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The BCL2 inhibitor venetoclax (VEN) in combination with azacitidine (5-AZA) is currently transforming acute myeloid leukemia (AML) therapy. However, there is a lack of clinically relevant biomarkers that predict response to 5-AZA/VEN. Here, we integrated transcriptomic, proteomic, functional, and clinical data to identify predictors of 5-AZA/VEN response. Although cultured monocytic AML cells displayed upfront resistance, monocytic differentiation was not clinically predictive in our patient cohort. We identified leukemic stem cells (LSC) as primary targets of 5-AZA/VEN whose elimination determined the therapy outcome. LSCs of 5-AZA/VEN-refractory patients displayed perturbed apoptotic dependencies. We developed and validated a flow cytometry-based “Mediators of apoptosis combinatorial score” (MAC-Score) linking the ratio of protein expression of BCL2, BCL-xL, and MCL1 in LSCs. MAC scoring predicts initial response with a positive predictive value of more than 97% associated with increased event-free survival. In summary, combinatorial levels of BCL2 family members in AML-LSCs are a key denominator of response, and MAC scoring reliably predicts patient response to 5-AZA/VEN. Significance: Venetoclax/azacitidine treatment has become an alternative to standard chemotherapy for patients with AML. However, prediction of response to treatment is hampered by the lack of clinically useful biomarkers. Here, we present easy-to-implement MAC scoring in LSCs as a novel strategy to predict treatment response and facilitate clinical decision-making.

FLT3 tyrosine kinase inhibitor (TKI) therapy evolved into a standard therapy in FLT3-mutated AML. TKI resistance, however, develops frequently with poor outcomes. We analyzed acquired TKI resistance in AML cell lines by multilayered proteome analyses. Leupaxin (LPXN), a regulator of cell migration and adhesion, was induced during early resistance development, alongside the tyrosine kinase PTK2B which phosphorylated LPXN. Resistant cells differed in cell adhesion and migration, indicating altered niche interactions. PTK2B and LPXN were highly expressed in leukemic stem cells in FLT3-ITD patients. PTK2B/FAK inhibition abrogated resistance-associated phenotypes, such as enhanced cell migration. Altered pathways in resistant cells, assessed by nascent proteomics, were largely reverted upon PTK2B/FAK inhibition. PTK2B/FAK inhibitors PF-431396 and defactinib synergized with different TKIs or daunorubicin in FLT3-mutated AML. Midostaurin-resistant and AML cells co-cultured with mesenchymal stroma cells responded particularly well to PTK2B/FAK inhibitor addition. Xenograft mouse models showed significant longer time to leukemia symptom-related endpoint upon gilteritinib/defactinib combination treatment in comparison to treatment with either drug alone. Our data suggest that the leupaxin-PTK2B axis plays an important role in acquired TKI resistance in AML. PTK2B/FAK inhibitors act synergistically with currently used therapeutics and may overcome emerging TKI resistance in FLT3-mutated AML at an early timepoint.

Transcriptomic and functional characterization of LSC-like cells

Cell line sensitivity and additional patient data

Comparison of MAC-Score and BH-3 profiling based response prediction

MAC-Score predict response within mutational subgroups

The BCL2 inhibitor venetoclax (VEN) in combination with azacitidine (5-AZA) is currently transforming acute myeloid leukemia (AML) therapy. However, there is a lack of clinically relevant biomarkers that predict response to 5-AZA/VEN. Here, we integrated transcriptomic, proteomic, functional, and clinical data to identify predictors of 5-AZA/VEN response. Although cultured monocytic AML cells displayed upfront resistance, monocytic differentiation was not clinically predictive in our patient cohort. We identified leukemic stem cells (LSC) as primary targets of 5-AZA/VEN whose elimination determined the therapy outcome. LSCs of 5-AZA/VEN-refractory patients displayed perturbed apoptotic dependencies. We developed and validated a flow cytometry-based “Mediators of apoptosis combinatorial score” (MAC-Score) linking the ratio of protein expression of BCL2, BCL-xL, and MCL1 in LSCs. MAC scoring predicts initial response with a positive predictive value of more than 97% associated with increased event-free survival. In summary, combinatorial levels of BCL2 family members in AML-LSCs are a key denominator of response, and MAC scoring reliably predicts patient response to 5-AZA/VEN.Significance:Venetoclax/azacitidine treatment has become an alternative to standard chemotherapy for patients with AML. However, prediction of response to treatment is hampered by the lack of clinically useful biomarkers. Here, we present easy-to-implement MAC scoring in LSCs as a novel strategy to predict treatment response and facilitate clinical decision-making.

Subpopulation specific Bcl-2 family levels and time on HMA/Venetoclax

Subpopulation specific Bcl-2 family levels and response to HMA/Venetoclax

Bcl-2 family gene expression in LSCs predicts in vitro response to HMA/VEN

7 Tables showing: molecular and clinical patient data, cell line info, antibody panels and overview of assays used

Engraftment and differentiation of donor hematopoietic stem cells is decisive for the clinical success of allogeneic stem cell transplantation (alloSCT) and depends on the recipient's bone marrow (BM) niche. A damaged niche contributes to poor graft function post-alloSCT, but the underlying mechanisms and the role of BM multipotent mesenchymal stromal cells (MSC) are ill defined. Upon multivariate analysis in 732 individuals, we observed a reduced presence of proliferation-capable MSC in BM aspirates from patients (N=196) who had undergone alloSCT (OR 0.61, p=.028). This was confirmed by paired analysis in 30 patients showing a higher frequency of samples with a lack of MSC presence post-alloSCT compared to pre-alloSCT. This reduced MSC presence was associated with reduced survival of patients following alloSCT and specifically with impaired graft function. Post-alloSCT MSC showed diminished in vitro proliferation along with a transcriptional antiproliferative signature, upregulation of epithelial-mesenchymal transition and extracellular matrix pathways and altered impact on cytokine release upon contact with hematopoietic cells. In order to avoid in vitro culture bias, we isolated the CD146+/CD45-/HLA-DR- BM cell fraction, which comprised the entire MSC population. The post-alloSCT isolated native CD146+MSC showed a similar reduction in proliferation capacity and shared the same antiproliferative transcriptomic signature as for post-alloSCT CFU-F-derived MSC. Taken together, our data show that alloSCT confers damage to the proliferative capacity of native MSC, which is associated with reduced patient survival after alloSCT and impaired engraftment of allogeneic hematopoiesis. These data represent the basis to elucidate mechanisms of BM niche reconstitution post-alloSCT and its therapeutic manipulation.

Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML., Cell Stem Cell, 30 (5), ISSN:1934-5909, ISSN:1875-9777

Patients with cancer are at high risk of severe coronavirus disease 2019 (COVID-19), with high morbidity and mortality. Furthermore, impaired humoral response renders severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines less effective and treatment options are scarce. Randomized trials using convalescent plasma are missing for high-risk patients. Here, we performed a randomized, open-label, multicenter trial (https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001632-10/DE) in hospitalized patients with severe COVID-19 (n = 134) within four risk groups ((1) cancer (n = 56); (2) immunosuppression (n = 16); (3) laboratory-based risk factors (n = 36); and (4) advanced age (n = 26)) randomized to standard of care (control arm) or standard of care plus convalescent/vaccinated anti-SARS-CoV-2 plasma (plasma arm). No serious adverse events were observed related to the plasma treatment. Clinical improvement as the primary outcome was assessed using a seven-point ordinal scale. Secondary outcomes were time to discharge and overall survival. For the four groups combined, those receiving plasma did not improve clinically compared with those in the control arm (hazard ratio (HR) = 1.29; P = 0.205). However, patients with cancer experienced a shortened median time to improvement (HR = 2.50; P = 0.003) and superior survival with plasma treatment versus the control arm (HR = 0.28; P = 0.042). Neutralizing antibody activity increased in the plasma cohort but not in the control cohort of patients with cancer (P = 0.001). Taken together, convalescent/vaccinated plasma may improve COVID-19 outcomes in patients with cancer who are unable to intrinsically generate an adequate immune response.

Background Patients with hematological malignancy or other cancers as well as immunosuppression bear a high risk for severe COVID-19. Monoclonal antibodies (mAb) are efficient at early stages of the disease but may lose potency with new variants. Trials on plasma from convalescent donors in unselected patients have not shown clinical benefit. No randomized trials focussing on patients with underlying disease have been published. Methods We conducted an open-label, multicenter, randomized controlled trial to evaluate efficacy of plasma (CVP - convalescent or after vaccination) in patients with COVID-19 at high risk for adverse outcome in Germany. We assessed the effect of high-titer CVP (2 units from different donors, 238-337 ml each, on subsequent days). Patients with hematological or other malignancy (group 1), immunosuppression (group 2), age >50 and ≤75 years and lymphopenia and/or high D-dimers (group 3) or age >75 years (group 4) who were hospitalized with confirmed SARS-CoV-2 infection and with an oxygen saturation ≤94% were included. Primary outcome measure was time to clinical improvement on a seven-point ordinal scale, secondary outcome was mortality (Janssen et al. Trials 2020 Oct 6;21(1):828). Results Overall, 133 patients were randomized, 68 received CVP with an additional 10 patients as a crossover on day 10. Median age (range) was 68 years (39-95) in the CVP group and 70 (38-90) in controls. For the entire cohort, no significant difference was seen in time to improvement (median days: CVP 12.5 vs. control 18; HR 1.24 (95% confidence interval (CI) 0.83-1.85), p=0.29). Subgroup analysis (group 1+2) revealed shortened time to improvement (median days CVP 13 vs. control 32; HR 2.03 (95%CI 1.17-3.6), p=0.01) and mortality was reduced (mortality CVP n=6 (18%) vs. control n=10 (29%). No significant differences in time to improvement were observed in group 3 or 4 (HR 0.72 (95%CI 0.41-1.28), p=0.26). No relevant adverse events were observed. Conclusion CVP improves time to clinical improvement and mortality for COVID-19 patients with underlying hematological disease/cancer or other reasons of impaired immune response. Even with new variants, high-titer CVP may offer a widely available and inexpensive therapy option in high-risk groups. Funding BMBF FKZ 01KI20152; EudraCT 2020-001632-10. Disclosures Uta Merle, MD, Gilead: Sponsored congress travel and accommodation Markus Weigand, MD, Bbraun: Speakers fee/ad boards fee|Biotest: Speakers fee/ad boards fee|Eumedica: Speakers fee/ad boards fee|Gilead: Speakers fee/ad boards fee|MSD: Speakers fee/ad boards fee|Pfizer: Speakers fee/ad boards fee|Shionogi: Speakers fee/ad boards fee|SOBI: Speakers fee/ad boards fee Martin Bornhäuser, MD, Alexion: Honoraria|Jazz Pharmaceuticals: Honoraria|MSD: Honoraria|Novartis: Honoraria Nael Alakel, MD, Amgen: personal fee, travel grant|Gilead: personal fee, travel grant|MSD Sharp and Dohme GmbH: personal fee, travel grant|Pfizer: personal fee, travel grant Timo Wolf, MD, Gilead Sciences: Lecture fee, travel grant|Janssen Pharmaceuticals: Lecture fee, travel grant|Merck Sharp Dome: Lecture fee, travel grant Maria Vehreschild, Prof. Dr., 3M: speaker fee|Astellas: Advisor/Consultant|Astellas: speaker fee|biologische heilmittel heel gmbh: Grant/Research Support|BioNtech: Grant/Research Support|EUMEDICA: Advisor/Consultant|Farmak International Holding: Advisor/Consultant|Ferring: Advisor/Consultant|Ferring: Speaker fee|Gilead Sciences: Advisor/Consultant|Immunic AG: Advisor/Consultant|MaaT: Advisor/Consultant|Merck: Advisor/Consultant|Merck: speaker fee|MSD: Advisor/Consultant|MSD: Grant/Research Support|MSD: speaker fees|Pfizer: speaker fee|Roche Molecular Systems: Grant/Research Support|Roche Molecular Systems: speaker fees|SocraRTec R&D GmbH: Advisor/Consultant|Takeda California: Grant/Research Support Hanns-Martin Lorenz, MD, Abbvie: Advisor/Consultant|Abbvie: Honoraria|Actelion: Advisor/Consultant|Actelion: Honoraria|Alexion: Advisor/Consultant|Alexion: Honoraria|Amgen: Advisor/Consultant|Amgen: Grant/Research Support|Astra Zeneca: Advisor/Consultant|Astra Zeneca: Honoraria|Baxter: Advisor/Consultant|Baxter: Advisor/Consultant|Baxter: Honoraria|Baxter: Honoraria|Bayer Vital: Advisor/Consultant|Bayer Vital: Honoraria|Biogen: Advisor/Consultant|Biogen: Honoraria|BMS: Advisor/Consultant|BMS: Honoraria|Boehringer Ingelheim: Advisor/Consultant|Boehringer Ingelheim: Honoraria|Celgene: Advisor/Consultant|Celgene: Honoraria|Fresenius: Advisor/Consultant|Fresenius: Honoraria|Genzyme: Advisor/Consultant|Genzyme: Honoraria|Gilead/Galapagos: Advisor/Consultant|Gilead/Galapagos: Honoraria|GSK: Advisor/Consultant|GSK: Honoraria|Hexal: Advisor/Consultant|Hexal: Honoraria|Janssen-Cilag: Advisor/Consultant|Janssen-Cilag: Honoraria|Lilly: Advisor/Consultant|Lilly: Honoraria|Medac: Advisor/Consultant|Medac: Honoraria|MSD: Advisor/Consultant|MSD: Honoraria|Mundipharm: Advisor/Consultant|Mundipharm: Honoraria|Mylan: Advisor/Consultant|Mylan: Honoraria|Novartis: Advisor/Consultant|Novartis: Honoraria|octapharm: Advisor/Consultant|octapharm: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Honoraria|Roche/Chugai: Advisor/Consultant|Roche/Chugai: Honoraria|Sandoz: Advisor/Consultant|Sandoz: Honoraria|Sanofi: Advisor/Consultant|Sanofi: Honoraria|Shire: Advisor/Consultant|Shire: Honoraria|SOBI: Advisor/Consultant|SOBI: Honoraria|Thermo Fisher: Advisor/Consultant|Thermo Fisher: Honoraria|UCB: Advisor/Consultant|UCB: Honoraria.

