Paula Savola

Clonal hematopoiesis (CH) becomes more prevalent with aging and may influence inflammatory diseases by altering immune function. While CH of indeterminate potential (CHIP) promotes inflammation in nonmalignant conditions, its relationship with rheumatoid arthritis (RA) remains unknown. We analyzed CHIP mutations in RA using two population-level cohorts and patients with newly diagnosed RA. CHIP was associated with prevalent RA in 10,089 FINRISK study participants with whole-exome sequencing (OR, 2.06; P = 0.029) and in the FinnGen cohort ( n = 520,210; OR, 1.49; P < 0.001) using single-nucleotide polymorphism array–based CHIP annotation. In FinnGen, CHIP was also associated with inferior overall survival in participants with RA ( P = 0.013). In newly diagnosed RA ( n = 573), DNMT3A -mutated seropositive patients had increased inflammatory markers and disease activity compared with patients without CHIP. In contrast, TET2 mutations were enriched in seronegative RA ( P = 0.009). Our findings provide further evidence for the context-dependent association between CHIP and inflammation, with potential therapeutic implications.

Although iron-deficiency anemia is common, interpreting iron laboratory test results can be challenging in patients with comorbidities. We aimed to study the accuracy of common iron biomarkers compared with bone marrow iron staining in a large retrospective data set of patients with hematologic disorders. We collected from 6610 patients (median age, 66 years) results of iron staining, with their concurrent ferritin, transferrin saturation, soluble transferrin receptor, transferrin, hemoglobin, and mean red blood cell volume results from Helsinki University Hospital electronic health records. In receiver operating characteristics analysis, ferritin had the highest area under the curve (AUC) with 88% (95% confidence interval [CI], 86-90) for females and 89% (95% CI, 87-91) for males in predicting reduced bone marrow iron. Using a ferritin cutoff of 30 μg/L resulted in high specificity rates of 97% in females and 99% in males. However, sensitivity rates were only 54% and 35%, respectively. Other studied biomarkers had inferior AUCs. Multivariate logistic regression models did not significantly perform better in prediction than ferritin alone. With 50% preprobability for reduced iron stores, a ferritin of 30 μg/L (females) and 51 μg/L (males) had a 95% positive predictive value for reduced iron stores. A 95% negative predictive value was achieved at 1750 μg/L (females) and 4967 μg/L (males). In our large population study, ferritin was the best single biomarker for iron deficiency in secondary care. Adding other blood tests in a multivariate model did not improve performance. However, in these patients with hematologic disorders, even a high ferritin did not rule out iron deficiency with 95% certainty.

Direct oral anticoagulants (DOAC) interfere in laboratory coagulation testing. The aim here was to study how commercial DOAC removal methods, DOAC Filter^® and DOAC-Stop™, perform to eliminate DOAC concentrations and false positive results in lupus anticoagulant (LAC) testing. We acquired 50 patient samples with high concentrations of DOACs: apixaban (n = 18, range 68–572 ng/mL), dabigatran (n = 8, range 47–154 ng/mL), edoxaban (n = 8, range 35–580 ng/mL) and rivaroxaban (n = 16, range 69–285 ng/mL). DOACs were removed ex vivo with either DOAC Filter^® (n = 28) or DOAC-Stop™ (n = 22). Additionally, commercial control and calibrator samples were studied (n = 13 for DOAC Filter^®, n = 14 for DOAC-Stop™). LAC screening was performed before and after DOAC removal. Both DOAC Filter^® and DOAC-Stop™ were effective in removing DOAC concentrations in samples: DOAC concentrations decreased to median of 0 ng/mL (range 0–48 ng/mL). Only one sample had more than residual 25 ng/mL of DOAC (apixaban). Before DOAC removal, 96% (48/50) of patient samples and over 90% (12/13 DOAC Filter^®, 13/14 DOAC-Stop™) of control/calibrator samples were positive in the LAC screening. In patient samples, LAC screening turned negative in 61% (17/28) after DOAC Filter^® and 45% (10/22) after DOAC-Stop™ treatment. All control samples became negative after DOAC removal. In conclusion, DOAC removal ex vivo reduces false positives in LAC screening. DOAC removal halved the need for confirmation or mixing tests- Although a subset of patients would require further testing, DOAC removal reduces unnecessary repeated LAC testing.

