Next Article in Journal
Ion Channel Integration and Functional Coupling in Salivary Gland Fluid Secretion
Next Article in Special Issue
Glycolytic Enzymes Are Part of an Oncogenic Network in AML
Previous Article in Journal
Plasma Extracellular Vesicles from Bronchopulmonary Dysplasia Infants Initiate Inflammation and Abnormal Angiogenesis in Neonatal Murine Retinas
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cells
Volume 15
Issue 4
10.3390/cells15040368
cells-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

From Cell Lines to Avatars: Charting the Future of Preclinical Modeling in T-Cell Malignancies

by
Pier Paolo Piccaluga
1,2,*,
Luigi Cimmino
1,2,
Valeriia Tsekhovska
1,2,
Pietro Cimatti
1,3,
Claudia Innocenti
1,
Sabrina Seidenari
1,
Giulia Calafato
1,
Floriana J. Di Paola
1 and
Giovanni Tallini
2,4
1
Biobank of Research, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti, 9, 40138 Bologna, Italy
2
Department of Medical and Surgical Sciences, Bologna University School of Medicine, 40138 Bologna, Italy
3
Reconstructive Orthopaedic Surgery Innovative Techniques–Musculoskeletal Tissue Bank, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
4
Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
*
Author to whom correspondence should be addressed.
Cells 2026, 15(4), 368; https://doi.org/10.3390/cells15040368
Submission received: 19 January 2026 / Revised: 9 February 2026 / Accepted: 18 February 2026 / Published: 19 February 2026
(This article belongs to the Special Issue Hematopoietic Cell Lines as Models for Leukemia and Lymphoma)
Browse Figures
Review Reports Versions Notes

Highlights

What are the main findings?
  • This review maps the current preclinical landscape of T-cell malignancies, showing a marked excess of T-ALL, CTCL, and ALCL cell lines contrasted with a near-complete lack of reliable models for most peripheral T-cell lymphoma (PTCL) entities.
  • It delineates how newer platforms—such as patient-derived xenografts, 3D cultures, and “avatar” models—better recapitulate tumor biology and microenvironmental dependence than conventional 2D cell lines.
What is the implication of the main finding?
  • The imbalance in available models helps explain why research and drug development are concentrated in a few T-cell lymphoma subtypes, underscoring the need for coordinated efforts to establish, authenticate, and share models of rare PTCLs.
  • Integrating advanced, patient-tailored models into preclinical pipelines may improve target discovery, refine drug sensitivity testing, and ultimately support more effective precision medicine strategies in T-cell malignancies.

Abstract

T-cell malignancies represent a complex spectrum of clinically and biologically heterogeneous diseases. Effective translational research and drug development are critically dependent on preclinical models that faithfully recapitulate this diversity. This review analyzes the current preclinical landscape, identifying a profound disparity between the clinical spectrum of T-cell neoplasms and the available in vitro tools. We demonstrate that the existing armamentarium of cell lines is heavily skewed, with an abundance of models for T-cell lymphoblastic leukemia/lymphoma (T-ALL), cutaneous T-cell lymphoma (CTCL), and anaplastic large cell lymphoma (ALCL). This skew is a direct result of a biological selection bias, as these entities are often driven by potent, TME-independent oncogenes (e.g., NOTCH1 mutations, NPM1-ALK fusions) conducive to immortalization. Conversely, the majority of peripheral T-cell lymphoma (PTCL) subtypes, which are frequently TME-dependent and clinically aggressive, remain “preclinical orphans” with few or no authenticated models. This “preclinical void” constitutes a major bottleneck, impeding mechanistic studies and therapeutic progress. We discuss the limitations of 2D cultures and highlight the necessity of adopting advanced platforms, such as patient-derived xenografts (PDX) and 3D organoid systems. These “avatar” models preserve vital tumor heterogeneity and microenvironmental context, offering superior predictive value. The systematic development and integration of these next-generation models are essential to bridge the translational gap and advance precision medicine for all patients with T-cell malignancies.

Graphical Abstract

1. Introduction

Human leukemia-lymphoma cell lines represent a cornerstone of modern cancer research. First established in the 1960s, these immortalized cell populations provide an unlimited, genetically stable, and globally accessible resource for investigating the fundamental pathophysiology of hematopoietic tumors [1,2,3]. Their utility in dissecting molecular pathways, identifying novel therapeutic targets, and conducting high-throughput drug screening is undisputed. However, the value of this armamentarium is contingent upon its ability to faithfully represent the vast clinical and biological diversity of the diseases under study. For T-cell malignancies, such an endeavor presents a formidable challenge [4].
T-cell neoplasms are not a monolithic entity but rather a complex collection of dozens of distinct diseases, as defined by the World Health Organization (WHO) and International Consensus Classification (ICC) systems [5]. These malignancies arise from T-lymphocytes at various stages of differentiation, ranging from immature thymic precursors, which give rise to T-cell lymphoblastic leukemia/lymphoma (T-ALL/LBL), to a wide spectrum of mature, post-thymic T-cells that form the basis of peripheral T-cell lymphomas (PTCLs) [6,7,8]. Our group and others have dedicated significant effort to elucidating the profound heterogeneity within the PTCL category, which is characterized by immense morphological, immunophenotypic, and molecular diversity [9]. This complexity makes PTCLs exceptionally difficult to classify, diagnose, and treat, earning them the unfortunate designation of “orphan diseases” [10,11].
This review sought to show that a critical disparity exists between the clinical diversity of T-cell malignancies and the preclinical models available to study them. The landscape of available cell lines is heavily skewed, with an abundance of well-characterized models for a few specific subtypes—namely, T-ALL/LBL, cutaneous T-cell lymphoma (CTCL), and anaplastic large cell lymphoma (ALCL). In stark contrast, for the majority of PTCL entities, which are often highly aggressive and associated with dismal prognoses, reliable and authenticated cell line models are either exceedingly rare or entirely non-existent [11,12]. This imbalance is not a mere artifact of historical collection but a direct consequence of the underlying biology of these tumors. Malignancies such as ALK-positive ALCL and many T-ALLs are driven by potent oncogenes, like the NPM1-ALK fusion protein or activating NOTCH1 mutations, which confer robust, cytokine-independent proliferative signals that are highly conducive to establishment and maintenance in artificial in vitro culture conditions [13]. Conversely, many rare PTCLs, such as angioimmunoblastic T-cell lymphoma (AITL), are intrinsically dependent on a complex tumor microenvironment (TME) for survival and proliferation—a condition that is lost in standard two-dimensional culture systems. This biological selection bias has created a vicious cycle: the availability of models drives research and drug discovery, leading to disproportionate progress in ALCL and T-ALL [14], while the entities lacking models remain neglected, perpetuating their status as preclinical and clinical orphans (Figure 1).

2. The Established Landscape: Available T-Cell Malignancy Cell Lines

The current collection of T-cell malignancy cell lines, curated by repositories such as the American Type Culture Collection (ATCC) and the German Collection of Microorganisms and Cell Cultures (DSMZ), reflects this skewed distribution. A systematic review reveals a clear pattern of abundance for some entities and a profound void for others (Table 1, Table 2 and Table 3).

