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Abstract Background: Waldenström Macroglobulinemia (WM) is an indolent B-cell lymphoplasmacytic lymphoma characterized by heterogeneous clinical behavior and variable progression patterns. While MYD88L265Pand CXCR4 mutations are frequently encountered, the broader genomic landscape underpinning disease evolution from smoldering WM (SWM) to active treatment naïve WM (TNWM), post-treatment remission WM (PTWM) and relapsed/refractory WM (RRWM) remains incompletely understood. To address this gap, we studied the mutational profile across the WM disease spectrum. Methods We performed whole exome sequencing (WES) on sorted (CD19 and/or CD138) bone marrow aspirates of patients across the spectrum of WM. Paired germline sequencing was performed using peripheral blood mononuclear cells (PBMCs). Unique molecular identifier-tagged reads were deduplicated using fgbio. WES data were processed through the Sarek pipeline. Any valid mutation from either caller, present on the index or in COSMIC was considered. Copy number variations (CNVs) were inferred via CNVkit and summarized at the gene and arm level (≥50% directional change). Mean tumor and germline coverages were 174X and 74X, respectively. Fisher's exact test was used for enrichment and hypergeometric test for overlap analyses. Results A total of 78 high-quality WM samples [15 SWM, 44 TNWM, 13 RRWM, 6 PTWM in remission (all with paired TNWM samples)] were sequenced. The mutational burden varied across disease states (median SWM=1.48 m/Mb, TNWM= 1.12 m/Mb, RRWM = 1.59 m/Mb; p=0.04). Density plots revealed bimodal VAF distributions, consistent with clonal and subclonal architectures in WM. Overall, 77% harbored a MYD88L265P mutation by WES. Among MYD88^WT^ patients, AS-PCR identified MYD88L265P in 4 (25%) not detected by WES, raising the overall mutation rate to 83%, plus one with a non-L265P variant. Low VAF (<10%) MYD88L265P mutations were seen in 6% of matched PBMCs. MYD88 copy number gain was seen in 2 (17%) MYD88WT TNWM patients. CXCR4 mutations were detected in 38% by WES (18% each frameshift and nonsense). Two (4%) CXCR4WT samples were positive by PCR, giving an overall rate of 40%. CXCR4 mutations were more frequent in RRWM (54%) vs TNWM (41%) and SWM (27%), though not statistically significant. Del(6q) and Del(17p) were noted in 16% (n=12) and 7% (n=5) of patient samples. All del(17p) events were noted in TNWM samples and in one paired post-treatment sample. Overall rate of del(17p) and TP53 mutation was 14% (n=11). RRWM was enriched in ARID1A alterations, a gene implicated in chromatin remodeling and plasma cell identity formation [38% (n=5) in RRWM vs. 11% (n=6) in TNWM and SWM], compared to TNWM and SWM p=0.03]. Alterations in NF-kB signaling gene MAP3K14 were more frequent in RRWM (31%, n=4) compared to the SWM/TNWM cohort [(7%), n=4; p=0.036). We noted an enrichment in the TP53 mutation rate in RRMW [n=3, 23%, OR 7.7] compared to TNWM and SWM cohorts [n=2, 4%), p=0.04]. Mutations in FUBP1 (p=0.03), an upstream regulator of MYC, and CARD11 (p=.043), a B-cell signaling scaffold protein were also more frequent in RRWM. Notably, none of the SWM patients harbored an ARID1A alteration (p=0.057), suggesting acquisition during progression. CNV analysis revealed that MAP3K14 amplifications were frequent in RRWM (n=3, 23%) compared to SWM/TNWM [n=2 (4%), p=0.043]. RRWM samples showed increased CD79B mutations (15%) and significant overlap with the C5/ABC diffuse large B-cell lymphoma (DLBCL) genomic cluster (Chapuy et al., Nat. Med. 2018; p=0.007). This overlap remained significant after excluding MYD88L265P (p=0.019), driven by CARD11, CD79B, and IGLL5, suggesting that a subset of RRWM may acquire an ABC DLBCL-like mutational profile. These findings warrant further investigation using transcriptional profiling. Conclusions This study provides a genomic map of WM evolution from precursor to relapse. Increasing somatic mutations and CNVs support stepwise progression. Enrichment of genes involved in chromatin remodeling and plasma cell identity formation (ARID1A), NF-kB or BCR signaling (CARD11, MAP3K14), and DNA damage repair (TP53) were noted at relapse, all of which have been shown to drive aggressive disease biology. Emergence of an ABC-like genotype in a proportion of RRWM indicates convergent evolution with aggressive lymphomas. Transcriptional studies are warranted to explore this further.

