Role of mutational signatures and clonality assessments in tailoring targeted therapies for lymphoma

 Corresponding author: Andoh Sheikh Atta-ullah Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Role of mutational signatures and clonality assessments in tailoring targeted therapies for lymphoma Andoh Sheikh Atta-ullah 1, *, Uche Philip 2 and Sampson Janice Candy 3 1 Department of Hematology, Fujian Provincial Cancer Hospital affiliated to Fujian Medical University, Fujian Medical University, China. 2 Department of Hematology and Oncology, Union Hospital affiliated to Fujian Medical University, China. 3 Department of Internal Medicine, First Affiliated Hospital of Fujian Medical University, Fujian Medical University, China. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 Publication history: Received on 30 March 2025; revised on 14 May 2025; accepted on 17 May 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1936 Abstract Lymphomas are highly heterogeneous malignancies characterized by diverse genetic, epigenetic, and phenotypic profiles, which complicate diagnosis, prognosis, and treatment. Traditional bulk sequencing methods mask the complexity of intratumoral variation, often overlooking rare subclones that may drive disease progression and therapeutic resistance. Single-cell genomics has emerged as a transformative approach to decipher tumor heterogeneity at unprecedented resolution. This technology enables the dissection of individual cellular populations within lymphomas, offering insights into clonal evolution, transcriptional diversity, and microenvironmental interactions. By applying single-cell RNA sequencing (scRNA-seq), chromatin accessibility assays (scATAC-seq), and single-cell DNA sequencing (scDNA-seq), researchers can unravel lineage relationships, identify resistant subpopulations, and track dynamic changes in response to therapy. In lymphomas such as diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), single-cell approaches have revealed distinct malignant and nonmalignant cell states that correlate with treatment outcomes. Moreover, integrating single-cell data with spatial transcriptomics and immune profiling enhances the understanding of the tumor microenvironment, including immune evasion mechanisms. These insights can inform personalized treatment strategies, identify novel therapeutic targets, and enable early detection of relapse. Despite technical challenges such as data complexity, sample viability, and cost, the application of single-cell genomics in lymphoma research is rapidly advancing. Future directions include multiomics integration, real-time patient monitoring, and clinical translation of predictive biomarkers. This review underscores the pivotal role of single-cell genomics in resolving tumor heterogeneity and predicting therapeutic resistance, positioning it as a cornerstone for next-generation precision oncology in lymphomas. Keywords: Single-cell genomics; Tumor heterogeneity; Therapeutic resistance; Lymphoma; Precision oncology; Clonal evolution 1. Introduction 1.1. Background on Lymphoma Subtypes (Hodgkin vs. Non-Hodgkin) Lymphomas represent a diverse group of hematologic malignancies originating from lymphoid cells, primarily affecting the lymph nodes and related immune tissues. These cancers are broadly categorized into Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL), each demonstrating distinct histopathological and molecular characteristics. Hodgkin World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2848 lymphoma, marked by the presence of Reed-Sternberg cells, accounts for approximately 10% of all lymphoma cases and is more prevalent in younger adults [1]. In contrast, non-Hodgkin lymphoma includes a wide array of subtypes such as diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), and Burkitt lymphoma (BL), with DLBCL being the most common [2]. NHLs vary greatly in terms of clinical aggressiveness, treatment response, and prognosis. Indolent forms, such as FL, tend to follow a slow course but are often incurable, whereas aggressive subtypes like BL may progress rapidly yet are more amenable to curative therapy [3]. The clinical heterogeneity of NHL underscores the necessity for nuanced classification systems that go beyond morphology and immunophenotyping.. 1.2. Limitations of Conventional Therapy Despite advances in immunochemotherapy regimens such as R-CHOP (rituximab with cyclophosphamide, doxorubicin, vincristine, and prednisone), the long-term outcomes for many patients with aggressive or relapsed lymphomas remain suboptimal [4]. Resistance to first-line therapy and disease relapse are frequent challenges, particularly in cases involving high-grade transformation or refractory disease biology. Furthermore, conventional therapies are associated with considerable toxicity, and their effectiveness is often compromised by the molecular diversity within and between tumors [5]. Another limitation lies in the lack of personalized treatment approaches. Most clinical protocols rely on a standardized approach, failing to consider the genetic and clonal landscape of individual tumors. This one-size-fits-all strategy overlooks biologically distinct disease subgroups that may respond differently to therapy, ultimately limiting the efficacy of systemic treatments [6]. 