Viswanatha, David S.

Here we review the ‘what and how’ of molecular techniques used in the context of haematopathological diagnostics of both lymphoid and myeloid neoplasms. Keeping in mind that the required resources for molecular testing are not universally available, we will not only discuss novel and emerging techniques that allow more high‐throughput and sophisticated analyses of lymphoid and myeloid neoplasms, but also the more classical, low‐cost alternatives and even some workarounds for molecular testing approaches. In this review we also address other key aspects around molecular techniques for haematopatholgy diagnostics, including preanalytics, data interpretation, and data management, bioinformatics, and interlaboratory precision and performance evaluation.

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores <50 are classified as non-contaminated and scores ≥50 require further investigation. Results The dataset was comprised of 10,990 samples. A total of 10,232 patient cases (93.1%) had non-contaminated MICon scores <50. Control replicates accounted for 261 samples (2.4%). There were 497 patient cases (4.5%) with MICon scores ≥50. Of the 497 patient cases, 137 (27.6%) had undergone hematopoietic stem cell transplantation (HSCT) and 114 (22.9%) contained gross chromosomal abnormalities, representing iatrogenic or tumor biological factors. An additional 135 (27.2%) of cases were contaminated from laboratory processing errors, 45 (9.1%) occurred due to chemistry failures during run processing, and 66 (13.2%) had an undetermined cause for a high score, representing laboratory-related and unknown factors. Overall, the 135 laboratory processing errors detected accounted for 1.3% of patient cases analyzed by the laboratory. These cases were repeated, and the non-contaminated results were accurately reported. Conclusions Herein it was shown the implementation of MICon has improved patient safety over the course of a year. The 1.3% of patient cases identified as arising from laboratory processing errors were successfully reprocessed. While the overall rate of laboratory processing errors appears low, the absolute number can be significant in a high-volume test setting, despite reliance on highly automated processes. Value is added by MICon through identifying errors that could result in the release of inaccurate patient results. By detecting previously unrealized errors, MICon has become an invaluable asset to the NGSHM assay by enhancing patient laboratory testing safety. References: [1] Balan J, et al. MICon Contamination Detection Workflow for Next-Generation Sequencing Laboratories Using Microhaplotype Loci and Supervised Learning. J Mol Diagn. 2023;25(8):602-610. doi:10.1016/j.jmoldx.2023.05.001. [2] Kidd KK, et al. Selecting microhaplotypes optimized for different purposes. Electrophoresis. 2018;39(21):2815-2823. doi:10.1002/elps.201800092.

Abstract The ZRSR2 gene is a member of the spliceosome gene family which are frequently mutated in myeloid neoplasms. ZRSR2 mutations (ZRSR2m) occur in less than 5% of MDS, CMML, and AML. Our study included 164 ZRSR2m patients (98.8% males) and 98 ZRSR2wt MDS control cohort. In the ZRSR2m group, there were 53 MDS (32%), 39 CCUS (24%), 33 MPN (20%), 23 MDS/MPN overlap (14%), 15 AML (9%), and 1 MPAL (0.6%). Most MDS patients were the low blast subtype (n=37, 70%). Twenty-seven MDS patients (51%), and 23 CCUS patients (59%) had absolute monocyte count ≥0.5 x109/L and 18 of 23 MDS/MPN overlap were CMML (78%). Mutations in ZRSR2 were spread across the entire gene. The median number of co-mutations was 2, with TET2 (51%) and ASXL1 (32%) being the most common. U2AF1 and SRSF2, previously described as mutually exclusive with ZRSR2, were found in 10.4% of patients. Median overall survival (OS) was 51 months, and significantly varied among MNs (p=0.004). ZRSR2m MDS patients had better mOS than the MDS control cohort with ZRSR2wt (35 vs 22 months, p=0.013). ZRSR2m patients with isolated TET2 co-mutation and higher hemoglobin showed improved survival, while patients with RUNX1m, higher WBC count showed worse OS.

