Exploiting Tumor Lineage Features for Precision Cancer Therapy

Abstract

Cancer cells often retain lineage- and tissue-of-origin-specific programs established prior to malignant transformation. This observation has been elaborated by advances in single-cell and lineage-tracing technologies, which provide high-resolution mapping of these features. Here, we provide an overview of these recent technological developments and examine how the tissue-of-origin shapes tumor behavior and vulnerabilities. We discuss how the preferential selection of oncogenic drivers by specific tissues leads to distinct genetic alterations across cancers. We then explore cancer cells’ continued dependence on lineage-specific physiological functions and signaling pathways, thereby revealing lineage-dependent therapeutic targets. Finally, we highlight how lineage-specific cell surface marker expression informs precision immunotherapies. Together, these insights are driving a shift toward therapies tailored to the developmental and functional identities of cancer cells.

Lineage-restricted dependencies in cancer

Cell lineage refers to the developmental trajectory through which stem or progenitor cells differentiate into specialized cell types, a process governed by tightly regulated molecular programs. In cancer, malignant transformation disrupts this hierarchical organization, leading to aberrant and often heterogeneous lineage architectures within tumors. These distorted differentiation pathways define distinct molecular and functional programs that sustain specialized cancer cell populations and contribute to tumor progression [1].

Understanding the structure and dynamics of these cancer-associated lineages has enhanced our ability to identify tumor liabilities that can be targeted. Historically, lineage-tracing techniques – for example, using genetic markers to track cell fate – resulted in phylogenetic insights that, when coupled with large-scale genetic sequencing across cancers and healthy tissues, provided early insights into cancer driver genes. More recently, single-cell technologies have revolutionized this field by enabling high-resolution mapping of cancer-initiating cells and their tissues-of-origin. These advances have reinforced a hierarchical model of oncogenesis, in which stepwise genetic and epigenetic alterations reshape cellular identity and drive malignant transformation [2–4]. Building on these insights, the concept of targeting lineage- and tissue-specific dependencies has gained considerable traction over recent years as a promising therapeutic strategy in oncology. These approaches take advantage of the fact that cancer cells often remain reliant on lineage-restricted survival programs of their tissue-of-origin, offering an avenue for effective treatments.

In this review, we describe how recent techniques and tools have enabled the identification of lineage-restricted features and dependencies in cancer. We then explore the critical components that define the lineage- and tissue-specific characteristics of tumors. First, we highlight that genetic alterations are preferentially selected and unevenly distributed across tissue types, leading to lineage-specific patterns of oncogenic driver representation across cancers. Second, we examine the persistence of specialized functions, established during embryonic development, which remain critical for tumor maintenance and survival. Third, we discuss how cancers remain dependent on lineage-restricted signaling pathways which are essential for the normal function of their tissue of origin, thereby creating therapeutic vulnerabilities. Together, these properties, which are shaped by both genetic and non-genetic factors, contribute to the distinct molecular profiles observed across tumor types (Figure 1). Finally, we consider how lineage-specific marker repertoires create unique vulnerabilities that can be exploited by advanced T cell-based therapies.

Figure 1. Lineage Features of Cancer Development.

Tumor lineage characteristics reflect three intrinsic properties of its tissue-of-origin: (1) preferential selection of oncogenic drivers, leading to their non-uniform distribution across tumor types; (2) dependence on lineage-restricted signaling pathways essential for the corresponding normal tissue; and (3) persistence of specialized functions established during embryonic development that remain critical for tumor maintenance and survival.

Technical Approaches to Uncover Lineage-Specific Features in Cancer

Early studies using morphological analyses revealed divergent cellular phenotypes in cancer cells originating from the same organ. Consequently, early cancer sub-classifications relied heavily on morphological features to identify and distinguish distinct cancer subtypes, such as non-small cell versus small cell lung cancer (NSCLC/SCLC) and non-seminomatous versus seminoma testicular germ cell tumors (NSGCT/SGCT). Researchers observed differential responses to treatment among morphologically distinct tumors, providing an early indication of lineage-specific responses to cancer therapies. The limited ability of morphological analyses to distinguish cancer subtypes based on cellular phenotype was later addressed by the implementation of bulk DNA and RNA sequencing technologies. These sequencing approaches established a foundation for understanding the relationship between tumor lineage and tumor-specific dependencies. Systematic sequencing of the exomes and whole genomes of diverse, histologically defined cancers by The Cancer Genome Atlas (TCGA) project (https://www.cancer.gov/ccg/research/genome-sequencing/tcga), the International Cancer Genome Consortium (ICGC), and other efforts revealed relationships between tumor lineages and their driver genes, mutational patterns, and structural genomic alterations. The Pan-Cancer Analysis of Whole Genomes project, expanding on TCGA and ICGC, revealed the accumulating diversification of driver genes during tumor evolution, delineating evolutionary trajectories of cancer and highlighting new opportunities to identify cancer-specific dependencies [4]. Further, despite the fact that bulk RNA sequencing provides only average gene expression data across heterogeneous cell populations, its application across histologically defined cancers revealed transcriptomic features of tumors linked to their tissue-of-origin. Finally, functional genomics resources like DepMap have married bulk DNA and RNA sequencing of tumor models with comprehensive functional analysis using genetic knockdown or knockout screens, revealing functional dependencies linked to not only mutational features, but also tumor cell lineage [5]. Similarly, databases such as the Human Cell Atlas Project depict genes expressed in normal cell types, which help to place cancers in the context of normal tissue developmental hierarchies with more accuracy and enable the potential selection of lineage- or cancer-restricted targets that minimize toxicity. Together, these approaches have been instrumental in predicting tissue- and lineage-specific cancer therapy targets [6] while also supporting the development and validation of novel lineage tracing tools [7–9].

Subsequent advances in single-cell sequencing, revealing the mutations present in single cells, enabled tracing of individual cells back to their origin. By identifying mutations that are shared (clonal) and heterogeneous (subclonal) in a tumor cell population, it is possible to map developmental trajectories and cellular hierarchies. In cancer, such maps revealed clonal evolution and key drivers of transformation. Building on this approach, CRISPR-based cellular barcoding allowed dynamic clonal tracing by integrating short DNA sequences heritable through cell divisions, tracked via high-throughput sequencing [10]. New CRISPR-based lineage-tracing (CbLT) models like DARLIN subsequently used terminal deoxynucleotidyl transferase (TdT) – a template-independent DNA polymerase – to enhance barcode diversity, improving clone identification [11]. While temporal resolution remains challenging, sequential mutations helped reconstruct developmental timelines. Sampling across time points refined our understanding of differentiation dynamics [12].

More advanced combinatorial approaches, such as integrating genomic barcoding with scRNA-seq, were then developed to enable a more accurate linking of lineage history to transcriptional states. When combined with signal-recording technologies, these methods captured lineage dynamics over time and revealed tumor evolution characteristics [13,14]. They are now used to reconstruct cell lineages in various cancers, uncover cancer-specific transcriptional programs, and identify tissue-specific drivers of transformation [15,16]. Single-cell mass spectrometry (scMS), paired with antibody-based FACS, enabled proteomic profiling across developmental stages [17]. Tools like macsGESTALT integrated transcriptomic and phylogenetic data to uncover survival-associated EMT transitions in pancreatic and lung cancers. In glioblastoma (GBM), lineage tracing with scRNA-seq has revealed both genetic and epigenetic resistance mechanisms [16,18].

