Citations

Abstract Objective Gastrointestinal (GI) symptoms are common in systemic mastocytosis (SM). We hereby report the clinicopathologic and molecular features of GI mastocytosis. Methods The study includes 104 patients with suspected SM. Of 258 GI biopsy ­specimens, 33 (20 patients, 19%) were mastocytosis-positive, with 19 (16 patients) initially missed. Results Tryptase (n = 29) was weakly positive/negative in two-thirds of the mastocytosis-positive GI biopsy specimens. Additional immunohistochemistry (n = 23) showed positive mast cell expression for CD30 (n = 2), CD123 (n = 7), and PD-L1 (n = 12), as well as increased PD-1–positive cells (n = 4). Sixty patients had positive bone marrow (BM) for SM (SM-BM+), including 19 with positive GI biopsy specimens (SM-GI+). Almost all (43/44) SM-BM– patients were SM-GI–. The SM-GI vs SM-BM status showed high positive predictive value (95%) and specificity (98%). Patients with SM with mastocytosis-positive vs mastocytosis-negative GI biopsy specimens had higher serum tryptase levels (P = .04). KIT D816V mutation was detected in 7 of 13 and 12 of 13 mastocytosis-positive GI biopsy specimens using allele-specific polymerase chain reaction and droplet digital polymerase chain reaction (ddPCR), respectively. Conclusions GI mastocytosis is highly predictive of SM in BM; however, most SM-BM+ patients are SM-GI–. The SM-BM– patients are very unlikely to be SM-GI+. Because GI neoplastic mast cells are often tryptase negative, diagnostic evaluation should include CD117 and CD25 immunohistochemistry, as well as KIT D816V mutation analysis by ddPCR as needed.

ABSTRACTThe current study includes 910 patients with systemic mastocytosis (SM) seen at the Mayo Clinic from 1968 to 2024. The primary objective was to examine the prognostic contribution of the International Consensus Classification (ICC), in the context of the Mayo Alliance Prognostic System (MAPS) for SM. World Health Organization classification (WHO‐HAEM5) subcategories included (i) indolent/smoldering SM (ISM/SSM; N = 518), (ii) SM associated with another hematological neoplasm (SM‐AHN; N = 273), with the latter including both myeloid and lymphoid neoplasms, (iii) aggressive SM (ASM; N = 106), and (iv) WHO‐defined mast cell leukemia (MCL; N = 13), which included mast cells with both “mature” and “immature” morphology. The ICC‐defined subcategories were mostly similar with the exception that SM‐AHN was replaced by SM associated with another myeloid neoplasm (SM‐AMN; N = 235) and WHO‐defined MCL was replaced by ICC‐defined MCL (N = 8), which included only those with immature MC morphology. Overall survival (OS) was similar between WHO‐defined MCL (median 1.8 years) vs. SM‐AHN (median 2.3 years; p = 0.3) but significantly different between ICC‐defined MCL (median 0.08 years) vs. SM‐AMN (median 2.0 years; p < 0.01). Significant difference in OS was also apparent between ICC‐defined SM‐AMN and SM associated with lymphoid neoplasm (SM‐ALN; median 8.1 years; HR 3.4; p < 0.01). Multivariable analysis confirmed the inter‐independent prognostic contributions of both ICC‐defined morphologic subcategories and MAPS risk variables, including age > 60 years, anemia, alkaline phosphatase > ULN, and platelet count < 150 × 109/L (p < 0.03 in all instances); the same was not true for WHO‐HAEM5‐defined SM subcategories. The addition of mutational information into the multivariable model resulted in ousting anemia and inclusion of ASXL1 (p < 0.01), SRSF2 (p = 0.01), and NRAS (p = 0.01) mutations as additional risk factors. Classification of SM by ICC is prognostically more accurate, compared to WHO‐HAEM5, and strengthens the prognostic contribution of morphology to current clinical and molecular risk models.

