Ferrer, Alejandro Tiana

Abstract The importance of bone marrow (BM) microenvironment and its immune interactions within and their role in chronic myelomonocytic leukemia (CMML) disease progression have not been well studied. Our previous work in CMML identified type 1 helper T cell (Th1) and CD4 terminal effector cell suppression in peripheral blood from untreated patients, in comparison to age-matched normal controls (Mangaonkar A et al. Blood Adv 2020). Furthermore, clonal dendritic cell (DC) aggregates in CMML are present in approximately 20-30% patients at diagnosis, display significant cell heterogeneity (plasmacytoid DCs + myeloid DCs and other myeloid/immune cells), express indoleamine 2,3-dioxygenase-1 (IDO), associate with T regulatory cell (Treg) expansion, and transformation to AML [blast transformation or BT, Lucas N et al. Leukemia 2019, Mangaonkar A et al. Blood Adv 2020, and Mangaonkar A et al. Blood 2023 (abstract)]. The purpose of this study was to define the myeloid-immune cell interactions within the BM microenvironment critical for CMML progression to AML. To phenotypically identify cell types and their interactions within the CMML BM, we used single-cell type spatial omics through two distinct modalities on archival formalin-fixed paraffin-embedded (FFPE) BM biopsy specimens. Ten serial CMML samples at chronic phase [CP, n=5, median BM blast% 5 (2-15)] and at BT [n=5, median BM blast% 60 (25-74)] were used for both techniques. Spatial transcriptomics was conducted through GeoMx Digital Spatial Profiler (DSP) platform that uses UV-photocleavable, spatial-barcoded oligo probes to profile the whole transcriptome in defined regions of interest (ROI). CD123 (marker for DC-myeloid aggregates) immunofluorescence antibody was used to identify and segment ROI. 16-color flow cytometry was done to validate cell-specific expression. Spatial cellular phenotyping of immune cells on the same samples (serial cuts) was done through multiplex imaging by co-detection by indexing (CODEX) using 27 markers: CD31, Ki67, CD11b, CD20, CD44, Ecad, CD123, CD107a, CD8, CD11c, CD45RO, FoxP3, CD14, PDL1, CD56, CD68, CD163, PD-1, CD38, HLA-DR, CD4, IDO, CD45, PanCK, Na/K/ATPase, CD3e, and DAPI. CD8T+ cell anergy was demonstrated through CFSE cell proliferation assay. Benjamini-Hochberg correction was used to adjust for multiple hypotheses. After filtering poor quality ROIs [ROIs with total read count < 1000 were removed, ROIs with trim, stitch, alignment < 80% or saturation < 50% were removed], 3rd-quartile normalization [BiocGenerics (v. 0.48.1)] and batch correction [limma (v. 3.58.1)], we compared transcriptomics signatures between CD123+ and CD123- ROIs in all samples. Results showed that LIPA, SLC4A1, HBA2, TIMD4 and HMOX1 werethe top five genes upregulated in CD123+ areas. Among all gene transcripts, TIMD4 expression increased the most (Log2FC=0.6, P-value=8.79E-10, FC=1.52, FDR=1.68E-07) when CD123+ ROIs were compared between CMML-CP and CMML-BT serial samples, suggesting a biological relevance in disease progression. Previous work in metastatic peritoneal cancer has established that TIM-4 expressing macrophages suppress Th1/Th2 ratios and impair CD8+ T cell function (Chow A et al. Cancer Cell 2021). To confirm expression at the protein level, we used a multicolor flow cytometry panel and confirmed that TIM4 is expressed on macrophages (Lineage-negative, HLA-DR+, CD68+ cells) in independent CMML BM aspirate samples (n=3). Next, we conducted spatial phenotyping using CODEX on serial cuts from the same serial samples used for spatial transcriptomics. Anatomical co-localization of macrophages with leukemic cells, monocytes and T cells was visually inspected and confirmed. Cell-interaction analysis (after Benjamini-Hochberg correction) indicated that macrophages-monocyte, macrophage-leukemic cells, and macrophage-memory CD4+T cells were significantly (P<0.05) increased in CMML-BT versus CMML-CP. CD8T+ cell anergy was confirmed through a CFSE cell proliferation assay in untreated CMML PBMC samples. In conclusion, our data suggests that TIM4-expressing macrophages and their interactions with monocytes/monocyte precursors and immune cells within a DC aggregate create a protective leukemic stem cell niche and T cell tolerance, promoting blast transformation in CMML.