2.1. Models for Immature T-Cell Neoplasms: T-Lymphoblastic Leukemia/Lymphoma (T-ALL/LBL)

T-ALL/LBL are aggressive neoplasms of T-cell precursors, accounting for approximately 15% of pediatric and 25% of adult ALL cases [7,15,16]. The molecular pathogenesis is characterized by a multi-step accumulation of genetic lesions. Key events include the aberrant expression of oncogenic transcription factors (e.g., TAL1, TLX1, HOXA), which often arise from chromosomal translocations involving T-cell receptor (TCR) loci and define distinct molecular subgroups that correspond to specific stages of T-cell developmental arrest [15,16]. These events are frequently coupled with hyperactive NOTCH1 signaling, due to activating mutations in over 60% of cases, and loss of the CDKN2A/B tumor suppressor locus in up to 70% of cases [15,17]. The strong proliferative signals conferred by these mutations have facilitated the establishment of numerous cell lines (Table 1).
A comprehensive analysis of 16 widely used T-ALL cell lines demonstrated that, despite their common classification, each line possesses a unique immunophenotype and TCR gene rearrangement status. This study proposed a categorization into pro-T, pre-T, cortical T, and mature T differentiation stages, underscoring the heterogeneity that these models collectively represent [18].

2.2. Models for Cutaneous T-Cell Lymphomas (CTCL)

CTCLs are a group of overall rare extranodal non-Hodgkin lymphomas defined by the infiltration of malignant T-cells into the skin, with Mycosis Fungoides (MF) and the related leukemic form, Sézary Syndrome (SS), being the most prevalent subtypes [19]. Several key cell lines have been established, primarily from patients with advanced-stage, aggressive disease (Table 2) [20,21]. These models have been instrumental in preclinical studies, particularly for evaluating novel therapeutic agents. For instance, the HUT 78 cell line was used to demonstrate the sensitivity of T-cell lymphoma to the histone deacetylase inhibitor (HDACi) depsipeptide [22], providing a key preclinical rationale for the development of this drug class for T-cell malignancies [23]. It should be noted, however, that CTCL remains an orphan disease and establishing reliable models is difficult, since the disease exhibits significant dependence on the tumor microenvironment (TME), such as interactions with keratinocytes and fibroblasts.

2.3. Models for Anaplastic Large Cell Lymphoma (ALCL)

ALCL is a mature T-cell lymphoma characterized by strong expression of the CD30 antigen [24]. It is divided into two main categories based on the expression of Anaplastic Lymphoma Kinase (ALK). ALK-positive ALCL, which is more common in younger patients, is defined by chromosomal rearrangements involving the ALK gene on chromosome 2p23, most frequently the t(2;5)(p23;q35) translocation that generates the potent NPM1-ALK fusion oncogene [25]. The constitutive kinase activity of NPM1-ALK drives robust downstream signaling through pathways including JAK/STAT and PI3K/AKT, promoting cell survival and proliferation [26]. This powerful oncogenic driver has made ALK+ ALCL one of the most successfully modeled PTCL subtypes, with a wealth of available cell lines (Table 3) [27]. These models have proven essential not only for in vitro studies but also for confirming tumorigenicity in vivo, as demonstrated by the characterization of novel lines in SCID mice [28]. In contrast, ALK-negative ALCL, which lacks ALK rearrangements and generally has a poorer prognosis, is less well-represented by cell line models, though some exist (Table 3; Figure 2) [5,29,30].

2.4. The Void: Scarcity of Models for Other Peripheral T-Cell Lymphomas (PTCL)

Beyond ALCL and CTCL, the landscape of preclinical models for mature T-cell malignancies is remarkably sparse. This gap is particularly acute for the diverse and aggressive subtypes that fall under the PTCL umbrella. As our group has demonstrated through extensive gene expression profiling studies, many of these entities arise from distinct functional T-cell subsets, such as the derivation of AITL from T-follicular helper (TFH) cells [9,31]. This cellular origin implies a profound dependence on signals from a complex TME, a feature that is lost in conventional cell culture and likely explains the extreme difficulty in establishing stable cell lines for these diseases.
It should be noted that the precise number of authenticated cell lines may vary across different literature sources and repository updates. However, in general, it is possible to claim that there are no widely available, authenticated cell lines for many of the most challenging PTCLs, including, among others, angioimmunoblastic T-cell lymphoma (AITL), hepatosplenic T-cell lymphoma (HSTCL) [32], enteropathy-associated T-cell lymphoma (EATL) and extranodal NK/T-cell lymphoma, nasal type (ENKTL) [33].
A notable and recent exception is the T8ML-1 cell line, the first authenticated model for PTCL, not otherwise specified (PTCL-NOS), harboring the recurrent t(14;19)(q11.2;q13.3) translocation [30,34]. This translocation results in the juxtaposition of the TCR alpha (TRA@) locus and the PVRL2 gene, leading to its overexpression [34]. The successful characterization of T8ML-1 provides a crucial and unique tool for studying this specific genetic subtype of PTCL-NOS, but it also highlights the profound scarcity of models for this “wastebasket” category of PTCL [34]. The complexity of this category is further exemplified by cell lines like FE-PD, a CD30-positive line originally established from a patient diagnosed with Hodgkin’s disease, later reclassified as an aggressive anaplastic large cell lymphoma, and now considered a model for ALK-negative ALCL or CD30+ PTCL-NOS [35,36].
This striking imbalance between diseases and models has tangible consequences. The entities with robust in vitro models, such as ALCL and CTCL, are precisely those that have seen some notable progress in targeted therapy, including the clinical approval of ALK inhibitors and the early indication of HDAC inhibitors, respectively [13]. In contrast, diseases like PTCL/NOS, AITL and HSTCL, for which cell lines are so scant, continue to be treated with conventional, non-specific chemotherapy regimens with poor outcomes [8,11,12,37,38,39]. This correlation underscores a direct link: the availability of a preclinical model system is a critical prerequisite for successful translational research and the development of targeted therapies.
Table 1. Characteristics of selected T-lymphoblastic leukemia/lymphoma (T-ALL/LBL) cell lines.
Table 1. Characteristics of selected T-lymphoblastic leukemia/lymphoma (T-ALL/LBL) cell lines.
Cell LineATCC/DSMZ No.Patient OriginKey Genetic AlterationsImmunophenotypic StageKey Reference(s) (PMID)
JurkatTIB-152/ACC 28214Y, M, Relapsed T-ALLPTEN null, CDKN2A del, TP53 R196*Cortical/Mature T[5]
CCRF-CEMCCL-119/ACC 2403Y, F, Relapsed T-ALLFBXW7 R465C, TP53 R248QCortical T[5]
MOLT-3CRL-1552/ACC 31619Y, M, Relapsed T-ALLFBXW7 R505C, TP53 R248QCortical T[5]
SUP-T1CRL-1942/ACC 1408Y, M, Relapsed T-LBLFBXW7 R465H, TP53 nullMature T (CD4+/CD8+)[7]
DND-41ACC 52513Y, M, T-ALLTLX3-BCL11B fusion, IL7R ins, NOTCH1 mutCortical T (Type III)[18,40]
HPB-ALLACC 48314Y, M, T-ALLNOTCH1 mut, TP53 C124*, FBXW7 mutCortical T (CD4+/CD8+)[18,40]
Abbreviations: Y, year; M, male; F, female; del, deletion.
Table 2. Characteristics of selected cutaneous T-cell lymphoma (CTCL) cell lines.
Table 2. Characteristics of selected cutaneous T-cell lymphoma (CTCL) cell lines.
Cell LineATCC/DSMZ No.Histotype/Mature T-Cell OriginHTLV-1 StatusKey Genetic AlterationsKey Reference(s) (PMID)
HUT 78TIB-161/ACC 398Sézary Syndrome (Central Memory T-cell, TCM)NegativeTP53 C176F[8]
H9HTB-176CTCL (HUT 78 clone; TCM phenotype)NegativeCDKN2A del, RB1 del, TP53 R196*[4]
HHCRL-2105/ACC 707Aggressive CTCL (Non-MF/SS; Mature Cytotoxic phenotype)NegativeFOXK2::TP63 fusion, TP53 mut[10,31]
MJ [G11]CRL-8294CTCL (Mature CD4+; Skin-homing phenotype)Positive-[10]
MyLaACC 285Mycosis Fungoides (Skin-resident Memory, TRM)NegativeCDKN2A del, PTEN mut[20]
Abbreviations: del, deletion; mut, mutation.
Table 3. Characteristics of selected anaplastic large cell lymphoma (ALCL) and other peripheral T-cell lymphoma (PTCL) cell lines.
Table 3. Characteristics of selected anaplastic large cell lymphoma (ALCL) and other peripheral T-cell lymphoma (PTCL) cell lines.
Cell LineATCC/DSMZ No.HistotypeKey Genetic AlterationsKey Reference(s) (PMID)
SU-DHL-1CRL-2955/ACC 356ALCL, ALK+NPM1-ALK t(2;5), TP53 R273H[9]
Karpas 299ACC 31ALCL, ALK+NPM1-ALK t(2;5)[40]
SUP-M2ACC 509ALCL, ALK+NPM1-ALK t(2;5)[41]
KI-JKACC 695ALCL, ALK+NPM1-ALK t(2;5)[25]
SR-786ACC 369ALCL, ALK+NPM1-ALK t(2;5)[2,13,28]
DELACC 338ALCL, ALK+NPM1-ALK t(2;5)[2,13,28,35]
FE-PD-PTCL-NOS/ALK-ALCLJAK1 G1097V, STAT3 G618R[29,35]
TLBR-2ACC 905BIA-ALCL, ALK-STAT3 D661Y[29]
T8ML-1-PTCL-NOSTRA@::PVRL2 t(14;19)[34]
Abbreviations: BIA-ALCL, Breast Implant-Associated ALCL; PTCL-NOS, Peripheral T-cell lymphoma, not otherwise specified.

