Normal platelet counts mask abnormal thrombopoiesis in patients with chronic myeloid leukemia

Prognostic value of platelet distribution width to lymphocyte ratio in patients with hepatocellular carcinoma following hepatectomy

BACKGROUND

Platelet distribution width (PDW), but not platelet count, was found to more comprehensively reflect platelet activity. The present study, thus, aimed to evaluate the prognostic value of PDW to lymphocyte ratio (PDWLR) in patients with hepatocellular carcinoma (HCC) following hepatectomy.

METHODS

Patients following hepatectomy were analyzed retrospectively. The Kaplan-Meier survival curves and Cox regression model were used to determine the prognostic value of PDWLR.

RESULTS

241 patients were analyzed eventually, and stratified into low and high PDWLR groups (≤ 9.66 vs. > 9.66). Results of comparing the baseline characteristics showed that high PDWLR was significantly associated with cirrhosis, and intraoperative blood loss (all P < 0.05). In multivariate COX regression analysis, PDWLR was demonstrated as an independent risk factor for OS (HR: 1.549, P = 0.041) and RFS (HR: 1.655, P = 0.005). Moreover, PDWLR demonstrated a superior capacity for predicting prognosis compared to other indicators.

CONCLUSION

Preoperative PDWLR has a potential value in predicting the prognosis of HCC patients following hepatectomy, which may help in clinical decision-making for individual treatment.

Prognostic Impact of Platelet-Large Cell Ratio In Myelodysplastic Syndromes

Background

Myelodysplastic syndromes (MDSs) are a very heterogeneous group of myeloid disorders with high prevalence and risk of developing acute myeloid leukemia. The more accurate risk stratification can provide a better guidance of treatment. The platelet–large cell ratio (P-LCR) is a parameter reported in complete blood cell count tests, and was associated with many diseases, but its role in MDS is not clear.

Purpose

This study aims to explore the impact of the P-LCR on the prognosis of patients with MDS, which is of great significance for clinical treatment.

Methods

In the retrospective study, 122 newly diagnosed MDS patients were enrolled. We used the bioinformatics tool X-tile to define a P-LCR threshold of 36.7% to predict prognosis. Patients were divided into P-LCR^low and P-LCR^high groups, and their characteristics were compared between the two groups.

Results

Results show that the P-LCR^low was associated with worse overall survival (OS) than the P-LCR^high patients (median OS, 18.53 months versus 25.77 months, p=0.0057), but there were no statistical differences in progression-free survival (PFS) between the two groups (p=0.2001). The results of univariate and multivariate Cox proportional hazard analyses adjusted for gender, bone marrow blast level, platelet count, and International Prognostic Scoring System scores showed that the P-LCR was useful in the evaluation of PFS [hazard ratio (HR) 0.212, 95%CI 0.064–0.702, p=0.011] and OS of MDS (HR 0.464, 95%CI 0.284–0.757, p=0.002).

Conclusion

This study is the first report showing that the P-LCR would be a simple and immediately available biomarker for predicting the prognosis of MDS.

Genetic associations of protein-coding variants in human disease

Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes¹. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery.

A meta-analysis combining whole-exome sequencing data from UK Biobank participants and imputed genotypes from FinnGen participants enables identification of genetic associations with human disease in the rare and low-frequency allelic spectrum

Sclerostin Depletion Induces Inflammation in the Bone Marrow of Mice

Romosozumab, a humanized monoclonal antibody specific for sclerostin (SOST), has been approved for treatment of postmenopausal women with osteoporosis at a high risk for fracture. Previous work in sclerostin global knockout (Sost^-/-) mice indicated alterations in immune cell development in the bone marrow (BM), which could be a possible side effect in romosozumab-treated patients. Here, we examined the effects of short-term sclerostin depletion in the BM on hematopoiesis in young mice receiving sclerostin antibody (Scl-Ab) treatment for 6 weeks, and the effects of long-term Sost deficiency on wild-type (WT) long-term hematopoietic stem cells transplanted into older cohorts of Sost^-/- mice. Our analyses revealed an increased frequency of granulocytes in the BM of Scl-Ab-treated mice and WT→Sost^-/- chimeras, indicating myeloid-biased differentiation in Sost-deficient BM microenvironments. This myeloid bias extended to extramedullary hematopoiesis in the spleen and was correlated with an increase in inflammatory cytokines TNFα, IL-1α, and MCP-1 in Sost^-/- BM serum. Additionally, we observed alterations in erythrocyte differentiation in the BM and spleen of Sost^-/- mice. Taken together, our current study indicates novel roles for Sost in the regulation of myelopoiesis and control of inflammation in the BM.

The molecular basis of immune-based platelet disorders

Platelets have a predominant role in haemostasis, the maintenance of blood volume and emerging roles as innate immune cells, in wound healing and in inflammatory responses. Platelets express receptors that are important for platelet adhesion, aggregation, participation in inflammatory responses, and for triggering degranulation and enhancing thrombin generation. They carry a cargo of granules bearing enzymes, adhesion molecules, growth factors and cytokines, and have the ability to generate reactive oxygen species. The platelet is at the frontline of a host of cellular responses to invading pathogens, injury, and infection. Perhaps because of this intrinsic responsibility of a platelet to rapidly respond to thrombotic, pathological and immunological factors as part of their infantry role; platelets are susceptible to targeted attack by the adaptive immune system. Such attacks are often transitory but result in aberrant platelet activation as well as significant loss of platelet numbers and platelet function, paradoxically leading to elevated risks of both thrombosis and bleeding. Here, we discuss the main molecular events underlying immune-based platelet disorders with specific focus on events occurring at the platelet surface leading to activation and clearance.

Identifying "Many-to-Many" Relationships between Gene-Expression Data and Drug-Response Data via Sparse Binary Matching

Identifying gene-drug patterns is a critical step in pharmacology for unveiling disease mechanisms and drug discovery. The availability of high-throughput technologies accumulates massive large-scale pharmacological and genomic data, and thus provides a new substantial opportunity to deeply understand how the oncogenic genes and the therapeutic drugs relate to each other. However, most previous studies merely used the pharmacological and genomic datasets without any prior knowledge to infer the gene-drug patterns. Here, we proposed a novel network-guided sparse binary matching model (NSBM) to decode these relationships hidden in the datasets. Not only the large-scale gene-expression data and drug-response data are jointly analyzed in our method, but also the additional prior information of genes and drugs are integrated into the form of network-based regularization. The essential structure of the NSBM model is a convex quadratic minimization problem with network-based penalties. It was demonstrated to be superior when compared with two benchmark methods through extensive experiments on both synthetic and empirical data. Posterior validation, including gene-ontology and enrichment analysis, confirmed the effectiveness of NSBM in revealing gene-drug patterns on a large-scale heterogeneous data source.