Abstract Introduction: Langerhans Cell Histiocytosis (LCH) is a rare histiocytic neoplasm, with approximately 90% of patients exhibiting alterations in the MAPK pathway. BRAF and MEK inhibitors have shown promising results in treating MAPK pathway-altered LCH. However, treatment interruptions due to adverse effects are common, and limited access to targeted therapies complicates care globally. While chemotherapy remains a common treatment for LCH, the outcomes for patients with MAPK pathway-altered LCH specifically receiving chemotherapy are still unclear. Methods: This multi-institutional international retrospective cohort study analyzed adult patients with BRAF or MAP2K (gain-of-function, GOF) altered LCH that were consecutively seen at Mayo Clinic, MN, AZ, and FL, University of Alabama at Birmingham, and Peking Union Medical College Hospital in China between 2007 and 2024. All patients who were included in this study were treated with chemotherapy, and those who have received targeted therapies or other modalities of frontline treatment were excluded. All time-to-event analyses were performed from the frontline therapy initiation, using the Kaplan-Meier method. Progression-free survival (PFS) included death and progression as events and was measured from the time of treatment initiation. The response assessment was based on radiographic evaluation in accordance with the most recent Histiocyte Society consensus guideline. Risk organ involvement was defined as disease involving the liver, spleen, bone marrow or central nervous system. Results: A total of 63 patients with LCH were included. The median age of the population was 34 years (range: 27-43), and males were 59%. Risk organ involvement was seen in 44 (70%) of patients and multisystemic LCH was observed in 48 (76%) patients. Among the 63 patients, BRAF gain-of-function alterations were observed in 79%, MAP2K mutations in 19%, and BRAF fusions in 2%. Of the BRAF gain-of-function alterations, BRAFV600E mutation was seen in 64% patients and [p.N486_P490del] deletion in 36%. There was no significant difference in organ involvement (single-system versus multisystem) or risk organ involvement between patients with BRAF and MAP2K alterations. All 63 patients received systemic therapy with chemotherapy and the regimens included Cytarabine (n=43), Cladribine (n=5), Etoposide (n=1), Methotrexate (n=1), and combined Methotrexate and Cytarabine (n=13). The median follow-up for the entire cohort was 3.8 years (range 3.2–5.0 years), with similar durations observed in patients with single-system vs. multisystem involvement and those with risk-organ involvement vs. no risk-organ involvement. The overall response rate among patients who received chemotherapy was 89%, with complete responses in 15 patients (26%) and partial responses in 40 patients (64.5%). The median progression-free survival (PFS) for the entire cohort was 2.61 years (95% CI: 2.06–NA). The 3-year PFS did not significantly differ based on risk organ involvement: 50.6% (95% CI: 36–71) for patients with risk organ involvement versus 47.4% (95% CI: 28–81) for those without, p=0.52. Patients with single-system disease experienced longer PFS compared to those with multisystem disease; the 3-year PFS was 80.2% (95% CI: 58.7–100) versus 42.2% (95% CI: 29–61.5), respectively, p=0.04. Conclusions: Our results indicate that LCH patients with a GOF alteration in the MAPK pathway derive significant clinical benefit from chemotherapy, achieving deep and durable responses. Therefore, patients should not be excluded from treatment due to the absence of targeted therapies, as they can still attain substantial responses and clinical improvement with chemotherapy, even in the context of MAPK pathway-driven disease.