To fully elucidate the cellular landscape of cancer drivers, high resolution technologies are required to sequence infrequent mutations contributing to the oncogenic network. New technologies now allow sequencing of rare mutations from both mitochondrial and nuclear genomes: Bottleneck sequencing (BotSeqS) improved sensitivity for rare somatic mutations using molecularly barcoded libraries [19], and subsequently developed nanorate sequencing (NanoSeq) reduced error rates generated with BotSeqS library preparation by 97.5%. This allowed for the reliable detection of somatic mutations in single DNA molecules and the mutation profile of non-dividing cells to be assessed, including comparison of stem cells vs terminally differentiated cells [20]. Duplex cDNA sequencing methods like CODEC identified true mutations on both DNA strands, as demonstrated in clonal hematopoiesis [21]. Perturb-Seq combined single cell RNA-seq and CRISPR-based perturbations to give insight into the transcriptional programs being controlled by cell lineage restricted components at the single cell level [22]. Further advancements in Perturb-Seq have resulted in greater power to learn genetic interactions through its integration with algorithms and machine learning techniques. The in vivo application of Perturb-Seq provided insights into the relevance of transcriptional programs in the cancerous niche [23,24]. This method of genetic perturbation at single cell level has generated rich phenotyping of gene function and regulatory networks in cancer.

Finally, more recent multiplexed single-cell lineage tracing techniques with integrated multi-omic layers have further enhanced our understanding of the complexity of lineage interactions in cancer biology. For example, in chronic lymphocytic leukemia (CLL), combining single-cell genetic, epigenetic, and transcriptomic data has refined our knowledge of how distinct lineages respond to treatment, thereby highlighting intra-tumoral lineage heterogeneity [25]. These approaches, among others, have contributed to the identification of lineage- and tissue-specific targets, and their continued development will drive the discovery of novel lineage-specific targets across cancers (Table 1).

Table 1.

Single-Cell Lineage-Tracing Technologies: Subtypes and Key Advances.

Approach	Database/Tool	Advancement	Ref.
scRNA-Seq-Based Methods	LARRY	scRNA-seq-compatible clonal labelling	[10]
	CITE-Seq	Quantifies cell surface protein and transcriptomic data with single-cell readout	[108]
	macsGESTALT	Inducible CRISPR-Cas9-based lineage recorder	[17]
	scGESTALT	CRISPR-Cas9 barcode editing for large-scale lineage-tracing combined with scRNA-seq	[109]
	CARLIN	A stable, genetically defined mouse line for CRISPR-based lineage tracing	[110]
	DARLIN	An inducible Cas9-barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites	[11]
Genome Sequencing-Based Methods	ClonTracer	High complexity DNA barcoding library	[111]
	DNA Typewriter	Sequential genome editing to overcome limitations in number of recorded unique k-mer insertions (symbols) and capture of order of events utilising a tandem array of partial inactive CRISPR-Cas9 targets, sequentially edited by the addition of specific gRNAs and an enzyme	[12]
	BotSeqS	Combines molecular barcoding with a dilution step just prior to library amplification for simultaneous quantification of rare somatic point mutations across mitochondrial and nuclear genomes	[19]
	NanoSeq	Combines BotSeqS with a restrictive end-repair method using deoxy nucleotides for deeper sequencing with higher sensitivity	[20]
Transposon-dependent methods	Tracer-Seq	An autonomous transposon system encoding a fully functional transposase	[112]
	Sleeping Beauty Transposon	Inserts specific DNA sequences to barcode cells	[45]
Integrated Approaches	peCHYRON	Cas9-reverse transcriptase fusion proteins are used in combination with co-expressed prime editing guide RNAs (pegRNAs), each containing unique triplet DNA sequences.	[113]
	LINNAEUS	Simultaneous lineage tracing and transcriptome profiling	[114]
	ScarTrace	Simultaneous single-cell transcriptomics and quantification of clonal history	[115]
Signal Recording Tools	CAMERA	Two CRISPR-mediated analogue multi-event recording apparatus	[116]
	ENGRAM	The activity and dynamics of multiple transcriptional reporters are stably recorded to DNA	[13]
	Protein-based ticker tape	Engineered protein fiber incorporating fluorescent marks during its growth to store a ticker tape-like history	[14]
CRISPR/Cas9 Loss-of-Function Screen Combined with scRNA-Seq	PERTURB-Seq	Understanding of regulators of cell lineage programs at the single cell level	[22]

Tissue Context Shapes the Oncogenicity of Driver Mutations

Most oncogene and tumor suppressor mutations that drive cell proliferation tend to be tumor-type biased and promote cancer only in certain tissues. Similarly, inherited mutations that predispose individuals to cancer often lead to increased risk in specific tissues rather than universally [9]. These “driver mutations” are relatively rare, occurring against a backdrop of numerous “passenger mutations” which accumulate but do not contribute to tumor growth. Notably, genome-scale overexpression screens suggest that approximately 90% of genes involved in regulating cell proliferation function in a tissue-specific manner, while only about 10% serve as core regulators with roles conserved across multiple tissues [26]. Although some core driver mutations are found across cancer types, clinical trials that target these mutations regardless of tumor origin, such as basket and umbrella trials, have had limited success, with median response rates of 14–18% [27]. This underscores the importance of incorporating tissue and lineage context into trial design. The specificity of driver mutations is shaped by interactions between genetic alterations, the cell-of-origin, and the tumor microenvironment [28], and even conventional chemotherapies show tissue-dependent efficacy, further highlighting the role of tumor origin in treatment response [29].

This important observation is further supported by genomic sequencing across tissues, which confirmed that genetic alterations are not universally oncogenic [9]. Instead, some mutations are more prevalent in cancers arising from specific cell types or tissues, indicating their role as lineage-biased oncogenic drivers that promote cancer primarily within these cell types (Table 2). For instance, the p210 BCR-ABL fusion, resulting from the t(9;22) translocation, when present in more immature hematopoietic stem cells, results in chronic myeloid leukemia (CML), and has been effectively targeted by tyrosine kinase inhibitors, transforming a once-fatal disease into a manageable chronic condition [30]. Conversely, when expressed in more committed progenitor cells the p210 BCR-ABL fusion can trigger B-cell acute lymphoblastic leukemia (B-ALL) [31]. Similarly, the fusion between two transcription factors, EWS-FLI1, which is unique to Ewing Sarcoma, reprograms neural crest-derived cells and drives tumor formation [32]. The fusion between the transcription factors PML and RARA is a unique driver feature of promyelocytic leukemia, disrupting normal cell differentiation; targeted treatment with all-trans retinoic acid and arsenic trioxide has dramatically improved patient outcomes [33]. Mutations of the tumor suppressor gene APC disrupt the delicate balance of Wnt pathway activity that is essential for normal colonic homeostasis and are observed in 80% of colon cancers yet relatively infrequently in other cancer types [34]. These examples illustrate how lineage- and tissue-specific oncogenic alterations promote cancer only in specific cellular contexts and can, in some cases, serve as precise therapeutic targets.

