Therapy for acute myeloid leukemia (AML) involves intense cytotoxic treatment and yet approximately 70% of AML are refractory to initial therapy or eventually relapse. This is at least partially driven by the chemo-resistant nature of the leukemic stem cells (LSCs) that sustain the disease, and therefore novel anti-LSC therapies could decrease relapses and improve survival. We performed in silico analysis of highly prognostic human AML LSC gene expression signatures using existing datasets of drug-gene interactions to identify compounds predicted to target LSC gene programs. Filtering against compounds that would inhibit a hematopoietic stem cell (HSC) gene signature resulted in a list of 151 anti-LSC candidates. Using a novel in vitro LSC assay, we screened 84 candidate compounds at multiple doses and confirmed 14 drugs that effectively eliminate human AML LSCs. Three drug families presenting with multiple hits, namely antihistamines (astemizole and terfenadine), cardiac glycosides (strophanthidin, digoxin and ouabain) and glucocorticoids (budesonide, halcinonide and mometasone), were validated for their activity against human primary AML samples. Our study demonstrates the efficacy of combining computational analysis of stem cell gene expression signatures with in vitro screening to identify novel compounds that target the therapy-resistant LSC at the root of relapse in AML.

Cancer research has considerably progressed with the improvement of in vitro study models, helping to understand the key role of the tumor microenvironment in cancer development and progression. Over the last few years, complex 3D human cell culture systems have gained much popularity over in vivo models, as they accurately mimic the tumor microenvironment and allow high-throughput drug screening. Of particular interest, in vitrohuman 3D tissue constructs, produced by the self-assembly method of tissue engineering, have been successfully used to model the tumor microenvironment and now represent a very promising approach to further develop diverse cancer models. In this review, we describe the importance of the tumor microenvironment and present the existing in vitro cancer models generated through the self-assembly method of tissue engineering. Lastly, we highlight the relevance of this approach to mimic various and complex tumors, including basal cell carcinoma, cutaneous neurofibroma, skin melanoma, bladder cancer, and uveal melanoma.

Combinations of immunosuppressive drugs are routinely used post-transplantation to prevent rejection and/or other complications and optimize outcomes. The prodrug ester mycophenolate mofetil (MMF) is frequently used in solid-organ and stem cell transplantation settings. A growing body of evidence supports therapeutic monitoring of this immunosuppressant to optimize its efficacy and reduce toxicity. Thus, pharmacodynamic monitoring of MMF is a strategy that could potentially improve patient outcomes. Pharmacodynamic measurements require evaluation of inosine-5'-monophosphate dehydrogenase (IMPDH) activity, the target enzyme of the active moiety mycophenolic acid. Various nonradioactive methods using chromatographic separations have been used to quantify xanthosine monophosphate, the catalytic product of the enzyme, to indirectly evaluate IMPDH activity. However, no methods have used mass spectrometry based detection, which provides more specificity and sensitivity. Here, we describe a liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) method for the quantification of xanthosine monophosphate and adenosine monophosphate (for normalization) in lysates of peripheral blood mononuclear cells (PBMCs) from hematopoietic stem cell transplant (HSCT) recipients. Linearity, precision, and accuracy were validated over a large range of concentrations for each compound. The method could measure analytes with high sensitivity, accuracy (93-116%), and reproducibility (CV < 7.5%). Its clinical application was validated in PBMC lysates obtained from healthy individuals (n = 43) and HSCT recipients (n = 19). This reliable and validated LC-MS/MS method could be a useful tool for pharmacodynamic monitoring of patients treated with MMF.

Recent evidence indicates that microRNAs might participate in prostate cancer initiation, progression and treatment response. Germline variations in microRNAs might alter target gene expression and modify the efficacy of prostate cancer therapy. To determine whether genetic variants in microRNAs and microRNA target sites are associated with the risk of biochemical recurrence (BCR) after radical prostatectomy (RP). We retrospectively studied two independent cohorts composed of 320 Asian and 526 Caucasian men with pathologically organ-confined prostate cancer who had a median follow-up of 54.7 and 88.8 months after RP, respectively. Patients were systematically genotyped for 64 single-nucleotide polymorphisms (SNPs) in microRNAs and microRNA target sites, and their prognostic significance on BCR was assessed by Kaplan-Meier analysis and Cox regression model. After adjusting for known clinicopathologic risk factors, two SNPs (MIR605 rs2043556 and CDON rs3737336) remained associated with BCR. The numbers of risk alleles showed a cumulative effect on BCR [perallele hazard ratio (HR) 1.60, 95% confidence interval (CI) 1.16-2.21, p for trend 5 0.005] in Asian cohort, and the risk was replicated in Caucasian cohort (HR 1.55, 95% CI 1.15-2.08, p for trend 5 0.004) and in combined analysis (HR 1.57, 95% CI 1.26-1.96, p for trend <0.001). Results warrant replication in larger cohorts. This is the first study demonstrating that SNPs in microRNAs and micro-RNA target sites can be predictive biomarkers for BCR after RP.