HAEM5:Primary cutaneous gamma/delta T-cell lymphoma

Haematolymphoid Tumours (WHO Classification, 5th ed.)

Primary Author(s)*

Mahzad Azimpouran, MD; Sumire Kitahara, MD; Cedars-Sinai, Los Angeles, CA

WHO Classification of Disease

Structure	Disease
Book	Haematolymphoid Tumours (5th ed.)
Category	T-cell and NK-cell lymphoid proliferations and lymphomas
Family	Mature T-cell and NK-cell neoplasms
Type	Primary cutaneous T-cell lymphoid proliferations and lymphomas
Subtype(s)	Primary cutaneous gamma/delta T-cell lymphoma

Related Terminology

Acceptable	N/A
Not Recommended	N/A

Gene Rearrangements

Driver Gene	Fusion(s) and Common Partner Genes	Molecular Pathogenesis	Typical Chromosomal Alteration(s)	Prevalence -Common >20%, Recurrent 5-20% or Rare <5% (Disease)	Diagnostic, Prognostic, and Therapeutic Significance - D, P, T	Established Clinical Significance Per Guidelines - Yes or No (Source)	Clinical Relevance Details/Other Notes
Arm‑level chromosomal alterations (e.g., 9p, 18q deletions; 1q, 7q,15q gains)	—	Copy number loss or gain → altered gene dosage of tumour suppressors/oncogenes	Other / chromosomal alteration	Recurrent (5‑20%) (9p del ~22%, 18q del ~22%; 1q/7q/15q gains ~33‑39%)	D / P	No	These structural changes suggest genomic instability and aggressive biology; may help risk stratification though not diagnostic per se
Fusion: FYN :: (probable partner TRAF3IP2)	TRAF3IP2	Structural alteration – deletion/exon8 deletion → (in other T‑cell lymphomas) FYN::TRAF3IP2 fusion leading to SRC‑family kinase activation; in this PCGDTCL case FYN exon8 deletion noted	Oncogene / Other	Rare (<5%) (single case reported)	T	No	Very recently described; may represent novel driver/target; further cases needed
Fusion: PCM1 :: JAK2	PCM1	Fusion → juxtaposition of dimerization domain of PCM1 with kinase domain of JAK2 → constitutive JAK2 activation	Oncogene	Rare (<5%) (single documented PCGDTCL case)	T	No	Known in other T‑cell and myeloid neoplasms; in PCGDTCL this double‐hit case had PCM1::JAK2 + TBL1XR1::TP63 fusion; patient refractory to JAK inhibitor
Fusion: TBL1XR1 :: TP63	TBL1XR1	Fusion → truncation/overexpression of ΔNp63 form → oncogenic p63 signalling	Oncogene / Other	Rare (<5%) (same single case) (	P / T	No	Associated with aggressive behaviour in T‑cell lymphomas; in the reported PCGDTCL case contributed to aggressive course and JAK inhibitor resistance

Individual Region Genomic Gain/Loss/LOH

Chr #	Gain, Loss, Amp, LOH	Minimal Region Cytoband and/or Genomic Coordinates [Genome Build; Size]	Relevant Gene(s)	Diagnostic, Prognostic, and Therapeutic Significance - D, P, T	Established Clinical Significance Per Guidelines - Yes or No (Source)	Clinical Relevance Details/Other Notes
1p	Loss	1p36.11	ARID1A	P	No	Deleted in ~28% of cases. Indicates epigenetic/chromatin modifier pathway involvement
1q	Gain (arm‐level amplification)	1q (approx chr1:144,000,000‑249,000,000)	Multiple genes on 1q (unspecified)	P / T	No	Amplification in ~33% of cases. Potential gene dosage effect; specific driver gene not yet defined
2q	Loss	2q37.3	PDCD1	P	No	Deletion in ~22% of cases. Immune checkpoint gene loss; potential therapeutic‑escape mechanism
7q	Gain (arm‐level)	7q (approx chr7:100,000,000‑159,000,000)	Multiple genes on 7q (unspecified)	P	No	Amplification in ~39% of cases. Suggests MAPK/other pathway involvement but specific gene not yet defined.
9p	Loss (deletion)	9p21.3 (~ chr9:21,900,000‑22,200,000)	CDKN2A, CDKN2B	P	No	High‐frequency homozygous or biallelic deletion (~61% of cases; 45% biallelic) in PCGDTCL. (PMC) Suggests aggressive biology, prognostic marker candidate
10q	Loss	10q24.1	FAS	P	No	Deletion in ~22% of cases. Loss of apoptosis regulator; may contribute to immune‑escape
15q	Gain (arm‐level)	15q (approx chr15:30,000,000‑102,000,000)	Multiple genes on 15q (unspecified)	P	No	Amplification in ~33% of cases. Likely reflects tumour evolution rather than diagnostic biomarker
18q	Loss	18q (arm level; no precise minimal region specified)	Putative tumour suppressors (unspecified)	P	No	Recurrent deletion ~22% in PCGDTCL cohort. May reflect genomic instability and poor outcome

