Subtype prediction in pediatric acute myeloid leukemia: Classification using differential network rank conservation revisited

Background: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family's simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types

Pharmacokinetically-guided dosing to improve the efficacy of brigatinib in non-small cell lung cancer patients

Brigatinib was recently approved for the treatment of anaplastic lymphoma kinase-positive non-small cell lung cancer and is dosed according to a one-dose-fits-all paradigm. We aimed to identify a pharmacokinetically-guided precision dosing strategy to improve treatment response with brigatinib through simulations using a previously published pharmacokinetic-pharmacodynamic model. Dosing strategies explored were the approved 180 mg QD; the highest tolerable dose tested in clinical trials: 240 mg QD; and two precision dosing strategies targeting the median trough concentrations following 180 mg QD, and 240 mg QD. We investigated the impact of alternative dosing regimens on progression-free survival (PFS), overall survival (OS) and the probability of developing a grade ≥2 rash or grade ≥2 amylase increase. Median PFS and OS increased by 1.6 and 7.8 months, respectively between the currently approved dosing strategy and precision dosing to the median trough concentration of the 240 mg dosing strategy, with only a minor increase in the probability of developing toxicity

Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs.OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs.METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate.RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity &gt;95%.CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity &gt;95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.</p

Acute activation of metabolic syndrome components in pediatric acute lymphoblastic leukemia patients treated with dexamethasone

Although dexamethasone is highly effective in the treatment of pediatric acute lymphoblastic leukemia (ALL), it can cause serious metabolic side effects. Because studies regarding the effects of dexamethasone are limited by their small scale, we prospectively studied the direct effects of treating pediatric ALL with dexamethasone administration with respect to activation of components of metabolic syndrome (MetS); in addition, we investigated whether these side effects were correlated with the level of dexamethasone. Fifty pediatric patients (3-16 years of age) with ALL were studied during a 5-day dexamethasone course during the maintenance phase of the Dutch Childhood Oncology Group ALL-10 and ALL-11 protocols. Fasting insulin, glucose, total cholesterol, HDL, LDL, and triglycerides levels were measured at baseline (before the start of dexamethasone; T1) and on the fifth day of treatment (T2). Dexamethasone trough levels were measured at T2. We found that dexamethasone treatment significantly increased the following fasting serum levels (P3.4) from 8% to 85% (P<0.01). Dexamethasone treatment also significantly increased the diastolic and systolic blood pressure. Lastly, dexamethasone trough levels (N = 24) were directly correlated with high glucose levels at T2, but not with other parameters. These results indicate that dexamethasone treatment acutely induces three components of the MetS. Together with the weight gain typically associated with dexamethasone treatment, these factors may contribute to the higher prevalence of MetS and cardiovascular risk among survivors of childhood leukemia who received dexamethasone treatment

Distinct migratory and non-migratory ecotypes of an endemic New Zealand eleotrid (Gobiomorphus cotidianus) – implications for incipient speciation in island freshwater fish species

Background: Many postglacial lakes contain fish species with distinct ecomorphs. Similar evolutionary scenarios might be acting on evolutionarily young fish communities in lakes of remote islands. One process that drives diversification in island freshwater fish species is the colonization of depauperate freshwater environments by diadromous (migratory) taxa, which secondarily lose their migratory behaviour. The loss of migration limits dispersal and gene flow between distant populations, and, therefore, is expected to facilitate local morphological and genetic differentiation. To date, most studies have focused on interspecific relationships among migratory species and their non-migratory sister taxa. We hypothesize that the loss of migration facilitates intraspecific morphological, behavioural, and genetic differentiation between migratory and non-migratory populations of facultatively diadromous taxa, and, hence, incipient speciation of island freshwater fish species. Results: Microchemical analyses of otolith isotopes (Sr-88, Ba-137 and Ca-43) differentiated migratory and non-migratory stocks of the New Zealand endemic Gobiomorphus cotidianus McDowall (Eleotridae). Samples were taken from two rivers, one lake and two geographically-separated outgroup locations. Meristic analyses of oculoscapular lateral line canals documented a gradual reduction of these structures in the non-migratory populations. Amplified fragment length polymorphism (AFLP) fingerprints revealed considerable genetic isolation between migratory and non-migratory populations. Temporal differences in reproductive timing (migratory = winter spawners, non-migratory = summer spawners; as inferred from gonadosomatic indices) provide a prezygotic reproductive isolation mechanism between the two ecotypes. Conclusion: This study provides a holistic look at the role of diadromy in incipient speciation of island freshwater fish species. All four analytical approaches (otolith microchemistry, morphology, spawning timing, population genetics) yield congruent results, and provide clear and independent evidence for the existence of distinct migratory and non-migratory ecotypes within a river in a geographically confined range. The morphological changes within the non-migratory populations parallel interspecific patterns observed in all non-migratory New Zealand endemic Gobiomorphus species and other derived gobiid taxa, a pattern suggesting parallel evolution. This study indicates, for the first time, that distinct ecotypes of island freshwater fish species may be formed as a consequence of loss of migration and subsequent diversification. Therefore, if reproductive isolation persists, these processes may provide a mechanism to facilitate speciation

t(2;11)(q33;q23) KMT2A/ABI2

Review on t(2;11)(q33;q23) with the gene fusion KMT2A/ABI

Hydrocortisone as an intervention for dexamethasone-induced adverse effects in pediatric patients with acute lymphoblastic leukemia: results of a double-blind, randomized controlled trial

Purpose Dexamethasone is a key component in the treatment of pediatric acute lymphoblastic leukemia (ALL), but can induce serious adverse effects. Recent studies have led to the hypothesis that neuropsychological adverse effects may be a result of cortisol depletion of the cerebral mineralocorticoid receptors. We examined whether including a physiologic dose of hydrocortisone in dexamethasone treatment can reduce neuropsychologic and metabolic adverse effects in children with ALL. Patients and Methods We performed a multicenter, double-blind, randomized controlled trial with a crossover design. Of 116 potentially eligible patients (age 3 to 16 years), 50 were enrolled and were treated with two consecutive courses of dexamethas

A limited sampling schedule to estimate individual pharmacokinetics of pemetrexed in patients with varying renal functions

Purpose: Pemetrexed is a widely used cytostatic agent with an established exposure–response relationship. Although dosing is based on body surface area (BSA), large interindividual variability in pemetrexed plasma concentrations is observed. Therapeutic drug monitoring (TDM) can be a feasible strategy to reduce variability in specific cases leading to potentially optimized pemetrexed treatment. The aim of this study was to develop a limited sampling schedule (LSS) for the assessment of pemetrexed pharmacokinetics. Methods: Based on two real-life datasets, several limited sampling designs were evaluated on predicting clearance, using NONMEM, based on mean prediction error (MPE %) and normalized root mean squared error (NRMSE %). The predefined criteria for an acceptable LSS were: a maximum of four sampling time points within 8 h with an MPE and NRMSE ≤ 20%. Results: For an accurate estimation of clearance, only four samples in a convenient window of 8 h were required for accurate and precise prediction (MPE and NRMSE of 3.6% and 5.7% for dataset 1 and of 15.5% and 16.5% for dataset 2). A single sample at t = 24 h performed also within the criteria with MPE and NRMSE of 5.8% and 8.7% for dataset 1 and of 11.5% and 16.4% for dataset 2. Bias increased when patients had lower creatinine clearance. Conclusions: We presented two limited sampling designs for estimation of pemetrexed pharmacokinetics. Either one can be used based on preference and feasibility