Table 2.

Genomic Alterations of Lineage Factor Genes Across Predominantly-Associated Neoplasms and Corresponding Small-Molecule Inhibitors Developed for Their Therapeutic Targeting.

Function	Lineage Factors	Alteration	Predominantly Associated With	Inhibitors	Ref.
Epigenetic & Transcriptional Regulator	MITF	Amplification Substitutions	Melanoma	N/A	[117]
	ERG	Fusion	ETS-Positive Prostate Cancer	N/A	[118]
	AR	Amplification Substitution	Prostate cancer	Enzalutamide Abiraterone (acetate) Bicalutamide Flutamide Apalutamide Darolutamide Ketoconazole Nilutamide	[56]
	EWS/FLI1	Fusion	Ewing Sarcoma	N/A	[32]
	PML/RARA	Fusion	Promyelocytic Leukemia	All-Trans Retinoic Acid + Arsenic Trioxide	[33]
	BRD4/NUTM1	Fusion	NUT Midline Carcinoma	BET Inhibitors: OTX015 GSK525762	[119]
Tyrosine Kinase	FLT3	Substitution Internal Duplication	Acute Myeloid Leukemia	Midostaurin Gilteritinib Quizartinib	[120]
	ERBB2 (HER2)	Amplification	HER2-Positive Breast Cancer	Antibodies: Trastuzumab Pertuzumab, Margetuximab Hersintuzumab Zanidatamab KN026 TKIs: lapatinib Pyrotinib Tucatinib Neratinib Fatinib, Dacomitinib Antibody drug conjugates: T-DM1 DS-8201 RC48 ARX788, SYD985 SHR-A1811 A166	[121]
	BCR/ABL	Fusion	Chronic Myelogenous Leukemia	Imatinib Dasatinib Ponatinib Asciminib Bafetinib Rebastinib Tozasertib HG-7-85-01	[30]
	EGFR	Amplification Deletion or Insertion Mutation Substitution	Lung Adenocarcinoma	TKIs: Gefitinib Erlotinib Afatinib Osimertinib Olumitinib Dacomitinib Lazertinib Antibodies: Amivantamab	[83]
Multifunctional Protein	NPM1	Insertion	Acute Myeloid Leukemia	Menin inhibitors: MI-2-2 VTP-50469 DS-1594b BMF-219. XPO1 inhibitors: Selinexor Eltanexor Leptomycin B CBS9106 KPT-185	[122]
E3 Ubiquitin Ligase	VHL	Deletion Substitution	Clear-Cell Renal Carcinoma	Belzutifan	[123]
DNA Repair	BRCA1/2	Substitution	Hereditary Breast and Ovarian Cancers	PARP inhibitors: Olaparib Niraparib Rucaparib Talazoparib	[124]

The genetic landscape of cancer is further complicated by the fact that, for a given oncogenic driver gene, certain alleles can promote cell growth in specific tissues while having little or no effect in others. KRAS, the most frequently altered oncogene, exemplifies this phenomenon. The A146T variant of KRAS is predominantly seen in colorectal cancer (CRC), while the G12D substitution is common in multiple cancers [35]. In CRC, different KRAS alleles were shown to drive distinct signaling profiles, with allele-specific activation of ERK2 substrates and unique proteomic and mutational patterns. In contrast, these differences were not observed in the spleen, highlighting the tissue-dependent nature of allele-specific oncogenic activity [36–38]. The KRAS G12R allele (the least common of the G12 variants) is more commonly found in pancreatic ductal adenocarcinoma (PDAC) but not in non-small cell lung cancer (NSCLC), or CRC. The context-specific oncogenic potency of the G12R variant can be attributed to its differential functional consequences in different cancer contexts. KRAS G12R is deficient in its capacity to bind the key effector PI3Kα, essential for mutated KRAS-driven cancer development, explaining the low prevalence of the KRAS G12R variant in cancer overall, including NSCLC and CRC. PDACs, however, circumvent this deficiency by overexpressing PI3Kγ, a compensatory mechanism which allows the expansion of KRAS G12R-driven PDAC [39].

Finally, the multifunctionality of cancer drivers such as the H3K27 histone methyltransferase, EZH2, can inform different therapeutic strategies depending on tissue context. High EZH2 expression is often observed in primitive malignant cells linked to cancer stem cell maintenance. In prostate cancer, EZH2 functions as a tissue-specific epigenetic driver of lineage plasticity, facilitating the transition from prostate adenocarcinoma to neuroendocrine prostate cancer (Box 1) [40]. Cellular plasticity is also reflected in the ability of cancer cells to switch between differentiation states. A notable example is neuroendocrine transdifferentiation which predominantly occurs in metastatic EGFR-mutant NSCLC treated with EGFR tyrosine kinase inhibitors. EGFR inhibition promotes dedifferentiation, thereby enabling cancer cells to acquire basal and stem cell-like characteristics before further differentiating into neuroendocrine lineage [41].

Box 1. EZH2: a context-dependent oncogenic driver.

EZH2 is the histone methyltransferase component of the Polycomb Repressive Complex 2 (PRC2) complex, regulating the epigenetic mark H3K27me3, which is associated with transcriptional repression. During normal development, EZH2 controls cell fate through bivalent promoters containing both repressive H3K27me3 and active H3K4me3 marks, thereby silencing lineage-specific transcription factors. Beyond this canonical role, EZH2 also has non-canonical functions, including methylation of non-histone substrates, transcriptional activation instead of repression, and scaffolding interactions with proteins or RNAs that are independent of PRC2. Its activity is highly context-dependent, showing either oncogenic or tumor-suppressive roles depending on tissue type, largely driven by differential interactions with PRC2 components and other partners. Loss-of-function (LOF) mutations in EZH2 are frequent in T-cell acute lymphoblastic leukemia (T-ALL), malignant peripheral nerve sheath tumors, myelodysplastic/myeloproliferative neoplasms, and myelofibrosis. In T-ALL, EZH2 deficiency leads to derepression of a PRC2-regulated MYCN enhancer, driving overexpression of the oncogene MYCN, replication stress, and dependency on CHK1, thereby revealing a targetable pathway using CHK1 inhibitors [94]. In contrast, gain-of-function (GOF) mutations in EZH2 are oncogenic in lymphomas, melanomas, and CML. In B-cell lymphoma, GOF mutations increase global H3K27me3 levels but redistribute this repressive mark to promote transcription at tumorigenic loci, thereby driving cancer progression [95]. In breast and prostate cancers, EZH2 is often overexpressed and associated with aggressive disease, metastasis, and poor clinical outcomes. Notably, in multiple myeloma and prostate or breast cancers, EZH2 overexpression can occur without coding region mutations, suggesting it results from copy number changes or epigenomic dysregulation. In CML, EZH2 overexpression is linked to transcriptional reprogramming and repression of splicing factors [96]. This dual role of EZH2, acting through both LOF and GOF mechanisms to drive oncogenesis in different tissues, underscores its complex, context-dependent functions and highlights the importance of dissecting cell-type-specific molecular pathways to identify lineage-specific therapeutic targets.

