Dasari, Surendra

Background: Multiple myeloma (MM) involves the proliferation of monoclonal plasma cells (PCs) in the bone marrow. The plasma cell labeling index (PCLI), indicating the percentage of PCs in the S-phase, is a key prognostic factor in newly diagnosed multiple myeloma (NDMM) patients. Methods: A retrospective study at Mayo Clinic analyzed bone marrow Affymetrix microarray datasets from 136 NDMM patients (2001-2012). Data normalization and quality assessment were followed by differential gene expression analysis using limma-voom, adjusted for age, BMPC percentage, and disease stage (RISS). Selected genes were dichotomized by median and subjected to logistic regression for high/low PCLI. Data from 594 patients with a Proliferation Index at baseline was pooled from the MMRF (CoMMpass) dataset for validation after normalization across assays. Results: The baseline characteristics of the patients are as follows: 62 (46%) were female and 74 (54%) were male, with a mean age at diagnosis of 61.6 ± 11.5 years. RISS staging included 36 patients (26.5%) at stage 1, 32 (23.5%) at stage 2, 29 (21.32%) at stage 3, and 39 patients (28.67%) with unknown stage. PCLI distribution showed 113 patients (83.7%) with <2%, and 23 (16.3%) ≥2%. Differential Analysis: The differential analyses reveal several critical insights. In the primary continuous differential analysis, 21,367 genes were analyzed, identifying 654 as differentially expressed with an FDR-adjusted p-value ≤0.05. Of these, 481 genes were upregulated (top 5: SPC25, TRAT1, PBK, E2F8, ASPM) and 173 were downregulated (top 5: SPARC, CD24, MYOF, CXCL12, VCAM1). Univariate and multivariate stratified analyses showed significant findings: 668 genes in age stratification, 395 genes in BMPC% stratification, and 536 genes in RISS stratification. In multivariate analysis, 408 genes were significant with an FDR-adjusted p-value <0.05 for PCLI and ≥0.05 for BMPC%, age, and RISS. 3-Fold Internal Validation: 240 significant genes were identified in fold 1, 25 in fold 2, and 167 in fold 3, with 21 genes common across all folds (e.g., MCM10, GTSE1, UBE2S, NCAPG, FOXM1, STMN1, NEK2, CENPF, MND1, HJURP, E2F8, TOP2A, SKA1, CCNB2, CCNF, CHAF1A, PBK, EXO1, AURKB, CDCA2, DHFR). 19 genes were common in the sensitivity analysis by excluding RISS 3. 20 genes were common in categorical analysis (Categories <1%, 1-2%, 2-3%, 3-4%, >4%). 3 Gene-Based Proliferation Score (3GPS): On univariate logistic regression analysis of 21 genes for high/low PCLI, 17 significant genes were identified. In the step-wise multivariate analysis, CDCA2 (OR=11.56), DHFR (OR=9.6), and CENPF (OR=6.11) were significant. Log-odds were used as weights. For the high expression(median and above) of gene as 1 and low expression as 0: 3GPS=2.4*CDCA2+ 2.3*DHFR +1.8*CENPF A cut-off of 2.4 was chosen as CDCA2 had the highest positive concordance rate (90.91%) versus DHFR (86.36%) and CENPF (77.27%). External Validation: In the CoMMpass dataset, the sensitivity and specificity for the presence of high expression of any of the 3 genes with ≥2 Proliferation Index (PI) were 92.4% and 73.8%, respectively (AUC=83.1%). The median 3GPS was 0 for <2 PI (n=238) and 6.5 for ≥2 PI (n=356). The sensitivity and specificity for the 3GPS in predicting ≥2 PI were 82.6% and 93.6%, respectively (AUC=88.1%). Pathway Analysis: Significant pathways using Reactome include polo-like kinase mediated events in G2/M transition, condensation of prometaphase chromosomes in metaphase, resolution of sister chromatid cohesion in anaphase, and APC-Cdc20 mediated degradation of Nek2A for regulation of the cell cycle. p53 signaling, cancer pathways, and microtubule regulation were also mapped. Survival Analysis: In our cohort, for CDCA2 quartiles (Q)3 and Q4, for CENPF Q3 and Q4, and for DHFR Q4 were associated with inferior overall survival. 3GPS ≥2.4 was associated with inferior survival of 60.28 months versus 111.5 months for <2.4 (p<0.01). In the validation dataset, median survival was not reached at 100 months (vs. high 79.7, p<0.01). The multivariate Cox model found 3GPS score ≥2.4 as an independent predictor (HR=1.64, 95% CI: 1.23 - 2.19), along with ECOG status, ISS stage, gender, age, and whether the patient underwent Stem Cell Transplant. Conclusion: CDCA2, DHFR, and CENPF are associated with ≥ 2% PCLI. The 3GPS score can serve as a measure of gene-based proliferation index. Prospective validation is needed for clinical practice.

