BL, GCB-DLBCL and DHL, because of commonly activated transcriptional programs.

Overall, the expression values of the literature signatures under study form two major clusters Figure 7c), one being governed by dark zone functionalities (BL_up, GCB_up, DHL_up), while the other one showing partly light zone characteristics (DLBCL_up, ABC_up). The different literature signatures and the module sets show overall comparable results when applied to classify lymphomas (Figure 7d,e). Small performance differences between GCB_up and the spot A signatures can be attributed to the ambiguous functional background of this signature overlapping with that of BL_up and DHL_up and also to the fact that genes of other spot modules such as K (B-cell differentiation) activate in GCB tumors (see the map of classifier genes of Scott et al. in Figure A9). The DZ-like characteristics of the GCB_up sets should be understood as a differential feature in comparison with ABC_up (Figure 7c).

In summary, classifiers distinguishing GCB and ABC from the literature mostly perform on comparable levels in comparison with the simple “spot” sets. The signatures analyzed split roughly into two groups mainly reflecting DZ- or LZ-characteristics, because the relevant differential expression (BL-vs-DLBCL, GCB-vs-ABC) spans the DZ-LZ axis. GCB_up and DHL_up signatures partly refer to similar cell functions rendering them per se relatively unspecific requiring proper pre-selection of the cases using additional criteria.

3.7. Pattern Types, Microenvironmental and GC-Related Categories

Transcriptomic portraits of lymphoma subtypes show “fingerprint”-like spot patterns which typically include more than one spot module (Figure 8a). We recently proposed a hierarchy of lymphoma strata of increasing granularity making use of topological features of the gene expression landscape in terms of so-called pattern types (PAT). PATs are defined as combinations of activated expressed spot-modules [4] (Figure 8a,b, left part). The majority of BL and FL assign virtually to one PAT-group, each in an almost one-to-one relation. In contrast, DLBCL and FL/DLBCL reveal a strong mixing among five to six PAT-groups, thus reflecting the high molecular diversity of these lymphoma types. There is also pronounced enrichment of ABC-, GCB- and FL/DLBCL tumors in the direction from proliferative DZ-related towards inflammatory/stromal LZ-like characteristics. The PAT-groups and the underlying order of spot activation thus follow roughly the course of the GC-reaction discussed above. The single-cell states suggested by Holmes et al. and the derived prognostic COO-groups I–V [24] order similarly (Figure 8a, right part). Another recent study classified DLBCL into four lymphoma microenvironment (LME) categories of proliferative (depleted of bystander cells), mesenchymal (enrichment of stromal cells and extracellular pathways), inflammatory (inflammatory bystander cells) and GC-like (enriched in GC-cells) characteristics [20], which notably agree with the functional background of the major spot modules (Figure 8c) as well as in the immune cell composition of the PATs [4]. For example, spot D in our landscape associates with DZ characteristics (group I, see [24]) and proliferative lymphoma types depleted from bystander cells [20], while spot F is representative for LZ-like (group III) and inflammatory LME lymphomas (Figure 8c). Interestingly, the four LME-types reveal correspondence with “hallmark” types of lymphomas related to “proliferative”, inflammatory, balanced inflammatory (stromal) and weakly cancerogenic (B-cell like) characteristics extracted from the PAT groups [4] (Figure 8a). Hence, diversification of subtypes using combinations of expression modules, single cell-derived COO or LME categories provides new insights about functional aspects of the tumors with impact for the selectivity of classification schemes.

4. Discussion

4.1. Footprints of the GC Reaction in the Lymphoma Transcriptome

SOM portrayal provided a holistic view on the transcriptome landscape of lymphomas and its multidimensional nature. It appears as a network of cellular programs, each governed by the transcription of a few dozen to a few hundred co-regulated genes of different functional context, related to, e.g., aberrant GC-biology, B-cell maturation and/or tumor microenvironment. Recent single cell expression signatures refined details of the GC reaction and enabled one to identify cell-of-origin footprints in the lymphoma transcriptome with impact for its subtyping, particularly as rationale for its continuous and multidimensional nature. The SOM landscape of lymphomas reflects the time-course of the GC reaction as series of transcriptional states from DZ via intermediate towards LZ and PBL. As for lymphomas, also the transcriptional states along the GC reaction form a continuum on cellular resolution rather than a set of well-separated single cell states [46]. Notably, SOM-landscapes in general are able to recapitulate time-dependent trajectories along such a continuum in gene state space [66] as was shown for a wide spectrum of time-scales ranging from cell and tissue development [67] to evolutionary processes [68,69] using transcriptomic and genetic features.