An increased understanding of mechanisms which drive cellular plasticity and subsequent therapy resistance presents the opportunity to pivot the therapeutic approach utilizing sequential or concomitant treatment strategies. Drug-tolerant persister (DTP) cells which survive targeted therapy, often exhibit a set of conserved behaviors that include mesenchymal differentiation. Several studies have demonstrated that targeting mesenchymal-selective vulnerabilities including GPX4 and PKN2, can help eliminate DTP cells [42,43]. Similarly, drugs targeting chromatin regulators, such as KDM5 enzymes, can ablate highly plastic DTP populations [44]. Alternatively, lineage plasticity mechanisms could be exploited to improve therapeutic outcomes by steering cells toward a secondary lineage or state that restores sensitivity to therapy.

Collectively, these findings highlight the notion that most cancer-driving mutations act in a manner that is templated by tissue context, influencing tumor development mainly within certain cell types. This specificity at least partially explains why targeting mutations without considering tumor origin often yields limited success. Tailoring therapies to the tissue and lineage context is therefore key to improving treatment effectiveness.

Specialized Functions of the Tissue of Origin as a Source of Lineage Vulnerability in Cancer

Cancers often co-opt the physiological functions of their cells of origin, leading to lineage-specific vulnerabilities tied to each tissue’s specialized role. Experimental models disrupting cellular homeostasis have shown that identical oncogenic events can produce distinct outcomes depending on the cell’s differentiation state or lineage context. A Sleeping Beauty transposon mutagenesis system implemented in mice introduced mutations at various stages of T-cell development revealed that the same genetic alterations can lead to different molecular and survival outcomes depending on the maturity status and function of the transformed cells. These findings provide an explanation for the presence of subtype-specific NOTCH1 mutation patterns in T-acute lymphoblastic leukemia [45]. Likewise, in pediatric AML, the lineage context in which fusion oncogenes like ETO2-GLIS2 are expressed shapes disease phenotype and aggressiveness by reprogramming lineage-defining transcription factors, thereby locking cells in an undifferentiated state that may be therapeutically reversible by targeting these transcription factors [46]. These findings demonstrate that the same mutation can drive divergent oncogenic behaviors depending on the cell functional identity and ontogeny, revealing inherently lineage-specific vulnerabilities.

Building on this, therapeutic susceptibilities often reflect the unique physiological demands of the tissue-of-origin. For instance, multiple myeloma arises from plasma cells specialized in immunoglobulin secretion. This function imposes chronic proteotoxic stress, making multiple myeloma cells highly dependent on proteasome activity. Proteasome inhibitors including the first-in-class compound bortezomib, the second-generation agent carfilzomib, and the first oral proteasome inhibitor, ixazomib, exacerbate proteostasis imbalance. As a result, these compounds constitute one of the backbone treatments for this cancer type [47,48]. This illustrates how tissue-specific functions not only shape tumor biology but also define therapeutic windows. Conversely, the absence of a clear lineage identity poses major clinical challenges. Acute leukemias of ambiguous lineage, for example, fail to commit to myeloid, B-, or T-lymphoid lineages, or display features of more than one, complicating diagnosis and treatment. This lack of lineage definition deprives clinicians of established, lineage-guided treatment strategies [49], highlighting the critical role of lineage in both disease biology and therapeutic design.

Responses to therapy can also be highly influenced by developmental stage and cell differentiation state. In AML for instance, the more advanced differentiation state of monocytic AML is associated with loss of expression of the venetoclax target, BCL-2, and increased reliance on MCL-1 for oxidative phosphorylation and survival. Therefore, resistance to venetoclax therapy is correlated to monocytic AML and phenotypically primitive AML is more sensitive to venetoclax therapy [50]. Importantly, a recent study indicates that monocytic differentiation and venetoclax resistance are two independent effects of RAS mutations. Leukemic transformation driven by mutations in RAS depends on the cellular milieu and chromatin landscape of granulo-monocytic progenitor cells, whereas venetoclax resistance is broadly conferred across hematopoietic stem and progenitor cell types by RAS mutations. This explains clinical observations in which monocytic subclones emerge during treatment: these subclones often correspond to RAS-mutant clones enriched in monocytic cells, and their expansion reflects selection at the leukemic stem cell level rather than monocytic differentiation per se driving therapy resistance [51].

Metabolism and hormone signaling represent specialized cellular functions shaped by lineage-specific gene regulation, contributing to the diverse physiological profiles of tissues and by extension, their cancers. Metabolic phenotypes vary across cancers in part due to lineage-specific expression of metabolic genes. Tumors often retain the metabolic signatures of their tissue lineage, as shown by studies integrating metabolomics, flux analysis, and functional genomics [52,53]. A notable case is the liver, whose intense metabolic activity corresponds to a high rate of somatic mutations with metabolic consequences in primary liver cancer (PLC) and in chronic liver disease contributing to PLC. Extensive metabolic rewiring in the context of liver cancer has resulted in the exploration of metformin, an inhibitor of gluconeogenesis, in this context. Metformin demonstrated substantial alleviation of the risk of hepatocellular carcinoma development and progression in vitro and in population studies [54,55]. Similarly, cancers of endocrine tissues such as breast, prostate, ovary, and endometrium sometimes retain dependence on hormone signaling, a hallmark of their lineage. Despite expressing the same nuclear hormone receptors, their therapeutic responses are lineage-specific: anti-androgen therapy, for example with enzalutamide, bicalutamide, flutamide, apalutamide, and darolutamide is effective in prostate cancer but not breast cancer. Similarly, selective estrogen receptor modulators like tamoxifen and fulvestrant benefit patients with breast but not prostate cancer [56–58].

Beyond exploiting specialized functions of their tissue of origin, cancer cells can also reawaken traits of embryonic cells, reflecting their inherent developmental plasticity. Many of these traits are not acquired anew but rather stem from the lineage-specific functions embedded in the tissue’s embryonic origin. Key embryonic signaling pathways have been shown to be reactivated during cancer development. Processes that revert cells to a more embryonic-like state that are associated with malignancy include increased ability for self-renewal in stem cells and epithelial-to-mesenchymal transition (EMT), both of which influence a disruption in cellular-lineage hierarchy in cancer [59–61]. The resulting reversion to a more stem- or mesenchymal-like state endows cells with unique, targetable vulnerabilities [62,63]. Tissues sharing a common embryonic origin often retain synapomorphies from their stem group, and their transcriptomic and proteomic profiles are more similar than those from different germ layers. Clonal asymmetry studies further suggest that somatic single nucleotide variants occurring during embryogenesis, gastrulation, and organogenesis shape long-term tissue patterning [64]. Using single-cell RNA sequencing of human pluripotent stem cells, germ-layer–specific transcriptional and drug-response programs were mapped and projected onto adult tumors. This work revealed the reactivation of embryonic lineage-specifying genes in cancers, indicating a return to early developmental states that could expose new therapeutic vulnerabilities [65]. Diffuse hemispheric gliomas, lethal brain tumors from interneuronal precursors, offer another example. These cancers segregate into subgroups defined by driver mutations tied to distinct developmental lineages. Each subgroup depends on embryonic-stage factors, and inhibiting these factors promotes differentiation and impairs tumor growth [66]. Similarly, primary central nervous system germ cell tumors likely arise from misrouted primordial germ cells and lack a dominant oncogenic driver. Instead, Genome-Wide Association Study studies revealed that multiple low-penetrance alleles disrupt pluripotency and apoptosis networks, hijacking early developmental circuits to sustain malignancy [67].