Background The detection, isotyping and quantitation of over-expressed immunoglobulin(s) (Igs) (M-protein) is key for diagnosing and monitoring patients with monoclonal gammopathies. Traditionally, our lab has used a combination of agarose gel protein electrophoresis (SPEP) and immunofixation electrophoresis (IFE) for these purposes. In 2018, a mass spectrometric method (Mass-Fix) was implemented to replace IFE for M-protein isotyping. This work documents the validation of a quantitative version (Q-Mass-Fix) to replace both SPEP and IFE. Methods 24,645 samples submitted for M-protein testing between 7/2018 and 9/2023 with quantitative IgG, IgA and IgM measurements, Mass-Fix analysis and SPEP results were utilized for this study. A separate cohort of 100,000 M-protein negative samples were utilized to mathematically model the spectral shapes IgG, IgA and IgM polyclonal background. To detect M-proteins, software was developed that fits the polyclonal Ig portion of the spectra and M-proteins are detected as peaks above the polyclonal background. M-proteins isotypes are determined by matching similar peaks in spectra in 5 sperate immuno enrichments (IgG, IgA, IgM, kappa and lambda). M-protein quantitation was obtained by multiplying total M-protein isotype quantity by the fraction area of the M-protein in the spectra. M-protein dilutional studies were used to examine the linearity and level of detection of Q-Mass-Fix. Results SPEP and Q-Mass-Fix M-protein quantitation had the best agreement between the range of 10 to 40 g/L. Below 10 g/L, Q-Mass Fix had on average, lower M-protein values. Above 40 g/dL, Q-Mass-Fix values were on average, higher than SPEP. The automated isotyping assignment was 98.8 percent consistent with the isotype assigned by visual inspection. Dilutional studies revealed that Q-Mass-Fix quantitation was linear over a range of 70 to 0.04 g/L (slope 1.0, r2 0.99) with an analytical limit of quantitation of 0.1 g/L and a limit of detection of 0.04 g/L. Conclusion Q-Mass-Fix represents a significant improvement in comparison to our current SPEP method. Q-Mass-Fix is more capable of removing the interference of polyclonal Igs below 10 g/L and is not subjective to the dye saturation effect at high M-protein levels. The increased analytical measuring range and improved limit of detection of detection could be beneficial in tracking patient's response. Q-Mass Fix is more automated than SPEP and the workflow in our laboratory.

Introduction The major primary genetic events in multiple myeloma (MM) include translocations of immunoglobulin heavy chain (IgH) and a hyperdiploidy (HRD) karyotype (Kuehl, 2002). The main drivers of pathogenesis in HRD MM are unknown (Fonseca, 2003), therefore further research is needed in this subgroup. Translational research in HRD MM has relied on analysis of genomic and transcriptomic alterations, however changes at the genome and transcriptome level do not always correlate directionally with protein abundance. Because proteins are considered the main and final effectors of most cellular processes, proteogenomics can improve our understanding of the drivers of HRD MM. We employed it here to identify potential biomarkers in HRD MM and determine whether any given trisomy may lead to a selective advantage. Methods: Protein and mRNA were extracted from CD138+ cells from 47 HRD MM and 14 t(11;14) MM samples. RNA-seq data was processed using MAPR-Seq pipeline using default parameters and gene-wise raw counts were extracted for each sample. Proteomics data was processed using Specranaut and protein-wise intensities were extracted for each sample. A generalized linear model configured with negative binomial distribution was used to analyze gene-wise count data in edgeR software. Genes with an adjusted p-value of <=0.05 were considered significantly differentially expressed. Protein intensity data were normalized using trimmed mean of M-values and log2 transformed. A generalized linear model configured with Gaussian distribution was used to compare protein intensities between the groups. Proteins with an adjusted p-value <= 0.05 were considered significantly differentially expressed. Two separate rank lists for protein and mRNA data were generated for pathway analysis. Results When comparing our small cohort of HRD MM patient samples to those with t(11;14) MM, out of approximately 16,600 total transcripts and 9,700 proteins evaluated we identified approximately 2,100 significantly variant transcripts and 500 significantly variant proteins in HRD MM. GSEA pathway analysis of the transcriptome and proteome revealed significantly upregulated translational and ribosomal processing pathways in HRD MM. Transcriptional MYC target pathways were also significantly upregulated. RNAseq revealed significantly higher expression of MYC (Rank 24.5, p=5.3E-6) in HRD MM. It is known that patients with t(11;14) MM have increased expression of CCND1 (Kuehl and Bergsagel, 2012). As expected, we found that the HRD MM subgroup had significantly lower expression of CCND1 compared to the t(11;14) MM subgroup (Rank -79.9, p=2.8E-21) and this was also true at the protein level (Rank -21.7, p=2.2E-5). The most significantly abundant protein in the HRD MM subgroup compared to t(11;14) MM was BAHD1, which is a protein that targets H3K27me3 and recruits transcriptional co-repressors (Fan, 2020). BAHD1 has been shown to repress erythroid genes and may recruit HDAC5, which plays a role in osteoblast