Vincristine (VCR) is the first-line chemotherapeutic medication often co-administered with other drugs to treat childhood acute lymphoblastic leukemia. Dose-dependent neurotoxicity is the main factor restricting VCR’s clinical application. VCR-induced peripheral neuropathy (VIPN) sometimes results in dose reduction or omission, leading to clinical complications or affecting the patient’s quality of life. With regard to the genetic basis of drug responses, preemptive pharmacogenomic testing and simultaneous blood level monitoring could be helpful for the transformation of various findings into individualized therapies. In this review, we discussed the potential associations between genetic variants in genes contributing to the pharmacokinetics/pharmacodynamics of VCR and VIPN incidence and severity in patients with acute lymphoblastic leukemia. Of note, genetic variants in the CEP72 gene have great potential to be translated into clinical practice. Such a genetic biomarker may help clinicians diagnose VIPN earlier. Besides, genetic variants in other genes, such as CYP3A5, ABCB1, ABCC1, ABCC2, TTPA, ACTG1, CAPG, SYNE2, SLC5A7, COCH, and MRPL47, have been reported to be associated with the VIPN, but more evidence is needed to validate the findings in the future. In fact, a variety of complex factors jointly determine the VIPN. In implementing precision medicine, the combination of genetic, environmental, and personal variables, along with therapeutic drug monitoring, will allow for a better understanding of the mechanisms of VIPN, improving the effectiveness of VCR treatment, reducing adverse reactions, and improving patients’ quality of life.

BackgroundAcute promyelocytic leukemia (APL) is characterized by the presence of coagulopathy at onset and translocation t (15; 17) (q22; 21), meanwhile, other translocation variants of APL have also been reported. The FIP1L1–RARA fusion gene has recently been reported as a novel RARA-associated fusion gene.ObjectivesWe report a case of de novo myeloid sarcoma (MS) type of APL with FIP1L1–RARA found by next-generation sequencing (NGS) that was not detected by conventional analyze analysis for RARA translocations.MethodsWe performed typical morphological, magnetic resonance imaging (MRI), conventional tests for PML–RARA dual-fusion translocation probe, high-through sequencing and NGS. Meanwhile, bioinformatics analyses were done by using public repositories, including ONCOMINE, COSMIC, and GeneMANIA analysis.ResultsA 28-month-old girl with a complex karyotype that includes 46,XX,t(4;17)(q12;q22)[9]/46,idem,del(16)(q22)[3]/45,idem,-x,-4,-9,-15,del(16)(q22),+marl,+mar2,+mar3[7]/46,xx[3], c.38G>A (p.Gly13Asp) in the KRAS gene, and a cryptic insertion of RARA gene into the FIP1L1 gene was diagnosed with APL complicated by the de novo MS.ConclusionWe report a FIP1L1–RARA fusion in a child with APL who presented with an extramedullary tumor in the skull without the classic karyotype using NGS, whom we treated with good results. NGS analysis should be considered for APL variant cases. Further experimental studies to the association between the mutation in KRAS gene and FIP1L1–RARA fusion on the clinical phenotype and progression of APL are needed to identify more effective therapeutic targets for APL.

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Virtually all cellular functions involve protein-protein interactions (PPIs). As an increasing number of PPIs are identified and vast amount of information accumulated, researchers are finding different ways to interrogate the data and understand the interactions in context. However, it is widely recognized that a significant portion of the data is scattered, redundant, not considered high quality, and not readily accessible to researchers in a systematic fashion. In addition, it is challenging to identify the optimal protein targets in the current PPI networks. The GeneSense server was developed to integrate gene annotation and PPI networks in an expandable architecture that incorporates selected databases with the aim to assemble, analyze, evaluate and disseminate protein-protein association information in a comprehensive and user-friendly manner. Three network models including nodenet, leafnet and loopnet are used to identify the optimal protein targets in the complex networks. GeneSense is freely available at www.biomedsense.org/genesense.php.

A method for monitoring l-asparagine (ASN) depletion in patients' serum using reversed-phase high-performance liquid chromatography with precolumn o-phthalaldehyde and ethanethiol (ET) derivatization is described. In order to improve the signal and stability of analytes, several important factors including precipitant reagent, derivatization conditions and detection wavelengths were optimized. The recovery of the analytes in biological matrix was the highest when 4% sulfosalicylic acid (1:1, v/v) was used as a precipitant reagent. Optimal fluorescence detection parameters were determined as λex = 340 nm and λem = 444 nm for maximal signal. The signal of analytes was the highest when the reagent ET and borate buffer of pH 9.9 were used in the derivatization solution. And the corresponding derivative products were stable up to 19 h. The validated method had been successfully applied to monitor ASN depletion and l-aspartic acid, l-glutamine, l-glutamic acid levels in pediatric patients during l-asparaginase therapy.