Recent advancements in sequencing technologies have improved our understanding of how developmental programming in gametes can influence cancer-inducing programs in offspring. A recent study demonstrated that positive selection of driver mutations in sperm leads to the accumulation of disease-causing mutations. The positively selected male germline mutations, whether activating or loss-of-function, were associated with developmental diseases or cancer [68]. The ability to perform extensive sequencing of gametes may reveal the extent to which specific cancers are pre-determined and the role of differential developmental programming in the emergence of cancerous lineages.

In particular molecular contexts, cancer therapies act as selective pressures resulting in release from lineage constraints and phenotypic switching. A prominent example involves the loss of differentiation regulators TP53 and RB1. In castration-resistant prostate cancer (CRPC), TP53- and RB1-deficient prostate cancers evade AR-targeted therapies by transitioning from Androgen Receptor (AR)-dependent luminal cells to AR-independent basal-like phenotypes. The loss of TP53 and RB1 results in the activation of microenvironmental signals, specifically the activation of JAK/STAT molecular program, producing AR-targeted therapy-resistant stem-like subclones. The restoration of TP53 and RB1 functions reverses this phenotypic switch and resensitizes to targeted therapy [69]. Similarly, TP53/RB1 co-mutation in lung cancer identifies a subset of patients at increased risk for small cell transformation and emergence of more stem-like clones associated with acquired resistance to targeted EGFR therapy [69,70]. These examples demonstrate how major alterations in differentiation state resulting from RB1/TP53 loss alter the expression of lineage-specific programs, leading to a more stem-like phenotype and resistance to targeted therapies in advanced cancers.

Emerging strategies aim to address the question of how to pharmacologically target embryonic dependencies such as those involved in the EMT process or regulation of the Hedgehog pathway which precision therapies have struggled to address. Early trials treating patients with MET-amplified gastric cancer using crizotinib, an inhibitor of the EMT-promoting proto-oncogene c-MET, demonstrated some clinical benefit [71]. Similarly, two inhibitors targeting the Hedgehog pathway regulator SMO, Vismodegib and Sonidegib, playing a role in embryonic development were approved by the FDA in 2012 and 2015, respectively, for the treatment of advanced basal cell carcinoma driven by PTCH gene loss-of-function alterations [72], [73]. In 2018, another SMO-targeting small-molecule inhibitor, Glasdegib, was approved for use in acute myeloid leukemia (AML) [74].

Lineage-Specific Signaling as a Source of Cancer Vulnerability

Cellular survival, proliferation, and function are regulated by a complex signaling network that maintains homeostasis within a specific lineage context. Targeting critical nodes in this lineage-specific network can thus block the progression of malignancies arising from that lineage. These mediators often exhibit lineage- or tissue-specific characteristics and distinct modes of pathway activation.

In some cases, the potential of targeting a tissue-specific signaling network has been translated into clinical outcomes. In certain lymphoid malignancies, the activation of the pro-survival NF-κB pathway is caused by a highly active BCR signaling cascade. Inhibitors that target intracellular BTK, a downstream propagator of BCR receptor signaling, have been transformative in the treatment of BCR-dependent diseases (Figure 2). In CLL, the BTK inhibitor, Ibrutinib, demonstrated a superior response rate and durability of response over chemotherapy with a significantly improved toxicity profile [75]. Genetically distinct forms of lymphoid malignancies show varying responses to BTK inhibitors. These differences reflect the underlying heterogeneity among tumor subtypes, which may appear similar phenotypically but respond differently to therapy. This variation can reveal resistance mechanisms and highlight lineage-specific trends, offering insights into the complexity of lineage networks within tissues [76].

Figure 2. Targetable Lineage-Specific Signaling Vulnerabilities in Blood Cancers.

Two examples of lineage-restricted signaling pathway modulators in CLL and AML highlight how small-molecule inhibitors can selectively impair cell growth in a disease-specific context. In CLL, BTK functions as a prosurvival mediator upstream of ERK and the NF-κB pathway. Selective inhibition of BTK disrupts these downstream prosurvival signals, leading to targeted killing of CLL cells. In AML, PI3Kγ uniquely regulates AKT signaling in an isoform-specific manner. Inhibiting PI3Kγ with small-molecule inhibitors such as IPI-549 or PROTAC degraders like ARM-165 blocks AKT signaling, resulting in the selective elimination of AML cells.

Global signaling regulators, including G-protein coupled receptors (GPCRs) and regulators of the activity of small guanine nucleotide-binding (G) proteins such as guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs), show variable expression and functional dependency across tissues. As the largest family of transmembrane proteins, GPCRs mediate key downstream signaling, yet their receptor/ligand distribution varies significantly across cancer types, creating distinct, subtype-specific expression patterns [77]. In AML, a lineage-specific dependency has emerged on the PI3Kγ isoform for GPCR-mediated survival and proliferation. PI3Kγ is selectively expressed and essential in AML, while other PI3K isoforms are dispensable, providing an opportunity to target this isoform selectively, sidestepping the inherent limitations of pan-PI3K inhibitors which cause on-target, off-tissue toxicities that limit their use (Figure 2) [78–80]. A PROTAC-based degrader molecule of PI3Kγ derived from the PI3Kγ inhibitor, AZ2, demonstrated high specificity and efficacy in AML cells with minimal toxicity in non-AML cells. This work exemplifies how targeting lineage-specific signaling dependencies can yield potent, cancer-selective therapies (Box 2). Similarly, due to the known oncogenicity of mutations in small G protein RAS across cancers, GAPs and GEFs have been postulated to play a role in cancer development and maintenance. A recently published study utilized functional screens to identify GAP ARHGAP45 as a selective functional dependency in myeloid malignancies compared to normal HSPCs. In myeloid cells, ARHGAP45 regulates GTP-bound RhoA levels and restrains overactivation of RhoA, subsequently blocking myeloid differentiation. T cells directed towards ARHGAP45-derived antigen significantly reduced tumor burden in an in vivo AML model [81]. These examples demonstrate how alternative strategies for drug development, including PROTACs and T-cell directed therapies, can be leveraged against novel lineage-specific targets where classical pharmacological inhibitors are insufficient or absent.

Box 2. PI3Kγ: a lineage-specific cell signaling mediator in myeloid cells.