3. Utility and Limitations: The Role of Cell Lines in Translational Research and Drug Screening

The available T-cell lymphoma cell lines, though limited in scope, have been indispensable for major advances in both basic and translational research. The ALCL cell lines, for example, were fundamental in validating ALK as a bona fide therapeutic target, elucidating its downstream signaling pathways, and serving as the primary preclinical platform for testing the first generation of ALK inhibitors like crizotinib [13,26]. Similarly, CTCL cell lines have been crucial for demonstrating the potent anti-lymphoma activity of HDACis, providing the preclinical proof-of-concept that paved the way for their successful clinical development and approval [22]. In T-ALL, cell lines have been workhorses for dissecting the complex transcriptional networks downstream of NOTCH1 activation and for evaluating the therapeutic potential of gamma-secretase inhibitors [15,40,42]. On the other hand, the lack of consistent models has so far failed to translate the genetic findings into efficient targeted approaches and valuable biomarkers. A remarkable example is how the strong evidence of PDGFRA expression in most PTCL/NOS could not translate into effective treatments with tyrosine kinase inhibitors, even if the only available model showed a striking dependence on its signaling. Similarly, VEGFR inhibition was not tested pre clinically in AITL, and still the evidence of T-cell receptor activation through mutations and translocations could not be translated into effective inhibition.

4. Key Scientific Questions Driving the Need for Better Models

While invaluable, the current armamentarium of cell lines primarily allows researchers to address questions pertinent to the biology of self-propagating, TME-independent tumors. Key scientific questions that have been successfully explored using these models include several issues. First, the dissection of oncogenic pathways. Cell lines like Karpas 299 and SU-DHL-1 have been instrumental in mapping the signaling cascades downstream of the NPM1-ALK fusion protein, identifying critical dependencies on pathways like JAK/STAT and PI3K/AKT [26,43]. Similarly, T-ALL lines have clarified the central role of NOTCH1 signaling in driving leukemogenesis [40,42,44]. Second, regarding target validation and preclinical drug screening, the availability of these models provides a high-throughput platform to test the efficacy of targeted drugs. The validation of ALK inhibitors in ALCL and HDAC inhibitors in CTCL are prime examples of successful translational research pipelines built upon cell line models. Third, cell lines are powerful tools for elucidating how novel drugs exert their anti-tumor effects and for investigating the molecular mechanisms that lead to acquired drug resistance.
Unfortunately, the most pressing challenges in the field of T-cell lymphomas lie in the entities for which these models are lacking. The scarcity of representative cell lines for the majority of PTCL subtypes means that numerous fundamental scientific questions remain largely unanswered, underscoring the urgent need for new and more sophisticated preclinical tools [41]. One of the most challenging issues in PTCLs is the definition of the role of the tumor microenvironment (TME) [45]. Many PTCLs, particularly AITL, are defined by a complex and pathologically integral TME. This TME consists of a dense network of non-malignant cells, including follicular dendritic cells, endothelial cells, and reactive B-cells, which provide essential survival signals through cytokine secretion and direct cell-to-cell contact. Standard cell lines, which are selected for growth in isolation, cannot be used to study the critical survival signals provided by the TME or to test therapies that target these tumor-stroma interactions. In addition, the cell of origin and even the normal counterparts of PTCLs are still largely unknown. While gene expression profiling has linked entities like AITL to T-follicular helper cells [46,47], the exact developmental stage and functional state of the cell of origin for most PTCL-NOS cases remain obscure [9,31,48,49]. Functional models derived from these specific subtypes are required to validate these hypotheses and understand the initial steps of transformation.
Another valuable point is understanding how rare, subtype-defining genetic lesions drive lymphomagenesis. The discovery of recurrent alterations like the TRA@::PVRL2 translocation in a subset of PTCL-NOS was a major step forward, and the T8ML-1 cell line provides a unique model to study its consequences [34]. However, for dozens of other recurrent but rare mutations, no models exist, preventing any functional investigation into their pathogenic role [50].
Of note, the lack of models for entities like AITL and HSTCL [12] creates a vicious cycle: without a platform for preclinical testing, rational drug development is stalled, and these patients continue to be treated with non-specific chemotherapy that yields poor outcomes [11,37,39,50]. Similarly, the mechanisms of intrinsic and acquired chemoresistance in PTCL are not well defined. Most PTCLs are notoriously resistant to standard chemotherapy regimens. Understanding the underlying biological basis for this resistance requires models that faithfully recapitulate the genetic and phenotypic heterogeneity of patient tumors, a feature that clonally selected, long-passaged cell lines often lose. Finally, the development of treatments like CAR-T cells requires preclinical models that allow for the assessment of immune-mediated killing [51]. This is a significant challenge in systems lacking a competent immune system or the relevant tumor antigens, highlighting the need for syngeneic models or humanized PDX models [52,53].
Addressing these fundamental questions is not possible with the current, skewed collection of cell lines. It necessitates a concerted effort to develop a new generation of preclinical models, including authenticated cell lines from rare entities and, critically, PDX models that better preserve the biological complexity of the primary disease [54].
Despite these successes, the inherent limitations of cell line models are significant and well-documented. Having been selected for robust growth in artificial, two-dimensional culture, they represent a clonally homogeneous population that fails to capture the extensive intra-tumoral heterogeneity that is a hallmark of cancer in patients and a major driver of therapeutic resistance [55,56]. Furthermore, these models are completely divorced from the native TME, which provides critical survival signals and contributes to drug resistance [57]. This is a particularly glaring deficiency for lymphomas like AITL, where the TME is not merely supportive but is a defining pathological feature of the disease [58]. The cumulative result of these shortcomings is a poor predictive value for clinical success; promising results from cell line studies frequently fail to translate into patient benefit, with over 90% of drugs that show preclinical activity ultimately failing in clinical trials [51,57].
The translational research efforts of our own group, which have focused on identifying molecular signatures and prognostic markers in PTCL, are directly impacted by this lack of representative models [11]. While our work and that of others have identified promising therapeutic avenues, such as targeting aberrantly activated tyrosine kinases or epigenetic regulators, the ability to rigorously test these hypotheses in a disease-specific context is severely hampered by the absence of appropriate preclinical tools [26,59].

