Predictive and Prognostic Molecular Factors in Diffuse Large B-Cell Lymphomas

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Review Predictive and Prognostic Molecular Factors in Diffuse Large B-Cell Lymphomas

Stefano A. Pileri 1,* , Claudio Tripodo 2,3, Federica Melle 1 , Giovanna Motta 1, Valentina Tabanelli 1, Stefano Fiori 1, Maria Carmela Vegliante 4, Saveria Mazzara 1, Sabino Ciavarella 4 and Enrico Derenzini 5,6

1 Division of Haematopathology, European Institute of Oncology, IEO IRCCS, Via Ripamonti 435, 20141 Milan, Italy; [email protected] (F.M.); [email protected] (G.M.); [email protected] (V.T.); stefano.ﬁ[email protected] (S.F.); [email protected] (S.M.) 2 Tumor Immunology Unit, University of Palermo, 90133 Palermo, Italy; [email protected] 3 Tumor and Microenvironment Histopathology Unit, IFOM, the FIRC Institute of Molecular Oncology, 20139 Milan, Italy 4 Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, Viale Flacco 65, 70124 Bari, Italy; [email protected] (M.C.V.); [email protected] (S.C.) 5 Division of Haemato-Oncology, European Institute of Oncology, IEO IRCCS, Via Ripamonti 435, 20141 Milan, Italy; [email protected] 6 Department of Health Sciences, University of Milan, Via di Rudinì 8, 20146 Milan, Italy * Correspondence: [email protected] or [email protected]

Abstract: Diffuse large B-cell lymphoma (DLBCL) is the commonest form of lymphoid malignancy, with a prevalence of about 40% worldwide. Its classiﬁcation encompasses a common form, also

termed as “not otherwise specified” (NOS), and a series of variants, which are rare and at least in part related to viral agents. Over the last two decades, DLBCL-NOS, which accounts for more than 80% of the neoplasms included in the DLBCL chapter, has been the object of an increasing number Citation: Pileri, S.A.; Tripodo, C.; Melle, F.; Motta, G.; Tabanelli, V.; of molecular studies which have led to the identification of prognostic/predictive factors that are Fiori, S.; Vegliante, M.C.; Mazzara, S.; increasingly entering daily practice. In this review, the main achievements obtained by gene expres- Ciavarella, S.; Derenzini, E. Predictive sion profiling (with respect to both neoplastic cells and the microenvironment) and next-generation and Prognostic Molecular Factors in sequencing will be discussed and compared. Only the amalgamation of molecular attributes will lead Diffuse Large B-Cell Lymphomas. to the achievement of the long-term goal of using tailored therapies and possibly chemotherapy-free Cells 2021, 10, 675. https://doi.org/ protocols capable of curing most (if not all) patients with minimal or no toxic effects. 10.3390/cells10030675 Keywords: diffuse large B-cell lymphoma; gene expression profiling; next-generation sequencing; Academic Editor: Caterina Marchiò classification; diagnosis; prognosis; therapy

Received: 12 January 2021 Accepted: 12 March 2021 Published: 18 March 2021 1. Classiﬁcation

Publisher’s Note: MDPI stays neutral Diffuse large B-cell lymphoma (DLBCL) is the most common form of lymphoid with regard to jurisdictional claims in malignancy, with a prevalence of about 40% worldwide [1]. It consists of medium or large published maps and institutional affil- B-lymphoid cells in which the nuclei are the same size as or larger than those of normal iations. macrophages, or more than twice the size of those of normal lymphocytes, with a diffuse growth pattern [1]. The concept of DLBCL has undergone fine-tuning over time, as is clear from the comparisons between the REAL and WHO classifications (third, fourth, and revised fourth editions) [1–4]. This produces some apparent terminological discrepancies

Copyright: © 2021 by the authors. throughout the text, which reflect the time of publication of each reference. Licensee MDPI, Basel, Switzerland. In the Revised Fourth Edition of the WHO Classification of Tumours of Haematopoi- This article is an open access article etic and Lymphoid Tissues, DLBCL is subdivided into morphologic variants, molecular sub- distributed under the terms and types, and distinct disease entities (Table1)[ 1]. Nevertheless, about 70% of all DLBCLs lack conditions of the Creative Commons features allowing their inclusion into one of the diagnostic categories listed in Table1[1] . Attribution (CC BY) license (https:// These cases are collectively termed as “not otherwise specified” (DLBCL-NOS) [1] and are creativecommons.org/licenses/by/ conventionally treated with the chemoimmunotherapy regimen R-CHOP [5,6]. 4.0/).

Cells 2021, 10, 675. https://doi.org/10.3390/cells10030675 https://www.mdpi.com/journal/cells Cells 2021, 10, 675 2 of 15

Table 1. Diffuse large B-cell lymphoma, high-grade B-cell lymphoma, and gray-zone lymphoma according to the Revised Fourth Edition of the WHO Classiﬁcation of Tumours of Haematopoietic and Lymphoid Tissues (italics indicate that an entity is provisional).

Diffuse Large B-Cell Lymphoma (DLBCL): DLBCL not otherwise specified (NOS) Morphological variants Centroblastic Immunoblastic Anaplastic Other rare variants Molecular subtypes Germinal centre B-cell subtype (GCB) Activated B-cell subtype (ABC) Other lymphomas of Large B-Cells: T-cell/histiocyte-rich large B-cell lymphoma Primary DLBCL of the CNS Primary cutaneous DLBCL, leg type EBV-positive DLBCL, NOS EBV-positive mucocutaneous ulcer DLBCL associated with chronic inflammation Lymphomatoid granulomatosis Large B-cell lymphoma with IRF4 rearrangement Primary mediastinal (thymic) large B-cell lymphoma Intravascular large B-cell lymphoma ALK-positive large B-cell lymphoma Plasmablastic lymphoma HHV8-positive DLBCL Primary effusion lymphoma High-Grade B-Cell Lymphoma: High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangement High-grade B-cell lymphoma, not otherwise specified (NOS) B-cell lymphoma, unclassifiable, with features intermediate between DLBCL and classic Hodgkin’s lymphoma

Based on a recent survey of 3550 DLBCL patients who mostly underwent R-CHOP with curative intent, the 5-year overall survival and cumulative incidence of relapsed/refractory disease corresponds to 65.3% and 23.1% of cases, respectively [6]. Thus, there is still an unmet need for optimal therapy for a significant proportion of DLBCL-NOS patients. In recent years, DLBCL-NOS has been the object of the extensive application of high- throughput technologies, which has led to the identification of prognostic/predictive factors that are increasingly entering daily practice. Although DLBCL-NOS is the main focus of this review, the borders between DLBCL- NOS and high-grade B-cell lymphoma (HGBCL) (Table1) will also be discussed. In fact, it is not uncommon to encounter cases that could be regarded as DLBCL-NOS but are ultimately classified as HGBCL due to the detection of double or triple hits (D/TH) of MYC, BCL2, and/or BCL6 (HGBCL-D/TH) by FISH, as underlined by Sehn and Salles Cells 2021, 10, 675 3 of 15

in their review on DLBCL published in the New England Journal of Medicine on 4 March 2021 [7] (see below).

2. Gene Expression Profiling 2.1. Cell of Origin (COO) At the beginning of this century, using gene expression profiling (GEP) Alizadeh and coworkers first reported that DLBCLs could be divided into two main subtypes with a gene signature related to the germinal center B-cell (GCB) and activated B-lymphocytes from the peripheral blood (ABC), respectively [8]. Such a distinction, not feasible on morphological grounds, had an important prognostic impact. In fact, the GCB forms had a significantly more favorable response to chemotherapy (CHOP) than those of ABC. This corresponded to a clear-cut difference in terms of overall and progression-free survival (OS and PFS, respectively). This subdivision was subsequently confirmed using cohorts consisting of hundreds of cases, and maintained its value in the era of chemoimmunotherapy [9–11]. By expanding the number of profiled cases, a third group between those of GCB and ABC emerged and was indicated as unclassified (U), corresponding to about 15% of DLBCLs [9–11]. Besides prognostic value, the distinction between GCB and ABC subtypes has biological relevance as it corresponds to different genetic aberrations as well as pathway perturbations (as detailed in the following). The main limitation of conventional GEP was the need for fresh or frozen (FF) samples, which were available for a small minority of patients followed up at reference centers. Therefore, many attempts were made to find surrogates for GEP through the search for immunohistochemical markers [12–18]. Several algorithms were proposed, with that of Hans et al. having the widest applications as it was based on the simple determination of CD10, BCL6, and IRF4/MUM1 [12]. However, none of these algorithms met their goal, for several reasons: (a) a lack of correspondence with GEP data in the same patients; (b) variability in the preanalytical and immunohistochemical techniques (including antibody and antigen retrieval, detection systems, and automatic platforms); and (c) subjectivity in result interpretation [19,20]. In 2014, a new approach was proposed based on targeted digital GEPFF and was successfully applied to mRNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissue samples (Lymph2Cx) [21]. In particular, a 20-gene panel (including 15 top genes and 5 housekeeping genes for normalization) was designed, which in 67 cases provided the same COO classification as conventional GEP from FF. Furthermore, the OS and PFS curves were over-imposable, irrespective of the type of GEP used (targeted digital vs. conventional). These preliminary results, which had been obtained by using the NanoString platform, were subsequently confirmed by independent studies based on several hundred cases [22–25]. The advantages of this approach over immunohistochemical algorithms are: (1) reproducibility in different laboratories; (2) the assessment of the absolute value of mRNA expressed by each gene; and (3) a lack of confounding factors (such as the variability of immunohistochemical techniques and subjective result interpretation). Moreover, targeted GEP subdivides DLBCLs-NOS into GCB, ABC, and U, like conventional profiling of FF samples. In contrast, immunohistochemical algorithms differentiate DLBCLs-NOS into GCB and non-GCB, with the latter group containing cases that are molecularly classified as GCB [21–25]. Interestingly, identical results were obtained by targeted profiling on different platforms and with different panels of genes confirming the prognostic relevance of the COO determination [21–25]. The COO determination provided less significant prognostic information when applied to cases enrolled in some trials [26–28]. This may be for several different reasons, i.e., (1) the adoption of protocols that are more intense than those used in real situations; (2) the selection of patients fit enough to await the completion of all the tests required for trial enrolment; and (3) the influence of other factors that can affect behavior within each subgroup defined by the COO. Cells 2021, 10, 675 4 of 15

The main limitation of targeted GEP applied to routine biopsies is the need for platforms which are not available in all pathology laboratories, unlike immunohistochemistry. This problem, as well as the test costs and need for basic bioinformatic skills, can be overcome by a hub-and-spoke organization, which is also required for the application of the array of molecular techniques at the basis of precision medicine (see below).

