Synthetic miR-34a mimics as a novel therapeutic agent for multiple myeloma: in vitro and in vivo evidence.

PURPOSE: Deregulated expression of miRNAs has been shown in multiple myeloma (MM). A promising strategy to achieve a therapeutic effect by targeting the miRNA regulatory network is to enforce the expression of miRNAs that act as tumor suppressor genes, such as miR-34a. EXPERIMENTAL DESIGN: Here, we investigated the therapeutic potential of synthetic miR-34a against human MM cells in vitro and in vivo. RESULTS: Either transient expression of miR-34a synthetic mimics or lentivirus-based miR-34a-stable enforced expression triggered growth inhibition and apoptosis in MM cells in vitro. Synthetic miR-34a downregulated canonic targets BCL2, CDK6, and NOTCH1 at both the mRNA and protein level. Lentiviral vector-transduced MM xenografts with constitutive miR-34a expression showed high growth inhibition in severe combined immunodeficient (SCID) mice. The anti-MM activity of lipidic-formulated miR-34a was further shown in vivo in two different experimental settings: (i) SCID mice bearing nontransduced MM xenografts; and (ii) SCID-synth-hu mice implanted with synthetic 3-dimensional scaffolds reconstituted with human bone marrow stromal cells and then engrafted with human MM cells. Relevant tumor growth inhibition and survival improvement were observed in mice bearing TP53-mutated MM xenografts treated with miR-34a mimics in the absence of systemic toxicity. CONCLUSIONS: Our findings provide a proof-of-principle that formulated synthetic miR-34a has therapeutic activity in preclinical models and support a framework for development of miR-34a-based treatment strategies in MM patients.

Single nucleotide polymorphisms of ABCC5 and ABCG1 transporter genes correlate to irinotecan-associated gastrointestinal toxicity in colorectal cancer patients: a DMET microarray profiling study.

Recent findings have disclosed the role of UDP-glucuronosyltransferase (UGT) 1A1*28 on the haematological toxicity induced by irinotecan (CPT-11), a drug commonly used in the treatment of metastatic colorectal cancer (mCRC). We investigated the pharmacogenomic profile of irinotecan-induced gastrointestinal (GI) toxicity by the novel drug-metabolizing enzyme and transporter (DMET) microarray genotyping platform. Twenty-six mCRC patients who had undergone to irinotecan-based chemotherapy were enrolled in a case (patients experiencing ≥ grade 3 gastrointestinal, (GI) toxicity) - control (matched patients without GI toxicity) study. A statistically significant difference of SNP genotype distribution was found in the case versus control group. The homozygous genotype C/C in the (rs562) ABCC5 gene occurred in 6/9 patients with GI toxicity versus 1/17 patients without GI toxicity (P=0.0022). The homozygous genotype G/G in the (rs425215) ABCG1 was found in 7/9 patients with GI toxicity versus 4/17 patients without GI toxicity (P=0.0135). The heterozygous genotype G/A in the 388G>A (rs2306283) OATP1B1/SLCO1B1 was found in 3/9 patients with grade ≥ 3 GI toxicity vs. 14/17 patients without GI toxicity (P=0.0277). DNA extracted from peripheral blood cells was genotyped by DMET Plus chip on Affymetrix array system. Genotype association was calculated by Fisher's exact test (two tailed) and relevant SNPs were further analyzed by direct sequencing. We have identified 3 SNPs mapping in ABCG1, ABCC5 and OATP1B1/SLCO1B1 transporter genes associated with GI toxicity induced by irinotecan in mCRC patients expanding the available knowledge of irinogenomics. The DMET microarray platform is an emerging technology for easy identification of new genetic variants for personalized medicine.

Classification of cancer cell death with spectral dimensionality reduction and generalized eigenvalues.

OBJECTIVE: Accurate cell death discrimination is a time consuming and expensive process that can only be performed in biological laboratories. Nevertheless, it is very useful and arises in many biological and medical applications. METHODS AND MATERIAL: Raman spectra are collected for 84 samples of A549 cell line (human lung cancer epithelia cells) that has been exposed to toxins to simulate the necrotic and apoptotic death. The proposed data mining approach for the multiclass cell death discrimination problem uses a multiclass regularized generalized eigenvalue algorithm for classification (multiReGEC), together with a dimensionality reduction algorithm based on spectral clustering. RESULTS: The proposed algorithmic scheme can classify A549 lung cancer cells from three different classes (apoptotic death, necrotic death and control cells) with 97.78%± 0.047 accuracy versus 92.22 ± 0.095 without the proposed feature selection preprocessing. The spectrum areas depicted by the algorithm corresponds to the 〉C O bond from the lipids and the lipid bilayer. This chemical structure undergoes different change of state based on cell death type. Further evidence of the validity of the technique is obtained through the successful classification of 7 cell spectra that undergo hyperthermic treatment. CONCLUSIONS: In this study we propose a fast and automated way of processing Raman spectra for cell death discrimination, using a feature selection algorithm that not only enhances the classification accuracy, but also gives more insight in the undergoing cell death process.