We looked at different lymphoma classes in the light of this complex transcriptional landscape and considered novel single-cell transcriptomics data which imply parallels between transcriptional programs in the GC and different lymphoma types. Knowledge emerging from transcriptomic studies increasingly identify genomic mechanisms driving lymphomagenesis towards different tumor phenotypes. In addition to the basic types BL, DLBCL and FL, we characterized provisional intermediate entities with specific genetic lesions affecting driver genes of lymphoma genesis, namely, double hits of MYC and BCL2 combining genetic features characteristic for BL and FL, of rearranged IRF4, a key player of plasma cell activation, as well as Chr. 11, giving rise to a BL-resembling transcriptome: however, via an alternative pathway. All these classes occupy intermediate positions in the transcriptomic landscape, thus partly providing a genetic reasoning of their phenotypes. Our transcriptomic characterization supports the new, 2022 adaptation of WHO nomenclature of lymphoid tumors regarding these classes where MYC/BCL6 rearrangements are not further considered as a hit-classifier and the other two types are now defined as IRF4-R LBCL and HGBL-11q, respectively (Table 1).

4.2. Navigating a Multidimensional Transcriptional Landscape

The continuous character of the transcriptome landscape not only complicates subtyping but also definition of gene expression classifiers. We find that existing classifier signatures developed for two-class comparisons in different studies are often redundant, i.e., they perform similarly regarding sensitivity and specificity even if the respective sets of marker genes only weakly overlap. Our analysis revealed two major causes of “redundancy”: firstly, “statistical-selection uncertainties”, due to the reservoir of correlated genes in the relevant modules exceeding the number of marker genes usually selected; and secondly, “biological uncertainties”, due to continuously distributed transcriptional states resulting in overlapping subtypes in a complex molecular landscape. We demonstrated that classifier signatures extracted from modules of this landscape performed as well as classifier signatures derived from pairwise comparisons of pre-selected subtypes. This result is not surprising, because both concepts use the same data, however, in a different order, namely, by selecting classes of interest in the data before or after bioinformatics diversity analysis, respectively.

Overall, it was not our aim to design another, “better”, classifier but simply to compare modules of co-regulated genes obtained by means of multidimensional clustering with the classifiers specifically designed for two-group classification tasks taken from the literature. We envisage an advantage of the holistic approach due to the fact that it better resolves the whole network of relevant cellular programs, and thus it enables the joint control of the “statistical” and “biological” redundancy problems. For example, the plasma cell characteristics in our landscape are consolidated by the consideration of plasma cell myeloma cases strongly expressing the plasma cell signature, which, in turn, appeared to be an essential characteristic of ABC-DLBCL (spot module H). In addition, inclusion of FL helped to better resolve FL/DLBCL and DHL, partly expressing joint characteristics of GCB-DLBCL, BL and FL (spot module A). Finally, sorted reactive B-cell specimens and tonsils extended the data space towards the GC B-cell and lymph node signatures and, particularly, enabled one to identify “outlier” samples strongly contaminated with reactive cells in a previous study [19]. Overall, the holistic approach allows to assemble the lymphoma transcriptome in terms of modular building blocks of concertedly expressed genes with underlying functional features. This view helps to stratify lymphomas in terms of combinatorial PATs and to interpret smaller subclasses in the context of larger ones.

4.3. Consenting Classifiers as a Trade-Off Balancing Accuracy and Stability

Molecular subtyping requires a set of bioinformatics tasks such as standardized preprocessing, cluster analysis (finding and defining classes), supervised classification (assigning cases to the classes) and classifier building (selecting features discriminating the classes) as well as molecular biological and clinical characterization of the classes [70]. For each of these tasks, numerous methods are available. Each of them associates with a certain level of noise, giving rise to uncertainties in accomplishing classification tasks regarding the diagnostic markers selected and the associated functions and predicted outcomes. Classifier selection typically tries to find minimum-sized signatures providing a maximum predictive accuracy (for example, the nearest shrunken centroid method [63] applied in [3] for BL versus non-BL discrimination (Table 2). Larger-sized signature sets are often considered as “overfitted”, a view, which underestimates the stability (i.e., consistency) of selected genes and thus their biological interpretability. Hence, the intention of shrinking the size of classifiers is opposed by the need for its increase for better interpretability. A more stable feature selection could be preferential over a less stable but slightly more accurate one. In a general sense, this problem can be understood as a trade-off between predictive accuracy and selection stability (Figure 9, see also [71]) where the accuracy relates to the statistical relevance of the selected features while stability is linked to their biological interpretability and consent application. It can be considered as a Pareto bi-optimal trajectory in accuracy versus stability coordinates from which a particular solution can be selected as a compromise between both aspects according to researchers’ needs [71]. Our results show that stability gains can be reached at small cost of classification accuracy by means of our spot-module selection. It provided larger-sized signatures than the minimum-sized classifiers taken from literature at virtually similar AUC values but higher stability regarding gene overlap and interpretability.

