BRCA1/2 genetic background-based therapeutic tailoring of human ovarian cancer: hope or reality?

Ovarian epithelial tumors are an hallmark of hereditary cancer syndromes which are related to the germ-line inheritance of cancer predisposing mutations in BRCA1 and BRCA2 genes. Although these genes have been associated with multiple different physiologic functions, they share an important role in DNA repair mechanisms and therefore in the whole genomic integrity control. These findings have risen a variety of issues in terms of treatment and prevention of breast and ovarian tumors arising in this context. Enhanced sensitivity to platinum-based anticancer drugs has been related to BRCA1/2 functional loss. Retrospective studies disclosed differential chemosensitivity profiles of BRCA1/2-related as compared to "sporadic" ovarian cancer and led to the identification of a "BRCA-ness" phenotype of ovarian cancer, which includes inherited BRCA1/2 germ-line mutations, a serous high grade histology highly sensitive to platinum derivatives. Molecularly-based tailored treatments of human tumors are an emerging issue in the "era" of molecular targeted drugs and molecular profiling technologies. We will critically discuss if the genetic background of ovarian cancer can indeed represent a determinant issue for decision making in the treatment selection and how the provocative preclinical findings might be translated in the therapeutic scenario. The presently available preclinical and clinical evidence clearly indicates that genetic background has an emerging role in treatment individualization for ovarian cancer 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.

Abstract 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 versus 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

Genetic variants associated with gastrointestinal symptoms in Fabry disease.

Gastrointestinal symptoms (GIS) are often among the earliest presenting events in Fabry disease (FD), an X-linked lysosomal disorder caused by the deficiency of α-galactosidase A. Despite recent advances in clinical and molecular characterization of FD, the pathophysiology of the GIS is still poorly understood. To shed light either on differential clinical presentation or on intervariability of GIS in FD, we genotyped 1936 genetic markers across 231 genes that encode for drug-metabolizing enzymes and drug transport proteins in 49 FD patients, using the DMET Plus platform. All nine single nucleotide polymorphisms (SNPs) mapped within four genes showed statistically significant differences in genotype frequencies between FD patients who experienced GIS and patients without GIS: ABCB11 (odd ratio (OR) = 18.07, P = 0,0019; OR = 8.21, P = 0,0083; OR=8.21, P = 0,0083; OR = 8.21, P = 0,0083),SLCO1B1 (OR = 9.23, P = 0,0065; OR = 5.08, P = 0,0289; OR = 8.21, P = 0,0083), NR1I3 (OR = 5.40, P = 0,0191) and ABCC5 (OR = 14.44, P = 0,0060). This is the first study that investigates the relationships between genetic heterogeneity in drug absorption, distribution, metabolism and excretion (ADME) related genes and GIS in FD. Our findings provide a novel genetic variant framework which warrants further investigation for precision medicine in FD

Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives

In the past decades, many efforts have been made to individualize medical treatments, taking into account molecular profiles and the individual genetic background. The development of molecularly targeted drugs and immunotherapy have revolutionized medical treatments but the inter-patient variability in the anti-tumor drug pharmacokinetics (PK) and pharmacodynamics can be explained, at least in part, by genetic variations in genes encoding drug metabolizing enzymes and transporters (ADME) or in genes encoding drug receptors. Here, we focus on high-throughput technologies applied for PK screening for the identification of predictive biomarkers of efficacy or toxicity in cancer treatment, whose application in clinical practice could promote personalized treatments tailored on individual’s genetic make-up. Pharmacogenomic tools have been implemented and the clinical utility of pharmacogenetic screening could increase safety in patients for the identification of drug metabolism-related biomarkers for a personalized medicine. Although pharmacogenomic studies were performed in adult cohorts, pharmacogenetic pediatric research has yielded promising results. Additionally, we discuss the current challenges and theoretical bases for the implementation of pharmacogenetic tests for translation in the clinical practice taking into account that pharmacogenomics platforms are discovery oriented and must open the way for the setting of robust tests suitable for daily practice

IL-11 synergizes with IL-3 in promoting the recovery of the immune system after irradiation

In our previous studies aiming at the design of appropriate to accelerate the recovery of the system after irradiation we found that recombinant murine (rmu) It-3 treatment induc3es differentiation and growth of thymocytes and splenic T and B lymphocytes in mice exposed to X-rays(200-500 cGy). These studies were extended to investigate the effectss of recombinant human (rhu)IL-11. Results indicate that rhuIL-11 is able to restore thymus and spleen cell numbers as well as T and B cell mitotic responsiveness in mice exposed to 200 cGy but not to 300cGy. However, recovery, of thymus and spfeen cell numbers and functions could be accelerated also in mice exposed to higherr doses it rhyuIL-11 was given with rmuIL-3. Recovery was complete as soon as 7 days after irradiation. A large doss range of both cytokines was explored and the synergiatic effect of the two cytokines was evident when a relatively small dose of rhyuIL-11 was injected with graded doses of rmuIL-3. The recovery of the immune system in irradiated mice injected with these cytokines was independent from Bcl-2 expression, suggesting that ellimination of damaged cells by apoptosis is uneffected by hamatopoistic cytokines

Scaling approaches for the prediction of human clearance of LNA-i-mir-221: A retrospective validation

LNA-i-miR-221 is a novel microRNA(miRNA)-221 inhibitor designed for the treatment of human malignancies. It has recently undergone phase 1 clinical trial (P1CT) and early pharmacokinetics (PKs) data in cancer patients are now available. We previously used multiple allometric interspecies scaling methods to draw inferences about LNA-i-miR-221 PKs in humans and estimated the patient dose based on the safe and pharmacodynamic (PD) active dose observed in mice, therefore providing a framework for the definition of safe starting and escalation doses for the P1CT. The preliminary data collected during the P1CT showed that the LNA-i-miR-221 anticipated doses, according to our human PK estimation approach, were indeed well tolerated and effective. PD data demonstrated concentration-dependent downregulation of miR-221 and upregulation of its CDKN1B/p27 and PTEN canonical targets as well as stable disease in 8 (50.0%) patients and partial response in 1 (6.3%) colorectal cancer case. Here, we detail the experimentally evaluated PK parameters of LNA-i-miR-221 in human, using both a non-compartmental and a population PKs approach. The population approach was adequately described by a three-compartments model with first-order elimination. The recorded age, sex and body weight of patients were evaluated as potential covariates. The estimated typical population parameter values were clearance (CL = 200 mL/h/kg), central volume of distribution (V1 = 45 mL/kg), peripheral volume of distribution (V2 = 200 mL/kg, volume of the second peripheral compartment V3 = 930 mL/h/kg) and inter-compartmental clearance (Q2 = 480 mL/h/kg and Q3 = 68 mL/h/kg). Age was found to be a predictor of Q3, with a statistically significant correlation. This work aimed also at retrospectively comparing the measured plasmatic clearance values with those predicted by different allometric scaling approaches. Our comparative analysis showed that the most accurate prediction was achieved by applying the single species allometric scaling approach and that the use of more than one species in allometric scaling to predict therapeutic oligonucleotides PKs would not necessarily generate the best prediction. Finally, our predictive approach was found accurate not only in predicting the main PK parameters in human but suggesting the range of effective and safe dose to be applied in the next clinic phase 2

OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes

Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field