Paroxysmal nocturnal hemoglobinuria-related thrombosis in the era of novel therapies: a 2043 patient/years analysis

Thrombophilia is one of the principal features of paroxysmal nocturnal hemoglobinuria (PNH) and constitutes the main cause of disease morbidity/mortality. Anti-complement treatment has revolutionized the natural history of PNH with control of the hemolytic process and abolition of thrombotic events (TE). However, no guidelines exist for the management of thromboembolic complications in this setting, with type and duration of anti-coagulation depending on individual practices. Besides, a scarcity of data is present on the efficacy of direct oral anti-coagulants (DOACs). Herein, we accrued a large real-world cohort of PNH patients from four US centers to explore features, predictors of TE and anti-coagulation strategies. Among 267 patients followed-up for a total of 2043 patient/years, 56 (21%) developed TE. This occurred at disease onset in 43% of cases, involving more frequently the venous system, typically as Budd-Chiari syndrome. Rate of TE was halved in patients receiving complement inhibitors (21 vs 40 TE per 1000 patient/years in untreated cases, with a 2-year cumulative incidence of thrombosis of 3.9% vs 18.3% respectively), and varied according to PNH granulocytes and erythrocytes clone size, type, disease activity parameters, as well as number (>2 mutations or less) and variant allelic frequency of PIGA mutations. Anti-coagulation with warfarin (39%), DOACs (37%), and low-molecular-weight heparin (16%) was administered for a median of 29 months (9-61.8). No thrombotic recurrence was observed in 19 patients treated with DOACs at a median observation of 17.1 months (8.9-45) while 14 cases discontinued anti-coagulation without TE recurrence at a median time of 51.4 months (29.9-86.8).

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia

BACKGROUND

TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis.

METHODS

We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely.

RESULTS

Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases.

CONCLUSION

Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Molecular patterns identify distinct subclasses of myeloid neoplasia

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

SARS-CoV-2 Vaccination IgG Antibody Responses in Patients with Hematologic Malignancies in a Myeloid Enriched Cohort: A Single Center Observation

OBJECTIVE

Patients diagnosed with hematologic malignancies are at increased risk for severe SARS-CoV-2 infection. We evaluated the serological IgG response following two doses of the SARS-CoV-2 vaccine in patients with hematologic malignancies.

METHODS

Patients treated at UT Southwestern Medical Center with a diagnosis of a myeloid or lymphoid neoplasm were included. SARS-CoV-2 vaccination response was defined as a positive quantifiable spike IgG antibody titer.

RESULTS

Sixty patients were included in the study and 60% were diagnosed with a myeloid neoplasm. The majority (85%) of the patients with a myeloid malignancy and 50% of the patients with a lymphoid malignancy mounted a serological response after receiving two doses of the vaccine.

CONCLUSION

Vaccination should be offered irrespective of ongoing treatment or active disease. Findings require validation in a larger cohort of patients.

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Machine learning prediction for COVID-19 disease severity at hospital admission

IMPORTANCE

Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation.

OBJECTIVE

To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data.

DESIGN, SETTING, AND PARTICIPANTS

We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods.

MAIN OUTCOMES AND MEASURES

Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation.

RESULTS

This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html .

CONCLUSIONS AND RELEVANCE

In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Immature Platelet Fraction as a Biomarker for Disease Severity in Pediatric Respiratory Coronavirus Disease 2019

In this retrospective single-institution cohort study of 113 hospitalized pediatric patients with respiratory coronavirus disease 2019, those admitted to the intensive care unit or requiring mechanical ventilation had significantly higher immature platelet fractions than those who did not require intensive care unit-level care or ventilation. Immature platelet fraction may be an accessible biomarker for disease severity in pediatric respiratory coronavirus disease 2019.