1.3. Rise of Precision Oncology in Hematologic Malignancies The advent of precision oncology has revolutionized the treatment landscape for solid tumors and is increasingly gaining momentum in hematologic malignancies. Precision oncology is characterized by the integration of genomic, transcriptomic, and proteomic data to guide individualized therapeutic strategies. In lymphomas, genomic profiling has enabled the identification of recurrent mutations, chromosomal aberrations, and pathway dysregulations, paving the way for more targeted and rational treatment designs [7]. Recent developments in high-throughput sequencing technologies have further facilitated the characterization of lymphoid malignancies at unprecedented resolution. For instance, large-scale efforts such as the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) have uncovered novel biomarkers and stratification tools that have already influenced clinical practice [8]. These initiatives highlight the feasibility of using molecular signatures to classify disease subtypes more accurately and predict therapy response. Moreover, precision oncology offers the potential to identify actionable mutations and assess disease dynamics over time. Unlike static diagnostic tools, real-time genomic surveillance can guide adaptive therapeutic interventions, particularly in diseases characterized by clonal evolution and resistance mechanisms [9]. 1.4. Define Mutational Signatures and Clonality: Relevance to Lymphoma Mutational signatures refer to characteristic patterns of somatic mutations that reflect specific DNA damage and repair processes. These patterns, identifiable through statistical modeling of genomic data, can reveal insights into the biological history of a tumor. Signature analysis has been extensively applied in solid tumors and is now increasingly employed in lymphomas to unravel the mutational processes that drive pathogenesis [10]. For example, activation-induced cytidine deaminase (AID) and APOBEC enzyme activity are known contributors to the mutational burden in B-cell lymphomas. These signatures not only help differentiate subtypes but may also carry prognostic and therapeutic implications [11]. Some signatures have been associated with increased tumor aggressiveness or resistance to standard treatments, making them valuable tools for risk stratification and therapy selection. Clonality, on the other hand, pertains to the genetic relatedness of tumor cell populations. Clonal assessments aim to determine whether a tumor is derived from a single progenitor cell or comprises multiple evolving subclones. In World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2849 lymphomas, clonality is especially relevant given the hierarchical structure of B-cell development and the frequent occurrence of somatic hypermutation and class-switch recombination [12]. Assessing clonality helps identify dominant clones, track subclonal dynamics over time, and evaluate the effects of therapy on tumor composition. Importantly, understanding clonal evolution can elucidate mechanisms of drug resistance and inform the timing of treatment escalation or de-escalation [13]. For instance, the expansion of minor subclones harboring resistance mutations during therapy often precedes clinical relapse and could be targeted preemptively in a personalized treatment plan. 1.5. Purpose and Scope of the Article Given the limitations of conventional approaches and the promise of precision medicine, this article aims to explore the role of mutational signatures and clonality assessments in tailoring targeted therapies for lymphoma. It seeks to synthesize current knowledge on the genomic underpinnings of lymphoid malignancies and demonstrate how these molecular features can guide more effective and personalized interventions. The subsequent sections will discuss the detection and interpretation of mutational signatures, highlight their prevalence in various lymphoma subtypes, and examine the tools used to assess clonal architecture. Additionally, the article will explore how integrating these data points supports clinical decision-making and enhances outcomes through more precise therapeutic targeting [14]. We will also address practical challenges, including data integration, assay standardization, and clinical implementation barriers. Real-world case studies and emerging technologies will be used to illustrate the translational impact of these approaches in modern lymphoma care. Ultimately, this article advocates for a paradigm shift—moving from generic protocols to genomically guided, clonality-informed therapy strategies that reflect the true complexity of lymphoid cancers [15]. 