Abstract Heterozygous germline variants in DDX41 are identified in ~5% of patients with Myelodysplastic Syndrome (MDS) or Acute Myeloid Leukemia (AML). Recognition of the hereditary nature of such cancers and a better understanding of the factors that affect progression to malignancy has deep implications for health surveillance of patients and relatives that share a pathogenic DNA variant. DDX41 has a role in ribosome biogenesis and in opposing R-loop accumulation at promoters, but the mechanisms through which DDX41 loss drives cancer development are poorly understood. In a collaboration between Cambridge, UK and Mayo Clinic, USA we identified 85 patients with germline DDX41 variants [73/85 with germline pathogenic variants (GPV) and 12 with variants of uncertain significance (VUS)]. The majority were identified through targeted myeloid NGS upon presentation with AML (30/85) or MDS (44/85). Average age at diagnosis was 65 (35-93) with male predominance (57/85). The most common GPV was the NM_016222.4(DDX41):c.415_418dup; p.D140Gfs*2 (15/85). The majority (53.4% 39/73), also acquired a somatic variants in the other DDX41 allele (referred to as GPV+Som). The most common somatic variant (82% 32/39) was the R525H, a variant predicted to reduce the ATPase function of the helicase domain. We hypothesise that the markedly reduced DDX41 activity in cells with biallelic variants, is a key cause of progression to overt malignancy. In our cohort, we observed that somatic variants occurred at a similar frequency in association with start loss, truncating, splice, in-frame deletion, and missense GPV. However, whereas MDS was the predominant diagnosis within GPV (67.6% 23/34, AML 26.4% 9/34), AML was the most frequent diagnosis within GPV+Som (AML 51%.2 20/39, MDS 38.4% 15/39). Overall, there was a significantly higher likelihood of AML in the presence of a somatic variant (p<0.05), suggesting that there might be a dosage effect on the phenotype. This pattern was observed across all the GPV, with the exception of missense, suggesting that many might be benign polymorphysms. In addition, although somatic variants are typically subclonal (overall mean variant allele fraction VAF=9.87%, 3-27.8%), we observed a higher somatic VAF in AML over MDS cases (12.4% vs 7.7% respectively p<0.05). A lower bone marrow cellularity was also characteristic of GPV+Som vs GPV alone (mean observed/expected cellularity for age 0.9 vs 1.32 respectively p<0.05). In two cases, serial bone marrow morphology and NGS data were available, and we observed marked reduction in bone marrow cellularity upon acquisition of the R525H clone. The mechanisms of AML evolution appear different in DDX41 HMMS compared to sporadic cases. Interestingly, over half of our patients (21/34 GPV and 23/39 GPV+Som) did not exhibit additional somatic drivers. Our work suggests that acquisition of a somatic variant is a critical event in disease progression. Furthermore, the near universally subclonal nature of somatic variants suggest that they may affect disease phenotype through non-cell autonomous mechanisms. Citation Format: Ludovica Marando, Talha Badar, Yael Kusne, Audrey Morris, Terra Lasho, Abhishek Mangaonkar, Christy Finke, Carles Crawley, James M. Foran, Aref Al-Kali, Andrew King, Hassan Alkhateeb, Naseema Gangat, Chi Wong, Rong He, David Viswanatha, Faisal Basheer, Mark Litzow, Alejandro Ferrer, George Vassiliou, Mrinal Patnaik. Investigating the role of the somatic DDX41 variant in the context of DDX41 hereditary myeloid malignancy syndromes (HMMS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A037.

