Multilineage hematopoietic recovery with concomitant antitumor effects using low dose Interleukin-12 in myelosuppressed tumor-bearing mice

<p>Abstract</p> <p>Background</p> <p>Interleukin-12 (IL-12) is a cytokine well known for its role in immunity. A lesser known function of IL-12 is its role in hematopoiesis. The promising data obtained in the preclinical models of antitumor immunotherapy raised hope that IL-12 could be a powerful therapeutic agent against cancer. However, excessive clinical toxicity, largely due to repeat dose regimens, and modest clinical response observed in the clinical trials have pointed to the necessity to design protocols that minimize toxicity without affecting the anti-tumor effect of IL-12. We have focused on the lesser known role of IL-12 in hematopoiesis and hypothesized that an important clinical role for IL-12 in cancer may be as an adjuvant hematological cancer therapy. In this putative clinical function, IL-12 is utilized for the prevention of cancer therapy-related cytopenias, while providing concomitant anti-tumor responses over and above responses observed with the primary therapy alone. This putative clinical function of IL-12 focuses on the dual role of IL-12 in hematopoiesis and immunity.</p> <p>Methods</p> <p>We assessed the ability of IL-12 to facilitate hematopoietic recovery from radiation (625 rad) and chemotherapy (cyclophosphamide) in two tumor-bearing murine models, namely the EL4 lymphoma and the Lewis lung cancer models. Antitumor effects and changes in bone marrow cellularity were also assessed.</p> <p>Results</p> <p>We show herein that carefully designed protocols, in mice, utilizing IL-12 as an adjuvant to radiation or chemotherapy yield facile and consistent, multilineage hematopoietic recovery from cancer therapy-induced cytopenias, as compared to vehicle and the clinically-utilized cytokine granulocyte colony-stimulating factor (G-CSF) (positive control), while still providing concomitant antitumor responses over and above the effects of the primary therapy alone. Moreover, our protocol design utilizes single, low doses of IL-12 that did not yield any apparent toxicity.</p> <p>Conclusion</p> <p>Our results portend that despite its past failure, IL-12 appears to have significant clinical potential as a hematological adjuvant cancer therapy.</p

Histone deacetylase inhibitors: clinical implications for hematological malignancies

Histone modifications have widely been implicated in cancer development and progression and are potentially reversible by drug treatments. The N-terminal tails of each histone extend outward through the DNA strand containing amino acid residues modified by posttranslational acetylation, methylation, and phosphorylation. These modifications change the secondary structure of the histone protein tails in relation to the DNA strands, increasing the distance between DNA and histones, and thus allowing accessibility of transcription factors to gene promoter regions. A large number of HDAC inhibitors have been synthesized in the last few years, most being effective in vitro, inducing cancer cells differentiation or cell death. The majority of the inhibitors are in clinical trials, unlike the suberoylanilide hydroxamic acid, a pan-HDACi, and Romidepsin (FK 228), a class I-selective HDACi, which are only approved in the second line treatment of refractory, persistent or relapsed cutaneous T-cell lymphoma, and active in approximately 150 clinical trials, in monotherapy or in association. Preclinical studies investigated the use of these drugs in clinical practice, as single agents and in combination with chemotherapy, hypomethylating agents, proteasome inhibitors, and MTOR inhibitors, showing a significant effect mostly in hematological malignancies. The aim of this review is to focus on the biological features of these drugs, analyzing the possible mechanism(s) of action and outline an overview on the current use in the clinical practice

Addition of rituximab to standard chemotherapy improves the survival of both the germinal center B-cell-like and non-germinal center B-cell-like subtypes of diffuse large B-cell lymphoma

Purpose: Diffuse large B-cell lymphoma (DLBCL) includes at least two prognostically important subtypes (ie, germinal center B-cell-like [GCB] and activated B-cell-like [ABC] DLBCL), which initially were characterized by gene expression profiling and subsequently were confirmed by immunostaining. However, with the addition of rituximab to standard chemotherapy, the prognostic significance of this subclassification of DLBCL is unclear. Patients and Methods: We studied 243 patient cases of de novo DLBCL, which included 131 patient cases treated with rituximab plus standard chemotherapy (rituximab group) and 112 patient cases treated with only standard chemotherapy (control group). The cases were assigned to GCB or non-GCB subgroups (the latter of which included both ABC DLBCL and unclassifiable DLBCL) on the basis of immunophenotype by using the Hans method. Clinical characteristics and survival outcomes of the two patient groups were compared. Results: The clinical characteristics of the patients in the rituximab and the control groups were similar. Compared with the control group, addition of rituximab improved the 3-year overall survival (OS; 42% v 77%; P < .001) of patients with DLBCL. Rituximab-treated patients in either the GCB or the non-GCB subgroups also had a significantly improved 3-year OS compared with their respective subgroups in the control group (P < .001). In the rituximab group, the GCB subgroup had a significantly better 3-year OS than the non-GCB subgroup (85% v 69%; P = .032). Multivariate analyses confirmed that rituximab treatment was predictive for survival in both the GCB and the non-GCB subgroups. Conclusion: In this retrospective study, we have shown that the subclassification of DLBCL on the basis of the cell of origin continues to have prognostic importance in the rituximab era. © 2008 by American Society of Clinical Oncology.link_to_subscribed_fulltex

Expression profile and specific network features of the apoptotic machinery explain relapse of acute myeloid leukemia after chemotherapy

<p>Abstract</p> <p>Background</p> <p>According to the different sensitivity of their bone marrow CD34+ cells to <it>in vitro </it>treatment with Etoposide or Mafosfamide, Acute Myeloid Leukaemia (AML) patients in apparent complete remission (CR) after chemotherapy induction may be classified into three groups: (i) normally responsive; (ii) chemoresistant; (iii) highly chemosensitive. This inversely correlates with <it>in vivo </it>CD34+ mobilization and, interestingly, also with the prognosis of the disease: patients showing a good mobilizing activity are resistant to chemotherapy and subject to significantly higher rates of Minimal Residual Disease (MRD) and relapse than the others. Based on its known role in patients' response to chemotherapy, we hypothesized an involvement of the Apoptotic Machinery (AM) in these phenotypic features.</p> <p>Methods</p> <p>To investigate the molecular bases of the differential chemosensitivity of bone marrow hematopoietic stem cells (HSC) in CR AML patients, and the relationship between chemosensitivity, mobilizing activity and relapse rates, we analyzed their AM expression profile by performing Real Time RT-PCR of 84 AM genes in CD34+ pools from the two extreme classes of patients (i.e., chemoresistant and highly chemosensitive), and compared them with normal controls.</p> <p>Results</p> <p>The AM expression profiles of patients highlighted features that could satisfactorily explain their <it>in vitro </it>chemoresponsive phenotype: specifically, in chemoresistant patients we detected up regulation of antiapoptotic BIRC genes and down regulation of proapoptotic APAF1, FAS, FASL, TNFRSF25. Interestingly, our analysis of the AM network showed that the dysregulated genes in these patients are characterized by high network centrality (i.e., high values of betweenness, closeness, radiality, stress) and high involvement in drug response.</p> <p>Conclusions</p> <p>AM genes represent critical nodes for the proper execution of cell death following pharmacological induction in patients. We propose that their dysregulation (either due to inborn or <it>de novo </it>genomic mutations selected by treatment) could cause a relapse in apparent CR AML patients. Based on this, AM profiling before chemotherapy and transplantation could identify patients with a predisposing genotype to MRD and relapse: accordingly, they should undergo a different, specifically tailored, therapeutic regimen and should be carefully checked during the post-treatment period.</p

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)