2. Understanding mutational signatures in lymphoma 2.1. Definition and Biological Basis Mutational signatures are characteristic patterns of somatic mutations within the cancer genome, reflecting the activity of underlying mutational processes. These processes may be endogenous, such as spontaneous deamination, replication errors, or the activity of enzymes like APOBEC or AID, or exogenous, such as exposure to ultraviolet radiation, tobacco smoke, or chemotherapeutic agents [16]. Each mutational process leaves a distinctive “fingerprint” in the DNA, which can be mathematically decomposed and classified into specific signatures. The most commonly used framework for categorizing these patterns is provided by the Catalogue Of Somatic Mutations In Cancer (COSMIC), which groups them into single base substitutions (SBS), double base substitutions (DBS), and insertions and deletions (indels) [17]. SBS signatures, the most extensively studied, describe point mutations occurring at single nucleotide sites, typically within specific trinucleotide contexts. For instance, SBS1 arises from spontaneous deamination of methylated cytosine, while SBS2 and SBS13 are attributed to APOBEC activity [18]. Double base substitutions are relatively rare but provide distinct insights into DNA damage caused by mutagens or reactive oxygen species. Indel signatures, meanwhile, reflect DNA repair pathway defects, including mismatch repair (MMR) or homologous recombination deficiencies. Notably, the interpretation of these signatures requires statistical modeling using computational techniques such as non-negative matrix factorization (NMF), which deconvolves mutation catalogs into distinct, biologically relevant components [19]. In lymphomas, these signatures serve as both etiological markers and predictive tools, enabling a deeper understanding of tumor development, progression, and therapeutic vulnerability [20]. As mutational signature databases grow, their application in lymphoid malignancies is expected to become increasingly robust and clinically actionable. 2.2. Methods of Detection and Analysis World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2850 Identifying mutational signatures in lymphoma requires high-resolution genomic sequencing data. Whole-genome sequencing (WGS) remains the gold standard for comprehensive signature analysis, as it captures both coding and noncoding regions, allowing for the detection of mutation patterns across the entire genome [21]. However, the high cost and computational demand of WGS limit its widespread clinical use. Whole-exome sequencing (WES), which focuses on protein-coding regions, is a more accessible alternative but provides a narrower view of mutational activity, potentially overlooking critical non-coding signatures [22]. Following sequencing, mutation calls are annotated using bioinformatics pipelines such as Mutect2, VarScan, or Strelka. These calls are then aggregated into trinucleotide or other contextual matrices to quantify the contribution of various mutational processes. Non-negative matrix factorization (NMF) and hierarchical Bayesian models are typically applied to deconvolute these data into discrete signatures that can be matched against the COSMIC reference database [23]. To standardize this process, computational tools such as SigProfiler, deconstructSigs, and MutationalPatterns have been developed, allowing researchers and clinicians to input variant data and receive signature profiles in return. These tools facilitate reproducibility and improve the comparability of studies across different platforms and laboratories [24]. Nevertheless, there are several limitations to current signature detection workflows. First, sample purity and sequencing depth can significantly affect the accuracy of mutation calls and the detection of low-frequency signatures. Second, technical artifacts introduced during library preparation or sequencing can confound genuine biological signals. Lastly, inter-sample variability and tumor heterogeneity necessitate larger cohorts and careful statistical modeling to avoid misclassification [25]. Standardization efforts are underway to address these challenges. The Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, for example, has proposed benchmarks and quality control metrics for signature attribution, enhancing the robustness of signature-based diagnostics [26]. Moreover, multi-institutional efforts are beginning to incorporate these tools into translational and clinical workflows, especially in trials investigating genomically guided therapy selection. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2851 Figure 1 Workflow diagram illustrating the process of mutational signature analysis in lymphoma, from sample preparation and sequencing to computational deconvolution and clinical interpretation As the field advances, integrating mutational signature analysis with real-time sequencing and decision-support systems may become routine in managing aggressive lymphomas or tracking therapy response, particularly where resistance is mediated by specific mutagenic processes. 2.3. Characterized Mutational Signatures in Specific Lymphoma Subtypes While most research on mutational signatures has focused on solid tumors, a growing body of work is now characterizing specific signatures across different lymphoma subtypes. These include DLBCL, FL, MCL, and BL, each of which exhibits unique mutational profiles that can inform diagnosis, prognosis, and treatment strategy [27]. In diffuse large B-cell lymphoma (DLBCL), one of the most prevalent and aggressive subtypes, signatures associated with activation-induced cytidine deaminase (AID) and APOBEC enzyme activity are frequently observed. AID, critical for somatic hypermutation and class-switch recombination in germinal center B cells, is implicated in off-target mutations leading to SBS9 and SBS84 signatures [28]. These mutations typically affect oncogenes such as MYC, BCL6, or PIM1 and may drive early lymphomagenesis. APOBEC-related signatures (e.g., SBS2 and SBS13) are also detected in a subset of DLBCL and are associated with a