The impact of HER2-directed targeted therapy on HER2-positive DCIS of the breast

BACKGROUND: In invasive breast cancer, HER2 is a well-established negative prognostic factor. However, its significance on the prognosis of ductal carcinoma in situ (DCIS) of the breast is unclear. As a result, the impact of HER2-directed therapy on HER2-positive DCIS is unknown and is currently the subject of ongoing clinical trials. In this study, we aim to determine the possible impact of HER2-directed targeted therapy on survival outcomes for HER2-positive DCIS patients. MATERIALS AND METHODS: The National Cancer Data Base (NCDB) was used to retrieve patients with biopsy-proven DCIS diagnosed from 2004–2015. Patients were divided into two groups based on the adjuvant therapy they received: systemic HER2-directed targeted therapy or no systemic therapy. Statistics included multivariable logistic regression to determine factors predictive of receiving systemic therapy, Kaplan-Meier analysis to evaluate overall survival (OS), and Cox proportional hazards modeling to determine variables associated with OS. RESULTS: Altogether, 1927 patients met inclusion criteria; 430 (22.3%) received HER2-directed targeted therapy; 1497 (77.7%) did not. Patients who received HER2-directed targeted therapy had a higher 5-year OS compared to patients that did not (97.7% vs. 95.8%, p = 0.043). This survival benefit remained on multivariable analysis. Factors associated with worse OS on multivariable analysis included Charlson-Deyo Comorbidity Score ≥ 2 and no receipt of hormonal therapy. CONCLUSION: In this large study evaluating HER2-positive DCIS patients, the receipt of HER2-directed targeted therapy was associated with an improvement in OS. The results of currently ongoing clinical trials are needed to confirm this finding

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines

Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling

<div><p>Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.</p></div

Inhibitor combination: model predictions and experimental validation.

<p>A) Heatmaps showing model simulations of the impact of 50% inhibitor I individually or in combination with 50% inhibitor II. As readout, the area under the curve of pAkt, pERK, and the sum of pAkt and pERK upon inhibitor treatment is compared to the area under the curve of the control condition. The change in the response induced by the inhibitor treatment is indicated as percentage to the control condition. B) Heatmap of synergistic effect of inhibitor combination treatment shown in panel (A). The synergy represents the efficiency of the double inhibitor treatment compared to individual inhibitor treatments. C) Inhibitor strength parameter estimation. Model 4_8_12 trajectories (solid lines) of the phosphorylation kinetic of pAkt and pERK measured in primary mouse hepatocytes treated with the indicated inhibitor or DMSO prior to HGF 40 ng/ml treatment (filled circles). The experimental data represent the average of two or more replica. D) Model predictions of pAkt and pERK kinetics and experimental validation of inhibitors combination treatment. Model predictions are based on the inhibitor strength estimated as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.g005" target="_blank">Fig 5C</a>. The experimental validation is based on primary mouse hepatocytes treated with the indicated inhibitors or DMSO and subsequently stimulated with 40 ng/ml HGF for the indicated time points. Quantification of the phosphorylation kinetics of Akt and ERK determined by quantitative immunoblotting (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.s019" target="_blank">S12 Fig</a>). Quantification of the area under the curve (AUC) of pAkt, pERK and their sum is indicated for the model trajectories and the experimental data. The experimental data is a representative dataset of an experiment performed in biological duplicates.</p

Selected minimal model structures, core and complete model.

<p>The compressed selected 16 minimal model structures that can explain the discretized data (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.g003" target="_blank">Fig 3B</a>) are shown. In addition, the complete model structure (that is the union of models 1–16) and the compressed core model structure are displayed. Arrows represent activating (positive) interactions; blunt-ended lines indicate inhibitory (negative) interactions. In each model, the core model is colored black, while the building block (the set of added candidate mechanisms) is shown in turquoise.</p

Interaction graph master model.

<p>The interaction graph master model was built from literature information. Detailed model documentation can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.s001" target="_blank">S1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.s003" target="_blank">S3</a> Tables. The core model is given in black, candidate mechanisms are depicted in turquoise. Arrows represent activating (positive) interactions, blunt-ended lines indicate inhibitory (negative) interactions. The measured species are marked with bold borders. The lightning symbol indicates that the respective species was experimentally targeted with a chemical inhibitor or siRNA.</p

ODE model fit.

<p>A) Structure of the best performing model 4_8_12. B-F) Plots showing representative model trajectories (solid lines) of the phosphorylation kinetic of the indicated proteins measured by quantitative immunoblotting in primary mouse hepatocytes pretreated with the indicated inhibitors and stimulated with 40 ng/ml of HGF for the indicated time (stars). y-axes show the concentration of the respective measured protein in arbitrary units on a logarithmic scale. The shadowed area surrounding the model trajectory represents the confidence interval delimited by the dashed line. Treatments are color-coded as indicated in the figure.</p

Model selection.

<p>A) Rankings represent the forward selection approach using selected minimal model structures; B) backward selection where the building blocks are removed from the complete model; C) the model combination selection. D) Comparison of the predictive power of the complete model and 4_8_12 model in respect of the kinetic of “active PI3K”. Confidence intervals of the predictions are indicated by shaded areas. E) Ranking of model selection including minimal model structures, model combinations and random models is shown. All rankings of model selection present the negative logarithmic likelihood penalized by parameter difference as described in Materials and Methods on the y-axis. Model identifiers are shown on the x-axis.</p

Negative crosstalk: experimental validation.

<p>A-B). Model prediction of active Akt and the loss of active Raf1 upon 3, 6 and 100 fold inhibition of active Akt. C-D) Experimental validation of the effect of Akt inhibition in primary mouse hepatocytes treated with 40 ng/ml of HGF alone or in combination with Akt inhibitor VIII. Quantification of the phosphorylation kinetics of Akt and Raf1 determined by quantitative immunoblotting (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.s016" target="_blank">S9</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004192#pcbi.1004192.s018" target="_blank">S11 Fig</a>).</p