Serum profiling identifies ibrutinib as a treatment option for young adults with B-cell acute lymphoblastic leukaemia

DCTPP1, an Oncogene Regulated by miR-378a-3p, Promotes Proliferation of Breast Cancer via DNA Repair Signaling Pathway

Breast cancer (BRCA) is one of the most deadly cancers worldwide, with poor survival rates that could be due to its high proliferation. Human all-alpha dCTP pyrophosphatase 1 (DCTPP1) is implicated in numerous diseases, including cancers. However, its role in BRCA is unclear. In this study, we used bioinformatic analyses of the ONCOMINE, UALCAN, and GEPIA databases to determine the expression pattern of DCTPP1 in BRCA. We found that elevated DCTPP1 levels correlate with poor BRCA prognosis. DCTPP1 silencing inhibited BRCA cell proliferation and induced apoptosis in vitro, as well as in vivo. Our data show that this tumorigenic effect depends on DNA repair signaling. Moreover, we found that DCTPP1 is directly modulated by miR-378a-3p, whose downregulation is linked to BRCA progression. Our results showed down-regulation of miR-378a-3p in BRCA. Upregulation of miR-378a-3p, on the other hand, can inhibit BRCA cell growth and proliferation. This study shows that reduced miR-378a-3p level enhances DCTPP1 expression in BRCA, which promotes proliferation by activating DNA repair signaling in BRCA.

Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia

Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.

Identification of Genes Whose Expression Overlaps Age Boundaries and Correlates with Risk Groups in Paediatric and Adult Acute Myeloid Leukaemia

Simple Summary

To better understand whether acute myeloid leukaemia differs between children and adults, we have analysed the expression of genes in samples from both patient groups. Using previously published data, we compared gene expression between patient risk subgroups. We examined patients who had a poor chance of survival, based on clinical assessments, and those with a good chance of survival, to see whether there was any difference in the genes expressed in their leukaemic cells. Then we compared the genes on these lists between adults and children with acute myeloid leukaemia. We believe that patients with good or poor survival chances express genes that provide insights into how leukaemic cells behave. We hope that this work will provide new information about the mechanisms that underlie acute myeloid leukaemia and answer questions on the ways this form of leukaemia is similar in adults and children, which will then tell us whether the same treatments could be used for both age groups of patients.

Abstract

Few studies have compared gene expression in paediatric and adult acute myeloid leukaemia (AML). In this study, we have analysed mRNA-sequencing data from two publicly accessible databases: (1) National Cancer Institute’s Therapeutically Applicable Research to Generate Effective Treatments (NCI-TARGET), examining paediatric patients, and (2) The Cancer Genome Atlas (TCGA), examining adult patients with AML. With a particular focus on 144 known tumour antigens, we identified STEAP1, SAGE1, MORC4, SLC34A2 and CEACAM3 as significantly different in their expression between standard and low risk paediatric AML patient subgroups, as well as between poor and good, and intermediate and good risk adult AML patient subgroups. We found significant differences in event-free survival (EFS) in paediatric AML patients, when comparing standard and low risk subgroups, and quartile expression levels of BIRC5, MAGEF1, MELTF, STEAP1 and VGLL4. We found significant differences in EFS in adult AML patients when comparing intermediate and good, and poor and good risk adult AML patient subgroups and quartile expression levels of MORC4 and SAGE1, respectively. When examining Kyoto Encyclopedia of Genes and Genomes (KEGG) (2016) pathway data, we found that genes altered in AML were involved in key processes such as the evasion of apoptosis (BIRC5, WNT1) or the control of cell proliferation (SSX2IP, AML1-ETO). For the first time we have compared gene expression in paediatric AML patients with that of adult AML patients. This study provides unique insights into the differences and similarities in the gene expression that underlies AML, the genes that are significantly differently expressed between risk subgroups, and provides new insights into the molecular pathways involved in AML pathogenesis.

New targets for therapy: antigen identification in adults with B-cell acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) in adults is a rare and difficult-to-treat cancer that is characterised by excess lymphoblasts in the bone marrow. Although many patients achieve remission with chemotherapy, relapse rates are high and the associated impact on survival devastating. Most patients receive chemotherapy and for those whose overall fitness supports it, the most effective treatment to date is allogeneic stem cell transplant that can improve overall survival rates in part due to a 'graft-versus-leukaemia' effect. However, due to the rarity of this disease, and the availability of mature B-cell antigens on the cell surface, few new cancer antigens have been identified in adult B-ALL that could act as targets to remove residual disease in first remission or provide alternative targets for escape variants if and when current immunotherapy strategies fail. We have used RT-PCR analysis, literature searches, antibody-specific profiling and gene expression microarray analysis to identify and prioritise antigens as novel targets for the treatment of adult B-ALL.