hypermutated phenotype that may influence response to immunotherapy or PI3K inhibitors [29]. In follicular lymphoma (FL), AID-related mutagenesis predominates, reflecting its germinal center origin. FLs also exhibit high clonal diversity and frequent mutations in epigenetic regulators such as EZH2, CREBBP, and KMT2D, many of which are shaped by recurring mutational processes [30]. Mantle cell lymphoma (MCL) often demonstrates less heterogeneity in terms of mutational signatures but may harbor indel patterns reflective of DNA repair deficiency, especially in blastoid variants. These indel signatures can portend poor prognosis and resistance to conventional regimens [31]. In Burkitt lymphoma (BL), characterized by MYC translocations and a highly proliferative phenotype, unique mutational signatures have been linked to mismatch repair deficiency (MMRd) and ultraviolet light exposure in certain endemic cases. SBS6, SBS15, and SBS44, often associated with MMRd, contribute to the elevated mutation burden observed in a subset of BL tumors, especially those occurring in immunocompromised individuals [32]. From a clinical standpoint, the presence of specific mutational signatures can aid in differential diagnosis, especially in ambiguous histologies. Moreover, signature profiling may assist in predicting therapy response or identifying therapeutic vulnerabilities. For instance, tumors with mismatch repair-deficient signatures may benefit from immune checkpoint blockade, while APOBEC-rich lymphomas may require novel agents that mitigate DNA hypermutation [33]. The clinical integration of mutational signatures remains an evolving field, but early evidence suggests that these patterns can stratify patients beyond conventional genomic biomarkers. As such, signature-guided classification could become an indispensable component of lymphoma precision medicine, particularly when combined with clonal profiling and real-time monitoring. 3. Clonality and clonal evolution in lymphoma 3.1. Concepts of Clonality and Intratumoral Heterogeneity Heterogeneity The concept of clonality in cancer describes the genetic lineage of tumor cells that originate from a common ancestral cell, known as the founder clone. In lymphomas, the founder clone typically arises from a single Bor T-cell precursor that has undergone malignant transformation [34]. However, due to ongoing genetic instability and selective pressures within the tumor microenvironment, this founder population often gives rise to subclones — genetically distinct offshoots that may vary in their proliferative capacities, resistance profiles, and metastatic potential. These subclonal populations form the basis of intratumoral heterogeneity, a hallmark of cancer evolution that complicates both diagnosis and treatment. Subclones may either remain minor or expand through clonal sweeps, where World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2852 selective advantages—such as resistance to therapy or enhanced proliferation—allow certain clones to dominate the tumor population [35]. An important related concept is clonal selection, whereby external forces like chemotherapy or immune surveillance preferentially eliminate sensitive clones while sparing or enriching resistant ones. This Darwinian process drives disease progression and relapse in lymphoma patients, especially in aggressive subtypes like diffuse large B-cell lymphoma (DLBCL) or mantle cell lymphoma (MCL) [36]. The degree of heterogeneity and clonal architecture has been directly associated with disease aggressiveness and prognosis. Tumors with high clonal diversity tend to be more adaptable and less responsive to monotherapy. For instance, a high clonal burden at diagnosis correlates with shorter progression-free survival in follicular lymphoma (FL) and poorer outcomes in relapsed/refractory DLBCL [37]. Understanding these evolutionary dynamics is critical for precision oncology. Accurate assessment of clonal structures enables oncologists to track disease progression, predict treatment response, and tailor therapeutic strategies that address both dominant and emerging resistant subclones [38]. 3.2. Technological Advances in Clonality Assessments The last decade has seen significant advances in technologies used to assess clonality in hematologic malignancies. Traditionally, clonality was inferred from bulk sequencing approaches, which analyze DNA or RNA from a pool of cells and provide an averaged view of mutational landscapes. While informative, bulk methods cannot distinguish between individual clones, especially when subclones are present at low frequencies [39]. To overcome this limitation, single-cell sequencing has emerged as a transformative tool in lymphoma research. It enables the direct analysis of individual tumor cells, revealing not only their mutational profiles but also transcriptomic states, lineage trajectories, and epigenetic modifications. This technology has uncovered extensive clonal diversity even within morphologically homogeneous tumors [40]. However, its clinical adoption remains constrained by cost, technical complexity, and data interpretation challenges. Another powerful tool is digital PCR (dPCR), which provides highly sensitive quantification of rare clonal variants. dPCR is particularly useful for detecting minimal residual disease (MRD), where traditional methods may lack the