Abstract Introduction: Suboptimal response to therapy, early disease relapse, and limited long-term survival remain critical challenges in a substantial number of patients with multiple myeloma (MM). While cross-sectional imaging studies are commonly obtained in clinical practice, only limited information is currently incorporated into risk stratification efforts and clinical decision making. Here, we investigate the use of novel image-based biomarkers to identify patients at high risk for adverse outcomes. Methods: We obtained the pretreatment 18F-FDG PET/CT scans of patients with newly diagnosed MM with available clinical and laboratory data seen at Mayo Clinic between 2006 and 2022. In this analysis, we included 637 patients with detectable metabolic tumor volume on their pretreatment scan. The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (nnU-Net v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical variability in PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. PyRadiomics was used to extract quantitative radiomics features for clinical prediction modeling. We evaluated the predictive power of tumor size-, shape-, and texture-related radiomics features for identifying high-risk disease (advanced ISS stage at diagnosis, the presence of high-risk FISH abnormalities, failure to achieve a stringent complete response [sCR] to first-line therapy by IWMG criteria, and early mortality). All radiomics features were quantile-normalized for downstream modeling. Forward and backward feature selection as well as testing for multicollinearity were used to remove redundant features. Results: Median age at diagnosis of the 637 myeloma patients was 64 years (range 22–90), and 387 patients (61%) were male. After a median follow-up of 4.4 years (95% CI 0.2–11.7), median overall survival (OS) was 7.3 years (95% CI 6.3–8.3). Increased total tumor volume (upper tertile vs lower tertiles), measured by the total tumor surface area, was associated with advanced ISS stage (III vs I+II; OR 1.73, 95% CI 1.24–2.42, p=0.001, n=637). Increased tumor density (dichotomized at the median), measured by the median gray level intensity (OR 1.93, 95% CI 1.94–3.12, p=0.007, n=303), and decreased tumor heterogeneity (lower tertile vs upper tertiles), measured by the variability of gray-level intensities (OR 1.88, 95% CI 1.49–3.12, p=0.012, n=303), were associated with the presence of high-risk cytogenetics. Both increased total tumor volume (4.9% increase per tertile, 95% CI 2.2–7.6, p<0.001, n=600) and tumor density (8.9% increase per tertile, 95% CI 4.5–13.3, p<0.001, n=600) were independently associated with more extensive bone marrow involvement at diagnosis. Increased tumor heterogeneity (upper tertiles vs lower tertile), as measured by rapid changes of gray level intensities, was associated with failure to achieve a stringent complete response to first-line therapy (OR 0.50, 95% CI 0.30–0.82, p=0.006, n=351). Increased tumor volume was associated with shorter OS (HR 1.35 per tertile, 95% CI 1.11–1.63, p=0.002, n=301), independent of age, sex, ISS stage, and the presence of high-risk cytogenetics. A simple additive score consisting of tertiles of total tumor volume, ISS stage, and plasma cell labeling index ≥2% identified patients at risk of mortality within 3 years of diagnosis (OR 2.00, 95% CI 1.40–2.85, p<0.001, n=126), with moderate discrimination (AUC=0.71). Sensitivity analysis demonstrated total tumor volume and texture as most predictive of overall survival in a random forest survival model (C-index 0.712, 95% CI 0.649–0.775) and tumor texture as most predictive of response to therapy in a random forest classification model (C-index 0.693, 95% CI 0.607–0.779). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans produces biomarkers that capture disease stage, burden, and risk in newly diagnosed MM. Total tumor volume and heterogeneity are strongly associated with important disease characteristics and can be used to identify patients at high risk for therapeutic resistance, early mortality, and inferior long-term outcomes.