PI3Kα, PI3Kβ, PI3Kδ and PI3Kγ are upstream mediators of AKT signaling. PI3Kγ is composed of the catalytic subunit, PIK3CG, and its exclusive regulatory subunit, PIK3R5. PI3Kγ is a selectively expressed functional dependency in myeloid cells, and it acts as the dominant PI3K isoform in propagating signals from GPCRs to downstream AKT in AML cells. Genetic loss of PI3Kγ in AML cells confers a survival advantage to mice, further exacerbated in combination with standard-of-care therapies. A dominant role for PI3Kγ in mediating cell signaling in myeloid cells has been characterized in different contexts, revealing different therapeutic avenues. Luo et al described a survival dependency of blastic plasmacytoid dendritic cell neoplasm (BPDCN) on PI3Kγ. They observed a sensitivity to PI3Kγ inhibition in a subset of AMLs with high expression of PIK3R5, correlated to expression of inflammation-associated genes which may act as sensitizers to PI3Kγ inhibition [78]. Guo et al. demonstrated, in the context of an MLL-AF9-driven mouse model, that PI3Kγ plays a critical role in LSC function and acts upstream of the pentose phosphate pathway (PPP). In this setting, PAK1 inhibitors can slow AML cell growth [79]. PI3Kγ targeting was demonstrated to be a sensitizer to standard of care therapies venetoclax and cytarabine, however the monotherapeutic potential of PI3Kγ inhibition in AML was limited. PI3Kγ inhibitors are unable to sustain their initial ablation of downstream AKT signaling and as a result these compounds are insufficient when given alone in a bulk AML setting. PI3Kγ has both scaffolding and non-scaffolding functions and so it has been hypothesized that enzymatic inhibition alone is insufficient to inhibit both scaffolding and catalytic functions of PI3Kγ. Therefore, our group synthesized a degrader compound (ARM-165) for sustained degradation of PIK3CG by the endogenous proteasome. ARM-165 demonstrated a greater efficacy than enzymatic PI3Kγ inhibition alone [80]. PI3Kγ is an essential cell signaling mediator specifically in myeloid cells, representing a lineage-specific target with the potential to reduce dose-limiting toxicities seen with existing therapies in AML. These recent studies justify further exploration into the mechanistic role and resulting anti-cancer potential of PI3Kγ targeting in specific myeloid cell contexts. To translate the potential of PI3Kγ as a lineage-specific target into clinical outcomes for AML patients, therapeutic strategies for specific patient subgroups should be defined based on the role of PI3Kγ as a cell signaling mediator in specific cellular contexts within the myeloid compartment.

As illustrated by studies on PI3Kγ, protein kinases play a central role in lineage addiction in cancer. Their impact on tumor progression arises from both kinase-dependent (catalytic phosphorylation) and kinase-independent functions, such as scaffolding, protein-protein interactions, allosteric regulation, subcellular localization, and DNA binding, functions whose relevance often varies by tissue context [82]. Further, both the signaling output of a kinase and the cancer cell’s tendency to die versus adapt to kinase inhibition are influenced by the particular signaling context present in a tissue. These factors influence the distribution of mutations impacting a given kinase across cancer types, which themselves may be differentially inhibitable with various classes of small molecule inhibitors. For example, EGFR is frequently mutated in both lung carcinoma and GBM, in each case resulting in EGFR activation. In GBM, mutations commonly occur in the extracellular domain, whereas in lung cancer, mutations more commonly impact the kinase domain. This selection for a different spectrum of EGFR mutations in GBM and lung carcinoma results in EGFR variants with context-dependent signaling functions, adaptive feedback mechanisms, and inhibitability with distinct classes of small molecules. As a result, EGFR inhibitors targeting the active kinase conformation are far more effective in lung carcinoma than in GBM (e.g. erlotinib) [83,84] (Figure 3). Similarly, BRAF-mutated CRCs and melanomas exhibit differential responses to BRAF and MEK inhibitors. BRAF mutations increase activation of the Ras-Raf-MEK-ERK (MAPK) pathway, driving cell proliferation and growth. However, BRAF mutations exist in distinct signaling contexts in these two diseases: In CRC, EGFR signaling has been well established to blunt the activity of BRAF and MEK inhibitor therapies, and constitutively active Wnt signaling may also contribute to these suboptimal responses relative to those observed in BRAF-mutant melanomas, which typically lack both. Further divergence of BRAF activity in different tissue contexts can be attributed to the presence of different BRAF variants, which vary in their representation between these tissues. The V600 variant mutation in BRAF exhibits strong kinase activity, while non-V600 variants range from RAS-independent hyperactive forms to low-activity or even ‘kinase-dead’ versions. As a result, whereas some BRAF variants yield MEK phosphorylation independently of RAS, others for example require RAS-dependent CRAF binding and activation. (Figure 4). Together, the differing BRAF signaling contexts and mutational distributions found in CRC versus melanoma lead to differential responses to BRAF and MEK inhibitor therapies, with responses in melanoma being significantly more robust [85].

Figure 3. Tissue‐Specific Diversity of EGFR Alterations and Downstream Signaling Cascade in Glioblastoma and Lung Cancer.

As illustrated by these two cancer types, alterations in EGFR function and response to targeted therapies are influenced by tissue-specific genetic variants, which can differentially affect catalytic and non-catalytic EGFR functions, as well as by the tissue-specific EGFR-dependent signaling context, in which certain signaling nodes are more highly expressed or active in one tissue than another. In glioblastoma, EGFR genetic alterations most commonly affect the extracellular domain, making degraders that disrupt non-catalytic functions particularly effective. In contrast, in lung cancer, EGFR mutations typically involve the tyrosine kinase domain, so drugs targeting the aberrant catalytic kinase activity are more efficacious.

Figure 4. Influence of Tissue‐Specific Signaling Pathways and BRAF Variants on Response to BRAF/MEK Inhibitors in Melanoma and Colorectal Cancers.

Responses to BRAF and MEK inhibitors in melanoma and colorectal cancer are shaped by tissue-specific signaling contexts and the distribution of catalytic and non-catalytic BRAF variants. In colorectal cancer, BRAF genetic alterations coexist with active Wnt and EGFR signaling pathways, which confer decreased sensitivity to BRAF and MEK targeting with small-molecule inhibitors despite the expression of BRAF mutations. This resistance is further compounded by a minority subset of non-V600 BRAF genetic variants, which are less sensitive to these inhibitors than BRAF^V600-mutated cells. In contrast, melanoma exhibits increased sensitivity to BRAF and MEK pharmacological inhibition due to lower EGFR expression, less active Wnt signaling, and the predominance of BRAF^V600 variants.

The baseline essentiality of signaling pathways across tissues contributes to toxicity following their inhibition in cancer therapy. The dosing of therapies targeting essential cell signaling pathways that function as lineage dependencies requires careful optimization to effectively target malignant tissue while ensuring that non-malignant tissues can tolerate pathway inhibition. For example, androgen receptor inhibition in the treatment of prostate cancer was associated with cardiac defects, likely due to the poorly characterized mechanism of cardioprotective functions of the androgen receptor [86]. Consequently, the therapeutic window is narrowed because of these toxic effects on cardiac tissue.