5. Future Perspectives: Expanding the Preclinical Toolkit with Patient-Derived Xenografts (PDX)

Addressing the profound deficit in preclinical models for T-cell lymphomas requires a multi-pronged approach. First, a renewed, concerted international effort to establish novel cell lines from underrepresented PTCL subtypes is warranted, perhaps employing advanced co-culture or three-dimensional organoid systems that better recapitulate the TME [41]. The rigorous authentication and characterization of the T8ML-1 cell line serve as a valuable blueprint for such endeavors [34].
However, the most promising and immediately impactful strategy is the development and widespread adoption of patient-derived xenograft (PDX) models [53,60,61]. Created by the direct implantation of fresh patient tumor tissue into severely immunodeficient mice (e.g., NOD-scid IL2Rgamma null or NSG mice), PDX models circumvent many of the critical limitations of traditional cell lines [54,55,61]. Direct transplantation of fresh tissue (rather than frozen or processed cells) not only largely preserves the original tumor architecture, maintains the cellular and genetic heterogeneity of the primary disease, but also retains key components of the human TME, including stromal and vascular elements. This maximizes the viability of both the malignant clones and the associated stromal elements, but it requires immediate proximity to clinical facilities [52,55]. Consequently, PDX models have demonstrated a significantly higher predictive value for clinical drug responses, making them a superior platform for translational research (Figure 3) [52,53].
Overall, PDX models offer the advantage of preserving tumor architecture and heterogeneity; however, they are limited by high costs, long establishment times, and the eventual loss of the human TME as it is replaced by murine stroma.
The establishment of public repositories has been a transformative step, democratizing access to these powerful but resource-intensive models. The Public Repository of Xenografts (PRoXe), founded at the Dana-Farber Cancer Institute, is a landmark initiative that has generated and made available a large collection of well-characterized hematologic malignancy PDXs [60,62,63]. Crucially, this and other efforts have succeeded in creating stable, transplantable PDX models for T-cell lymphomas that have historically been impossible to culture in vitro [62].
An AITL PDX has been successfully established, maintaining the characteristic CD4+/PD1+ immunophenotype of TFH cells and harboring hallmark mutations in genes such as TET2 [62]. The ability to propagate this TME-dependent disease in vivo is a breakthrough, finally providing a renewable model to study AITL biology and test therapies targeted against its unique genetic drivers, such as mutations in RHOA, TET2, and IDH2 [5,39]. In addition, a PTCL-NOS PDX was shown to retain the clonal TCR gene rearrangement of the primary tumor and recapitulate a complex microenvironment containing non-malignant human T- and B-cells [62].
PDX models of both systemic and breast implant-associated ALK-negative ALCL have been developed, providing invaluable tools for a disease with a poorer prognosis than its ALK-positive counterpart [29,30].
Finally, a PDX model derived from an SS patient demonstrated appropriate disease dissemination, with malignant cells trafficking to the skin, spleen, and bone marrow, thus faithfully recapitulating the systemic nature of the disease [62].
It should be noted, however, that PDX models offer the advantage of preserving tumor architecture and heterogeneity; however, they are limited by high costs, long establishment times, and the eventual loss of the human TME as it is replaced by murine stroma. On the other hand, in addition to PDX, emerging platforms such as 3D organoids and microfluidic co-culture systems offer promising avenues to study TME interactions in a high-throughput manner.
The availability of these models enables a paradigm shift in preclinical drug development toward “co-clinical trials” or “mouse avatar” studies. In this approach, cohorts of mice bearing PDXs from different patients can be used to conduct randomized, phase II-like trials, allowing for the robust evaluation of novel agents and the identification of biomarkers that predict response or resistance [55,64].

6. Conclusions

The landscape of preclinical models for T-cell malignancies is one of stark contrasts. While a rich and well-characterized collection of cell lines exists for T-ALL, CTCL, and ALK-positive ALCL, this abundance is a direct reflection of a biological selection bias favoring tumors with potent, culture-permissive oncogenic drivers. For the majority of PTCL subtypes—many of which are rare, aggressive, and lack effective therapies—the preclinical toolkit is profoundly deficient. This gap has created a significant bottleneck in research, hindering our understanding of disease biology and impeding the development of novel targeted therapies. To break this cycle of neglect, the field must move beyond its reliance on a small, biased panel of historical cell lines. The future of translational research in T-cell lymphoma lies in the development and utilization of more clinically relevant models. Patient-derived xenografts, which preserve the heterogeneity and microenvironment of the original tumor, represent the most powerful tool currently available. The expansion and broad accessibility of PDX models through public repositories like PRoXe offer the most promising path forward to bridge the translational gap, enabling biologically informed drug development and ultimately improving outcomes for patients with the full spectrum of T-cell lymphomas and leukemias [60,63].