differentiation (Rank 72.3, p=2.9E-20; Xu, 2021). The second most abundant protein in HRD MM was CKS2, which binds to the catalytic subunit of cyclin-dependent kinases (CDKs) and therefore positively regulates cell proliferation, invasion, and migration and has been shown to be implicated in tumor progression in several solid tumors (Rank 72.3, p=2.9E-20; You, 2015). TRIM proteins promote tumorigenesis and cancer development. TRIM14 mediates cancer development through mediating the JAK/STAT, PI3K/AKT, NFkB, and p53 pathways and was found to be significantly higher in HRD MM (Rank 70.6, p=9.0E-20; Huang, 2022). We also found increased protein abundance of LAMP5 in HRD MM (Rank 20.1, p=6.0E-5). LAMP5 may play an important role in MM progression and has been shown to be highly expressed in patients with mixed-lineage leukemia and is associated with a poor prognosis (Wang, 2023; Gracia-Maldonado, 2022). We identified many proteins (not all discussed here) that have not been well described in HRD MM and thus may be biomarkers/targets of disease pathogenesis of this subgroup. Conclusion Proteins are the final effectors of most cellular processes. Proteogenomics can improve our understanding of the drivers of HRD MM. Using a proteogenomic approach we identified proteins that may lead to selective advantage and/or pathogenesis of trisomies in HRD MM.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction: In myeloma, accurate risk stratification remains a challenge due to substantial biological heterogeneity and highly variable survival outcomes. Cross-sectional imaging is commonly obtained at the time of diagnosis, but imaging findings are not currently incorporated into risk stratification efforts in a standardized manner, despite their prognostic significance. Here, we investigate the use of novel image-based prognostic biomarkers obtained by automated image segmentation and radiomics feature extraction using machine learning methods. Methods: We obtained the pre-treatment 18F-FDG PET/CT scans of 443 patients with newly diagnosed myeloma and complete demographic and cytogenetic data (seen at Mayo Clinic between 2005 and 2019). The native DICOM images were pre-processed and subjected to automated image segmentation using 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 differences related to PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. The quality of the automated image segmentation was evaluated by comparing the radiomics features obtained from ground-truth segmentation (manual lesion segmentation) and model segmentation in a separate cohort of patients with hematologic malignancies (n=72). We selected 42 radiomics features that moderately (r≥0.30, p<0.005, n=23) or strongly (r≥0.50, p<0.001, n=19) correlated between ground-truth (manual) and model (automated) segmentation for further evaluation in myeloma patients. PyRadiomics was used to extract discrete radiomics features for clinical prediction modeling. We evaluated the prognostic significance of tumor size-, shape-, and texture-related radiomics features and compared their predictive power to established prognostic factors (International Staging System [ISS], Revised ISS [R-ISS] stage and high-risk FISH cytogenetics). The outcome of interest was overall survival and the predictive power of competing proportional hazards regression models was assessed using Harrell's C-statistic. For modeling purposes, patients without measurable tumor volume were categorized as having undetectable radiomics features (0 for all features). All radiomics features were median dichotomized for downstream modeling. Forward feature selection and testing for multicollinearity were used to remove redundant features. Select radiomics features were divided into quintiles to generate an additive radiomics risk score and associated risk strata. Results: Median age at diagnosis of the 443 myeloma patients was 63 years (32-91) and 270 patients (62%) were men. After a median follow-up of 2.4 years (95% CI 0.1-7.4), median overall survival was 6.5 years (95% CI 5.5-NR). Most patients had detectable metabolic tumor volume (n=309, 70%) on their pre-treatment 18F-FDG PET/CT scan. Several of the 42 radiomics features were intercorrelated and two features were selected for further investigation based on their independent prognostic significance (forward feature selection). Higher maximum axial tumor diameter (HR 1.90, 95% CI 1.31-2.76, p=0.001) and a lower concentration of low gray-level values in the tumor (HR 1.73, 95% CI 1.20-2.49, p=0.004) were independently associated with overall survival in newly diagnosed myeloma. These associations were independent of age at diagnosis, sex, and R-ISS stage. We devised a simple additive risk score representing the sum of quintiles of the two radiomics features and divided the patients into three risk categories akin to the R-ISS. The predictive power of this radiomics risk score (Harrell's C=0.60) was comparable to the ISS (C=0.59) and the R-ISS (C=0.57). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans is feasible. This approach yields image-based biomarkers that correlate with results obtained by manual imaging segmentation. Several radiomics features such as tumor size and heterogeneity have independent prognostic significance in newly diagnosed myeloma. These novel biomarkers rival the predictive power of the currently established risk stratification methods and may be used to improve risk stratification in myeloma.