2.2. Key Genes FISH analyses have shown that B-cell lymphomas, regarded as DLBCLs-NOS based on morphology and phenotype, could carry double or triple rearrangements of MYC, BCL2, and/or BCL6 [1]. These cases, which overall have a significantly worse prognosis and may require therapies that are more intense than standard R-CHOP, are nowadays included in the provisional category of high-grade B-cell lymphomas with double/triple hits (HGBL D/TH). Based on this observation, FISH should ideally be applied to all DLBCL-NOS cases. As FISH analyses are rather expensive, attempts have been made to find surrogates for FISH results through immunohistochemistry. This has led to the identification of a group of DLBCL-NOS cases which show double expression of MYC and BCL2 at the protein level (the so-called double expressors (DEs)) [29,30]. According to the Revised Fourth Edition of the WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, at least 50% and 40% of neoplastic cells should express BCL2 and MYC, respectively, in order to consider a DLBCL-NOS patient as a DE [1]. However, discrepancies exist as to the reproducibility of the cut-off value of MYC positivity, which has been moved to 70% by some groups [31]. Most importantly, as there is no actual correspondence between the results of immunohistochemistry and FISH [32], these cases with MYC and BCL2 double- expression but lacking D/TH remain within the bounds of DLBCL-NOS but more often belong to the ABC/non-GCB subtype and require further studies to definitively assess their prognostic and/or therapeutic relevance [1,32]. In 2018, investigators from two groups used GEP signatures to identify high-risk patients with DLBCL in FFPE series. Sha et al. [33] used a Burkitt lymphoma-like signature, whereas Ennishi et al. [34] used a signature derived from genes differentially expressed between MYC/BCL2 DH and non-DH GCB-DLCBLs. With their respective signatures, these investigators were, as expected, able to identify most DH lymphomas, as well as many non-DH lymphomas which were actually found in about half of the identified patients and showed a poorer response to standard chemoimmunotherapy. These findings suggest that many of the patients harbored genetic or even epigenetic alterations that produced similar gene expression changes in the tumor cells, as recognized by the respective signatures. This does not come as a surprise. In fact, activation of an oncogene or oncogenic pathway can be produced by multiple mechanisms besides MYC/BCL2 DH, for example MYC upregulation through translocation and gene amplification. Apart from structural alterations, MYC expression or activity can be enhanced through transcriptional and posttranscriptional events. Derenzini et al. [25] used a targeted GEP panel combining the Lymph2Cx signature for COO classification, with additional targets including MYC, BCL-2, and NFKBIA (the latter encoding for the IkB-α protein, an endogenous inhibitor of NF-kB signaling [35]), in 186 FFPE cases originally diagnosed as DLBCL-NOS from two randomized trials (discovery cohorts NCT00355199 and NCT00499018) and in three independent validation cohorts. By integrating the COO, MYC/BCL-2 DE status, and NFKBIA expression, a three-gene signature was designed combining MYC, BCL-2, and NFKBIA (MBN signature). The high-risk (MBN Sig-high) subgroup, characterized by higher expression levels of MYC and BCL-2 and a lower expression of NFKBIA, could be used to identify a significant fraction of ABC DLBCLs and the vast majority of double-hit cases, allowing for further risk stratification within the GCB/U subset. These results were validated in three independent series including Sha’s cohort based on the REMoDL-B trial [33,36], a phase III randomized trial investigating the efficacy of the addition of bortezomib to standard first-line chemoimmunotherapy. In line with the biological activity of bortezomib, which increases the protein abundance of IkB-α, leading to inhibition of NF-kB signaling [37], an exploratory ad hoc analysis of the latter cohort showed that the addition of bortezomib in the MBN Sig-high subgroup provided Cells 2021, 10, 675 5 of 15

a progression-free survival advantage compared with standard chemoimmunotherapy. These data suggest that a simple three-gene signature based on MYC, BCL-2, and NFKBIA can refine the prognostic stratification in DLBCL. Finally, Mottok et al. [38] developed a robust and accurate molecular classification assay (Lymph3Cx) for the distinction of primary mediatinal B-cell lymphoma (PMBCL) from DLBCL subtypes based on gene expression measurements in formalin-fixed, paraffin- embedded tissue. A probabilistic model accounting for classification error comprising 58 gene features was trained on 68 cases of PMBCL and DLBCL. Model performance was subsequently evaluated in an independent validation cohort of 158 cases and showed a high agreement of the Lymph3Cx molecular classification with the clinicopathological diagnosis of an expert panel (frank misclassification rate, 3.8%). In the authors’ view, Lymph3Cx represents a molecular tool that is potentially helpful for the diagnosis of PMBCL in light of the use of ad hoc therapeutic approaches [1]. In fact, on central review, cases enrolled in trials as DLBCL-NOS are not infrequently reclassified as PMBCL and vice versa, as seen in the authors’ experience at a reference center for several trials of the Italian Lymphoma Foundation.

3. Tissue Microenvironment (TME) By means of a gene profiling analysis of nearly 500 FF DLBCL samples, in 2008 Lenz et al. first demonstrated that the expression of peculiar gene sets, namely “Stromal-1” and “Stromal-2” signatures, respectively correlated with good- and poor-outcome subgroups of R-CHOP-treated patients independently of COO [39]. Although they were selectively enriched in genes encoding extracellular matrix proteins and histiocyte infiltration (Stromal-1) or reflecting angiogenesis (Stromal-2), these signatures resulted mechanistically uninformative, and their practical use was limited by the lack of standardized GEP assays for FFPE samples. A number of subsequent research attempts were aimed at identifying TME-related prognostic factors, but none provided biomarkers reproducible enough to be translated into daily clinical practice [40–43]. To overcome this limitation, in 2018 Ciavarella and co-workers [44] generated a 1028-gene matrix incorporating the signatures of 17 cytotypes and applied the computational method CIBERSORT to deconvolve Lenz’s GEP dataset. The work clarified the prognostic asso- ciations between patient outcome and quantitative proportions of tumor-infiltrating cell types. A panel of 45 genes related to myofibroblasts (MFs), dendritic cells, and CD4+ T-cells was selected and digitally validated by a NanoString-based approach on an independent cohort of 175 FFPE DLBCLs from two randomized trials. All tissue samples consisted of pretreatment biopsies of advanced-stage nodal DLBCLs treated by comparable R-CHOP/R- CHOP-like regimens. The expression of the 45 TME genes positively correlated with better outcomes and predicted the patient risk of overall and progression-free survival. In a multivariate Cox model, the TME panel retained high prognostic performance independently of COO, and integration of the two prognostic factors (COO + TME) improved survival prediction. Finally, a model to assign single DLBCL cases to a “COO–TME” risk category was built and successfully applied to an independent cohort of 40 “real-life” cases. In a parallel work, Staiger et al. [45] proposed a lymphoma-associated macrophage interaction signature (LAMIS) interrogating features of the microenvironment, once again using a NanoString assay applicable to FFPE. The clinical impact of the signature was validated in a cohort of 466 patients enrolled in prospective clinical trials at the German High- Grade Non-Hodgkin Lymphoma Study Group (DSHNHL). Patients with high expression of the signature (LAMIS-high) had shorter event-free survival (EFS), progression-free survival (PFS), and overall survival (OS). Multivariate analyses revealed independence from Inter- national Prognostic Index (IPI) factors in EFS (HR 1.7, 95%CI 1.2–2.4, p-value = 0.001), PFS (HR 1.8, 95%CI 1.2–2.5, p-value = 0.001), and OS (HR 1.8, 95%CI 1.3–2.7, p-value = 0.001). Multivariate analyses adjusted for the IPI factors showed the signature was independent of COO, MYC rearrangements, and double-expressor (DE) status. LAMIS-high and simul- taneous DE status characterized a patient subgroup with dismal prognosis and greater probability of early relapse. Cells 2021, 10, 675 6 of 15

Beyond the prognostic value of TME, only a few studies have provided comprehensive biological insights into the putative link between B-cell genomics and functional patterns of immune and stromal components while suggesting new rationales for future therapeutic approaches. Tripodo et al. [46] reported on a spatially resolved 53-gene signature comprising key genes of the dark-zone (DZ) mutational machinery, and light-zone (LZ) immune and mesenchymal milieu. This signature was applied to the transcriptomes of 543 cases of GCB- DLBCL and HGBCL-DH. According to the DZ/LZ signature, the GC-related lymphomas were sub-classified into two clusters. The subgroups differed in the distribution of DH cases and survival, with most DH cases displaying a distinct DZ-like profile. The clustering analysis was also performed using a 25-gene signature composed of DZ/LZ genes positively enriched in the non-B, stromal sub-compartments, for the first time achieving DZ/LZ discrimination based on stromal/immune features. The report offers new insight into the GC microenvironment, hinting at a DZ microenvironment of origin in DH lymphomas. Intriguing research integrating transcriptomic, genetic, and immunophenotypic data of 347 DLBCLs demonstrated that MHC loss, particularly in GC-derived tumors originating from the centroblast-rich DZ, is associated with a strong enrichment of EZH2 mutations, lower T cell infiltration, and poorer outcome [47]. Such results paved the way for the potential use of EZH2 inhibitors to treat the tumor by simultaneously modulating its immune microenvironment. Finally, very recent work by Kotlov et al. [48] provided a relevant classification of DL- BCL based on the transcriptomic characterization of TME from 4655 cases. Four major TME categories were identified as being associated with peculiar genetic/epigenetic aberrations of the malignant component, clinical behavior, and potential therapeutic targeting. Beyond its classification value, to date this work represents the most extensive translational and biological analysis of malignant and non-malignant DLBCL components. By all means, in-depth TME analysis still represents an approach that can significantly improve the prognostication of DLBCL and even further tune the identification of different risk groups within the same COO category, predicting the response to targeted therapies.