Abstract Objectives VEXAS syndrome is an adult-onset autoinflammatory disease caused by a somatic pathogenic mutation in the UBA1 (ubiquitin-like modifier activating enzyme 1) gene. Patients present with rheumatologic manifestations and cytopenias and may have an increased predisposition to myelodysplastic syndrome (MDS) and plasma cell neoplasms. Prior studies have reported on the peripheral blood and bone marrow findings in patients with VEXAS syndrome. Due to the protean clinical presentation and lack of specificity of morphologic features (eg, vacuoles in early erythroid and granulocytic precursors), an optimal screening methodology to identify these patients in a timely fashion is desirable. Methods To further evaluate and describe the salient diagnostic morphologic features in VEXAS syndrome, we carried out a comprehensive study of the largest single-institution cohort to date. Diagnostic and follow-up bone marrow biopsy specimens from 52 male patients with molecularly identified VEXAS syndrome underwent central review. Results Cytopenias were common in all cases, primarily macrocytic anemia, monocytopenia, and thrombocytopenia. Bone marrow aspirate and biopsy were often hypercellular, with an increased myeloid/erythroid ratio, granulocytic hyperplasia with left shift, erythroid left shift, and megakaryocyte hyperplasia, which exhibited a range of striking morphologic findings. Distinctly vacuolated myeloid and erythroid precursors were seen in more than 95% of cases. Conclusions Our data reveal potential novel diagnostic features, such as a high incidence of monocytopenia and distinct patterns of atypical megakaryopoiesis, that appear different from dysmegakaryopoiesis typically associated with MDS. In our experience, those findings are suggestive of VEXAS, in the appropriate clinical context.

INTRODUCTION: The Stromal Antigen 2 ( STAG2), on Xq25, is the most common cohesin complex gene mutated in myeloid neoplasms (MN) (5-10%). STAG2 mutations ( STAG2m) were incorporated in the ELN2022 as AML-MDS defining genetic mutations inferring poor prognosis and is a genetic marker in IPSS-M risk stratification. The PHD-finger protein 6 ( PHF6) gene, on Xq26.3, is a tumor suppressor gene, mutated in 3% of MN, and is more common in T-ALL. PHF6m is believed to be a late event in MN with little data on its prognostic implications in MN. Both STAG2m and PHF6m are more prevalent in males, but their implications on the clinical presentation of MN have not been compared prior. METHOD: Our study is a multicenter study at Mayo Clinic (Rochester, Florida, Arizona). After IRB approval, we retrospectively analyzed charts of 7935 patients who had NGS between 2016-2022. STAG2m was found in 96 patients, and PHF6m in 116 (including 3 with concurrent STAG2m and PHF6m). We excluded 4 PHF6m patients who had T-ALL. Data was captured at time of NGS. MDS risk stratification was by IPSS-M and AML by ELN2022. Overall survival (OS) was calculated from date of NGS to date of last follow-up. BlueSky Software was used for statistical analysis. RESULTS: Characteristics: We compared 96 STAG2m vs 112 PHF6m MN patients. Of STAG2m cases, 78% were