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Background Adult xanthogranuloma (AXG) is a rare clonal histiocytic neoplasm (HN) with a poorly defined mutational landscape. Clinically and immunophenotypically, AXG can resemble other HNs such as Erdheim-Chester Disease (ECD) which frequently harbor MAPK pathway alterations and carry BRAF V600E mutations in close to 50% of cases. However, due to the rarity of AXG, the prevalence of the BRAF V600E mutation and the broader mutational landscape is not well known. In this study, we characterize the mutational profile of AXG. Methods We conducted a retrospective analysis of patients diagnosed with AXG at our institution between January 1, 2016, and July 15, 2025. The study was approved by the institutional review board. To ensure inclusion of true AXG cases, only patients with a confirmed histopathologic diagnosis and without multisystemic involvement or characteristic imaging findings of ECD, such as symmetric long bone osteosclerosis, were included. BRAF alterations were identified by immunohistochemistry (IHC) and/or next-generation sequencing (NGS). We also performed an extensive literature review of mutational studies in AXG and included cases that underwent comprehensive NGS. Results Seventeen patients with AXG were identified. The median age at diagnosis was 40 years (range: 18–75 years). Out of the 17 patients, 11 (65%) reported the face as the primary site of disease, and 11 (65%) had solitary lesions. Of the 17 patients, 13 (76%) underwent BRAF V600E IHC staining, and none tested positive. Twelve (71%) patients had tissue samples analyzed via NGS, and none exhibited the BRAF V600E mutation. Eight of the 12 (67%) patients with NGS testing harbored other alterations involving the MAPK pathway. These included BRAF fusions (n=3), KRAS mutations (n=2), and individual cases with TRAF7 mutation, ALK1 fusion and FGFR1 fusion. One patient was found to have a mutation in WRN, which is not previously reported in AXG. Three patients had no identifiable pathogenic alterations on NGS. Ten patients were monitored without treatment, 2 underwent surgical resections and 3 received chemotherapy (1 had a complete response [CR], 2 achieved a partial response). One patient with FGFR1 fusion was treated with pemigatinib and achieved a CR for over 3 years. One patient with TRAF mutation obtained a CR after treatment with rituximab. We also identified 51 cases of AXG with adequate mutational testing reported in the literature, which reported findings similar to ours. In a retrospective study from France involving 29 patients, MAPK alterations were observed in 55% of cases: however, only 10% harbored BRAF V600E mutations (1). Another analysis of 270 patients with various HN subtypes included 13 individuals with AXG, none had BRAF mutations. MAP2K1 mutations were seen in 31% of patients (2). Taken together, across a combined cohort of 68 AXG patients, the most common alterations were: KRAS mutations (16%), fusions (14%), MAP2K1 mutations (7%) and BRAF V600E (6%). Among fusions, the most involved gene was BRAF (50%), and GAB2 was the most common fusion partner (n=2). In total, 59% of patients had MAPK alterations. These findings collectively underscore the diverse mutational landscape of AXG and highlight the rarity of BRAF V600E mutations relative to closely related HNs. Conclusion We present a series of AXG cases with mutational profiling, from our institution and the published literature. We demonstrate that MAPK alterations are frequently observed in AXG and are driven by heterogeneous mutations. In contrast to other HNs where BRAF V600E mutations are common, we identified no such mutations in our cohort and found only rare cases in the literature. Our institutional data, along with findings from the existing literature, suggest that AXG may have a distinct molecular profile compared to other HNs. A deeper understanding of its mutational landscape may offer insights into disease biology and identify therapeutic targets. ReferencesAnnabi E, Mahévas T, Chasset et al. Clinical, Histopathologic, and Molecular Features and Treatment of Multiple Xanthogranuloma in Adults. JAMA Dermatol. 2025;161(7):765-768. doi:10.1001/jamadermatol.2025.0886.Durham BH, Lopez Rodrigo E, Picarsic J, et al. Activating mutations in CSF1R and additional receptor tyrosine kinases in histiocytic neoplasms. Nat Med. 2019;25(12):1839-1842. doi:10.1038/s41591-019-0653-6