Zusammenfassung Die Digitalisierung findet längst auch im Gesundheitswesen und mithin in seiner herausragenden Subinstitution, dem Krankenhaus, statt. Konkrete Digitalisierungsprojekte sind dabei häufig von Mehrdeutigkeiten und Widerständen begleitet, die auf die komplexen soziotechnischen Herstellungszusammenhänge verweisen, in denen sie realisiert werden. Dieser Artikel arbeitet anhand einer empirischen Fallstudie die Ge- bzw. Misslingensbedingungen für die Integration multipler Interessen in eine digitale Überwachungsplattform heraus und geht den strukturellen Effekten des Entwicklungsprozesses für das beteiligte Universitätskrankenhaus nach. Dabei zeigt sich: Erwartungs-, Deutungs- und Konstellationsstrukturen der organisierten Krankenbehandlung können insbesondere dann verändert werden, wenn auch die mehrdimensionale Kontrolle der ökonomischen Übersetzung, die Herstellung zeitlicher Kohärenz und die Kongruenz von digitaltechnischer und sozialer Konstellationsstruktur gelingen. Diese Momente situativer Schließung lassen sich ausgehend von den empirischen Übersetzungen einer digitalen Plattform rekonstruieren und als Bedingung der Möglichkeit für strukturelle Effekte aus dem Plattformentwicklungsprozess behandeln.

Inter-patient variability and the similarity of healthy and leukemic stem cells have impeded the characterization of leukemic stem cells (LSCs) in acute myeloid leukemia (AML), and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. While residual healthy cells dominated the dormant stem cell compartment, active leukemic stem cells resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors were highly aberrant and constituted the disease-defining compartment: Their gene expression and differentiation state determined both chemotherapy response and the leukemia’s ability to differentiate to transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers mis-regulated specifically in leukemic cells by intra-patient comparisons. Taken together, CloneTracer revealed a differentiation landscape that mimics its healthy counterpart and determines biology and therapy response in AML.