Inhibiting essential signaling pathways by targeting protein isoforms with lineage-specific expression is a promising strategy to minimize toxicity in normal tissues. For example, targeting the pro-survival PI3K-AKT-mTOR pathway with PI3Kα inhibitors for the treatment of solid tumors incurs side effects in non-malignant tissues as a result of the broad importance PI3Kα plays in propagating PI3K-AKT-mTOR signaling in diverse tissues (NCT01219699). In contrast, targeting the PI3Kγ isoform offers a way to avoid such therapy-related toxicity, as its role in promoting PI3K-AKT-mTOR signaling is largely restricted to myeloid cells [80]. This example illustrates that while targeting lineage-based dependencies can pose inherent toxicity risks, selective targeting of lineage-specific protein isoforms can expand the therapeutic window and mitigate side effects, providing a clear rationale for how such strategies can be translated safely to the clinic.

The Tissue-of-Origin Antigen Repertoire of the Tumor as a Lineage-Restricted Liability for Advanced T Cell–Based Therapies

CAR-T cell therapy, which engineers patient-derived T cells to express receptors targeting tumor-associated antigens, has gained momentum. Tumor- and tissue-restricted antigens are excellent potential targets for CAR-T cell therapies, and as such a complete definition of the tumor- and tissue-restricted antigen landscape is of crucial importance (as earlier exemplified by T cells directed towards ARHGAP45-derived antigen in AML) [81,87]. Recent advances linking intratumoral T-cell phenotypes to antigen specificity, aided by multi-dimensional single-cell technologies, have pushed T-cell therapies toward more tissue- and lineage-specific precision approaches.

CD19, which is predominantly expressed across nearly all stages of B-cell development, was historically the first lineage-specific marker to be used for CAR-T cell-based therapies, with significant anti-leukemia effects in relapsed/refractory B-ALL and diffuse large B-cell lymphoma [88]. Building on this, other B-cell markers such as CD20, CD22, and CD79b, each with distinct but largely B cell-restricted expression patterns, have been explored as additional lineage-specific targets and are currently being tested in clinical trials (Table 3). CD20 is mainly expressed on mature B cells, CD22 is present on mature and some late-stage B cells, and CD79b, a component of the B-cell receptor complex, is expressed throughout most B-cell stages except plasma cells [89]. Many next-generation CAR-T systems now incorporate bispecific designs targeting combinations like CD19 and CD22, or CD19 and CD70 [90]. Following the success of CD19-based CAR-T therapies, T-lineage-associated markers such as CD5 and CD7 have also been investigated as CAR-T targets [91,92]. Of note, CD7-based CAR-T therapies often require gene-editing strategies to prevent fratricide activity [93]. In multiple myeloma, B-cell maturation antigen (BCMA) has become a major therapeutic target, with CAR-T cell therapies like idecabtagene vicleucel and ciltacabtagene autoleucel already FDA-approved [94]. GPRC5D, a newer plasma cell-associated marker with low expression in normal tissues, is now under clinical investigation, including for BCMA-refractory patients [95].

Table 3.

Lineage-Specific Targeting by CAR-T cell systems and corresponding clinical trials.

Lineage Target	Associated Neoplasm	System	Phase	Clinical Trial / Ref.
CD19	B Cell Malignancies	CAR-T	Completed	[88]
CD19/22		Bi-Specific CAR-T	Phase I/ II	NCT05432882
CD19/70		Bi-Specific CAR-T	Phase I/ II	NCT05436496
CD19/79b		Bi-Specific CAR-T	Phase I/ II	NCT05436509
CD19/20		Sequential CAR-T	Phase I/ II	NCT02737085
CD20/22		Bi-Specific CAR-T	Early Phase I	NCT04283006
CD5	T Cell Malignancies	CAR-T	Early Phase I	NCT06633354
CD7		CAR-T	Completed	[91]
BCMA	Multiple Myeloma	CAR-T	Phase I	NCT04706936
GPRC5D		CAR-T	Phase II	NCT06297226
BCMA/GPRC5D		Bi-Specific CAR-T	Phase I	NCT07003568
CD33	Myeloid Malignancies	CAR-T	Phase I/ II	NCT02958397
CD123		CAR-T	Phase I/II	NCT04265963
PSMA	Prostate Cancer	CAR-T	Phase I/ II	NCT04429451
PSCA		CAR-T	Phase I	NCT05805371
HER2/GD2/CD44v6	Breast cancer	Multi-CAR T	Phase I/ II	NCT04430595
GPC3	Hepatocarcinoma	CAR-T	Phase I	NCT04121273
CLDN18.2	Colorectal Cancer	CAR-T + CAR-DC	Phase I	NCT06946615

In myeloid neoplasms, CD33 and CD123 are the primary targets for CAR-T strategies. CD33 is highly expressed on normal myeloid progenitors and most AML cells, and its clinical relevance is supported by the FDA-approved ADC gemtuzumab ozogamicin [96]. However, its expression on normal precursors limits this agent’s therapeutic index. CD123, which is more selectively expressed on leukemic stem cells and blasts than on normal pluripotent hematopoietic progenitors, represents a potentially safer and more targeted therapeutic option. This is exemplified by ongoing trials using CD123-directed CAR-T cells and the FDA-approved recombinant fusion protein tagraxofusp, which combines the CD123 ligand IL-3 with a truncated diphtheria toxin for the treatment of blastic plasmacytoid dendritic cell neoplasm [97]. Dual antigen targeted CAR-T therapies can compensate for the heterogeneity of single antigen expression, resulting in improved specificity and efficacy. In AML, dual-targeted CD33 and CD-123 CAR-Ts were able to eradicate AML from patient derived xenograft models [98]. Multi-specific engagers show promise to improve the selectivity of CD-123 based T cell therapy for cancer cells relative to normal cells, reducing the risk of cytokine release [99].

Antigens like CD19 are homogeneously expressed in hematologic malignancies but solid tumors often display variable and patchy antigen expression, a factor that has made them more difficult to target with antigen-specific T cell based therapies. Approaches such as NGS have been applied to identify better tumor-specific targets. In CRPC, this led to development of CAR-T cells against PSMA and PSCA [100,101]. To minimize risk of antigen escape in solid tumors due to heterogeneous antigen marker expression, newer CAR T-cell strategies such as the Multi‑4SCAR‑T trial (NCT04430595) in breast cancer are testing simultaneous targeting of HER2, GD2, and CD44v6 (Table 3). By focusing on multiple antigens linked to both lineage and cancer stem cells, this approach reduces the risk of immune evasion and toxicity to healthy tissues and enhances tumor clearance. Lineage-specific markers have proven essential for the precision and efficacy of CAR T-cell therapies, especially in hematologic cancers. Exploiting these markers remains key to enhancing therapeutic impact and limiting off-target toxicity, even in the more complex context of solid tumors.

An elegant approach to improve antigen-specific immunotherapy in the absence of cancer restricted surface markers involves the engineering of healthy hematopoietic stem and progenitor cells to delete or modify the target antigen, thereby reducing the risk of toxicity caused by immune destruction of healthy cells. For example, in AML, CD33-targeted CAR-T therapy is restricted by the destruction of healthy myeloid cells due to the widespread expression of CD33 in healthy and malignant myeloid compartments. The engraftment of HSPCs with CD33-deleted but otherwise normal functions was shown to yield effective cancer targeting while sparing the normal, CD33-deficient donor cells that comprised the reconstituted hematopoietic system [102]. This method has since been applied to markers beyond CD33, including the combinatorial targeting of FLT3, KIT and CD123 through the alteration of key epitopes in donated normal HSPCs [103]. In principle, epitope engineering of T cell target antigens in healthy compartments has the potential to be applied beyond hematological malignancy in solid cancers whereby CAR-T therapies remain limited, largely due to the unrestricted expression of lineage-related antigens in essential solid tissues.