4. Genetic Classiﬁcation Over the last few years, several proposals for a genetic classiﬁcation of DLBCL have been published. Hereunder, the main contributions will be summarized and discussed based on the technical approach used.

4.1. Whole-Exome Sequencing (WES)-Based Studies In 2017, Reddy and coworkers [49] reported on the whole-exome sequencing (WES) of 1001 FF DLBCLs and 400 paired germline DNAs. They found 150 driver genes to be recurrently mutated. The 60 top genes frequently exhibited a pattern of either predominant missense and/or copy number gains consistent with an oncogene or truncating mutations and/or copy number losses consistent with a tumor suppressor gene. When the mutational pattern was matched with the COO, 20 genes were differentially mutated between the two groups, including EZH2, SGK1, GNA13, SOCS1, STAT6, and TNFRSF14, which were mutated in GCB tumors, and ETV6, MYD88, PIM1 and TBL1XR1, which were mutated in ABC tumors. Interestingly, MLL2 mutations were associated with those of MYC, while TP53 mutations occurred in a mutually exclusive fashion with KLHL6. CRISPR screening revealed that knockout of EBF1, IRF4, CARD11, MYD88, and IKBKB was selectively lethal in ABC DLBCL cell lines, as was knockout of ZBTB7A, XPO1, TGFBR2, and PTPN6 in the GCB lines. On prognostic grounds, MYC mutations were strongly associated with poorer survival, as were mutations in CD79B and ZFAT. Mutations in NF1 and SGK1 were associated with more favorable survival. Furthermore, in ABC DLBCLs, genetic alterations in KLHL14, BTG1, PAX5, and CDKN2A were associated with signiﬁcantly poorer survival, while those in CREBBP were associated with favorable outcomes. In the GCB-DLBCL group, genetic alterations in NFKBIA and NCOR1 were associated with poorer prognosis, while alterations in EZH2, MYD88, and ARID5B were all associated with a signiﬁcantly better prognosis. The authors developed a multivariate supervised learning approach for Cells 2021, 10, 675 7 of 15

defining the association of survival with combinations of genetic markers (150 genetic driver genes) and gene expression markers (cell of origin, MYC, and BCL2). This led to the proposal of a three-subgroup molecular risk model that was found to outperform all existing predictors (i.e., COO, MYC/BCL2 DE, and IPI). However, the recent application of this model to 499 DLBCLs by Bolen et al. [50] did not provide independent validation. This might reflect the technical differences between the two studies (WES of FF samples by Reddy et al. vs. targeted NGS of DNA extracted from FFPE biopsies by Bolen et al.). Two studies published in 2018 proposed a molecular subclassification of DLBCLs that had potential prognostic and therapeutic implications [51,52]. They both were based on WES and copy-number analysis of a large series of FF DLBCLs (304 and 574, respectively) [51,52]. Chapuy et al. [51] described five clusters characterized by different genetic lesions that were capable of identifying subgroups within the COO categories showing different behaviors. Most cases included in clusters (Cs) 1 and 5 were classified as ABC. However, they showed important differences on molecular and prognostic grounds. C1 cases were thought to derive from marginal-zone B-cells, as they showed a stable mutational pattern, structural variants (SVs) of BCL6, and mutations of genes involved in the NOTCH2 and NF-kB pathways (NOTCH2, SPEN, BCL10, TNFAIP3, and FAS). Besides the multiple genetic lesions of genes involved in immune escape (BM2, CD70, FAS, PD-L1, PD-L2), these C1 cases carried MYD88 mutations which were non-L265P, unlike what was observed in the cases included in C5. Notably, C1 cases had a rather favorable course and revealed potential therapeutic targets related to NOTCH2 and BCL6 signaling and immune evasion mechanisms. C5 tumors, which behaved more aggressively than the C1 ones, showed mutations of MYD88L265P, CD79B, PIM1, TBL1XR1, GRHPR, and BTG1, SV of 18q, and activation of the NF-kB pathway. In addition, they carried ongoing mutations, being at least in part under the effect of AID. Potential targets for C5 cases corresponded to BCR/TLR signaling and BCL2. Cs 3 and 4 were significantly enriched in GCB cases but were characterized by different genetic lesions and responses to chemoimmunotherapy. The majority of DLBCLs in C3 harbored BCL2 mutations with concordant SVs. They also exhibited frequent mutations in chromatin modifiers, KMT2D, CREBBP, and EZH2, and increased transcriptional abundance of EZH2 targets by gene set enrichment analisys (GSEA). These tumors also had alterations in the B-cell transcription factors MEF2B and IRF8, and indirect modifiers of BCR and PI3K signaling (TNFSF14(HVEM), HCNV1, and GNA13). In addition, C3 tumors had two alternative mechanisms of inactivating PTEN: focal 10q23.31/PTEN loss and predominantly truncating PTEN mutations, events that play a role in the process of lymphomagenesis. C4 DLBCLs were characterized by mutations in four linker and four core histone genes, multiple immune evasion molecules (CD83, CD58, and CD70), BCR/PI3K signaling inter- mediates (RHOA, GNA13, and SGK1), NF-kB modifiers (CARD11, NFKBIE, and NFKBIA), and RAS/JAK/STAT pathway members (BRAF and STAT3). Comparison of the C3 and C4 genetic signatures further revealed that these GCB-DLBCLs utilized distinct mechanisms to perturb common pathways such as PI3K signaling. In contrast to C3 DLBCLs, C4 tumors rarely exhibited PTEN alterations but harbored more frequent RHOA mutations. In addition, C4 DLBCLs rarely exhibited BCL2 alterations and had higher mutational density. The distinct genetic features of C3 and C4 GCB-DLBCLs led Chapuy et al. to suggest specific targeted therapies including inhibition of BCL2, PI3K, and the epigenetic modifiers EZH2 and CREBBP in C3 GCB tumors, and JAK/STAT and BRAF/MEK1 blockade in C4 GCB-DLBCLs. Last but not least, C3 cases had a far worse prognosis. C2 DLBCLs harbored frequent biallelic inactivation of TP53 by mutations and 17p copy loss. In addition, they often exhibited copy loss of 9p21.13/CDKN2A and 13q14.2/RB1, perturbing chromosomal stability and cell cycle. C2 tumors also had significantly more driver somatic copy number alterations (SCNAs) and a higher proportion of genome doubling events. This cluster included both GCB- and ABC-DLBCLs, as did prior DLBCL cohorts with TP53 mutations in targeted analyses [53]. Prognostically significant SCNAs, including 13q31.31/miR-17-92 copy gain and 1q42.12 copy loss, were also more common in these DLBCLs, which were characterized by a rather unfavorable prognosis. Cells 2021, 10, 675 8 of 15