This collaborative meta-analysis was supported by the Swiss National Science Foundation and the Laura and John Arnold Foundation (grant supporting the post-doctoral fellowship at the Meta-Research Innovation Center at Stanford (METRICS), Stanford University). The funders had no role in the design of this collaborative meta-analysis; in the collection, analysis, and interpretation of data; or in the report writing., We would like to express our warm gratitude to all participating patients and convalescent plasma donors. We thank Katja Suter and Sina Ullrich, University of Basel, for their administrative assistance. For their helpful contribution to individual trials, we thank Erica Wood, Iain Gosbell, Richard Charlewood, Thomas Hills, Veronica Hoad, Kristina Kairaitis, Aikaj Jindal, John Gerrard, Hong Foo, Adam Stewart, and Nanette Trask (ASCOT trial); Amalia Bravo-Lindoro, Ral Carrillo-Esper, Karla Maldonado-Silva, Catalina Casillas-Suárez, Orlando Carrillo-Torres, Sandra Murrieta, Elizabeth Diaz-Padilla, Eli Omar Zavaleta, Yadira Bejar-Ramirez, and Evelyn Cortina-de la Rosa (CPC-SARS trial); Sheri Renaud, Roel Rolando-Almario, and Jacqueline Day (NCT04385199 trial); Sandra Tingsgrd, MD, Karen Brorup Heje Pedersen, MD, Michaela Tinggaard, MD, Louise Thorlacius-Ussing, MD, Clara Lundetoft Clausen, MD, Nichlas Hovmand, MD, Simone Bastrup Israelsen, MD, Cecilie Leding, MD, Katrine Iversen, MD, Maria Engel Miller, MD, Hkon Sandholdt, MSc biostatistician (CCAP-2 trial). On behalf of the IRCT20200310046736N1 trial, we thank the Khuzestan Blood Transfusion Organization for specialized assistance in the preparation and maintenance of plasma samples. On behalf of the NCT04332835 trial, we thank all the members of the "PC-COVID-19 Group" at the Clinica del Occidente and Hospital Universitario Mayor Mederi in Bogota, and Clinica CES in Medellin. On behalf of the NCT04403477 trial, we thank Miles Carroll for his support regarding the antibody testing in Bangladesh. The Co-CLARITY team would like to extend their gratitude to the Department of Science and Technology Philippine Council for Health Research and Development and the UP-Philippine General Hospital for all their support in the setting up and conduct of the trial. For helpfully communicating details of their trial, we thank members of the PlasmAr trial, NCT04359810 trial (Max O´Donnell), NCT04468009 trial, and CTRI/2020/05/025299 trial. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute (https://www.cancer.gov/)., During the conduct of the study, the following is reported: Dr. Berry reports being employee with ownership role at Berry Consultants (receives payments for statistical modeling and design of REMAP-CAP). Dr. Castillo reports grants from DOST PCHRD. Dr. Daly reports grants from Medical Research Future Fund (Australian Govt) and RBWH. Dr. Denkinger reports grants from German Ministry for Education and Research. Dr. Dumagay reports grants from Philippine Council for Health Research and Development. Dr. Dunachie reports grants from UK Department of Health and Social Care, grants from UK National Institute of Health Research. Dr. Gauiran reports grants from Department of Science and Technology—Philippine Council for Health Research and Development. Dr. Gordon reports grants from NIHR, grants from NIHR Research Professorship (RP-2015-06-18), and non-financial support from NIHR Clinical Research Network. Dr. Higgins reports grants from NHMRC and from the Minderoo Foundation. Dr. Hills reports grants from Health Research Council of New Zealand. Dr. Holm reports grants from Swedish Government Funds for Clinical Research (ALF). Dr. Janssen and Dr. Müller-Tidow report grants from the Federal Ministry of Education and Research in Germany (BMBF) to the RECOVER clinical trial. Dr. Krapp reports grants from Department of Foreign Affairs, Trade, and Development of Canada, grants from Fundación Telefónica del Perú. Dr. J. Lim reports grants from the Department of Science and Technology, Philippine Council for Health Research and Development. Dr. Lucero reports grants from Philippine Council for Health Research and Development. Dr Manrique reports economic support from Grupo ISA Intercolombia for the project development of trial NCT04332835. Drs. McQuilten and Wood report grants from Medical Research Future Fund. Dr. McVerry reports grants from The Pittsburgh Foundation, Translational Breast Cancer Research Consortium, and from UPMC Learning While Doing Program. Mr. Mouncey reports grants from National Institute for Health Research and from the European Union FP7: PREPARE. Dr. Najdovski reports payment from KUL Leuven to Belgian Red Cross for supply of convalescent plasma. Dr. Nichol reports grants from Health Research Board of Ireland. Dr. D. Roberts reports grants from the National Institute for Health (UKRIDHSC COVID-19 Rapid Response Rolling Call—Grant Reference Number COV19-RECPLAS) and the European Commission (SUPPORT-E #101015756). Dr. Rowan reports grants from the European Commission and from the UK National Institute for Health Research. Dr. Shankar-Hari reports grants from National Institute for Health Research UK, grants from UKRI-National Institute for Health Research UK. Dr. Turgeon reports grants from Canadian Institutes of Health Research. Dr. Venkatesh reports grants from Baxter. Dr. Webb reports grants from National Health and Medical Research Council, grants from Minderoo Foundation. Dr. Zacharowski reports grants from EU Horizon 2020. The ASCOT trial team (Drs Bowen, Daly, Davis, Denholm, Hammond, Jha, L. Lim, McQuilten, Molton, Morpeth, O’Sullivan, Paterson, Perry, Price, Rees, Roberts, Rogers, Sasadeusz, Snelling, Tong, Venkatesh, Wood) is funded by grants from from Royal Brisbane and Women’s Hospital Foundation, Pratt Foundation, Minderoo Foundation, BHP Foundation, Hospital Research Foundation, Macquarie Group Foundation, Health Research Council of New Zealand, Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE), and the collection and supply of convalescent plasma was conducted within Lifeblood’s funding arrangements. The CONFIDENT trial is funded by the Belgian KCE (blood establishments received payment for the convalescent plasma supplied in the clinical trial). REMAP-CAP was supported in part by funding from UKRIDHSC COVID-19 Rapid Response Rolling Call (Grant Reference Number COV19-RECPLAS). Collection of convalescent plasma for REMAP-CAP was funded by the Department of Health and Social Care, UK. The IRCT20200310046736N1 trial was supported by the Ahvaz Jundishapur University of Medical Sciences (Grant No. R.AJUMS.REC.1399.003, Dr. Pouladzadeh). The PC-COVID-19 Group is supported by the Universidad del Rosario, IDCBIS, ISA Group and Suramericana (Colombia). Outside the submitted work, the following is reported: Dr. Axfors reports postdoctoral grants from the Knut and Alice Wallenberg Foundation, Uppsala University, the Swedish Society of Medicine, the Blanceflor Foundation, and the Sweden-America Foundation. Dr. Aomar reports personal fees from SOBI, GEDEON RICHTER, and GSK. Dr. Benfield reports grants from Novo Nordisk Foundation, Simonsen Foundation, Lundbeck Foundation, Kai Hansen Foundation, Erik and Susanna Olesen’s Charitable Fund; grants and personal fees from GSK, Pfizer, Gilead; and personal fees from Boehringer Ingelheim, MSD, and Pentabase ApS. Dr. Estcourt reports being an investigator on the RECOVERY trial. Dr. Gordon reports personal fees from GlaxoSmithKline, Bristol Myers Squibb, and 30 Respiratory. Dr. Jha reports grants and personal fees from Baxter Healthcare, personal fees from Astra Zeneca, grants from NephroPlus. Dr. Laterre reports personal fees from Adrenomed. Dr. McVerry reports grants from NIH/NHLBI and Bayer Pharmaceuticals, Inc. Dr. Mondragon reports financial activities outside the submitted work (employment at Johnson & Johnson). Dr. Perry reports partner being employed at CSL and owning shares in CSL. Dr. Paterson reports involvement with ALLIANCE trial of COVID-19 treatments. Dr. J. Roberts reports other COVID-19 related trials (in different patient groups): tocilizumab in ICU patients; hydroxychloroquine dosing in ICU patients; planned study of remdesivir pharmacokinetics in patients during expanded access program; and in silico evaluation of ivermectin dosing. Dr Sasadeusz reports grants from various Pharma companies including Gilead Sciences, Abvvie, Merck, and Takeda. Dr. Zacharowski reports personal fees from Biotest AG, CSL Behring, GE Heathcare, and is President of the ESAIC., Background: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX). Methods: In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results: A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions: Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care., Amalia Bravo-Lindoro, BHP Foundation, Blanceflor Foundation, Department of Foreign Affairs, Trade, and Development of Canada, Eli Omar Zavaleta, Erik and Susanna Olesen’s Charitable Fund, Federal Ministry of Education and Research in Germany, Fundación Telefónica del Perú, IDCBIS, ISA Group and Suramericana, Kai Hansen Foundation, Katja Suter and Sina Ullrich, Khuzestan Blood Transfusion Organization, Macquarie Group Foundation, Medical Research Future Fund, NephroPlus, RBWH, Roel Rolando-Almario NCT04385199, Royal Brisbane, Sheri Renaud, Simonsen Foundation, UKRI-National Institute for Health Research UK, UKRIDHSC COV19-RECPLAS, UP-Philippine General Hospital CTRI/2020/05/025299, NCT04359810, NCT04468009, Women’s Hospital Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, Pittsburgh Foundation, Pfizer, Baxter International, Stanford University, Gilead Sciences, National Institute on Handicapped Research RP-2015-06-18, Meso Scale Diagnostics, Universität Basel, Laura and John Arnold Foundation, Health Research Board, Pratt Foundation, Canadian Institutes of Health Research, National Institute for Health Research, Department of Health and Social Care, European Commission 101015756, National Health and Medical Research Council, Department of Science and Technology, Ministry of Science and Technology, India, Health Research Council of New Zealand, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Sweden-America Foundation, Bundesministerium für Bildung und Forschung, Lundbeckfonden, Knut och Alice Wallenbergs Stiftelse, Seventh Framework Programme, Ahvaz Jundishapur University of Medical Sciences, Université Pierre et Marie Curie, Uppsala Universitet, Horizon 2020, Svenska Läkaresällskapet, Universidad del Rosario, Pharmaceuticals Bayer, Novo Nordisk Fonden, Department of Science and Technology, Philippines, Philippine Council for Health Research and Development, Minderoo Foundation