4.4. New Concepts: PATs, Machine Learning of Molecular Portraits and Multiomics Classifiers

In a practical sense, the Pareto concept discussed in the previous subsection intends increased, more stable marker signatures instead of the minimalistic sets often used. One option to achieve this would be using genes included in the spot modules extracted from multidimensional analyses such as SOM or non-negative matrix factorization methods [72] as classifier signatures. However, selection of single modules as marker sets can be insufficient due to the ambiguous nature of most of the spot modules; for example, spot A is up in BL, FL, DHL and GCB while spot D is up in BL and DLBCL as well. The concept of patterns types (PAT) applied previously [4] refines classification by using combinatorial patterns of more than one activated spot-modules: e.g., the ambiguity of spot A partly resolves because in parallel either spot D or G can be found activated assigning the tumors alternatively as BL (or DH) or FL, respectively (Figure 1e). However, in practice, PATs do not completely resolve the uncertainty of subtype classification mainly because of the continuous character of the transcriptomic landscape (Figure 8a). The next conceptual step to increase granularity of classifier selection consists of considering the whole transcriptome portraits of the tumors with metagene resolution. For a proof of principle, we applied fully connected neuronal network machine learning for molecular portrait recognition in analogy with face recognition in security identification applications (Appendix A.4, Figure A2). The method is trained by the class labels of the lymphoma portraits and returns class prediction of newly added lymphoma portraits. We find that the machine indeed learns relevant features beyond the spots and, overall, can be seen as an option to exploit maximum information content of the transcriptome landscape. Its limitation, on the other hand, is the averaging inherent in the metagene patterns as well as the lack of clear-cut borderlines between the transcriptomes of the subtypes, which possibly limits its resolution by principle reasons.

One way to overcome this problem can be seen in using multi-omics molecular information and particularly, genetic data as another way to improve classification. For example, subtyping of gliomas has been based mostly on a series of genetic markers [73]. For lymphomas, the genetic patterns look overall more complicated. However, well-defined genetic lesions such as double hits, IRF4-rearrangements and aberrant Chr. 11q are now considered as genetic markers for specific lymphoma sub-entities [32]. Transcriptomics enabled us to interpret their functional phenotypes in the transcriptomic landscape of lymphomas. On a wider scale, genetic data of lymphomas become increasingly available and provided novel insights about genetic drivers [11,12,74], particularly of BL [75], FL [54] and DLBCL [11,12,74] (see also [14,76] for a review). Seven genetic DLBCL subtypes with distinct outcomes were extracted using whole-exome sequencing (WES) [13] (Figure 8a, right part). They revealed a similar shift of molecular function as the transcriptomic classes, and particularly, our PAT groups. A recent whole-genome sequencing (WGS) study [65] distinguished nine subtypes partly resembling the WES-based ones (Figure 8a). It was found that B-cell lymphomas can be traced back to mutated cells that emerge from the GC and/or have passed the GC reaction. This result rationalizes the parallels between the cellular states of the GC reaction and lymphoma transcriptomes discussed. We expect that genetic classification schemes will extend and refine the molecular subtyping of lymphomas. Integrative views and bioinformatics methods linking them with the gene expression landscape as well as with epigenetic, proteomic and metabolomic data are, however, required for a holistic understanding of factors governing the heterogeneity of lymphomas.

5. Conclusions

The whole-transcriptome SOM landscape of GC-derived lymphomas in combination with portrayal of the individual tumors offers novel perspectives for consenting existing redundant classifier signatures, for their better functional understanding in the light of the GC reaction and for increasing granularity of classification towards personalized diagnostics. Consent between different gene expression signatures as well as their extension towards existing novel classification schemes such as PAT grouping, hallmark types, LME groups and single cell-derived COO classes, are needed to combine their different functional perspectives into a common view on the molecular phenotypes of lymphoma. The spot-modules of co-regulated signature genes represent “mountain peaks” used as classification landmarks in an otherwise continuous transcriptomic landscape of GC-derived lymphomas, which leaves classification uncertain to some residual degree. Still existing inconsistencies between genetic, transcriptomic and microenvironmental classifiers need further research with larger tumor cohorts to better resolve rare classes. Improved experimental techniques such as single-cell omics measurements on the data side and probabilistic classification schemes and integrative multi-omics bioinformatics in combination with machine learning on the analytics side are the next milestones along the avenue towards further improved, classification of lymphomas.