Author Contributions

Conceptualization, P.P.P.; methodology, L.C. and P.P.P.; investigation, L.C., V.T., P.C., C.I., S.S., G.C. and F.J.D.P.; resources, P.P.P.; data curation, L.C., V.T. and P.C.; writing—original draft preparation, P.P.P. and L.C.; writing—review and editing, P.P.P. and G.T.; supervision, P.P.P.; project administration, P.P.P.; funding acquisition, P.P.P. All authors have read and agreed to the published version of the manuscript.

Funding

The work reported in this publication was funded by the Italian Ministry of Health, RC-2025-2797263.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Pier Paolo Piccaluga is currently affiliated with the University of Nairobi (Nairobi, Kenya) and the University of Botswana (Gaborone, Botswana). V.T. and L.C. were supported in part by PRIN 2022NXK38S.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Drexler, H.G.; Matsuo, Y. Malignant hematopoietic cell lines: In vitro models for the study of natural killer cell leukemia-lymphoma. Leukemia 2000, 14, 777–782. [Google Scholar] [CrossRef] [PubMed]
  2. Drexler, H.G.; MacLeod, R.A.F. Leukemia-lymphoma cell lines as model systems for hematopoietic research. Ann. Med. 2003, 35, 404–412. [Google Scholar] [CrossRef] [PubMed]
  3. Drexler, H.G. Establishment and culture of leukemia-lymphoma cell lines. In Cancer Cell Culture: Methods and Protocols; Humana Press: Totowa, NJ, USA, 2011; Volume 731, pp. 181–200. [Google Scholar]
  4. Sehn, L.H.; Soulier, J. Introduction to the review series on T-cell malignancies. Blood 2017, 129, 1059–1060. [Google Scholar] [CrossRef] [PubMed]
  5. Savage, K.J.; De Leval, L. Erratum to: Introduction to the peripheral T-cell lymphoma review series: Advances in molecular characterization, classification refinement and treatment optimization. Haematologica 2024, 109, 693–694. [Google Scholar] [CrossRef]
  6. Gootenberg, J.E.; Ruscetti, F.W.; Mier, J.W.; Gazdar, A.; Gallo, R.C. Human cutaneous T cell lymphoma and leukemia cell lines produce and respond to T cell growth factor. J. Exp. Med. 1981, 154, 1403–1418. [Google Scholar] [CrossRef]
  7. Kroeze, E.; Loeffen, J.L.C.; Poort, V.M.; Meijerink, J.P.P. T-cell lymphoblastic lymphoma and leukemia: Different diseases from a common premalignant progenitor? Blood Adv. 2020, 4, 3466–3473. [Google Scholar] [CrossRef]
  8. Lage, L.A.d.P.C.; Cabral, T.C.D.S.; Costa Rde, O.; Gonçalves, M.d.C.; Levy, D.; Zerbini, M.C.N.; Pereira, J. Primary nodal peripheral T-cell lymphomas: Diagnosis and therapeutic considerations. Rev. Bras. Hematol. Hemoter. 2015, 37, 277–284. [Google Scholar] [CrossRef]
  9. Piccaluga, P.P.; Fuligni, F.; De Leo, A.; Bertuzzi, C.; Rossi, M.; Bacci, F.; Sabattini, E.; Agostinelli, C.; Gazzola, A.; Laginestra, M.A.; et al. Molecular profiling improves classification and prognostication of nodal peripheral T-cell lymphomas: Results of a phase III diagnostic accuracy study. J. Clin. Oncol. 2013, 31, 3019–3025. [Google Scholar] [CrossRef]
  10. Agostinelli, C.; Piccaluga, P.P.; Went, P.; Rossi, M.; Gazzola, A.; Righi, S.; Sista, T.; Campidelli, C.; Zinzani, P.L.; Falini, B.; et al. Peripheral T cell lymphoma, not otherwise specified: The stuff of genes, dreams and therapies. J. Clin. Pathol. 2008, 61, 1160–1167. [Google Scholar] [CrossRef]
  11. Piccaluga, P.P.; Agostinelli, C.; Gazzola, A.; Mannu, C.; Bacci, F.; Sabattini, E.; Pileri, S.A. Prognostic markers in peripheral T-cell lymphoma. Curr. Hematol. Malig. Rep. 2010, 5, 222–228. [Google Scholar] [CrossRef][Green Version]
  12. Bron, D.; De Leval, L.; Michiels, S.; Wittnebel, S. EuroBloodNet for rare diseases. Hepatosplenic T-cell lymphoma: Treatment challenges. Curr. Opin. Oncol. 2021, 33, 406–411. [Google Scholar] [CrossRef]
  13. Subbiah, V.; Kuravi, S.; Ganguly, S.; Welch, D.R.; Vivian, C.J.; Mushtaq, M.U.; Hegde, A.; Iyer, S.; Behrang, A.; Ali, S.; et al. Precision therapy with anaplastic lymphoma kinase inhibitor ceritinib in ALK-rearranged anaplastic large cell lymphoma. ESMO Open 2021, 6, 100172. [Google Scholar] [CrossRef]
  14. Gazdar, A.F.; Carney, D.N.; Bunn, P.A.; Russell, E.K.; Jaffe, E.S.; Schechter, G.P.; Guccion, J.G. Mitogen requirements for the in vitro propagation of cutaneous T-cell lymphomas. Blood 1980, 55, 409–417. [Google Scholar] [CrossRef][Green Version]
  15. Spinella, J.F.; Cassart, P.; Richer, C.; Saillour, V.; Ouimet, M.; Langlois, S.; St-Onge, P.; Sontag, T.; Healy, J.; Minden, M.D.; et al. Genomic characterization of pediatric T-cell acute lymphoblastic leukemia reveals novel recurrent driver mutations. Oncotarget 2016, 7, 65485–65503. [Google Scholar] [CrossRef] [PubMed][Green Version]
  16. Bardelli, V.; Arniani, S.; Pierini, V.; Di Giacomo, D.; Pierini, T.; Gorello, P.; Mecucci, C.; La Starza, R. T-Cell Acute Lymphoblastic Leukemia: Biomarkers and Their Clinical Usefulness. Genes 2021, 12, 1118. [Google Scholar] [CrossRef] [PubMed]
  17. Moharram, S.A.; Shah, K.; Kazi, J.U. T-cell Acute Lymphoblastic Leukemia Cells Display Activation of Different Survival Pathways. J. Cancer 2017, 8, 4124. [Google Scholar] [CrossRef][Green Version]
  18. Burger, R.; Hansen-Hagge, T.E.; Drexler, H.G.; Gramatzki, M. Heterogeneity of T-acute lymphoblastic leukemia (T-ALL) cell lines: Suggestion for classification by immunophenotype and T-cell receptor studies. Leuk. Res. 1999, 23, 19–27. [Google Scholar] [CrossRef]
  19. PDQ Adult Treatment Editorial Board. Peripheral T-Cell Non-Hodgkin Lymphoma Treatment (PDQ®): Health Professional Version; National Cancer Institute: Bethesda, MD, USA, 2002. [Google Scholar]
  20. Starkebaum, G.; Loughran, T.P.; Waters, C.A.; Ruscetti, F.W. Establishment of an IL-2 independent, human T-cell line possessing only the p70 IL-2 receptor. Int. J. Cancer 1991, 49, 246–253. [Google Scholar] [CrossRef]