Immunotherapies, such as chimeric antigen receptor (CAR) modified T cells and antibody-drug conjugates (ADCs), have revolutionized the treatment of cancer, especially of lymphoid malignancies. The application of targeted immunotherapy to patients with acute myeloid leukemia (AML) has been limited in particular by the lack of a tumor-specific target antigen. Gemtuzumab ozogamicin (GO), an ADC targeting CD33, is the only approved immunotherapeutic agent in AML. In our study, we introduce a CD33-directed third-generation CAR T-cell product (3G.CAR33-T) for the treatment of patients with AML. 3G.CAR33-T cells could be expanded up to the end-of-culture, that is, 17 days after transduction, and displayed significant cytokine secretion and robust cytotoxic activity when incubated with CD33-positive cells including cell lines, drug-resistant cells, primary blasts as well as normal hematopoietic stem and progenitor cells (HSPCs). When compared to second-generation CAR33-T cells, 3G.CAR33-T cells exhibited higher viability, increased proliferation and stronger cytotoxicity. Also, GO exerted strong antileukemia activity against CD33-positive AML cells. Upon genomic deletion of CD33 in HSPCs, 3G.CAR33-T cells and GO preferentially killed wildtype leukemia cells, while sparing CD33-deficient HSPCs. Our data provide evidence for the applicability of CD33-targeted immunotherapies in AML and its potential implementation in CD33 genome-edited stem cell transplantation approaches.

Somatic mutations in DNA Methyltransferase 3A (DNMT3A) are among the most frequent alterations in clonal hematopoiesis (CH) and Acute Myeloid Leukemia (AML), with a hotspot in exon 23 at arginine 882 (DNMT3A-R882). Here we demonstrate that DNMT3A-R882H-dependent CH- and AML cells are specifically susceptible to the hypomethylating agent azacytidine (AZA). Addition of AZA to chemotherapy prolonged AML survival solely in patients with DNMT3A-R882 mutation, suggesting its potential as a novel predictive marker for AZA response. AML and CH mouse models confirmed AZA susceptibility specifically in DNMT3A-R882H-expressing cells. Hematopoietic stem and progenitor cells expressing DNMT3A-R882H exhibited cell-autonomous viral mimicry response as a result of focal DNA hypomethylation at retrotransposon sequences. Administration of AZA boosted hypomethylation of retrotransposons specifically in DNMT3A-R882H expressing cells and maintained elevated levels of canonical interferon-stimulated genes (ISGs), thus leading to suppressed protein translation and increased apoptosis.

