Low RAB6C expression is a predictor of tamoxifen benefit in estrogen receptor-positive/progesterone receptor-negative breast cancer

Cross-continental admixture in the Kho population from northwest Pakistan

Northern Pakistan is home to many diverse ethnicities and languages. The region acted as a prime corridor for ancient invasions and population migrations between Western Eurasia and South Asia. Kho, one of the major ethnic groups living in this region, resides in the remote and isolated mountainous region in the Chitral Valley of the Hindu Kush Mountain range. They are culturally and linguistically distinct from the rest of the Pakistani population groups and their genetic ancestry is still unknown. In this study, we generated genome-wide genotype data of ~1 M loci (Illumina WeGene array) for 116 unrelated Kho individuals and carried out comprehensive analyses in the context of worldwide extant and ancient anatomically modern human populations across Eurasia. The results inferred that the Kho can trace a large proportion of their ancestry to the population who migrated south from the Southern Siberian steppes during the second millennium BCE ~110 generations ago. An additional wave of gene flow from a population carrying East Asian ancestry was also identified in the Kho that occurred ~60 generations ago and may possibly be linked to the expansion of the Tibetan Empire during 7th to 9th centuries CE (current era) in the northwestern regions of the Indian sub-continent. We identified several candidate regions suggestive of positive selection in the Kho, that included genes mainly involved in pigmentation, immune responses, muscular development, DNA repair, and tumor suppression.

Stratification of Estrogen Receptor-Negative Breast Cancer Patients by Integrating the Somatic Mutations and Transcriptomic Data

Patients with estrogen receptor-negative breast cancer generally have a worse prognosis than estrogen receptor-positive patients. Nevertheless, a significant proportion of the estrogen receptor-negative cases have favorable outcomes. Identifying patients with a good prognosis, however, remains difficult, as recent studies are quite limited. The identification of molecular biomarkers is needed to better stratify patients. The significantly mutated genes may be potentially used as biomarkers to identify the subtype and to predict outcomes. To identify the biomarkers of receptor-negative breast cancer among the significantly mutated genes, we developed a workflow to screen significantly mutated genes associated with the estrogen receptor in breast cancer by a gene coexpression module. The similarity matrix was calculated with distance correlation to obtain gene modules through a weighted gene coexpression network analysis. The modules highly associated with the estrogen receptor, called important modules, were enriched for breast cancer-related pathways or disease. To screen significantly mutated genes, a new gene list was obtained through the overlap of the important module genes and the significantly mutated genes. The genes on this list can be used as biomarkers to predict survival of estrogen receptor-negative breast cancer patients. Furthermore, we selected six hub significantly mutated genes in the gene list which were also able to separate these patients. Our method provides a new and alternative method for integrating somatic gene mutations and expression data for patient stratification of estrogen receptor-negative breast cancers.