Introduction MYD88 L265P mutations (MUT) are found in 90% of patients with Waldenstrom macroglobulinemia (WM) and lead to the activation of pro-survival signaling. Limited literature has demonstrated that patients with MYD88 wild-type (WT) WM have poorer responses to conventional therapies and a higher likelihood of histological transformation. Increasingly the role of non-coding genomics in lymphomagenesis has demonstrated importance and currently, the epigenetic landscape of MYD88 WT WM remains underexplored. This study utilized a multi-omics approach to assess DNA methylation in MYD88 WT WM compared to MYD88 MUT patients and controls. Methods Bone marrow samples were prospectively collected from 20 subjects [3 MYD88 WT/CXCR4 WT WM, 12 MYD88 MUT WM and 5 normal controls] and positive selection for CD19+ and/or CD138+ B cells was performed. The analysis focused on the comparison between MYD88 WT WM to normal controls and MYD88-WT to MUT WM. Genome-wide DNA methylation analysis was performed, and CpG methylation ratios were segmented into 200-bp regions. Differentially methylated regions (DMRs) were determined using a Q-value <0.05 and absolute methylation difference ≥10%. Next, RNA-seq data was interrogated and differentially expressed genes (log2fold change>0.5 or <-0.5, false discovery rate<0.05) with corresponding promoter hypo- or hypermethylation were assessed. Ingenuity Pathway Analysis was used to determine potentially epigenetically regulated pathways using a corrected p<0.05. Results Comparing MYD88 WT WM samples to controls, a statistically greater number of hypermethylated DMRs were observed across genomic regions (hyper- vs hypomethylated): promoter (29129 vs 6300), 3'untranslated region (UTR) (2068 vs 1551), 5'UTR (10169 vs 735), intron (30344 vs 24511), exon (2224 vs 6855), CpG island (30372 vs 1240) and CpG shore (14477 vs 5872) (all p<0.001). Next, mRNA analysis demonstrated that 214 genes were differentially up-/downregulated with concordant promoter hypo-/hypermethylation. On the assessment of promoter-methylation-based pathways, multiple pathways involved in cell signaling, proliferation, gene regulation, cytokine and immune signaling and metabolism were found to be deregulated. Most pathways were found to be negatively enriched in MYD88 WT patients compared to controls including several members of interleukin (IL) signaling (IL-2, IL-3, IL,7) and regulators of cell proliferation and signaling including mTOR, PI3K/AKT, and p53. Positive enrichment of the PTEN, FGFR3 and ERK/MAPK pathways were observed. Underlying multiple pathways was promoter hypermethylation with corresponding mRNA downregulation of AKT3 and PIK3CA. Furthermore, promoter hypomethylation with transcript upregulation of PTEN was observed. Next, in comparison of MYD88 WT to MUT samples, a statistically greater number of hypermethylated DMRs were observed across genomic regions (hyper- vs hypomethylated): promoter (24829 vs 2544), 3'UTR (3534 vs 378), 5'UTR (6111 vs 605), intron (52292 vs 5360), exon (21066 vs 2187), CpG island (18334 vs 1551) and CpG shore (16351 vs 1957) (all p<0.001). Assessing mRNA expression, 354 genes were up-/downregulated with concordant promoter hypo-/hypermethylation. Pathway analysis revealed positive enrichment of the mitotic G1 phase to G1/S transition pathway with upregulation of the CDK4, LYN, and PSMA6 transcripts in MYD88 WT compared to MUT samples. Further, negative enrichment of cell signaling regulators including the RHO GTPase family, IL pathways (IL-4, IL-8, IL-13, IL-17) and integrin signaling were found in MYD88 WT compared to MUT samples. Underlying multiple pathways were downregulation with promoter hypermethylation of BCL2, an important regulator of apoptosis. Conclusion Genome-wide methylation analysis of MYD88 WT WM revealed a unique DNA methylation profile, with potential epigenetic alteration of key oncogenic transcripts and pathways. The negative enrichment of cell signaling and cytokine/inflammatory pathways in MYD88 WT samples compared to controls and MYD88 MUT WM suggests an epigenetic mechanism for immune evasion and potentially underlies the reduced response to conventional WM-directed therapies. Overall, this study reveals that MYD88 WT WM is epigenetically distinct from MYD88 MUT WM and regulators such as methylation may underlie the unique clinical and phenotypic properties observed.