BackgroundAfter infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2), Immunoglobulin G (IgG) antibodies and virus-specific neutralizing antibodies (nAbs) develop. This study describes antibody responses in a cohort of recovered COVID-19 patients to identify predictors.MethodsWe recruited patients with confirmed SARS-CoV-2 infection from Heidelberg, Germany. Blood samples were collected three weeks after COVID-19 symptoms ended. Participants with high antibody titers were invited for follow-up visits. IgG titers were measured by the Euroimmun Assay, and nAbs titers in a SARS-CoV-2 infection-based assay.Results281 participants were enrolled between April and August 2020 with IgG testing, 145 (51.6%) had nAbs, and 35 (12.5%) had follow-up. The median IgG optical density (OD) ratio was 3.1 (Interquartile range (IQR) 1.6-5.1), and 24.1% (35/145) had a nAb titer>1:80. Higher IgG titers were associated with increased age and more severe disease, and higher nAbs were associated with male gender and CT-value of 25-30 on RT-PCR at diagnosis. The median IgG OD ratio on follow-up was 3.7 (IQR 2.9-5.9), a median increase of 0.5 (IQR −0.3-1.7). Six participants with follow-up nAbs all had titers ≤ 1:80.ConclusionsWhile age and disease severity were correlated with IgG responses, predictive factors for nAbs in convalescent patients remain unclear.

Background Venetoclax (VEN)-based combination therapy with hypomethylating agents (HMA) has been approved for first-line treatment in patients ineligible for intensive treatment based on two randomized trials. There is some evidence for efficacy also in the in relapsed/ refractory setting (R/R), but comparative controlled data is lacking. Here, we report our experience of VEN-Azacitidine (AZA) in R/R AML salvage treatment and bridge to allogeneic cell transplantation (allo-HCT) in fit patients compared to historical data from the Study Alliance Leukemia (SAL) registry (ClinicalTrials.gov Identifier: NCT03188874). Design/Methods We analyzed all patients with R/R AML after initial intensive therapy, who started VEN-AZA salvage treatment at the University Hospital Heidelberg, between October 2018 and October 2020. Patients, who were bridged to allo-HCT were compared in a multivariable analysis to data of R/R AML patients from the SAL registry receiving an allo-HCT. Results: A total of 26 patients (median age 60 years, range 23 to 79) were included. All patients initially received intensive therapy, 16 patients (62%) had been refractory to intensive induction therapy with DA (daunorubicin, cytarabine) (11 patients)/ CPX-351 (2 patients) or to an intensive salvage therapy regime with HAM (2 patients)/ Cytarabin-Bortezomib (1 patient). Ten patients (38%) had morphologic (7 patients) or molecular relapse (3 patients) after intensive first line therapy. The distribution of AML according to WHO-2016 classification was n=10 recurrent genetic abnormalities (n= 7, mutated NPM1; n=1, biallelic CEBPA mutations; n=1, mutated RUNX1; n=1, CBFB-MYH11), n=10 AML with MRC, n=6 AML NOS. According to the 2017 ELN classification, 9 patients (34,5%) had low risk, 8 (31%) intermediate risk and 9 (34,5%) adverse risk disease. All patients received AZA 75mg/m² for 7 days combined with VEN 400mg/day after initial ramp up or a reduced dose of 100mg/day in case of co-medication with azoles in 28 days cycles. Best response was CR/CRi in 58% (n=15), PR in 23% (n=6) patients. Day 30-mortality was 0%, day 60-mortality was 4% (n=1). Allo-HCT was performed in 20 patients (77%). Pre-Allo-HCT remission status was CR/CRi in 11 (55%), PR in 4 (20%) patients and MLFS in 1 patient and 4 patients had active disease (n=3, relapse after achieving CR/CRi on VEN-AZA, n=1 refractory to VEN-AZA.). At the time of analysis 15 (75%) of the 20 bridged patients were alive and 11 (55%) are still in CR resulting in a median relapse-free survival in bridged patients of 406 days, whereas all patients not proceeding to allo-HCT died after a median of 139 days. In total, 63 patients with R/R AML were identified in the SAL-registry proceeding to allo-HCT with non VEN-based salvage attempt. Pre-Allo-HCT remission status was CR/CRi in 18 (28%), PR in 15 (24%), unknown in 13 patients (21%) and 17 (27%) patients had active disease (n=9 relapsed, n=8 refractory). Patients of the SAL registry were younger (median, 55 years; range, 22-75 years) and more patients were ELN-int (low risk, 32%, n=20; int, 52%, n=33, adv, 16%, n=10). Median follow-up in the VEN-AZA and the SAL cohorts were 1.4 years and 4.6 years, respectively. Cox-regression modeling of survival measured from the date of being refractory/relapsed revealed a non-significant effect of the cohorts favoring the VEN-AZA salvage therapy (HR, 0.87, p=0.73). However, stratified univariable survival analysis revealed in trend better survival (p=0.10) in the VEN-AZA compared to the SAL cohort with 77% (95%-CI, 62-95%) and 74% (95%-CI, 57-97%) as well as 84% (95%-CI, 76-94%) and 52% (95-CI, 41-68%) 1- and 2-years survival, respectively. Conclusion: Our data confirms the efficacy of VEN-AZA in patients with R/R AML and underlines its potential as an effective strategy for bridging to successful allo-HCT. Disclosures Unglaub: JazzPharma: Consultancy, Other: travel costs/ conference fee; Novartis: Consultancy, Other: travel costs/ conference fee. Schlenk: Boehringer Ingelheim: Research Funding; AstraZeneca: Research Funding; Roche: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria; Neovio Biotech: Honoraria; Hexal: Honoraria; Daiichi Sankyo: Honoraria, Research Funding; Celgene: Honoraria; Astellas: Honoraria, Research Funding, Speakers Bureau; Abbvie: Honoraria; Agios: Honoraria. Middeke: Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Gilead: Consultancy; Abbvie: Consultancy, Honoraria; Jazz: Consultancy; Pfizer: Consultancy, Honoraria; Sanofi: Honoraria, Research Funding; Astellas: Consultancy, Honoraria; Novartis: Consultancy; Glycostem: Consultancy; UCB: Honoraria. Krause: Siemens: Research Funding; Takeda: Honoraria; Pfizer: Honoraria; art-tempi: Honoraria; Kosmas: Honoraria; Gilead: Other: travel support; Abbvie: Other: travel support. Schliemann: Philogen S.p.A.: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Other: travel grants; Astellas: Consultancy; AstraZeneca: Consultancy; Boehringer-Ingelheim: Research Funding; BMS: Consultancy, Other: travel grants; Jazz Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy; Roche: Consultancy; Pfizer: Consultancy. Haenel: GSK: Consultancy; Jazz: Consultancy, Honoraria; Bayer Vital: Honoraria; Takeda: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy; Celgene: Consultancy, Honoraria. Crysandt: Pfizer: Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria. Fransecky: Medac: Honoraria; Abbvie: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria; Novartis: Honoraria. Einsele: Janssen, Celgene/BMS, Amgen, GSK, Sanofi: Consultancy, Honoraria, Research Funding. Seggewiss-Bernhardt: Astra-Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; ipsen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; EusaPharma: Honoraria, Membership on an entity's Board of Directors or advisory committees. Platzbecker: AbbVie: Honoraria; Celgene/BMS: Honoraria; Janssen: Honoraria; Novartis: Honoraria; Geron: Honoraria; Takeda: Honoraria. Baldus: Amgen: Honoraria; Celgene/BMS: Honoraria; Jazz: Honoraria; Novartis: Honoraria. Dreger: Bluebird Bio: Consultancy; AstraZeneca: Consultancy, Speakers Bureau; BMS: Consultancy; AbbVie: Consultancy, Speakers Bureau; Janssen: Consultancy; Gilead Sciences: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Roche: Consultancy, Speakers Bureau. Müller-Tidow: Pfizer: Research Funding; Janssen: Consultancy, Research Funding; Bioline: Research Funding. Sauer: Takeda: Consultancy, Other: DSMB/SAB Member; Matterhorn Biosciences AG: Consultancy, Other: DSMB/SAB Member; Abbvie: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau. OffLabel Disclosure: off-label use of Venetoclax-based combination therapy in relapsed or refractory AML