“Ideal” parathyroid hormone in erythropoietin‐stimulating agents‐resistant anemia

Erythropoietin‐stimulating agents (ESAs) have revolutionized anemia treatment in end‐stage renal disease (ESRD), but ESA resistance is increasingly identified. Secondary hyperparathyroidism (SHP) is one cause of ESA resistance. We describe a patient with ESA‐resistant, transfusion‐dependent anemia and mild SHP with remodeling and reticulin fibrosis on bone marrow biopsy, all of which resolved with stricter SHP management. We identified 64 patients with anemia, ESRD, and bone marrow biopsy. The parathyroid hormone (PTH) range for bony remodeling was 183–16,161.9 pg/ml versus 90.8–3283 pg/ml. The PTH range for fibrotic changes was 183–2487 pg/ml versus 90.8–16,161.9 pg/ml. We found no clear PTH range predictive for bone marrow changes.

Immature platelets as a biomarker for disease severity and mortality in COVID-19 patients

COVID-19, caused by SARS-CoV-2, is a contagious life-threatening viral disease that has killed more than three million people worldwide to date. Attempts have been made to identify biomarker(s) to stratify disease severity and improve treatment and resource allocation. Patients with SARS-COV-2 infection manifest with a higher inflammatory response and platelet hyperreactivity; this raises the question of the role of thrombopoiesis in COVID-19 infection. Immature platelet fraction (IPF, %) and immature platelet counts (IPC, ×109 /l) can be used to assess thrombopoiesis. This study investigates whether the level of thrombopoiesis correlates with COVID-19 severity. A large cohort of 678 well-characterized COVID-19 patients was analyzed, including 658 (97%) hospitalized and 139 (21%) admitted to the intensive care unit (ICU). Elevated percentage IPF at presentation was predictive of length of hospitalization (P < 0·01) and ICU admission (P < 0·05). Additionally, percentage IPF at the peak was significantly higher among ICU patients than non-ICU patients (6·9 ± 5·1 vs 5·3 ± 8·4, P < 0·01) and among deceased patients than recovered patients (7·9 ± 6·3 vs 5·4 ± 7·8, P < 0·01). Furthermore, IPC at the peak was significantly higher among ICU patients than non-ICU patients (18·5 ± 16·2 vs. 13·2 ± 8·3, P < 0·05) and among patients on a ventilator than those not (22·1 ± 20·1 vs.13·4 ± 8·4, P < 0·05). Our study demonstrated that elevated initial and peak values of percentage IPF and IPC might serve as prognostic biomarkers for COVID-19 progression to severe conditions.

Thrombopoietic status of patients on haemodialysis

Thrombocytopenia is a potential dialysis-related treatment complication. Developments in bio-compatible dialyser membranes have decreased the occurrence of thrombocytopenia. We investigated whether thrombopoiesis is impaired in haemodialysis patients by measuring the thrombopoietin level and absolute immature platelet number (AIPN) in the blood of patients undergoing haemodialysis. Samples were collected from the dialysis tubing pre- and post- haemodialysis in a cohort of 45 well-characterized haemodialysis patients. Thrombopoietin levels and AIPN increased following haemodialysis, despite no change in platelet count. Observed increase in release of immature platelets from the bone marrow following haemodialysis indicates possible complement activation secondary to interaction between blood constituents and the dialysis membrane.

Functional Niche Competition Between Normal Hematopoietic Stem and Progenitor Cells and Myeloid Leukemia Cells