male vs 76% in PHF6m MN. Median age was 72 vs 73 years, respectively. Diagnoses included AML (29% of STAG2m vs 34% of PHF6m MN), MDS (54% vs 28%), MDS/MPN (10% vs 21%) and MPN (3% vs 9%). MDS was more common among STAG2m MN (p<.001) while MDS/MPN was more common among PHF6m MN (=.05). MDS-IB was the most common subtype in both (69% vs 52% in STAG2m vs PHF6m MDS). MDS-LB and 5q subtypes were only found in PHF6m MDS, and there was more PHF6m MDS-RS (13% vs 2%). Median IPSS-M score was 0.9 vs 0.8 in STAG2m vs PHF6m MDS (both high-risk). Majority of STAG2m AML were adverse risk (96%) while PHF6m AML were intermediate (79%) (p<.001). AML cases included 43% vs 30% secondary AML (sAML) in STAG2m vs PHF6m AML. Among STAG2m and PHF6m MN, 17% of each were tMN. Cytogenetic abnormalities were more common in PHF6m MN (50% vs 31%, p=.005). The median bone marrow (BM) blasts count among STAG2m MN was 7% vs 4% in PHF6m MN (p=.03). Genetic characteristics: STAG2m were nonsense, frameshift and splice site (54%, 36% and 11%). PHF6m were nonsense, frameshift, splice site, missense and start loss variant mutations (44%, 33%, 12%, 10% and 1%). The median variant allele frequency (VAF) of STAG2m MN was 50% compared to 32% in PHF6m MN (p=.008). Co-mutations: The median number of co-mutations was 3 in STAG2m and 4 in PHF6m MN (p=.04). The most common were TET2m (37% in STAG2m MN vs 45% in PHF6m MN), RUNX1m (31% in both), SRSF2m (39% vs 20%), ASXL1m (66% vs 38%), BCORm (19% vs 6%), U2AF1m (13% vs 14%) and IDH2m (18% vs 7%). ASXL1m, SRSF2m, BCORm, IDH2m and SF3B1m were more common among STAG2m MN (p=<.001, .003, .006, .02, .09). JAK2m, IDH1m are more common among PHF6m MN (p=.02, .06). Survival: Of STAG2m and PHF6m MN, 70% and 72% of patients received treatment. HSCT was performed in 23 (24%) and 23 (21%) of STAG2m and PHF6m patients, including AML (n=9, n=10), MDS (n=12, n=8), MDS/MPN (n=2, n=2). OS among STAG2m and PHF6m MN was 20.5 and 22.1 months, respectively. OS among AML cases was 13.5 vs 21 months (p=.6) in STAG2m vs PHF6m, was NR vs 22 months (p=.4) in MDS, 11.7 vs 17.6 months in MDS/MPN (p=.4), and 10.4 vs 27.4 months (p=.5) in MPN. There was no difference between males and females in either STAG2m or PHF6m MN. HSCT led to longer OS among both STAG2m (NR vs 19.9 months, p=.01) and PHF6m MN patients (NR vs 17.6 months, p=.07). Among AML patients, HSCT did not significantly impact OS (18 vs 9.6 months, p=.3 and 25.1 vs 21 months, p=.2, in STAG2m and PHF6m). HSCT improved OS in STAG2m MDS (NR vs 20.5 months, p=.03). In PHF6m MDS, OS was 22.1 vs 13.1 months in HSCT vs non-HSCT (p=.3). CONCLUSION: STAG2m and PHF6m are 2 X-linked mutations that occur in MN. Both commonly occur in elderly males. MDS was the most common diagnosis in STAG2m MN. STAG2m co-occurred particularly with SRSF2m ASXL1m and BCORm, which are associated with poor prognosis. In our cohort, STAG2m AML carried a poorer prognosis than PHF6m AML. Our study confirms the ELN2022 AML risk stratification. MDS risk classification and OS did not differ between STAG2m and PHF6m in our cohort. A larger cohort is required to confirm our findings and may provide stronger validation.