Abstract Introduction: Somatic mutations in splicing factors (SF) - SF3B1, SRSF2, U2AF1, and ZRSR2 – represent one of the most common drivers of premalignant clonal hematopoesis. While SF mutations are potent drivers of clonal expansion (Fabre et al., Nature 2022), multiple SF mutations are rarely observed in the same individual (negative epistasis). Prior studies have identified rare cases of concurrent SF mutations (<4%) and described specific hotspot combinations that escape negative epistasis (Taylor et al., Blood 2020). We conducted this study to further characterize the frequency and clonal relationship between concurrent SF mutations using a larger patient cohort. Methods: After IRB approval, we assessed 3,369 myeloid next generation sequencing (NGS) results that identified somatic variants in SF3B1, SRSF2, U2AF1, or ZRSR2, in 2,500 adult patients from 08/2015 to 07/2025. Patient demographics, ICD-9/10 codes, and complete blood counts with differentials were obtained, retaining only those datapoints assigned 30 days before or after NGS testing. To allow comparison of clone size between autosomal and the X-linked SF ZRSR2, VAFs were multiplied by two to estimate the cancer cell fraction (CCF), except for ZRSR2 mutations in males. For patients harboring concurrent SF mutations, we categorized them as co-dominant if the difference in respective CCFs was < 10% of the greater CCF. Specific SF variants were categorized into common hotspots (n=13), except for ZRSR2 variants, which do not occur in defined hotspots. Fisher's exact testing was used to assess the enrichment of SF hotspot variants in patients with concurrent SF mutations, as well as combinations of SF variants in patients with co-dominant SF mutations. Results: We identified 2,331 patients with at least 1 pathogenic SF mutation, of which 6.7% (n=156) had > 1 SF mutation (multi-SF MT). The total cohort comprised cases of AML (n=549, 24%), MDS (n=977, 42%), MDS/MPN (n=233, 10%), MPN (n=228,10%), and uncategorized ICD codes (“Other”, n=326), of which the largest category was clonal cytopenia(s). Notably, the prevalence of multi-SF cases was higher than previously described and our cohort included 5 patients with 3 SF mutations and 1 with 4 SF mutations. Of the multi-SF MT patients, we identified 42 (27%) with co-dominant CCFs among their respective SF mutations. Thirty-three multi-SF MT patients had multiple NGS sampling timepoints (median 2, range 2-4) spanning a median of 0.97 years (range 0.06-5.69 years); we found that 17 of these 33 demonstrated correlative SF CCFs over multiple timepoints, suggesting their SF mutations occurred in the same clone (“co-clonal”). Of these 17 patients, 82% had co-dominant CCFs at the single-timepoint level, whereas none of the 16 non-co-clonal demonstrated co-dominance. Thus, we used SF co-dominance as a surrogate for cases likely to be co-clonal. ZRSR2 (p=8.8x10-29), U2AF1Q157 (p=9.4x10-9), SF3B1K666 (p=1.4x10-4) and infrequent SF3B1 (“SF3B1-Other”, p=.0029) mutations were overrepresented in the multi-SF MT cohort compared to patients with a single SF mutation. Within the multi-SF MT cohort, 3 SF mutation combinations showed overrepresentation in codominant cases – SRSF2P95R/H/L+ ZRSR2 (p=0.014), SRSF2P95R/H/L+ SF3B1K666 (p=0.044), and U2AF1S34 + U2AF1Q157 (p=0.024), although not significant after multiple hypothesis correction. Dual ZRSR2 mutations were the most common combination seen in the multi-SF MT cohort but were underrepresented in codominant cases (p=0.025). Although there was no disease subtype uniquely associated with total or co-dominant multi-SF MT cases, occurrence of multiple SF mutations was more common in males (8% vs. 3% in females), an effect driven by ZRSR2 mutations. Hemoglobin and WBC were lower in multi-SF MT cases, with the lower hemoglobin strongly driven by the codominant multi-SF MT cases (median 8.3 g/dL vs 9.4 g/dL, p=0.00046). Conclusions: These findings suggest that SF mutation co-occurrence in myeloid neoplasms is more prevalent than previously estimated (~7%). Furthermore, we identified 6 individuals harboring > 2 SF mutations, which has not been previously described. We validate specific SF variant pairs that appear to exist in the same clone and are working to confirm the clonal architecture and characterize global splicing aberration in selected cases with single-cell studies to fully uncover the mechanism by which negative epistasis is overcome.