Common variable immunodeficiency and other late-onset immunodeficiencies often co-manifest with autoimmunity and lymphoproliferation. The pathogenesis of most cases is elusive, as only a minor subset harbors known monogenic germline causes. The involvement of both B and T cells is however implicated. To study whether somatic mutations in CD4+ and CD8+ T cells associate with immunodeficiency, we recruited 17 patients and 21 healthy controls. Eight patients had late-onset common variable immunodeficiency and nine patients other immunodeficiency and/or severe autoimmunity. In total, autoimmunity occurred in 94% and lymphoproliferation in 65%. We performed deep sequencing of 2533 immune-associated genes from CD4+ and CD8+ cells. Deep T-cell receptor beta sequencing was used to characterize CD4+ and CD8+ T-cell receptor repertoires. The prevalence of somatic mutations was 65% in all immunodeficiency patients, 75% in common variable immunodeficiency and 48% in controls. Clonal hematopoiesis-associated variants in both CD4+ and CD8+ cells occurred in 24% of immunodeficiency patients. Results demonstrated mutations in known tumor suppressors, oncogenes, and genes that are critical for immune- and proliferative functions, such as STAT5B (two patients), C5AR1 (two patients), KRAS (one patient), and NOD2 (one patient). Additionally, as a marker of T-cell receptor repertoire perturbation, common variable immunodeficiency patients harbored increased frequencies of clones with identical complementarity determining region 3 sequences despite unique nucleotide sequences when compared to controls. In conclusion, somatic mutations in genes implicated for autoimmunity and lymphoproliferation are common in CD4+ and CD8+ cells of patients with immunodeficiency. They may contribute to immune dysregulation in a subset of immunodeficiency patients.

The role of somatic variants in diseases beyond cancer is increasingly being recognized, with potential roles in autoinflammatory and autoimmune diseases. However, as mutation rates and allele fractions are lower, studies in these diseases are substantially less tolerant of false positives, and bio-informatics algorithms require high replication rates. We developed a pipeline combining two variant callers, MuTect2 and VarScan2, with technical filtering and prioritization. Our pipeline detects somatic variants with allele fractions as low as 0.5% and achieves a replication rate of >55%. Validation in an independent data set demonstrates excellent performance (sensitivity > 57%, specificity > 98%, replication rate > 80%). We applied this pipeline to the autoimmune disease multiple sclerosis (MS) as a proof-of-principle. We demonstrate that 60% of MS patients carry 2–10 exonic somatic variants in their peripheral blood T and B cells, with the vast majority (80%) occurring in T cells and variants persisting over time. Synonymous variants significantly co-occur with non-synonymous variants. Systematic characterization indicates somatic variants are enriched for being novel or very rare in public databases of germline variants and trend towards being more damaging and conserved, as reflected by higher phred-scaled combined annotation-dependent depletion (CADD) and genomic evolutionary rate profiling (GERP) scores. Our pipeline and proof-of-principle now warrant further investigation of common somatic genetic variation on top of inherited genetic variation in the context of autoimmune disease, where it may offer subtle survival advantages to immune cells and contribute to the capacity of these cells to participate in the autoimmune reaction.