PURPOSE BRAF and MEK inhibitors are standard treatments in histiocytic disorders, such as Erdheim-Chester disease (ECD). Some patients lack MAPK-pathway alterations, making these treatments less effective. METHODS We describe three patients with histiocytic disorders who have novel non-MAPK pathway alterations. These alterations were studied through genomic and in silico analyses when applicable, then treated with off-label medications rationally selected on the basis of genomic alterations. RESULTS Patient 1 had rapidly progressive ECD involving the CNS. A CSF1R in-frame deletion (p.S560_P566del) was identified, and in silico modeling predicted a gain-of-function mutation. This alteration was targeted with pexidartinib, which led to a clinical complete response (CR) within 2 months, and a partial response (PR) on imaging after 3 months. After 15 months, the disease became resistant to pexidartinib and transformed to histiocytic sarcoma. Patient 2 has skin-only involvement of a xanthogranuloma disorder. A KIF5B-FGFR1 fusion was identified on RNA sequencing and targeted with pemigatinib. At 24 months of follow-up, she remains in a clinical PR. Patient 3 has ECD involving the bone marrow, gastrointestinal tract, and subcutaneous tissues. A MEF2C-FLT3 fusion was identified and targeted with sorafenib. He achieved a clinical CR and radiographic PR within 3 months, which has continued for 30 months. CONCLUSION We report three patients with histiocytic disorders harboring novel alterations who had sustained responses to off-label kinase inhibitors specific to their histiocytic disorder. Pathogenic variants outside of the MAPK pathway, including variants of unknown significant, may be targeted with readily available small molecules.

AbstractObjectiveTo describe the phenotypes, oncological associations, biomarker profiles, and outcomes across different age groups in patients with ANNA1 (anti‐Hu) autoimmunity.MethodsA retrospective review of patients with ANNA1‐IgG in serum/CSF between January 1, 2001, and December 31,2019 was performed. Patients were classified into three groups based on the age of symptom onset. Phage immunoprecipitation sequencing (PhIP‐Seq) and neurofilament light chain (NfL) measurements were done in patient sera/CSF with archived samples.ResultsOf 122 patients, 81 (66%), 20 (16%), and 21 (17%) patients belonged to older adults, young adults, and pediatric groups, respectively. Lung cancer and neuromuscular presentations were more common in older adults (p < 0.001), while limbic encephalitis and neuroblastoma were more common in pediatric patients (p < 0.005). Most young adults (75%) did not have cancer identified. Proportions of patients with a favorable response to immunotherapy were 20%, 30%, and 52% among older adults, young adults, and pediatric groups, respectively. PhIP‐Seq demonstrated significant enrichment for ELAVL4 peptides especially for amino acids 240–289, in the majority of samples evaluated (36/67, 54%). ZIC and SOX2 peptides were significantly enriched in those with central nervous system presentations. Serum NfL levels were elevated in patients with cancer and those with poor long‐term outcomes.InterpretationYoung adults with ANNA1 autoimmunity phenotypically resembled older adults but rarely had an underlying cancer. Pediatric patients frequently presented with limbic encephalitis and neuroblastoma and often responded favorably to immunotherapy. Distinct antigenic signatures may underlie differences in clinical presentations. Serum NfL levels may be a biomarker of poor long‐term outcomes in ANNA1 autoimmunity.