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Background: While myeloproliferative neoplasms (MPN), including essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (MF), are usually sporadic, adult-onset neoplasms, an increasing number of germline risk alleles have been described (e.g. EGLN1, ETV6, SH2B3, and ATM) in these disorders. DDX41MT Germline Predisposition Syndrome (GPS) is the most common myeloid GPS in adults and while there is extensive data on MDS/AML, little is known regarding MPN occurring in this context. We carried out this study to define the prevalence and characteristics of DDX41MT-GPS associated MPN. Methods: After IRB approval, we retrospectively assessed the Mayo Clinic cohort of 174 patients with DDX41MT-GPS, which included both pathogenic and clinically relevant germline variants of undetermined significance (VUS), based on our previous analysis (Badar et al Haematologica 2023). Standard statistical measures were applied. Results: We identified 12 (6.9%) patients with MPN (ET-5, PV-2, PMF-4, MPN-NOS-1). The median age at MPN diagnosis was 61.2 years (range, 8.-70.4); 58.3% (n=7) were male, and 83.3% (n=10) were Caucasian. A family history of hematologic malignancy was present in 16.7% (n=2, AML and T-LGL), while 50% (n=6) had a family history of visceral malignancies and 41.7% (n=5) had no family history of malignancies. Among ET patients (n=5) with DDX41MT-GPS, JAK2V617F was the MPN driver in 4 (80%) while type 1-CALR (L367fs*46) was a driver in 1 (20%). This included a 9-year-old child diagnosed with JAK2V617FMT ET. The DDX41 variant was classified as pathogenic in 40% (n=2; E113Kfs*14, D140Gfs*2) and VUS in 60% (n=3; V544L, V591I, P38L). Bone marrow (BM) aspirates in the ET patients were hypercellular in 40% (n=2), normocellular in 40% (n=2) and hypocellular in 20% (n=1), and none had increased blasts, or reticulin fibrosis (RF). In addition to JAK2V617F, somatic mutations seen included SF3B1 (K666T, VAF 16%) in 1 patient and IDH2 (R140Q, VAF 6%) and DNMT3A (R882H, VAF 7%) in 1 patient, respectively. With a median follow-up from diagnosis of 3.9yrs (range 2.9-8.9), there were no thrombotic events and no disease progressions was documented. There were 2 patients with JAK2V167FMT PV in whom the DDX41MT were clinically significant VUS (c.138+5G>T and M155I). Both patients had hypercellular BM, with no increase in blasts, no RF, normal karyotypes and no additional somatic mutations. With a median follow up of 7.5yrs (range, 2.1-12.9), 1 patient developed a deep vein thrombosis (DVT) and there were no fibrotic or leukemic transformations. There were 4 patients with PMF; 3 (75%) with JAK2V617F and 1(25%) with a type 1-CALR driver mutation (L367fs*46). DIPPS-plus scores ranged from 1 to 4 (median 2.5). The DDX41 variants were pathogenic in 50% (n=2, E256K and P78Qfs*3) and VUS in 50% (n=2, P510S and M155I). The median grade of RF at PMF diagnosis was grade 2 (range, 2-3). BM were hypercellular in 75% (n=3) and hypocellular in 25% (n=1), and 1 patient had 3% peripheral blasts (and no BM blasts). Somatic co-mutations included TET2 in 1 patient with a JAK2V617F, and CBL, EZH2 and SRSF2 in 1 patient with a CALR driver-mutation, respectively. PMF patients were treated with ruxolitinib (n=3), stem cell transplant (n=1), and pIFNα (n=1). With a median follow up of 3.9yrs (range,1.1-21.9), all patients were alive, and none had leukemia transformation. There was 1 patient with a JAK2V617F MPN-NOS with trisomy 9, where the DDX41MT-GPS was a VUS (I215L). There were no somatic co-mutations and at last follow up (3.1 years), there was no evidence for progression. Conclusions: While associations between late onset MDS/AML and DDX41MT-GPS has been well established, we systematically describe the MPN landscape seen in this context. MPN was diagnosed in 7% of our cohort, with JAK2V617F being the most common driver mutation. Most importantly, unlike in AML/MDS, where somatic DDX41 mutations are frequently associated with neoplastic transformation (30-50%; R525H being most common), none of our MPN patients had a somatic DDX41 mutation. Our work underscores the importance of germline mutation testing in patients with MPN.

Abstract Introduction: Most rare disease patients (75 − 50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genetic databases to enable rapid review of genetic findings. Methods We tested RENEW in an unselected cohort of 1,066 undiagnosed cases from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. Results 5,741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 seconds per variant (32 hours total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. Discussion New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.