Genomics of -Positive Breast Cancer in Young Women Before and After Exposure to Chemotherapy Plus Trastuzumab.

Purpose: Erb-B2 receptor tyrosine kinase 2 ()-positive breast cancer (BC) is particularly common in young women. Genomic features of -positive tumors before and after chemotherapy and trastuzumab (chemo + H) have not been described in young women and are important for guiding study of therapeutic resistance in this population.

Methods: From a large prospective cohort of women age 40 years or younger with BC, we identified patients with -positive BC and tumor tissue available before and after chemo + H.

Adjuvant paclitaxel and trastuzumab for node-negative, HER2-positive breast cancer: final 10-year analysis of the open-label, single-arm, phase 2 APT trial.

Background: We aimed to report on long-term outcomes of patients with small, node-negative, HER2-positive breast cancer treated with adjuvant paclitaxel and trastuzumab and to establish potential biomarkers to predict prognosis.

Methods: In this open-label, single-arm, phase 2 study, patients aged 18 years or older, with small (≤3 cm), node-negative, HER2-positive breast cancer, and an Eastern Cooperative Oncology Group performance status of 0-1, were recruited from 16 institutions in 13 cities in the USA. Eligible patients were given intravenous paclitaxel (80 mg/m) with intravenous trastuzumab (loading dose of 4 mg/kg, subsequent doses 2 mg/kg) weekly for 12 weeks, followed by trastuzumab (weekly at 2 mg/kg or once every 3 weeks at 6 mg/kg) for 40 weeks to complete a full year of trastuzumab.

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies.

Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host.

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer.

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape.

A new discrete dynamic model of ABA-induced stomatal closure predicts key feedback loops.

Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated.

Structure-based control of complex networks with nonlinear dynamics.

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory.

Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes.

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We aim to identify network perturbations that suppress TGFβ-driven EMT, with the goal of suppressing invasive properties of cancer cells.

Cell fate reprogramming by control of intracellular network dynamics.

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state.

Network modeling of TGFβ signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation.

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is TGFβ, which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC.

An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks.

Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.