He, Rong

Pulmonary infections are commonly observed in hematologic malignancy (HM) patients.For HM patients with low immune functionality and high susceptibility to pulmonary infections, timely identification of pathogens and administration of correct medications can greatly improve patient outcomes[1, 2].The next generation sequencing can achieve an early diagnosis of respiratory infection.To date, the systematic evaluation of the clinical use of mNGS and tNGS for pneumonia in HM patients has been insufficient. A retrospective analysis was conducted on HM patients with suspected pneumonia admitted to the Hematology Department of The Third Xiangya Hospital between April 2019 and April 2024.A total of 234 patients were identified for analysis,including 126 patients used mNGS to detect bronchoalveolar lavage fluid(BALF）and 108 patients used mNGS to detect BALF.The purpose was to compare the diagnostic efficacy of mNGS and tNGS in pulmonary infection pathogens. The overall microbial detection rate of mNGS and tNGS were 88.9% (112/126) and 93.5% (101/108), respectively. And there was no significant difference between them ( p = 0.217).Viruses,bacteria and fungi accounted for 42.2%(117/277), 33.9% (94/277) and 23.8% (66/277) among the microorganisms detected in mNGS, respectively. Meanwhile,Viruses,bacteria and fungi accounted for 61.0%(147/241), 21.2% (51/241) and 17.8% (43/241) among the microorganisms detected in mNGS.In order to compare the diagnostic and concordance between mNGS and tNGS.We used the final clinical diagnosis as the “gold standard”.The sensitivity, specificity, positive predictive value and accuracy of mNGS were 92.7%,37.5%,91.1% and 85.7%, respectively, compared with 97.9%,45.5%,94.1% and 92.6% for tNGS.Further more,we compared the clinical impact of mNGS and tNGS on antibiotic usage.The mNGS results led to adjustments in antibiotic usage for 63 out of the 126 patients(56.3%). Following these alterations, 56 patients (88.9%) reported significant improvements.In tNGS,there are 59 out of the 108 patients(58.4%%) adjustmented in antibiotic usage and 56 patients(94.9%) reported significant improvements.The total death of mNGS and tNGS were 9.8%(11/126) and 4.0%（4/108), respectively. There were no significant differences between the two groups (P ＞0.05).Compared to the microbial range detected by tNGS, mNGS detected an additional 14 pathogenic microorganisms, in which had a very low proportion of pathogenic microorganisms (3.6%,4/112), which did not have any adverse effects on the clinical outcomes of patients. Collectively,we found that tNGS is comparable with mNGS in HM patients with suspected pneumonia for pathogenic microorganism detection.What's more,the ability of tNGS in guiding treatment was consistent with those of mNGS.

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.

AbstractThe current study was inspired by observations from exploratory analyses of an institutional cohort with chronic myelomonocytic leukemia (CMML; N = 398) that revealed no instances of blast transformation in the seven patients with plant homeodomain finger protein 6 (PHF6) mutation (PHF6MUT). A subsequent Mayo Clinic enterprise‐wide database search identified 28 more cases with PHF6MUT. Compared with their wild‐type PHF6 counterparts (PHF6WT; N = 391), PHF6MUT cases (N = 35) were more likely to co‐express TET2 (89% vs. 45%; p < .01), RUNX1 (29% vs. 14%; p = .03), CBL (14% vs. 2%; p < .01), and U2AF1 (17% vs. 6%; p = .04) and less likely SRSF2 (23% vs. 45%; p < .01) mutation. They were also more likely to display loss of Y chromosome (LoY; 21% vs. 2%; p < .01) and platelets <100 × 109/L (83% vs. 51%; p < .01). Multivariable analysis identified PHF6MUT (HR 0.28, 95% CI 0.15–0.50) and DNMT3AMUT (HR 5.8, 95% CI 3.3–10.5) as the strongest molecular predictors of overall survival. The same was true for blast transformation‐free survival with corresponding HR (95% CI) of 0.08 (0.01–0.6) and 9.5 (3.8–23.5). At median 20 months follow‐up, blast transformation was documented in none of the 33 patients with PHF6MUT/DNMT3AWT but in 6 (32%) of 19 with DNMT3AMUT and 74 (20%) of 374 with PHF6WT/DNMT3AWT (p < .01). The specific molecular signatures sustained their significant predictive performance in the context of the CMML‐specific molecular prognostic model (CPSS‐mol). PHF6MUT identifies a unique subset of patients with CMML characterized by thrombocytopenia, higher prevalence of LoY, and superior prognosis.

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.

7050 Background: The management of Richter transformation (RT) of CLL represents an unmet clinical need. We previously reported modest efficacy of pembrolizumab monotherapy in an initial cohort of patients with RT (Ding, et al, Blood 2017). Here we report updated results of this trial, including an expansion cohort and a combination therapy cohort. Methods: Patients with biopsy proven RT to diffuse large B-cell lymphoma were enrolled and treated with single agent pembrolizumab (200 mg IV Q3W). Patients with progressive disease (PD) or stable disease (SD) after 3 months of pembrolizumab monotherapy were allowed to add a B-cell receptor (BCR) kinase inhibitor. The primary end point was overall response rate (ORR). Results: A total of 26 patients with RT were enrolled (9 in initial cohort and 17 in expansion cohort). The median age was 68.5 years. Ten (38%) patients had del(17p) or TP53 mutation. The median number of prior lines of therapy was 3 (range 1-10). Fifteen (58%) patients had received ibrutinib previously (14 of whom had PD on ibrutinib). With pembrolizumab monotherapy (n=26), the ORR was 23.1%, with 2 (7.7%) complete response (CR), 4 (15.4%) partial response (PR), and 7 (26.9%) SD. Of the 6 patients with confirmed CR or PR, responses occurred after a median of 2 cycles of monotherapy. The median duration of response (DOR) was 3.2 months (95% CI 2.8-NA). The median progression-free survival (PFS) was 2.6 months (95% CI 1.6-3.6). Sixteen patients went on to receive a BCR kinase inhibitor (ibrutinib [n=15], idelalisib [n=1]) in combination with pembrolizumab, after SD or PD on pembrolizumab monotherapy. The ORR to combination therapy was 62.5%, with 4 (25.0%) CR and 6 (37.5%) PR. Of the 10 patients with confirmed CR or PR to combination therapy, responses occurred after a median of 4 cycles of therapy. The median DOR was 4.5 months (95% CI 1.6-NA). The median PFS was 7.6 months (95% CI 2.3-13.3). After a median follow-up of 50.4 (95% CI 34.0-56.0) months, the median overall survival (OS) for all 26 patients with RT was 11.6 months (95% CI 7.5-20.1), and the 2-year OS rate was 27%. Treatment-related adverse events (TRAEs) of any grade occurred in 24 (92%) patients, including anemia (54%), thrombocytopenia (58%), neutropenia (54%), leukopenia (35%), lymphopenia (23%), dyspnea (23%), diarrhea (19%), and nausea (23%). Treatment-related grade 3 or above AEs occurred in 21 (81%) patients, including neutropenia (38%), leukopenia (31%), thrombocytopenia (27%), anemia (15%), febrile neutropenia (12%), and lung infection (12%). Conclusions: This expanded analysis confirmed that pembrolizumab has modest single agent activity in RT. Combination therapy with pembrolizumab and a BCR kinase inhibitor is associated with increased efficacy (ORR >60%), similar to other reports. These results support further investigation of immune checkpoint inhibitor-based combination therapy in RT. Clinical trial information: NCT02332980 .