sensitivity to identify residual malignant clones post-treatment. MRD monitoring via dPCR has become a validated prognostic tool in multiple lymphoma subtypes, guiding therapy duration and intensification decisions [41]. In B-cell lymphomas, clonality is also assessed by examining B-cell receptor (BCR) gene rearrangements. During normal lymphocyte development, unique recombinations of V(DJ) gene segments occur in the immunoglobulin heavy chain (IGH), generating a diverse BCR repertoire. The detection of a dominant, monoclonal IGH rearrangement indicates a clonal expansion, while the presence of multiple rearrangements may suggest biclonal disease or subclonal diversification [42]. Similarly, T-cell receptor (TCR) rearrangements are used to evaluate clonality in T-cell lymphomas. High-throughput sequencing platforms such as Adaptive Biotechnologies' ImmunoSEQ or ArcherDx's VariantPlex enable deep interrogation of immune receptor repertoires, supporting both diagnostic and MRD applications [43]. Despite their utility, clonality assessment techniques vary in sensitivity, resolution, and clinical applicability. The following table summarizes key comparative features: Table 1 Comparison of Techniques for Clonality Detection in Lymphoma Method Resolution Sensitivity Clinical Utility Bulk DNA Sequencing Low Moderate Standard for mutation calling Single-Cell Sequencing High High Research-grade; not yet routine clinically Digital PCR (dPCR) Moderate Very High MRD detection and therapy monitoring World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2853 BCR/TCR Rearrangement PCR High (clone-specific) High Diagnosis, MRD, and clonality confirmation Standardizing these tools for clinical practice remains a priority, especially as next-generation sequencing (NGS) becomes integrated into lymphoma diagnostic algorithms [44]. 3.3. Clonal Dynamics in Therapy Response and Relapse Therapy exerts powerful selective pressure on tumor populations, often altering their clonal architecture. This phenomenon, known as clonal evolution, plays a central role in the development of resistance and relapse in lymphoma. At diagnosis, a tumor may appear to be dominated by a single clone, but therapy can lead to the expansion of previously minor subclones that harbor survival advantages [45]. A classic example is the emergence of chemo-resistant subclones following R-CHOP therapy in DLBCL. While initial response rates are high, a significant proportion of patients relapse with tumors that possess new mutations in genes associated with drug resistance, such as TP53 or BCL2. These genetic alterations are often absent or subclonal at baseline, highlighting the dynamic nature of clonal selection [46]. Longitudinal monitoring of clonal composition can provide early warnings of therapeutic failure. In follicular lymphoma, studies have shown that the appearance of a dominant EZH2-mutant clone during therapy correlates with poor prognosis and suggests early clonal divergence [47]. In mantle cell lymphoma, high clonal complexity post-induction therapy has been associated with rapid progression and limited benefit from maintenance regimens. Importantly, not all resistant clones arise de novo. Some are therapy-induced as a result of treatment-associated mutagenesis. For example, cytotoxic drugs can introduce new mutations that confer fitness advantages to previously quiescent clones, leading to therapy-driven evolution rather than simple selection from pre-existing populations [48]. The integration of serial sequencing, both at the bulk and single-cell level, enables researchers and clinicians to map the trajectory of clonal changes over time. This includes identifying when a resistant subclone first emerges, how rapidly it expands, and how it responds to second-line therapy. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2854 Figure 2 Longitudinal clonal evolution in lymphoma showing clonal dynamics at diagnosis, during therapy, and postrelapse, with mutation tracking across time points These insights inform adaptive therapeutic strategies. Instead of waiting for relapse, oncologists may intervene proactively based on clonal trends—escalating therapy, switching agents, or adding novel treatments that target resistant clones [49]. Moreover, understanding the clonal basis of resistance opens the door to rational drug combinations designed to target both dominant and emergent clones simultaneously. This could reduce the risk of relapse and improve long-term disease control. For example, combining BCL2 inhibitors with immune checkpoint blockade has shown promise in targeting multiple clonal compartments in relapsed FL and DLBCL [50]. Ultimately, incorporating clonal assessments into standard care can personalize treatment across all stages of lymphoma—from initial diagnosis to salvage therapy—marking a key advancement in precision hematology. 