Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment. After this treatment patients who achieve a pathologic Complete Response (pCR) usually have a favorable prognosis than those without. Therefore, pCR is now considered as the best prognosticator for patients with neoadjuvant chemotherapy. However, not all patients can benefit from this treatment. As a result, we need to find a way to predict what kind of patients can induce pCR. Various gene signatures of chemosensitivity in breast cancer have been identified, from which such predictors can be built. Nevertheless, many of them have their prediction accuracy around 80%. As such, identifying gene signatures that could be employed to build high accuracy predictors is a prerequisite for their clinical tests and applications. Furthermore, to elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithm (GA) and Sparse Logistic Regression (SLR) along with t-test were employed to identify one signature. It had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between pCR and Residual Disease (RD) and was used to build an SLR predictor of pCR (SLR-28). This predictor tested on a training set (n = 81) and validation set (n = 52) had very precise predictions measured by accuracy, specificity, sensitivity, positive predictive value, and negative predictive value with their corresponding P value all zero. Furthermore, this predictor discovered 12 important genes in the 28 probe set signature. Our findings also demonstrated that the most discriminative genes measured by SLR as a group selected by GA were not necessarily those with the smallest P values by t-test as individual genes, highlighting the ability of GA to capture the interacting genes in pCR prediction as multivariate techniques. Our gene signature produced superior performance over a signature found in one previous study with prediction accuracy 92% vs 76%, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer.

A novel avian-origin H7N9 influenza virus was discovered in March in China and has caused a total of 131 people infected including 39 deaths in China as of June 9, 2013. Adaptation of avian viruses to efficiently infect humans requires the viral hemagglutinin (HA) binding switches from avian to human type receptors with help of some mutations in HA. As such it is critical for pandemic assessment to discover these mutations as hallmarks of adaptation. To continue our previous study of this novel H7N9 virus, we identified two sets of mutations in HA. The first set of mutations are present in the current circulating strains of 2013 H7N9 in China, and the second set are potential mutations that were found when compared to the HAs of previous human H7 subtype. These two sets of mutations exhibited unique features. The first group of mutations, on average, enhanced the HA binding to human type receptors whereas reduced that to avian types. Further the reduction of avian binding was almost three times of the increase of the human binding. The second group increased the binding to both human and avian types. But the increase in human types was almost three times of that in the avian types. Though different in their way of changing the binding preference, these two sets of mutations both contained more mutations to decrease the avian binding and increase the human binding than those that did the opposite. Our research highlighted the pandemic potential of this novel virus by showing the important mutations that could potentially help it to adapt to human hosts. Our findings offered new insights into the current state of evolution of this virus, which might be helpful for the continued surveillance of the emergence of H7N9 strains having the ability of human-to-human transmission.