  21. Popovic, M.; Sarin, P.S.; Robert-Gurroff, M.; Kalyanaraman, V.S.; Mann, D.; Minowada, J.; Gallo, R.C. Isolation and transmission of human retrovirus (human t-cell leukemia virus). Science 1983, 219, 856–859. [Google Scholar] [CrossRef]
  22. Irimia, R.; Piccaluga, P.P. Histone Deacetylase Inhibitors for Peripheral T-Cell Lymphomas. Cancers 2024, 16, 3359. [Google Scholar] [CrossRef]
  23. Piekarz, R.L.; Robey, R.W.; Zhan, Z.; Kayastha, G.; Sayah, A.; Abdeldaim, A.H.; Torrico, S.; Bates, S.E. T-cell lymphoma as a model for the use of histone deacetylase inhibitors in cancer therapy: Impact of depsipeptide on molecular markers, therapeutic targets, and mechanisms of resistance. Blood 2004, 103, 4636–4643. [Google Scholar] [CrossRef]
  24. Epstein, A.L.; Levy, R.; Kim, H.; Henle, W.; Henle, G.; Kaplan, H.S. Biology of the human malignant lymphomas. IV. Functional characterization of ten diffuse histiocytic lymphoma cell lines. Cancer 1978, 42, 2379–2391. [Google Scholar] [CrossRef]
  25. Shimakage, M.; Dezawa, T.; Tamura, S.; Tabata, T.; Aoyagi, N.; Koike, M.; Inoue, H.; Yutsudo, M.; Hakura, A.; Ikegami, N. A Ki-1-positive cell line expressing Epstein-Barr virus antigens, established from a child with Ki-1-positive lymphoma. Intervirology 1993, 36, 215–224. [Google Scholar] [CrossRef]
  26. Piccaluga, P.P.; Cascianelli, C.; Inghirami, G. Tyrosine kinases in nodal peripheral T-cell lymphomas. Front. Oncol. 2023, 13, 1099943. [Google Scholar] [CrossRef]
  27. Drexler, H.G.; MacLeod, R.A.F. Malignant hematopoietic cell lines: In vitro models for the study of anaplastic large-cell lymphoma. Leukemia 2004, 18, 1569–1571. [Google Scholar] [CrossRef]
  28. Merz, H.; Lange, K.; Gaiser, T.; Müller, A.; Kapp, U.; Bittner, C.; Harder, S.; Siebert, R.; Bentz, M.; Binder, T.; et al. Characterization of a novel human anaplastic large cell lymphoma cell line tumorigenic in SCID mice. Leuk. Lymphoma 2002, 43, 165–172. [Google Scholar] [CrossRef] [PubMed]
  29. Marra, A.; Viale, G.; Pileri, S.A.; Pravettoni, G.; Viale, G.; De Lorenzi, F.; Nolè, F.; Veronesi, P.; Curigliano, G. Breast implant-associated anaplastic large cell lymphoma: A comprehensive review. Cancer Treat. Rev. 2020, 84, 101963. [Google Scholar] [CrossRef] [PubMed]
  30. Chai, K.X.Y.; Lee, E.C.Y.; Li, Z.; Muhammad, N.A.B.; Lim, J.Q.; Huang, D.; Lim, S.T.; Chan, J.Y.; Ong, C.K. Establishment and characterization of a novel patient-derived TP63-rearranged anaplastic large-cell lymphoma model PTCL-S1. Hum. Cell 2025, 38, 135. [Google Scholar] [CrossRef] [PubMed]
  31. Piccaluga, P.P.; Agostinelli, C.; Tripodo, C.; Gazzola, A.; Bacci, F.; Sabattini, E.; Pileri, S.A. European T-cell Lymphoma Study Group. Peripheral T-cell lymphoma classification: The matter of cellular derivation. Expert Rev. Hematol. 2011, 4, 415–425. [Google Scholar] [CrossRef]
  32. Schwartz, L.F.; Devine, K.J.; Xavier, A.C. Hepatosplenic T-cell lymphoma in children and adolescents. Blood Adv. 2025, 9, 1847–1858. [Google Scholar] [CrossRef]
  33. Feng, Y.; Ma, Y.; Li, T.; Liu, M.; Hong, Z.; Yin, Q.; Zheng, M. Angioimmunoblastic T-cell lymphoma: A concise overview encompassing the pathogenetic, pathological, clinical, therapeutical characteristics, and recent advances. Clin. Exp. Med. 2025, 25, 218. [Google Scholar] [CrossRef]
  34. Ehrentraut, S.; Nagel, S.; Pommerenke, C.; Dirks, W.G.; Quentmeier, H.; Kaufmann, M.; Meyer, C.; Zaborski, M.; Geffers, R.; Fujiwara, H.; et al. Peripheral T-cell lymphoma cell line T8ML-1 highlights conspicuous targeting of PVRL2 by t(14;19)(q11.2;q13.3). Haematologica 2017, 102, e356–e359. [Google Scholar] [CrossRef][Green Version]
  35. del Mistro, A.; Leszl, A.; Bertorelle, R.; Calabro, M.L.; Panozzo, M.; Menin, C.; D’Andrea, E.; Chieco-Bianchi, L. A CD30-positive T cell line established from an aggressive anaplastic large cell lymphoma, originally diagnosed as Hodgkin’s disease. Leukemia 1994, 8, 1214–1219. [Google Scholar]
  36. Piccaluga, P.P.; Rossi, M.; Agostinelli, C.; Ricci, F.; Gazzola, A.; Righi, S.; Fuligni, F.; A Laginestra, M.; Mancini, M.; Sapienza, M.R.; et al. Platelet-derived growth factor alpha mediates the proliferation of peripheral T-cell lymphoma cells via an autocrine regulatory pathway. Leukemia 2014, 28, 1687–1697. [Google Scholar] [CrossRef] [PubMed]
  37. PDQ Adult Treatment Editorial Board. Mycosis Fungoides and Other Cutaneous T-Cell Lymphomas Treatment (PDQ®): Health Professional Version; National Cancer Institute: Bethesda, MD, USA, 2002. [Google Scholar]
  38. Devine, K.J.; Schwartz, L.; El-Mallawany, N.K. Peripheral T-cell lymphoma-NOS in children and adolescents: A review from the Children’s Oncology Group NHL Committee. Blood Adv. 2025, 9, 1420–1431. [Google Scholar] [CrossRef]
  39. Mohammed Saleh, M.F.; Kotb, A.; Abdallah, G.E.M.; Muhsen, I.N.; El Fakih, R.; Aljurf, M. Recent Advances in Diagnosis and Therapy of Angioimmunoblastic T Cell Lymphoma. Curr. Oncol. 2021, 28, 5480–5498. [Google Scholar] [CrossRef] [PubMed]
  40. Girardi, T.; Vicente, C.; Cools, J.; De Keersmaecker, K. The genetics and molecular biology of T-ALL. Blood 2017, 129, 1113–1123. [Google Scholar] [CrossRef] [PubMed]
  41. Mulloy, J.C. Peripheral T cell lymphoma: New model + new insight. J. Exp. Med. 2010, 207, 911–913. [Google Scholar] [CrossRef]
  42. Smith, S.D.; Morgan, R.; Link, M.P.; McFall, P.; Hecht, F. Cytogenetic and immunophenotypic analysis of cell lines established from patients with T cell leukemia/lymphoma. Blood 1986, 67, 650–656. [Google Scholar] [CrossRef]