Hematopoietic stem and progenitor cells (HSPCs) reside in a specialized niche that regulates their proliferative capacity and their fate. There is increasing evidence for similar roles of marrow niches on controlling the behavior of leukemic cells; however, whether normal hematopoietic stem cell (HSC) and leukemic cells reside in or functionally compete for the same marrow niche is unclear. We used the mixed lineage leukemia-AF9 (MLL-AF9) murine acute myeloid leukemia (AML) in a competitive repopulation model to investigate whether normal HSPC and leukemic cells functionally compete for the same marrow niches. Irradiated recipient mice were transplanted with fixed numbers of MLL-AF9 cells mixed with increasing doses of normal syngeneic whole bone marrow (WBM) or with purified HSPC (LSK). Survival was significantly increased and leukemic progression was delayed proportional to increasing doses of normal WBM or normal LSK cells in multiple independent experiments, with all doses of WBM or LSK cells studied above the threshold for rapid and complete hematopoietic reconstitution in the absence of leukemia. Confocal microscopy demonstrated nests of either leukemic cells or normal hematopoietic cells but not both in the marrow adjacent to endosteum. Early following transplantation, leukemic cells from animals receiving lower LSK doses were cycling more actively than in those receiving higher doses. These results suggest that normal HSPC and AML cells compete for the same functional niche. Manipulation of the niche could impact on response to antileukemic therapies, and the numbers of normal HSPC could impact on leukemia outcome, informing approaches to cell dose in the context of stem cell transplantation.

Active thrombopoiesis is associated with worse severity and activity of chronic GVHD

Chronic GVHD (cGVHD) is a major complication of allogeneic hematopoietic SCT. Post transplant thrombocytopenia in patients with cGVHD has been associated with poor outcome and its etiology is unclear. We investigated whether thrombopoiesis, assessed via measurement of the absolute immature platelet number (AIPN) in the blood, is impaired in cGVHD, and whether the level of thrombopoiesis correlates with the severity and activity of cGVHD as assessed via the National Institutes of Health (NIH) organ scoring system. We used a cohort of 110 well-characterized cGVHD patients, including 83 (75%) with severe cGVHD per NIH global score. Higher AIPN was associated with active therapeutic intent (P=0.026), lower Karnofsky score (P=0.0013), worse joint/fascia cGVHD (P=0.0005) and worse skin cGVHD (P=0.0044). AIPN correlated with platelet counts and was not correlated with ANC, WBC, C-reactive protein (CRP), absolute lymphocyte count (ALC), albumin, total and average NIH scores, or number of prior systemic therapies. AIPN values for cGVHD patients substantially overlapped those of the normal population. Higher AIPN, as marker of active thrombopoiesisis, was associated with worse severity and activity of cGVHD, especially skin and joints/fascia manifestations. Among patients with stable moderate or severe cGVHD, there was no evidence of hypoproduction of platelets. Future studies should further investigate the role of thrombopoiesis in cGVHD.

Measurement of the absolute immature platelet number reflects marrow production and is not impacted by platelet transfusion

BACKGROUND

The ability to distinguish increased platelet (PLT) destruction from PLT hypoproduction is important in the care of patients with marrow failure syndromes and patients receiving high-dose chemotherapy. The measurement of immature circulating PLTs based on RNA content using an automated counter is now feasible. This study evaluated the impact of recent PLT transfusion on measurement of immature PLT variables.

STUDY DESIGN AND METHODS

The immature PLT fraction (IPF) and absolute immature PLT number (AIPN) were measured using a hematology analyzer before and after PLT transfusion in nine transfusion-dependent patients with marrow failure secondary to aplastic anemia, myelodysplasia, or transplantation conditioning. IPF and AIPN were also measured serially over 5 days of storage in three plateletpheresis components collected from normal donors.

RESULTS

PLT transfusion did not significantly change the mean AIPN in transfused patients. In contrast, IPF decreased significantly from 6.6 ± 4.6% on Day -1 to 2.3 ± 1.4% on Day 0 before returning to 4.3 ± 2.3% on Day +1. In the PLT component, AIPN and IPF% increased significantly over 5 days of storage, most likely due to an artifact of the staining and detection process for stored PLTs, no longer detected in vivo once the PLTs were transfused.

CONCLUSION

PLT transfusion decreases the IPF due to the resultant increase in circulating PLT count. However, PLT transfusion does not change the circulating AIPN, validating this assay as a reflection of ongoing PLT production by the marrow in various clinical settings, regardless of proximity to PLT transfusion.