Abstract Background:Erdheim-Chester Disease (ECD) is a rare, incurable histiocytic neoplasm. Since its recognition in 1930, our understanding of ECD's pathophysiology has evolved. The identification of its clonal nature after 2015 established ECD as a cancer, prompting increased disease awareness and treatment advances. Targeted therapies show promising responses, but changes in ECD mortality over time have not been extensively documented. Methods:A multi-institutional retrospective cohort study analyzed adults with ECD consecutively seen at Mayo Clinic MN, AZ, and FL and the University of Alabama at Birmingham between 2007 and 2024. Clinical and prognostic data were abstracted. Overall survival was estimated using the Kaplan-Meier method and compared to expected survival using U.S. Census data, based on age at diagnosis (by calendar year), date of diagnosis, and sex. Standardized mortality ratios (SMRs) were calculated by dividing the number of observed deaths in the study population by the expected number of deaths using U.S. population data. Risk of death was compared by date of diagnosis (pre- and post-2015) using Cox proportional hazards analysis. Statistical analysis was performed with R software. Results:A total of 159 ECD patients were included (median age 58 years [range 19-82], 65% male). Forty-nine patients were diagnosed between 1990-2014 and 110 between 2015-2024. Median follow-up for the entire cohort was 4.0 years (95% CI: 3.2-4.4). Median follow-up for those diagnosed between 1990-2014 was 10.4 years (CI: 3.2-16.8), compared to 3.5 years (CI: 3.0-4.2) for those diagnosed between 2015-2024. The 5-year overall survival rate was 80% (95% CI: 72-88). Patients with ECD had an increase in mortality over expected, with 28 deaths compared to an expected 11.2, an SMR of 2.5 (95% CI: 1.7-3.6, p<0.001). Of 17 patients with an identified cause of death, death was attributable to ECD in 11 (65%), secondary malignancy in 1 (7%), and unrelated causes in 5 (29%). Cause of death was unknown in 11 patients. Among deaths, 11 (39%) were treated with targeted therapy, 8 (29%) with chemotherapy, and 1 (4%) received both. One patient (4%) received steroids, 1 received immunomodulator and targeted therapy (4%), and 6 (21%) received no disease-directed therapy. Of the patients whose death was related to ECD, 10 (91%) had multisystem disease. For 49 patients diagnosed pre-2015, 14 deaths occurred during the follow-up, significantly higher than the expected 5.2 deaths based on U.S. population data (SMR of 2.7 [95% CI: 1.5-4.4, p=0.001]). Among those diagnosed between 2015 and 2024, 14 deaths occurred, compared to expected 6.1 (SMR of 2.3 [95% CI: 1.3-3.8, p=0.006]). In an age- and sex-adjusted Cox proportional hazards model, diagnosis after 2015 vs. diagnosis prior to 2015 yielded a hazard ratio of 0.59 (95% CI: 0.27-1.31, p=0.19). Conclusion:The mortality rate of ECD patients remains higher than the general population, despite advancements in disease recognition and development of targeted therapies. While our data suggest a possible trend toward improved survival among patients diagnosed after 2015, we did not find statistically significant evidence of decreased mortality compared to those diagnosed prior to 2015. Larger cohorts with longer follow-up periods are needed to draw definitive conclusions regarding mortality.