A further cluster, termed 0, was also detected, which apparently lacked significant genetic alterations. However, as the C0 group consisted almost exclusively of T-cell rich/histiocyte-rich B-cell lymphomas, the obtained results might have been largely influ- enced by the small number of neoplastic cells. The authors further evaluated BCL2 and MYC alterations. Tumors with cooccurring BCL2 and MYC SVs were significantly more frequent in C3 DLBCLs. Importantly, the coordinate genetic signatures reported by Chapuy et al. predicted outcomes independent of IPI which could suggest new combination treatment strategies and, more broadly, provide a roadmap for actionable DLBCL classification [51]. By their integrated approach, Schmitz et al. [52] identified four prominent genetic subtypes among 574 DLBCLs which they termed MCD (based on the co-occurrence of MYD88L265P and CD79B mutations), BN2 (based on BCL6 fusions and NOTCH2 mutations), N1 (based on NOTCH1 mutations), and EZB (based on EZH2 mutations and BCL2 translocations). Interestingly, Schmitz and co-workers enriched their series with unclassified DLBCLs. The latter turned out to frequently carry mutations affecting SPEN and NOTH2 as well as BCL6 fusions. ABC cases were enriched in MYD88L265P and CD79B or NOTCH1 mutations, with the two conditions being mutually exclusive. GCB tumors showed the co-occurrence of EZH2 mutations and BCL2 translocations. The MCD and N1 subtypes were dominated by ABC cases, while EZB included mostly GCB tumors, and BN2 had contributions from all GEP subgroups. Overall, about 45% of the samples were classified into the genetically pure subtypes of DLBCL. The MCD subtype displayed 82% of cases carrying MYD88L265P or CD79B aberrations (mutation or amplification), with 42% bearing both abnormalities. The MCD subtype showed a frequent gain or amplification of SPIB, encoding a transcription factor that, with IRF4, defines the ABC phenotype and promotes plasmacytic differentiation. Known tumor suppressors in MCD include CDKN2A, ETV6, BTG1, and BTG2, and putative tumor suppressors include TOX, SETD1B, FOXC1, TBL1XR1, and KLHL14. The tumor suppressor TP53 was mutated significantly less often in MCD as compared to other subtypes. Immune editing appeared prominent in MCD genomes, with 76% acquiring a mutation or deletion of HLA-A, HLA-B, or HLA-C and 30% acquiring truncating mutations targeting CD58. BN2 was dominated by NOTCH pathway aberrations, with 73% acquiring a NOTCH2 mutation or amplification, SPEN mutation, or mutation in DTX1, a NOTCH target gene. BCL6 fusion, the other BN2 hallmark, occurred in 73% of cases. BCL6 fusions were enriched in cases with NOTCH2, SPEN, or DTX1 lesions to a significantly greater extent in BN2 than in non-BN2 cases. Genetic aberrations (mutations or amplifications) targeting regulators of the NF-kB pathway were a prominent feature of BN2. These more often affected TNFAIP3, PRKCB, and BCL10. Other likely gain-of-function events included mutations targeting cyclin D3 and CXCR5, whereas inactivating lesions targeting the immune regulator CD70 suggested immune escape. N1 was characterized by NOTCH1 mutations and aberrations targeting transcriptional regulators of B-cell differentiation (IRF4, ID3, and BCOR), which may contribute to its plasmacytic phenotype. TNFAIP3 mutations in N1 could reinforce this phenotype by fostering NF-KB-induced IRF4 expression. EZB was enriched for most of the genetic events previously ascribed to GCB-DLBCL, including BCL2 translocation, EZH2 mutation, and REL amplification, as well as inactivation of the tumor suppressors TNFRSF14, CREBBP, EP300, and KMT2D. The germinal- center homing pathway involving S1PR2 and GNA1314 was disrupted in 38% of EZB cases. JAK-STAT signaling was promoted in about half cases by a STAT6 mutation or amplification or by a mutation or deletion targeting SOCS1. PI3K target of rapamycin signaling turned out to be activated in 23% of cases by MTOR mutations or the amplification of MIR17HG. Immune editing was of interest in EZB genomes since 39% acquired lesions in the major histocompatibility complex class II pathway genes CIITA and HLA-DMA. The four subtypes differed significantly in PFS and OS, with the BN2 and EZB subtypes having much more favorable outcomes than the MCD and N1 subtypes. The predicted 5-year OS rates for the MCD, N1, BN2, and EZB subtypes were 26%, 36%, 65%, and 68%, respectively. Within ABC DLBCL, patients with MCD had significantly inferior survival Cells 2021, 10, 675 9 of 15

as compared with those with BN2, and patients with either MCD or N1 had significantly inferior survival as compared with patients with ABC tumors that were not genetically classified. Within GCB-DLBCL, there was a trend toward inferior OS among patients with EZB as compared with those with other GCB tumors. The COO subgroups and genetic subtypes independently contributed to survival in a multivariate analysis. Conversely, the IPI score did not vary significantly among the genetic subtypes, but the latter significantly added to IPI. A trend toward increased extranodal involvement (e.g., CNS) was a feature of MCD, which reflected the frequent CD79B and MYD88L265P mutations. On therapeutic grounds, constitutive BCR signaling activation was most frequent in MCD and least frequent in EZB, but genetic alterations involving the BCR cascade occurred in all genetics subtypes, suggesting that constitutive BCR signaling is a pervasive aspect of DLBCL pathogenesis. BN2 was notably enriched for BCR–NF-kB and IKK regulator aberrations. In addition to NF-kB, survival of DLBCL cells turned out to be promoted by antiapoptotic BCL2 family members, which were targeted by genomic amplification or translocation in 17.4% of cases. As expected, BCL2 mRNA levels were significantly higher in EZB tumors with BCL2 translocations than in other EZB tumors. MCD tumors also had high BCL2 mRNA expression as compared with other cases, a finding due to mechanisms other than translocation or amplification.

4.2. Targeted NGS and Bioinformatic-Based Studies Lacy et al. [54] applied a 293-gene chip to DNA extracted from FFPE tissue samples by using a Covaris LE220. The authors sequenced a large, unselected cohort consisting of 928 DLBCL patients all treated with R-CHOP and provided with full clinical follow-up. Bernoulli mixture-model clustering was applied, and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1, and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2, and MYD88 recapitulated the above-mentioned studies showing good, intermediate, and poor prognosis, respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. The results obtained by Lacy et al. are summarized in Table2, where they are also compared with the subtypes reported by Chapuy et al. [51], and Schmitz et al. [52]. Ennishi et al. [55] performed an integrative genomic and transcriptomic analysis of DLBCL using a British Columbia population-based registry. They uncovered recurrent biallelic TMEM30A loss-of-function mutations which were associated with a favorable outcome and were uniquely observed in DLBCL. Using TMEM30A-knockout systems, increased accumulation of chemotherapy drugs was observed in TMEM30A-knockout cell lines and TMEM30A-mutated primary cells, accounting for the improved treatment outcome. Furthermore, they found increased tumor-associated macrophages and an enhanced effect of anti-CD47 blockade limiting tumor growth in TMEM30A-knockout models. By contrast, Ennishi et al. showed that TMEM30A loss-of-function increased B-cell signaling following antigen stimulation—a mechanism conferring selective advantage during B-cell lymphoma development. These findings suggested intrinsic and extrinsic vulnerabilities of cancer cells that can be therapeutically exploited. Finally, Wright et al. [56] developed an algorithm to determine the probability of a patient’s lymphoma belonging to one of seven genetic subtypes based on its genetic features. This represented a probabilistic classification tool (LymphGen) using any combination of mutational, copy number, and BCL2/BCL6 rearrangement data. Schmitz’s cohort was used as training set, while those from Chapuy et al. and Ennishi et al. were used for validation. Wright et al. developed a model that is summarized in Table3, which also includes information on potential therapeutic targets related to the genetic subtype of DLBCL. CellsCells Cells2021 2021 ,2021 10, ,10 x, 10,FOR x, FORx FORPEER PEER PEER REVIEW REVIEW REVIEW 10 10of10 of16 of 16 16

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C4 NFKBIA CREBBP CREBBP CREBBPBRAFSOCS1NFKBIE NFKBIEBRAFNFKBIESOCS1 NFKBIEBRAFSOCS1 SOCS1/SGK1 C4

C4 NFKBIA SOCS1/SGK1 SOCS1/SGK1 C4 NFKBIA SOCS1/SGK1 NFKBIANFKBIENFKBIENFKBIE

CREBBP2 CREBBP2 CREBBP2TET2BRAFSOCS1 SOCS1TET2SOCS1BRAF SOCS1TET2BRAF A Aless Aless lessstrongly strongly strongly identifiable identifiable identifiable subtype. subtype. subtype. Has Has Has SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 CD83CD83NFKBIESOCS1 NFKBIESOCS1 SOCS1NFKBIE PredominantlyPredominantly GCB. GCB. Shares Shares genetic genetic and and

CD83 BRAFBRAFSOCS1BRAF Predominantly GCB. Shares genetic and TET2 TET2TET2 A AlessA less lessstrongly strongly strongly identifiable identifiable identifiable subtype. subtype. subtype. Has Has Has BRAFBRAFBRAF veryveryvery strong strong strong similarity similarity similarity to to SOCS1/SGK1 to SOCS1/SGK1 SOCS1/SGK1 but but but

SOCS1/SGK1 BRAF SOCS1/SGK1 SOCS1/SGK1 HIST1H1EHIST1H1EHIST1H1ESOCS1SOCS1SOCS1 genegenegene expression expression expression features features features of ofPMBCL. ofPMBCL. PMBCL. Associ- Associ- Associ- BRAF BRAF BRAFTET2 TET2BRAFBRAFTET2 veryA differsAverylessA differsveryless strong less stronglystrong stronglystrongby stronglyby similaritythe thesimilarity identifiableadditionsimilarity identifiableaddition identifiable to to SOCS1/SGK1of to ofSOCS1/SGK1 TET2subtype. SOCS1/SGK1 TET2subtype. subtype. and and Has BRAF Hasbut BRAF Hasbut but SGK1SGK1SGK1 SGK1TET2 SGK1 SGK1 atedated atedwith with with thediffers the most theA most lessmost favorableby favorable strongly thefavorable addition prognosis. prognosis. identiﬁable prognosis. of TET2 subtype. and BRAF Has very

BRAF BRAFBRAF TET2BRAFTET2BRAFTET2BRAF andAdiffersvery andveryAdifferslessAvery anddifferslessthe strong less the stronglystrongby lackthe stronglystrongby lackthestrongly by lackthesimilarity of additionthesimilarity of SOCS1identifiablesimilarityaddition of SOCS1identifiableaddition SOCS1identifiable toandof to andSOCS1/SGK1of TET2 to and ofSOCS1/SGK1 CD83.subtype.TET2 SOCS1/SGK1 CD83.TET2subtype. CD83.subtype.and and Favorable and BRAFFavorable Has FavorableBRAF Hasbut BRAF Has but but C4 C4 NFKBIANFKBIASGK1SGK1SGK1 strong similarity to SOCS1/SGK1 but differs by