Background: Dysfunction of systemic vascular endothelial cells (ECs) is common in heart failure with preserved ejection fraction (HFpEF). We sought to determine if organ-specific EC translational heterogeneity is present in HFpEF. Using a transgenic mouse model (RiboTag EC ), we analyzed the translatome (the mRNAs bound with ribosomes) of organ-specific ECs in a mouse HFpEF model induced by hypertensive/metabolic stresses. Methods: Six-month-old male RiboTag EC ( Cdh5 CreERT2+ / Rpl22 HA/+ ) mice, expressing a hemagglutinin tag in EC ribosomal protein Rpl22, were fed a high-fat diet and L-NAME water (N=4) or a normal diet and water (N=5) for 7 weeks. Organs were collected for Translating Ribosome Affinity Purification, RNA isolation, and RNA-seq. RNA-seq data were visualized using uniform manifold approximation and projection (UMAP). Differentially expressed genes (DEGs; absolute fold change ≥ 2 and false discovery rate (FDR) adjusted p-value ≤ 0.05). Pathway enrichment analyses were conducted (Ingenuity Pathways Analysis (IPA)). Results: In UMAP, the EC translatome clustered by organ (heart, lung, skeletal muscle, and liver) highlighting organ-specific EC heterogeneity and by disease (HFpEF) status with cardiac ECs showing the most dramatic changes with HFpEF (Fig A). Cardiac ECs in HFpEF (vs Control) had 283 downregulated and 23 upregulated DEG (Fig B) out of 14,923 genes identified. There were significant differences in 142 canonical pathways (16 upregulated, 126 downregulated), including LXR/RXR activation, DHCR 24 signaling, regulation of insulin-like growth factors, eNOS signaling, acute phrase response (all down regulated) and IL-1b-mediated inhibition of RXR and unfolded protein response (both up regulated) (Fig C). Conclusion: HFpEF induction caused extensive translational changes in EC but particularly cardiac ECs. In cardiac ECs, metabolism, anti-inflammatory, vasodilation, tissue repair, and angiogenesis pathways were inhibited, while pro-inflammatory response, insulin resistance, and proteostasis pathways were activated.

Abstract Most TERT promoter (TERTp) clinically relevant sequence variants in CNS tumors are the canonical C228T and C250T mutations. Non-canonical TERTp mutations have been reported mainly in non-CNS tumors. We describe 12 CNS tumors with non-canonical TERTp variants interpreted as clinically relevant. Using clinical targeted NGS panels (2020-2024), we detected c.-100_-79dup (n=2), c.-92_-91insGGCGGCCCCGCCCCTTCCTTTC (n=1), c.-118_-117insTCCCCGGCCCAGCCCCTTCCGGG (n=1), c.-123_-76dup (n=1), c.-110_-89dup (n=2), and c.-57A&gt;C (n=5). Most cases had chromosomal microarray (n=11) and/or methylation array data analyzed using the NCI/Bethesda v.2 classifier (n=9). All non-canonical TERTp variants occurred at a variant allele frequency suggestive of somatic clonal origin (mean, 34%; range, 18-46), and in adults (mean age, 60 years; range, 36-88). Except for the c.-57A&gt;C variant, all non-canonical TERTp variants occurred in tumors with histological and/or molecular diagnostic features of glioblastoma, IDH-wildtype (GBM), often epigenetically aligning to this tumor type. All five tested cases matched to superfamily glioblastoma (score ≥ 0.9), with three to class GBM_RTK_I (&gt;0.9 in 2, 0.86 in 1) and two to GBM_RTK_II (&gt;0.9). The size and sequence of such variants suggest that these may be functionally equivalent to canonical mutations or generate favorable TERTp activation motif conformations like previously reported activating duplications. The c.-57A&gt;C variant is a well-established oncogenic mutation observed in other cancer types but has not been reported in CNS tumors. We describe five cases with the c.-57A&gt;C variant: one GBM matching to methylation class GBM_MES_TYP (0.99); two oligodendroglioma, IDH-mutant and 1p/19q-codeleted, one of which had methylation data matching to class O_IDH (0.99); one diffuse glioma, IDH-mutant, which was partially 1p/19q-codeleted and matched to class O_IDH (0.99); and one anaplastic meningioma, aligning to superfamily meningioma (0.99)/MNG_MAL (0.79). Our findings show that non-canonical TERTp variants typically occur in tumor types that often harbor TERTp canonical mutations, expanding the spectrum of TERTp clinically relevant variants that are molecular biomarkers in CNS tumors.