Characteristic Chromosomal or Other Global Mutational Patterns

Chromosomal Pattern	Molecular Pathogenesis	Prevalence - Common >20%, Recurrent 5-20% or Rare <5% (Disease)	Diagnostic, Prognostic, and Therapeutic Significance - D, P, T	Established Clinical Significance Per Guidelines - Yes or No (Source)	Clinical Relevance Details/Other Notes
Arm‑level somatic copy‑number variation (SCNV) (average ~4 arm‑level events per case; median ~166.5 SCNVs per sample)	Reflects genomic instability; multiple gains and losses of whole chromosome arms likely contribute to oncogenesis and progression by altering gene dosage of multiple oncogenes/tumour suppressors simultaneously. (PMC)	Common (>20%) — nearly all cases show multiple arm‑level events (median 4 per sample) (PMC)	P	No	High genomic complexity may explain aggressive behaviour and poor response to therapy. Could impact prognosis or treatment resistance but not yet in guidelines.
High burden of somatic copy‑number variants (SCNVs) relative to single‐nucleotide variants (SNVs) (e.g., median ~166.5 SCNVs per sample)	Suggests that structural genomic alterations dominate the mutational landscape, perhaps more so than classical hotspot SNVs, indicating a biology driven by large‑scale genomic disruption rather than just point mutations.	Common (>20%)	P	No	Recognising this pattern may guide expectation of complexity, but this is not currently used clinically for diagnosis or treatment.
Distinct cell‑of‑origin signature: Vδ1 vs Vδ2 subtype (epidermal/dermal Vδ1 vs panniculitic Vδ2)	Different tissue compartments (epidermis/dermis vs subcutaneous) correspond to distinct γδ T‑cell subsets (Vδ1 vs Vδ2). The cell‑of‑origin influences mutational signatures (eg UV signature in Vδ1) and clinical phenotype (Vδ2 more aggressive)	Recurrent (5‑20%) — this pattern applies in a subset of cases defined by tissue involvement and TCR subtype.	D / P	No	This dichotomy may help stratify patients clinically (Vδ2 subtype worse prognosis) but is not currently part of formal diagnostic or therapeutic guidelines.
Ultraviolet (UV) mutational signature in Vδ1 subtype	The epidermal/dermal Vδ1 γδ T‑cell lymphomas exhibit a UV signature in their mutation spectrum, likely reflecting skin localization and UV exposure contributing to oncogenesis.	Recurrent (5‑20%) — seen in Vδ1 cases but not all.	P	No	Could suggest etiology and may influence prognosis; though not yet used for therapy selection.
Frequent deletions of 9p21.3 (CDKN2A region) (part of the SCNV pattern)	Loss of CDKN2A/p14^ARF leads to cell‑cycle deregulation, loss of tumour suppressor control: a hallmark of many aggressive lymphomas	Common (>20%) (approx 61% of cases)	P	No	Among the most prevalent genomic events in PCGDTCL — potential prognostic marker though not yet guideline‑endorsed.
Multiple gains of oncogenic arms (e.g., 1q, 7q, 15q) and corresponding losses (eg 18q)	Gains may increase dosage of oncogenes; losses may reduce tumour suppressor dosage—together contributing to malignant phenotype	Recurrent (5‑20%) for specific arm‑level changes (e.g., 1q gain ~33%, 7q ~39%, 15q ~33%)	P	No	These arm‑level events indicate complexity; may correlate with poorer prognosis; not yet actionable in therapy.
TCR chain repertoire restriction / non‑random Vγ or Vδ usage (eg Vγ3Vδ2 in panniculitic cases)	Suggests antigen‑driven or tissue‐resident γδ T‑cell proliferation; highlights non‑random selection of malignant clones	Recurrent (5‑20%) in defined subtypes	D	No	Might help refine subclassification of PCGDTCL; not currently used in routine diagnostic algorithms.

Gene Mutations (SNV/INDEL)

Gene	Genetic Alteration	Tumor Suppressor Gene, Oncogene, Other	Prevalence - Common >20%, Recurrent 5-20% or Rare <5% (Disease)	Diagnostic, Prognostic, and Therapeutic Significance - D, P, T	Established Clinical Significance Per Guidelines - Yes or No (Source)	Clinical Relevance Details/Other Notes
STAT5B	Activating missense (e.g., p.N642H) → constitutive downstream STAT5 signalling	Oncogene	Recurrent (~5‑20 %) — e.g., in the 2020 genomic study: JAK/STAT mutations ~21 % of cases	T / P: Therapeutic potential (JAK/STAT inhibition); Prognostic implication (pathway addiction/resistance)	No	Mutant STAT5B (especially N642H) shown to induce T‑cell neoplasia in models; in PCGDTCL JAK/STAT addiction shown clinically
STAT3	Activating missense (SH2 domain) → constitutive STAT3 signalling	Oncogene	Rare (<5 %) to Recurrent (≈5‑10 %) (in NK/γδ‑T lymphomas earlier)	T / P	No	Less frequent than STAT5B in PCGDTCL; part of JAK/STAT pathway involvement
JAK3	Activating mutation (e.g., p.R657W) → JAK3 tyrosine kinase activation	Oncogene	Rare (<5 %) (noted in the Daniels et al. cohort)	T	No	Supports JAK/STAT involvement; one case report showed response to JAK inhibition
KRAS	Activating hotspot mutations (e.g., G12D, Q61H, D119N) → RAS/MAPK activation	Oncogene	Recurrent (~5‑20 %) — “KRAS was the most frequently mutated oncogene”	T / P	No	MAPK pathway appears relevant; patients with MAPK‑pathway driver mutations had worse survival in the cohort
NRAS	Activating hotspot mutation → RAS/MAPK activation	Oncogene	Rare (<5 %) to Recurrent (~5‑10 %)	T / P	No	Part of the same RAS/MAPK pathway as KRAS; less common.
MAPK1	Activating mutation → MAPK1 signalling activation	Oncogene	Rare (<5 %)	T	No	Also in MAPK pathway; limited data in PCGDTCL
MYC	Activating missense mutation (e.g., p.P74L) → MYC pathway up‑regulation	Oncogene	Rare (<5 %)	P / T	No	MYC pathway involvement may contribute to the aggressive phenotype; direct targeting not yet established
MYCN	Activating mutation (e.g., p.G34R) → MYCN pathway activation	Oncogene	Rare (<5 %)	P / T	No	Highlights involvement of MYC‑family beyond MYC itself in this disease

Note: A more extensive list of mutations can be found in cBioportal, COSMIC, and/or other databases. When applicable, gene-specific pages within the CCGA site directly link to pertinent external content.

Epigenomic Alterations

N/A

Genes and Main Pathways Involved

Gene; Genetic Alteration	Pathway	Pathophysiologic Outcome
DNMT3A (DNA methyltransferase)	Loss‑of‑function mutations or deletions → reduced de novo DNA methylation; “epigenetic writer” defect (DNA methylation pathway)	Deregulation of gene silencing; tumour suppressor genes may remain unmethylated or aberrantly methylated → genomic instability, aberrant T‑cell differentiation/activation
TET2 (methylcytosine dioxygenase)	Loss‑of‑function mutations → failure of DNA 5‑mC → 5‑hmC demethylation (“epigenetic eraser” defect)	Aberrant hypermethylation or demethylation patterns; influences T‑cell development and malignant transformation (e.g., in T‑fh lymphomas)
IDH2 (metabolic enzyme altering epigenome)	Gain‑of‑function mutation (e.g., R172) → produces 2‑hydroxyglutarate → inhibits TET family → epigenetic dysregulation	Oncometabolite‑driven methylation changes, impaired differentiation, proliferation of malignant T cells
ARID1A (SWI/SNF chromatin‑remodeller)	Loss‑of‑function mutation/deletion → impaired nucleosome remodelling, altered chromatin accessibility (“chromatin remodeller”)	Reduced tumour‑suppressor gene expression due to chromatin compaction; may influence immune microenvironment and genomic instability
KMT2D / KMT2A (H3K4 methyltransferases)	Loss‑of‑function mutations (“histone‑writer” defect) → decreased H3K4 methylation (activating mark)	Impaired activation of gene expression programs (differentiation, apoptosis) → contributes to malignant transformation
KDM6A (H3K27 demethylase)	Loss‑of‑function → accumulation of H3K27me3 (repressive histone mark) (“histone‑eraser” defect)	Further chromatin repression of tumour‑suppressor genes; may enhance survival of malignant T cells
EZH2 (PRC2 complex methyltransferase)	Overexpression/gain of function → increased H3K27me3 (“histone‑writer” overactivity)	Enhanced silencing of differentiation/apoptosis genes; contributes to aggressive lymphoma phenotypes
CREBBP / EP300 (histone acetyl‑transferases)	Loss‑of‑function mutations (“histone‑writer” defect) → reduced histone acetylation and gene activation	Diminished transcriptional activation of tumour‑suppressor/immune genes; may drive malignant progression
DNA methylation of specific tumour‑suppressor loci (e.g., CDKN2A promoter; FAS promoter)	Hypermethylation of promoter CpG islands → silencing of tumor suppressor / apoptosis‑initiator genes	Loss of cell‑cycle control or apoptosis leads to malignant T‑cell survival/proliferation

Genetic Diagnostic Testing Methods

Method	Description	Type of Alteration Detected	Advantages	Limitations	Clinical Use in PCGDTCL
Next-Generation Sequencing (NGS)	High-throughput sequencing of targeted gene panels, whole-exome, or whole-genome sequencing	SNVs, INDELs, copy number variants (CNVs), some fusions (if RNA-seq included)	Comprehensive mutation detection; scalable; can detect multiple variants simultaneously	Requires high-quality DNA/RNA; bioinformatics expertise needed; cost-intensive	Main tool for mutational profiling in PCGDTCL; used in research and increasingly in clinical labs
Targeted Gene Panels (amplicon or hybrid capture-based)	Sequencing of a defined set of genes known to be relevant	SNVs, INDELs, limited CNVs, hotspot fusions (if included)	Faster, cheaper than WES/WGS; focused on clinically relevant genes	May miss novel or unexpected mutations; limited to panel content	Often used clinically to screen for mutations in JAK/STAT, RAS pathways in PCGDTCL
Fluorescence In Situ Hybridization (FISH)	DNA probes hybridize to metaphase or interphase chromosomes	Structural chromosomal alterations, gene fusions, amplifications, deletions	Visualizes gene rearrangements and copy number changes; established clinical use	Limited to known targets; low resolution; labor-intensive	Used to detect known translocations or gene amplifications (e.g., MYC) in lymphoma diagnosis
Array Comparative Genomic Hybridization (aCGH) / SNP Arrays	Genome-wide detection of copy number alterations and LOH	Copy number gains, losses, LOH (Loss of heterozygosity)	Genome-wide coverage; detects submicroscopic CNVs	Cannot detect balanced translocations or point mutations; resolution depends on array density	Useful for detecting large chromosomal alterations in lymphoma samples
RNA Sequencing (RNA-Seq)	Sequencing of transcriptome	Gene fusions, splice variants, expression levels	Detects novel and known fusions; measures gene expression; alternative splicing	RNA quality sensitive; bioinformatics expertise needed	Research use for identifying novel fusion partners or expression signatures in PCGDTCL
Sanger Sequencing	Chain termination sequencing of PCR-amplified regions	SNVs and small indels	Gold standard for validation; high accuracy	Low throughput; not suitable for large panels	Used to confirm NGS-identified mutations
Digital Droplet PCR (ddPCR) / qPCR	Highly sensitive quantification of known mutations or gene rearrangements	Known point mutations, copy number changes	Very sensitive, quantitative; fast turnaround	Limited to known mutations; not comprehensive	Useful for monitoring known mutations (e.g., STAT5B N642H) in minimal residual disease (MRD) or treatment response
Immunohistochemistry (IHC) (surrogate genetic marker)	Antibody staining of protein expression	Protein expression reflecting genetic alterations (e.g., pSTAT5B, MYC)	Widely available; easy to implement	Indirect; may not perfectly correlate with mutation status	Supportive role in diagnosis and prognosis, not definitive genetic test

Familial Forms

There are currently no well-established familial or hereditary forms described in the literature.

Additional Information

N/A

Links

N/A

Notes

*Citation of this Page: Azimpouran M, Kitahara S. “Primary cutaneous gamma/delta T-cell lymphoma”. Compendium of Cancer Genome Aberrations (CCGA), Cancer Genomics Consortium (CGC), updated 01/6/2026, https://ccga.io/index.php/HAEM5:Primary_cutaneous_gamma/delta_T-cell_lymphoma.

*Primary authors will typically be those that initially create and complete the content of a page.  If a subsequent user modifies the content and feels the effort put forth is of high enough significance to warrant listing in the authorship section, please contact the Associate Editor or other CCGA representative.  When pages have a major update, the new author will be acknowledged at the beginning of the page, and those who contributed previously will be acknowledged below as a prior author.

Prior Author(s): N/A