C4 NFKBIAKLHL6KLHL6 KLHL6

BRAF TET2/SGK1 TET2/SGK1 TET2/SGK1 TET2BRAFTET2BRAFBRAFTET2 Avery veryAlessvery Aless strong less stronglystrong stronglystrong strongly similarity similarity identifiablesimilarity identifiable identifiable to to SOCS1/SGK1 to SOCS1/SGK1 subtype. SOCS1/SGK1 subtype. subtype. Has Hasbut Has but but NFKBIE NFKBIE NFKBIEID3KLHL6SGK1 ID3SGK1KLHL6 SGK1ID3KLHL6 prognosis.anddiffersprognosis.differsanddiffersprognosis.andthe the the additionby lackthe by lack the by lackthe of theaddition of ofSOCS1addition of SOCS1 TET2addition SOCS1 andof andof TET2 and ofBRAF CD83.TET2 CD83.TET2 CD83.and andand Favorable and BRAFFavorable the FavorableBRAF BRAF lack of SOCS1/SGK1 SOCS1/SGK1 TET2/SGK1 TET2/SGK1 TET2/SGK1 SOCS1/SGK1 SOCS1SOCS1SOCS1BRAFSGK1 SGK1SGK1BRAF SGK1BRAF verydiffersdiffersverydiffersverySOCS1 strong strongby strongby the andby similaritythe theadditionsimilarity CD83. additionsimilarity addition Favorable to of to SOCS1/SGK1of TET2to ofSOCS1/SGK1 TET2 SOCS1/SGK1 TET2 prognosis. and and and BRAF BRAF but BRAF but but BCL10ID3KLHL6 BCL10KLHL6ID3 KLHL6BCL10ID3 Notprognosis.andandNotprognosis.andprognosis.Not associatedthe theassociated theassociatedlack lack lack of of withSOCS1 of withSOCS1 SOCS1with any anyand anyandCOO. and COO.CD83. COO. CD83. CD83.Shares Shares Favorable Shares Favorable Favorable muta- muta- muta- TET2/SGK1 TET2/SGK1 TET2/SGK1 BRAF BRAFBRAFSGK1KLHL6 KLHL6KLHL6SGK1 KLHL6SGK1 differsandprognosis.prognosis.anddiffersandprognosis.differs the the by thelack by lackthe by lackthe of additionthe of SOCS1addition of SOCS1addition SOCS1 andof andof TET2 and of CD83.TET2 CD83.TET2 CD83.and and Favorable and BRAFFavorable FavorableBRAF BRAF TET2/SGK1 ID3ID3ID3 BCL10BCL10BCL10 NotNotNot associated associated associated with with with any any any COO. COO. COO. Shares Shares Shares muta- muta- muta-

TET2/SGK1 TET2/SGK1

TET2/SGK1 tionaltionaltional similarity similarity similarity to to MZL to MZL MZL but but butnot not notenriched enriched enriched TET2/SGK1 TET2/SGK1 TNFAIP3TNFAIP3TNFAIP3 TET2/SGK1 TET2TET2TET2 A lessA Aless stronglyless strongly andstronglyand prognosis.and identifiablethe identifiablethe identifiable lackthe lack lack of subtype. of SOCS1subtype. of subtype.SOCS1 SOCS1 Has Hasand Hasand and CD83. CD83. CD83. Favorable Favorable Favorable KLHL6ID3 ID3ID3KLHL6 ID3KLHL6 prognosis.prognosis.prognosis.

TET2/SGK1 BCL10BCL10 NotNot associated associated with with any any COO. COO. Shares Shares muta- muta- TET2/SGK1 BCL10 Not associated with any COO. Shares muta- TET2/SGK1 BRAFBRAFTNFAIP3 TNFAIP3 TNFAIP3 very very strong strongfortionalfortional similaritytransformedfortional similarity transformed similarity transformed similarity similarity to toSOCS1/SGK1 SOCS1/SGK1 MZLs.to MZLs.to MZL MZLs.to MZL MZL Less butbut Less butbut Less butstronglynot stronglynot stronglynotenriched enriched enriched de- de- de- BRAFNOTCH2 NOTCH2NOTCH2 very strong similarityprognosis.prognosis. to SOCS1/SGK1 but

ID3BCL10 BCL10ID3 BCL10ID3 prognosis.NotNotNot associated associated associated with with with any any any COO. COO. COO. Shares Shares Shares muta- muta- muta- SGK1SGK1SGK1 BCL6NOTCH2TNFAIP3BCL10 BCL6TNFAIP3NOTCH2 TNFAIP3BCL6NOTCH2 translocation translocation translocationdiffers differs differs by by thefinedfortional by thefinedtionalfor addition thetransformedtionalfinedforNot addition transformedthan additionsimilarity transformed thanassociatedsimilarity thanofsimilarity other ofTET2 other ofTET2 other TET2 MZLs.andtosubgroups. with MZLs.toand subgroups.MZL BRAFMZLs.toandsubgroups. MZL BRAF anyMZL LessBRAF but Less COO.but Less but stronglynot stronglynot Shares stronglynotenriched enriched enriched de- mutational de- de-

BCL10 andand the the lack lack ofNot ofSOCS1 SOCS1 associated and and CD83. CD83. with Favorable Favorable any COO. Shares muta- C1 C1 C1 KLHL6KLHL6KLHL6BCL10TNFAIP3 BCL10TNFAIP3 TNFAIP3 and the lackNottional oftionalNot tionalsimilaritySOCS1 associated similarityassociated similarity andsimilarity toCD83. MZLwith to with to FavorableMZL to but anyMZL anyMZL not butCOO. but COO. enriched butnot notShares not enrichedShares enriched forenriched muta- muta- BN2 BN2 BN2 NOTCH2NOTCH2 finedforforfined transformedforfined transformedthan transformed than than other other other MZLs.subgroups. MZLs.subgroups. MZLs.subgroups. Less Less Less strongly strongly strongly de- de- de- TET2/SGK1 TET2/SGK1 NOTCH2 TET2/SGK1 CCND3BCL6TNFAIP3CCND3BCL6CCND3BCL6 translocation translocation translocation

ID3ID3 prognosis.prognosis.prognosis. transformed MZLs. Less strongly deﬁned than C1 C1 C1 ID3 TNFAIP3TNFAIP3NOTCH2TNFAIP3 tionalfortionalfor transformedfortional transformed similarity transformed similarity similarity MZLs.to MZLs.to MZL MZLs.to MZL MZL Less but Less but Less butstronglynot stronglynot stronglynotenriched enriched enriched de- de- de- NOTCH2 NOTCH2 NOTCH2 NOTCH2 NOTCH2NOTCH2 for transformed MZLs. Less strongly de- BN2 BN2 BN2 BCL6BCL6NOTCH2BCL6 translocation translocation translocation fined fined fined than than than other other other subgroups. subgroups. subgroups.

CCND3CCND3CCND3

BCL10BCL10BCL10SPEN NOTCH2SPEN SPEN NotNot associatedNot associated associated otherwith with with any subgroups. any COO.any COO. COO. Shares Shares Shares muta- muta- muta- C1 C1 C1 NOTCH2NOTCH2 forforfined transformedfinedfor transformed transformedthan than other other MZLs. MZLs.subgroups. MZLs.subgroups. Less Less Less strongly strongly strongly de- de- de- NOTCH2 NOTCH2 NOTCH2 NOTCH2BCL6 translocationfinedfined fined than than than other other other subgroups. subgroups. subgroups. BN2 BN2 BCL6BCL6 translocation translocation BN2 BCL6BCL6BCL6 translocation translocation translocation

CCND3CCND3 TNFAIP3TNFAIP3SPENUBE2ASPENCCND3UBE2ASPEN tional tionaltional similarity similarity similarity to toMZL toMZL MZL but but not but not enriched not enriched enriched

C1 TNFAIP3UBE2A C1 C1 C1 C1 C1 BCL6 translocation BN2 BN2 BN2 BN2 BN2 finedfined than than other other subgroups. subgroups. BN2

NOTCH2 NOTCH2 fined than other subgroups. NOTCH2 BCL6BCL6BCL6 translocation translocation translocation NOTCH2NOTCH2NOTCH2CCND3CCND3 CCND3 for for transformedfor transformed transformed MZLs. MZLs. MZLs. Less Less Lessstrongly strongly strongly de- de- de- C1 SPENSPENSPEN

C1 UBE2AUBE2AUBE2A C1 C1 CD70CD70 CD70 BN2

NOTCH2 NOTCH2 NOTCH2 NOTCH2 NOTCH2 BN2 BN2 NOTCH2 BN2 BCL6BCL6BCL6 CCND3SPENtranslocation CCND3SPENCCND3translocation SPENtranslocationCCND3 fined fined fined than than than other other other subgroups. subgroups. subgroups.