Abstract Background Extramedullary disease (EMD) in multiple myeloma (MM) represents an aggressive phenotype with poor outcomes. We performed single-cell RNA sequencing (scRNA-seq) on clonal plasma cells (CPCs) from EMD tissue, solitary plasmacytomas (SPM), and bone marrow aspirates (BMAs) from patients with MM to identify transcriptional states unique to EMD. Methods We processed FFPE tumor samples from EMD and SPMs and cryopreserved CD138+ BMAs from MM, using the 10x Genomics FLEX platform. Cells/nuclei were fixed, hybridized with Human WTA probes, pooled, and sequenced (10,000–40,000 cells/library) on NovaSeq X. After removing low quality cells, data was normalized and integrated using RPCA in Seurat. Plasma cell identity was assigned via Cellarium and canonical markers. Clonality was inferred by light chain restrictions and refined via random forest modeling. Cell cycle effects were regressed out to reduce proliferation bias. Results We analyzed 128, 908 CPCs from 16 samples [8 EMD (62, 669 CPCs), 4 SPM (20, 607 CPCs), 4 BMA (45, 632 CPCs)] and identified eight transcriptionally distinct cell clusters. Differential expression and pathway comparisons were performed between EMD/SPM/BMA within each of these clusters. Integration of gene expression and pathway analyses revealed consistent upregulation of cell cycle and E2F pathways in EMD. We identified two highly proliferative cell cycle clusters (Cluster 2 with S-phase genes and Cluster 4 with a G2/M transcriptional program) that were expanded in EMD compared to BMA and SPM. Cluster 0 comprised a quiescent CPC population enriched for RNA processing pathways alongside downregulation of ER stress and autophagy. EMD cells demonstrated suppressed interleukin signaling [fold change (FC) –33.8), and class I MHC antigen processing (FC –23.5) relative to BMA, suggestive of an immune suppressive transcriptional program. Angiogenesis-related genes [VEGFA (log2FC 2.4), ADM2 (log2FC 2.2)] and EZH2 [log2FC 2.1; q<0.0001)] were upregulated in EMD. Compared to SPM, EMD plasma cells in this cluster demonstrated marked downregulation of CD74 (log2FC –2.6; q<0.0001), a key modulator of MHC class II antigen presentation. Cluster 1 comprised of metabolically stressed secretory CPCs and displayed downregulation of early growth response pathways in EMD compared to BMA, concurrent with upregulation of E2F signaling. Suppression of IL-2/STAT5, IL-6/JAK/STAT3, and TGF-β pathways in EMD suggests reduced reliance on bone marrow–derived cytokines and impaired immunoregulatory signaling. CD44 was upregulated in EMD versus BMA (log2FC 3.2), consistent with its role in EMD spread and marrow independence. Cluster 3 was enriched for extracellular matrix–interacting plasma cells (COL1A1, MMP2) with upregulated epithelial–mesenchymal transition (EMT) pathways in EMD compared to both BMA (FC 7.5) and SPM (FC 7.07). Cluster 6 was defined by a migratory, motility-primed plasma cell population (CD44, AHNAK, LMNA and S100A10). Notably, IL32 was overexpressed in EMD compared to BMA (log2FC 5.1; adj. p = 2.02×10⁻¹⁴), a finding linked to hypoxia-driven proliferation. Compared to SPM, EMD cells also had elevated MYC/E2F activity and DNA repair, hallmarks of a proliferative phenotype. Cluster 5 displayed a strong interferon-stimulated gene signature (ISG15, IFIT1, TNFSF10), with enrichment of type I IFN, JAK–STAT, and TNF pathways. Within this inflammatory cluster, EMD exhibited marked suppression of immune signaling, metabolism (TCA, OXPHOS), and TP53 programs, consistent with an immune-evasive, metabolically suppressed, and deregulated state. Finally, within Cluster 7, a smaller subset defined by NFκB–driven anti-apoptotic transcriptional activity, EMD exhibited significant downregulation of KLF2 (log2FC –3.7; adj. p = 1.77×10⁻²⁷), likely reflecting disruption of the KDM3A–KLF2–IRF4 axis, a transcriptional network critical for bone marrow homing and plasma cell survival. We consistently observed downregulation of the MAPK pathway in EMD compared to BMA, aligning with prior bulk RNA-seq data and contrasting with the near-universal presence of MAPK mutations, highlighting a disconnect between mutational status and pathway activation. Conclusion In the largest yet reported single-cell transcriptional atlas of EMD and SPM, we demonstrate EMD to be highly proliferative with metabolic reprogramming, EMT, and downregulation of key immunologic signaling pathways, indicating immune escape and microenvironmental independence.