4. Integration of mutational signatures and clonality in precision oncology 4.1. Linking Genomic Profiles to Targetable Mutations Genomic profiling in lymphoma has enabled the identification of actionable driver mutations that contribute to oncogenesis and tumor maintenance. These include alterations in genes involved in B-cell receptor signaling (e.g., CARD11, CD79B), apoptosis regulation (BCL2, TP53), and chromatin remodeling (EZH2, CREBBP, KMT2D) [51]. Such driver mutations often emerge from selective evolutionary pressures and represent attractive therapeutic targets for pathway-directed agents. Integrating mutational signatures enhances the interpretive power of genomic profiling by connecting mutation patterns to specific biological mechanisms. For example, tumors exhibiting AID-related mutational signatures often harbor rearrangements or mutations in MYC, BCL6, and other genes that drive germinal center-derived lymphomas [52]. Similarly, the presence of APOBEC signatures may point to genomic instability, rendering cells more susceptible to synthetic lethal strategies targeting DNA repair pathways. The principle of synthetic lethality—where co-occurring gene disruptions lead to cell death—offers a compelling strategy for targeting tumors with defined mutational contexts. For instance, lymphomas with mutations in DNA damage response genes (e.g., ATM, CHEK2) may be vulnerable to PARP inhibitors [53]. The combination of signature analysis and mutation profiling can thus pinpoint critical vulnerabilities not evident through traditional sequencing alone. Furthermore, these signatures often illuminate pathway dependencies, allowing for rational drug development and repurposing. Tumors with MMR-deficient signatures (e.g., SBS6, SBS15) exhibit heightened sensitivity to immune checkpoint blockade due to increased neoantigen burden [54]. Integrating this information with clonality data allows clinicians to design therapies that selectively disrupt the evolutionary core of the disease, preventing recurrence and clonal escape. Ultimately, linking genomic profiles to actionable mutations via mutational signature interpretation transforms static sequencing data into dynamic, clinically actionable insights, enabling targeted interventions tailored to individual tumor biology [55]. 4.2. Predictive Value of Signatures for Treatment Sensitivity Mutational signatures serve not only as etiological clues but also as predictive biomarkers of treatment sensitivity. Certain signatures correlate strongly with therapeutic response, providing a framework for refining patient stratification and guiding the selection of precision therapies. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2855 A prominent example is mismatch repair (MMR) deficiency, often reflected by mutational signatures such as SBS6, SBS15, and SBS26. These tumors exhibit high tumor mutation burden (TMB), a condition that enhances the generation of neoantigens and subsequently renders the malignancy more responsive to immune checkpoint inhibitors (ICIs) like anti-PD-1 or anti-CTLA-4 therapies [56]. Although primarily studied in solid tumors, MMR deficiency has also been observed in subsets of aggressive lymphomas, including primary mediastinal B-cell lymphoma and Richter ’s transformation [57]. Another signature of therapeutic relevance is APOBEC-associated hypermutation, typically represented by SBS2 and SBS13. These mutations generate localized clusters of cytosine-to-thymine substitutions, contributing to increased mutational burden and genomic instability. In lymphoma, APOBEC activity is frequently associated with resistance to conventional chemotherapy but paradoxically may confer sensitivity to PI3K inhibitors due to pathway rewiring [58]. Emerging data suggest that APOBEC-enriched tumors are more likely to harbor PIK3CA, PTEN, and AKT1 mutations— alterations that make the PI3K/AKT/mTOR axis an actionable therapeutic target. Thus, mutational signatures provide a layer of context that enhances the predictability of drug response beyond simple mutation presence or absence [59]. Importantly, signatures also inform resistance prediction. In DLBCL, for instance, SBS17—a pattern linked to oxidative stress—has been associated with early relapse after R-CHOP therapy, suggesting an aggressive phenotype and potential resistance to anthracyclines [60]. Identifying such patterns allows for early therapeutic escalation or inclusion in clinical trials exploring novel agents. The utility of mutational signatures in predicting therapeutic efficacy is rapidly gaining traction and is now being incorporated into biomarker panels alongside traditional genomic markers, immune cell infiltration metrics, and MRD status [61]. These integrative approaches offer a holistic view of tumor behavior, enhancing personalized treatment strategies and improving outcomes in patients with refractory or high-risk lymphoma. 4.3. Clonality-Guided Risk Stratification and Therapy Design The evolving understanding of tumor clonality has shifted the paradigm of therapy design from targeting a uniform disease entity to addressing a heterogeneous population of evolving clones. Stratifying patients based on clonal architecture allows oncologists to anticipate disease trajectory and optimize treatment selection. One of the most significant advantages of clonality analysis is its ability to differentiate dominant from emerging subclones. Dominant clones, often responsible for bulk disease at presentation, may be effectively targeted by first-line therapies. However, minor subclones—frequently overlooked in bulk analyses—can harbor mutations conferring resistance, such as TP53 loss or MYD88 mutations, and expand under treatment pressure [62]. Therapies designed to eliminate dominant clones while suppressing the outgrowth of minor subclones are more likely to yield durable remissions. For