NOTCH2 UBE2AUBE2AUBE2A C1 C1 CD70 CD70CD70

C1 NOTCH1NOTCH1 A Adefault default category, category, containing containing cases cases that that NOTCH2 NOTCH1 A default category, containing cases that NOTCH2 NOTCH2 BN2 BN2 BN2 CCND3CCND3CCND3SPEN SPENSPEN SPEN

UBE2A UBE2AUBE2AUBE2A

NOTCH2 NOTCH2 CD70CD70 NOTCH2 RELNOTCH1 RELNOTCH1CD70NOTCH1REL amplification amplification amplification couldA couldAdefaultcouldA default defaultnot not notcategory,be category,be classified category,be classified classified containing containing elsewhere containing elsewhere elsewhere cases casesand cases and thatand no that no that de- no de- de- SPENSPENSPEN UBE2A

UBE2ACD70 UBE2ACD70CD70

UBE2ACD70 UBE2AUBE2AUBE2ATP53RELNOTCH1 NOTCH1TP53REL NOTCH1TP53REL amplification amplification amplification tectedcouldA tectedAcoulddefaultA tectedcoulddefault defaultnotmutation. notmutation. notbecategory,mutation. category,be classified category,be classified classifiedLikely Likely containing Likely containing elsewhere containingto elsewhere to alsoelsewhere to also also containcases containcases and containcases and thatand no thatcases no that casesde- no casesde- de- NEC NEC NEC Other Other

Other CD70 CD70CD70 NOTCH1NOTCH1 AA default default category, category, containing containing cases cases that that NOTCH1 A default category, containing cases that CD70 CD70CD70 NOTCH1TP53RELCD70 NOTCH1RELTP53 NOTCH1RELTP53 amplification amplification amplification bAtectedcould elonging couldAbtecteddefaultelongingAcould tectedbdefaultelonging default notmutation. notmutation. not category,bemutation.to category, be toclassified bothcategory, beto classifiedboth classifiedbothLikely NOTCH1 Likely containing NOTCH1Likely containingNOTCH1 elsewhere containingto elsewhere to alsoelsewhere to alsoand also andcontaincases and containcasesTP53/CNA and containcases TP53/CNAand TP53/CNAthatand no thatcases no that cases de-no casesde- de-

NEC NEC NEC

Other Other Other NOTCH1 NOTCH1NOTCH1 NOTCH1RELRELNOTCH1 amplification amplification A Adefault default Acould category, Acoulddefault category,could Adefault defaultnot not containing notcategory,be containing category,be classified becategory, classified classified cases containingcases containing thatelsewhere containing thatelsewhere elsewhere cases casesand cases and thatand no that no that de-no de- de- NOTCH1RELTP53NOTCH1TP53REL RELTP53 amplification amplification amplification A default category,subgroups.btected elongingtectedcouldsubgroups.belongingAtectedbsubgroups.elonging default mutation. containing notmutation. mutation.to beto category, both to classified both cases bothLikely NOTCH1Likely NOTCH1 Likelythat containing NOTCH1 to elsewhere to also to alsoand also and casescontain and containTP53/CNA contain TP53/CNAand thatTP53/CNA cases nocould cases casesde- not

NEC NEC

NEC

Other Other Other REL REL RELamplification amplification amplificationREL amplification could couldcould not not be not beclassifiedcould be couldclassified classified not elsewherenot elsewherebe elsewherebe classified classified and and noand no de-elsewhere no de-elsewhere de- and and no no de- de-

RELTP53TP53RELTP53 amplification amplification couldtectedbelongingtectedbelongingtectedbbeelonging not classiﬁedmutation. mutation. be mutation.to to classifiedboth to both elsewhere bothLikely NOTCH1Likely NOTCH1Likely NOTCH1 elsewhere to and to also to also noand also andcontain detected and contain TP53/CNAand containTP53/CNA TP53/CNA no cases mutation. casesde- cases

NEC subgroups.subgroups.subgroups. NEC NEC NEC NEC TP53TP53TP53 CharacterizedCharacterizedCharacterized by by TP53 by TP53 TP53 mutation mutation mutation and and and wide- wide- wide- NEC Other Other Other Other Other Other TP53TP53 REL ampliﬁcationtectedtectedtected mutation. mutation. mutation. Likely Likely Likely to toalso toalso alsocontain contain contain cases cases cases

NEC TP53 C2 C2

NEC NEC C2 Other tected mutation. Likely to also contain cases

NEC tectedtectedLikely mutation. mutation. to also containLikely Likely casesto to also also belonging contain containto cases bothcases Other Other TP53TP53TP53 belongingbelongingbelonging to to both to both both NOTCH1 NOTCH1 NOTCH1 and and and TP53/CNA TP53/CNA TP53/CNA Other belonging to both NOTCH1 and TP53/CNA

NEC subgroups.subgroups. NEC subgroups. NEC TP53TP53TP53 CharacterizedspreadCharacterizedspreadCharacterized copy copy bynumber numberby TP53 by TP53 TP53 changes.mutation changes.mutation mutation and and and wide- wide- wide- Other spread copy number changes. Other FrequentFrequentFrequent deletions deletions deletionsbelongingbelonging to toboth both NOTCH1 NOTCH1 and and TP53/CNA TP53/CNA Other TP53 belonging to bothNOTCH1 NOTCH1 and and TP53/CNA TP53/CNA subgroups. C2 C2 C2

belongingbsubgroups.elongingsubgroups.belonging to to both to both both NOTCH1 NOTCH1 NOTCH1 and and and TP53/CNA TP53/CNA TP53/CNA

subgroups. subgroups.subgroups.subgroups. NoFrequentTP53 NoTP53Frequent detectedTP53NoFrequent detected detected deletions deletionssubgroups.abnor- deletions subgroups.abnor-subgroups. abnor- CasesspreadCharacterized CasesCharacterizedspread CharacterizedCasesspread with copy with copy with copyno number no detectable bynumberno detectablenumberby TP53bydetectable TP53 changes.TP53 changes.mutation changes.mutation mutationmutation mutation and andwere and werewide- werewide- al-wide- al- al-

C2 C2

C2 TP53 Characterized by TP53 mutation and widespread C0 C0 subgroups. C0 subgroups.

subgroups. TP53 TP53 TP53 TP53Frequent TP53Frequent TP53Frequent deletions deletionsCharacterized deletionsCharacterizedCharacterized Characterizedspread spreadCharacterized byCharacterizedspread by TP53 by copyTP53 TP53copy mutation copy mutation number mutation bynumber numberby TP53andby TP53and TP53changes.wide-and changes.mutationwide- changes.wide-mutation mutation and and and wide- wide- wide- C2 NoNo detectedNo detected detected abnor- abnor- abnor-CasesCasesCases with with with no no detectable no detectable detectable mutation mutation mutation were were were al- al- al- C2 C2 malities malitiesmalities locatedlocatedlocated to to the to the NECthe NEC NEC group. group. group. C2 C2 C2 C2

C2 C2

C2 spreadspread copy copy numbercopy number number changes. changes. changes. C0 C0 C0 FrequentFrequent deletions deletionsspread copy number changes. Frequent deletionsTP53 Characterized by TP53 mutation and wide- TP53Frequent FrequentTP53FrequentFrequent deletions deletions deletions deletions Characterizedspread Characterizedspreadspread copy copy copy number bynumber numberby TP53 TP53 changes. changes.mutation changes.mutation and and wide- wide-

malitiesNoNomalities detectedNomalities detected detected abnor- abnor- abnor-locatedCasesCaseslocatedCaseslocated with towith to withthe tono the no NECthe detectableno NECdetectable NECdetectable group. group. group. mutation mutation mutation were were were al- al- al-

C2 C2 NOTCH1NOTCH1 CharacterizedCharacterized by by NOTCH1 NOTCH1 mutation, mutation, this this C2 NOTCH1 Characterized by NOTCH1 mutation, this

NoNo detected detected abnor- abnor-CasesCases with with no no detectable detectable mutation mutation were were al- al-

No detected abnor- Cases with no detectable mutation were al- C0 C0 C0 Frequent deletions spreadspread copy copy number number changes. changes.

Frequent deletions spread copy number changes. Frequent deletions C0 C0 No No detectedNo detected detected abnor- abnor- abnor-CasesCasesCases with with with no no detectableno detectable detectable mutation mutation mutation were were were al- al- al- C0 NoNo detectedNo detected detected abnor- abnor- abnor-CasesCasesCases with with with no no detectableno detectable detectable mutation mutation mutation were were were al- al- al- malitiesmalitiesNOTCH1malities malitiesNOTCH1 malitiesNOTCH1 located located towasCharacterizedlocated tothelocatedwasCharacterized thelocatedwasCharacterized significantlyNEC NECsignificantly tosignificantly group. to thegroup. to the the NEC by NEC by elevatedNOTCH1NEC by elevatedgroup.NOTCH1 elevatedgroup.NOTCH1 group. in mutation, in Lacy’s mutation,in Lacy’s mutation, Lacy’s NEC NECthis NECthis this C0 C0 malities located to the NEC group. C0

C0 C0

N1 N1 N1 NoNomalities detectedmalitiesNo detected detected abnor- abnor- abnor-CasesCaseslocatedlocatedCases with with towith noto the no thedetectable no NECdetectable NEC detectable group. group. mutation mutation mutation were were were al- al- al- NOTCH1 NOTCH1 NOTCH1malitiesNOTCH1 malitiesNOTCH1 malitiesNOTCH1 Characterized Characterized CharacterizedlocatedwasCharacterizedlocatedCharacterizedwas bylocatedCharacterizedwas significantly by NOTCH1 significantlyby NOTCH1 tosignificantly NOTCH1to the to the mutation,theNEC by mutation, NECby mutation,elevatedNEC NOTCH1by elevatedgroup.NOTCH1 elevatedNOTCH1group. this group. this this in mutation, in Lacy’s mutation,in Lacy’s mutation, Lacy’s NEC NECthis NECthis this

C0 groupgroupgroup but but butonly only only mutated mutated mutated in in 2.5% in 2.5% 2.5% of of samples. of samples. samples. C0 C0

N1 N1 N1 malitiesNOTCH1malitiesNOTCH1NOTCH1malities was was wassignificantly significantly significantlylocatedCharacterizedlocatedCharacterizedCharacterizedlocated elevated elevatedto elevated to the to the in NEC the byinLacy’s NEC inbyLacy’s NOTCH1NEC byLacy’s group.NOTCH1 NEC NOTCH1group. NEC group. NEC mutation, mutation, mutation, this this this NOTCH1 AssociatedgroupwasAssociatedwasCharacterizedgroupwasAssociatedgroup significantly significantlybut significantlybut butonly with only with only withmutated bypoor mutatedelevated poor mutatedelevatedNOTCH1 poor elevated outcome. outcome. in outcome. in 2.5% in 2.5%Lacy’s mutation,in 2.5%Lacy’s ofLacy’s of samples. NECof samples. NEC samples. NECthis N1 N1 N1

N1 N1 groupgroup but but only only mutated mutated in in2.5% 2.5% of ofsamples. samples. N1 NOTCH1group but only Characterizedmutated in 2.5% of by samples. NOTCH1 mutation, this NOTCH1 NOTCH1 CharacterizedwasAssociatedgroupgroupwasCharacterizedAssociatedwasgroupAssociated significantly significantlybut significantlybut butonly with only with onlyby withmutated bypoor elevatedmutatedNOTCH1 poor mutatedelevatedNOTCH1 poor elevated outcome. outcome. in outcome. in 2.5% mutation,in Lacy’s2.5% mutation,in 2.5%Lacy’s ofLacy’s of samples. NECof samples. NEC thissamples. NECthis

N1 N1 N1 N1 AssociatedAssociated with with poor poor outcome. outcome. N1 Associatedwasgroup withwasgroupgroupwas significantlypoor significantlybut significantlybut outcome. butonly only only mutated elevatedmutated mutatedelevated elevated in in 2.5% in Lacy’s2.5% in 2.5%Lacy’s ofLacy’s of samples. NECof samples. NEC samples. NEC AssociatedAssociatedgroupAssociated but with only with with poor mutated poor poor outcome. outcome. outcome. in 2.5% of samples. N1 N1 N1 groupAssociatedAssociatedgroupAssociatedgroup but but butonly with only with only withmutated poor mutated poor mutated poor outcome. outcome. in outcome. in 2.5% in 2.5% 2.5% of of samples. of samples. samples. AssociatedAssociatedAssociated with with with poor poor poor outcome. outcome. outcome.

