Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms.

Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify 'population-differentiated' SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug-gene databases to identify 'population-differentiated' drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify 'population differentiated' drugs could help to facilitate data-driven decision-making for a more personalized medicine.

The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

BACKGROUND: Evidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk. METHODS: Summary-level genotype data from 23andMe, Inc (breast size, n = 33 790), the Breast Cancer Association Consortium (breast cancer risk, n = 228 951) and the Genetic Investigation of ANthropometric Traits (BMI, n = 183 507) were used for our analyses. In assessing causal relationships, four complementary MR techniques [inverse variance weighted (IVW), weighted median, weighted mode and MR-Egger regression] were used to test the robustness of the results. RESULTS: The genetic correlation (rg) estimated between BMI and breast size was high (rg = 0.50, P = 3.89x10-43). All MR methods provided consistent evidence that higher genetically predicted BMI was associated with larger breast size [odds ratio (ORIVW): 2.06 (1.80-2.35), P = 1.38x10-26] and lower overall breast cancer risk [ORIVW: 0.81 (0.74-0.89), P = 9.44x10-6]. No evidence of a relationship between genetically predicted breast size and breast cancer risk was found except when using the weighted median and weighted mode methods, and only with oestrogen receptor (ER)-negative risk. There was no evidence of reverse causality in any of the analyses conducted (P > 0.050). CONCLUSION: Our findings indicate a potential positive causal association between BMI and breast size and a potential negative causal association between BMI and breast cancer risk. We found no clear evidence for a direct relationship between breast size and breast cancer risk.

Characterization of stem-like cells in mucoepidermoid tracheal paediatric tumor.

Stem cells contribute to regeneration of tissues and organs. Cells with stem cell-like properties have been identified in tumors from a variety of origins, but to our knowledge there are yet no reports on tumor-related stem cells in the human upper respiratory tract. In the present study, we show that a tracheal mucoepidermoid tumor biopsy obtained from a 6 year-old patient contained a subpopulation of cells with morphology, clonogenicity and surface markers that overlapped with bone marrow mesenchymal stromal cells (BM-MSCs). These cells, designated as MEi (mesenchymal stem cell-like mucoepidermoid tumor) cells, could be differentiated towards mesenchymal lineages both with and without induction, and formed spheroids in vitro. The MEi cells shared several multipotent characteristics with BM-MSCs. However, they displayed differences to BM-MSCs in growth kinectics and gene expression profiles relating to cancer pathways and tube development. Despite this, the MEi cells did not possess in vivo tumor-initiating capacity, as proven by the absence of growth in situ after localized injection in immunocompromised mice. Our results provide an initial characterization of benign tracheal cancer-derived niche cells. We believe that this report could be of importance to further understand tracheal cancer initiation and progression as well as therapeutic development.