example, the use of venetoclax, a BCL2 inhibitor, has shown activity in BCL2-dominant follicular lymphomas, while combination with agents targeting EZH2-mutated subclones can further delay resistance [63]. Clonal data also guide combination therapy decisions. By characterizing the subclonal architecture of a tumor, clinicians can choose drug pairs or triplets that simultaneously disrupt multiple evolutionary pathways. In relapsed/refractory DLBCL, dual inhibition of CD79B (via antibody-drug conjugates) and BCL6 (a transcriptional repressor) has demonstrated efficacy in tumors with bifurcated clonal drivers [64]. Importantly, understanding clonality enhances risk stratification. Patients with high clonal diversity, rapid clonal turnover, or therapy-induced clonal reshaping are more likely to experience relapse and may benefit from early hematopoietic stem cell transplantation or enrollment in adaptive clinical trials [65]. Table 2 Targeted Therapies Associated with Specific Mutational Signatures and Clonal Architectures Mutational Signature / Clonal Feature Associated Targetable Pathway Therapeutic Agent(s) World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2862 [29] Lacy SE, Barrans SL, Beer PA, Painter D, Smith AG, Roman E, et al. Targeted sequencing in DLBCL reveals subsetspecific mutations that correlate with outcome. Haematologica. 2020;105(3):e109-12. [30] Green MR, Kihira S, Liu CL, Nair RV, Salari R, Gentles AJ, et al. Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation. Proc Natl Acad Sci USA. 2015;112(10):E1116-25. [31] Bea S, Valdes-Mas R, Navarro A, Salaverria I, Martin-Garcia D, Jares P, et al. Landscape of somatic mutations and clonal evolution in mantle cell lymphoma. Proc Natl Acad Sci USA. 2013;110(45):18250-5. [32] Schmitz R, Young RM, Ceribelli M, Jhavar S, Xiao W, Zhang M, et al. Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics. Nature. 2012;490(7418):116-20. [33] Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357(6349):409-13. [34] Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194(4260):23-8. [35] Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481(7381):306-13. [36] Landau DA, Carter SL, Stojanov P, McKenna A, Stevenson K, Lawrence MS, et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell. 2013;152(4):714-26. [37] Araf S, Wang J, Korfi K, Pangault C, Kotsiou E, Rio-Machin A, et al. Genomic profiling reveals spatial intra-tumor heterogeneity in follicular lymphoma. Leukemia. 2018;32(5):1261-5. [38] Damm F, Mylonas E, Cosson A, Yoshida K, Della Valle V, Mouly E, et al. Acquired initiating mutations in early hematopoietic cells of CLL patients. Cancer Discov. 2014;4(9):1088-101. [39] Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;17(3):175-88. [40] Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396-401. [41] Kurtz DM, Green MR, Bratman SV, Scherer F, Liu CL, Kunder CA, et al. Noninvasive monitoring of diffuse large Bcell lymphoma by immunoglobulin high-throughput sequencing. Blood. 2015;125(24):3679-87. [42] van Dongen JJ, Langerak AW, Brüggemann M, Evans PA, Hummel M, Lavender FL, et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia. 2003;17(12):2257-317. [43] Carlson CS, Emerson RO, Sherwood AM, Desmarais C, Chung MW, Parsons JM, et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat Commun. 2013;4:2680. [44] Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391-405. [45] Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012;481(7382):506-10. [46] Rossi D, Spina V, Deambrogi C, Rasi S, Laurenti L, Stamatopoulos K, et al. The genetics of Richter syndrome reveals disease heterogeneity and predicts survival after transformation. Blood. 2011;117(12):3391-401. [47] Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL, Corbett RD, et al. Frequent mutation of histonemodifying genes in non-Hodgkin lymphoma. Nature. 2011;476(7360):298-303. [48] Holstein SA, McCarthy PL. Immunomodulatory drugs in multiple myeloma: mechanisms of action and clinical experience. Drugs. 2017;77(5):505-20. [49] Anderson KC, Alsina M, Atallah E, Biermann JS, Chandler JC, Costello C, et al. NCCN Guidelines Insights: Multiple Myeloma, Version 3.2018. J Natl Compr Canc Netw. 2018;16(1):11-20. [50] Ansell SM, Lesokhin AM, Borrello I, Halwani A, Scott EC, Gutierrez M, et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma. N Engl J Med. 2015;372(4):311-9. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2863 [51] Reddy A, Zhang J, Davis NS, Moffitt AB, Love CL, Waldrop A, et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell. 2017;171(2):481-94.e15. [52] Pasqualucci L, Dalla-Favera R. Genetics of diffuse large B-cell lymphoma. Blood. 2018;131(21):2307-19. [53] Lord CJ, Ashworth A. PARP inhibitors: Synthetic lethality in the clinic. Science. 2017;355(6330):1152-8. [54] Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 Blockade in Tumors with MismatchRepair Deficiency. N Engl J Med. 2015;372(26):2509-20. [55] Moffitt AB, Dave SS. Clinical applications of the genomic landscape of aggressive non-Hodgkin lymphoma. J Clin Oncol. 2017;35(9):955-62. [56] Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124-8. [57] Xiao W, Gandhi M, Wang J, Li Z, Zhao X, Gopalakrishnan R, et al. Mismatch repair deficiency is associated with increased mutation rate in advanced cutaneous T-cell lymphoma. Blood. 