CONCLUDING REMARKS AND FUTURE DIRECTIONS

Lineage-tracing techniques have helped uncover a new generation of lineage- and tissue-specific dependencies across cancers. Recent advances in lineage-tracing and single-cell technologies have enabled high-resolution mapping of these programs, leading to the development of targeted therapies that exploit the molecular context of the cell-of-origin. Continued efforts to define these lineage-specific targets and dependencies are likely to expand the repertoire of precision cancer therapies. Achieving this goal requires a deeper understanding of how cellular hierarchies evolve and diverge in healthy versus malignant tissues (see outstanding questions). Improved lineage-tracing methods, particularly when coupled with single-cell and multi-omics approaches, are helping clarify how genetic alterations interact with non-genetic features across tissue contexts. In immunotherapy, these tools have enabled the identification of tissue-specific antigen expression patterns for selective T cell-based targeting. Finally, recent advancements in artificial intelligence and deep learning methods have identified novel causal drivers of lineage-specificity and plasticity in the cancer context [104].

This emerging precision strategy marks a shift from broad cytotoxic treatments to more selective interventions that improve efficacy and reduce toxicity. However, current lineage-tracing methods have limitations. For instance, they often rely on assumptions about the rarity of identical mutations arising independently, potentially introducing bias in clonal relationships. Moreover, the interaction of intrinsic and extrinsic factors—including genetic and epigenetic plasticity—can enable cancer cells to escape lineage-targeted therapies.

Careful mining of existing data from resources like DepMap has identified several historically ‘undruggable’ lineage-specific targets, most notably including transcription factors [5]. Excitingly, emerging drug design strategies employing chemoproteomics, molecular glues, PROTACs, and other technologies are rapidly changing the landscape of druggability, suggesting that these lineage-selective targets may soon become pharmacologically addressable. Cellular plasticity also remains a major obstacle in this field. One therapeutic approach involves using combination regimens to steer cells into a fixed lineage state, thereby increasing sensitivity to lineage-specific cytotoxic agents. Future efforts should then focus on improving lineage-tracing resolution, understanding the molecular basis of plasticity, and developing novel delivery platforms for lineage-restricted therapies (Box 3; see outstanding questions). This interdisciplinary frontier will benefit from collaboration between chemists, biologists, and clinicians to translate biological specificity into therapeutic precision.

Box 3. Exploiting lineage plasticity as a therapeutic strategy.

Lineage plasticity is widely viewed as a major obstacle to the design of effective, long-lasting cancer treatments. This phenomenon often occurs because therapy directed towards a particular molecular target or program generates a ‘selective pressure’ under which the (epi)genetic and molecular identity of the lineage shifts. For example, in prostate cancer, the use of therapy targeting androgen signaling has led to a heightened frequency of neuroendocrine prostate cancer (NEPC), an androgen receptor (AR)-independent form of the disease. This shift in cancer lineage towards NEPC means alternative targeted therapies need to be explored to combat these androgen-independent prostate cancers. BRD4 has been identified as an emerging regulator of lineage plasticity in prostate cancer through the direct regulation of hundreds of genes involved in lineage plasticity programs. A BRD4 inhibitor in clinical development, AZD153, has been shown to inhibit growth of de novo and treatment-induced NEPC PDX models [105]. Alternatively, LSD1 is a histone demethylase that promotes stemness and cell survival in prostate cancer. Recently, LSD1 and its interactors have been described as key mediators of lineage plasticity in BRAF-mutated neuroendocrine cancer whose inhibition blocks lineage plasticity to improve therapeutic response [106]. Similarly, LSD1 has been identified as upregulated and important for survival in NEPC, suggesting a role for LSD1 in driving prostate cancer cells towards the NEPC phenotype. The inhibition of the scaffolding and catalytic function of LSD1 reduces NEPC growth in in vivo models and therefore has been posed as a promising treatment strategy to combat NEPC [107]. Characterizing the lineage-specific mechanisms regulating plasticity may enable the design of combination therapies that render cells more durably susceptible to targeted treatments by restricting plasticity. Of particular note, the shapeshifting capabilities of cells often involve changes in chromatin state and accessibility, and therefore the epigenetic and genetic regulators of chromatin organization may be especially attractive targets to restrict cellular plasticity.

GLOSSARY OF TERMS

Adaptive response

the process by which a cell responds to environmental cues to enhance survival, including increased resistance following prior mild stress.

Basket/umbrella trial

a clinical trial design in which a therapy is tested or repurposed across multiple diseases or cancer contexts.

Cell lineage

the developmental history of a cell, showing how it and its descendants arise from a common progenitor.

Cellular barcoding

The marking of individual cells with unique heritable identifiers to track their progeny.

Cellular hierarchy

The organization of cells within a system, reflecting relationships between undifferentiated cells and specialized populations.

CRISPR

Clustered Regularly Interspaced Short Palindromic Repeats; a genome-editing technology used to selectively modify DNA.

Dedifferentiation

process by which a specialized cell loses its differentiated features and reverts to a more primitive or stem-like state.

Developmental trajectory

The sequential process of cellular differentiation and maturation that a cell or lineage follows during development.

Differentiation dynamics

The timing and progression of cell fate commitment during development.

Driver versus passenger mutations

Genetic changes that either promote cancer development (drivers) or have no functional impact on it (passengers).

Drug-Tolerant Persister (DTP) cells

Cancer cells that survive therapy via reversible, non-genetic adaptations.

Embryogenesis

The formation and development of an embryo.

Epithelial-to-Mesenchymal Transition (EMT)

The biological process during which polarized epithelial cells undergo molecular changes to acquire a mesenchymal phenotype.

Fratricide activity

The cross-killing of genetically identical cells.

Gastrulation

The early developmental process in which a single-layered embryo forms multiple layers.

Lineage plasticity

The ability of cells to switch phenotypes in response to environmental cues.

Lineage-tracing

The process of labeling a single cell with markers so that all its progeny can be identified and tracked over time.

Morphological analysis

The study of the shape, structure, and form of cells or organisms.

Multi-specific engagers

Therapeutic agents designed to simultaneously target multiple molecules or pathways.

Organogenesis

The formation and development of organs during embryonic development.

Self-renewal

The ability of an undifferentiated cell to produce identical progeny that retain the same undifferentiated characteristics.

Selective pressure

An environmental factor that favors the survival or reproduction of a particular phenotype.

Synapomorphy

An inheritable trait present in a common ancestor and shared exclusively by its evolutionary descendants.

Tissue-of-origin

The tissue from which a tumor or cell type arises, shaping its characteristics and therapy response.

Undruggable

A term describing a cellular target that cannot be effectively modulated with current pharmacological agents.

Universal pathway essentiality

The dependence of a cell or tissue on a particular gene or factor across most or all of its signaling pathways.