Abstract Introduction Surface proteins in cancer not only reflect tumor–microenvironment interactions but also serve as targets for diagnostic flow cytometry and precision therapies, including monoclonal antibodies and CAR-T cell therapies. A better understanding of plasma cell immunophenotypes could inform flow cytometric prognostic assays and development of targeted therapies. Methods RNA-seq and Seq-FISH data from CD138-enriched bone marrow plasma cells, along with corresponding clinical annotations, were obtained from the Multiple Myeloma Research Foundation-CoMMpass Study. A curated list of surface genes was assembled by integrating three sources: the Human Protein Atlas (plasma membrane and cell junction annotations), the Gene-Set Enrichment Analysis membrane-associated gene set (Ontology:0016020), and the Cell Surface Protein Atlas (validated high-confidence category). A total of 4,579 genes coding for cell surface proteins were identified. Genes below the 25th variance percentile were removed. PCA (≥90% variance) followed by k-means (k=2, silhouette) and confirmed by hierarchical clustering. A Plasma Cell Maturity (PBM) score was computed using singscore on 84 mature plasma cell–associated genes to compare cluster phenotypes. Pathway analysis was done using GSEA software for Hallmark pathways. Clinical biomarkers and survival kinetics were compared between the clusters. Results 761 newly diagnosed patients were included. (Age: 27-93 years, median overall survival/OS: 29 months/mo). Clustering revealed two groups which we termed Atypical/Aggressive Cluster 1 (C1, n=105, 13.8%) and Classical Cluster 2 (C2, n=656, 86.2%), based on the patterns and clinical characteristics. Among non-missing cases, high-risk disease per the International Myeloma Society–International Myeloma Working Group (IMS–IMWG) definition was observed in 27.7% of patients in C2 (n = 552) and 24.7% in C1 (n = 90) (p = 0.4).C1 exhibited enrichment for t(14;16)-MAF (OR = 2.6, 95% CI: 1.1–6.1, p = 0.04) and t(11;14)-CCND1 (OR = 1.7, CI: 1.01–2.8, p = 0.04). In contrast, hyperdiploidy was more common in C2 (OR for C1 = 0.6, CI: 0.4–0.9, p = 0.04). Elevated LDH was observed in C1 (OR = 2.6, CI: 1.6–4, p < 0.001). Median serum Beta-2 microglobulin was lower in C1.(p=0.009) Upregulated genes in C1 in decreasing order of log Fold Change(logFC): AHNAK, MT-ND1, EEF2, EZR, HLA-B, TXNIP, HLA-E, TRIB1, MCL1, RHOB, CD74, CFLAR, HLA-A, HERPUD1, RPSA, ERN1, HSP90AB1, RAPGEF2, ADGRE5, and LAPTM5. Downregulated genes in C1 in descending order of |logFC|: B2M, SDC1 (CD138), HSP90B1, SEL1L, CALR, CANX, PECAM1, SLAMF7, STT3A, TNFRSF17 (BCMA), FCRL5, STT3B, PALM2AKAP2, CD53, PARP1, CCR2, RPN1, CD38, EDNRB, and LMAN2. Upregulated pathways in C1: proliferation (MYC, E2F, PI3K/AKT/mTOR, KRAS, Wnt); evasion of apoptosis (p53, TGF-β, Notch, NF-κB, IL6/JAK/STAT3); Epithelial to Mesenchymal Transition (angiogenesis, apical junction); inflammatory (interferon, TNF-α, complement, IL2/STAT5); metabolic reprogramming (oxidative phosphorylation, lipid/cholesterol); and stress (DNA repair, unfolded protein response). The PBM score for C1 was significantly lower, suggesting a less differentiated phenotype. C1 patients had inferior survival (n = 93, median OS 43.9 mo, p = 0.04) compared to those in the C2 cluster (n = 559, median OS 102.5 mo). Patients in the C1 + IMS–IMWG high-risk status had the shortest survival (20.7 mo), compared to C1+low (50 mo), C2/high (68.3 mo), and C2+low (not reached). In a multivariable analysis, C1 status (HR = 1.93; 95% CI: 1.1–3.4, p = 0.02) was an independent predictor, along with older age (1.05), UAMS70 (1.02), EMC-92 (1.4). In C1, early (<18 mo) progression occurred in 76.3% (n=71) (p < 0.001). In patients receiving first line of treatment(LOT1), median PFS was 39.7 mo in C1vs. 47.3 mo in C2 (p=0.8). Among patients who received stem cell transplant in LOT1, the median survival for the C2 was 102.5 mo, compared to 44.5 mo for C1 (n = 52) (p = 0.01). Among non-SCT patients on Immunomodulator + Proteasome Inhibitor therapy, C1 (n=28) had shorter survival of 50.0 vs. 71.7 C2 (n=137; p =0.8). Conclusion Our transcriptomic analysis identified a higher-risk group of patients (C1) exhibiting downregulation of myeloma specific markers (CD38, CD138, BCMA, SLAMF7, FCRL5) and inferior outcomes. It is important to diagnose and find appropriate therapy for these patients as they don't obtain the same benefit from the current standard of therapy as the remaining patients.