2020;135(10):737-47. [58] Petljak M, Dananberg A, Chu K, Bergstrom EN, Striepen J, von Morgen P, et al. Mechanisms of APOBEC3 mutagenesis in human cancer cells. Nature. 2022;607(7920):799-807. [59] Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127(20):2375-90. [60] Ennishi D, Jiang A, Boyle M, Collinge B, Grande BM, Ben-Neriah S, et al. Double-hit gene expression signature defines a distinct subgroup of germinal center B-cell-like diffuse large B-cell lymphoma. J Clin Oncol. 2019;37(3):190-201. [61] Scott DW, Mottok A, Ennishi D, Wright GW, Farinha P, Ben-Neriah S, et al. Prognostic significance of diffuse large Bcell lymphoma cell of origin determined by digital gene expression in formalin-fixed paraffin-embedded tissue biopsies. J Clin Oncol. 2015;33(26):2848-56. [62] Landau DA, Sun C, Rosebrock D, Herman SEM, Fein J, Sivina M, et al. The evolutionary landscape of chronic lymphocytic leukemia treated with ibrutinib targeted therapy. Nat Commun. 2017;8(1):2185. [63] Davids MS, Roberts AW, Seymour JF, Pagel JM, Kahl BS, Wierda WG, et al. Phase I First-in-Human Study of Venetoclax in Patients With Relapsed or Refractory Non-Hodgkin Lymphoma. J Clin Oncol. 2017;35(8):826-33. [64] Wilson WH, Young RM, Schmitz R, Yang Y, Pittaluga S, Wright G, et al. Targeting B cell receptor signaling with ibrutinib in diffuse large B cell lymphoma. Nat Med. 2015;21(8):922-6. [65] Jain N, Keating MJ, Thompson PA, Burger JA, Ferrajoli A. Ibrutinib and venetoclax for first-line treatment of CLL. N Engl J Med. 2023;388(4):327-39. [66] Roschewski M, Dunleavy K, Pittaluga S, Moorhead M, Pepin F, Kong K, et al. Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study. Lancet Oncol. 2015;16(5):541-9. [67] Kurtz DM, Esfahani MS, Scherer F, Soo J, Jin MC, Liu CL, et al. Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction. Cell. 2019;178(3):699-713.e19. [68] Morschhauser F, Tilly H, Chaidos A, McKay P, Phillips T, Assouline S, et al. Tazemetostat for patients with relapsed or refractory follicular lymphoma: an open-label, single-arm, multicentre, phase 2 trial. Lancet Oncol. 2020;21(11):1433-42. [69] Wright GW, Huang DW, Phelan JD, Coulibaly ZA, Roulland S, Young RM, et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell. 2020;37(4):551-68.e14. [70] Delfau-Larue MH, van der Gucht A, Dupuis J, Jais JP, Nel I, Beldi-Ferchiou A, et al. PET-driven strategy in patients with limited-stage diffuse large B-cell lymphoma: a randomized controlled trial. J Clin Oncol. 2021;39(31):3473-82. [71] Younes A, Hilden P, Coiffier B, Hagenbeek A, Salles G, Wilson W, et al. International Working Group consensus response evaluation criteria in lymphoma (RECIL 2017). Ann Oncol. 2017;28(7):1436-47. World Journal of Advanced Research and Reviews, 2025, 26(02), 2847-2864 2864 [72] Flaherty KT, Gray RJ, Chen AP, Li S, McShane LM, Patton D, et al. Molecular Landscape and Actionable Alterations in a Genomically Guided Cancer Clinical Trial: National Cancer Institute Molecular Analysis for Therapy Choice (NCIMATCH). J Clin Oncol. 2020;38(33):3883-94. [73] Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med. 2017;376(22):2109-21. [74] Morschhauser F, Le Gouill S, Feugier P, Bailly S, Nicolas-Virelizier E, Bijou F, et al. Obinutuzumab versus Rituximab in Previously Untreated Patients with Follicular Lymphoma: A Propensity Score Analysis of the GALLIUM Study. J Clin Oncol. 2021;39(16):1805-16. [75] Chapuy B, Stewart C, Dunford AJ, Kim J, Wienand K, Kamburov A, et al. Genomic analyses of PMBL reveal new drivers and mechanisms of sensitivity to PD-1 blockade. Blood. 2019;134(26):2369-82. [76] Kurtz DM, Scherer F, Jin MC, Soo J, Craig AFM, Esfahani MS, et al. Circulating Tumor DNA Measurements As Early Outcome Predictors in Diffuse Large B-Cell Lymphoma. J Clin Oncol. 2018;36(28):2845-53. [77] Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, et al. Feasibility of Large-Scale Genomic Testing to Facilitate Enrollment Onto Genomically Matched Clinical Trials. J Clin Oncol. 2015;33(25):2753-62. [78] Jennings LJ, Arcila ME, Corless C, Kamel-Reid S, Lubin IM, Pfeifer J, et al. Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn. 2017;19(3):341-65. [79] Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15(7):565-74. [80] Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types. Cell. 2018;173(2):291-304.e6. [81] Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023-31. [82] Jiao W, Atwal G, Polak P, Karlic R, Cuppen E, Danyi A, et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun. 2020;11(1):728. [83] Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX. DeepProg: an ensemble of deep-learning and machinelearning models for prognosis prediction using multi-omics data. Genome Med. 2021;13(1):112. [84] Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, et al. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep. 2018;23(1):172-80.e3. [85] Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9. [86] Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-15. [87] Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054-6. [88] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. [89] Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021;596(7871):211-20. [90] Rodrigues SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463-7. [91] Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of breast cancer. Nature. 2020;578(7796):615-20. [92] Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9-14.