CellsCells Cells2021 2021 ,2021 10, ,10 x, 10,FOR x, FORx FORPEER PEER PEER REVIEW REVIEW REVIEW 10 10of10 of16 of 16 16

Cells 2021, 10, x FOR CellsPEER 2021 REVIEW, 10, x FOR PEER TableREVIEWTableTable 2. 2.Molecular 2.Molecular Molecular subtypes subtypes subtypes of ofDLBCL of DLBCL DLBCL according according according to toLacy to Lacy Lacy et etal. etal. in al. incomparison in comparison comparison with with with10 those of those 16those of ofCh of Chapuy Chapuyapuy et10 et al.of etal. 16and al. and andSchmitz Schmitz Schmitz et et et

al.al. al.

LacyLacyLacy et etal. et al. al. Chapuy Chapuy Chapuy et etal. et al. al. Schmitz Schmitz Schmitz et etal. et al. al. Notes Notes Notes Table 2. MolecularTable subtypes 2. Molecular of DLBCL subtypes according of DLBCLto Lacy etaccording al. in comparison to Lacy et withal. in those comparison of ChMMYD88apuy MYD88withYD88 et those al. and of SchmitzChapuyStronglyStronglyStrongly et al. associated and associated associated Schmitz with etwith with ABC. ABC. ABC. The The The most most most ro- ro- ro- al. al.

CD79BCD79BCD79B bustbustb groupust group group in in all in all reports. all reports. reports. Contains Contains Contains the the themost most most

bust group in all reports. Contains the most CD79B

Lacy et al. ChapuyLacy et al. et al. Chapuy Schmitz et etal. al. Schmitz et al. PIM1PIM1PIM1 Notes primaryprimary Notesprimary PCNSL PCNSL PCNSL and and and testicular testicular testicular lymphoma. lymphoma. lymphoma. C5 C5 C5 C5 PoorPoor prognosis. prognosis. MCD MCD Poor prognosis. MCD ETV6ETV6ETV6 Poor prognosis. MYD88 MYD88StronglyMCD associatedETV6Strongly with ABC.associated The most with ro- ABC. The most ro- MYD88 MYD88 MYD88 MYD88

CD79B CD79Bbust group in CDKN2AallCDKN2A breports.CDKN2Aust group Contains in all reports. the most Contains the most

PIM1 PIM1primary PCNSL TBL1XR1 TBL1XR1 primaryandTBL1XR1 testicular PCNSL lymphoma. and testicular lymphoma. C5 C5 Poor prognosis.EZH2 EZH2PoorEZH2 prognosis. StronglyStronglyStrongly associated associated associated with with with GCB. GCB. GCB. Contains Contains Contains MCD ETV6MCD ETV6 EZH2 Strongly associated with GCB. Contains MYD88 MYD88 CDKN2A CDKN2A BCL2BCL2BCL2 mostmostmost transformed transformed transformed FLs FLs FLsand and and cases cases cases with with with a con-a con-a con-

BCL2BCL2BCL2 translocation translocation translocation current current current FL. FL. FL.Generally Generally Generally favorable favorable favorable prognosis, prognosis, prognosis,

TBL1XR1 TBL1XR1 BCL2 translocation current FL. Generally favorable prognosis, althoughalthoughalthough enriched enriched enriched for for casesfor cases cases of of double-hit of double-hit double-hit C3 C3 EZH2C3 EZH2Strongly associatedKMT2DKMT2DStronglyKMT2D with GCB. associated Containsalthough with GCB. enriched Contains for cases of double-hit

C3 KMT2D EZB EZB EZB BCL2 BCL2 BCL2 EZB BCL2 BCL2 BCL2most transformedTNFRSF14TNFRSF14mostTNFRSF14 FLs transformedand cases withlymphoma FLslymphomalymphoma a andcon- cases and and and MHG.with MHG. MHG. a con-

BCL2 translocationBCL2 current translocation FL. GenerallyCREBBP CREBBPcurrentCREBBP favorable FL. Generally prognosis, favorable prognosis,

although enriched CREBBP2 CREBBP2althoughCREBBP2 for cases enriched of double-hit for cases of double-hit C3 C3 KMT2D KMT2D CREBBP2 EZB EZB BCL2 BCL2 TNFRSF14 TNFRSF14lymphoma andCD83 CD83MHG.lymphomaCD83 and MHG.PredominantlyPredominantlyPredominantly GCB. GCB. GCB. Shares Shares Shares genetic genetic genetic and and and

CREBBP CREBBP HIST1H1EHIST1H1EHIST1H1E genegenegene expression expression expression features features features of of PMBCL. of PMBCL. PMBCL. Associ- Associ- Associ- SGK1SGK1SGK1 atedatedated with with with the the mostthe most most favorable favorable favorable prognosis. prognosis. prognosis.

CREBBP2 CREBBP2 SGK1 ated with the most favorable prognosis.

C4 C4 CD83C4 CD83Predominantly NFKBIA GCB.NFKBIAPredominantlyNFKBIA Shares genetic GCB. and Shares genetic and C4 NFKBIA HIST1H1E HIST1H1Egene expression NFKBIENFKBIE genefeaturesNFKBIE expression of PMBCL. features Associ- of PMBCL. Associ- SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 SOCS1/SGK1 SGK1 SGK1ated with the mostSOCS1SOCS1atedSOCS1 favorable with the prognosis. most favorable prognosis.

BRAF BRAFBRAF C4 C4 NFKBIA NFKBIA BRAF

NFKBIE NFKBIE TET2TET2TET2 A AlessA less lessstrongly strongly strongly identifiable identifiable identifiable subtype. subtype. subtype. Has Has Has

SOCS1/SGK1 SOCS1/SGK1 SOCS1 SOCS1 BRAFBRAFBRAF veryveryvery strong strong strong similarity similarity similarity to to SOCS1/SGK1 to SOCS1/SGK1 SOCS1/SGK1 but but but BRAF BRAF SGK1SGK1SGK1 differsdiffersdiffers by by the by the additionthe addition addition of of TET2 of TET2 TET2 and and and BRAF BRAF BRAF

TET2 TET2A less stronglyKLHL6 identifiableKLHL6AKLHL6 less strongly subtype. identifiableandand Hasand the the lackthe lack subtype. lack of of SOCS1 of SOCS1 SOCS1 Has and and and CD83. CD83. CD83. Favorable Favorable Favorable TET2/SGK1 TET2/SGK1 TET2/SGK1 TET2/SGK1 prognosis.prognosis.prognosis. BRAF BRAFvery strong similarity ID3 ID3very ID3 strong to SOCS1/SGK1 similarityprognosis. butto SOCS1/SGK1 but SGK1 SGK1differs by the additionBCL10BCL10differsBCL10 of by TET2 the addition andNotNot BRAFNot associated ofassociated associated TET2 andwith with withBRAF any any any COO. COO. COO. Shares Shares Shares muta- muta- muta- KLHL6 KLHL6and the lack ofTNFAIP3 SOCS1TNFAIP3andTNFAIP3 the and lack CD83. of SOCS1tional Favorabletionaltional andsimilarity similarity similarityCD83. to Favorable to MZL to MZL MZL but but butnot not notenriched enriched enriched TET2/SGK1 TET2/SGK1 ID3 ID3prognosis. NOTCH2NOTCH2prognosis.NOTCH2 forfor transformedfor transformed transformed MZLs. MZLs. MZLs. Less Less Less strongly strongly strongly de- de- de-

BCL10 BCL10Not associatedBCL6 withBCL6NotBCL6 translocation anyassociated translocation translocation COO. Shares withfined fined finedany muta- than thanCOO. than other other Sharesother subgroups. subgroups. subgroups. muta- C1 C1 C1 C1 BN2 BN2 BN2 TNFAIP3 TNFAIP3tionalBN2 similarity CCND3CCND3 totionalCCND3 MZL similarity but not enriched to MZL but not enriched NOTCH2 NOTCH2 NOTCH2 NOTCH2 NOTCH2 NOTCH2for transformed SPEN SPENMZLs.forSPEN transformed Less strongly MZLs. de- Less strongly de-

BCL6 translocationBCL6 fined translocation than otherUBE2A UBE2A finedsubgroups.UBE2A than other subgroups. C1 C1 BN2 CCND3BN2 CCND3 CD70 CD70CD70 NOTCH2 NOTCH2 SPEN SPEN NOTCH1NOTCH1NOTCH1 A AdefaultA default default category, category, category, containing containing containing cases cases cases that that that