Introduction: Germline DEAD-box RNA helicase-1 mutant (DDX41) predisposition syndrome ( DDX41-MT-GPS) is one of the most frequent hereditary predisposition syndromes in adults with myeloid neoplasms (MN-2-5%). Somatic mosaicism involving the unmutated DDX41 allele (e.g., R525H) as a potential mechanism for myeloid evolution has been reported in 39-50% of patients (pts). In addition, given the later age of onset, somatic mosaic states composed of age-related clonal hematopoiesis (ARCH) (e.g., DNMT3A, ASXL1, TET2, and TP53), with reported frequencies less than expected in MDS and AML, have been documented. We conducted this study to define the somatic mosaic landscape in DDX41-MT-GPS . Methods: After approval from the Mayo Clinic IRB the prospective GPS database was queried for pts with DDX41-MT GPS. Germline confirmation was conducted on skin fibroblast-, or hair follicle-derived DNA. Somatic CH testing was conducted using a targeted NGS panel as previously described. Given the high frequency of variants of undetermined significance (VUS), comparisons were made between pathogenic ( DDX41path) and DDX41VUS. Results: We identified 106 pts with DDX41-MT-GPS, of which 83 (78%) met criteria for MN, 16 (15%) for CH/CCUS (clonal cytopenias of undetermined significance), while 7 (6%) were unaffected carriers. Somatic mosaicism in DDX41-GPS patients without MN Sixteen DDX41-MT pts without MN met criteria for CH/CCUS ( DDX41path [n=4] and DDX41VUS [n=12]; median age 69 years (yrs) (range [R], 19-78) ( DDX41path [69 yrs] and DDX41VUS [67 yrs], p=0.85), with a male predilection (75%). Four (25%) pts had somatic DDX41 MT (R525H [n=3] and A376T [n=1]) with median variant allele frequency (VAF) 10.5% (R, 5-27); 2 (50%) pts with germline DDX41path had R525H (VAF; 5% and 13%) and 1 (8%) pt each with germline DDX41VUS had R525H (VAF 5%) and A376T (VAF 27%) somatic variants, respectively. Abnormal cytogenetics (CG) were observed in 2/13 evaluable (15%) pts ([+8 and del20q]) in the germline DDX41path group only (p= 0.05). ARCH co-mutations were observed in 11 (69%) pts, not significantly (NS) different between DDX41path (75%) and DDX41VUS (75%), p= 0.66. Individual co-mutations: DNMT3A (25% vs 16%, p= > 0.99), ASXL1 (25% vs 8%, p=0.71), TET2 (25% vs 8%, p= >0.99), and JAK2 (0% vs 8%, p= >0.99) were NS different between the two groups. Somatic mosaicism in DDX41-GPS patients with MN Eight-three pts met criteria for MN ( DDX41path [n=43] and DDX41VUS [n=40]; median age 67 yrs (range [R], 27-92), with a male predilection (58%). Fifty-four (65%) pts had MDS ( DDX41path [55%] and DDX41VUS [45%], p=0.55) and 29 (35%) had AML ( DDX41path [41%] and DDX41VUS [59%], p=0.65). Overall, 15 (18%) pts had somatic DDX41 MT; 12 (80%) pts with DDX41path and 3 (20%) pts with DDX41VUS (p= 0.003). The median VAF of somatic DDX41 MT was 12% (R, 5-37), NS different between DDX41path (10.5%) and DDX41VUS (12%) (p=0.08). The most common somatic DDX41 MT was R525H (66% [n=10]), significantly associated with DDX41path (9/10 [90%]), p=0.01. ARCH was observed in 17 (20%) pts, significantly higher in DDX41path (76%) compared to DDX41VUS (24%), p= 0.03. Abnormal CG were observed in 10/73 evaluable (14%) pts (-Y [n=2], del 20 [n=3], del 5q [n=2], del 7q [n=1], t (3;8) [n=1] and complex CG [n=1]), higher in DDX41path (70%) compared to DDX41VUS (30%) with p value >0.99. We compared the spectrum of CH between DDX41 MT CH/CCUS and MN pts ( Table 1) and found no difference in the proportion of pts with somatic DDX41 MT (p= 0.52) and ARCH/myeloid MT (p= 0.56). The individual occurrences of DNMT3A (p= 0.49), ASXL1 (p= 0.71), TET2 (p=0.59), JAK2 (p>0.99) and TP53 (p= 0.56) mutations were NS different between the two groups ( Figure 1). Conclusion: We define the somatic mosaic landscape in DDX41-MT GPS and demonstrate that somatic mutations involving the other allele, especially R525H, are seen in ~20% of pts (>80% of R525H present in MN). The frequency and composition of ARCH and myeloid driver mutations was lower than expected for the age of presentation, in comparison to de novo and secondary MDS/AML and occurred more frequently in DDX41path compared to DDX41VUS. Forty-two % (n=41), including 37% with MN, did not have somatic mosaicism, underscoring the fact that mechanisms of leukemia progression remain to be defined in subsets of affected pts.