e18596 Background: The International Consensus (ICC), but not the 5 th edition of the WHO classification (WHO5), distinguishes mature from immature mast cell leukemia (MCL) and systemic mastocytosis (SM) associated with myeloid neoplasm (SM-AMN) from SM associated with myeloid or lymphoid neoplasm (SM-AHN). The objectives of the current study were i) to assess prognostic input from disease classification by ICC vs. WHO5, and ii) to compare the predictive performance of the Mayo Alliance Prognostic scoring (MAPS) with other prognostic models and its prognostic interaction with the ICC-based morphologic classification. Methods: Study patients met diagnostic criteria per ICC/WHO5. MAPS risk variables included i) age >60 years, ii) hemoglobin below sex-adjusted lower limit of normal, iii) platelets <150 × 10⁹/L, iv) alkaline phosphatase above upper limit of normal, and v) advanced, including MCL, SM-AMN, and aggressive (ASM) vs. indolent/smoldering (ISM/SSM) SM. ICC-defined MCL includes only MCL-immature whereas WHO-defined MCL includes both MCL-immature and MCL-mature. WHO-defined SM-AHN includes SM-AMN and SM associated with lymphoid neoplasm (SM-ALN). Results: 910 Mayo Clinic patients with SM were diagnosed between 1968 and 2024 (median age 56 years). ICC categories were ISM/SSM (N=552), ASM (N=115), SM-AMN (N=235), and MCL (N=8). At median follow-up of 4 years, 345 (38%) deaths and 35 (3.8%) leukemic transformations were documented. Overall survival (OS) predictive performance at 5 years was superior with ICC (AUC 0.85), compared to WHO5 (AUC 0.83); median survival was 24.3 years in ISM/SSM, 5.8 years for ASM, 2.0 years for SM-AMN, 2.3 years for SM-AHN, 1.8 years for MCL-mature and 0.08 years for MCL-immature; the difference in OS was significant between MCL-immature and SM-AMN (p<0.01) but not between MCL-mature and SM-AHN (p=0.3). Similarly, OS was similar between MCL-mature and ASM (p=0.8) and between SM-ALN and ASM (p=0.08) but different between MCL-immature and ASM (p<0.01), SM-AMN and ASM (p<0.01), and MCL-mature and MCL-immature (p<0.01). Predictive performance at 5 years was superior with MAPS (AUC 0.91) and MAPS-molecular (AUC 0.91), compared to the mutation-adjusted risk score (MARS; AUC 0.82). Median OS estimates were not reached, 24.2, 11.7, 5.7, 2.6, and 0.7 years, in the presence of 0, 1, 2, 3, 4, or 5 of the forementioned MAPS risk factors. Among informative cases, ASXL1 mutation was the only genetic risk factor with MAPS-independent prognostic relevance. Prognostic inter-independence between ICC and MAPS was confirmed by multivariable analysis. Conclusions: The current study confirms the superior performance of MAPS, in regard to survival prediction, with little additional contribution from mutations. SM sub-classification by the ICC is prognostically independent of MAPS and more valuable than classification by WHO5.

Abstract Background The lupus anticoagulant (LAC) is an important laboratory criterion in the diagnosis of antiphospholipid antibody syndrome. LAC testing at the Mayo Clinic Special Coagulation Laboratory includes up to 13 tests, which are interpreted by trained physicians to identify the presence of LAC and rule out anticoagulant interferences. This feasibility study explored the use of two deep neural network (DNN) architectures for multilabel classification of LAC profiles as a first step toward automating interpretation. Methods Seven thousand two hundred and two retrospective cases were randomly split (64:16:20) for training, validation, and test, respectively. LAC positivity by dilute Russell's viper venom time (LAC-DRVVT) and activated partial thromboplastin time (LAC-APTT) and the presence of warfarin (WAR) and heparin (HEP) were adjudicated by one expert. DNN architectures included: single-output DNNs using domain-knowledge input feature selection and a single-column multioutput DNN using all 13 inputs. Results Domain-knowledge-naïve multioutput DNN achieved similar or improved performance for all 4 label prediction tasks compared with domain knowledge optimized DNNs: F1 scores of 0.977 vs 0.968 for LAC-DRVVT, 0.954 vs 0.945 for LAC-APTT, 0.961 vs 0.957 for HEP, and 0.995 vs 0.977 for WAR, respectively. Conclusions The comparable performance of the 4 domain knowledge optimized DNNs and the multioutput DNN using all 13 input features suggests that the DNN may learn feature importance or mapping to a task without explicit input selection. Given its relative simplicity and versatility, the multioutput DNN is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.