UBE2A UBE2A RELRELREL amplification amplification amplification could couldcould not not notbe be classified be classified classified elsewhere elsewhere elsewhere and and and no no de- no de- de- Cells 2021, 10, 675 11 of 15 CD70 CD70 TP53TP53TP53 tectedtectedtected mutation. mutation. mutation. Likely Likely Likely to to also to also also contain contain contain cases cases cases NEC NEC NEC Other Other Other NEC NOTCH1 NOTCH1AOther default category,A default containing category, casesbelongingb containingelongingthatbelonging to to both tocases both both NOTCH1 thatNOTCH1 NOTCH1 and and and TP53/CNA TP53/CNA TP53/CNA

subgroups.subgroups.subgroups. REL amplificationREL could amplification not be classified couldTable notelsewhere 2. Cont. be classified subgroups.and no elsewhere de- and no de-

TP53 TP53tected mutation.TP53TP53 tectedLikelyTP53 mutation. to also contain CharacterizedLikelyCharacterizedCharacterized cases to also containby by TP53 by TP53 TP53 cases mutation mutation mutation and and and wide- wide- wide- C2 C2 C2

NEC NEC Other Other Lacy et al.C2 Chapuy et al. Schmitz et al. Notes belonging to bothFrequent FrequentbFrequent elongingNOTCH1 deletions deletions deletions to and both TP53/CNAspread NOTCH1spreadspread copy copy copy and number number TP53/CNAnumber changes. changes. changes.

NoNo detectedNo detected detected abnor- abnor- abnor-CasesCasesCases with with with no no detectable no detectable detectable mutation mutation mutation were were were al- al- al- subgroups. NoNosubgroups. detected detected abnor- CasesCases with with no nodetectable detectable mutation mutation were allocatedal- C0 C0 C0 C0

TP53 TP53Characterized malities byabnormalitiesmalitiesCharacterized malitiesTP53 mutation by andlocated TP53locatedlocated towide- themutation to NECto the to the NECthe group. NECand NEC group. wide- group. group.

C2 C2 Frequent deletions Frequent spread deletions copy numberNOTCH1 NOTCH1spreadNOTCH1 changes. copy number CharacterizedCharacterizedCharacterized changes. by by NOTCH1 by NOTCH1 NOTCH1 mutation, mutation, mutation, this this this

waswaswas significantly significantly significantly elevated elevated elevated in in Lacy’s in Lacy’s Lacy’s NEC NEC NEC No detected abnor-NoCases detected with abnor- no detectableCases with mutation no detectablewas wereCharacterized significantly al- mutation byelevated were NOTCH1 al- in mutation,Lacy’s NEC this was

N1 N1 N1

C0 C0 Cells 2021, 10 , x FOR PEER REVIEW N1N1 13 of 18 malities malitieslocated to the NECNOTCH1located group. to the NECgroupgroup group.groupsigniﬁcantly but but butonly only only elevatedmutated mutated mutated in in Lacy’s in 2.5% in 2.5% 2.5% NECof of samples. of groupsamples. samples. but only mutated in 2.5% of samples. Associated with NOTCH1 NOTCH1Characterized byCharacterized NOTCH1 mutation, byAssociated NOTCH1AssociatedAssociated this mutation,with with with poor poor poor thisoutcome. outcome. outcome. poor outcome. was significantlywas elevated significantly in Lacy’s elevated NEC in Lacy’s NEC PCNSL, primary central nervous system lymphoma; FL, follicular lymphoma; MHG, molecular high grade; MZL, marginal zone lymphoma. N1 N1 group but only mutatedgroup but in 2.5%only mutatedof samples. in 2.5% of samples. Associated with Associatedpoor outcome. with poor outcome. Table 3. Implications of genetic subtypessubtypes ofof DLBCLDLBCL forfor therapytherapy (from(from WrightWright etet al.,al., modiﬁed).modified). Genetics GEP Signature Related LNH Targets 5y-OS My-T-BCR dependent NF- B-cell activation Primary extranodal BCR-dep. NF-kB 40% All kB; Immune evasion-MHC NF-kB DLBCL PI3K 37% ABC class I; Cell survival; BCL2 IRF4 mTORC1

UNC expression; Altered B-cell MYC Transformed WM BCL2-BCLXL-

MCD differentiation; G1-S cell Proliferation MCL1 ABC cycle/p53 checkpoint; JAK1

BCR: IgM>>IgG; IgVH 4-34++ IRAK4 IRF4 NOTCH1 signaling NOTCH NOTCH1 mutated NOTCH1 27% All UNC Altered B cell Quiescence CLL Immune 22% ABC

N1 differentiation Plasma cells checkpoints ABC BCR: IgM>IgG T cell-Myeloid-FDC

TP53 inactivation—DNA p53 - BCR-dep. NF-kB 63% All UNC Damage Immune low 33% ABC Aneuploidy 100% GCB GCB A53 Immune evasion—B2M ABC loss BCR: IgM>>IgG;

IgVH 4-34++ NOTCH2 signaling B-cell activation MZL BCR-dep. NF-kB 67% All Altered B cell NF-kB Transformed MZL PI3K 76% ABC UNC differentiation BCR- NOTCH mTORC1 100% GCB GCB dependent NF-kB Immune Proliferation BCL2 38% UC BN2 ABC evasion—CD70 loss NOTCH2 Proliferation—Cyclin D3 BCR: IgM>>IgG; IgVH 4-34++ JAK/STAT3 signaling GC B cell NLPHL PI3K 84% All UNC NF-kB activation PI3K signaling JAK2 81% GCB P2RY8-GNA13 activation JAK2 signaling THRLBCL GCB ST2 Altered B cell Glycolysis ABC differentiation Stromal

BCR: IgM>>IgG − + Chromatin modification GC LZ (MYC ) FL PI3K 48% MYC + Anti-apoptosis GC IZ (MYC+) Transformed FL mTORC1 82% MYC− UNC + + MYC PI3K signaling BCL6 (MYC ) BL (EZB-MYC ) EZH2 GCB S1PR2-GNA13 inactivation TCF3 (both) BCL2-MCL1

ABC − Altered TFH interactions TFH cells (MYC−) MYC (EZ-MYC+) Stromal (MYC−) MYC BCR: IgG > IgM Immune low (MYC+) WM, WaldenströmWaldenström macroglobulinaemia; macroglobulinaemia; CLL, CLL, chronic chronic lymphocytic lymphocytic leukemia; leukemia; MZL, MZL, marginal marginal zone lymphoma; zone lymphoma; NLPHL, NLPHL, nodular lymphocyte-predominantnodular lymphocyte-predominant Hodgkin lymphoma; Hodgkin THRLBCL, lymphoma; T cell THRLBCL, histiocyte-. T cell histiocyte-.

Cells 2021, 10, 675 12 of 15

5. Open Issues and Perspectives The paper of Lacy et al. [54] was accompanied by a commentary from Morin and Scott [57], who concluded that comprehensive sequencing of a larger number of tumors with the combination of whole-genome and transcriptome sequencing is warranted to develop a new molecular taxonomy which may be concretely translated into clinical benefits. In fact, between 7.5% and 55% of the cases reported by Chapuy et al., Schmitz et al., and Lacy et al. did not fit into any of the major genetic categories they identified [51,53,58]. The fact that the genomic studies hitherto reported show a certain variability in terms of results may depend on different factors, such as the size of the analyzed cohort or heterogeneity of the techniques used (e.g., FF vs. FFPE tissue, whole exome vs. targeted sequencing, and the statistical approach applied), but also on the actual heterogeneity of the lesions occurring in these tumors. For instance, divergent evolution within the same biopsy, which corresponded to different morphologic, phenotypic, and COO features [59], has been reported. Although the distinct components had a common clonal origin and shared the bulk of genetic aberrations, each revealed private mutations, in keeping with the above-mentioned morpho-phenotypic and molecular differences. The heterogeneity of genetic lesions is much greater than was thought until a couple of years ago. This has been highlighted by liquid biopsy (LB) [59–61]. By ultradeep sequencing of the cell-free circulating tumoral DNA (cfDNA) released by neoplastic cells undergoing apoptosis, it has been shown that the global mutational landscape of DLBCL is indeed wider than that observed in diagnostic biopsies, which means that different mutations can occur at different anatomic sites. Once a standardized methodology is developed and the cost per test is reduced, LB can represent a real-time noninvasive tool for disease monitoring. In fact, patients achieving early molecular response (a 2-log decrease of ctDNA after one cycle of standard chemoimmunotherapy) and major molecular response (a 2.5-log decrease after two cycles) show a significantly superior outcome at 24 months independently of IPI and interim positron emission tomography. Conversely, among treatment-resistant subjects, new mutations are acquired in cfDNA, marking resistant clones selected during the clonal evolution. The continuous development of sequencing and bioinformatic techniques will allow us to achieve the long searched-for goal of using customized therapies based on the molecular characteristics of each individual tumor. Some approaches do appear to be more easily and cheaply applicable to daily life. Nevertheless, the more comprehensive the bioinformatic approach is, the higher the likelihood of overcoming today’s standard chemoimmunotherapy and designing chemotherapy-free protocols capable of curing most (if not all) patients, with minimal or no toxic effects.

Funding: The Ministry of Health, Italian Government, Funds R.C. 2021 (to SC and SAP). Acknowledgments: This review was conducted thanks to the support of the Italian Association for Cancer Research (AIRC 5x1000 grant n. 21198 to SAP) and the Ministry of Health, Italian Government, Funds R.C. 2021 (to SC and SAP). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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