An improved model to study tumor cell autonomous metastasis programs using MTLn3 cells and the Rag2−/− γc−/− mouse

The occurrence of metastases is a critical determinant of the prognosis for breast cancer patients. Effective treatment of breast cancer metastases is hampered by a poor understanding of the mechanisms involved in the formation of these secondary tumor deposits. To study the processes of metastasis, valid in vivo tumor metastasis models are required. Here, we show that increased expression of the EGF receptor in the MTLn3 rat mammary tumor cell-line is essential for efficient lung metastasis formation in the Rag mouse model. EGFR expression resulted in delayed orthotopic tumor growth but at the same time strongly enhanced intravasation and lung metastasis. Previously, we demonstrated the critical role of NK cells in a lung metastasis model using MTLn3 cells in syngenic F344 rats. However, this model is incompatible with human EGFR. Using the highly metastatic EGFR-overexpressing MTLn3 cell-line, we report that only Rag2−/−γc−/− mice, which lack NK cells, allow efficient lung metastasis from primary tumors in the mammary gland. In contrast, in nude and SCID mice, the remaining innate immune cells reduce MTLn3 lung metastasis formation. Furthermore, we confirm this finding with the orthotopic transplantation of the 4T1 mouse mammary tumor cell-line. Thus, we have established an improved in vivo model using a Rag2−/− γc−/− mouse strain together with MTLn3 cells that have increased levels of the EGF receptor, which enables us to study EGFR-dependent tumor cell autonomous mechanisms underlying lung metastasis formation. This improved model can be used for drug target validation and development of new therapeutic strategies against breast cancer metastasis formation

Adjustments to Model Predictions of Depth of Shower Maximum and Signals at Ground Level using Hybrid Events of the Pierre Auger Observatory

We present a new method to explore simple ad-hoc adjustments to the predictions of hadronic interaction models to improve their consistency with observed two-dimensional distributions of the depth of shower maximum, X$_{max}$, and signal at ground level, as a function of zenith angle. The method relies on the assumption that the mass composition is the same at all zenith angles, while the atmospheric shower development and attenuation depend on composition in a correlated way. In the present work, for each of the three leading LHC-tuned hadronic interaction models, we allow a global shift ΔX$_{max}$ of the predicted shower maximum, which is the same for every mass and energy, and a rescaling R$_{Had}$ of the hadronic component at ground level which depends on the zenith angle. We apply the analysis to 2297 events reconstructed by both fluorescence and surface detectors at the Pierre Auger Observatory with energies 10$^{18.5}$−10$^{19.0}$ eV. Given the modeling assumptions made in this analysis, the best fit reaches its optimum value when shifting the X$_{max}$ predictions of hadronic interaction models to deeper values and increasing the hadronic signal at both extreme zenith angles. The resulting change in the composition towards heavier primaries alleviates the previously identified model deficit in the hadronic signal (commonly called the muon deficit), but does not remove it. Because of the size of the required corrections ΔX$_{max}$ and R$_{Had}$ and the large number of events in the sample, the statistical significance of the corrections is large, greater than 5σ$_{stat}$ even for the combination of experimental systematic shifts within 1σ$_{sys}$ that are the most favorable for the models