Abstract This retrospective analysis aimed to provide evidence-based risk stratification of TP53-mutated (TP53mut) myeloid neoplasms (MNs). Of 580 MNs harboring TP53mut with variant allele frequency (VAF) ≥2%, 219 (37.8%), 194 (33.4%), 92 (15.9%), and 75 (12.9%) were classified as acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) with low blasts (MDS-LB), MDS with excess blasts (MDS-EB)-2, and -EB1 according to the revised fourth edition of the World Health Organization (WHO) classification, respectively. Hierarchical analysis identified the following 4 risk groups with distinct survival: (1) MDS-LB, (2) MDS-EB1/EB2/AML VAF &lt;10%, (3) MDS-EB1/EB2 VAF ≥10%, and (4) AML VAF ≥10%. We next evaluated the impact of allelic status, VAF, and complex karyotype (CK). In our cohort, the significance of biallelic status was limited to MDS with &lt;5% blasts and not for blasts 5% to 9%, as proposed by the International Consensus Classification (ICC), or 5% to 19%, as proposed by the fifth edition of the WHO (WHO-5). MDS-EB1 and -EB2 with VAF ≥10% had comparable survival (9.6 vs 7.2 months; P = .12), regardless of allelic status. Contrary to the ICC proposal, MDS-EB1/EB2 with VAF &lt;10% and CK had poor survival compared with those without CK, comparable to MDS-EB1/EB2 with VAF ≥10% (5.6 vs 26.2 vs 6.3 months; P = .003). Survival of TP53mut AML was poor (median 3.9 months) regardless of allelic/CK status. Thus, using ICC or WHO-5 may underestimate prognosis of MDS with blasts 5% to 19% and 5% to 9%, respectively. Collectively, the hierarchical model acknowledges poor survival of 91.9% TP53mut MDS and AML compared with 36.5% and 80.7% by WHO-5 and ICC, respectively.

ABSTRACTIntroductionVon Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter‐rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.MethodsWe trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.ResultsThe model achieved 91% accuracy compared to Expert 1 (macro‐averaged precision = 0.851, recall = 0.757, F1‐score = 0.786) and 87% accuracy compared to Expert 2 (macro‐averaged precision = 0.653, recall = 0.653, F1‐score = 0.641). Inter‐rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.ConclusionAutomated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.

Abstract Objectives Fluorescence resonance energy transfer (FRET)–based ADAMTS13 activity assays are critical for the diagnosis of thrombotic thrombocytopenic purpura. However, these assays are susceptible to interference. As iodide has been suggested to interfere in laboratory testing via fluorophore quenching or promotion, we aimed to determine whether iodinated contrast (Omnipaque) interferes with the ATS-13 ADAMTS13 Activity Assay 2.0. Methods We evaluated the excitation, emission, and absorbance spectrum of Omnipaque alone and spiked in patient plasma with known ADAMTS13 activity and ADAMTS13 activity on Omnipaque alone, an abnormal control of patient plasma previously observed to display elevated baseline relative fluorescent units, and variable concentrations of patient plasma with known ADAMTS13 activity spiked with Omnipaque. Results No atypical fluorescent peaks were observed on any sample (Omnipaque alone or spiked in plasma) between 250 and 700 nm. There was no difference in the mean ADAMTS13 activity among the various concentrations of plasma spiked with Omnipaque or plasma spiked with saline. Conclusions Iodinated contrast does not appear to interfere—either via spectral overlap of the fluorophore or through physiologic inhibition of the ADAMTS13 enzyme—with ADAMTS13 activity FRET-based assays based on the findings from this in vitro analysis. Delaying sample collection for ADAMTS13 activity testing from suspected patients with thrombotic thrombocytopenic purpura following administration of iodinated radiocontrast agents is not necessary, and recent contrast administration should not yield erroneous ADAMTS13 activity results.