  43. Havre, P.A.; Dang, L.H.; Ohnuma, K.; Iwata, S.; Morimoto, C.; Dang, N.H. CD26 expression on T-anaplastic large cell lymphoma (ALCL) line Karpas 299 is associated with increased expression of versican and MT1-MMP and enhanced adhesion. BMC Cancer 2013, 13, 517. [Google Scholar] [CrossRef]
  44. Tan, S.H.; Bertulfo, F.C.; Sanda, T. Leukemia-Initiating Cells in T-Cell Acute Lymphoblastic Leukemia. Front. Oncol. 2017, 7, 218. [Google Scholar] [CrossRef] [PubMed]
  45. Gaulard, P.; de Leval, L. The microenvironment in T-cell lymphomas: Emerging themes. Semin. Cancer Biol. 2014, 24, 49–60. [Google Scholar] [CrossRef] [PubMed]
  46. de Leval, L.; Rickman, D.S.; Thielen, C.; de Reynies, A.; Huang, Y.-L.; Delsol, G.; Lamant, L.; Leroy, K.; Brière, J.; Molina, T.; et al. The gene expression profile of nodal peripheral T-cell lymphoma demonstrates a molecular link between angioimmunoblastic T-cell lymphoma (AITL) and follicular helper T (TFH) cells. Blood 2007, 109, 4952–4963. [Google Scholar] [CrossRef] [PubMed]
  47. Piccaluga, P.P.; Agostinelli, C.; Califano, A.; Carbone, A.; Fantoni, L.; Ferrari, S.; Gazzola, A.; Gloghini, A.; Righi, S.; Rossi, M.; et al. Gene expression analysis of angioimmunoblastic lymphoma indicates derivation from T follicular helper cells and vascular endothelial growth factor deregulation. Cancer Res. 2007, 67, 10703–10710. [Google Scholar] [CrossRef]
  48. Piccaluga, P.P.; Agostinelli, C.; Califano, A.; Rossi, M.; Basso, K.; Zupo, S.; Went, P.; Klein, U.; Zinzani, P.L.; Baccarani, M.; et al. Gene expression analysis of peripheral T cell lymphoma, unspecified, reveals distinct profiles and new potential therapeutic targets. J. Clin. Investig. 2007, 117, 823–834. [Google Scholar] [CrossRef]
  49. Iqbal, J.; Wright, G.; Wang, C.; Rosenwald, A.; Gascoyne, R.D.; Weisenburger, D.D.; Greiner, T.C.; Smith, L.; Guo, S.; Wilcox, R.A.; et al. Gene expression signatures delineate biological and prognostic subgroups in peripheral T-cell lymphoma. Blood 2014, 123, 2915–2923. [Google Scholar] [CrossRef]
  50. Kuczynski, E.A.; Morlino, G.; Peter, A.; Coenen-Stass, A.M.L.; Moss, J.I.; Wali, N.; Delpuech, O.; Reddy, A.; Solanki, A.; Sinclair, C.; et al. A preclinical model of peripheral T-cell lymphoma GATA3 reveals DNA damage response pathway vulnerability. EMBO Mol. Med. 2022, 14, e15816. [Google Scholar] [CrossRef]
  51. Andreu-Sanz, D.; Gregor, L.; Carlini, E.; Scarcella, D.; Marr, C.; Kobold, S. Predictive value of preclinical models for CAR-T cell therapy clinical trials: A systematic review and meta-analysis. J. Immuno Ther. Cancer 2025, 13, e01169. [Google Scholar] [CrossRef]
  52. Goto, T. Patient-Derived Tumor Xenograft Models: Toward the Establishment of Precision Cancer Medicine. J. Pers. Med. 2020, 10, 64. [Google Scholar] [CrossRef]
  53. Hidalgo, M.; Amant, F.; Biankin, A.V.; Budinská, E.; Byrne, A.T.; Caldas, C.; Clarke, R.B.; de Jong, S.; Jonkers, J.; Mælandsmo, G.M.; et al. Patient-derived xenograft models: An emerging platform for translational cancer research. Cancer Discov. 2014, 4, 998–1013. [Google Scholar] [CrossRef]
  54. Idrisova, K.F.; Simon, H.U.; Gomzikova, M.O. Role of Patient-Derived Models of Cancer in Translational Oncology. Cancers 2022, 15, 139. [Google Scholar] [CrossRef] [PubMed]
  55. Jung, J.; Seol, H.S.; Chang, S. The Generation and Application of Patient-Derived Xenograft Model for Cancer Research. Cancer Res. Treat. 2018, 50, 1153620. [Google Scholar] [CrossRef] [PubMed]
  56. Yu, K.; Chen, B.; Aran, D.; Charalel, J.; Yau, C.; Wolf, D.M.; van ‘t Veer, L.J.; Butte, A.J.; Goldstein, T.; Sirota, M.; et al. Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types. Nat. Commun. 2019, 10, 3574. [Google Scholar] [CrossRef] [PubMed]
  57. Xing, Y.; Huang, J.; Zhang, Y.; Wang, Y.; Qi, S. Advancing the understanding and management of angioimmunoblastic T-cell lymphoma: Insights into its pathogenesis, clinical features, and emerging therapeutic strategies. Front. Oncol. 2025, 15, 1479179. [Google Scholar] [CrossRef]
  58. Mhaidly, R.; Krug, A.; Gaulard, P.; Lemonnier, F.; Ricci, J.E.; Verhoeyen, E. New preclinical models for angioimmunoblastic T-cell lymphoma: Filling the GAP. Oncogenesis 2020, 9, 73. [Google Scholar] [CrossRef]
  59. Pileri, S.A.; Piccaluga, P.P. New molecular insights into peripheral T cell lymphomas. J. Clin. Investig. 2012, 122, 3448–3455. [Google Scholar] [CrossRef]
  60. Huo, K.G.; D’Arcangelo, E.; Tsao, M.S. Patient-derived cell line, xenograft and organoid models in lung cancer therapy. Transl. Lung Cancer Res. 2020, 9, 2214–2232. [Google Scholar] [CrossRef]
  61. Steel, C.; James, E.R.; Matthews, J.D.; Turner, S.D. Establishing Patient-Derived Xenograft (PDX) Models of Lymphomas. Methods Mol. Biol. 2025, 2865, 429–448. [Google Scholar]
  62. Christie, A.L.; Ng, S.Y.; Koch, R.; Christodoulou, A.N.; DeSouza, T.; Murakami, M.A.; Tseng, M.; Peng, A.; Molla, V.; Jacobson, C.A.; et al. T-Cell Lymphoma Patient-Derived Xenografts and Newly Developed Cell Lines Recapitulate Aspects of Disease Biology and Represent Novel Tools for Preclinical Drug Development. Blood 2016, 128, 3015. [Google Scholar] [CrossRef]
  63. Murakami, M.A.; Christodoulou, A.N.; Christie, A.L.; DeSouza, T.; Louissaint, A.; Vojinovic, U.; Koch, R.; Li, L.S.; Kallgren, S.P.; Rao, P.; et al. Proxe: A Public Repository of Xenografts to Facilitate Studies of Biology and Expedite Preclinical Drug Development in Leukemia and Lymphoma. Blood 2015, 126, 3252. [Google Scholar] [CrossRef]
  64. Townsend, E.C.; Murakami, M.A.; Christodoulou, A.; Christie, A.L.; Köster, J.; DeSouza, T.A.; Morgan, E.A.; Kallgren, S.P.; Liu, H.; Wu, S.-C.; et al. The Public Repository of Xenografts Enables Discovery and Randomized Phase II-like Trials in Mice. Cancer Cell 2016, 30, 183. [Google Scholar] [CrossRef]
Cells 15 00368 g001