Background. Acquired aplastic anemia (AA) is a bone marrow failure syndrome, in which patients' hematopoietic stem cells are destroyed, resulting in pancytopenia. The exact mechanism and biological process leading to AA remain largely unknown. Bone marrow destruction is perceived as an immune-mediated process, which is supported by elevated cytotoxic T lymphocyte (CTL) counts, responsiveness to immunosuppressive therapy and skewed CTL T cell receptor (TCR) repertoire. Although there is a well-established role of T cells in the pathology of AA, the putative antigen behind the autoimmune response, and thus, the TCRs recognising the antigen are still unrevealed. Methods. Our cohort comprised of 130 samples, consisting of bone marrow and/or peripheral blood samples from AA patients (n=52) from diagnosis and/or follow-up phases and from healthy controls (n=27). We performed TCRβ sequencing (immunoSEQ, Adaptive Biotechnologies) on sorted AA and healthy CTLs (n=25 and n=27) or AA mononuclear cells (MNCs, n=27). To gain in-depth understanding of the TCR-repertoire, we built two novel analysis methods: 1) an unsupervised clustering method to characterize epitope-specific T cells based on the amino acid -level similarities of TCRs and 2) a probability based classifier to identify AA based on TCR data in the CTL (discovery) and MNC (validation) cohorts. Results. To fully appreciate the complex nature of AA pathology, we divided the cytotoxic T cell response in two distinct categories, private and public response. Private response comprises T cell clones found only in individual patients, and this compartment could explain the variation in treatment responses and disease severity across patients. In the CTL TCR repertoire, we identified patient exclusive expanded T cell clones (>1% frequency in TCR repertoire, n = 317) and treatment responding clones (n = 364). Clustering analysis revealed that there is significant amino acid -level similarity between the TCRs of the private clones. We found several epitope-specific TCR clusters that were associated with different treatment responses, disease severities and HLA-DR15 risk-allele positivity. Interestingly, we discovered that the public response (CTL clones that are statistically enriched in AA patients compared to healthy controls) could discriminate AA from healthy TCR repertoire. Based on these publicly enriched TCRs, we built a classifier which could identify AA CTL TCR repertoire from healthy controls with 97% accuracy (F-score, revieved from leave-one-out cross-validation). We tested our classifier with the validation cohort. The accuracy to diagnose AA based on the TCR repertoire data in the MNC cohort was 0.72. Furthermore, the public clones that differentiated best the AA cases from healthy controls showed statistically significant peptide similarity. These public TCRs occurred also in healthy controls, but with smaller frequencies. When combining the private and public response TCRs, we discovered that some of AA patients' most expanded and treatment responding clones clustered in the same putative epitope-specific clusters with the public TCRs, showing an interesting intersection between public and private signatures. In addition, the comparison of the interesting private and public TCRs against databases of TCR sequences of known antigen specificities hinted that some of these clones may originally target known viral species (CMV, EBV and Influenza A), suggesting a role of these common pathogens in the development of AA. Discussion. CTLTCR repertoire analysis of AA patients revealed a TCR signature that was typical of AA patients, but varied between different patients, and it was validated with an independent dataset. Thus, we could design a TCR-based framework which could identify AA patients based on their TCR repertoire, independently of sample type. A future application for our classifier could be distinguishing AA from other AA-like diseases, like hypoplastic myelodysplastic syndrome. Furthermore, we found groups of TCRs that look similar on amino acid level, and hence these clones may target the same epitope. These TCR clusters were associated with clinical features. Amino acid similarity -based TCR signatures or TCR classifiers have not previously been published for AA or any other autoimmune diseases, and thus our pioneering tools could be utilized to study pathogenesis of other T cell mediated diseases as well.

Significance: Our analysis shows that sex can be associated with the degree to which HLA molecules propagate selection and expansion of T cells as characterized by their T cell receptor variable beta chain (TCRBV). Furthermore, CD8 T cells, especially in men with autoimmune diseases such as multiple sclerosis or rheumatoid arthritis, are capable of expanding in unison with other CD8 T cells, even without expressing TCRBVs with biochemical similarity in pivotal HLA-binding regions. Our findings add to the understanding of sex bias in diseases with immune system involvement: autoimmunity, infection, and cancer. These results also reveal pathology-associated TCRBVs of interest for future studies and support the argument for sex-separated analysis of HLA disease associations in general.

Felty syndrome is a rare disease defined by neutropenia, splenomegaly, and rheumatoid arthritis. Sometimes the differential diagnosis between Felty syndrome and large granular lymphocyte leukemia is problematic. Recently, somatic STAT3 and STAT5B mutations were discovered in 30-40% of patients with large granular lymphocyte leukemia. We now aimed to study whether these mutations can also be detected in Felty syndrome, which would imply for a common pathogenic mechanism between these two disease entities. We collected samples and clinical information from 14 Felty syndrome patients who were monitored at the rheumatology outpatient clinic for Felty syndrome. Somatic STAT3 mutations were discovered in 43% (6/14) of Felty syndrome patients with deep amplicon sequencing targeting all STAT3 exons. Mutations were located in the SH2 domain of STAT3 which is a known mutational hotspot. No STAT5B mutations were found. In blood smears, overrepresentation of large granular lymphocytes was observed, and in the majority of cases the CD8+ T-cell receptor repertoire was skewed when analyzed by flow cytometry. In bone marrow biopsies, an increased amount of phospho-STAT3 positive cells was discovered. Plasma cytokine profiling showed that 10 of the 92 assayed cytokines were elevated both in Felty syndrome and large granular lymphocyte leukemia, and three of these cytokines were also increased in patients with uncomplicated rheumatoid arthritis. In conclusion, somatic STAT3 mutations and STAT3 activation are as frequent in Felty syndrome as in large granular lymphocyte leukemia. Considering that the symptoms and treatment modalities are also similar, unified re-classification of these two syndromes is warranted.