Background: The strength, assistance walking, rise from a chair, climb stairs, and falls (SARC-F) questionnaire is a well-established instrument for screening of sarcopenia and sarcopenia-related functional impairments. As it is based on self-reporting, its use precludes patients who are unable to answer the questionnaire as a consequence of severe acute diseases or cognitive impairment. Therefore, we aimed to validate a proxy-reported version of the SARC-F for both ad-hoc as well as retrospective screening for severe sarcopenia-related functional impairments. Methods: Patients aged ≥60 years completed the SARC-F and performed the short physical performance battery (SPPB) at baseline (T1). Proxies in Cohort A gave a simultaneous assessment of the patients' functional status with the proxy-reported SARC-F at T1 and again, retrospectively, after 3 months (T2). Proxies in Cohort B only completed the SARC-F retrospectively at T2. The questionnaires' performances were assessed through sensitivity/specificity analyses and receiver operating characteristic (ROC) curves. For non-inferiority analyses, results of both the patient-reported and proxy-reported SARC-F were correlated with the SPPB total score as well as the results of the chair-rise test subcategory; the respective correlation coefficients were tested against each other. Results: One hundred and four patients and 135 proxies participated. Using a SPPB score

Objectives Primary objectives • To assess the time from randomisation until an improvement within 84 days defined as two points on a seven point ordinal scale or live discharge from the hospital in high-risk patients (group 1 to group 4) with SARS-CoV-2 infection requiring hospital admission by infusion of plasma from subjects after convalescence of SARS-CoV-2 infection or standard of care. Secondary objectives • To assess overall survival, and the overall survival rate at 28 56 and 84 days. • To assess SARS-CoV-2 viral clearance and load as well as antibody titres. • To assess the percentage of patients that required mechanical ventilation. • To assess time from randomisation until discharge. Trial design Randomised, open-label, multicenter phase II trial, designed to assess the clinical outcome of SARS-CoV-2 disease in high-risk patients (group 1 to group 4) following treatment with anti-SARS-CoV-2 convalescent plasma or standard of care. Participants High-risk patients >18 years of age hospitalized with SARS-CoV-2 infection in 10-15 university medical centres will be included. High-risk is defined as SARS-CoV-2 positive infection with Oxygen saturation at ≤ 94% at ambient air with additional risk features as categorised in 4 groups: • Group 1, pre-existing or concurrent hematological malignancy and/or active cancer therapy (incl. chemotherapy, radiotherapy, surgery) within the last 24 months or less. • Group 2, chronic immunosuppression not meeting the criteria of group 1. • Group 3, age ≥ 50 - 75 years meeting neither the criteria of group 1 nor group 2 and at least one of these criteria: Lymphopenia < 0.8 x G/l and/or D-dimer > 1μg/mL. • Group 4, age ≥ 75 years meeting neither the criteria of group 1 nor group 2. Observation time for all patients is expected to be at least 3 months after entry into the study. Patients receive convalescent plasma for two days (day 1 and day 2) or standard of care. For patients in the standard arm, cross over is allowed from day 10 in case of not improving or worsening clinical condition. Nose/throat swabs for determination of viral load are collected at day 0 and day 1 (before first CP administration) and subsequently at day 2, 3, 5, 7, 10, 14, 28 or until discharge. Serum for SARS-Cov-2 diagnostic is collected at baseline and subsequently at day 3, 7, 14 and once during the follow-up period (between day 35 and day 84). There is a regular follow-up of 3 months. All discharged patients are followed by regular phone calls. All visits, time points and study assessments are summarized in the Trial Schedule (see full protocol Table 1). All participating trial sites will be supplied with study specific visit worksheets that list all assessments and procedures to be completed at each visit. All findings including clinical and laboratory data are documented by the investigator or an authorized member of the study team in the patient's medical record and in the electronic case report forms (eCRFs). Intervention and comparator This trial will analyze the effects of convalescent plasma from recovered subjects with SARS-CoV-2 antibodies in high-risk patients with SARS-CoV-2 infection. Patients at high risk for a poor outcome due to underlying disease, age or condition as listed above are eligible for enrollment. In addition, eligible patients have a confirmed SARS-CoV-2 infection and O2 saturation ≤ 94% while breathing ambient air. Patients are randomised to receive (experimental arm) or not receive (standard arm) convalescent plasma in two bags (238 - 337 ml plasma each) from different donors (day 1, day 2). A cross over from the standard arm into the experimental arm is possible after day 10 in case of not improving or worsening clinical condition. Main outcomes Primary endpoints: The main purpose of the study is to assess the time from randomisation until an improvement within 84 days defined as two points on a seven-point ordinal scale or live discharge from the hospital in high-risk patients (group 1 to group 4) with SARS-CoV-2 infection requiring hospital admission by infusion of plasma from subjects after convalescence of a SARS-CoV-2 infection or standard of care. Secondary endpoints: • Overall survival, defined as the time from randomisation until death from any cause 28-day, 56-day and 84-day overall survival rates. • SARS-CoV-2 viral clearance and load as well as antibody titres. • Requirement mechanical ventilation at any time during hospital stay (yes/no). • Time until discharge from randomisation. • Viral load, changes in antibody titers and cytokine profiles are analysed in an exploratory manner using paired non-parametric tests (before – after treatment). Randomisation Upon confirmation of eligibility (patients must meet all inclusion criteria and must not meet exclusion criteria described in section 5.3 and 5.4 of the full protocol), the clinical site must contact a centralized internet randomization system (https://randomizer.at/). Patients are randomized using block randomisation to one of the two arms, experimental arm or standard arm, in a 1:1 ratio considering a stratification according to the 4 risk groups (see Participants). Blinding (masking) The study is open-label, no blinding will be performed. Numbers to be randomised (sample size) A total number of 174 patients is required for the entire trial, n=87 per group. Trial Status Protocol version 1.2 dated 09/07/2020. A recruitment period of approximately 9 months and an overall study duration of approximately 12 months is anticipated. Recruitment of patients starts in the third quarter of 2020. The study duration of an individual patient is planned to be 3 months. After finishing all study-relevant procedures, therapy, and follow-up period, the patient is followed in terms of routine care and treated if necessary. Total trial duration: 18 months Duration of the clinical phase: 12 months First patient first visit (FPFV): 3rd Quarter 2020 Last patient first visit (LPFV): 2nd Quarter 2021 Last patient last visit (LPLV): 3rd Quarter 2021 Trial Report completed: 4th Quarter 2021 Trial registration EudraCT Number: 2020-001632-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001632-10/DE, registered on 04/04/2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2). The eCRF is attached (Additional file 3).