Abstract Introduction Multiple myeloma (MM) is clinically heterogeneous, and therapeutic response is profoundly influenced by the complex cytokine milieu of the bone marrow microenvironment. Unlike most cancers, MM staging incorporates non–tumor-burden markers such as serum albumin, β₂-microglobulin, and LDH, which are prognostically useful but biologically nonspecific. Although individual cytokines (e.g., IL-6, IL-17, TGF-β) have been linked to disease activity, a comprehensive, systems-level assessment comparing an exhaustive list of cytokines, chemokines, and growth factors across large patient cohorts is lacking. Here, we present evidence for an actionable cytokine panel, using gene expression–derived cytokine bioactivity as a surrogate measure. Methods CD138 enriched bone marrow plasma cell RNA-seq, Seq-FISH, and clinical data were obtained from the MMRF CoMMpass study. Data were TMM-normalized, log₂(x + 1) transformed, and mean-centered. Bioactivity scores for 64 cytokines, chemokines, and growth factors were inferred using CytoSig (National Cancer Institute). CytoSig leverages transcriptomic data from over 20,000 samples treated with individual molecules to model gene expression changes of cytokine regulators, targets, and response elements to provide z-scores. Reverse validation of bioactivity between samples scoring top and bottom quartiles of each cytokine was done using pathway analysis via Gene Set Enrichemnent analysis platform that verifies bioactivity across 7561 well-studied gene sets.(MSigDB C2 2025). Cytokines with upregulation of associated pathways were considered for downstream analyses (Criteria: Normalized enrichment score > 1). Genes were ranked by sign(logFC) × –log₁₀(p), identifying 44 significantly enriched pathways. Univariate Cox regression identified prognostic cytokines. To account for variable correlation and improve prediction, Elastic Net Cox regression (α = 0.5) was used, combining LASSO (L1) and Ridge (L2) penalties. For biological validation, pathway analysis was done between samples with high vs low serum C-Reactive Protein(CRP). Survival kinetics and clinical characteristics were analyzed for the derived model. Results A total of 767 NDMM patients were included (median age 63 years, range 27–93); median overall survival (OS) was 44.4 months (IQR 70.9). Revised International Staging System (R2-ISS) distribution was: Stage I 14.7%, II 24.0%, III 34.7%, and IV 6.4%. Based on the International Myeloma Society–International Myeloma Working Group (IMS–IMWG) definition, 32.2% of patients were classified as high-risk. Supervised analysis identified 30 univariate prognostic cytokines. Elastic Net retained BMP-7, IGF-1, IL-3, NRG1, IL-17A, IL-2, IL-21, and TGF-A (positive coefficients), and IL-4, IL-18, BMP-4, palmitic acid, DHT, and BMP-2 (negative coefficients). In sensitivity analysis, after removing cytokines not passing GSEA validation, the cytokine score (CS) was established using BMP-7, IL-3, TGF-A, IL-17A, BMP-2, IL-18, BMP-4, and IL-4. High serum CRP–associated gene expression was linked to upregulation of IL-17 and BMP pathways and downregulation of IL-4 signaling. Patients with CS ≥ median had higher β2M (p < 0.001) and LDH (p = 0.005); creatinine (p = 0.07) and albumin (p = 0.6) were not significant. CS was higher in patients with del(17p), 1q21 gain, high-risk IGH, t(8;14), and del(1p), and lower in those with hyperdiploidy (all p < 0.01). CS achieved a C-index of 0.67. Median OS was 58.7 months in the high-CS group vs. 102.5 months in the low-CS group (p < 0.001). Adding continuous CS to a model with continuous β2M + continous albumin improved C-index from 0.65 to 0.72. ISS improved from 0.62 to 0.68; R2-ISS from 0.65 to 0.69. A multivariate model with β2M ≥ 5.5, del(1p) + 1q gain, t(4;14), deep del(1p), and del(17p) improved from 0.63 to 0.68 with CS. Discussion These findings require functional validation in patient samples through direct measurement of cytokine levels. BMP-7 remains understudied in MM. Elevated TGF-α, IL-3, and IL-17 promote angiogenesis, bone resorption, and immune dysfunction, while BMP-2 and BMP-4 act as tumor suppressors. IL-18 is being utilised to boost T-cell responses, and IL-4 is consistently reduced in MM. Conclusion This study provides evidence for a robust, clinically translatable cytokine, chemokine, and growth factor panel. IL-17, IL-4, and BMPs emerged as consistently regulated across all analyses. CS is associated with high-risk disease and inferior survival.

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.