Figure 1. Disparity in Preclinical Models: Clinical Incidence vs. In Vitro Representation of T-Cell Malignancies. Panel A illustrates the approximate clinical incidence of major T-cell malignancy subtypes. Panel B shows the number of currently available, authenticated cell line models for these subtypes. The marked imbalance highlights a major research gap, particularly for Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), Angioimmunoblastic T-cell lymphoma (AITL), and Hepatosplenic T-cell lymphoma (HSTCL), which lack suitable in vitro models for mechanistic and preclinical studies. For cutaneous T-cell lymphomas (CTCL), please note that they represent a heterogeneous category, accounting for different entities, only a few of which are represented by cell lines.
Figure 1. Disparity in Preclinical Models: Clinical Incidence vs. In Vitro Representation of T-Cell Malignancies. Panel A illustrates the approximate clinical incidence of major T-cell malignancy subtypes. Panel B shows the number of currently available, authenticated cell line models for these subtypes. The marked imbalance highlights a major research gap, particularly for Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), Angioimmunoblastic T-cell lymphoma (AITL), and Hepatosplenic T-cell lymphoma (HSTCL), which lack suitable in vitro models for mechanistic and preclinical studies. For cutaneous T-cell lymphomas (CTCL), please note that they represent a heterogeneous category, accounting for different entities, only a few of which are represented by cell lines.
Cells 15 00368 g001
Cells 15 00368 g002
Figure 2. Constitutively active signaling pathways in amenable T-cell lymphoma subtypes. This diagram illustrates potent, self-sustaining oncogenic signaling mechanisms in ALCL (NPM1–ALK fusion) and T-ALL (NOTCH1 mutation) that drive robust cell line establishment and survival in vitro. In ALCL, the NPM1–ALK fusion results in constitutive activation of the JAK/STAT3 and PI3K/AKT/mTOR pathways, promoting MYC-driven proliferation and survival. In T-ALL, activating NOTCH1 mutations lead to ligand-independent cleavage and release of the NOTCH1 intracellular domain (NICD), which interacts with CSL/MAML to upregulate target genes such as MYC and CYCLIN D1, sustaining uncontrolled cell growth. This highlights a biological selection bias favoring cell line establishment from subtypes with strong autonomous signaling activity, such as ALCL and T-ALL.
Figure 2. Constitutively active signaling pathways in amenable T-cell lymphoma subtypes. This diagram illustrates potent, self-sustaining oncogenic signaling mechanisms in ALCL (NPM1–ALK fusion) and T-ALL (NOTCH1 mutation) that drive robust cell line establishment and survival in vitro. In ALCL, the NPM1–ALK fusion results in constitutive activation of the JAK/STAT3 and PI3K/AKT/mTOR pathways, promoting MYC-driven proliferation and survival. In T-ALL, activating NOTCH1 mutations lead to ligand-independent cleavage and release of the NOTCH1 intracellular domain (NICD), which interacts with CSL/MAML to upregulate target genes such as MYC and CYCLIN D1, sustaining uncontrolled cell growth. This highlights a biological selection bias favoring cell line establishment from subtypes with strong autonomous signaling activity, such as ALCL and T-ALL.
Cells 15 00368 g002
Cells 15 00368 g003
Figure 3. Schematic representation of Patient-Derived Xenograft (PDX) generation and application in translational research. Patient tumor biopsies are directly implanted into immunodeficient NSG mice, preserving the original tumor architecture and heterogeneity. After successful engraftment, PDX models are expanded for subsequent preclinical testing. These “mouse avatar” models allow evaluation of drug efficacy (e.g., Drug A, Drug B, Control) and identification of predictive biomarkers of response or resistance.
Figure 3. Schematic representation of Patient-Derived Xenograft (PDX) generation and application in translational research. Patient tumor biopsies are directly implanted into immunodeficient NSG mice, preserving the original tumor architecture and heterogeneity. After successful engraftment, PDX models are expanded for subsequent preclinical testing. These “mouse avatar” models allow evaluation of drug efficacy (e.g., Drug A, Drug B, Control) and identification of predictive biomarkers of response or resistance.
Cells 15 00368 g003
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Piccaluga, P.P.; Cimmino, L.; Tsekhovska, V.; Cimatti, P.; Innocenti, C.; Seidenari, S.; Calafato, G.; Di Paola, F.J.; Tallini, G. From Cell Lines to Avatars: Charting the Future of Preclinical Modeling in T-Cell Malignancies. Cells 2026, 15, 368. https://doi.org/10.3390/cells15040368

AMA Style

Piccaluga PP, Cimmino L, Tsekhovska V, Cimatti P, Innocenti C, Seidenari S, Calafato G, Di Paola FJ, Tallini G. From Cell Lines to Avatars: Charting the Future of Preclinical Modeling in T-Cell Malignancies. Cells. 2026; 15(4):368. https://doi.org/10.3390/cells15040368

Chicago/Turabian Style

Piccaluga, Pier Paolo, Luigi Cimmino, Valeriia Tsekhovska, Pietro Cimatti, Claudia Innocenti, Sabrina Seidenari, Giulia Calafato, Floriana J. Di Paola, and Giovanni Tallini. 2026. "From Cell Lines to Avatars: Charting the Future of Preclinical Modeling in T-Cell Malignancies" Cells 15, no. 4: 368. https://doi.org/10.3390/cells15040368

APA Style

Piccaluga, P. P., Cimmino, L., Tsekhovska, V., Cimatti, P., Innocenti, C., Seidenari, S., Calafato, G., Di Paola, F. J., & Tallini, G. (2026). From Cell Lines to Avatars: Charting the Future of Preclinical Modeling in T-Cell Malignancies. Cells, 15(4), 368. https://doi.org/10.3390/cells15040368

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop