Recurrent IL4R Somatic Mutations in Diffuse Large B-Cell Lymphoma Lead to an Altered Gene Expression Profile and Changes in Tumor Microenvironment Composition
Bibliographic record
Abstract
Abstract The Janus kinase-signal transducers and activators of transcription (JAK-STAT) pathway is an important and conserved pathway in lymphocytes which is activated by extracellular stimuli such as cytokines and growth factors. Aberrant activation of the JAK-STAT pathway is a hallmark of a variety of lymphomas which leads to increased proliferation/survival as well as immune evasion. Regarding the latter, it has been previously described that activation of the JAK-STAT signaling pathway can alter the secretome of lymphoma cells and the composition of the tumor microenvironment (TME). Specifically, our group reported PTPN1 loss-of-function as well as IL4R gain-of-function (p.I242N) mutations up-regulate the expression of the immune regulatory chemokine CCL17 through a STATs-dependent mechanism in primary mediastinal B cell lymphoma (PMBCL). Here, we assembled a cohort of 340 diffuse large B cell lymphoma (DLBCL) patients uniformly treated with R-CHOP to investigate JAK-STAT signaling mutations (targeted gene sequencing), copy number alteration (SNP arrays), gene expression (RNAseq) and TME composition (Cibersort, IHC). We confirmed the presence of mutations in SOCS1, STAT6 and 9p24 amplification with a frequency of 13.8%, 2.5%, 11.4%, respectively. Interestingly, we also identified the presence of somatic IL4R mutations in DLBCL, including the hotspot p.I242N mutation previously reported in PMBCL. Similarly to what was reported for other alterations in the JAK-STAT pathway, IL4R mutations were significantly enriched in GCB-DLBCL as compared to the ABC subtype, unclassified or double hit lymphomas with DLBCL morphology (p=0.045). Within the GCB group, patients carrying mutations in IL4R showed inferior disease-specific survival (p=0.029) and time to progression (p=0.023) after R-CHOP therapy. Mutational analysis revealed IL4R mutation being significantly concurrent together with mutations in ACTB, KLML6, MYC, STAT6, NLRC5, TNFAIP3 and mutually exclusive with EZH2 mutations (p<0.05). However, among those gene mutations, only mutations in IL4R risk-stratified patients with GCB DLBCL and showed inferior patient outcomes. Moreover, mutations in the extracellular and transmembrane domains of IL4R resulted in gain-of-function leading to constitutive activation of the JAK-STAT pathway in vitro. Gene expression analysis of primary patient samples carrying IL4R mutations displayed increased CCL17 expression (p=0.027), which positively correlated with the level of the T-regulatory marker FOXP3 by IHC (p=0.005). In addition, in silico TME composition analysis revealed a role of IL4R mutations in inducing changes in macrophage polarization. Specifically, we observed an enrichment of an M2-like macrophage phenotype in primary patient specimens carrying IL4R mutations (p=0.01) and a high M2/M1 ratio was significantly associated with inferior patient outcomes (time to progression, log rank p=0.027). In summary, our data suggest a common mechanism between PMBCL and DLBCL where aberrant JAK-STAT activation mediated by mutations in IL4R plays a significant role in altering chemokine expression profiles and TME changes. Disclosures Gascoyne: NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies. Scott:Celgene: Consultancy, Honoraria; Roche: Research Funding; NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Janssen: Research Funding. Steidl:Roche: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: patent holding; Tioma: Research Funding; Juno Therapeutics: Consultancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".