Human leucocyte antigen G (HLA-G) is a non-classical HLA-class I antigen that exerts immunoregulatory functions. The polymorphisms 14-base pair (bp) insertion/deletion (ins/del) (rs1704) and +3142C > G (rs1063320) could modify the expression level of HLA-G. We genotyped 175 kidney recipients (41 with acute rejection and 134 without rejection) and additionally the corresponding donors for both polymorphisms in order to assess their impact on acute rejections one year after transplantation. In addition, we analyzed soluble HLA-G (sHLA-G) levels in sera of 32 living kidney donors and compared the sHLA-G levels in terms of the present genotype. In kidney transplant recipients we did not observe an impact of the 14-bp ins/ins and the +3142GG genotypes on acute rejection. In contrast, we found a higher frequency of these genotypes in the donors of the no-rejection collective compared to the rejection collective (4.9% vs. 24.6%; p = 0.010; 9.8% vs. 31.3%; p = 0.006). Soluble HLA-G levels were highest in healthy kidney donors homozygous for the 14-bp insertion. We conclude that the HLA-G polymorphisms of the donor are of importance for susceptibility of acute rejection in kidney transplantation. We suggest that the 14-bp ins/ins and the +3142GG genotypes are protective against kidney transplant rejection.

Purpose Although we know that HRM practices can have a huge impact on employees’ innovative work behaviour (IWB), we do not know exactly which practices make the difference and how they affect IWB. Thus, the purpose of this paper is to determine the best HRM practices for boosting IWB, to understand the theoretical reasons for this, and to discover mediators and moderators in the relationship between HRM practices and IWB. Design/methodology/approach Based on a systematic review of the literature, the authors carried out a content analysis on 27 peer-reviewed journal articles. Findings Working with the definitions and items provided in the articles, the authors were able to cluster HRM practices according to the ability-motivation-opportunity framework. The best HRM practices for enhancing IWB are training and development, reward, job security, autonomy, task composition, job demand, and feedback. Practical implications The results of this study provide practical information for HRM professionals aiming to develop an HRM system that generates innovative employee behaviours that might help build an innovative climate. Originality/value A framework is presented that aggregates the findings and clarifies which HRM practices influence IWB and how these relationships can be explained.

Expression of human leukocyte antigen G (HLA-G) has been associated with increased graft survival and decreased rejection episodes. It has been described that the HLA-G 14-base pair (bp) insertion/deletion (ins/del) (rs66554220) and +3142CG (rs1063320) gene polymorphisms modify the expression level of HLA-G. The aim of the study was to investigate whether these HLA-G polymorphisms have an impact on acute rejection after liver transplantation. In total, 146 liver transplant recipients (57 with acute rejection and 89 without acute rejection) and 99 corresponding liver donors were genotyped for both polymorphisms. In liver transplantation the 14-bp ins/ins and the +3142GG genotypes are more frequent in recipients without rejection compared to recipients with rejection (3.5% vs. 31.5%, p=0.001; 12.3% vs. 41.6%, p=0.001) demonstrating an association with protection from acute rejection. In contrast, in liver donors we could not reveal an association. We conclude that 14-bp ins/ins and +3142GG genotypes of HLA-G in liver transplant recipients are of importance for prediction of acute rejection after liver transplantation. Thus genotyping of liver recipients for both polymorphisms might be useful to stratify liver transplant recipients according to the risk of acute liver transplant rejection.